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Orange waste, generally discarded, is a source of many bioactive compounds that could be used for the development of high-value-added products in the food, cosmetic, and pharmaceutical industries. The objective of this study was to evaluate the influence of extraction method (automated Soxhlet extraction and temperature-controlled maceration), solvent system, and extraction time on the recovery of bioactive compounds from Valencia orange (Citrus sinensis) by-products. Proximate characterization of the dried orange residue (DOR) was performed prior to extraction. The type of solvent (ethanol and methanol), solvent:water ratio (50, 75, and 100%), and extraction time (60 and 120 min) were evaluated in terms of total extraction yield (TEY), total phenolic content (TPC), and antioxidant capacity determined by ABTS and DPPH assays, for each extraction method. ASE generally provided higher extraction yield and total phenolic content, particularly with 75:25 ethanol:water at 120 min, whereas TCM combined with methanol produced the highest antioxidant capacity. Extracts with up to 46.32% TEY, 5.57 mg GAE/g dm, and antioxidant capacities of 66.49 and 11.10 µmol TE/g dm determined by ABTS and DPPH assays, respectively, were obtained. The results demonstrated that Valencia orange by-products are a source of phenolic compounds and antioxidants with potential for product development across different industrial sectors. Full article

Safe and sustainable water sources are a serious global concern because of growing population, urbanization, industrialization, and climate change. The conventional water surveillance systems that rely on periodic sampling and laboratory analysis fail to provide time-sensitive and high-resolution data utilized for proactive water management. Artificial Intelligence of Things (AIoT) offers a viable solution, as they can provide tools of constant active monitoring and predictive analytics. The integration of IoT sensor networks with machine learning (ML) methods enables real-time data-driven water resource monitoring and intelligent decision-making, enhances water quality assessment, supports early detection of anomalies, improves predictive capabilities for floods and droughts, and facilitates efficient irrigation and reservoir management, ultimately leading to sustainable and resilient water management systems. The paper presents an extensive overview of AIoT solutions for water quality monitoring and water resource management, including IoT sensor networks for real-time data acquisition, machine learning methods for prediction, classification, anomaly detection, and edge computing platforms for data processing and decision support. This study also highlights existing possibilities, obstacles, and research gaps identified through a review of the recent literature. Key challenges reported across multiple studies include limited data availability, sensor calibration bias, integration of heterogeneous data, and insufficient model interpretability. Advanced paradigms such as digital twin systems, TinyML, federated learning, and explainable AI (XAI) are examined as enabling technologies to enhance system efficiency, flexibility, and transparency. Future research directions are outlined to develop scalable, interpretable, and real-time water management solutions. Full article

Over the past decade, Building Information Modelling (BIM) has become increasingly adopted across the Architecture, Engineering, Construction, and Operation (AECO) industry. As its use in practice has expanded, BIM has also received growing scholarly attention. Existing research has largely concentrated on specific applications of BIM, such as construction management, sustainable building design, infrastructure development, and facility management. However, comparatively limited attention has been given to examining BIM implementation from a global perspective. This study addresses this gap by applying a scientometric approach to analyse global BIM implementation research published between 2004 and 2023. The analysis is conducted using co-authorship, co-word, and co-citation analysis to map the structure and development of the research field. A total of 1349 published articles were obtained from the Scopus database for the analysis. The study identifies the most productive and influential contributors to BIM implementation research, including leading researchers, research institutions, countries, subject areas, and academic journals. In addition, the analysis highlights several key thematic domains within global BIM research. These include topics related to Industry Foundation Classes (IFC), Internet of Things (IoT), Geographic Information Systems (GIS), Historic Building Information Modelling (HBIM), and Digital Twin technologies, which appear as prominent keywords within the BIM implementation literature. Beyond mapping these trends, this paper integrates dispersed scientometric evidence into a coherent global perspective, revealing how BIM implementation research has evolved, matured, and diversified across regions and disciplines. It also establishes a structured knowledge base that can serve as a benchmark for future comparative studies, performance assessments, and policy development initiatives in the digital construction domain. These findings provide valuable insights for researchers, practitioners, and policymakers by illustrating landscape of BIM-related research and highlighting potential directions for future investigation. Full article

The electronic and electrical architecture of vehicles has rapidly evolved to centralized. At present, there is no unified consensus on the R&D strategy of the platform in the industry, and there is also a lack of a quantitative decision-making framework that can be implemented. This study takes the centralized architecture platform as the research object, constructs a two-stage analysis framework of “advanced constraint screening-cost quantitative evaluation”, uses a fuzzy-set qualitative comparative analysis method to screen feasible R&D strategy combinations that meet the requirements of the architectural advancement, builds a total cost of ownership evaluation system around the software and hardware elements related to the architecture platform, and systematically analyzes the optimal cost R&D strategy combinations of car enterprises with different mass production scales under the two scenarios of Multi-Box and One-Board. The research results show that adaptive platform middleware and framework middleware are the core necessary elements to realize the advanced architecture; the amortization cost of architecture is negatively correlated with the scale of mass production, and the cost of in-house R&D is highly dependent on large-scale amortization; and there are differentiated optimal solutions in the framework selection and R&D strategy combination of automakers with different mass production scales. This study can provide quantitative reference and practical guidance for R&D decision making of centralized architecture platform for automakers. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article

Biofouling is an issue of global significance that impairs marine infrastructure, causes increased fuel consumption and greenhouse gas emissions, and threatens biodiversity. Since the year 2000, self-polishing copolymer (SPC) coatings and fouling release coatings (FRCs) dominate the fouling protection coatings market. SPC technology is based on the controlled release of biocides using a mixture of acrylic and natural binders as a delivery system. FRC technology is based on PDMS providing surface properties that resist attachment of fouling organisms. FRCs often contain surface modifying agents, such as free silicone oils, to tune the physicochemical properties of the surface. However, the long-term efficacy of these agents and their migration and distribution in PDMS-based coatings have not been well studied. In this study, we employed time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate analysis to examine the distribution of silicone oils as a function of exposure to artificial seawater (ASW). The results show that pure PDMS-based coatings allow uniform distribution of silicone oils with robust behavior upon ASW exposure. In contrast, epoxy–PDMS-based coatings displayed phase separation of the oils, which strongly altered their surface chemistry. Our findings suggest that the modification of mobile oils is critical to the performance of marine antifouling coatings. Furthermore, the presence of other ingredients of commercial coating formulations strongly affected the distribution of mobile oils. This study lays the foundation for future systematic research aimed at developing predictive models to optimize fouling protection coatings for the marine industry. Full article

The low absolute strength and insufficient room-temperature ductility remain key bottlenecks that restrict the engineering application of magnesium alloys in high-end industrial fields. In the present study, 1 vol.% carbon nanotubes (CNTs)-reinforced Mg-xAl (x = 0, 1, and 1.5 wt.%) composites were synthesized via a powder metallurgy route coupled with hot extrusion–rolling processing to realize a simultaneous improvement in mechanical properties. The hot extrusion–rolling processed 1 vol.% CNTs/Mg-1Al composite exhibits an ultimate tensile strength of 300 MPa and an elongation to failure of 9%, showing an excellent strength–ductility synergy. Microstructural characterization reveals a well-bonded interface between CNTs and the Mg matrix. Deformation incompatibility between CNTs and the magnesium matrix during hot extrusion–rolling induces a high density of dislocations, providing an important strengthening contribution. Moreover, an increased proportion of low-angle grain boundaries and the development of a bimodal texture promote significant grain refinement and effectively activate non-basal slip systems, thereby alleviating plastic deformation constraints. The synergistic effects of interfacial strengthening, dislocation strengthening, grain boundary strengthening, and texture regulation together contribute to the simultaneous improvement of strength and ductility in CNTs-reinforced Mg-Al composites. Full article

Alkali-activated cementitious materials (AACMs) are recognized as promising green building materials and a viable alternative to traditional cement due to their low carbon footprint, high durability, and superior mechanical properties. These materials primarily utilize industrial by-products such as coal gangue, steel slag, and gasification slag. The alkali activation process offers an environmentally friendly pathway for the construction industry. To address the need for the large-scale utilization of bulk solid wastes, this study established a ternary solid waste synergy system comprising coal gangue, steel slag, and gasification slag. The preparation and performance optimization of AACMs based on this system were investigated. An optimal mix proportion was identified through orthogonal experiments, and the influence of various factors on the mechanical properties at different curing ages was analyzed. The results indicate that the fluidity of all AACMs meets the requirements for general backfilling applications. Among the alkali activators, Na₂SO₄ had the smallest effect on fluidity. Under single-activator conditions, sodium silicate (water glass) and sodium hydroxide exerted a greater influence on strength development compared to anhydrous sodium sulfate. For the composite activator system, the significance of parameters affecting compressive strength followed the order: silicate modulus > alkali activator content. The maximum 28-day unconfined compressive strength reached 7.653 MPa with a mix proportion of 55% coal gangue, 45% steel slag, and 5% gasification slag, as well as a silicate modulus of 1.2 and a water glass content of 8%. This represents increases of 540.95% and 299.25% compared to the non-activated group and single-activator groups, respectively. Microstructural analysis revealed that the enhanced integrity and strength of AACMs are attributed to pore-filling by hydration products, predominantly C–S–H and C–A–S–H gels. This study successfully developed high-performance AACMs based on a coal gangue–steel slag–gasification slag ternary system, elucidating the critical regulatory role of silicate modulus in composite activators and the underlying microstructural strengthening mechanisms. The findings provide a theoretical foundation and technical support for the high-value, large-scale utilization of bulk industrial solid wastes in building materials. Full article

Digital mining has become a tangible reality in recent years and the digital revolution enables and requires data exchange for autonomous machines and operational flow management. LoRa technology and its underground propagation behavior can make an important contribution to this digitalization. This paper presents a Data Mule approach that enabled progress in digitalization at refueling stations in active underground mining areas of a mine near Werra, Germany, operated by the K+S Group. This demonstration aimed to automate manual data collection at fuel gauges by using a dynamic LoRa network. We used specially developed LoRa Data Mule modules for operations over many square kilometers. LoRa was chosen for its industrial functionality and long-range capabilities, particularly in underground environments. The Data Mule modules used were in-house-designed units with underground mining-rated casing and connectors, as well as commercial LoRa boards and custom communication protocols. Connectivity between all systems was realized at travel speeds of 20 to 40 km/h, with connection data successfully relayed for 180 to 770 m, despite 90° turns and no line of sight. It was shown that the LoRa Data Mule approach can be used in a network of remote but active data generation points. Full article

Flexible automated assembly lines (FAALs) in Industry 4.0 require robust quality management that integrates operational data with systematic risk analysis. However, Process Failure Mode and Effects Analysis (PFMEA) documents are often developed during the design phase and not systematically updated with actual production data, leading to a gap between formal risk assessment and operational reality. This study addresses this gap by developing and validating an integrated data-driven framework that combines classical quality tools (process flow charts, check sheets, cause-and-effect diagrams, and Pareto analysis) with data-driven PFMEA, creating traceable links from operational logs to risk ratings. While individual quality tools are well-established, the core contribution of this work is a structured data transformation pipeline that creates traceable, auditable linkages from raw operational event logs to calibrated PFMEA ratings with quantified uncertainty—a combination not previously demonstrated for flexible assembly systems. The framework was applied to FMS-200, a modular FAAL for bearing units, consisting of eight stations and a common transfer system. Analysis of 186 failure events across 2743 assembly cycles, including 18 product configurations, identified 40 distinct failure modes with risk priority number (RPN) values ranging from 60 to 378, revealing that approximately 90% of the aggregated risk is associated with pneumatic systems. Monte Carlo uncertainty analysis (10,000 iterations) demonstrated robust rank stability, with the top five failure modes maintaining their relative ordering in over 90% of simulations. The framework provides production and quality managers with a systematic methodology to maintain PFMEA relevance through continuous data integration, enabling evidence-based prioritization of improvement actions. Full article

Digital twin (DT) technology is a key element of Industry 4.0. Despite its rapid development, current research is mainly focused on industrial optimisation and machine-level monitoring. However, its implementation in the educational process lags significantly behind practice. Moreover, existing DT implementations in education often emphasise visualisation or simulation, while neglecting synchronisation and verification of functional equivalence between the physical and virtual systems. This study presents the design, development and experimental verification of a digital twin of a laboratory material handling system. The virtual model created in Tecnomatix Plant Simulation is connected to the physical system controlled by a Siemens PLC SIMATIC S7-1200 and equipped with industrial sensors and an HMI interface. Real-time bidirectional communication is established via the OPC UA protocol using KEPServerEX, ensuring synchronisation between the physical and virtual systems. Experiments confirmed the functional synchronisation of both systems. Additionally, the study presents that DT technology can be adapted for educational purposes and implemented in engineering education. Full article

This review critically evaluates current PA6 recycling technologies, with a specific focus on caprolactam-oriented chemical recycling pathways, including hydrolysis, pyrolysis, glycolysis, ammonolysis, hydrothermal treatment, ionic-liquid-assisted depolymerization, and microwave-assisted processes. Reported caprolactam yields vary significantly depending on reaction conditions and catalyst systems, ranging from below 60 wt% in conventional hydrolysis to above 90 wt% under optimized catalytic, hydrothermal, or microwave-assisted conditions. Among these approaches, microwave-assisted hydrolysis and catalytic depolymerization have emerged as particularly promising, offering substantially reduced reaction times (minutes rather than hours), improved energy efficiency, and high monomer selectivity at moderate temperatures (typically 200–350 °C). This review integrates kinetic modeling approaches, analytical methods for monitoring depolymerization, and downstream separation considerations that govern monomer purity and recyclability. Key challenges, including energy demand, feedstock contamination, scalability, and economic competitiveness, are critically discussed in relation to industrial implementation. Overall, hydrolysis-based and microwave-assisted chemical recycling routes are the most viable pathways for closed-loop recycling of PA6. Future progress will rely on integrated reaction–separation–repolymerization designs, catalyst optimization, and process intensification to enable sustainable and industrially relevant PA6 circularity. Full article

This article compares selected fire safety regulatory systems in Japan, China, the United States, and the EU/UK, interpreted through the lens of responsive architecture and the implementation of digital technologies—building information modelling (BIM), digital twins (DTs), artificial intelligence (AI), and the Internet of Things (IoT). The study adopts a qualitative approach based on a structured review of legal acts, technical standards, public-sector reports, and the scientific and professional literature, organised using a common analytical framework. First, the analysis identifies shared foundations across regimes: the primacy of life safety, mandatory detection and alarm functions, fire compartmentation, requirements for protected means of exit, and the increasing importance of documenting the operational status of protection measures. Then, it contrasts key differences, including the permissibility of performance-based design (PBD), the degree to which digital documentation is formally recognised, organisational enforcement models, and cybersecurity approaches for integrated fire alarm/voice alarm/building management/IoT ecosystems. Japan and selected Chinese cities combine stringent requirements with openness to dynamic solutions and urban-scale data platforms. The USA relies on a decentralised code-based ecosystem with a strong role for professional and industry bodies, while the EU/UK continues to strengthen harmonised standards and digital building registers, reinforced by lessons after the Grenfell Tower fire. Against this background, Poland is discussed as broadly aligned in goals and baseline technical requirements yet lagging behind in implementing PBD pathways, digital registers, formal BIM/DT integration, and minimum cybersecurity requirements. The proposed directions for change aim to create a more predictable regulatory and technical framework for the development of responsive architecture and dynamic fire safety systems in Poland. The study contributes to the sustainability literature by framing regulatory readiness for digital fire safety as a lifecycle resilience strategy, directly relevant to safe, resource-efficient, and inclusive built environments. Full article

Automated Storage and Retrieval Systems (AS/RSs) are pivotal hubs in modern intelligent logistics, yet their operational efficiency is often constrained by the complex coupling of storage allocation, equipment scheduling, and path planning. This study proposes a systematic optimization framework to address these three critical control challenges. First, a multi-objective mathematical model for storage location allocation is established, considering efficiency, stability, and correlation. To solve this high-dimensional discrete problem, a Tabu Variable Neighborhood Search (TVNS) algorithm is proposed, integrating short-term memory mechanisms with multi-structure exploration to prevent premature convergence. Second, regarding stacker crane and forklift collaborative scheduling, a Pheromone-guided Artificial Hummingbird Algorithm (PT-AHA) is introduced. By incorporating pheromone feedback into foraging behavior, the algorithm significantly enhances global search capability to minimize total task completion time. Third, stacker crane path planning is modeled as a constrained Traveling Salesman Problem (TSP) and solved using a hybrid Simulated Annealing-Whale Optimization Algorithm (SA-WOA). Quantitative simulation results demonstrate that the TVNS algorithm improves storage allocation fitness by 1.1% over standard Genetic Algorithms, while the PT-AHA reduces task completion time (Makespan) by 21.9% for small-scale batches and consistently outperforms ACO by up to 3.6% in large-scale operations. Validation through an Intelligent Warehouse Management System (WMS) confirms that the integrated framework maintains high industrial resilience by triggering fault alarms and initiating recovery within 3.2 s during simulated equipment failures, providing a robust solution for enterprise-level deployments. Full article

Masonry bricks and blocks are among the most widely used construction materials worldwide; however, their conventional production relies on energy-intensive firing processes and virgin raw materials, leading to significant environmental impacts. In response to increasing sustainability and decarbonization demands in the construction sector, numerous novel ceramic and refractory materials have been proposed for masonry applications. This systematic review provides a comprehensive assessment of recent advances in ceramic and refractory materials for masonry bricks and blocks, focusing on material classification, processing routes, microstructure–property relationships, and sustainability performance. Following the PRISMA 2020 guidelines, the peer-reviewed literature published between 2018 and 2025 was systematically identified, screened, and analyzed. An analytical framework based on well-established relationships from ceramic science was adopted to support consistent comparison of mechanical, thermal, acoustic, durability, and sustainability-related properties across heterogeneous material systems. Conventional fired ceramics, waste-derived ceramics, lightweight and porous systems, alkali-activated and unfired materials, and advanced engineered ceramics were comparatively evaluated. The results reveal a clear shift from dense traditional fired ceramics toward materials incorporating industrial and agricultural residues, engineered porosity, and low-temperature or unfired processing routes. Waste-derived and geopolymer-based systems demonstrate significant potential for reducing CO${}_2$ emissions and energy consumption while maintaining functional performance suitable for masonry applications. Lightweight and porous ceramics exhibit enhanced thermal and acoustic behavior, often accompanied by reduced mechanical strength, highlighting application-dependent trade-offs. Overall, this review provides an integrated perspective linking composition, processing, microstructure, performance, and environmental impact, identifying key research trends and knowledge gaps relevant to sustainable masonry construction. Full article