Search Results (3,131)

Waste glass recycling generates waste streams such as fine glass fraction, waste ceramics containing fine glass, and waste polyethylene plastics. All of the aforementioned streams contain contaminants of organic and inorganic origin that are difficult to remove. This research was conducted to determine technological processes aimed at achieving a circular economy (CE) in the recycling of waste glass. Foam glass was made from the fine-grained, multicolored fraction of contaminated glass, an effective method for recycling glass waste at a low cost. A frothing system based on manganese oxide (MnO₂) and silicon carbide (SiC) was proposed, and an optimum weight ratio of MnO₂/SiC equal to 1.0 was determined. The possibility of controlling the process to achieve the desired foam glass densities was demonstrated. Statistical analysis was used to determine the effect of the MnO₂/SiC ratio and MnO₂ content on the density of the resulting foam glass products. Waste ceramics contaminated with different-colored glass were transformed into ceramic–glass granules. The characteristic temperature curve of the technological process was determined. The metal content in water extracts from ceramic–glass granules and pH value indicate their potential use for alkalizing areas degraded by industry and agriculture. Waste polyethylene-based plastics were converted into polyethylene waxes by thermal treatment carried out in two temperature ranges: low temperature (155–175 °C) and high temperature (optimum in 395 °C). The melting temperature range of the obtained waxes (95–105 °C) and their FTIR spectral characteristics indicate the potential application of these materials in the plastics and rubber industries. The integrated management of all material streams generated in the glass recycling process allowed for the development of a CE model for the glass recycling plant. Full article

This paper introduces a novel sensor fusion approach for the detection of multiple static and mobile targets by autonomous mobile agents. Unlike previous studies that rely on theoretical sensor models, which are considered as independent, the proposed methodology leverages real-world sensor data, which is transformed into sensor-specific probability maps using object detection estimation for optical data and converting averaged point-cloud intensities for LIDAR based on a dedicated deep learning model before being integrated through a large language model (LLM) framework. We introduce a methodology based on LLM transfer learning (LLM-TLFT) to create a robust global probability map enabling efficient swarm management and target detection in challenging environments. The paper focuses on real data obtained from two types of sensors, light detection and ranging (LIDAR) sensors and optical sensors, and it demonstrates significant improvement in performance compared to existing methods (Independent Opinion Pool, CNN, GPT-2 with deep transfer learning) in terms of precision, recall, and computational efficiency, particularly in scenarios with high noise and sensor imperfections. The significant advantage of the proposed approach is the possibility to interpret a dependency between different sensors. In addition, a model compression using knowledge-based distillation was performed (distilled TLFT), which yielded satisfactory results for the deployment of the proposed approach to edge devices. Full article

The aim of the work was to develop a highly effective catalyst for the conversion of furfural into furfuryl alcohol through catalytic transfer hydrogenation, which is an important process for converting biomass-derived compounds into valuable chemicals. A highly mesoporous silica was modified with various zirconium and aluminium precursors to obtain Lewis acid centres. The materials were characterised by nitrogen adsorption, FTIR spectroscopy, pyridine adsorption, thermogravimetry, SEM and XRD. The catalytic properties of the materials versus acid site concentration, alcohol type, zirconium content and reaction time were investigated in a batch reactor. The zirconium propoxide-modified materials appeared to be the most active and selective catalysts in the reaction studied. They showed complete furfural conversion with ca. 99% selectivity of furfuryl alcohol, which was attributed to the predominantly Lewis acidic character of these catalysts. High productivity, 15.2 mol_FA/mol_Zr·h, was obtained for the most active catalyst. Good catalytic stability was confirmed in repeated cycles. The oxide form of zirconium and aluminium species resulted in the mixed Lewis and Brönsted acidity, which encouraged further transformation of furfuryl alcohol into butyl furfuryl ether, angelica lactone and butyl levulinate. The elaborated catalyst offers a promising approach for converting renewable resources into industrially relevant chemicals. Full article

With the rapid rise in electric vehicle (EV) adoption, the deployment of EV charging infrastructure—particularly fast-charging stations—has expanded significantly to meet growing energy demands. While fast charging offers the advantage of reduced charging time and improved user convenience, it imposes considerable stress on existing power distribution systems due to its high power and current requirements. This study investigated the impact of EV fast charging on power quality within Thailand’s distribution network, emphasizing compliance with accepted standards such as IEEE Std 519-2014. We developed a control-oriented EV-charging station model in power systems computer-aided design and electromagnetic transients, including DC (PSCAD/EMTDC), which integrates grid-side vector control with DC fast-charging (CC/CV) behavior. Active/reactive power setpoints were mapped onto $dq$ current references via Park’s transformation and regulated by proportional integral (PI) controllers with sinusoidal pulse-width modulation (SPWM) to command the voltage source converter (VSC) switches. The model enabled dynamic studies across battery state-of-charge and staggered charging schedules while monitoring voltage, current, and total harmonic distortion (THD) at both transformer sides, charger AC terminals, and DC adapters. Across all scenarios, the developed control achieved grid-current THDi of <5% and voltage THD of <1.5%, thereby meeting IEEE 519-2014 limits. These quantitative results show that the proposed, implementation-ready approach maintains acceptable power quality under diverse fast-charging patterns and provides actionable guidance for planning and scaling EV fast-charging infrastructure in Thailand’s urban networks. Full article

Sustainable intensification requires metrics that are able to capture both economic performance and the often-hidden environmental inputs that support agriculture. Emergy analysis (EmA) meets this need by converting all inputs—free environmental flows and purchased goods/services—into a common unit (solar emjoules, sej). We conducted a PRISMA-documented bibliometric review of EmA in agroecosystems (Web of Science + Scopus, 2000–2022) using Bibliometrix and synthesized farm-scale indicators (ELR, EYR, ESI, %R). Our results show output has grown but is concentrated in a few countries (China, Italy and Brazil) and journals, with farm-level assessments dominating over regional and national assessments. Across cases, mixed crop–livestock systems tend to show lower environmental loading (ELR) and higher sustainability (ESI) than crop-only or livestock-only systems. %R is generally modest, indicating continued reliance on non-renewables, with fertilizers (crops) and purchased feed (livestock) identified as recurrent drivers. Thematic mapping reveals well-developed niche clusters but no single motor theme, consistent with the presence of incongruous baselines, transformities and boundaries that limit comparability. We recommend adoption of the 12.1 × 10²⁴ sej yr⁻¹ baseline, transparent transformity reporting and multi-scale designs that link farm diagnostics to basin and national trajectories. Co-reporting with complementary sustainability assessment methods (such as LCA and carbon footprint), along with appropriate UEV resources, would increase its reputation among policymakers while preserving EmA’s systems perspective, converting dispersed case evidence into cumulative knowledge for circular, resilient agroecosystems. Full article

In real-world decision-making situations, experts often rely on subjective and imprecise judgments, frequently expressed using linguistic terms. While fuzzy logic offers a valuable tool to capture and process such uncertainty, traditional methods often convert fuzzy inputs into crisp values too early in the process. This premature defuzzification can result in significant loss of information and reduced interpretability. To address this issue, the present study introduces an enhanced fuzzy TOPSIS model that utilizes expected interval representations instead of early crisp transformation. This approach allows the original fuzzy data to be preserved throughout the analysis, leading to more transparent, realistic, and informative decision outcomes. The practical application of the proposed method is demonstrated through a supplier selection case study, which illustrates the model’s capability to handle real-world, complex, and qualitative decision environments. By explicitly linking the method to this domain, the study provides a concrete anchor for practitioners and decision-makers seeking transparent and robust evaluation tools. Full article

Indoor positioning systems based on received signal strength (RSS) achieve indoor positioning by leveraging the position-related features inherent in spatial RSS fingerprint images. Their positioning accuracy and robustness are directly influenced by the quality of fingerprint features. However, the inherent spatial low-resolution characteristic of spatial RSS fingerprint images makes it challenging to effectively extract subtle fingerprint features. To address this issue, this paper proposes an RSS-based indoor positioning method that combines enhanced spatial frequency fingerprint representation with fusion learning. First, bicubic interpolation is applied to improve image resolution and reveal finer spatial details. Then, a 2D fast Fourier transform (2D FFT) converts the enhanced spatial images into frequency domain representations to supplement spectral features. These spatial and frequency fingerprints are used as dual-modality inputs for a parallel convolutional neural network (CNN) model with efficient multi-scale attention (EMA) modules. The model extracts modality-specific features and fuses them to generate enriched representations. Each modality—spatial, frequency, and fused—is passed through a dedicated fully connected network to predict 3D coordinates. A coordinate optimization strategy is introduced to select the two most reliable outputs for each axis (x, y, z), and their average is used as the final estimate. Experiments on seven public datasets show that the proposed method significantly improves positioning accuracy, reducing the mean positioning error by up to 47.1% and root mean square error (RMSE) by up to 54.4% compared with traditional and advanced time–frequency methods. Full article

Brewer’s spent grain (BSG), a by-product originating from the brewing industry, contains substantial amounts of fibers, proteins, and bioactive compounds; however, its utility is restricted by anti-nutritional factors. Solid-state fermentation (SSF) presents a viable method for improving the nutritional and functional properties of BSG. Microorganisms such as Rhizopus oligosporus have been demonstrated to enhance nutrient bioavailability, facilitate the degradation of complex carbohydrates, and improve protein digestibility while simultaneously reducing anti-nutritional components. Furthermore, this fermentation process yields bioactive compounds that exhibit antioxidant, anti-inflammatory, and prebiotic properties, thereby contributing to improved gut health, the prevention of metabolic disorders, and enhanced nutritional outcomes. Additionally, SSF seeks sustainability by repurposing agro-industrial by-products, reducing waste, and promoting the principles of a circular economy. Collectively, these advantages underscore the transformative potential of SSF in converting BSG into a functional food ingredient, effectively addressing contemporary health and environmental challenges and offering innovative solutions for food security and sustainable development. Full article

The 2 × 25 kV flexible traction power supply system (FTPSS), using a three-phase-single-phase converter as its power source, effectively addresses the challenges of neutral section transitions and power quality issues inherent in traditional power supply systems (TPSSs). However, the bidirectional fault current and low short-circuit current characteristics degrade the effectiveness of traditional TPSS protection schemes. This paper analyzes the fault characteristics of FTPSS and proposes a fault identification method based on empirical wavelet transform (EWT) and 1-sequence faulty energy. First, a composite sequence network model is developed to reveal the characteristics of three typical fault types, including ground faults and inter-line short circuits. The 1-sequence differential faulty energy is then calculated. Since the 1-sequence component is unaffected by the leakage impedance of autotransformers (ATs), the proposed method uses this feature to distinguish the TPSS faults from disturbances caused by electric multiple units (EMUs). Second, EWT is used to decompose the 1-sequence faulty energy, and relevant components are selected by permutation entropy. The fault variance derived from these components enables reliable identification of TPSS faults, effectively avoiding misjudgment caused by AT excitation inrush or harmonic disturbances from EMUs. Finally, real-time digital simulator experimental results verify the effectiveness of the proposed method. The fault identification method possesses high tolerance to transition impedance performance and does not require synchronized current measurements from both sides of the TPSS. Full article

Activated peroxymonosulfate (PMS) processes have emerged as a highly effective advanced oxidation technique for the removal of emerging organic contaminants in water. This study successfully converted δ-MnO₂ into α-MnO₂ through a crystal phase transformation method via the application of a mild water bath heating process, enhancing its catalytic properties. α-MnO₂ (k = 0.092 ± 0.0059 min⁻¹) exhibited significantly higher activity than δ-MnO₂ (k = 0.027 ± 0.0075 min⁻¹) in the PMS-activated degradation of sulfamethoxazole (SMX). Importantly, ¹O₂ was identified as the primary reactive oxygen species in the α-MnO₂ + PMS system for SMX degradation. XPS and O₂-TPD characterizations demonstrated that α-MnO₂ possesses a higher concentration of active lattice oxygen a and lower concentration of Mn(III) than δ-MnO₂. Further analysis reveals that both surface Mn(III) and active lattice oxygen in α-MnO₂ are crucial for PMS activation. Notably, ¹O₂ is predominantly generated through the interaction between PMS and reactive lattice oxygen. Moreover, a heterogeneous PMS activation mechanism toward α-MnO₂ was proposed. This research underscores the critical role of active lattice oxygen in MnO₂ for PMS activation, providing valuable insight relevant to the design of catalysts aimed at pollutant elimination in environmental applications. To the best of our knowledge, our study is the first to report a pathway for MnO₂ crystal phase transition under relatively mild conditions. Full article

Traditional LLC resonant converters face significant challenges in wide-output-voltage-applications, such as limited voltage gain, efficiency degradation under wide-gain range, and increased complexity in magnetic component design. For example, in electric vehicle charging power modules, achieving wide output voltage typically relies on changing the transformer turns ratio or switching the series-parallel circuit configuration via relays, which prevents real-time dynamic adjustment. To overcome these limitations, this paper proposes a wide-gain-range control method based on a full-bridge T-type three-level LLC resonant converter, capable of achieving a voltage gain range exceeding six times. By integrating a T-type three-level bridge arm with PWM modulation and employing a variable-topology and variable-frequency control strategy, the proposed method achieves synergistic optimization for wide-output-voltage-applications. PWM modulation enables wide-range voltage output by dynamically adjusting both the converter topology and switching frequency. Finally, the proposed method is validated through circuit simulations and experimental results based on a full-bridge T-type three-level LLC converter prototype, demonstrating its effectiveness and feasibility. Full article

This systematic review examines and critically classifies the strategies applied to abandoned urban voids through the analysis of peer-reviewed scientific and technical literature, addressing the lack of integrative studies on the topic. A systematic search was conducted in the SCOPUS database for articles published between 2000 and 2025, following the PRISMA 2020 guidelines. Sixteen studies that met the inclusion criteria were analyzed using the digital tool Parsifal. The results reveal that adaptive reuse and architectural regeneration are the predominant strategies for transforming abandoned urban voids, aimed at converting underutilized or abandoned spaces into functional and socially integrated urban assets. In contrast, approaches such as rehabilitation and environmental remediation appear less frequently but highlight the importance of ecological and preparatory interventions. This review also identifies gaps in the psychological and participatory dimensions, proposing their integration into future urban regeneration frameworks. This work provides an updated conceptual foundation for sustainable architectural interventions and emphasizes the need to expand research in diverse geographical contexts, particularly in Latin America. Full article

Enzyme-based biosensors have emerged as a transformative technology, leveraging the specificity and catalytic efficiency of enzymes across various domains, including medical diagnostics, environmental monitoring, food safety, and industrial processes. These biosensors integrate biological recognition elements with advanced transduction mechanisms to provide highly sensitive, selective, and portable solutions for real-time analysis. This review explores the key components, detection mechanisms, applications, and future trends in enzyme-based biosensors. Artificial enzymes, such as nanozymes, play a crucial role in enhancing enzyme-based biosensors by mimicking natural enzyme activity while offering improved stability, cost-effectiveness, and scalability. Their integration can significantly boost sensor performance by increasing the catalytic efficiency and durability. Additionally, lab-on-a-chip and microfluidic devices enable the miniaturization of biosensors, allowing for the development of compact, portable devices that require minimal sample volumes for complex diagnostic tests. The functionality of enzyme-based biosensors is built on three essential components: enzymes as biocatalysts, transducers, and immobilization techniques. Enzymes serve as the biological recognition elements, catalyzing specific reactions with target molecules to produce detectable signals. Transducers, including electrochemical, optical, thermal, and mass-sensitive types, convert these biochemical reactions into measurable outputs. Effective immobilization strategies, such as physical adsorption, covalent bonding, and entrapment, enhance the enzyme stability and reusability, enabling consistent performance. In medical diagnostics, they are widely used for glucose monitoring, cholesterol detection, and biomarker identification. Environmental monitoring benefits from these biosensors by detecting pollutants like pesticides, heavy metals, and nerve agents. The food industry employs them for quality control and contamination monitoring. Their advantages include high sensitivity, rapid response times, cost-effectiveness, and adaptability to field applications. Enzyme-based biosensors face challenges such as enzyme instability, interference from biological matrices, and limited operational lifespans. Addressing these issues involves innovations like the use of synthetic enzymes, advanced immobilization techniques, and the integration of nanomaterials, such as graphene and carbon nanotubes. These advancements enhance the enzyme stability, improve sensitivity, and reduce detection limits, making the technology more robust and scalable. Full article

In order to solve the problem that it is difficult to take into account the performance constraints between the core functions of insulation, current flow and arc extinguishing of high-voltage vacuum circuit breakers at the same time, this paper proposes a flexible control strategy for the motor operating mechanism of high-voltage vacuum circuit breakers. The relationship between the rotation angle of the motor and the linear displacement of the moving contact of the circuit breaker is analyzed, and the ideal dynamic curve is planned. The motor drive control device is designed, and the phase-shifted full-bridge circuit is used as the boost converter. The voltage and current double closed-loop sliding mode control strategy is used to simulate and verify the realization of multi-stage and stable boost. The experimental platform is built and the experiment is carried out. The results show that under the voltage conditions of 180 V and 150 V, the control range of closing speed and opening speed is increased by 31.7% and 25.9% respectively, and the speed tracking error is reduced by 51.2%. It is verified that the flexible control strategy can meet the ideal action curve of the operating mechanism, realize the precise control of the opening and closing process and expand the control range. The research provides a theoretical basis for the flexible control strategy of the high-voltage vacuum circuit breaker operating mechanism, and provides new ideas for the intelligent operation technology of power transmission and transformation projects. Full article

With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. Distributed Acoustic Sensing (DAS), leveraging Rayleigh backscattering to convert standard optical fibers into kilometer-scale, real-time vibration sensor networks, presents a transformative alternative. This paper provides a comprehensive review of the principles and system architecture of DAS for roadway traffic monitoring, with a focus on signal processing techniques, feature extraction methods, and recent advances in vehicle detection, classification, and speed/flow estimation. Special attention is given to the integration of deep learning approaches, which enhance noise suppression and feature recognition under complex, multi-lane traffic conditions. Real-world deployment cases on highways, urban roads, and bridges are analyzed to demonstrate DAS’s practical value. Finally, this paper delineates emerging research trends and technical hurdles, providing actionable insights for the scalable implementation of DAS-enhanced ITS infrastructures. Full article