The research contributions of this paper are:
-An SFD-BIS framework for vegetation-based OANs.
-A link established between channel response and sugarcane sucrose content using finite element analysis (FEA).
-A deep learning-based sucrose content estimator developed as a use case for the SFD-BIS framework.
Visually impaired students typically face intense
difficulties when handling electronic learning materials such as PDF
textbooks, academic papers, and study guides. Traditional assistive
technologies like screen readers have the basic text-to-speech
functionality without enabling smart interaction and understanding
document structure. This paper outlines the design and
implementation of an AI-Based Tutor system to enhance the
learning experience for visually impaired students. The system
integrates Optical Character Recognition (OCR) to extract
structured text from diverse PDF structures, including accurate line
and section detection. The system has high-quality Text-to-Speech
(TTS) capabilities for natural-sounding speech, and a Speech-toText (STT) functionality for real-time voice queries and
instructions. Essentially, the system employs leading-edge Natural
Language Processing (NLP) models such as GPT or Gemini to
provide intelligent question answering, context-based
conversations, and content summarization. A voice-enabled
interface affords hands-free and intuitive control of the learning
process. Preliminary evaluation with blind students shows increased
accessibility, comprehension, and interaction efficacy compared to
existing solutions. The current paper contributes a new, integrated
solution that provides blind students greater independence and
control over their studies
Visually impaired students typically face intense
difficulties when handling electronic learning materials such as PDF
textbooks, academic papers, and study guides. Traditional assistive
technologies like screen readers have the basic text-to-speech
functionality without enabling smart interaction and understanding
document structure. This paper outlines the design and
implementation of an AI-Based Tutor system to enhance the
learning experience for visually impaired students. The system
integrates Optical Character Recognition (OCR) to extract
structured text from diverse PDF structures, including accurate line
and section detection. The system has high-quality Text-to-Speech
(TTS) capabilities for natural-sounding speech, and a Speech-toText (STT) functionality for real-time voice queries and
instructions. Essentially, the system employs leading-edge Natural
Language Processing (NLP) models such as GPT or Gemini to
provide intelligent question answering, context-based
conversations, and content summarization. A voice-enabled
interface affords hands-free and intuitive control of the learning
process. Preliminary evaluation with blind students shows increased
accessibility, comprehension, and interaction efficacy compared to
existing solutions. The current paper contributes a new, integrated
solution that provides blind students greater independence and
control over their studies
The main contributions of this paper are summarized as follows:
• Development of a low-cost IoT-based hotel room occupancy detection system using radar sensing for both static and dynamic presence.
• Cloud-based storage and visualization through Firebase and a Django dashboard, enabling real-time and historical monitoring.
• Energy-efficient and sustainable operation using deep sleep, Wi-Fi Manager, and OTA-enabled remote maintenance.
• Demonstration of scalability across domains beyond hospitality, including smart homes, healthcare, and intelligent building systems.
• Introduced the Explainable Ensemble Learning Framework (EELF) a Voting Classifier, that integrates Logistic Regression, Random Forest, and K-Nearest Neighbors with optimized hyperparameters to predict diabetes.
• Incorporated SHAP and LIME to enhance model interpretability by identifying key feature contributions, thereby improving clinician trust in the decision-making process.
• Conducted a comparative analysis, where the EELF achieved an accuracy of 81.16%, demonstrating strong potential for clinical application.
The significant research contribution of this paper lies in proposing a semi-automatic digital system for validating references in scholarly articles using RoBERTa-based semantic similarity analysis.
Key contributions include:
Introducing a novel framework that leverages RoBERTa embeddings with K-similar search to verify references against cited works.
Overcoming BERT’s input length limitations by applying document segmentation and preprocessing strategies for handling long research papers.
Achieving higher accuracy (F1-score: 0.777) compared to BERT and SBERT, demonstrating the effectiveness of RoBERTa for contextual similarity in reference validation.
Reducing reliance on manual cross-checking and peer reviewers, thereby streamlining the academic publication process while preserving reference authenticity.
This paper presents a
comparative analysis of three recent YOLO variants—YOLOv8,
YOLOv10, and YOLOv11—evaluated on a traffic sign detection
task under variable real-world visual conditions. The nano
variant of each model was evaluated in terms of precision, recall,
mean average precision (mAP), training efficiency, F1-confidence,
and runtime speed. This study offers practical insights for
deploying object detection models in intelligent transportation systems, aiming to balance real-time performance with detection accuracy. The results indicated that YOLOv8 achieved the highest mAP (0.92), followed by YOLOv11 (0.908) and YOLOv10 (0.873). In terms of runtime performance, YOLOv8 and YOLOv11 demonstrated comparable speeds on the test data, whereas YOLOv10 required more time to complete the inference process
An ‘emojian’ algorithm was developed to manage this procedure. The enhanced caption produced, is then translated through Google/Argosopentech translate API, to the particular natural language picked manually, or through the automatic reading of the apparatus. Eventually, the translated enhanced mixed caption is combined with the scanned image, derived through the initial stage of this process. To assess and verify the effectiveness of the Emojian deep learning algorithm, we scrutinized the complete word count in the given text caption parameter relayed to our algorithm, and the complete emoji count delivered by the algorithm, each time a positive result is posted.
This is important for power system
The relentless drive towards extremely fast switching frequency, elevated operating voltage, increased thermal capabilities, and reduced switching losses have positioned Silicon Carbide (SiC) MOSFETs based converters at the forefront in high-performance power electronics applications. This brings in the benefits of enhanced switching frequencies, improved power density, and enhanced dynamic response. Unfortunately, this is critically constrained by severe Electromagnetic Interference (EMI), high-frequency ringing and undesirable switching oscillations. These challenges are addressed through a systemic modelling and analysis mitigation measures, the holistic co-optimizing ferrite bead on the gate loop, RC snubber, and gate drive impedance simultaneously. An LTspice simulation framework was developed, incorporating the manufacturer’s spice models to accurately model parasitics and quantify losses. The proposed methodology shows that the addition of the mitigation technique offers a practical trade-off between EMI suppression and switching performance, without increasing the switching losses.
The increasing penetration of renewable energy
and inverter-based distributed generation has introduced
significant challenges for fault detection in modern smart grids.
Traditional protection schemes often struggle to identify weak
or evolving fault signatures, leading to delayed fault isolation
and compromising system reliability. To address this gap, this
study introduces a hybrid deep learning framework that
integrates convolutional neural networks (CNNs) with long
short-term memory (LSTM) networks for intelligent fault
detection. The approach leverages phasor measurement unit
(PMU) image data, where CNN layers extract spatial fault
characteristics and LSTM layers capture temporal dynamics,
enabling a more comprehensive representation of fault
progression. Data preprocessing included normalization, class
rebalancing, and synthetic noise augmentation to ensure
robustness. Model performance was validated using stratified 5-
fold cross-validation, achieving 99% classification accuracy
while maintaining lower computational requirements compared
to CNN-only and ensemble-based baselines. Comprehensive
evaluation with ROC–AUC, PR–AUC, per-class accuracy, and
confusion matrices further demonstrated reliability and
interpretability. The findings highlight the potential of the
proposed method to enhance fault detection mechanisms,
contributing to improved grid stability, faster protection
response, and greater resilience of future smart energy systems
The increasing connectivity of Electric Vehicle Supply Equipment (EVSE) within smart grid networks has compounded the threat of cyber exposures, particularly Distributed Denial-of-Service (DoS) attacks. This paper introduces a lightweight, real-time intrusion detection system based on a hybrid deep learning structure that intermixes Transformer encoders and a Multilayer Perceptron (MLP) classifier. The proposed model uses kernel-level event logs to capture both temporal dependencies and high-dimensional feature interactions. A well-structured preprocessing pipeline includes feature leakage prevention, normalization, class balancing, and stratified cross-validation. This approach ensures data integrity and effective learning. Experimental evaluation on a real-world dataset confirms the model’s superior performance with 100% accuracy, precision, recall, and F1-score, and ideal ROC-AUC and PR-AUC scores. Furthermore, the framework is streamlined for deployment on resource-constrained edge devices to enable decentralized, on-device threat detection. These results accentuate the effectiveness and viability of the suggested solution in enhancing the cybersecurity posture of modern EV charging ecosystems
Reliable transformer operation is critical for
minimizing downtime and ensuring power system stability.
Dissolved Gas Analysis (DGA) is the most widely used diagnostic
tool, yet ratio-based methods such as the Duval Triangle and
Key Gas Method often fail when signatures overlap or appear
at early fault stages. While machine learning has improved
accuracy, models remain vulnerable to noise, imbalance, and
overfitting. This paper proposes a CatBoost-based framework
that combines statistical and energy features of H₂, CO, C₂H₂,
and C₂H₄ gases with engineered ratios to capture complex intergas dependencies. With tuned hyperparameters, the model
achieved 97.6% overall accuracy and strong class-wise
performance: 98.9% (Normal), 94.7% (Partial Discharge),
93.9% (Low-Energy Discharge), and 89.0% (Low-Temperature
Overheating). Feature importance analysis identified H₂ and gas
ratios as key contributors, while training dynamics showed
rapid and stable convergence. The results demonstrate
robustness, interpretability, and efficiency, highlighting the
framework’s potential for real-time transformer fault detection
and improved power system resilience
• We have introduced a highly developed Ensemble-Based Thyroid Disorder Detector (ETDD) that uses Random Forest, CatBoost, LightGBM, and XGBoost in sequence, and feature engineering has been used to enhance the detector’s effectiveness.
• We have improved model interpretability and clinician trust by integrating explainable AI techniques, including SHAP and LIME, to explain predictions and validate model decisions.
• We have conducted a comprehensive evaluation against state-of-the-art models, demonstrating that the ETDD system achieves a high accuracy of 96.24%, establishing its effectiveness as a clinical decision-support tool.
Solar photovoltaic (PV) systems are highly vulnerable to changes in irradiance, and shading is one of the most detrimental issues affecting their efficiency. Even minor shading can cause disproportionate power losses, current mismatch, and the emergence of multiple peaks in I–V and P–V characteristics, complicating maximum power point tracking (MPPT). To address these challenges, this study develops a modeling and simulation framework to systematically analyze shading effects on PV arrays. The model evaluates different irradiance distributions, partial shading scenarios, and the effectiveness of bypass diodes in minimizing mismatch losses. Simulation outcomes demonstrate the direct influence of shading patterns on the I–V response, the reduction in maximum power output, and the nonlinear behavior that hinders MPPT accuracy. Incorporating bypass diodes is shown to mitigate hotspot risks and recover part of the lost power under shaded conditions. Overall, this research highlights the value of simulation tools in predicting real-world PV behavior, offering a practical framework for researchers and system designers to quantify shading impacts and optimize PV performance for enhanced energy yield and reliability.
This research presents a novel, waterless, self-sustaining autonomous solar-panel cleaner that combines LDR sensing for dust monitoring, LRA vibration for dust removal, ML-based scheduling, and heat to energy harvesting using TEGs, achieving 95% dust clearance, consuming only 0.366 Wh/day, costing approximately $35 per unit, and delivering an estimated 70% reduction in maintenance costs.
Abstract. The article presents the results of a research of the CoAP, MQTT and 6LoWPAN protocols used in Internet of Things (IoT) telecommunications networks. A comparative analysis of the protocols’ performance in transmitting data under resource-constrained conditions typical of IoT environments, such as low bandwidth, high latency, and limited power consumption, is conducted. The research examines the key characteristics and features of each protocol: CoAP implements a lightweight application-layer protocol for exchanging data between devices with limited computing resources; MQTT is focused on transmitting messages in a publish/subscribe mode, ensuring high reliability and scalability of systems, 6LoWPAN provides efficient adaptation of IPv6 to low-data-rate networks. Experimental analysis showed that CoAP exhibits the lowest packet transmission latency for small data volumes, while MQTT performs better with a large number of nodes and high loads. 6LoWPAN, in turn, optimizes the network layer and reduces node power consumption. The obtained results allow us to conclude that the combined use of these protocols is feasible depending on the requirements of specific IoT scenarios—from environmental monitoring to smart home systems and the Industrial Internet of Things.
The paper presents the calculation of the reliability of sensors and sensors in the intelligent telecommunication network system using fuzzy set methods. The relevance of using fuzzy set methods and fuzzy neural networks in the analysis and study of the main reliability parameters of Internet of Things sensors is shown. It has been shown that neural networks can effectively predict sensor failure, time to failure, or classify the state of a sensor, which increases the reliability of the entire system and allows for timely measures to be taken for its maintenance. Genetic algorithms provide a powerful tool for optimizing mathematical models, especially in problems with many variables and complex dependencies, such as calculating the reliability of sensors. This method allows for efficient search for optimal solutions in multidimensional spaces and can be used to predict failures, estimate time to failure, or other characteristics that affect the reliability of a system. Modeling the reliability parameters of sensors using genetic algorithms allows for efficient optimization of complex models that include many variables and nonlinear dependencies.
The desiccated basin of the Aral Sea, once a major inland water body, exemplifies the severe ecological consequences of long-term mismanagement and climate-induced stress. Addressing this environmental crisis requires decision-making frameworks capable of balancing environmental, social, and economic objectives amidst significant uncertainty and data incompleteness. This study proposes a decision-support model grounded in Intuitionistic Fuzzy Set (IFS) theory, integrated within a Multi-Criteria Decision-Making (MCDM) approach, to manage the complex trade-offs inherent in the sustainable restoration of the Aral Sea region. By incorporating hesitation degrees alongside traditional membership and non-membership values, the model captures expert uncertainty and conflicting information more effectively than classical fuzzy or deterministic methods. A real-world case study – focused on water allocation and land rehabilitation – demonstrates the practical utility of the framework. The results highlight the model’s ability to enhance decision robustness, transparency, and reliability in uncertainty-dominated ecological systems, offering a scalable tool for environmental policy and sustainability planning in similar high-risk regions.
This paper discusses the creation and assessment
of MediPath, a smart web-based emergency medical assistance
system. It connects patients, hospitals, and ambulance services
in one digital platform. The system uses technologies like
Natural Language Processing (NLP) to match hospital
specialties with patient symptoms. It also uses real-time location
services and smart route planning to address gaps in emergency
medical response. MediPath has a three-part structure that
supports logins for patients or attendants, hospital management
portals, and navigation systems for ambulance drivers. The
platform uses machine learning to link patient symptoms with
hospital specialties, Firebase Realtime Database for easy data
syncing, and mapping APIs for better traffic-aware routes.
Performance tests show average response times of under 30
seconds for hospital matches and 95% accuracy in matching
symptoms to specialties. There is also a significant drop in
ambulance dispatch delays. User studies at different healthcare
facilities indicate improved coordination, shorter emergency
response times, and better use of resources. Although there are
challenges with highly specialized medical conditions and
connectivity in rural areas, MediPath greatly enhances
emergency medical service delivery by creating a unified
ecosystem that connects all parties in real time. This research
presents a scalable, smart solution that addresses disconnected
emergency medical services. It uses modern web technologies
alongside healthcare knowledge to help save lives through
quicker and more coordinated emergency responses.
understanding urban citizens behavior
Environmental challenges and their risks to human health have drawn the attention of academics, policymakers, and industry leaders. This study examines the factors influencing purchase intentions for energy-efficient LED bulbs in the context of sustainable development, which are relevant from business, marketing, and economic perspectives. Moreover, energy-efficient LED bulbs contribute to the attainment of the Sustainable Development Goals (SDGs) through their direct and indirect impacts. By promoting clean energy and sustainable urban growth, energy-efficient LED bulbs are consistent with SDGs 7, 11, and 13.
Theoretically, the research extends UTAUT2 to explain consumer behavior in the context of green technology adoption. Practically, it provides insights for segmentation, targeting, and brand positioning in emerging markets. Managers and stakeholders are encouraged to focus on product design, packaging, quality, energy efficiency, and cost to enhance adoption. Social factors, including family and peer influence, play a key role, suggesting strategies such as social proof, influencer marketing, and media campaigns to boost engagement and loyalty.
From a societal perspective, promoting energy-efficient LED bulbs supports sustainable consumption, energy savings, and community well-being. Economically, the findings guide producers and marketers in building brand value, competitive pricing, and using digital platforms to educate consumers. At the national level, the study emphasizes government support in regulating fair pricing, ensuring sustainable production, and offering financial incentives such as subsidies, tax breaks, and loans. Policymakers can align environmental objectives with national strategies, foster global collaborations, and promote adoption to reduce carbon emissions.Overall, the study contributes theoretically by enhancing UTAUT2, offers actionable insights for business and policy, and supports Bangladesh’s progress toward sustainable development through widespread adoption of energy-efficient technologies.
We propose an energy-efficient blockchain-IoT framework for smart farming that integrates a lightweight Tangle consensus protocol with renewable energy sources to reduce energy consumption and carbon emissions while maintaining security and scalability.
Dr. Waziha Farha – Conceptualization, Project Administration, Supervision, Writing – Original Draft, Writing – Review & Editing
Dr. Umme Habiba Rahman – Writing – Review & Editing
Rejaul Karim – Writing – Review & Editing
Md. Alauddin Sany – Data Curation
Md. Limon Hosen Miazi – Data Curation
Sanid Al Rafsan – Data Curation
Tanvir Hossain Ashiq – Data Curation
1) Integrated Hardware and Software Solution: We de-
veloped a comprehensive system combining a smart
medicine reminder box with a mobile application and a
web dashboard, ensuring seamless interaction between
physical medication management and digital health
monitoring.
2) Enhanced Medication Adherence and Emergency Re-
sponse: The system features intelligent reminders
(LEDs, buzzer, app notifications) for timely medication
intake and includes a critical panic button for immediate
emergency alerts via SMS and app notifications to
caregivers
3)
This work develops a low-cost, solar-powered IoT hydroponic system integrating multi-sensor monitoring, automated nutrient control, cloud logging, and seamless grid fallback, validated on Nutrient Film Technique (NFT) spinach with stable performance and predictive dataset generation.
1) A novel multimodal fusion framework for TB severity
classification, integrating chest X-ray images via pre-
trained Vision Transformer (ViT) and clinical data via
hyperparameter-tuned Tabular Transformer, with early
feature concatenation for joint learning.
2) Extensive hyperparameter tuning and regularization
(e.g., dropout, learning rate scheduling) to enhance generalization.
3) Achieved 94.01% accuracy on the MIMIC-CXR dataset
using pneumonia as TB proxy, outperforming unimodal
baselines and state-of-the-art methods.
A summary of the contributions of our study is as follows:
1. A new graph-based framework leveraging social media data for suicide risk detection is proposed.
2. The integration of semantic (GloVe), syntactic (dependency parsing), and emotional (SenticNet) features in a single graph structure is proposed, correlating different perspectives of the data.
3. Here, we demonstrate the creation of more context-sensitive and interpretable models that would outperform certain recent ML, DL, and LLM approaches.
4. The performance of both GraphSAGE and HGNN was significantly improved when the dataset size was increased from 4K to 8K. GraphSAGE kept its reliability high, while HGNN achieved consistent best scores due to its capacity to scale and hence improve the accuracy, recall, and robustness with larger data.
Proposal of a Correction Algorithm for Undetected Timber Using Machine Learning.
While about 93% of timber are detected by machine learnin, about 98% of timber are detected by the proposed method without degrading the F1 score.
The following are the primary contributions of this study:
* Proposed a remarkable 2-step feature extraction technique that blends statistics and ML to identify ASD-genetic features.
* Introduced a novel Gene Pathway Analysis framework to identify ASD biomarkers from high-dimensional genomic data.
* Conducted Ontological Analysis to discover critical gene connections, that aids in the exact diagnosis and an awareness of autism.
* Performed Survival Analysis on gene data, uncovering key time-linked biomarkers associated with autism progression.
In this work, we propose a hybrid framework that integrates autoencoder-based latent representation, GAN-driven minority-class augmentation, and graph-based classification with GraphSAGE. Our key contributions are as follows:
1. We propose a latent-space augmentation strategy that synthesizes minority-class embeddings within the autoencoder feature manifold, avoiding artifacts common in pixel-level augmentation.
2. We design a graph-based relational learning framework that embeds leukocyte representations into a similarity graph and applies inductive classification to leverage contextual neighborhood information.
3. Through extensive experiments on the AML-Cytomorphology-LMU dataset, our method achieves ~91% accuracy and improved macro-F1 scores, particularly enhancing recall for rare subtypes.
4. We show that latent augmentation improves minority-class sensitivity and reduces cross-validation variability, yielding more robust and clinically deployable models.
Network Intrusion Detection Systems( NIDS) are critical for securing modern network infrastructures; still, being deep knowledge- predicated approaches constantly suffer from high conclusion quiescence, limiting their connection in real time and edge computing surroundings. This paper presents an optimized Convolutional Neural Network( CNN)- predicated NIDS frame designed to achievesub- 5 ms anomaly discovery while maintaining high discovery delicacy. The proposed ar chitecture employs depthwise separable complications to reduce computational exodus, INT8 quantization for conclusion accel eration, and channel community to overlap packet internee, preprocessing, and type tasks. Evaluation was conducted on three standard datasets — CICIDS2017, UNSW- NB15, and NSL- KDD — demonstrating delicacy situations above 96 and increment exceeding 15,000 packets per second on an NVIDIA Jetson Xavier NXedge device. The frame achieved up to 60 × hastily conclusion than LSTM- predicated births and demonstrated strong zero day discovery performance, achieving 94.8 recall on unseen Botnet attacks. These results validate the proposed system’s capability to deliver both high- speed and high- delicacy discov ery, making it suitable for deployment in quiescence-sensitive, resource- constrained surroundings. future advancements will explore confederated knowledge for distributed model updates, bettered inimical robustness, and adaptive explainability features to ensure secure decision- making in dynamic network surrounds.
The significant research contribution of this work lies in the development of FashFit, a personalized outfit recommendation and visualization system that goes beyond typical e-commerce virtual try-on tools. Instead of focusing on sales, it acts as a personal fashion advisor by analyzing user-specific features such as face shape, body size, skin tone, and fabric preferences, and then matching them with garment attributes like color, silhouette, and fabric type. Using computer vision and image processing, the system generates a virtual illusion of the user wearing the suggested outfits, allowing individuals to visualize suitability before making clothing choices. This contributes to fashion technology by filling the gap between simple visualization and true personalization, helping users make confident outfit decisions and reducing the trial-and-error process in clothing selection.
This research presents a vision-based dynamic traffic signal control system that leverages computer vision and deep learning (YOLOv8) to improve pedestrian safety and traffic efficiency. Unlike traditional fixed-time signals, the system dynamically adjusts pedestrian crossing times based on real-time pedestrian density, ensuring safe crossings while minimizing vehicle delays. It further incorporates automated violation detection, including red-light running, jaywalking, and stop-line violations, supported by license plate recognition for enforcement. With high detection accuracy (95.4%) and near real-time response, the proposed system offers a scalable and intelligent solution for modern urban mobility. Its contribution lies in combining adaptive traffic light optimization with automated violation monitoring, laying the groundwork for integration into future smart city infrastructure.
This paper identifies and outlines the challenges regarding cybersecurity in centralized access control architectures.
The paper proposes a decentralized access control architecture using smart contracts deployed on a blockchain. This system eliminates centralized points of failure and provides automated, tamper-resistant access decision-making using role-based policies.
This research pioneers the application of artificial intelligence for predicting brucellosis in dairy cattle in Bangladesh by developing a highly accurate deep learning model (up to 93.94%). A significant contribution is the use of the SMOTE technique to effectively manage imbalanced veterinary data, a common challenge in disease diagnostics. Furthermore, the study identifies and ranks critical clinical risk factors, establishing that a retained placenta is the most significant predictor. By creating association rules to clarify the interplay between these factors, this work provides veterinarians and farmers with a powerful and practical tool for early diagnosis, paving the way to mitigate substantial economic losses in the dairy industry.
This paper proposed a method to improve the accuracy of pedestrian flow analysis by applying machine learning-based object detection to urban video data. To handle occlusions and maintain robust tracking, a pedestrian modeling approach was introduced, which allows detection even when objects overlap and enables class identification using past detection results. The effectiveness of the proposed method was verified in a real-world setting. It was confirmed that the method can achieve stable detection and reduce class assignment errors, achieving a mean absolute percentage error of 1.0% for pedestrians and 2.2% for bikes.
The current paper brings a design, development and evaluation of an AI-based legal assistant named Clegora to suit the requirements of the Indian legal system. However, based on a state-of- the-art large language model, Groq Llama 3.2, deployed in a Retrieval-Augmented Generation (RAG) framework, Clegora delivers contextual and high-quality responses through the dynamic retrieval of relevant legal documents in a specialized vector database. The system uses a new token management approach that scales input lengths based on query complexity with the aim of being efficient in resource utilization without compromising on the quality of responses. Clegora has privacy and ethical standards, as it is built with secure authentication of the user by Firebase and strong content safety measures. Performance tests show short response times of less than five seconds to simultaneous users and citation efficiency of more than 95%. The responses of the users point out how the assistant could be used to simplify complex legal cases, multi-turn conversations, and provide users with confidence when handling legal cases. Though the existing constraints are the processing of language diversity in the region and very narrow domain queries, the modular design and continual data refreshing makes Clegora a scalable solution that will democratize the access to legal information, enabling informed decision-making in diverse Indian populations.
This research presents the integration of IoT and Mechatronic Systems in a compact, automated biogas digester designed for Small-Scale household use. The system enhances biogas yield efficiency through control of key parameters and real-time monitoring. The system offers a scalable solution for decentralized renewable energy and sustainable waste management in Small Island Developing States.
Recognition of handwritten characters is an important task in the field of image processing. Bangla handwritten characters exhibit greater complexity and variation in shape, along with high inter-class similarity, making their recognition particularly challenging. To address this, we propose the “EfficientNetV2S” model, designed to recognize complex and visually similar characters in the Bangla language. Although Bangla is one of the most spoken languages in the world, research on Bangla character recognition is still comparatively limited. In this study, we apply a deep learning model trained and tested on a custom dataset of Bangla handwritten characters. The model is utilized both for feature extraction and for its proven efficiency in image classification tasks, enabling it to learn image features effectively and recognize characters with remarkable accuracy. Our approach achieves an impressive 96.63% accuracy, highlighting the strength and reliability of the proposed method. To broaden the range of classes, we combined the “BanglaLekha-Isolated” and “Matrivasa-raw (Ekush)” datasets. The results show that the End Ensemble technique effectively tackles recognition challenges and offers strong potential for real-world applications like automation and education. This research advances Bangla character recognition and encourages further exploration in the field.
This study’s primary contribution is the development of a novel ViT-GNN hybrid deep learning model callled IGNN for plant disease classification. By combining the feature-learning prowess of a Vision Transformer with the relational-modeling capability of a Graph Neural Network, the model achieves superior performance and interpretability over traditional methods. This work demonstrates a new paradigm for leveraging both rich visual features and structural dataset relationships, advancing the field of automated plant disease diagnosis.
It contributes a lot to provide information about selection of best machine learning models for anomaly detection.
The selection of suitable battery technologies is a key lever in shaping the global transition to sustainable mobility. This study investigates the techno-economic potential of two emerging battery types – solid-state batteries and sodium-ion batteries. While solid-state batteries promise higher energy densities, sodium-ion batteries offer advantages in material availability and potential cost stability due to the abundance of sodium. A MATLAB-based evaluation is used to conduct a holistic analysis focusing on their application within the Tesla Model Y and the VW ID.3. The assessment considers key indicators such as energy density, specific costs, CO2 emissions, and achievable driving range. The results highlight technological trade-offs and demonstrate how different battery choices impact the ecological and economic performance of electric vehicles. Moreover, the flexibility of the modeling approach enables its application across a range of current and future vehicle configurations, supporting strategic decision making in battery selection.
The research by Mohammed Alghassab, conducted at Shaqra University, significantly advances renewable energy systems through the application of advanced machine learning (ML) models—Random Forest, Support Vector Regressor, Gradient Boosting, and CatBoost. Key contributions include achieving high prediction accuracy (Gradient Boosting: 94.2% classification accuracy, 1.86 MW RMSE) and perfect scalability prediction (CatBoost: 1.0 accuracy) for solar, wind, hydro, and geothermal systems. These models enhance energy output forecasting, resource classification, and performance optimization by leveraging feature engineering and hyperparameter tuning. The study demonstrates a 12% accuracy improvement and 10% error reduction over baselines, supporting grid stability, cost-efficiency, and CO2 reduction. By addressing intermittency, scalability, and dependability challenges, the research aligns with global sustainability goals, fostering innovation in smart grids and policy-driven energy planning for a low-carbon future.
The paper introduces and experimentally validates an end-to-end data-resilience layer for MQTT-based CPS that maintains AI subsystem reliability under adverse network conditions. Concretely, it contributes practical mechanisms (loss-aware buffering/retransmission, QoS/retention strategies, deduplication/back-pressure, and gap-tolerant ingestion for AI) and shows, through controlled tests with packet loss and instability, that operational continuity and model performance can be sustained despite communication faults.
The paper proposes an integrated Green Finance–AI framework and platform that links financial allocation with AI-enabled monitoring, verification, and reporting. This framework allows banks to transition from symbolic commitments to measurable decarbonization strategies. By aligning green finance with AI capabilities, the proposed solution supports environmental protection, social inclusion, economic resilience, governance accountability, and digital innovation; thereby operationalizing sustainable economic growth across all five pillars
An integrated, market-based pricing framework for isolated microgrids that jointly optimizes hourly PV–wind–battery–diesel dispatch and derives LCOE-consistent tariffs
This study is significant because it directly addresses the emerging quantum threat to IoT-based healthcare systems, which represent one of the most critical and vulnerable sectors of modern infrastructure. While much of the existing literature explores general post-quantum cryptography (PQC) migration strategies, there is a lack of frameworks specifically tailored to the unique constraints of healthcare IoT ecosystems. By proposing a phased, hybrid migration framework that accounts for the layered IoT architecture, device heterogeneity, and resource limitations, this paper provides a practical and structured pathway for ensuring security in healthcare systems against future quantum-enabled attacks.
The framework’s emphasis on crypto-agility, interoperability, and phased adoption is particularly important for healthcare environments where continuous operation and patient safety are paramount. Furthermore, the study bridges a critical gap by integrating both technical and operational perspectives, making it valuable for policymakers, system architects, and healthcare providers preparing for the quantum era. Ultimately, this work contributes to safeguarding patient data, ensuring the reliability of medical devices, and preserving trust in digital healthcare services in the face of advancing quantum computing capabilities.
This paper discusses the creation and assessment
of MediPath, a smart web-based emergency medical assistance
system. It connects patients, hospitals, and ambulance services
in one digital platform. The system uses technologies like
Natural Language Processing (NLP) to match hospital
specialties with patient symptoms. It also uses real-time location
services and smart route planning to address gaps in emergency
medical response. MediPath has a three-part structure that
supports logins for patients or attendants, hospital management
portals, and navigation systems for ambulance drivers. The
platform uses machine learning to link patient symptoms with
hospital specialties, Firebase Realtime Database for easy data
syncing, and mapping APIs for better traffic-aware routes.
Performance tests show average response times of under 30
seconds for hospital matches and 95% accuracy in matching
symptoms to specialties. There is also a significant drop in
ambulance dispatch delays. User studies at different healthcare
facilities indicate improved coordination, shorter emergency
response times, and better use of resources. Although there are
challenges with highly specialized medical conditions and
connectivity in rural areas, MediPath greatly enhances
emergency medical service delivery by creating a unified
ecosystem that connects all parties in real time. This research
presents a scalable, smart solution that addresses disconnected
emergency medical services. It uses modern web technologies
alongside healthcare knowledge to help save lives through
quicker and more coordinated emergency responses.
1) FedFall framework: a novel architecture that fuses federated and transfer learning to enable privacy-preserving and personalized fall detection.
2) Edge-based deployment strategy: an end-to-end system
that supports real-time inference and training on low-
power IoT devices.
3) Simulation-based validation: demonstration of FedFall’s feasibility using simulated multi-client datasets, high lighting accuracy, latency, and communication efficiency.
4) Adaptation for constrained devices: lightweight models and compression techniques to ensure compatibility with embedded platforms.
Firstly, we introduce a histogram-based decoding method for 4SQR-Code that improves efficiency over traditional approaches.
Secondly, we demonstrate substantial improvements in
both decoding time and memory usage on 3,000 test
samples, achieving an average reduction of approximately 98.66% in decoding time and nearly 88.91% in memory consumption compared to baseline methods.
Finally, we conduct a comparative analysis with the
traditional OpenCV QR Code detector to demonstrate the advantages of our method.
Integrating renewable energy into microgrids requires advanced power conversion systems that ensure stability, power quality, and harmonic suppression. This paper presents the design, analysis, and optimization of a five-level cascaded H-bridge (CHB) inverter integrated with an LCL filter and controlled by a PI controller using sinusoidal pulse-width modulation (SPWM). This work focuses on enhancing stability and harmonic performance in sustainable energy microgrids through parametric tuning of PI controller gains (Kp, Ki) and comparative analysis with alternative control strategies. Simulation results demonstrate that optimal PI tuning achieves a total harmonic distortion (THD) of 2.8% in grid current, with a robust transient response under dynamic loads. The sinusoidal pulse width modulation (SPWM) technique offers straightforward implementation while supporting compatibility with multilevel inverter topologies, making it scalable for high-voltage applications. Comparative analysis with proportional-resonance (PR) and hysteresis controllers emphasizes the practicality of PI controllers, achieving an effective balance between computational simplicity and control performance. Our proposed system addresses the main challenges of integrating renewable energy and provides a better and more cost-efficient way to enhance microgrid resilience and power quality. Future research should focus on more reliable and hardware-based authentication to transition key findings from the simulation into real-world applications
Partial shading significantly affects the performance of photovoltaic (PV) systems, resulting in significant power losses and decreased efficiency. This study examines the effect of partial shading on the output power and electrical parameters of photovoltaic modules through field case studies and outdoor experimental tests. Additionally, an I-V tracer, PROVA 1011, was used to experimentally examine 10W and 20W polycrystalline panels under controlled partial shade sizes of 20%, 40%, 50%, 60%, and 80%. The results show that the voltage remains stable at lower shading levels but drops sharply beyond 50%. The current declines linearly with increasing shading, and the power losses reach 88.4% under 80% shading. Five case studies have been considered in this study to analyze rooftop-based partial shading size and the loss calculated using the experimental data. The study shows that power losses vary from 83.87 % to 87.91% due to the partial shading size of 40% to 60% for a 100W module. These findings emphasize the critical impact of rooftop obstacle-based partial shading on PV system losses and the need for practical design of PV installation to mitigate the shading.
Electric vehicles (EVs) are central to sustainable transportation; however, their battery lifespan and charging efficiency are significantly impacted by seasonal temperature and humidity variations. This study investigates how Dhaka’s distinct climate periods—winter, summer, monsoon, and post-monsoon—influence EV battery degradation and charging performance. Laboratory tests at 10 °C, 25 °C, and 40 °C quantify capacity fade and impedance growth over 1,000 cycles, while field-deployed battery packs record real-world temperature and humidity impacts each season. Charging behavior is evaluated using 7 kW AC Level 2- and 50-kW DC fast chargers, measuring charge time and efficiency under ambient conditions. Results show that sustained summer heat (≥ 35 °C) accelerates irreversible capacity loss by approximately 15% per 1,000 cycles, and monsoon humidity (≥ 85% RH) reduces charging efficiency by about 8%. Recommendations include adaptive liquid-cooled battery thermal management, ambient-aware charger derating, and humidity-resistant connector designs. These strategies aim to extend battery life, improve charging reliability, and support EV adoption in tropical, monsoon-prone environments.
1. By overcoming current limitations like production cost, operational technology, and non-availability of advanced technology, this work designed a multi-axial movable bed system for bedridden individuals.
2. This design entails the integration of locally sourced components and parts, and real-time control of multidirectional kinematics.
3. The proposed design aspires to enhance patient autonomy, reduce caregiver workload, and provide a scalable, sustainable assistive solution tailored to the biomechanical and socio-technical requirements of domiciliary care in resource-constrained environments.
• The developed an advanced intrusion detection system
achieved 0.97 accuracy and 0.99 precision for attack clas-
sification, significantly outperforming baseline models
• This study innovatively integrates SHAP and LIME in-
terpretability methods, providing both global and local
explanations that validate the model’s reliance on mean-
ingful network features such as packet time-to-live (sttl)
and protocol types (proto).
• Through rigorous evaluation metrics (AUC=0.9974) and
error analysis , the study establishes the reliability of
the model for real-world deployment in security-sensitive
environments.
• This study contributes to cybersecurity research by
demonstrating how interpretable machine learning can
effectively detect modern network threats while maintain-
ing operational transparency for security analysts.
• The study outlines critical pathways for subsequent work,
including adaptive learning mechanisms for evolving
threats and optimization of edge computing architectures
in distributed networks
1.A universal ensemble system with high performance levels was developed according to metric measures and compared to the baseline ML and DL models on the EMBER dataset.
2.Designed an integrated system that uses RandomForest, Extra Trees, XGBoost, LightGBM, Hist-GradientBoosting and unified deep-learning ensembles to control bias-variance in a balanced way.
3. Both traditional and deep learning models incorporate soft-voting, stacking, and meta-learning to ensure adaptability across diverse malware feature spaces.
4. Demonstrated scalability using memory-mapped data handling and standardized preprocessing for large datasets
Providing the first systematic comparative analysis of the practical trade-offs between the EU’s rights-based, the U.S.’s market-driven, the UK’s flexible, and China’s state-controlled AI governance models, offering a crucial framework for global policymakers.
This research develops a Transactive Memory System-based visualization framework that leverages Social Network Analysis to enhance team collaboration in project management. The contribution lies in integrating SNA metrics with an interactive dashboard to provide managers with actionable insights into communication structures and knowledge sharing. The framework was validated through pre- and post-analysis in real organizational settings, demonstrating its potential to improve coordination, decision-making, and overall project performance.
This research introduces a modular hydroponic system with robotic integration for automated indoor plant care, supported by a CNN-based disease detection model. The system also monitors indoor air quality and controls ventilation, providing both health benefits and energy efficiency. It offers a sustainable alternative to traditional air purifiers, aligning with biophilic design to enhance wellbeing.
A significant part of my contribution lies in advancing RFID tag identification technology. I have developed and refined knowledge-based query tree algorithms that enhance identification efficiency through innovative mechanisms, including bit-tracking, shortcutting, and distinguished-bit techniques. These contributions have significantly enhanced both the speed and accuracy of RFID systems, providing critical improvements for IoT environments where rapid and reliable tag processing is crucial.
A significant part of my contribution lies in advancing RFID tag identification technology. I have developed and refined knowledge-based query tree algorithms that enhance identification efficiency through innovative mechanisms, including bit-tracking, shortcutting, and distinguished-bit techniques. These contributions have significantly enhanced both the speed and accuracy of RFID systems, providing critical improvements for IoT environments where rapid and reliable tag processing is crucial.
Md. Shohanur Rahman Shohan: Data analysis, Software, Methodology, Validation, Writing – original draft. Md Habibur Rahman: Conceptualization, Methodology, Software, Validation, Writing – reviewing, editing, and Supervision. Md Abbas Mahmud Suzon: Data curation, Validation, Writing –review and editing. Md Imran Hasan: helped in the preparation of the tables and figures. Md Shofiqul Islam: Review and Editing, Md Aktaruzzaman: Review and Editing.
Md. Shohanur Rahman Shohan: Data analysis, Software, Methodology, Validation, Writing – original draft. Md Habibur Rahman: Conceptualization, Methodology, Software, Validation, Writing – reviewing, editing, and Supervision. Md Abbas Mahmud Suzon: Data curation, Validation, Writing –review and editing. Sabrina Ferdous: helped in the preparation of the tables and figures. Md Shofiqul Islam: Review and Editing, Md Aktaruzzaman: Review and Editing.
Solar energy integration in the future of rooftops, windows, and interiors of glass as urban areas grow, and the need to provide power rises. Building-integrated photovoltaics (BIPV) is a new concept, where semi-transparent solar cells can turn glass facades into power plants without compromising comfort, daylight, or styling. This recent generation of solar technological solutions can solve the problem of sustainable energy production. The current work investigates the impact of transparency levels on energy generation by modeling the optical and electrical responses of ST-PSCs using finite-difference time-domain (FDTD) and SCAPs-1D software. Additionally, the study is devoted to performance assessment in the building-integrated environment and offers an optimized design architecture to be implemented in practice. The simulation outcomes show that decent efficiencies of 16.41% can be acquired with moderate transparency of 31.7% which sheds light on the significance of ST-PSCs in vertical and urban designs. The paper uses cell design software to simulate and optimize these semi-transparent panels. The findings highlight the feasibility of using semi-transparent solar panels as an optimal solution that addresses land scarcity, energy efficiency, and urban energy needs, providing valuable insights for the future integration of renewable energy systems in urban infrastructure and agriculture.
This study proposes and verifies a method to estimate speech endings in medical interviews using non-verbal information like face direction and tone. This could lead to the development of more realistic virtual patients for Objective Structured Clinical Examination (OSCE), addressing the shortage of human simulated patients and improving training for medical students.
Md Abbas Mahmud Suzon: Data analysis, Software, Methodology, Validation, Writing – original draft. Md Habibur Rahman: Conceptualization, Methodology, Software, Validation, Writing – reviewing, editing, and Supervision. Md Imran Hasan: Data curation, Validation, Writing –review and editing. Md. Shohanur Rahman Shohan: helped in the preparation of the tables and figures. Md. Robiul Hoque: Review and Editing, Md Aktaruzzaman: Review and Editing, Md. Farukuzzaman Khan: Review and Editing.
Menstrual hygiene is really a concerning issue and using commercial sanitary napkins during this period not only effects on maintaining hygiene but also effects our environment. We tried to innovate one more available waste in pads to make it biodegradable and also cost effective so that the feminine can learn about menstrual hygiene and use this pad which is affordable for them and the feminine of Bangladesh and India who thinks its a matter of shy as the pad will not be biodegradable(commercially sells);this biodegradable pads will remove their concern as the aerial root and bamboo is available in this country.
Traditional AMR navigation methods require prior mapping or the setup of a coordinate system for interested space, which can impose significant overhead.
We propose and implement an autonomous mobile robot (AMR) navigation system based on impulse radio ultra-wideband (IR-UWB), which relies solely on IR-UWB ranging between IR-UWB terminals mounted on the AMR and those placed at designated locations. Additionally, we incorporate transmit power control (TPC) to mitigate multipath effects and apply a moving average filter to smooth route fluctuation. We implement the navigation system using this approach and validate its effectiveness through experimental measurements.
This research provides the first detailed quantification of atmospheric moisture recycling in the Sudd Wetland, a globally significant yet understudied hydrological system. By integrating ERA5 reanalysis with the WAM-2layers model, it reveals the wetland’s substantial role in sustaining regional precipitation and its vulnerability to climate and land-use changes. The methodology offers a transferable framework for assessing land-atmosphere feedbacks in data-scarce regions, directly supporting sustainable water management, climate resilience planning, and transboundary governance in the Nile Basin and beyond.
The paper contributes both theoretically (by linking ICT with sustainable tourism in a Pacific Island context) and practically (by developing and testing a real app prototype that empowers local communities and promotes eco-tourism).
The research clearly presents the critical issues faced by taxi drivers in Fiji, outlines the proposed solution (the Smart Taxi Monitoring System), and highlights the potential benefits of implementing this system. It succinctly conveys the urgency of the problem and the innovative approach taken to address it, making it relevant to stakeholders in transportation safety, technology, and public policy.
Additionally, the emphasis on real-time monitoring and predictive analytics demonstrates a forward-thinking solution that aligns with current trends in technology and safety management. This combination of practical implications and technological advancement makes the content suitable for a broader audience interested in industry improvements and public safety.
The paper proposes a KDN-enabled IoT framework for resilient smart grid protection, integrating AI/ML-based anomaly detection, predictive fault recovery, and adaptive routing within a three-plane architecture. Simulations and testbed results show notable gains over baseline SDN—25.2% lower latency, 4.5% higher PDR, 43.8% faster recovery, 7.7% better attack detection, and 15.6% lower energy use—demonstrating a scalable, secure, and efficient smart grid communication solution.
The paper proposes a KDN-enabled IoT framework for resilient smart grid protection, integrating AI/ML-based anomaly detection, predictive fault recovery, and adaptive routing within a three-plane architecture. Simulations and testbed results show notable gains over baseline SDN—25.2% lower latency, 4.5% higher PDR, 43.8% faster recovery, 7.7% better attack detection, and 15.6% lower energy use—demonstrating a scalable, secure, and efficient smart grid communication solution.
A key contribution of this study is the formulation of a holistic mathematical model that simultaneously considers upstream sites selection, transportation routing, and vehicle choice, and evaluates their combined effects on downstream sustainability outcomes. The model employs a general sustainability index that can captures the specific sustainability priorities of the enterprise.
* Synthesis of Silicon-rich-silica from local river sand using a low-cost process
* Enhancement of seed germination and seedling growth parameters by priming Maize seed with Silicon-rich-silica.
This paper embodies a novel Pulse Width Modulation (PWM) strategy with an advanced closed-loop control technique for grid-connected three-level Neutral Point Clamped (NPC) converters. The proposed method efficiently adjusts the power factor to meet grid demands, supporting unity, lagging, or leading power factors. It achieves significant reductions in voltage and current Total Harmonic Distortion (THD), with voltage THD at 20.92% and current THD at 1.64%, both complying with the Institute of Electrical and Electronics Engineers (IEEE)-519 standard. These improvements are due to modifications in the modulating signal and carrier waveform. The method also demonstrates stable THD performance across varying switching frequencies and modulation indices. Simulation results validate the proposed approach, showing better THD performance than conventional PWM techniques such as Sinusoidal PWM (SPWM), Third Harmonic PWM (THPWM), Triangular PWM (TRPWM), and Bus-Clamping PWM (BCPWM). This work demonstrates the potential of the proposed method to enhance the performance of NPC converters in grid-connected systems, ensuring improved power quality and robust control.
Incorporating a dynamic energy routing system on a light electric vehicle using TAB converter and efficient utilization of solar and electrical energy which will help reduce grid dependence and lower emissions, contributing to sustainable transportation.
Design, developement,deployment , and evaluation of a Lightweight Image Classification Model for Resource-Constrained Environments Based on Enhanced MobileNet
This study offers a novel, physician-centered analysis of the adoption and integration of AI-enabled robotic systems for vital sign monitoring, identifying nine interrelated enablers and barriers. By combining thematic insights from interviews with recent literature, it advances understanding of both the technical and organizational factors influencing successful deployment, providing a practical framework to guide healthcare institutions in overcoming adoption challenges.
The main objective of this research was to study the proposed measures, which include using electric buses instead of diesel buses and installing photovoltaic systems in the available area of the bus station. The Jordan Valley’s new bus station in Irbid was used as a case study. Two scenarios for switching to electric buses were studied. The first was to convert the currently operating diesel buses to electric buses, and the second was to use new electric buses to replace the old buses. After that, a proposal was made to build a partial photovoltaic system covering the available space in the bus station.
This study aimed to perform a comprehensive evaluation of the life cycle assessment for a current 500MW oil shale power plant installed in the south of Jordan, compared to a proposed PV power plant with the same production capacity. The study was performed using Open LCA software and based on the Ecoinvent 3.7 database. The PV power plant was divided into 5 identical substations, each one has 500MW power capacity with 2.5 GW in total. The design was carried out through specialized software, which showed that each substation could generate 1027108 MWh annually. The LCA study was performed to find the environmental impact for five categories: climate change, ozone depletion, fossil depletion, human toxicity, and particulate matter formation.
The significant research contribution of this conference paper lies in its development of a high-resolution, sector-integrated geospatial electrification planning.
Key contributions include:
1) Multi-sector Demand Integration: Unlike many prior geospatial studies that focus only on household electricity demand, this study integrates electricity demand from health and education facilities, enabling more realistic and inclusive planning.
2) Use of High-Resolution Spatial Data: By employing fine-grained population data (e.g., HRSL) and georeferenced infrastructure/facilities, the study increases the accuracy of least-cost technology selection across diverse terrains and settlement types.
3) Scenario-Based Planning: It evaluates electrification pathways under low, medium, and high demand scenarios, accounting for future consumption growth, and identifies how technology preferences shift with changing demand levels.
Overall, the paper contributes to a policy-relevant framework for achieving universal electricity access in challenging geographies, with implications for similar contexts across Sub-Saharan Africa.
This study makes a significant contribution by quantitatively demonstrating the critical impact of incorporating hourly solar radiation, temperature, and electricity demand data into MG sizing, revealing substantial implications for system capacity and cost metrics in tropical rural communities. The findings show that neglecting hourly temperature variations leads to a considerable underestimation of the required PV and battery capacities, alongside a dramatic increase in the Levelized Cost of Electricity (LCOE) and Net Present Cost (NPC). Specifically, the analysis across three Mozambican communities indicates that accounting for temperature necessitates an increase in PV capacity ranging from 74% to 114% and battery storage from 85% to 122%. This translates directly into a significant increase in project costs, with LCOE increasing by 116% to 165% and NPC by 116% to 147%. These percentage increases are not minor adjustments but rather highlight a fundamental oversight in mini-grid sizing methodologies that do not account for temperature effects.
This study makes a pioneering contribution by conducting the first comprehensive investigation of the lead-free perovskite compound K3TlBr6 for high-efficiency solar cell applications. It integrates first-principles Density Functional Theory (DFT) and device-level SCAPS-1D simulations to assess the material’s electronic, mechanical, optical, and photovoltaic properties. Key findings reveal that K₃TlBr₆ exhibits a direct band gap, mechanical flexibility, strong UV-visible light absorption, and robust thermal and defect tolerance. The optimized device configuration achieves a power conversion efficiency (PCE) of 22.61%, confirming its viability as an eco-friendly, stable absorber layer for next-generation solar cells. This work bridges the gap between material properties and device performance, offering a valuable framework for future experimental development of K3TlBr6-based photovoltaics.
This study provides a comprehensive analysis of IoT adoption in Saudi higher education, highlighting its alignment with Vision 2030 goals. It contributes new empirical insights by using a mixed-methods approach to assess adoption levels, opportunities, and barriers. The research identifies key institutional disparities, offers practical recommendations for overcoming implementation challenges, and outlines strategic pathways for leveraging IoT to enhance educational quality, operational efficiency, and global competitiveness in Saudi universities.
This paper examines the factors influencing Coefficient of Performance (COP) and Seasonal COP for air-to-air heat pump systems subject to frost and defrost cycles, emphasizing economic performance modeling over theoretical thermodynamics. We integrate real-world data from field installations in Rhode Island and Pennsylvania, highlighting cost-performance tradeoffs, break-even COP thresholds for cost savings, and the impact of supplemental heating. The paper underscores the
importance of climate-specific analysis and smart control (e.g.
defrost minimization, optimal switchover to backup heat) in
maximizing both the economic and environmental benefits of
heat pumps in cold regions.
This study presents a comprehensive UAV-based inspection system that leverages AI, high-resolution imaging, thermal sensors, and LiDAR for efficient and automated monitoring of power transmission lines and solar farms. By integrating real-time cloud analytics and AI-driven fault detection, the research significantly enhances inspection speed, safety, and diagnostic accuracy. It offers a scalable and cost-effective alternative to traditional methods, contributing to the development of smart, resilient, and sustainable energy infrastructure.
Deepfake technology, leveraging advancements in deep learning, has become a significant threat to digital media authenticity, enabling the creation of hyper-realistic yet deceptive videos that challenge existing detection methods. This paper presents a hybrid approach combining ResNext Convolutional Neural Networks (CNN) for frame-level feature extraction and Long Short-Term Memory (LSTM) networks for analyzing temporal dependencies to improve deepfake detection accu racy. The study utilized a balanced dataset comprising videos from FaceForensic++, Celeb-DF, and custom-crafted deepfakes, with preprocessing steps that included facial region cropping, frame standardization, and noise reduction. The proposed model achieved an accuracy of 94.87%, outperforming existing methods by effectively capturing both static and dynamic video fea tures. Key innovations include leveraging the complementary strengths of CNNs and LSTMs to address frame-level and se quential inconsistencies in fake media. This approach is validated through extensive experimentation, demonstrating robustness against evolving generative adversarial techniques. The results establish a strong foundation for scalable and real-time detection applications, with future work aiming to enhance detection for multi-modal data and improve computational efficiency for deployment in resource-constrained environments.
Since deepfake films allow for the production of extremely convincing manipulated media, they represent serious threats to the integrity of information. These videos are produced utilising sophisticated machine learning models such as Gen erative Adversarial Networks (GANs). This study introduces a hybrid deep learning framework that efficiently detects deepfakes by combining Long Short-Term Memory (LSTM) networks for temporal analysis with ResNext Convolutional Neural Networks (CNNs) for spatial feature extraction. By applying transfer learning, the model reduces computing overhead while achieving great accuracy and efficiency. For training and assessment, a meticulously selected dataset of 1,000 videos that was evenly dis tributed between authentic and fraudulent content was utilised. During preprocessing, video frames’ facial features were sepa rated and cropped to provide a high-quality face-only dataset. The suggested model proved its resilience in detecting modified information with an astounding 95% detection accuracy on the test set. The model’s superiority over baseline techniques was demonstrated through performance validation using metrics like precision, recall, and F1-score. In order to combat the swift advancement of deepfake technology, this study highlights the significance of creating flexible detection methods. Subsequent efforts will concentrate on extending detection capabilities to encompass full-body movements and incorporating the frame work into easily available tools such as browser-based plugins for continuous use.
We introduce PotatoGANs, a hybrid augmentation approach using CycleGAN and Pix2Pix to generate synthetic diseased potato images from healthy samples, enhancing dataset diversity and model generalization while reducing data collection costs. To support model interpretability, we combine GradCAM, GradCAM++, and ScoreCAM with DenseNet169, ResNet152 V2, and InceptionResNet V2, offering transparent visual explanations of model predictions. Unlike existing work focused solely on leaf-level analysis, our method addresses whole-crop disease localization using advanced segmentation tools like Detectron2. Validated by the Bangladesh Agricultural Research Institute, this study aims to support the advancement of agricultural disease diagnosis and management in Bangladesh.
By implementing these recommendations, South Sulawesi can position itself as a leader in sustainable industrial development, contributing to global efforts to combat climate change and promote circular economy principles.
We report the size dependent uptake of dsRNA loaded MSNPs into the leaves and roots of Nicotiana benthamiana plants and accessed for their relative reduction in Tomato leaf curl New Delhi viral load. A non-GMO method of RNA interference (RNAi) has been recently in practice through direct delivery of double stranded RNA into the plant cells. Tomato leaf curl New Delhi virus (ToLCNDV), a bipartitie begomovirus, is a significant viral pathogen of many crops in the Indian subcontinent. Conventional RNAi cargo delivery strategies for instance uses viral vectors and Agrobacterium-facilitated delivery, exhibiting specific host responses from the plant system. In the present study, we synthesized three different sizes of amine-functionalized mesoporous silica nanoparticles (amino-MSNPs) to mediate the delivery of dsRNA derived from the AC2 (dsAC2) gene of ToLCNDV and showed that these dsRNA loaded nanoparticles enabled effective reduction in viral load. Furthermore, we demonstrate that amino-MSNPs protected the dsRNA molecules from nuclease degradation, while the complex was efficiently taken up by the leaves and roots of Nicotiana benthamiana. The real time gene expression evaluation showed that plants treated with nanoparticles of different sizes ~ 10 nm (MSNPDEA), ~ 32 nm (MSNPTEA) and ~ 66 nm (MSNPNH3) showed five-, eleven- and threefold reduction of ToLCNDV in N. benthamiana, respectively compared to the plants treated with naked dsRNA. This work clearly demonstrates the size dependent internalization of amino-MSNPs and relative efficacy in transporting dsRNA into the plant system, which will be useful in convenient topical treatment to protect plants against their pathogens including viruses. Mesoporous silica nanoparticles loaded with FITC, checked for its uptake into Nicotiana benthamiana.
This paper signifies the importance of replacing current Ordinary
Portland Cement (OPC) manufacturing processing with low carbon emission geopolymer based cements in construction industry and addressing the challenges for the supply chain in Australia.
At Bantaeng in South Sulawesi and Kwinana in
Western Australia new industrial scale ports will be built to serve
the industrial precincts at these locations. At both these sites a 1-
2Mtpa GPC plant is proposed for precast production of some 1,600
port modules as well as other infrastructure requiring some 750,000
cum of concrete and thereafter the plant can be repurposed for other
products for local markets such as reef modules and wall panels.
Geopolymer concrete can be the replacement for conventional
concrete and be made from waste-derived materials while having a
lower carbon footprint. The plant is designed to be operated by
renewable energy and an energy audit estimated that a 1Mtpa
geopolymer production plant needs up to 200 GWh pa to operate.
This could be served by 6-10 on-land wind turbines combined with
solar PV farm at a total cost $45-55 million USD. The electricity
generated @ say $100/MWh was worth $12-20M pa that could
result in a payback of 2-5 years. In Kwinana, planning is already
underway for a large wind farm as part of the overall
decarbonisation strategy for this industrial precinct. Feedstock
materials can be harnessed for use in the geopolymer production
plant by means of Circularity Hubs. These hubs can be established
through the KIC4 and 6-Capitals models of Industrial Symbiosis and
to optimise the proposed geopolymer plant within the industrial
precincts at Bantaeng and Kwinana. Such an approach can
contribute to Regenerative Development when both of the ports are
built.
Diversification of seaweed products can also open up new economic opportunities, create jobs, and reduce dependence on imported products. As an archipelagic country rich in water resources, Indonesia has unique characteristics in the water-energy-food relationship. Additional investigation is required to explore the most effective types of seaweed and optimal processing methods to maintain iodine content and other bioactive components.