Search Results (462)

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

Smart meters are the core terminals for distribution network data acquisition in smart cities, yet their collected data commonly suffer from quality issues caused by harsh operating environments, communication failures, hardware degradation, and human factors. This paper presents a systematic review of anomaly detection and data repair methods for smart meter data based on a critical analysis of many publications. First, we characterize five typical anomalies—sudden jumps, reading stagnation, reverse readings, pulse spikes, and gradual drifts—from physical root causes to data manifestations and provide unified mathematical definitions with explicit traceability to the existing literature. Additional anomaly types including meter replacement jumps, data duplication from retransmission, complete missing segments, and timestamp errors are also discussed to present a more complete picture of operational data quality challenges. Second, existing anomaly detection methods are systematically reviewed and classified into four categories—statistical, machine learning, deep learning, and dedicated time-series methods—with representative studies, quantitative performance metrics, and scenario-specific applicability examined for each. Third, data repair approaches are reviewed across four categories—traditional interpolation, matrix completion, generative models, and time-series prediction—with systematic comparison of their accuracy and limitations across different anomaly types and durations. Based on the synthesized evidence, we identify three cross-cutting structural limitations that persist across method categories: the performance ceiling of data-only detection without physical constraint embedding, the open-loop architecture that separates detection from repair and allows error propagation, and the exclusive reliance on statistical error metrics that fails to distinguish physically plausible repairs from those violating conservation laws. To address these gaps, we discuss a physics-guided integrated framework incorporating physical constraint embedding, joint anomaly diagnosis, scenario-adaptive repair, and posterior verification as a promising forward-looking direction. Finally, open challenges and future research directions are outlined, including parameter adaptation in unlabeled scenarios, multi-source data fusion for physical disambiguation, new power system extensions, explainable AI integration, edge-computing deployment, and standardized benchmark development. This review provides a comprehensive theoretical reference and technical roadmap for smart meter data quality research in the context of smart city energy systems. Full article

The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends. Full article

Functional nanomaterials, such as functionalized nanoparticles, nanofibers, nanocrystals, MXene and liposomes, have emerged as game-changers in tissue engineering, enabling precise modulation of cellular behaviors and dynamic biomimetic microenvironments. This review comprehensively summarizes and discusses the cutting-edge applications of nanomaterials in tissue regeneration (including bone, skin, neural and cardiac tissue regeneration), with a focus on their unique physicochemical properties (e.g., stimuli-responsiveness, nano-topography) and hybrid system design. Recent breakthroughs include 4D-printed shape-memory nanocomposites for irregular bone defects and “smart” wound dressings integrating antibacterial nanoparticles with real-time biosensing. However, clinical adoption remains constrained by unresolved challenges in biocompatibility, scalability of nanomanufacturing, and regulatory ambiguities. We critically analyze these barriers and propose a translational roadmap leveraging AI-driven material design and multi-omics validation platforms to accelerate commercialization. Full article

HfO₂ based thin-film ferroelectrics are metastable at room temperature and transited to the dielectric monoclinic phase upon heating. The thermal stability of such ferroelectrics increases when thin-film oxides are buried (BOX) in silicon–ferroelectric–silicon (SFS) structures formed by SmartCut^®, where thin ferroelectric layers are stabilized by oxygen vacancies and tensile stresses in the BOX, which is similar to silicon-on-insulator (SOI) structures. The main characteristics of the ferroelectrics in MFS and SFS structures are residual polarization P_r and coercive field E_c, which are determined by the fraction of the metastable ferroelectric phases that are also stabilized due to the inserted Al impurity in HfO₂:Al₂O₃ 10:1 (HAO) and (HfO₂:ZrO₂):Al₂O₃ (1:1)₅:1 (HZAO) nanolaminates. SFS structures and SFS CMOS ICs were tested after all thermal treatments at temperatures 900–1000 °C with t_BOX = 10–20 nm (or equivalent oxide thickness EOT = 1–2 nm) in an industrial process as gate insulators for CMOS and dual-gate DG SFS transistors. Their characteristics simulated in TCAD Sentaurus and analytic models in LTspice are investigated for an analog content addressable memory (ACAM). Full article

Supplying shore-side electricity to ships at berth, a practice known as cold ironing, removes the emissions of their auxiliary engines, yet the resulting electricity demand is large, highly seasonal and hard to serve economically from the grid at the small and medium-sized ports that make up most of the European network. This study asks how to meet that demand affordably and cleanly. It develops a smart-sizing and energy-management framework for a grid-connected hybrid renewable energy system that jointly optimizes solar photovoltaic and wind capacity together with a combined battery-and-hydrogen storage envelope. An energy-conserving stochastic reconstruction of the hourly cold-ironing demand is embedded within a genetic algorithm that minimizes the levelized cost of energy and the carbon footprint, and the system is operated by a transparent, priority-based controller. On a full year of real operational data from a Mediterranean port, the optimizer selects 380 kilowatts of photovoltaic capacity and a 2064 kilowatt-hour, battery-dominated storage envelope, reaching a renewable penetration equal to 76 percent of annual demand, with 57 percent of demand met without the grid. Relative to grid-only cold ironing it lowers the levelized cost of energy by about 10 percent on a screening basis, before life-cycle costs bring it to roughly grid parity, while cutting greenhouse-gas emissions by 45 percent; emissions fall 72 percent relative to auxiliary engines. Storage capacity, not oversized renewable generation, proves decisive for deep decarbonization, and battery storage dominates the cost-optimal design for this diurnal load. The framework gives port operators a transferable, data-driven decision-support tool. Full article

This review systematically summarizes recent advances in the field of surface finishing for wood products, with a focus on three cutting-edge directions: functionalization, intelligence, and sustainability. The article first outlines the fundamental theories and the evolution and modernization of traditional surface-finishing techniques, then delves into the construction mechanisms and performance characteristics of advanced functional surfaces such as superhydrophobic, self-cleaning, and smart-responsive coatings. Surface finishing of wood products is transitioning from conventional passive protection and aesthetic enhancement toward active functional empowerment and intelligent interaction. Functionalization, intelligence, and sustainability have become mainstream trends in technological development, supported by the deep integration of materials science, digital technologies, and design disciplines. Finally, the paper identifies current research challenges and prospects for key future research directions, aiming to provide a systematic knowledge framework and developmental guidance for academic studies in wood-product surface finishing. Full article

Selective harvesting requires tree-level decisions that balance operational productivity, stand development, and sustainable forest management, yet current AI and sensing studies often remain fragmented across remote sensing, machine perception, optimization, and forestry domains. This review synthesizes research on AI-assisted selective harvesting and smart forest management, with emphasis on multimodal sensing, decision support, and harvester-oriented deployment. A PRISMA-informed scoping review was conducted using Scopus, Web of Science Core Collection, IEEE Xplore, ScienceDirect, and SpringerLink, resulting in 82 studies retained for qualitative synthesis. The reviewed literature was organized into eight thematic groups covering smart forestry, harvesting operations, RGB-based perception, LiDAR and point-cloud processing, multimodal fusion, edge deployment, decision-support systems, and forecasting-oriented digital forestry. The analysis shows that RGB imaging provides semantic tree recognition, LiDAR enables spatial localization and structural assessment, and decision-support methods can translate tree-level observations into transparent cut/keep recommendations. However, integrated harvester-mounted systems remain underdeveloped, particularly regarding real-time RGB–LiDAR fusion, operator-facing recommendations, and forecasting integration. This review proposes a reference architecture for human-in-the-loop AI-assisted selective harvesting and identifies future research priorities for field validation and smart forest-management integration. Full article

Integratingartificial intelligence (AI), the Internet of Things (IoT), and Building Information Modeling (BIM) holds considerable promise for modernizing construction management, yet a unified real-time framework connecting these technologies for heavy civil earthmoving remains lacking. This paper presents BIM-iDT, a BIM-Integrated Digital Twin framework that couples multi-source IoT sensing with an IFC-based BIM model to enable intelligent fleet coordination and automated progress control. The research follows a design-science methodology comprising framework formulation, modular development, field deployment, and multi-project validation. The framework comprises a heterogeneous sensor fusion layer aligning GPS, IMU, fuel-consumption, and LiDAR data within the BIM coordinate system; a spatio-temporal graph attention network (ST-GAT) that recognizes equipment states and predicts short-horizon productivity by modeling fleet-level spatial dependencies; a temporal point cloud differencing module that quantifies cut/fill volumes against BIM design surfaces; and a constrained multi-objective evolutionary optimizer (CMOEO) that generates Pareto-optimal dispatch plans balancing fuel, cycle time, utilization, and schedule adherence. Validation on a highway project with instrumented machines shows that ST-GAT achieves a macro-averaged F1 of 0.943, volume MAPE stays below 3%, and CMOEO reduces fuel consumption by 12.6% and cycle time by 9.3% while maintaining schedule adherence above 96%, yielding an estimated 168-ton CO₂ emission reduction. End-to-end latency averages 600 ms, satisfying real-time requirements. Cross-project transfer experiments on a secondary dam construction site further confirm framework generalizability, establishing BIM-iDT as a scalable paradigm for AI-and-IoT-enabled smart construction in infrastructure engineering. Full article

Public sector organizations are under increasing pressure to modernize service delivery while preserving transparency, interoperability, accountability, and citizen trust. Blockchain technology offers relevant capabilities for these objectives, particularly through shared ledgers, cryptographic verification, and programmable rules. However, its adoption in public sector contexts remains constrained by the lack of architectural guidance tailored to inter-organizational services. This study proposes BRA-PS, a Blockchain Reference Architecture for Public Sector Citizen-Centric Applications, developed from a real-world digitalization project in Quebec, Canada. The architecture organizes components into six layers (presentation, business, communication, smart contract, blockchain, and data) with cross-cutting concerns addressing governance, access control, security, and monitoring. A key design principle is the public–private workflow separation, which enables inter-organizational collaboration while preserving each organization’s operational autonomy and data confidentiality. We validated the architecture through a case study involving a vehicle registration process between two public agencies, supported by a proof-of-concept implementation using Hyperledger Fabric. An Architecture Trade-off Analysis Method (ATAM) evaluation, conducted with a panel of five domain experts, identified six architectural risks, including InterPlanetary File System (IPFS) confidentiality exposure and smart contract inflexibility, six non-risks, six sensitivity points, and six trade-offs across three key quality attributes: autonomy, collaboration, and functional suitability. The results show that BRA-PS can support implementation decisions, clarify stakeholder responsibilities, and expose relevant architectural trade-offs. The recommendations derived from the evaluation provide practical guidance for the adoption of blockchain in citizen-centric public sector services. Full article

Smart manufacturing requires real-time monitoring of multidimensional forces at the interface between the tool and workpiece in computer numerical control (CNC) machining. In this study, an innovative iKIT wireless extrasensory toolholder is introduced that is capable of high-fidelity, in situ, high-frequency sensing and monitoring of the cutting force, torque, and two-way bending moments. The hardware design of the system is outlined, highlighting a high-bandwidth miniature wireless transmission method and noncontact power supply and energy storage solution suitable for rotating machining environments. To assess the system performance, comprehensive milling tests were performed on aluminum alloy materials, and the relationship between the process parameters and changes in multidimensional mechanical loads was thoroughly examined. The experimental findings demonstrate that the smart toolholder detects precisely how parameter variations affect the loads. Multidimensional mechanical signals (torque and two-way bending moments) show a strong positive correlation with the feed rate and axial depth of cut, confirming the impact of the material removal rate on the system loads. Conversely, these signals are negatively correlated with spindle speed, accurately reflecting the effects of thermal softening and a reduced friction coefficient in aluminum alloys during high-speed cutting. This study not only offers a dependable hardware framework for integrating miniaturized sensors into toolholders, but also delivers accurate data to support digital twin models and adaptive control in machining processes. Full article

Low-Power Wide-Area Networks based on LoRa are widely deployed in smart city, agricultural, and environmental monitoring, where their constrained energy budget makes them vulnerable to radio-frequency jamming. (1) Background: a node that keeps transmitting into a jammed channel wastes energy on undeliverable packets, yet detection and energy management are usually treated separately. (2) Methods: we present J-DEAS, a Jamming-Driven Exponential Adaptive Sleeping technique that couples a lightweight, threshold-based detector with an exponential sleep back-off scheduler. The detector uses only the RSSI and SNR reported by commodity transceivers, and a single exponentially weighted confidence variable drives the sleep interval; we analyze the decision boundary, confidence dynamics, steady-state duty cycle, and latency–energy trade-off in closed form. (3) Results: on a measurement dataset the detector reaches an AUC of 0.985 and an F1 of 0.969; under sustained jamming, J-DEAS cuts the duty cycle from 100% to 5.5% and wasted transmissions from 94.3% to 8.3%, with a single-slot median latency and a sub-2% false-sleep rate on clean channels. (4) Conclusions: the technique needs no training and no extra hardware, making it suitable for resource-constrained end devices. Full article

Purpose: This research intends to explore the relationships between digital risk management practices and the successful implementation of innovative banking services with the mediating effect of digital capabilities and the moderating effect of digital culture. Methodology Approach: In this study, the data was gathered using a quantitative approach and the cross-sectional survey method with responses from participants who were chosen as the unit of analysis of being investigated for the study. Islamic finance institutions in Jordan were used as the unit of analysis in this study. Responses of different Islamic finance institutions were surveyed in a structured manner to collect data with 281 valid responses. The current study then used structural equation modeling using SmartPLS3 to investigate the relationship between the variables. Findings: The results show that utilizing digital risk management, advanced analytics, artificial intelligence, and automated compliance systems is essential to fostering innovation while upholding Shariah compliance. The study also shows that efficient digital risk management boosts users’ confidence increases service effectiveness and facilitates the launch of cutting-edge Shariah-compliant products. The findings supported a significant meditating effect of the digital capabilities but did not support a moderating effect of the digital culture between digital risk management and innovative banking services respectively. Originality: By investigating digital risk management in the particular context of Islamic innovative banking services, this study provides novel insight. In contrast to earlier research that focuses on innovation in Islamic finance, this paper examines how digital risk management frameworks impact the sustainability of innovative banking services that adhere to Shariah. Moreover, building institutional capacity and resilience requires training programs that emphasize emerging technologies and digital risk awareness. Full article

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. Full article

Fruit harvesting systems are undergoing a paradigm shift toward sustainable and energy-efficient mechanized platforms driven by robotics, artificial intelligence, and advanced sensing technologies. This review synthesizes recent engineering developments in fruit harvesting, focusing on system architecture, fruit detachment mechanics, and mechanized harvesting strategies. It examines harvesting classifications, mechanical principles governing detachment, and pre-harvest factors affecting performance, along with principal mechanisms including shaking, cutting, and alternative detachment techniques. Post-detachment handling and fruit recovery processes are also analyzed, together with economic and sustainability-related trade-offs between manual and mechanized harvesting systems. Recent progress in robotic harvesting systems, machine vision, and multi-sensor fusion is evaluated within the framework of smart orchard engineering, with increasing emphasis on energy-efficient design, resource optimization, reduced postharvest losses, and environmental sustainability as key performance drivers. Despite these advancements, current technologies remain constrained by fruit damage susceptibility, biological variability, limited cross-crop adaptability, and high implementation costs, limiting large-scale adoption in commercial orchards. The novelty of this review lies in establishing a unified engineering framework that links mechanical detachment principles with robotic systems and intelligent sensing technologies under an energy-efficient sustainability perspective, enabling a system-level understanding of harvesting performance and supporting the development of next-generation adaptive and sustainable fruit harvesting systems. Full article