Search Results (16,026)

This study aims to identify and prioritize digital economy factors affecting inflation and to determine effective policy strategies for managing digitally driven inflationary pressures in the context of financial systems and risk dynamics. The analysis considers twelve key digital economy indicators, including e-commerce penetration, digital payment systems, internet infrastructure, price transparency, digital advertising, Industry 4.0 technologies, data-driven inventory and demand systems, fintech adoption, cryptocurrency usage, and digital financial access. In parallel, eight policy strategies are evaluated, covering digital price transparency, expansion of digital payments, digital logistics optimization, digital public services, smart manufacturing, intelligent-based demand forecasting, fintech integration, and digital workforce development. The study employs a novel intelligent-supported decision-making framework integrating an attention-based expert weighting approach, generalized fractal fuzzy sets, the MEREC method, and the ARLON technique. The empirical design is based on expert evaluations obtained from ten specialists with at least 12 years of experience in digital economy, finance, and policymaking. Rather than relying on country-specific or time-series inflation datasets, the study examines the structural relationship between digitalization and inflation through a multi-criteria expert-based approach, with data collected in 2025. The findings indicate that e-commerce penetration and the prevalence of digital payment systems are the most influential factors affecting inflation. In addition, digital price transparency and the expansion of digital payment systems emerge as the most effective strategies for mitigating inflationary pressures. These results provide important insights into how digital transformation reshapes inflation dynamics, monetary transmission mechanisms, and inflation-related financial risks. The proposed model offers a robust and systematic framework for analyzing inflation in digitalized economies and supports policymakers and financial decision-makers in managing emerging risks in intelligent-driven economic environments. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

The rapid transformation of urban centers into smart environments introduces complex challenges at the intersection of technological advancement, environmental stewardship, and social justice. This study evaluates Lisbon’s smart city transition by establishing an integrated framework that links digital equity with Environmental, Social, and Governance principles. Employing a convergent qualitative research design, this paper triangulates a comprehensive regulatory policy analysis with primary empirical data gathered from twenty-five semi-structured interviews with municipal officials, academic experts, and residents of marginalized communities. The findings expose critical systemic disparities in digital infrastructure deployment, device affordability, and platform literacy across socio-economic strata, demonstrating how localized digital divides directly impede the execution of urban ESG objectives. While green financing mechanisms offer robust pathways for sustainable energy and transit infrastructure, their equity outcomes remain constrained without mandatory, transparent information disclosure systems that mitigate agency costs. Cultivating urban resilience requires shifting from tokenistic e-governance to genuine citizen empowerment. This study offers a novel theoretical contribution by operationalizing corporate ESG metrics within public urban governance frameworks, providing an empirical roadmap for municipal policymakers globally to balance digital innovation with structural inclusion and environmental accountability in smart city agendas. Full article

With the increasing complexity of distribution networks, higher demands have been placed on systems for efficient power conversion. The solid-state transformer (SST), which integrates power electronic converters with a high-frequency transformer (HFT), has become a major research focus in end-user power supply applications. This paper first compares the technical advantages of SSTs over conventional transformers and systematically explains their operating principles. It then reviews the development trajectory of SSTs in terms of topological evolution, prototype-based engineering validation, and the application of emerging materials. Next, it classifies and summarizes the current mainstream topologies and identifies core devices and key control technologies, including SiC devices and advanced soft magnetic materials. Finally, it introduces representative SST applications in data centers, smart grids, and charging stations and summarizes and discusses future research directions and challenges. This paper clarifies the technological evolution and existing bottlenecks of SSTs, provides a useful reference for the high-quality and highly flexible operation of distribution networks, and offers clear guidance and directions for the subsequent engineering deployment of SSTs. Full article

As modern life accelerates, demand for housekeeping services is rising. However, safety concerns deter potential users, causing a persistent imbalance that poses significant challenges to social sustainability. This research aims to design a home service system that is not only operationally efficient but also socially and economically sustainable. Using a user behavior analysis method, this study investigated the safety and hygiene needs of potential users. From a user-centered design perspective, an innovative housekeeping service system, along with its key service touchpoints: a mobile application and smart products. The system’s design is underpinned by the “Internet+” and Online-to-Offline (O2O) business models, integrating service design and sustainability principles. We present key system architecture and technologies, along with an analysis of implementation challenges. The findings suggest that the proposed system can enhance resource efficiency and economic viability (e.g., reduce operational costs by an estimated 15–25% compared with traditional models); improve user well-being and social equity (e.g., increase user trust by 15–20% through integrated credibility mechanisms and reduce household labor hours by approximately 4–6 h per week through optimized scheduling); and offer a replicable design framework for promoting sustainable service-sector development. Provide a scalable platform for the housekeeping industry’s sustainable transition. This research contributes a design paradigm for service systems that aligns business viability with Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 5 (Gender Equality), and SDG 8 (Decent Work and Economic Growth), by fostering trust, efficiency, and responsible consumption. Full article

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions. Full article

With the rapid development of general artificial intelligence, large models have gradually become the key force driving the digital transformation of the field. Agriculture has distinct domain characteristics, and traditional deep learning models are difficult to meet its cross-regional and cross-task requirements. Large models specifically designed for the agricultural field can integrate multi-source data and prior knowledge to break through this bottleneck. Therefore, tracking the development trend of large agricultural models is an important prerequisite for building new, quality productive forces in smart agriculture and promoting the digital transformation of agriculture. This article conducts a literature search and review around the research on large agricultural models, following the PRISMA guidelines. It combines the keywords of large models, crops, livestock breeding, etc., and only includes journal papers from 2022 to 2026, totaling 713 articles. Then, it performs topic modeling to deeply clarify the current research and application status, and summarizes the challenges faced and makes future research prospects. Existing evidence indicates that current large agricultural models are gradually developing towards agents and embodied intelligence, and are widely applied in scenarios such as agricultural knowledge services, pest and disease diagnosis and prevention, livestock and fishery breeding, and smart agricultural machinery control. However, they still face many key challenges, and further exploration is needed in theoretical methods and practical applications. In the future, research can be further deepened and expanded in areas such as the construction of high-quality data sets, the construction of domain evaluation systems, strengthening model reliability, building multi-agent systems, and lightweight deployment of large models and embodied intelligence. Full article

With the advancement of smart city technologies and the proliferation of Mobility as a Service (MaaS), realizing seamless navigation that continuously connects heterogeneous mobility modes and indoor–outdoor spaces has emerged as a critical challenge. However, existing navigation services operate in a fragmented, siloed manner, segmented by transport mode and spatial environment, and thus possess fundamental limitations in supporting continuous mobility. This study establishes an analytical framework comprising the four core components of navigation systems (positioning, navigation data, route planning, and guidance) and critically reviews 108 prior studies identified through purposive sampling from Web of Science, Scopus, and Google Scholar to evaluate the technical requirements and the level of seamless integration achieved for each component. The analysis reveals that while each component has reached a high level of maturity within its individual domain, four critical technical gaps persist across all components: positioning handover discontinuities at indoor–outdoor transition zones, structural and semantic inconsistencies between heterogeneous spatial datasets, static route planning that fails to account for transition-space uncertainties, and guidance systems whose context resets upon changes in transport mode. These gaps originate not from insufficient performance of individual technologies but from a systematic lack of research at the interface points between components. Overcoming these challenges necessitates a comprehensive redesign of the integrated system architecture, encompassing dynamically adaptive multi-sensor fusion positioning, hierarchical heterogeneous data integration models, probabilistic cost modeling for transition spaces, and adaptive guidance systems based on automatic context handover. Full article

Periprosthetic joint infection (PJI) remains a major clinical challenge after total joint arthroplasty because of its association with prolonged antimicrobial therapy, repeated surgery, implant failure, functional disability, and substantial socioeconomic burden. Current strategies, including systemic antibiotics, debridement with implant retention, staged revision, and antibiotic-loaded cement spacers, remain indispensable but are limited by mature biofilm tolerance, protected microbial reservoirs, insufficient local drug penetration, persistent inflammation, and compromised periprosthetic bone repair. Increasing evidence indicates that PJI is not merely bacterial colonization of an implant surface, but a dynamic prosthetic interface disorder involving biofilm persistence, immune dysregulation, inflammatory osteolysis, and failed osseointegration. This review summarizes recent advances in anti-infective prosthetic interface design, emphasizing the transition from passive antibacterial coatings toward multifunctional immuno-antibacterial osseointegrative systems. The pathogenic basis of PJI is first discussed, including conditioning film formation, bacterial adhesion, biofilm maturation, protected reservoirs, immune evasion, and osteolysis. Current clinical management limitations are then evaluated, followed by emerging biomaterial strategies, including anti-adhesive and contact-killing surfaces, active antimicrobial coatings, mature biofilm disruption, biological antibiofilm therapies, smart infection-responsive delivery systems, and osteoimmunomodulatory interfaces. Particular attention is given to balancing early antibacterial activity with cytocompatibility, immune resolution, angiogenesis, mechanical durability, and long-term osseointegration. Finally, key translational barriers are highlighted, including load-bearing and tribological constraints, insufficiently standardized mature biofilm and animal models, limited clinical evidence for advanced smart materials, manufacturing reproducibility, sterilization compatibility, regulatory complexity, and application-specific clinical readiness. Future anti-PJI interfaces should evolve beyond unidirectional bacterial killing toward stage-specific systems integrating biofilm control, immune restoration, vascularized bone regeneration, and durable mechanical performance. Full article

Chemotherapy has been used in cancer treatment for decades; however, standard chemotherapy treatments still have significant weaknesses, including collateral damage to healthy tissue, rapid development of drug resistance, and dose-limiting toxicity that limits therapeutic value. There is now an alternative approach using polymer materials that are responsive to biological stimuli that will allow for improved treatment of cancer while avoiding the limitations. Responsive polymer materials are designed to be inert during circulation until they reach their site of action; then, they will respond to specific triggers. These smart carriers respond to stimuli present in the tumor microenvironment (e.g., low pH, high glutathione levels, and increased proteolytic activity) or external stimuli applied at the bedside (e.g., localized heat, light, ultrasound, and applied magnetic fields). In both cases, there is a consistent pattern where the drug is released exactly where/when it is needed, with minimal drug release occurring outside that location and timeframe. Therefore, it is theorized that the use of polymeric-based delivery systems with stimuli-regulated drug release will significantly increase the concentration of drug delivered intratumorally, decrease the drug toxicity, and provide a potential mechanism to overcome the development of multidrug resistance from a variety of cancer treatments. To date, various types of responsive polymers have been developed and could be combined to give rise to a wide variety of different vehicle systems (e.g., micelles, nanogels, hydrogels, and hybrid delivery systems), with many of these carriers designed to respond to multiple stimuli simultaneously. Nonetheless, significant challenges remain in the clinical application of these materials due to tumor heterogeneity, immune system interactions, reproducibility issues, polymer chemistry advances, surface chemistry, and other interaction mechanisms. As a result of all of these evolving regulatory systems, as well as some of the emerging areas of polymer chemistry and surface engineering, theranostic integration will allow for new routes to provide therapy for patients with cancer. Additionally, because of these scientific advances, there will also be more opportunities to provide targeted, controllable, and on-demand treatments to patients using stimuli-responsive polymers. Full article

The increasing demand for clean, reliable, and sustainable energy has intensified the need for advanced control strategies in modern wind energy conversion systems. Although conventional backstepping control (BC) offers strong stability and robustness, its performance may deteriorate under parameter uncertainties and dynamic operating conditions, leading to power fluctuations and reduced energy quality. To overcome these challenges, this study proposes an intelligent fuzzy fractional-order BC (FFOBC) strategy for multi-machine wind energy systems. By integrating fuzzy logic with fractional-order calculus into the classical BC framework, the proposed approach enhances adaptability, dynamic response, and robustness against system disturbances and nonlinearities. The controller is implemented at the machine-side inverter and validated in MATLAB/Simulink under varying wind and load conditions. Comparative results demonstrate that the proposed FFOBC significantly outperforms conventional sliding mode control in terms of overshoot reduction, steady-state accuracy, response smoothness, and total harmonic distortion minimization. Furthermore, the proposed strategy improves energy conversion efficiency, reduces mechanical and electrical stress, and ensures stable power injection into the grid. These findings highlight the potential of the proposed intelligent control framework to support sustainable, resilient, and high-quality wind energy integration in future smart power systems. Full article

The increasing penetration of photovoltaic (PV) systems requires accurate and stable short-term forecasting to ensure reliable grid operation under operational constraints. This paper investigates short-horizon multi-step PV power forecasting using one full year of high-resolution (5 min) real-world data from a 111-kW grid-connected rooftop installation. The forecasting problem is formulated as a direct multi-output supervised learning task with a 30 min prediction horizon. A comprehensive comparative evaluation is conducted across baseline (persistence), tree-based (XGBoost), and deep learning architectures (LSTM, GRU, and Temporal Convolutional Networks—TCN). Results show that deep learning models significantly outperform conventional baselines, with LSTM achieving the lowest normalized RMSE (≈10.3%), while TCN provides a competitive trade-off between predictive accuracy, temporal stability, and computational efficiency. The direct multi-step formulation was adopted to reduce potential error propagation effects commonly observed in recursive forecasting approaches. Beyond forecasting accuracy, the study evaluates computational complexity and inference latency to assess practical deployability in resource-constrained environments. The proposed framework demonstrates that high-resolution real-world PV forecasting can achieve both strong predictive performance and operational feasibility. These findings contribute to the development of robust short-term forecasting strategies for distributed renewable energy systems operating under regulatory export constraints. Full article

This paper evaluates residential energy management systems (EMSs) that combine on-site renewable generation and battery energy storage in an all-electric house. This work compares four levels of control complexity: baseline operation, deterministic rule-based control, an optimization-based benchmark, and adaptive control using machine learning, predictive control, and a transactive framework. A calibrated gray-box house model based on the Archetype Sustainable House in Vaughan, Ontario, was used to test each strategy under the same operating assumptions. The comparison shows a clear trade-off between simplicity and performance. Deterministic load-shifting strategies are easy to implement but deliver the lowest savings. The optimized controller provides a practical upper bound on achievable performance. The machine-learning controller, trained from optimized historical operation, produced the strongest annual savings and outperformed deterministic control by a range of about 15–22%. Predictive control showed promise, but its demonstration was limited by forecast-data quality; more than 40% of collected forecast files were unusable, leaving only a 10-day continuous case study. A transactive energy management system delivered moderate direct savings, but its main value was flexibility, agent-based coordination, and future applicability to community-scale control. Experimental work further showed that 98% of an air-source heat pump peak-hour load could be shifted using battery control hardware. Despite these technical benefits, this study finds that battery-supported residential EMSs remain financially unattractive under the electricity prices and battery costs considered here. The results suggest that the most realistic path forward is not a one-size-fits-all controller, but a staged transition from simple battery logic to adaptive and transactive control as hardware prices fall, data quality improves, and homes become more connected. Full article

The growing demand for real-time data synchronisation has increased the importance of supervisory control systems in industrial automation, smart grids, healthcare monitoring, and environmental applications. Low-power wireless sensor networks (LPWSNs) have emerged as key enablers of scalable and energy-efficient monitoring. However, achieving reliable synchronisation remains challenging due to latency, energy constraints, scalability limitations, security vulnerabilities, and data integrity concerns. This review examines the role of time synchronisation in supervisory control systems and evaluates how LPWSNs support real-time monitoring and decision-making. Established synchronisation protocols, including Reference Broadcast Synchronisation (RBS), the Flooding Time Synchronisation Protocol (FTSP), and the Timing-Sync Protocol for Sensor Network (TPSN), are analysed in terms of accuracy, energy efficiency, and scalability. Key optimisation strategies, such as clock drift compensation, data aggregation and compression, and edge computing, are also discussed. Recent advances, including artificial intelligence and machine learning (AI/ML)-based predictive synchronisation, blockchain, software-defined networking (SDN), and 5G-enabled LPWSNs, are reviewed across industrial, energy, healthcare, and agricultural applications. The review critically evaluates their benefits and trade-offs and identifies remaining challenges related to cybersecurity, energy efficiency, and large-scale deployment. Finally, future research directions are outlined to support robust, scalable, and efficient real-time synchronisation in LPWSNs. Full article

Fifth-generation (5G), Beyond 5G (B5G), and sixth-generation (6G) wireless networks, along with the Internet of Things (IoT), are core communication infrastructure in smart cities. Their increased deployments create high-dimensional optimization and resource management challenges. Consequently, researchers have increasingly explored the use of Artificial Intelligence (AI) models for optimizing networks. The Proximal Policy Optimization (PPO) is one such algorithm that optimizes networks. This Systematic Literature Review (SLR) follows the PRISMA 2020 protocol to review 76 studies published between 2023 and 2026 to synthesize recent PPO-based approaches to optimize communication systems. This study examines key PPO variants in major communication domains. It outlines the primary obstacles to real-world deployment and provides a cross-domain classification. According to this study, PPO provides continuous action spaces with good training stability for AI models. Its stable policy-learning capabilities make it suitable for next-generation communication systems. However, sim-to-real transfer, reward design, and multi-agent scalability are a few key challenges encountered. Future directions emphasize robust, deployable PPO frameworks for 6G, IoT, and internet architecture. Full article