Search Results (22,257)

Biomass–plastic co-pyrolysis has emerged as a promising thermochemical route for the valorization of mixed biomass and plastic waste streams, addressing growing challenges in waste management and resource efficiency. This review summarizes current knowledge on feedstock interactions, reactor technologies, operating conditions, and resulting product distributions. The literature indicates that co-processing biomass with plastics can enhance process performance compared to single-feedstock pyrolysis. Improvements are mainly observed in liquid product quality, increased energy content of gaseous fractions, and modified char properties, although outcomes strongly depend on feedstock composition and process conditions. Beyond technical aspects, the review highlights the relevance of co-pyrolysis within circular economy systems. Oil can be considered a secondary feedstock for the refining and chemical industries, process gas can support internal energy integration, and char may be utilized in material or environmental applications, contributing to partial closure of carbon and resource loops. Despite these advantages, the transition from laboratory-scale studies to large-scale implementation remains the major challenge for biomass–plastic co-pyrolysis. This limitation is associated with feedstock heterogeneity, contamination issues, scale-up difficulties, regulatory uncertainty, and the need for downstream upgrading of products. Overall, biomass–plastic co-pyrolysis represents a promising pathway toward circular waste valorization, but its practical relevance depends on successful system-level integration rather than laboratory-scale performance alone. Full article

The absence of overhead catenary systems in contactless trams imposes stringent requirements on onboard energy efficiency and real-time power management. Hybrid energy storage systems combining batteries and supercapacitors provide an effective solution; however, conventional energy management strategies often suffer from limited global optimality under frequent traction–braking conditions. To address this issue, this paper proposes a variable-horizon model predictive control (MPC)-based energy management strategy for a battery–supercapacitor hybrid power supply system in contactless trams. A power-level-matching method is first adopted for capacity configuration, and the MPC prediction horizon is then dynamically adjusted to cover the entire traction phase, enabling global energy loss optimization while satisfying voltage, current, and SOC constraints. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed strategy effectively suppresses excessive battery current and premature supercapacitor depletion. Compared with the conventional single-step MPC, the total energy loss is reduced by 9.88%, indicating improved energy efficiency and operational performance. Full article

Cyber-physical power systems (CPPSs) are a crucial component of smart grids, integrating physical power systems with advanced information and communication technologies to achieve efficient, reliable, and intelligent energy management and control. However, with the widespread deployment of information technology, CPPSs face increasingly severe security threats and privacy protection challenges, such as data leakage, identity forgery, and impersonation, which can compromise the secure and stable operation of CPPSs. To counter these threats and protect privacy, cryptographic technique is developed to provide fundamental and powerful tools, supporting secure authentication, data integrity checking, privacy preservation, and trusted communication between connected devices in CPPSs. We systematically review the research progress on security authentication and privacy protection in CPPSs from a cryptographic perspective. Our survey analyzes the major security threats faced by CPPSs, along with the impact of various attacks on system data. We explore mainstream cryptographic algorithms, including digital signatures, key agreement protocols, signcryption authentication, homomorphic encryption, and blockchain-based security mechanisms that are capable of resisting cyber attacks and ensuring reliable decision-making and control in CPPSs. This work also provides the trends and challenges regarding the intersection of cryptography and networked control, blockchain scalability, and convergence of cryptography and artificial intelligence in CPPSs. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

Energy efficiency interventions are typically evaluated and implemented at the single-firm level, yet energy use and savings are shaped by interdependent decisions distributed across the supply chain, spanning sourcing, production, inventory, logistics, and financing. A foundational observation motivating this paper is that some energy efficiency actions are only possible through inter-firm cooperation: they require changes to partners’ processes or technologies, create benefits that accrue to different actors than those bearing the investment costs, and demand governance mechanisms (e.g., cost-sharing contract, buyer-financed supplier development, supply chain finance instruments) to be financially viable. This paper proposes CHAIN-EE (Collaborative Holistic Approach for Integrated Network Energy Efficiency), an action-oriented framework that operationalizes systems thinking into a practical roadmap for supply chain decision-makers. CHAIN-EE integrates three interconnected phases: (A) supply-chain energy diagnosis, covering boundary definition, baseline construction, and hotspot identification across nodes and flows; (B) action portfolio design, structured around a six-lever intervention taxonomy and multi-criteria evaluation embedding a cost–benefit alignment map that makes governance feasibility an explicit selection criterion; and (C) governance and continuous improvement, including incentive alignment, investment architecture and ISO 50001-compatible performance management. Evidence from four European research projects spanning the food cold chain, dairy, food-and-beverage/transport value chains, and HORECA illustrates how each phase operates in practice across different sectors and governance contexts. The paper contributes an integrative, sector-adaptable structure for supply chain energy efficiency programmes, grounded in both analytical research and applied project experience, and a targeted research agenda on cross-node rebound effects, data-enabled energy flow mapping, and multi-tier coordination mechanisms. Full article

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments. Full article

Gait symmetry analysis has emerged as an important tool in rehabilitation engineering and neurological disorder assessment, as it provides clinically relevant indicators of mobility impairment and gait abnormalities. The proposed framework integrates gait symmetry variability, statistical gait features and Dynamic Time Warping (DTW)-based temporal alignment to enhance robustness against gait variations and irregular walking patterns. A hybrid feature vector comprising DTW similarity scores, the hybrid gait symmetry index (GSI), and statistical gait descriptors was employed to classify gait patterns into five categories: normal, slow, medium, fast, and abnormal. The system was implemented as a wearable edge-computing platform using an NI myRIO device equipped with a tri-axial Inertial Measurement Unit (IMU) mounted on the subject’s body. The onboard FPGA performs real-time signal preprocessing, GSI computation, feature extraction, constrained DTW matching, and gait classification using fixed-point streaming architectures and BRAM-based buffering. Meanwhile, the embedded ARM processor manages TCP/IP communication and transmits real-time gait information to a remote monitoring workstation via a WiFi interface for visualization and analysis. Operating at a clock frequency of 100 MHz, the complete architecture achieves an end-to-end processing latency of approximately 4 ms. The proposed FPGA-based implementation provides low-latency, energy-efficient, and real-time gait analysis, making it well suited for wearable rehabilitation systems, assistive healthcare devices, and continuous mobility monitoring applications. Full article

This research presents a physics-informed deep learning framework for investigating the magnetohydrodynamic flow of a dissipative non-Newtonian micropolar nanofluid induced by a stretching sheet, incorporating Stefan blowing, internal heat generation, and the zero nanoparticle mass flux condition. The physical model consists of the interplay between the microrotation dynamics, resistance of porosity on the microrotation, Brownian diffusion, and thermophoretic transport phenomenon. The numerical solutions for the nonlinear yielded equations that result from the above interaction are obtained by employing a PINN that considers the laws of physics and boundary conditions. With this technique, the flow behavior, temperature, concentration, and microrotation fields can be predicted accurately without requiring huge datasets. This shows the ability of PINNs to numerically treat highly-coupled nonlinear transport equations in a very efficient manner compared to other traditional methods. The important discoveries from this study include that the porous and magnetic factors increased the skin friction coefficient, but the magnetic effect and viscous dissipation decreased the rate of heat transfer, and the thermophoresis effect decreased the rate of mass transfer while the Brownian effect increased it. The precision of the PINN algorithm is confirmed by comparison of the results with the earlier findings, which proves very high accuracy and hence the robustness of the current computing framework. Results of this research are useful for the development of some thermal management systems, energy converters, cooling methods, chemical reaction processes, fuel cell technology, porous media reactors, and ocean engineering involving the transport of complicated non-Newtonian nanofluids. Full article

Background and Objectives: Radial nerve palsy is one of the most clinically important neurological complications associated with humeral shaft fractures. This study aimed to evaluate the incidence of radial nerve palsy in adult humeral shaft fractures and to investigate recovery patterns and functional outcomes in surgically treated patients. Materials and Methods: This single-center retrospective observational study included adult patients with humeral shaft fractures treated between November 2022 and January 2025. All adult humeral shaft fractures during the study period were screened to determine the overall rate of radial nerve palsy. The final functional analysis included surgically treated patients with radial nerve palsy. Demographic data, fracture characteristics, injury energy, fracture type, timing of radial nerve palsy, treatment modality, operative time, radial nerve exploration, additional surgery, time to nerve recovery, and QuickDASH scores were evaluated. Conservatively managed patients with radial nerve palsy were included in the calculation of overall incidence and recovery rates but were not included in the final functional analyses. Results: A total of 469 adult patients with humeral shaft fractures were evaluated. Radial nerve palsy was identified in 77 patients, corresponding to an overall rate of 16.4%. The rate was 46.4% in open fractures and 14.5% in closed fractures. Among the 77 patients with radial nerve palsy, 49 underwent surgical treatment and constituted the final analysis cohort, whereas 28 were managed conservatively. Recovery was observed in 25 of 28 conservatively managed patients (89.3%) and in 35 of 49 surgically treated patients (71.4%). Overall, radial nerve function recovered in 60 of 77 patients (77.9%). New-onset postoperative radial nerve palsy developed in 29 of 129 surgically treated patients without preoperative palsy (22.5%); however, recovery occurred in 22 of these 29 patients (75.9%), and the rate of persistent postoperative palsy was 5.4% among patients without preoperative palsy. In the surgical cohort, open fracture, longer operative time, and higher initial Cobb angle were associated with non-recovery. Recovery of radial nerve function was the only factor independently associated with better QuickDASH scores. Conclusions: Radial nerve palsy was more frequent in open humeral shaft fractures. Although recovery was high among conservatively managed patients, surgically treated cases showed a lower recovery rate, likely reflecting greater injury complexity. New-onset postoperative radial nerve palsy was relatively frequent, but most cases recovered. Recovery of radial nerve function was strongly associated with better upper extremity functional outcomes. 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

With the global shift toward green energy, solar photovoltaic (PV) power has expanded rapidly. However, the unpredictable nature of PV generation, caused by changing weather conditions like irradiance and temperature, challenges grid stability and power scheduling. Therefore, developing smart forecasting models for high-precision PV power prediction is essential for modern grid management. This paper introduces an optimized forecasting framework using Long Short-Term Memory (LSTM) networks. By integrating historical power generation data with localized meteorological factors, a multivariate predictive model was developed and validated using empirical data from a 100 MW PV plant. Based on Pearson correlation analysis, four key features—temperature, direct normal irradiance (DNI), relative humidity, and cloud cover—were chosen as the main drivers of PV output. A multivariate LSTM model was then trained and carefully tested using time series cross-validation. Results show the multi-feature architecture consistently outperforms and surpasses single-feature benchmarks. Specifically, the model achieved a peak R² of 0.9864 and minimum MAE of 1.4057 kW. Across four validation sets, R² remained stable (0.9755–0.9836), with most errors tightly bounded within ±5 kW. These findings confirm the model’s excellent accuracy and its value for improving power system dispatch and resource planning. Full article

Blockchain technology has introduced a rapidly expanding range of consensus mechanisms, each designed to satisfy different operational requirements related to security, scalability, decentralization, transaction throughput, and energy efficiency. Selecting an appropriate consensus mechanism has consequently become a complex multi-criteria decision problem, particularly for developers and organizations without extensive expertise in distributed systems and blockchain architectures. Existing tools primarily address protocol benchmarking and static documentation, leaving the decision-support dimension largely unaddressed. This paper presents a web-based expert system designed to support the selection of blockchain consensus mechanisms according to specific user-defined operational requirements. The proposed solution was implemented using the MERN technology stack, consisting of MongoDB, Express.js, React, and Node.js, enabling a modular and scalable architecture suitable for future expansion and maintenance. The recommendation process is based on a two-phase filtering and scoring algorithm. In the first phase, mechanisms incompatible with mandatory user-defined constraints, including network type and key resource type, are systematically eliminated. In the second phase, the remaining mechanisms are ranked using attribute matching across criteria encompassing energy efficiency, scalability, security, decentralization, and transaction speed. The system returns the three most suitable consensus mechanisms for the given operational scenario together with their key characteristics. In addition to recommendation functionality, the application supports user authentication, recommendation history management, and administrative maintenance of the consensus mechanism database, which currently contains 38 distinct blockchain consensus protocols. Experimental evaluation through twelve representative usage scenarios demonstrated that the system consistently produces contextually relevant recommendations aligned with user-specified requirements. A comparative analysis with existing tools confirms that the proposed system occupies a distinct decision-support role currently absent from the available tooling landscape. A comparative analysis against four representative existing tools indicates that the proposed system combines a set of decision-support capabilities not jointly offered by any one of them. The presented approach contributes a transparent and extensible decision-support framework intended to simplify architectural planning and management of blockchain-based distributed systems. Full article

Modern energy internets and large-scale industrial systems are becoming increasingly complex. Consequently, the rapid response capability of energy infrastructure during sudden failures has become a core element to ensure the stable operation of the social economy. Emergency repair path planning (ERPP) is a complex nonlinear combinatorial optimization problem. It is characterized by dynamic uncertainties, such as fluctuating task durations and variable traffic accessibility. This paper proposes a cross-regional emergency repair path planning (CR-ERPP) optimization model considering dynamic path conditions. The model takes into account jurisdiction ownership, cross-regional dispatch costs, path weights (congestion coefficient, grade coefficient, quality coefficient) and accident risk levels. The primary objective of this model is to minimize the total repair cost. Furthermore, an Adaptive Bitterling Fish Optimization with Evolutionary Game Theory (ABFO-EGT) is developed. It introduces adaptive mechanisms, evolutionary game theory, and a symmetric mutation strategy. These enhancements are designed to overcome the inherent limitations of traditional swarm intelligence algorithms, namely unbalanced search behavior and premature convergence to local optima. Performance analysis demonstrates that the ABFO-EGT algorithm exhibits superior convergence stability and global search capability. Case study results show that the proposed method significantly reduces the total repair cost. Specifically, the cost is reduced by 33.2% compared to manual decision-making, 27.6% compared to the GWO algorithm, and 9.1% compared to both the ACO and PSO algorithms. This study provides an efficient and reliable decision support tool for emergency management of large-scale energy systems. Full article

Automotive paint sludge (APS) is a hazardous industrial waste generated during automotive coating operations and is characterized by high moisture content, variable organic and inorganic composition, volatile organic compounds, pigments and heavy metals. Conventional disposal methods, including landfilling and direct incineration, present increasing environmental and regulatory challenges, thereby motivating interest in thermochemical conversion technologies for APS valorization and energy recovery. This review evaluates the current state of research on APS thermochemical conversion through incineration, pyrolysis and gasification pathways. The review compares the major operational characteristics of thermochemical pathways, including reactor conditions, temperature ranges, product yields, energy recovery potential, pollutant formation and downstream cleanup requirements. Also, techno-economic considerations such as drying energy demand and scale-up limitations are discussed. According to the current literature, incineration is the most industrially mature route for APS destruction, whereas pyrolysis offers more flexibility for fuel and material recovery. Gasification shows potential for syngas and hydrogen production but remains insufficiently studied for APS applications. Despite growing interest in APS valorization, a lot of research gaps remain regarding standardized feedstock classification, pilot-scale validation, process integration, environmental risk assessment and techno-economic optimization. Conclusively, future approaches towards managing APS would need to incorporate process optimization for specific APS types, incorporation of co-processing techniques, as well as an overall assessment for both environmental and economic feasibility. Full article

The shear characteristics of soil–root systems are dynamic indicators for assessing soil erosion resistance. Vegetation type influences shear characteristics by altering soil properties and root traits. However, the mechanisms by which mixed vegetation roots affect shear characteristics remain unclear. We selected naturally restored forestland (with Dicranopteris dichotoma, Baeckea frutescens, and their combinations) and artificially managed orchard systems (with Paspalum wettsteinii, Gardenia jasminoides, and their combinations) in the erosion-prone red soil region of southern China. In situ shear tests were conducted to explore the shear characteristics of soil–root systems under different vegetation types, identify the main influencing factors, and clarify the underlying mechanisms. Shear fracture energy (SFE), peak shear stress (PSS), and peak shear stress displacement (DPS) decreased with increasing soil depth across all sites. The average SFE and PSS in the forestland were 2.44 and 3.11 times higher, respectively, than those in the orchards. The main factors influencing shear characteristics in forestland included root volume density, tensile strength, bulk density, and mean weight diameter of aggregates (MWD), whereas those at the orchard sites included root length density, tensile strength, and organic matter content. Root factors had a stronger impact on shear fracture energy than soil properties. Shear fracture energy equations were constructed for forestland and orchard sites, showing high R² and Nash–Sutcliffe efficiency values, indicating adequate predictive performance. These findings contribute to our understanding of the mechanical mechanisms of soils under vegetation restoration, provide scientific evidence for soil and water conservation evaluations, and help optimize vegetation restoration strategies in the Southern Red Soil Region. Full article