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.