Journal of Engineering Systems and Digital Innovation
Journal of Engineering Systems and Digital Innovation is an international, peer-reviewed academic journal dedicated to publishing high-quality research on modern engineering systems, digital transformation, intelligent technologies, and innovation-driven industrial applications. The journal provides a scholarly platform for researchers, engineers, practitioners, and policy makers to explore how digital technologies reshape engineering design, system optimization, production management, infrastructure development, and organizational innovation. The journal welcomes original research articles, review papers, case studies, and applied research that address both theoretical advancement and practical implementation. Key areas of interest include engineering systems modeling, digital manufacturing, smart infrastructure, artificial intelligence in engineering, data-driven decision-making, industrial digitalization, cyber-physical systems, sustainable engineering, technology management, and innovation strategy. By emphasizing interdisciplinary research and real-world relevance, Journal of Engineering Systems and Digital Innovation aims to promote the integration of engineering knowledge, digital tools, and innovative methodologies. The journal is committed to supporting rigorous academic discussion and advancing solutions for complex engineering and industrial challenges in the digital era.
Contextual Emotion Detection in Mental Health Support Conversations Using Large Language Models
Mental health support increasingly takes place through text-based platforms, including crisis services, telepsychology portals, peer support forums, and mobile counselling applications. In these settings, emotional states must be inferred from written language, often across multiple conversational turns and under conditions of significant ambiguity. Contextual emotion detection therefore requires systems that can interpret not only discrete words but also dialogue history, user goals, social context, and clinical risk. Large language models offer substantial potential for this task because they encode rich linguistic context and can adapt to complex mental health expressions. However, their deployment in mental health infrastructures raises structural, ethical, and operational challenges that extend well beyond model accuracy. This paper presents a system-level analysis of contextual emotion detection in mental health support conversations using large language models. It examines architectural configurations, representation choices, model adaptation, deployment trade-offs, robustness, fairness, governance, sustainability, and clinical integration. The discussion emphasizes that effective emotion detection in mental health is not a standalone classification problem but a socio-technical system design problem. It requires careful balancing of inference quality, latency, privacy, interpretability, and accountability. The paper argues for modular architectures with human oversight, strong documentation practices, and policy alignment. It further considers cross-domain comparisons, energy costs, and the implications of using general-purpose foundation models in sensitive clinical contexts. The analysis is intended to inform researchers, system designers, and policy stakeholders who seek to build safer and more sustainable mental health text analysis infrastructures.
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Parameter-Efficient Fine-Tuning for Multilingual Natural Language Understanding
Parameter-efficient fine-tuning has become a decisive architectural and operational strategy for adapting large pretrained language models to multilingual natural language understanding tasks without retraining or storing full model replicas. This paper presents a systems-oriented analysis of parameter-efficient adaptation, focusing on structural trade-offs among adapter modules, low-rank decompositions, prefix-based approaches, and selective parameter updates. The discussion emphasizes cross-lingual transfer dynamics, in which task-specific and language-specific knowledge must be balanced under limited parameter budgets. Rather than treating parameter efficiency solely as a compression problem, the paper examines multilingual adaptation as a socio-technical infrastructure challenge involving deployment constraints, evaluation fairness, energy consumption, and governance. The analysis shows how small parameter updates can preserve strong zero-shot cross-lingual capacity while reducing memory and communication overhead in distributed training and serving environments. The paper also addresses risks of linguistic performance asymmetry, tokenizer-induced cost disparities, and evaluation benchmarks that obscure underperformance in low-resource languages. Through a synthesis of architectural strategies, deployment considerations, and policy implications, the paper argues that parameter-efficient multilingual fine-tuning should be governed by explicit fairness, sustainability, and auditability principles. The analysis contributes a system-level framework for designing multilingual natural language understanding systems that are not only computationally tractable but also institutionally accountable and linguistically inclusive.
Context-Aware Multimodal Recommendation Framework for Personalized Healthcare Information Retrieval Using Large Language Models
The rapid proliferation of large language models has catalyzed a transformation in healthcare information retrieval, yet most systems remain tethered to unimodal textual inputs and static relevance models that disregard temporal, situational, and individual patient contexts. This paper presents a context-aware multimodal recommendation framework that integrates clinical text, medical imaging, structured patient records, and environmental sensor streams into a unified retrieval and personalization pipeline orchestrated by large language models. Drawing upon system-level design principles, the framework employs modular encoders, cross-modal attention fusion, and adaptive context encoders to generate rich representations of user information needs. A dynamic personalization layer continuously refines recommendations by modeling longitudinal health profiles, activity patterns, and domain-specific preferences without retaining raw sensitive data. The architecture is evaluated through the lenses of scalability, infrastructure governance, fairness, robustness, and sustainability. We discuss structural trade-offs between centralized large-model inference and edge-distributed fine-tuning, the integration of federated learning with differential privacy, and the challenges of maintaining retrieval quality under evolving clinical knowledge. Policy implications surrounding algorithmic accountability, data sovereignty, and equitable access are examined, emphasizing that technical design choices directly shape socio-technical outcomes. The framework is positioned as a composite infrastructure that reconciles cutting-edge multimodal understanding with the stringent demands of healthcare ecosystems, offering a reference architecture for deployable, responsible, and personalized medical information systems.
Adaptive E-Commerce Search Optimization Using Large Language Models and Dynamic User Interest Memory Networks
The rapid growth of e-commerce platforms has made product search a critical interface between consumers and vast digital catalogs, yet traditional retrieval and ranking pipelines struggle to capture the fluid and context-dependent nature of user intent. This paper presents a comprehensive system-level investigation of adaptive e-commerce search optimization that integrates large language models with dynamic user interest memory networks. We examine the architectural fusion of transformer-based language understanding with dynamically updating memory components that continuously encode short-term and long-term user preferences, session context, and cross-session behavioral signals. The discussion is organized around structural trade-offs, infrastructure design, deployment considerations, and governance challenges, including fairness, robustness, privacy, and sustainability. By situating the proposed approach within the broader evolution from feature-based learning to rank and neural retrieval models, we analyze how memory-augmented large language models can enable more coherent and personalized query matching and intent modeling. The paper does not present a single system implementation but instead develops a holistic research perspective that connects advances in dense retrieval, interest evolution networks, and zero-shot ranking capabilities with the systemic demands of production-scale e-commerce environments. Throughout, we emphasize the interplay between model expressiveness and operational constraints, the necessity of continuous model updating to combat concept drift, the policy implications of profiling user memory, and the environmental costs of serving large generative models at scale. The analysis is grounded in recent literature and provides forward-looking perspectives on building inclusive, privacy-preserving, and energy-efficient search systems that respect user autonomy while delivering high-quality discovery experiences.
Computational Social Science Approach to Online Community Dynamics: Discovering Collective Identity Formation through Language Models
Online communities function as dynamic socio-technical systems in which collective identity continuously emerges through discourse, shared practices, and evolving norms. Understanding how collective identity forms and transforms within these digitally mediated spaces remains a central challenge for both social theory and platform design. This paper presents a computational social science framework that leverages large-scale language models to discover and trace collective identity formation across diverse online communities. We articulate a systems-level architecture that integrates natural language processing pipelines with theoretical constructs from social identity theory, enabling the extraction of community-level narratives, value structures, and boundary-making language. The discussion emphasizes structural trade-offs in designing such discovery systems, including tensions between batch and stream processing, model interpretability, computational scalability, and privacy preservation. We examine governance implications arising from algorithmic surfacing of collective identity, addressing fairness, representation, and the risk of essentializing community voices. Further, we analyze infrastructure requirements for sustainable deployment and the robustness of identity models under linguistic drift and platform evolution. Through cross-domain case illustrations spanning open-source developer communities, health support forums, and fan subcultures, we highlight how language model outputs can inform platform moderation policies, community health diagnostics, and inclusive design. The paper contributes a holistic, systems-oriented perspective on computationally studying collective identity, foregrounding the interplay between technical architecture, social theory, and responsible deployment in large-scale online environments.
Energy-Efficient LLM Inference for Real-Time Edge Intelligence Applications
Large language models have become central to natural language processing, but their inference demands create substantial energy, memory, and latency challenges for real-time edge intelligence systems. Edge devices operate under strict power envelopes, thermal limits, and connectivity constraints, while many language model inference workloads are memory-bound and produce highly variable token-level computational demands. This paper examines energy-efficient large language model inference from a system-level perspective rather than treating efficiency as an isolated algorithmic concern. It analyzes structural trade-offs across model architecture, compression, quantization, runtime scheduling, memory management, heterogeneous hardware, and edge-cloud partitioning. The discussion emphasizes interdependence among these architectural and operational choices, showing that gains in one layer can be undermined by inefficiencies in another. The paper further considers the governance, fairness, sustainability, and policy dimensions of deploying compressed language models at the edge. It argues that efficiency cannot be reduced to floating-point operations per inference, because the organizational and environmental costs of edge artificial intelligence are shaped by data movement, lifecycle carbon emissions, auditability, and the distribution of performance across diverse linguistic and demographic groups. A coordinated research agenda is proposed in which efficiency, robustness, equity, and regulatory compliance are treated as co-design requirements rather than post hoc constraints. The conclusion identifies future directions for energy-aware runtime systems, transparent reporting standards, and hardware-software co-design for sustainable edge intelligence.
Reinforcement Learning-Based Code Generation and Program Repair with Language Models
Large language models have transformed automated software engineering by enabling the generation and repair of source code from natural language intent. However, the standard supervised fine-tuning paradigm often fails to capture long-horizon correctness, execution semantics, and maintainability requirements that real-world software systems demand. Reinforcement learning offers a complementary mechanism for aligning code-producing models with execution feedback, test outcomes, and human preferences. This paper presents a system-level analysis of reinforcement learning-based code generation and program repair. It examines structural trade-offs across model architectures, training workflows, reward design, deployment infrastructure, and governance. The discussion emphasizes the interaction between policy optimization, semantic feedback, and software engineering constraints such as test coverage, regression risk, fairness, and auditability. Rather than focusing on algorithmic notation, the paper develops a conceptual framework for integrating reinforcement learning into code-oriented language model pipelines. It further explores deployment concerns including inference cost, safety, monitoring, and the organizational processes required for responsible operation. By connecting reinforcement learning with program repair, the analysis shows that learned repair policies can improve code quality but also introduce risks related to reward hacking, distributional drift, and reduced human oversight. The paper concludes with implications for sustainable system design and policy development in AI-assisted software engineering.
Fine-Grained Opinion Mining for E-Commerce Reviews with Hierarchical Attention Mechanisms
E-commerce platforms generate vast volumes of unstructured review text in which consumers express opinions about products, services, sellers, logistics, and other granular aspects of their purchasing experience. Document-level sentiment classification is insufficient for decision support because a single review often contains mixed evaluations across multiple aspects. Fine-grained opinion mining addresses this limitation by identifying aspect-specific sentiment expressions and aggregating them into interpretable structured representations. This paper presents a system-level examination of fine-grained opinion mining for e-commerce reviews using hierarchical attention mechanisms. It situates hierarchical attention within broader architectures that combine word-level and sentence-level encoding, contextual representation learning, and contrastive representation objectives. The discussion emphasizes structural trade-offs among recurrent, transformer-based, and hybrid encoders, the role of attention interpretability, and the governance requirements associated with large-scale review processing. The paper further analyzes data governance, infrastructure design, fairness, robustness, deployment sustainability, and policy implications. It argues that hierarchical attention architectures offer meaningful advantages for aspect-level review understanding, but their deployment must be accompanied by rigorous documentation, auditability, energy-aware engineering, and fairness monitoring. The analysis provides a forward-looking perspective on how such systems can be integrated into e-commerce platforms in a responsible and sustainable manner.
Emotion Recognition in Conversational AI Using Context-Aware Transformer Networks
Emotion recognition in conversational AI has moved from isolated utterance classification to context-sensitive modeling of multi-turn dialogue. Context-aware transformer networks provide a flexible architectural basis for capturing long-range dependencies, speaker dynamics, and evolving emotional states. This paper examines these systems from a systems research perspective, considering not only predictive performance but also structural trade-offs, data infrastructure, deployment constraints, fairness, governance, and sustainability. The discussion begins with the evolution from recurrent and graph-based dialogue models to pretrained transformer encoders, then analyzes how context can be encoded through speaker segments, turn-level positional signals, memory mechanisms, and attention regularization. It further addresses annotation quality, dataset biases, label subjectivity, and evaluation protocol limitations. Rather than proposing a single optimal architecture, the paper argues that emotion recognition in conversational AI should be treated as a socio-technical infrastructure problem in which model architecture is coupled with data governance, monitoring, and organizational accountability. The analysis includes considerations of latency, memory, energy consumption, fine-tuning efficiency, and cross-domain generalization. It also explores regulatory and ethical implications when emotion inference is deployed in education, healthcare, customer service, and public-sector settings. The paper concludes that context-aware transformer networks are promising but require careful system design and institutional safeguards to ensure robust, fair, and sustainable deployment.
Curriculum Learning for Robust Reasoning in Small-Scale Transformer Models
Large-scale transformer models exhibit strong reasoning capabilities, but their deployment in resource-constrained settings remains limited by computational cost, energy consumption, latency, and data governance requirements. Small-scale transformer models provide a tractable alternative, yet they frequently suffer from brittle reasoning, limited generalization, and sensitivity to spurious correlations. This paper examines curriculum learning as a systemic intervention for improving robust reasoning in compact transformer architectures. Rather than treating curriculum design as merely a scheduling heuristic, the analysis positions it within a broader socio-technical framework that includes architectural constraints, infrastructure trade-offs, robustness evaluation, fairness, alignment, deployment, and sustainability. The paper reviews evidence from foundational and recent work on progressive training schedules, reasoning path filtering, and alignment for small parameter language models. It argues that carefully staged exposure to concepts, tasks, and reasoning patterns can strengthen internal representations while reducing unnecessary computation. The discussion further addresses structural trade-offs involving memory footprint, throughput, distillation, and quantization, as well as governance and policy implications related to auditing, accountability, and equitable access. A central claim is that robust reasoning in small-scale systems is not primarily a matter of adding parameters or data; it requires coordinated design of training curricula, evaluation protocols, and deployment safeguards. The paper concludes by identifying open challenges and forward-looking research directions for building reasoning-capable small models that are more transparent, efficient, and aligned with institutional and societal expectations.
Behavioral Biases in Football Lottery Decision-Making: An Integrated Prospect Theory and Machine Learning Approach
The football lottery represents a complex socio-technical domain in which millions of participants routinely make decisions under uncertainty, often exhibiting systematic departures from rationality. This paper presents an integrated analytical framework that synthesizes prospect theory with modern machine learning architectures to examine, detect, and contextualize behavioral biases in large-scale lottery decision-making environments. Rather than proposing a single predictive model, the study adopts a systems-level perspective to interrogate the structural trade-offs, infrastructural requirements, governance mechanisms, and ethical implications inherent in deploying such integrated systems. We discuss how core prospect theory constructs such as reference dependence, loss aversion, and probability weighting can be operationalized within machine learning pipelines that combine real-time transactional data, contextual match information, and user interaction streams. The analysis is organized around a set of interconnected architectural axes, including the tension between interpretability and predictive fidelity, the design of streaming data fabrics to capture time-varying behavioral signatures, the maintenance of algorithmic fairness across heterogeneous demographic segments, the robustness of inference under adversarial contamination and concept drift, and the sustainability of computational workloads at population scale. Policy considerations regarding consumer protection, responsible gambling mandates, and transnational regulatory fragmentation are examined as constitutive elements of the system design space rather than external constraints. Through cross-domain comparisons with financial trading and insurance underwriting, we identify recurring patterns and critical divergences that inform forward-looking deployment strategies. The paper contributes a conceptual blueprint for the responsible, transparent, and resilient integration of behavioral economic theories into the automated decision-support infrastructures that increasingly mediate collective gambling behavior.
Cross-Lingual Sentiment Classification with Attention-Based Multilingual Language Models
Cross-lingual sentiment classification has become a critical capability for organizations that need to interpret subjective language across linguistically diverse digital environments. This paper examines the problem from a system-level perspective, focusing on attention-based multilingual language models as infrastructural components rather than isolated algorithms. The discussion covers architectural foundations, cross-lingual transfer dynamics, deployment trade-offs, robustness, fairness, governance, and sustainability. Multilingual pretrained models built on transformer self-attention have improved zero-shot and few-shot sentiment transfer, but they introduce structural tensions between parameter sharing, language coverage, inference cost, and downstream accuracy. The paper argues that successful deployment requires coordinated attention to training data provenance, model lifecycle management, computational efficiency, and evaluation practices that account for linguistic and cultural diversity. It further examines how governance mechanisms, including model documentation, auditing, and participatory language community engagement, can address disparities across high-resource and low-resource languages. Cross-domain illustrations from consumer analytics, public health monitoring, and content moderation show that sentiment classification is not a context-free task. Instead, it is embedded in social, economic, and regulatory systems. The paper concludes by identifying forward-looking research directions that integrate architectural innovation, infrastructure planning, and policy development for more robust and equitable multilingual sentiment systems.
Cross-Modal Generative Models for Remote Sensing Image Enhancement and Earth Observation Applications
Cross-modal generative models are becoming a central architectural paradigm for remote sensing image enhancement and large-scale Earth observation workflows. These models integrate heterogeneous data sources, including multispectral imagery, synthetic aperture radar, text-based environmental reports, and geospatial metadata, to reconstruct degraded scenes, fill missing observations, and generate physically plausible interpretations of the Earth surface. This paper provides a systems-level analysis of cross-modal generative approaches in remote sensing, focusing not on narrow algorithmic performance but on structural trade-offs, data governance, deployment architectures, robustness, fairness, sustainability, and policy implications. It examines how adversarial and diffusion-based generative mechanisms are reorganized within Earth observation pipelines and how they interact with transformer-based representation learning. The discussion emphasizes the importance of multi-sensor data governance, calibration under distribution shift, edge-to-cloud deployment, and the regulatory pressures emerging around foundation models. The paper further considers how generative outputs are increasingly used in humanitarian mapping, climate monitoring, and environmental policy, requiring careful attention to uncertainty communication and equitable access. By connecting architectural decisions to broader infrastructural and societal constraints, the paper argues that the next generation of Earth observation systems must treat generative enhancement not merely as an image reconstruction task but as a governed socio-technical infrastructure.
Sentiment-Aware Stock Market Prediction Using Financial Text and Temporal Deep Learning
Financial market prediction has traditionally relied on quantitative price and volume data, but such representations often ignore the interpretive discourse that shapes investor expectations and trading behavior. This paper presents a system-level examination of sentiment-aware stock market prediction that integrates financial text analysis with temporal deep learning architectures. The study conceptualizes sentiment not as an exogenous indicator but as an intermediate representation derived from heterogeneous textual sources, including news, analyst commentary, regulatory filings, and social media. The system design couples contextual language modeling with recurrent and attention-based temporal learners to capture sequential dependencies in market dynamics. The paper emphasizes structural trade-offs in latency, interpretability, scalability, and data governance. It further examines infrastructure requirements for reliable ingestion, annotation, model retraining, and deployment. Robustness and fairness concerns are analyzed through the lens of distributional shift, noise propagation, and the risk of amplifying existing market asymmetries. Policy implications related to algorithmic trading, disclosure, auditability, and emerging regulatory frameworks are also considered. The discussion argues that sustainable deployment of sentiment-aware prediction requires not only predictive accuracy but also organizational oversight, data provenance, model risk management, and careful alignment with public interest. The paper contributes a systemic framework for designing, deploying, and governing integrated financial text and temporal deep learning systems in modern financial markets.
Trustworthy Reasoning and Hallucination Mitigation in Large Language Models
Large language models are increasingly embedded in decision support systems across medicine, law, science, and public administration. Their capacity to produce fluent and seemingly well-reasoned text has made them attractive components of socio-technical infrastructures. At the same time, hallucination remains a persistent source of risk, and trustworthy reasoning cannot be achieved through a single architectural or algorithmic intervention. This paper provides a systems-level analysis of reasoning reliability and hallucination mitigation in large language models. It examines why hallucination arises from the interaction between generative pretraining, probabilistic decoding, evaluation constraints, and deployment context. The discussion moves beyond model accuracy to consider the structural trade-offs associated with reasoning depth, inference cost, verification, alignment, runtime guardrails, and institutional oversight. The paper reviews the role of chain-of-thought and search-based reasoning, verifier training, process supervision, human feedback alignment, lightweight trajectory filtering, self-evaluation, and continuous deployment monitoring. It further addresses fairness, robustness, sustainability, and policy implications of building reliable language model systems. The central argument is that hallucination should be treated as an emergent property of a larger pipeline rather than a narrow defect of a single model. Trustworthy reasoning therefore requires layered technical safeguards, transparent documentation, and governance mechanisms that enable failures to be detected, contained, and corrected before they produce institutional harm. Future directions include adaptive risk-based verification, stronger separation between linguistic generation and logical inference, cross-domain evaluation, and policy frameworks that focus on systemic accountability rather than isolated benchmark performance.
Hardware-Aware Optimization of Vision-Language Models for Resource-Constrained Autonomous Vehicles
Vision-language models have emerged as powerful instruments for unified perception, scene understanding, and reasoning in autonomous systems. Their ability to align visual and textual representations enables zero-shot semantic interpretation, natural-language interaction, and richer uncertainty communication. However, deployment in resource-constrained autonomous vehicles introduces stringent constraints on latency, memory, energy, thermal budget, hardware heterogeneity, and safety certification. This paper presents a system-level analysis of hardware-aware optimization for vision-language models in automotive contexts, treating the problem not merely as model compression but as a cross-layer challenge involving architecture design, runtime infrastructure, safety assurance, sustainability, and governance. The analysis examines structural trade-offs among pruning, quantization, knowledge distillation, and efficient backbone selection, and discusses how these interact with accelerator capabilities, memory hierarchies, and real-time scheduling. The discussion emphasizes that optimization decisions cannot be reduced to accuracy-throughput curves; they must account for degradation under distribution shift, redundancy requirements, fairness implications, energy accountability, and compliance with emerging regulatory frameworks. Drawing on developments in efficient deep learning, multi-modal perception, and edge computing, the paper provides an interdisciplinary perspective on how hardware-aware vision-language model deployment can support dependable, equitable, and sustainable autonomous mobility. The paper also highlights the importance of transparent reporting, institutional oversight, and cross-domain collaboration between automotive engineering, machine learning, and policy communities.
Explainable Artificial Intelligence for Social Media Sentiment Mining: Integrating Transformer-Based Attention and Human-Centric Interpretation
The proliferation of user-generated content on social media platforms has created enormous demand for automated sentiment mining tools that can accurately capture nuanced opinion, emotion, and stance at scale. While transformer-based deep learning architectures have achieved state-of-the-art performance in text sentiment classification, their inherent opacity presents a critical barrier to adoption in high-stakes domains such as public health surveillance, financial market analysis, and political discourse monitoring. This paper presents a comprehensive interdisciplinary examination of explainable artificial intelligence (XAI) approaches tailored to social media sentiment mining, focusing on the integration of transformer-based attention mechanisms with human-centric interpretation frameworks. We survey contemporary architectural paradigms including bidirectional encoder representations, generative pre-trained transformers, and their fine-tuning strategies, then systematically analyze how intrinsic attention weights can be repurposed to generate post hoc explanations. The discussion extends to the structural tension between explanation fidelity and model complexity, the infrastructure requirements for deploying interpretable models in real-time social media pipelines, and the governance frameworks necessary to ensure fairness and accountability. By examining cross-domain case illustrations from healthcare misinformation detection, crisis response coordination, and consumer sentiment tracking, we identify key trade-offs in latency, transparency, and robustness. The paper further addresses sustainability concerns related to the computational carbon footprint of large-scale transformer fine-tuning and inference, proposing lightweight distillation and modular explanation proxies as viable paths forward. Policy implications concerning algorithmic transparency mandates and auditability standards are synthesized with technical design choices, culminating in a set of architectural principles for building socially responsible sentiment mining systems. The analysis reveals that effective explainability cannot be reduced to a single technical method but must be constructed as a socio-technical assemblage where attention visualization, contrastive example generation, and structured natural language rationales are combined under participatory design with domain stakeholders.
Robust Sentiment Classification Under Noisy, Informal, and Adversarial Text Conditions
Sentiment classification has become a central capability in large-scale computational social science, market intelligence, and content moderation systems. However, real-world deployment remains constrained by pervasive noisy input, nonstandard orthography, code-switching, informal registers, and deliberate adversarial manipulation. This paper develops a system-level analysis of robust sentiment classification under such conditions. Rather than treating robustness as a narrow model architecture problem, the discussion frames it as a property emerging from interactions among data governance, annotation infrastructure, training objectives, evaluation protocols, and deployment monitoring. The paper examines structural trade-offs between representational expressiveness and vulnerability to perturbation, between data augmentation and distributional drift, and between interpretability and performance under attack. It provides a conceptual taxonomy of robustness failures, including phonetic substitutions, Unicode confusables, syntactic paraphrases, and social-contextual ambiguity. The discussion further addresses fairness implications when robustness interventions systematically benefit majority dialects while marginalizing minority language varieties. A forward-looking perspective integrates dynamic attention mechanisms, supervised contrastive learning, adversarial training, and human-in-the-loop governance into a coherent system design. The analysis emphasizes that sustainable robustness cannot be achieved by model retraining alone; it requires institutional arrangements for continuous evaluation, shared threat intelligence, provenance tracking, and regulatory alignment. The paper concludes by outlining research directions that couple technical robustness with organizational accountability, thereby enabling sentiment classification systems to remain reliable, equitable, and auditable in contested information environments.
Multilingual Hate Speech Detection Through Sentiment and Semantic Representation Fusion
Multilingual hate speech detection remains a persistent challenge that sits at the intersection of language technology, platform governance, and public safety. This paper develops a systems-oriented analysis of detection architectures that combine sentiment and semantic representation fusion for multilingual environments. Rather than treating hate speech detection as a monolingual classification problem or as a simple extension of sentiment analysis, the argument positions affective signals and cross-lingual semantic encoders as complementary sources of evidence that must be integrated through carefully designed fusion mechanisms. The analysis emphasizes structural trade-offs between fine-grained affect modeling and large-scale multilingual semantic representation, including issues of representation alignment, data scarcity, operational latency, and annotation reliability. It also examines the implications of fusion design for robustness, fairness, auditability, and long-term deployment sustainability. The paper draws on cross-domain comparisons with social media content moderation, cross-lingual natural language processing, and human-in-the-loop review systems. It argues that effective multilingual hate speech detection requires not only advances in model architecture but also governance frameworks that account for dataset bias, evaluation validity, and the institutional contexts in which automated systems operate. The discussion contributes a system-level perspective for researchers and practitioners seeking to build detection infrastructures that are technically sound and socially accountable.
Deep Learning-Based Spatiotemporal Modeling of Bicycle Crash Risk Using Mobile Sensing and Urban Built Environment Data
Bicycle transportation is increasingly recognized as a sustainable urban mobility mode, yet pervasive safety concerns inhibit realization of its full societal potential. Conventional crash analysis methods rest on aggregate police-reported datasets that suffer from underreporting, spatiotemporal sparseness, and limited characterization of near-miss events. This paper presents a system-level investigation of deep learning architectures designed for spatiotemporal bicycle crash risk modeling by integrating heterogeneous streams of mobile sensing data and detailed urban built environment information. The discussion is oriented around structural trade-offs inherent in sensor fusion, computational graph construction, attention-driven memory mechanisms, and adversarial robustness. Particular emphasis is placed on the governance of large-scale mobility data pipelines, including privacy preservation, algorithmic fairness, infrastructure interoperability, and long-term deployment sustainability. Drawing from cross-domain comparisons with motor vehicle crash prediction and urban flow forecasting, we analyze the interplay between model complexity, interpretability, and real-world policy actionability. We further examine how varying spatial resolutions, temporal update frequencies, and built environment feature encodings influence distributive equity in risk assessments across sociodemographic groups. The paper concludes with an exploration of anticipatory governance frameworks that reconcile the promise of proactive infrastructure safety interventions with the public accountability demands of municipal planning systems, offering a forward-looking perspective on deep learning as an infrastructural component for sustainable urban safety systems.
Human–Computer Interaction Optimization through LLM-Based Cognitive Modeling and Personalized Dialogue Memory
The rapid evolution of large language models has transformed human–computer interaction, yet persistent challenges remain in aligning system behavior with user intent, context, and long-term preferences. This paper presents a system-level examination of how cognitive modeling and personalized dialogue memory can be integrated into LLM-based interactive systems to optimize human–computer interaction. We argue that treating interaction as a continuous cognitive process, rather than a series of stateless exchanges, requires architectures that embed working memory, episodic recall, and user-specific semantic representations directly into the inference loop. By analyzing structural trade-offs across centralized and federated memory designs, latency-sensitive retrieval mechanisms, and adaptive personalization engines, the paper maps the design space for scalable, robust, and fair cognitive dialogue systems. We explore the interplay between memory decay, forgetting policies, and the right to be forgotten under governance frameworks such as the GDPR, revealing tensions between personalization depth and data minimization. Through case illustrations in healthcare and enterprise deployment, we show how architectural decisions around memory persistence, knowledge conflict resolution, and model retraining frequency directly impact fairness, robustness, and sustainability. The discussion extends to infrastructure implications, including on-device versus cloud-based processing, the carbon footprint of lifelong memory stores, and the long-term socio-technical risks of feedback loops that amplify cognitive biases. The paper concludes by identifying actionable policy pathways and design principles that guide the responsible deployment of LLM-based cognitive interaction systems, highlighting future research directions at the intersection of human-centered AI and systems engineering.
Multimodal Large Language Models for Understanding Human Self-Concept Evolution in Digital Social Environments
The digitally mediated social world is now a primary arena in which human self-concept is formed, revised, and expressed. The proliferation of multimodal platforms, from image-centric social networks to short-video ecosystems, has transformed identity construction into a continuous, cross-channel, and algorithmically shaped process. Understanding how individuals’ self-representations evolve across these environments poses profound challenges for methods rooted in single-modality analysis or episodic self-report. This paper examines the potential of multimodal large language models (MLLMs) as systems instruments capable of capturing the textured, longitudinal dynamics of self-concept within digital social environments. We adopt a systems-level perspective that moves beyond model accuracy to interrogate the broader sociotechnical infrastructure required to deploy MLLMs responsibly. The discussion is organized around structural trade-offs, data governance, architectural choices, fairness considerations, robustness, sustainability, and policy implications. We argue that MLLMs, by virtue of their capacity to process and integrate text, image, video, and interaction metadata at scale, can serve as computational mirrors that reveal how platform affordances and algorithmic curation co-produce evolving self-views. However, harnessing this capacity demands rigorous attention to the inherent tensions between representational richness and privacy, between personalization and ecological validity, and between interpretability and model scale. The paper provides a conceptual analysis of these tensions, sketches design principles for a federated MLLM-based research infrastructure, and maps the regulatory landscape that must be navigated if such systems are to be deployed in ways that uphold human autonomy, fairness, and long-term trust.
Operando Electronic Structure Engineering of Zhang–Rice Singlet Catalysts for Simultaneous Water Oxidation and Volatile Organic Compound Mitigation in Urban Transportation Microenvironments
The urban transportation microenvironment constitutes a pervasive and underexplored exposure landscape for volatile organic compounds (VOCs), with subways, buses, and taxis exhibiting distinct chemical fingerprints and adverse health outcomes. Simultaneously, electrochemical water oxidation driven by earth-abundant catalysts offers a pathway toward decentralized oxygen generation and coupled pollutant degradation, yet catalysts capable of performing both functions with high efficiency under realistic, fluctuating conditions remain elusive. This paper advances a systems-level framework for operando electronic structure engineering of Zhang–Rice singlet (ZRS) catalysts to achieve concurrent oxygen evolution reaction (OER) and VOC mineralization within vehicular microenvironments. The ZRS, a correlated quantum state arising from hole coupling between copper 3d and oxygen 2p orbitals, has recently been demonstrated as a powerful motif for triggering water oxidation under operando conditions. We argue that external stimuli such as applied bias, illumination, and humidity can dynamically modulate the ZRS formation energy and hole delocalization, thereby tuning both OER selectivity and the complete oxidation of aromatic and aliphatic VOCs. Moving beyond molecular-scale optimization, the article examines structural trade-offs embedded in the design of catalytic modules for integration across heterogeneous vehicle fleets—including energy supply architecture, real-time feedback control through environmental sensing, modular cartridge-based replacement systems, and communications protocols with vehicle climate control units. Detailed analysis addresses robustness against poisoning, thermal cycling, and mechanical vibration, as well as sustainability through reliance on copper-based oxides that circumvent critical raw material bottlenecks. Governance, fairness, and policy dimensions are explored from the perspective of differentiated exposure burdens across socioeconomic strata and the need for regulatory frameworks that encourage equitable deployment of advanced catalytic air purification in both private and public transit. The study ultimately outlines a socio-technical roadmap in which operando electronic structure engineering converges with distributed infrastructure thinking to transform urban mobility into a site of active environmental remediation.
Intelligent Enterprise Knowledge Management via Domain-Specific Retrieval-Augmented Generation and User-Centric Query Understanding
Modern enterprises face an escalating challenge in managing and leveraging vast, heterogeneous knowledge repositories while ensuring that information retrieval and synthesis align precisely with end-user intent. This paper presents a systemic examination of intelligent enterprise knowledge management through the synthesis of domain-specific retrieval-augmented generation (RAG) and user-centric query understanding. We propose a conceptual architecture that integrates fine-grained query interpretation, contextual disambiguation, and dynamic retrieval from curated document bases with large language model (LLM) generation to produce accurate, traceable, and contextually grounded responses. The discussion extends beyond algorithmic design to encompass the structural trade-offs between retrieval precision and generative fluency, the governance of proprietary knowledge, infrastructure scalability, and deployment sustainability. We analyze how user-centric query modeling—incorporating role-based, task-oriented, and temporal signals—can mitigate hallucinations and improve relevance in high-stakes enterprise settings. Further, we address cross-domain fairness, robustness against distributional shifts, and the policy implications of automated knowledge synthesis. Through a multidisciplinary lens, we explore how RAG systems can be hardened for production through continuous feedback loops, audit trails, and ethical guardrails. The paper contributes a holistic framework for designing knowledge management systems that balance generative capability with retrieval accountability, ensuring that enterprises can harness AI-driven insights without sacrificing reliability, compliance, or user trust. We conclude with a roadmap for future research that emphasizes human-in-the-loop validation, federated knowledge indexing, and the alignment of retrieval objectives with organizational values.
Multimodal Emotion Understanding with Vision-Language Models: A Cross-Modal Attention Framework for Sentiment Analysis
The emergence of vision-language models has enabled substantial progress in multimodal sentiment analysis, where emotional cues arise from the interplay between visual expressions and textual content. This paper presents a systems-oriented investigation of a cross-modal attention framework for emotion understanding using large-scale vision-language models. We examine the architectural design space, discussing the trade-offs inherent in dual-encoder versus fusion-based attention mechanisms, and the challenges of aligning heterogeneous modalities for reliable sentiment classification. Beyond algorithmic performance, we focus on infrastructure requirements for training and deploying these models, including distributed computing, model serving, and edge-optimized variants. Robustness to noisy or adversarial multimodal inputs, fairness across demographic groups in facial affect recognition, and privacy concerns in real-world deployment are analyzed in depth. We further articulate governance frameworks and policy guidelines necessary for responsible fielding of emotion AI systems. The paper contributes a comprehensive perspective that bridges technical architecture, operational sustainability, and socio-technical considerations, illustrating how cross-modal attention frameworks can be designed not only for accuracy but also for equity, transparency, and deployability at scale.
Personalized Healthcare Recommendation via LLM-Based Patient Memory Modeling and Medical Query Understanding
The integration of large language models into healthcare recommender systems represents a paradigm shift in the delivery of personalized medical information and decision support. Traditional clinical recommendation engines often rely on structured electronic health records and static rule-based logic, failing to capture the fluid, longitudinal narrative of a patient’s lived health experience. This paper presents a system-level investigation into a novel architecture that constructs a dynamic patient memory model using the generative and contextual capabilities of large language models, coupled with advanced medical query understanding. Rather than proposing a single algorithm, we examine the structural trade-offs, integration pathways, and socio-technical implications of embedding such memory-augmented models within real-world healthcare infrastructures. The discussion extends to the architecture’s handling of temporal dynamics, multi-modal health signals, and the disambiguation of clinically ambiguous language in patient queries. A central theme is the governance of such systems, addressing fairness across heterogeneous populations, interpretability for clinical stakeholders, and the sustainability of large-scale deployment under computational and energy constraints. Through conceptual analysis and cross-domain comparison with consumer personalization systems, the paper identifies unique challenges in the medical domain, including the irreversibility of harmful recommendations and the imperative of value alignment between patient preferences and evidence-based medicine. We argue that the memory model must not be a passive log but an active, ethically-aware construct that supports continuity of care while respecting privacy boundaries and evolving patient identities. The analysis further explores federated learning paradigms to distribute model training across institutions without sharing sensitive data, and examines policy implications for regulatory approval of continuously learning recommendation agents. The paper concludes with a forward-looking perspective on how patient-centric memory architectures could reshape chronic disease management, remote monitoring, and shared clinical decision-making, ultimately contributing to more resilient and equitable health systems.
Reinforcement Learning and Vision-Language Models for Intelligent Clinical Report Generation and Medical Image Understanding
The automated generation of clinical reports from medical images is a grand challenge at the intersection of computer vision, natural language generation, and healthcare delivery. Recent advances in vision-language models (VLMs) have enabled remarkable zero-shot and few-shot image understanding, while reinforcement learning (RL) offers a principled framework for optimizing sequence-level clinical objectives that are non-differentiable and align with diagnostic quality. This paper presents a system-level interdisciplinary analysis of the integration of RL and VLMs for clinical report generation and medical image understanding. We examine architectural paradigms that combine large-scale pretrained multimodal representations with reward-driven fine-tuning tailored to radiological correctness, factual consistency, and clinical utility. The discussion moves beyond algorithm design to consider structural trade-offs involving data governance, infrastructure federation, model robustness, fairness across populations, and regulatory compliance. We analyze how RL-guided relation extraction and graph-structured reasoning can sharpen the alignment between visual findings and textual narratives, while confronting challenges such as hidden stratification, demographic bias, and the risk of fluent but clinically inaccurate generations. The paper further addresses deployment sustainability, energy consumption of foundation models, continuous monitoring in live hospital environments, and the evolving policy landscape defined by FDA action plans and the EU AI Act. By synthesizing technical architectures with sociotechnical governance, we argue that the next generation of intelligent clinical reporting systems must be designed as accountable, transparent, and continuously validated socio-technical infrastructures. The analysis identifies open problems and advocates for interdisciplinary collaboration across machine learning, medicine, ethics, and law to realize safe and equitable clinical AI.
Physics-Informed Large Language Models for Predictive Maintenance and Fault Diagnosis of Renewable Energy Systems
The accelerating global transition toward renewable energy systems introduces unprecedented operational complexity in asset management, where predictive maintenance and fault diagnosis must reconcile high-dimensional sensor streams, physical degradation laws, and stochastic environmental forcing. Conventional data-driven machine learning models often fail under distributional shift, limited fault samples, and the absence of physically grounded reasoning, while purely physics-based approaches struggle to scale across heterogeneous fleets. This paper presents a system-level investigation of physics-informed large language models (LLMs) as a unifying architectural paradigm for maintenance intelligence in wind, solar, and hybrid renewable installations. We examine how the generative and reasoning capabilities of LLMs can be structurally coupled with physics-informed priors—through digital twin surrogates, embedded domain knowledge, and differentiable physical constraints—to enable interpretable anomaly narration, causal fault localization, and context-aware prescriptive guidance. Departing from component-level algorithm design, the analysis emphasizes cross-system trade-offs among model fidelity, computational latency, data governance, and deployment topology. We discuss infrastructure requirements for federated fine-tuning across geographically distributed assets, edge-cloud orchestration, and the integration of real-time supervisory control and data acquisition streams with latent physical representations. Critical attention is devoted to governance challenges, including auditability of LLM-generated maintenance recommendations, fairness in resource allocation across interconnected energy nodes, and the policy implications of delegating diagnostic authority to hybrid neuro-symbolic systems. Robustness under adversarial sensor degradation and concept drift is analyzed alongside sustainability considerations related to model update frequency and carbon-aware inference scheduling. By synthesizing insights from industrial AI, prognostics and health management, and socio-technical systems theory, the paper articulates a forward-looking research agenda for physics-informed LLMs as resilient cognitive infrastructure in next-generation renewable energy operations.
Privacy-Preserving On-Device Generative AI for Personalized Healthcare Image Synthesis
Generative artificial intelligence is increasingly positioned as a transformative force in medical imaging, offering to produce patient-specific synthetic views that can support augmentation, simulation, education, and rare pathology representation. At the same time, personalized image synthesis creates acute privacy risks because generative models trained on sensitive health data can leak patient-specific visual information. On-device generative AI has emerged as a promising architectural response, preserving data locality while enabling personalization at the edge. This paper provides a system-level analysis of privacy-preserving on-device generative AI for healthcare image synthesis. It examines the structural relationships among learning paradigms, model architectures, edge hardware, privacy mechanisms, governance, fairness, and sustainability. We argue that technical privacy guarantees alone are insufficient without careful attention to deployment context, lifecycle management, and regulatory alignment. The paper discusses federated learning, differential privacy, model compression, hardware-aware generation, and adversarial threats such as membership inference and reconstruction. It further contrasts edge-native generative models with cloud-centric foundation models and considers the implications for energy use, bias amplification, auditability, and patient consent. By integrating perspectives from machine learning, systems engineering, and health policy, the analysis provides a comprehensive framework for designing and evaluating next-generation personalized medical image synthesis systems. The discussion concludes with future research directions that emphasize standardized evaluation, federated benchmarking, sustainable hardware co-design, and governance structures that preserve public trust.
Temporal Sentiment Dynamics Modeling in Online Communities: A Deep Learning Approach with Adaptive Attention Mechanisms
The proliferation of online communities has created dynamic ecosystems in which collective sentiment shifts substantially over short timescales, influencing public discourse, market behavior, and societal trust. Traditional sentiment analysis methods treat textual expressions as static points within a fixed classification space, overlooking the deeply temporal and context-dependent nature of affective language in interactive settings. This paper proposes a system-level deep learning framework for temporal sentiment dynamics modeling that integrates recurrent sequence architectures with an adaptive attention mechanism capable of recalibrating its focus based on evolving historical contexts and community interaction patterns. The architecture decouples feature encoding, temporal gating, and interpretable attention weighting, allowing for modular updates and resource-aware deployment in large-scale content moderation pipelines. We examine the structural trade-offs inherent in designing adaptive attention that remains sensitive to concept drift, fairness constraints across demographic groups, and adversarial manipulations that target sentiment classifiers. The discussion spans architectural decisions, infrastructure requirements for real-time inference, bias auditing, robustness to distributional shifts, and governance frameworks necessary for responsible deployment. By embedding adaptive attention within a holistic socio-technical perspective, the framework not only improves predictive accuracy over volatile sentiment trajectories but also provides a transparent basis for auditing, policy alignment, and sustainable operation in platform governance settings.
Spatiotemporal Dynamics of Residential VOC Emissions and Their Contribution to Indoor–Outdoor Air Pollution Coupling and Urban Atmospheric Chemistry
Residential buildings represent a pervasive yet under-characterized source of volatile organic compounds (VOCs) that profoundly influence both indoor air quality and the reactive chemical environment of the outdoor urban atmosphere. The spatiotemporal dynamics of these emissions—governed by building materials, consumer product usage, occupant behavior, and ventilation regimes—create a complex coupling between indoor and outdoor domains that remains poorly integrated into air quality management frameworks. This paper advances a systems-level perspective on residential VOC emissions, examining the multiscale spatial heterogeneity from individual material outgassing to neighborhood-level aggregate fluxes, as well as the temporal signatures shaped by diurnal occupancy patterns, seasonal climate forcing, and episodic events. We analyze the mechanisms of indoor-to-outdoor transport and their implications for ozone and secondary organic aerosol formation in urban canopies, highlighting structural trade-offs between energy-efficient building envelopes and dilution of indoor pollutants. Computational architectures that couple building-level emission models with urban microclimate and atmospheric chemistry simulations are reviewed, along with the deployment of low-cost sensor networks and data assimilation strategies necessary to capture ground truth across heterogeneous housing stocks. The paper further addresses governance challenges, including regulatory fragmentation between indoor and outdoor air quality standards, equity concerns arising from differential exposures in vulnerable communities, and the resilience of monitoring infrastructure under climate change. We argue that achieving sustainable urban air quality necessitates an integrated socio-technical infrastructure paradigm in which residential VOC emissions are treated as a controllable boundary flux within city-scale atmospheric chemistry management. The analysis culminates in policy recommendations for harmonized indoor-outdoor standards, anticipatory building codes, and community-based sensing frameworks that collectively address the intertwined fates of indoor environments and urban airsheds.