Search Results (10,881)

Cyber-physical systems (CPSs) represent sophisticated intelligent architectures that tightly couple computational elements, communication networks, and physical processes. Their deployments now span virtually every industrial and civilian domain—from power grids and manufacturing plants to autonomous transportation networks. Ensuring the secure operation of CPSs relies fundamentally on effective time series anomaly detection, which remains a challenging task due to the complex, often unknown system dynamics and non-negligible sensor noise present in real-world environments. To address these challenges, we introduce a Neural Network-Enhanced Kalman Filter (NNEKF), a novel anomaly detection framework that combines model-based filtering with data-driven learning. The NNEKF employs a two-stage trained neural network with a specialized architecture: the first stage learns the underlying dynamics of the CPS, while the second stage optimizes the computation of the Kalman gain during the update step. At inference time, the enhanced Kalman filter recursively estimates the likelihood of observed sensor measurements to identify anomalies, supported by a batched parallel inference scheme that delivers substantial speedups. Extensive experiments on benchmark datasets demonstrate that the NNEKF attains an average F1-score of 0.935, coupled with rapid inference and minimal model footprint—surpassing all competitive baselines and facilitating dependable real-time anomaly detection for CPS environments. Full article

Biomass-based adsorbents for methylene blue (MB) currently face critical bottlenecks including raw material homogenization, insufficient adsorption capacity, and an unclear structure–activity relationship. To address these limitations, we prepared porous super activated carbon (SAC) with ultra-high specific surface area via KOH activation, using industrial balsa wood (Ochroma pyramidale) waste from the wind power industry as the precursor. The adsorption behavior and underlying mechanism of the as-prepared SAC towards MB were systematically investigated. The as-prepared SAC has an ultra-high specific surface area of 3833 m²/g, with a well-developed microporous structure matching the molecular size of MB. It exhibited a maximum monolayer MB adsorption capacity of 1037.76 mg/g, superior to similar biomass-based materials. Near-complete removal of high-concentration MB was achieved at an SAC dosage of 0.4 g/L, and the material maintained stable performance across a wide pH range of 4 to 10. The adsorption of MB onto SAC fitted well with the Langmuir isotherm and pseudo-second-order kinetic models, dominated by monolayer physisorption. The outstanding adsorption performance originated from the synergistic contribution of the pore confinement effect, π-π conjugation, electrostatic interaction, and hydrogen bonding. This work provides a new strategy for high-value utilization of balsa wood industrial waste and efficient treatment of dye wastewater. Full article

The current literature on electrical system maintenance highlights three technology domains—building information modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article

The use of solar photovoltaic technology is among the most promising approaches to achieving SDG7—Affordable and Clean Energy—which seeks to provide modern, reliable, sustainable, and efficient energy for everyone globally, especially in developing areas with high irradiation, where both energy access and decarbonization are major challenges. South Khorasan Province, Iran, is one of the most highly irradiated regions in the world. However, despite the abundance of solar resources, most previous research in Iran on solar potential has focused on technical potential, with little emphasis on actual energy consumption patterns and economic viability. To the best of our knowledge, this is the first demand-driven assessment at the county level and the first national-scale implementation of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method for selecting solar energy sites in Iran. A spatially explicit integrated framework based on GIS-MARCOS was established for each of the eleven counties of South Khorasan Province, and five benefits were used as criteria (solar irradiance, population, per capita electrical consumption in residential, industrial, and agricultural sectors). Objective weights were calculated using Shannon’s Entropy. The analysis indicates that residential electricity demand emerges as the most influential factor in the prioritization process. Therefore, the counties of Birjand, Qaenat, and Tabas were identified as top priority counties, while counties with high irradiation levels but low demand (for example, Boshruyeh) received the least priority. These results clearly indicate the need to transition from irradiation-based to demand-based planning to minimize transmission losses and maximize the ability to integrate solar-generated electricity into the electric power grid. This proposed methodology provides a transferable decision-support tool for other high-irradiation, demand-heterogeneous regions around the globe. Full article

This study presents an integrated approach for producing antioxidant-rich polar fractions from Inula salicina L. via pressurised ethanol/water extraction (PLE-EtOH/H₂O), optimised by coupling a central composite design and response surface methodology (CCD-RSM) with Aspen Plus^® simulation. The effects of PLE temperature, extraction time, and EtOH/H₂O ratio for yield, total phenolic (TPC) and flavonoid (TFC) content, and Trolox equivalent antioxidant capacity (TEAC) measured in ABTS^•+-scavenging, cupric ion reducing antioxidant (CUPRAC) and oxygen radical absorbance (ORAC) assays were assessed via a multi-response optimisation approach. Optimal conditions were set at 82 °C, 27 min, and 60% EtOH (v/v), yielding ~29 g extract per 100 g plant material, characterised by high TPC (227 mg GAE/g), TFC (34 mg QE/g), and TEAC values in the CUPRAC (1473 mg TE/g), ABTS (869 mg TE/g), and ORAC assays (1165 mg TE/g). The TPC and TEAC values of the post-extraction residue were >92% lower than those of unextracted I. salicina, confirming efficient recovery of the major portion of antioxidant-active constituents by PLE-EtOH/H₂O. The high in vitro radical scavenging capacity, reducing power, and photoprotective potential (sun protection factor ~50 at 0.5 mg/mL) of the I. salicina extract are consistent with its phenolic-rich composition, with chlorogenic acid (~97 mg/g extract) and its derivatives being the major constituents. The validated Aspen Plus^® model closely aligned with the CCD-RSM predictions, supporting process scale-up and energy feasibility and demonstrating an industry-relevant, green-solvent PLE process for producing higher value-added I. salicina fractions with potential applications in the food, pharmaceutical, nutraceutical, and cosmetic sectors. Full article

The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through embedding-based semantic analysis. Using abstracts from 86,674 mobile industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise in policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based changepoint detection with topic lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design. Full article

This study aims to achieve the efficient preparation of sunflower calathide flavonoids (SCF) through optimized processes and to elucidate their composition and bioactivity. Total flavonoids were prepared by optimizing the ultra-high-pressure extraction (UHPE) process using a combination of single-factor experiments and response surface methodology, followed by purification and enrichment via macroporous resin. The components were identified with UPLC-QTOF-MS/MS technology, and their antioxidant activity and inhibitory capacity against xanthine oxidase (XOD) were systematically evaluated. The optimal extraction conditions were determined as follows: an extraction pressure of 290 MPa, a holding time of 8 min, an ethanol concentration of 67%, and a solid-to-liquid ratio of 1:14 g/mL. Under these conditions, the total flavonoid extraction yield reached 13.52 mg/g, which was further enriched to 16.74 mg/g after purification by macroporous resin. A total of 32 flavonoid compounds were identified, and the purified extract exhibited stronger free radical scavenging ability, total reducing power, ferric ion reducing activity, and XOD inhibitory effect compared to the unpurified extract. The combination of UHPE with macroporous resin separation technology effectively enriches SCF, and the resulting extract possesses both antioxidant and xanthine oxidase inhibitory activities, providing a theoretical basis and technical support for its industrial production and application. Full article

High spatio-temporal resolution satellite imagery has become increasingly accessible thanks to advancements in the aerospace industry which, combined with a growing computational power, has enabled the spring of novel techniques regarding recognition in remote sensing (RS) images. However, there is still a lack of studies around 3D convolutions for spatio-temporal data applied to classification problems in RS. Hence, this study investigates the feasibility of 3D convolutional neural networks (3DCNNs) within a spatio-temporal perspective for scene classification and semantic segmentation in RS images, focusing on the identification of mining sites. We firstly developed a dataset covering several parts of Brazil based on MapBiomas products and Planet imagery, then we evaluated the effectiveness of 3DCNNs in capturing temporal information from a sequence of monthly captured images. Moreover, not only for scene classification but also for semantic segmentation, we compared 3D and 2D approaches. As for scene classification, a 3DCNN was better than the corresponding 2D model, while a 2D U-Net was better than a U-Net3D for semantic segmentation. The main explanation for this lies in the fact that a less costly annotation and training time strategy was adopted, but this may have harmed spatio-temporal approaches for semantic segmentation but not for scene classification. However, U-Net3D presented the highest Precision of all models, meaning that it is highly accurate when it predicts a positive. Moreover, 3DCNN (U-Net3D) presented significantly better performance with respect to semantic segmentation compared to other spatio-temporal approaches like ConvLSTM+U-Net and TempCNN. Sensitivity analysis revealed that the near-infrared (NIR) band played a decisive role in distinguishing mining areas, emphasizing its importance in highlighting subtle spectral variations associated with land-cover disturbances. Full article

Early prediction of semen quality in young boars is crucial to reduce operational costs associated with low-productivity boars at boar studs, where high genetic merit is key. B-ultrasound imaging, a non-invasive method using echo signals to visualize tissue density, has been evaluated as a potential tool for this purpose. Stronger echoes indicate denser tissues, such as the seminiferous tubules responsible for sperm production. This study focuses on investigating the predictive power of using B-ultrasound imaging from boars around 6 to 9 months old to predict semen quality over the next six months in industrial settings. Several scanning factors were considered, including image brightness, imaging area of the testicle, and the imaging angle of the probe. The studied dataset included 1417 images and 3254 semen records from 107 boars. Results showed that the model’s performance was significantly influenced by the imaging area, the angle of the testicle, and the pixel brightness of the image without being standardized. While the accuracy of B-ultrasound imaging is not yet sufficient to replace traditional assessments, this study highlights key features in testicular images that may significantly impact model predictions, providing practical guidance for leveraging B-ultrasound imaging in predicting semen quality in young boars. Full article

The thermal safety and longevity of lithium-ion batteries are critically constrained by excessive temperature rise and spatial thermal non-uniformity, particularly during high-rate discharges. Most existing numerical investigations rely on simplified heat generation models that fail to capture the spatiotemporal heterogeneity of electrochemical heat sources, leading to compromised predictive accuracy. To address this deficiency, this study develops a comprehensive three-dimensional electrochemical–thermal coupled framework, integrating the Newman pseudo-two-dimensional (P2D) electrochemical model with conjugate heat transfer and laminar flow dynamics. The predictive robustness of this framework is rigorously validated against experimental data across multiple discharge rates (3 C and 5 C). The validated model is then deployed to evaluate a water-cooled microchannel cold plate designed for prismatic $\mathrm{L}\mathrm{i}\mathrm{M}{\mathrm{n}}_2{\mathrm{O}}_4$/graphite cells under a demanding 5 C discharge. A systematic parametric investigation is conducted to quantify the effects of ambient temperature (293–343 K), microchannel number (2–6), and coolant inlet velocity (0.1–0.6 m/s) on the maximum battery temperature (${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$) and temperature difference ($\mathsf{\Delta }\mathrm{T}$). Results demonstrate that the proposed system exhibits exceptional environmental robustness: over a 50 K ambient temperature span, ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ increases by merely 2.0 K, remaining safely below the 323 K industry limit. Densifying the channel count from 2 to 6 further reduces ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ by 1.55 K and narrows $\mathsf{\Delta }\mathrm{T}$ to 4.25 K, successfully satisfying the strict 5 K temperature uniformity standard. Furthermore, the thermal benefit of elevating inlet velocity exhibits a pronounced diminishing-return trend governed by the asymptotic reduction in bulk coolant temperature rise, dictating a critical trade-off against the quadratically escalating pumping power. Ultimately, these findings provide robust theoretical guidelines for the rational design of safe and energy-efficient battery thermal management systems. Full article

Data centers are critical digital infrastructure supporting cloud computing, artificial intelligence, and global information services. Despite their high-reliability design, they remain vulnerable to fire incidents due to continuous operation, high electrical loads, dense power systems, and the increasing use of lithium-ion batteries. Although such events are rare, their consequences can be severe, including service disruption, equipment damage, financial loss, and risks to data integrity. This study presents a systematic literature review of fire safety risk management frameworks in data centers, following PRISMA guidelines. Peer-reviewed studies published between 2020 and 2025 were retrieved from Scopus and Web of Science, screened, and appraised using structured quality criteria. Twelve empirical studies were synthesized and benchmarked against NFPA 75 and NFPA 76 standards. The findings are organized into three domains: Strategic Management, Fire Risk, and Fire Preparedness. The results show a strong focus on technical prevention and electrical hazards, while organizational readiness, emergency response, and recovery remain underexplored. Benchmarking indicates that industry standards adopt a more comprehensive lifecycle approach than the academic literature. This study reframes data center fire safety as a socio-technical reliability system and highlights critical gaps, providing a foundation for future research and improved fire safety governance and resilience. Full article

Against the backdrop of the dual-carbon strategy promoting the green and low-carbon transformation of the shipping industry, pollutant emissions generated during vessel berthing operations have become a critical challenge in port environmental governance. To address the combined effects of the priority berthing policy for new energy vessels and time-of-use electricity pricing, a joint optimization model for berth and shore power allocation is developed with the objectives of minimizing the total economic cost of vessels and the environmental tax cost associated with pollutant emissions. An improved Adaptive Large Neighborhood Search algorithm (ALNS-II) is further designed to solve the model. Numerical experiments based on actual port data verify the effectiveness of the proposed model and the superiority of the algorithm. The results indicate that, under time-of-use electricity pricing, the priority berthing policy for new energy vessels can shorten their waiting time at anchorage and encourage fuel-powered vessels to shift toward electrification. When the peak-to-valley electricity price ratio increases from 4.1:1 to 7.5:1, the environmental tax cost of pollutant emissions decreases slightly, whereas the total economic cost of vessels rises by 4.17%, suggesting that the peak-to-valley electricity price ratio should not be set excessively high. In addition, increasing the proportion of new energy vessels to 70% is more conducive to improving the overall economic and environmental performance of ports. The findings provide a theoretical basis and decision support for the optimal allocation of port resources under the coordination of multiple policies. Full article

Duplex stainless steel is widely used in nuclear power, the chemical industry, coastal infrastructure, and other fields due to its excellent mechanical properties, physical properties, and corrosion resistance. This paper focuses on the narrow-gap groove laser welding with wire filling conducted on 25 mm S32101 duplex stainless steel. It analyzes the microstructural features of various regions within the welded joint and evaluates its mechanical properties and corrosion resistance. Research indicates that the thermal cycle effect during multi-layer and multi-pass welding significantly affects the microstructure and properties of the joint. Austenite in the weld seam area mainly precipitates along the dendrite boundaries; in the overlap area of the weld beads, due to the secondary thermal cycle effect, the austenite content significantly increases to 56.2%, and the grain size is refined; in the heat-affected zone (HAZ) near the seam, austenite appears in stripes, and its content decreases to 39.4%. Mechanical property tests reveal that the welded joint exhibits an average tensile strength of 705 MPa, surpassing that of the base material. The corrosion resistance of the weld zone closely mirrors that of the base material, yet the corrosion resistance of the heat-affected zone (HAZ) is diminished due to the reduction in austenite content and the potential precipitation of harmful phases. Full article

Construction and demolition activities generate over one-third of all waste produced within the European Union, with the largest fraction being mineral materials, and concrete representing up to 90% of this volume. In this context, the recycling of this type of waste is an important research topic with growing scientific and industrial relevance. While numerous studies have examined the influence of recycled concrete and other industrial waste on the technical performance of Portland cement-based composites, the antimicrobial resistance of these composites remains largely unexplored. Therefore, in this study we evaluate the effects of three different waste materials on the key properties of Portland cement mortar, as well as on its antimicrobial resistance; the investigated waste materials were fly ash (produced in thermal power plants), recycled concrete fines resulted from the mechanical processing of concrete waste generated in construction and demolition activities, as well as dried concrete slurry (a byproduct of concrete batching plants). The partial replacement of Portland cement with these concrete wastes slightly increased the mortar’s workability (up to 4.6%). However, it also led to an 11–12% reduction in compressive strength after 28 days of hardening. After 60 days of curing, the antimicrobial properties of these mortars were evaluated by assessing their effect on planktonic microbial growth and their anti-adherent capacity against the most common pathogenic strains (S. aureus, E. coli, P. aeruginosa, C. albicans, and C. parapsilosis). Antimicrobial assays were performed at two different concentrations of microbial suspensions, and the mortars exhibited significant antibiofilm properties against all strains, especially against E. coli. The study identified mortar formulations in which partial replacement of cement with construction, demolition, and industrial waste materials resulted in compressive strength and antimicrobial resistance comparable to those of conventional reference mortars. These findings highlight the potential to integrate recycled waste into Portland cement-based materials, supporting both structural integrity and microbial resistance and advancing sustainable construction practices. Full article

Additive manufacturing by Selective Laser Melting (SLM) or, precisely, Laser Powder Bed Fusion (L-PBF), offers new opportunities for producing electrically functional metal components with tailored geometric designs and material properties. In this study, the electrical contact resistance and related properties of 3D-printed samples made from Al5086 aluminum alloy are tested. The benefits of Al5086 include flexibility without cracking, welding ability and exceptional resistance to corrosion in saltwater and industrial environments. This makes it an excellent candidate for power electric applications due to its good electrical conductivity and corrosion resistance. In this study, an analysis is performed to assess the impact of internal volumetric properties and surface parameters on general contact resistance performance. This analysis combines advanced testing procedures and parameter identification of the electric contact resistance model. This study investigates how these parameters affect contact resistance, which is a critical factor in the reliability of electrical devices. Electrical contact resistance was measured using a dedicated test setup that applied consistent pressure and maintained directional alignment. The results show that the printing direction of the samples slightly affects resistance values due to the continuity of current paths along the build direction, likely due to homogenous inter-layer boundaries and mechanical stress distribution. These findings suggest that both print orientation and internal structure must be considered when designing 3D-printed contact elements for electrical applications. Overall, this study demonstrates the feasibility of using L-PBF-fabricated aluminum components in electric applications where both electrical and structural performances are essential. Full article