Gongcheng Kexue Yu Jishu/Advanced Engineering Science

The aggressive scaling of CMOS technologies and the widespread adoption of Internet of Things (IoT) systems demand highly power-efficient, temperature-robust, and frequency-stable on-chip power management and clock generation circuits. This work presents an integrated design of a low-power bandgap reference (BGR), a temperature-adaptive low-dropout regulator (LDO), and a low power crystal oscillator to achieve reliable timing under wide operating conditions. Finally, a low power crystal oscillator is implemented using a Pierce configuration with negative resistance of approximately 500Ω at 48MHz, satisfying the oscillation startup criterion of three times the crystal ESR. The oscillator achieves rail-to-rail output swing, startup time below 2ms, and total current consumption below 75µA, corresponding to power consumption of approximately 90µW at 1.2V. The combined architecture demonstrates that co-design of reference, regulation, and clock generation circuits significantly enhances frequency stability, reduces power dissipation, and improves system-level power efficiency, making the proposed framework highly suitable for next-generation IoT, wearable, and battery-operated edge devices.

Molecular structures are assigned numerical v-alues known as topological indices, which serve as important descriptors in chemical graph theory. In the present study, degree-based topological indices of the total graph of alkanes, a significant class of saturated hydrocarbons, are investigated. Several widely used indices, including the first and second Zagreb indices, Randić index, harmonic index, atom-bond connectivity index, forgotten index, sum connectivity index, symmetric division degree index, geometric–arithmetic index, and the first and second hyper Zagreb indices, are computed for the total graphs of linear alkanes. A detailed numerical comparison of these indices is presented along with their graphical behaviour as the carbon chain increases. Furthermore, regression analysis is performed to examine the relationship between the computed indices and selected physicochemical properties. The results highlight the influence of molecular structure on topological characteristics and demonstrate the applicability of total graph of alkanes-based descriptors in chemical graph theory, molecular modeling, and predictive chemistry.

Graph operations are essential in many graph theory applications because they enable the creation of large graphs from smaller ones. This study focuses on a particular graph-theoretical operation: the Cartesian product graph. Our research is focused on alkanes, the simplest hydrocarbons made only of hydrogen (H) and carbon (C) atoms with no functional groups. In this paper, we compute several topological indices for the Cartesian product of alkanes namely, Atom Bond Connectivity Index ( ABC), Geometric Arithmetic ( GA ), Randic Index ( ), Reciprocal Randic Index ( RR ), First Zagreb Index ( ), Second Zagreb Index ( ), Hyper Zagreb Index ( HM ), Forgotten Index ( F ), Inverse Sum Indeg Index ( ISI ), Symmetric Division Deg Index ( SDD ). Additionally, we provide a graphical and numerical comparison of these topological indices.

The rapid growth of IoT (Internet of Things) networks has brought new challenges in ensuring the safety and reliability of devices and data. Automated Teller Machines (ATMs) are increasingly targeted by physical attacks, theft, and sophisticated intrusion attempts, necessitating intelligent and privacy-preserving security mechanisms. This paper proposes a federated learning–based multi-level threat detection framework for ATM security that integrates both external sensors (vibration, magnetic, tilt) and internal sensors (door status, liveness detection, face masks or static images). Each ATM operates as an edge node, locally analysing sensor data to classify operational states into three threat levels: Normal, Theft, and Critical. To ensure reliability and safety, a hybrid decision strategy is adopted, combining rule-based safety checks for critical events with a simple machine learning model for nuanced threat identification. Collaborative model training across several ATMs is made possible by federated learning. without transferring raw sensor data to a central server, protecting data in the process and reducing communication overhead. Critical conditions such as forced entry attempts, abnormal cash handling, and ATM communication failures are detected in real time and escalated through centralized monitoring using visual alert or notify the bank’s higher authorities. The findings show that federated learning is a scalable and secure solution for distributed ATM threat monitoring in real-world banking infrastructures, maintaining data privacy, reducing communication overhead.

Healthcare systems face an unprecedented surge in sophisticated cyberattacks, with ransomware, data breaches, and advanced persistent threats (APTs) posing severe risks to patient safety and data privacy. Traditional security information and event management (SIEM) systems struggle to keep pace with the evolving threat landscape, demonstrating detection rates below 75% for novel attacks and generating excessive false positives that overwhelm security operations centers. This paper presents a comprehensive AI-driven cybersecurity framework specifically designed for healthcare environments. Our approach integrates multiple machine learning algorithms including Isolation Forest for anomaly detection, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and ensemble methods combining Random Forest, Convolutional Neural Networks (CNN), and Deep Neural Networks (DNN) to achieve superior threat detection and automated response capabilities. Through extensive evaluation across simulated and real-world healthcare network environments, our ensemble model achieved 98.5% accuracy in threat detection, with precision of 98.2%, recall of 97.9%, and an F1-score of 98.0%. The false positive rate was reduced to 1.8%, compared to 12.5% for traditional SIEM systems. Average threat detection time improved from 45-240 minutes (traditional methods) to 1.8-5.2 minutes (AI-enhanced system), enabling rapid response to critical threats. The framework incorporates automated response mechanisms, continuous learning capabilities, and HIPAA-compliant data handling procedures, making it practical for deployment in resource-constrained healthcare environments. Implementation costs range from $400K-$800K, with projected ROI of 220-340% within 24 months through reduced breach incidents, minimized downtime, and lower operational overhead.

Effective healthcare delivery and research increasingly depend on the ability to share clinical data across organizational boundaries. However, fragmented systems, escalating cyber threats, and stringent regulatory requirements continue to limit secure and trustworthy health information exchange. Traditional perimeter-based security models, which assume implicit trust within institutional networks, are no longer adequate in modern, highly distributed healthcare environments. This paper presents a comprehensive framework for secure health data sharing grounded in continuous verification principles, layered encryption, and coordinated governance across participating institutions. The proposed architecture enforces verification of every access request regardless of user location, encrypts data throughout its lifecycle, and maintains detailed audit trails to ensure accountability and regulatory compliance. In addition, the framework supports privacy-preserving collaborative research through federated and distributed analysis models that allow institutions to generate shared insights without exposing individual patient records. Practical implementation strategies, governance structures, and phased deployment pathways are outlined to enable incremental adoption without disrupting clinical workflows. By integrating technical safeguards with organizational coordination, this framework demonstrates how healthcare organizations can balance data accessibility, patient privacy, and system security. The approach offers a scalable and practical foundation for advancing interoperable care, collaborative clinical intelligence, and public trust in health data exchange.

The increasing use of multi-layer composite materials in high-performance engineering applications has created a growing demand for accurate modeling of transient heat conduction. The heterogeneous and anisotropic nature of composite systems, combined with the presence of multiple material interfaces, introduces significant complexity in predicting time-dependent thermal behavior. This study presents a qualitative and systematic synthesis of existing research on transient heat conduction modeling in multi-layer composite materials with the objective of developing an integrated conceptual framework. A systematic literature review was conducted using major scientific databases, and the selected studies were analyzed using thematic analysis to identify dominant modeling approaches, key influencing parameters, and persistent research challenges. The findings reveal the predominance of numerical techniques, particularly finite element modeling, supported by analytical methods for theoretical validation. The analysis highlights the critical role of thermal interface resistance, anisotropic material behavior, and the increasing importance of multi-scale and multi-physics modeling. Emerging trends such as machine learning and digital twin technologies were identified as promising directions for improving predictive accuracy and reducing computational cost. The study identifies significant gaps in experimental validation, interface characterization, and integration of physics-based and data-driven approaches. Based on the synthesis, an integrated conceptual framework for transient heat conduction modeling is proposed. The findings contribute to a more unified understanding of thermal behavior in layered composite systems and provide guidance for future research in advanced thermal management and composite material design.

Detection of images in low-visibility poses challenges for safety management of autonomous vessels, coastal surveillance, and maritime situational awareness. Our emotional convolution neural network suffers from performance degradation when raw images are captured under adverse weather, fog and in low-light (dark) illumination. We propose a light-weight Vision Transformer (ViT) based model which provides real-time nautical image enhancement and object detection for lower visibility conditions. We propose a paradigm shift from using a convolution neural network (CNN) as the work-horse of ViTs are good models for capturing long range information but are computationally light-weight and amenable to low-Size Area Power (SAP) embedded systems space applications. Having surveyed and re-used some existing light-weight magic tricks we integrate three ingredients: (1) - a contrast-guided image augmentation module on the basis of dark channel prior, or non-uniform illumination correction techniques [essentially re-establishing “daylight” illumination], (2) a light-weight vision transformer backbone based on Rostrum Rep ViT allowing us to utilize a proportionately lower number of parameters along with the beauty of transformers, and (3) a scale-adaptive feature fusion module for distributing information through cross stage connections. We provide inner workings Anglo-Saxon Esque explanations plus expectations in terms of increasing transparency to underpin the advantageous use of our cherries here. Furthermore, recipe ingredient/Ton berry poison testing using ablation studies confirms miraculously that each of our features/components have “magical” merit. We perform experiments in several data sets RUOD, UTDAC etc and also low-visibility maritime situations (including dense fog). Testing shows a mean Average Precision (mAP) of 94.7% on 321, LOL380 speeds on edge systems via the Py Torch framework. Our model has direct applicability in dense fog and heavy rain amongst other tests, while we show our method achieves required performance with a truly marginal added computational increase 12.8M parameters 18.4 GFLOPs compared to a number of other CANN solutions including YOLOv11 based and RT-DETR. Our work closes the crucial gap in maritime automation that now exists, providing a simple, deployable method to enable real-time object detection on low-resource platforms without sacrificing accuracy, directly impacting autonomous surface vessel (ASV) navigation, maritime port security, fishing vessel monitoring, and climate resilient coastal management systems in keeping with the IMO 2030/2050 decarbonisation targets.

The emergence of artificial intelligence in textual production marks a profound transformation in the structure of digital literature and cultural expression. Unlike earlier digital innovations that primarily altered the format or distribution of texts, AI-driven systems actively participate in generating language itself. Large language models produce coherent prose, poetry, and narrative structures through probabilistic pattern recognition, drawing from vast datasets of human-authored material. As a result, textual creation shifts from intentional authorship toward computational synthesis, challenging long-standing assumptions about originality, creativity, and meaning. Traditionally, literary meaning has been associated with human experience, authorial intention, and interpretive context. However, AI-generated texts complicate this framework by demonstrating linguistic fluency without consciousness or subjective awareness. This development raises critical theoretical questions: If language can be produced without lived experience, does meaning remain tied to intention, or does it emerge entirely through reader interpretation? In the digital environment, meaning increasingly becomes relational and interactive, shaped by algorithmic generation, platform circulation, and audience engagement. At the same time, artificial intelligence operates within broader cultural infrastructures that influence which texts are produced, amplified, and consumed. Algorithmic systems not only generate language but also rank, recommend, and distribute content, thereby shaping cultural visibility and symbolic power. Consequently, digital texts in the age of AI reflect both creative possibility and structural constraint. The transformation of literary culture under artificial intelligence is therefore not merely technological but epistemological. It redefines language as data-driven synthesis and repositions cultural expression within computational systems, prompting a re-evaluation of authorship, creativity, and the nature of literary meaning.

A novel polymer was synthesized through the condensation of 2, 4-dihydroxybenzoic acid with acrylamide and formaldehyde. The polymer was comprehensively characterized by elemental analysis, physico-chemical methods, and spectrometry such as Ultraviolet Visible spectroscopy, Infrared spectroscopy and Proton Nuclear Magnetic Resonance. Semi-crystalline nature of polymer was exported using electron Beam microscopy. GPC (gel permeation chromatography) was implemented to figure out the molecular weight of the polymer. To assess its metal adsorption capabilities, a batch separation performance was used for the selective extraction of toxic traces metallic cation such as Co²⁺, Cd²⁺, and Pb²⁺. Experimental outcomes indicated that the synthesized polymer possesses a highly permeable and major surface area, contributing to its enhanced metal ion uptake. When differentiate with marketable available phenolic and polystyrene resins, the polymer demonstrated superior adsorption performance. By employing thermogravimetric analysis (TGA), the polymer's thermal stability has been assessed. Detailed thermal decomposition behavior was studied, and kinetic parameters were evaluated using both the Freeman–Carroll and Sharp–Wentworth methods. The consistency between the results from both methods confirmed the reliability of the thermal analysis.

Narrative voice functions as a critical medium through which identity is constructed, negotiated, and reconstructed in contemporary English literature. In contexts shaped by globalization, migration, postcolonial legacies, gender politics, and socio-cultural fragmentation, literary narratives increasingly foreground voice as a site of memory, resistance, and power negotiation. This study examines how contemporary English literature reconstructs identity through linguistic strategies, narrative perspective, and memory articulation. Drawing from postcolonial theory, feminist narratology, and discourse analysis, the paper argues that narrative voice is not merely a stylistic device but a political and epistemological instrument that shapes subjectivity and challenges dominant power structures. By analyzing selected thematic patterns in contemporary texts, the study explores how fragmented narration, multilingual expression, unreliable narrators, and memory-based storytelling contribute to identity formation. The paper concludes that narrative voice serves as a transformative space where marginalized identities reclaim agency and contest hegemonic discourses.

The impact of increased CO2 in environment is a major factor of climate change and has cascading effects on ecosystem, human societies and agriculture. Increased CO2 levels can affect crop yield and food security. This paper aims to examine carbon credit of a greenhouse integrated solar photovoltaic thermal (GiSPVT) system that uses Multi crystalline solar cell material with semitransparent (SPVT) collectors for control environment while promoting the friendly atmosphere. GiSPVT-integrated air collectors supply thermal and electrical energy, which is used to regulate the structure's humidity and temperature. In this paper analytical calculations are done to derive the expressions that can find temperature of the plant and air within the greenhouse, solar cell temperature, also the efficiency of electrical energy of the GiSPVT system along with the collectors. The expressions are derived with the help of climatic variables like solar irradiations and ambient air temperature, as well as some design parameters such as temperature coefficients, heat transfer coefficients, area of the PV module, electrical efficiency under standard test conditions (STC). The performance of the proposed system is evaluated using different energy performance indicators, namely energy payback time (EPBT), energy production factor (EPF), and life cycle conversion efficiency (LCCE). Thermal modelling is specifically carried out for a multi-crystalline silicon (mc-Si)–based GiSPVT system integrated with air collectors. The total electrical and thermal energy outputs of the system are estimated in order to assess these energy matrices. In addition, the total carbon emissions, carbon mitigation, and carbon credits associated with the proposed system over its entire lifetime are also evaluated.

The stagnation of commercial productivity in okra [Abelmoschus esculentus (L.) Moench] due to narrow genetic bases and climatic fluctuations necessitates a paradigm shift from mere yield maximization to the development of "Climate Smart" genotypes. Understanding Genotype × Environment (G × E) interactions are critical to identifying hybrids that maintain stability across diverse agro-climatic zones.The present investigation evaluated the phenotypic stability and adaptability of 37 genotypes (8 diverse parents, 28 F1 hybrids derived from a half-diallel mating design, and one standard check, Parbhani Kranti). The experiment was conducted across three distinct environments (E1, E2, E3) using a Randomised Block Design. Stability parameters were estimated using the Eberhart and Russell (1966) model to partition G × E interaction into linear (predictable) and non-linear (unpredictable) components for eleven quantitative traits. Results: Pooled analysis of variance revealed highly significant differences among genotypes and significant G × E interactions for key economic traits, including fruit yield per plant. Notably, the linear component of G × E interaction was significant for fruit yield, indicating that the performance shifts of these hybrids are predictable. The stability analysis identified Phule Utkarsha × AKOV-107 as a specific adaptor for favourable, high-input environments, recording the highest mean yield of 163.65 g/plant with a regression coefficient (bi) of 2.04. Conversely, the hybrid AKOV-107 × GO-6 emerged as a widely adapted, resilient genotype with a stable mean yield of 118.90 g/plant and a regression coefficient near unity (bi ≈ 0.82). For resource-constrained conditions, GOA-5 × GJO-3 demonstrated stress tolerance with a distinct stability profile (bi < 0). Conclusion: The study successfully identified novel heterotic combinations that break the current yield plateau while offering specific adaptation strategies. The identification of these stable and responsive hybrids provides a roadmap for "Climate Smart" okra breeding, ensuring nutritional security under fluctuating climatic conditions.

IoV is becoming a major facilitator of intelligent transport systems, which can encompass vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communications. Nonetheless, IoV networks can be susceptible to fake information transmission, Sybil attacks, and malicious nodes that decrease the reliability of vehicle exchange of data. The current centralized trust authorities have a single point of failure, bottlenecks in scalability, and poor transparency. In response to those challenges, the paper suggests blockchain-based decentralized trust and reputation management system of the IoV. The system proposed combines a lightweight blockchain consensus among Road Side Units (RSUs) and edge servers with a smart contract-based trust model to assign reputation scores to vehicles dynamically, depending on the accuracy, consistency, and behavior of the data. The blockchain holds trust scores that are imperishable and enable reputation management that is both transparent and incorruptible. The framework is feasible as evidenced by a simulated environment of synthetic vehicular transactions Simulation results indicate faster trust convergence, effective malicious vehicle discrimination, and scalability trends under increasing vehicular density. The findings are based on comparative and theoretical examination, which provides a base to further implementation based on SUMO and Hyperledger Fabric. This study offers a safe and scalable solution to trust management in IoV and opens the gate to safer and more trusted intelligent transport systems.

Traditional artificial intelligence (AI)–based inspection systems in manufacturing are typically deployed as static classifiers, trained on historical datasets and rarely updated to reflect evolving production conditions. However, contemporary manufacturing environments are characterized by concept drift, rare defect emergence, process variability, and increasing regulatory scrutiny, rendering static inspection architectures inadequate. This study proposes a socio-technical framework of Adaptive Artificial Intelligence (AAI) for industrial quality inspection. We conceptualize AAI as an enterprise-level dynamic capability that integrates adaptive learning mechanisms, uncertainty-aware decision engines, dynamic inspection policies, human-in-the-loop governance, and enterprise system alignment into a unified architecture. Grounded in adaptive systems theory, dynamic capabilities theory, socio-technical systems research, and cyber-physical systems literature, the framework formalizes five interrelated constructs and articulates their structural relationships through moderated and mediated theoretical propositions. Specifically, adaptive learning moderates the relationship between production volatility and inspection robustness, while dynamic inspection policy mediates the impact of uncertainty-aware decision signals on cost–quality efficiency outcomes. Governance and enterprise alignment act as cross-cutting enablers influencing organizational trust and learning. The paper further redefines inspection performance through resilience-oriented metrics, including robustness under drift, adaptive capacity, inspection elasticity, cost–quality efficiency, and trust-based compliance. Cross-industry illustrations from automotive, electronics, and pharmaceutical manufacturing demonstrate the applicability of the framework. By repositioning inspection as a dynamic, enterprise-embedded capability rather than a static algorithmic function, this study contributes to operations and AI research and advances the theoretical foundations of human-centric Industry 5.0 quality systems.

Financial literacy is extremely significant in today's complex and interconnected global environment. It encompasses the knowledge, skills, and understanding of financial concepts that empower individuals to make informed and responsible decisions about their personal finances. The present study focuses on determining the financial literacy among students at higher educational institutes, the challenges they encounter in managing their personal finances, and explores the measures that can be taken to improve their financial literacy and overall financial well-being. Using both main and secondary sources, a survey-based research design gathered data from 101 college students. Participants were divided into low, medium, and high literacy groups based on self-evaluation and knowledge of fundamental financial concepts. The results indicate that 20.79% of students exhibit low literacy, 28.71% exhibit high literacy, and 50.5% exhibit moderate financial literacy. While only a small fraction turns to professional financial counsellors, most students (44.6%) depend on self-directed research for financial knowledge. Lack of financial knowledge (25.74%), peer pressure causing overspending (23.76%), and high cost of living (20.79%) were among the main issues discovered. Interestingly, 68.32% of pupils advocate making financial literacy instruction required in every field of study. The research supports the conclusion that better personal financial management is strongly correlated with higher financial literacy and that financial education initiatives successfully raise pupils' financial awareness. But financial literacy levels were not much different between various academic fields. The study advises including financial literacy in academic programs, holding frequent seminars, and using digital tools to help pupils improve their financial capacity. For governments trying to enhance financial education programs for college students as well as for educational institutions, these results offer insightful information.

In this paper, we introduce the concept of neutrosophic soft regular semi baire spaces in neutrosophic soft topological spaces. Also, we define neutrosophic soft regular semi-nowhere dense, neutrosophic soft regular semi first category and neutrosophic soft regular semi second category sets. Some of its characterizations of neutrosophic soft regular semi baire spaces are also studied. Several examples are given to illustrate the concepts.

Dehydration and electrolyte derangement constitute critical physiological stressors, particularly in environments characterized by extreme thermal indices and high physical exertion. Despite the clinical significance, continuous monitoring of hydration status remains a challenge due to the lack of non-invasive, cost-effective, and integrated real-time tracking systems. This paper proposes a conceptual Internet of Things (IoT) framework designed for autonomous physiological and environmental telemetry. The proposed architecture leverages an ESP32 microcontroller as a central processing unit, integrating high-fidelity sensors for heart rate (HR), body temperature, ambient humidity, and atmospheric pressure. A novel mathematical Hydration Index (HI) model is developed using min-max normalization and a weighted multi-sensor data fusion algorithm. Unlike traditional reactive measures, this framework proactively estimates fluid-electrolyte status by quantifying the interplay between internal cardiovascular stress and external environmental variables. Initial simulation-based analysis demonstrates the model's logical consistency in differentiating between normal hydration, mild dehydration, and acute risk states. The system architecture emphasizes a layered IoT approach, ensuring high scalability and low-power consumption for integration with mobile health (mHealth) platforms. This research provides a structured foundation for non-invasive health supervision in sports science, geriatric care, and occupational safety. Future extensions will focus on longitudinal experimental validation and the integration of predictive machine learning models for personalized hydration risk assessment.

In recent years, speech recognition technology has gained significant importance in web-based applications due to its ability to enable hands-free interaction, enhance user convenience, and improve accessibility for individuals with physical or hearing impairments. The rapid advancement of artificial intelligence and natural language processing techniques has made voice-driven systems more accurate, responsive, and adaptable across multiple domains such as education, healthcare, customer service, and smart environments. Voice-to-text conversion, in particular, has become a widely adopted feature in modern applications, allowing spoken language to be transformed into written text efficiently and in real time. This article presents the design and implementation of a Voice-to-Text Based Threat Detection and Alert System developed using Python and the Django web framework. The system is designed as a web-based application that captures spoken input directly through a browser interface, processes the audio using the SpeechRecognition library, and converts it into readable textual format. The transcribed text is displayed instantly on the application interface, ensuring smooth user interaction and minimal processing delay. By leveraging Django’s structured backend architecture, the system ensures secure data handling, efficient request processing, and scalable deployment capabilities. Unlike conventional voice-to-text systems that focus solely on transcription accuracy and speed, this system extends functionality by incorporating an intelligent threat detection module. After the speech is converted into text, the system analyzes the content using predefined keywords, logical filtering rules, and pattern recognition techniques to identify potentially harmful, abusive, or threatening language. This additional layer of analysis transforms the system from a simple transcription tool into a proactive safety monitoring solution. When suspicious or dangerous content is detected, the system automatically triggers an alert mechanism. A notification is generated and displayed on a dedicated monitoring dashboard designed for parents, guardians, or administrators. The alert includes relevant details such as the identified keyword and transcript segment, enabling timely awareness and intervention. All conversation logs and alert records are securely stored in the database for documentation and future review. By combining real-time speech processing, web technologies, structured data management, and safety monitoring mechanisms, the proposed system offers an efficient, scalable, and user-friendly solution for secure communication environments. The integration of voice recognition with automated threat detection demonstrates how modern technologies can be utilized not only to enhance accessibility and convenience but also to strengthen digital safety and proactive supervision.

Crop diseases are a major threat to agricultural productivity and food security worldwide, directly impacting the achievement of SDG 2 – Zero Hunger established by the United Nations. Early detection of plant diseases is crucial to prevent yield loss, enhance food availability, and ensure sustainable farming practices. Traditional methods of identifying crop diseases rely on manual inspection by experts, which can be time-consuming, costly, and prone to human error. This project presents an intelligent system for early detection of crop diseases using image processing techniques and Convolutional Neural Networks (CNN). The proposed method begins with capturing high-resolution images of crop leaves using a mobile camera. The images undergo pre-processing steps such as noise removal, resizing, and contrast enhancement to improve image quality. After pre-processing, image segmentation is performed to isolate the diseased region from the healthy portion of the leaf. Feature extraction is automatically handled by the CNN model, which identifies patterns related to color variations, texture irregularities, and shape distortions. The extracted features are passed into the trained CNN classifier, which categorizes the leaf as healthy or affected by a specific disease. The system provides accurate and real-time disease prediction. By enabling early intervention, this approach reduces dependency on manual diagnosis, minimizes crop loss, increases agricultural productivity, and strengthens food security. Therefore, the proposed system directly contributes to achieving SDG 2 – Zero Hunger by supporting sustainable agriculture and improving global food production through precision farming technologies.

Detecting chronic diseases at an early stage is a major contributing factor to reducing healthcare expenses and increasing survival rates. Most diagnostic methods utilize laboratory tests and the physician’s expertise; therefore, it could take time for patients to receive timely treatment.This research presents a Multi-Disease Prediction and Appointment Management System that employs machine learning techniques in order to create an integrated solution for providing healthcare. The proposed system will predict four major diseases, including: diabetes, heart disease, Parkinson’s disease, and skin diseases. Specifically, Support Vector Machine (SVM) will be used to predict diabetes and Parkinson’s Disease, Logistic Regression will be used to predict heart disease, and Convolutional Neural Networks (CNN) will be used to categorize dermatological images. Medical datasets and images of skin conditions were collected from Kaggle; the datasets were then trained and tested in Google Colab. After training and testing the model in Google Colab, it was deployed via a web interface created with Streamlit. Additionally, Firebase will be utilized as a mechanism for both secure doctor/patient authentication and real-time appointment management. This study contributes to sustainable healthcare development by enhancing public health, improving health system access, and supporting inclusive health through machine learning–based disease prediction.

Electrical cable theft is a major problem in Indian Railways. It leads to service interruptions, financial losses, and serious safety risks. Traditional monitoring systems rely on manual checks or limited CCTV surveillance. They frequently fail to catch theft in real time, causing delays in response and increased damage. This project presents an Electrical Cable Theft Detection System using IoT sensors, microcontrollers, and GSM communication modules to monitor cable continuity and voltage levels along railway tracks. The system promptly detects unusual changes like cable cuts or voltage drops. When it identifies theft, it sends immediate alerts to railway authorities, enabling quick actions to reduce damage. By providing real-time monitoring, the system enhances the security of railway electrical infrastructure, lowers financial losses due to theft or damage, and ensures passenger safety. Moreover, it decreases the need for manual inspections and lessens manpower requirements. It supports continuous monitoring even in remote or hard-to-reach railway areas. Overall, this IoT-based solution offers a reliable, effective, and affordable way to safeguard crucial railway infrastructure

New trends in power system includes the optimal placement of Flexible AC transmission System (FACTS) devices to improve the performance of the system by managing the power by managing the load. A meta heuristic method is proposed for the optimal placement and sizing of FACTS devices, namely the Thyristor-Controlled Series Compensators (TCSCs), Shunt VARs Compensators (SVCs), and Unified Power Flows Controllers (UPFCs). To find the optimal locations of these devices in a network, weak buses and lines are determined through the line sensitivity index, Then, Hybrid GA-PSO is employed not only to find an ideal rating for these devices but also the optimal coordination of SVC, TCSC, and UPFC with the reactive power sources already present in the network (tap settings of transformers and reactive power from generators). The Line sensitivity index (LSI) in which three scenarios are considered with line outage and also with different loading conditions, to calculate the power loss. The objective here is the minimization voltage deviation and reduction of power losses in the network to improve the system performance. The methods are applied to the IEEE 14 bus test system.

In modern power distribution networks, Distributed Generation (DG) significantly enhances system performance and operational efficiency. Demand Side Management (DSM) serves as a strategic approach to optimize DG deployment. This study introduces a novel optimization framework that integrates DSM with demand response mechanisms and DG placement. The methodology in this paper identifies optimal DG capacities and locations by evaluating real and reactive power losses alongside voltage profiles. To ensure environmentally sustainable and economically viable operations, the system's daily performance is optimized both with and without demand response integration. A Non-Dominated Sorting Firefly Algorithm (NSFA) is employed for multi-objective optimization, while a fuzzy decision-making model selects the most suitable solution from the Pareto front. The proposed approach is validated using the Practical 32-bus test system, demonstrating its effectiveness through simulation results.