Search Results (77,111)

Titanium alloys are irreplaceable in aerospace, biomedical and marine industries due to their low density, high specific strength and excellent biocompatibility. Conventional manufacturing methods suffer from low material utilization and difficulty in fabricating complex components, while additive manufacturing (AM) realizes near-net-shape forming of customized structures but introduces unique non-equilibrium microstructures and defects, which significantly alter the corrosion behavior and limit the long-term service reliability of additively manufactured (AMed) titanium alloys. This work systematically analyzes the corrosion behavior of titanium alloys fabricated by four mainstream AM processes: LPBF (laser powder bed fusion)/SLM (selective laser melting), EBM (electron beam melting), DED (directed energy deposition) and WAAM (wire arc additive manufacturing). It quantitatively summarizes the key electrochemical parameters and discusses the regulatory effects of matrix composition, post-treatment and service environment on their corrosion behaviors. The universal corrosion mechanisms—namely, passive film breakdown, micro-galvanic corrosion, and defect-induced localized corrosion—as well as process-specific corrosion mechanisms inherent to AMed titanium alloys are systematically elucidated. This study offers theoretical foundations for optimizing corrosion resistance and ensuring the reliable engineering implementation of AMed titanium alloys. Full article

Although coconut water is recognized for its desirable sensory appeal and nutritional composition, its broader industrial use is constrained by the rapid deterioration that occurs after extraction. In this study, crossflow microfiltration of coconut water with a silicon carbide membrane was optimized by investigating pressure and temperature through a face-centered design (FCD) and artificial neural network modeling coupled with a genetic algorithm (ANN–GA). Permeate flux and fouling index were used as process responses, and the optimized condition was further examined in terms of hydraulic resistance, fouling behavior, and retention of minerals and primary metabolites. Pressure and temperature affected the process differently: permeate flux showed marked nonlinear behavior, whereas fouling index was governed mainly by pressure. At the sample level, ANN described permeate flux more accurately than FCD (R² = 0.99 vs. 0.96), whereas FCD showed better grouped cross-validated predictivity across unseen pressure–temperature conditions (Q² = 0.85 vs. 0.57). For the fouling index, FCD outperformed ANN in both sample-level fit and grouped validation (R² = 0.95 vs. 0.60; Q² = 0.70 vs. 0.61). Both approaches converged on the same favorable operating window, and experimental validation at 60 kPa and 35 °C yielded 1085.23 ± 23.12 L h⁻¹ m⁻² and 83.56 ± 1.56%. During concentration mode, flux decline was severe but predominantly reversible, with high clean-water permeance recovery after chemical cleaning. Resistance partition and fouling modeling indicated that the main hydraulic limitation was associated with concentration polarization and external cake-layer buildup rather than irreversible membrane damage. The clarified fraction also preserved high transmission of major minerals and relevant primary metabolites, indicating that the selected condition combined high productivity, manageable fouling, and satisfactory nutritional retention. Full article

Clay, as a sediment material, is an attractive option for the production of porous ceramics due to its low price and high abundance. Porous ceramics possess a combination of essential properties of clay-based materials, including high porosity and thermal and chemical stability, making them suitable for various industrial applications, such as filters, heat insulators, and absorbents. In this study, thermally and chemically purified clay was mixed with boric acid as a pore-forming agent. Obtained results reveal that different contents of boric acid (2 wt.% and 0.5 wt.%) and variations in synthesis conditions, including low pressing pressures up to 60 MPa and low sintering temperatures of 1150 °C and 1300 °C, optimize the production of a filter medium with good separation and mechanical properties. Further, these findings indicate that an adequate combination of boric acid content and synthesis conditions positively affects mechanical properties, including values of hardness, Young’s modulus, compressive and tensile strength of clay-based filters. The clay-based filter with 2 wt.% boric acid exhibited a larger maximum pore diameter of nearly 0.2 mm, compared to the one with 0.5 wt.% boric acid. The filtering efficiencies of both filters were tested on pharmaceutical-grade ciprofloxacin with removal efficiency above 80% for two tested concentrations (6 μM and 9 μM). Full article

This study examines how energy-security shocks shape macroeconomic resilience in import-dependent economies and which energy-management strategies remain robust under alternative shock conditions. Using a balanced panel of 18 mainly European energy-importing economies and Türkiye for 2000–2024, the study constructs a Macroeconomic Resilience Index (MRI) from five dimensions: GDP growth, inflation, unemployment, current account balance, and industrial production growth. Inflation and unemployment are treated as inverse resilience indicators, and a Principal Component Analysis (PCA)-based alternative index is used as a robustness check. Methodologically, the study develops a hybrid framework that first applies a Shock-Augmented Cross-Sectionally Dependent Panel Quantile ARDL model to estimate heterogeneous shock effects across resilience levels, and then translates the econometric evidence into a Shock-Conditioned Bayesian Network–Regret MCDM model for strategy prioritization. The findings show that exchange-rate pressure is the most consistent long-run vulnerability channel, while energy intensity weakens resilience across short-run, benchmark, and quantile robustness results. The renewable energy share supports resilience under some conditions, but its effect depends on complementary investments in storage, grid flexibility, and demand-side capacity. Scenario results indicate that exchange-rate pressure produces the weakest resilience profile. The positive MRI value observed during combined-crisis years should be interpreted cautiously, as additional sensitivity evidence indicates that it mainly reflects the 2021–2022 post-pandemic rebound rather than a beneficial effect of shocks. Bayesian Network results identify macro-financial stabilization, import-dependency reduction, energy efficiency, and grid reliability as key resilience drivers. The regret-based MCDM results rank energy efficiency improvement as the most robust strategy, followed by energy import diversification. The study contributes by linking dynamic macroeconometric shock analysis with probabilistic scenario modeling and regret-sensitive decision support, offering an evidence-informed framework for prioritizing energy-security strategies in the sampled import-dependent economies. Full article

(1) Background. Food sovereignty and local sustainability are ensured by large agro-industrial holdings and small-scale farms; this synergy forms a complementary model of the agrifood system. Maintaining this model’s balance requires the creation of a unified digital ecosystem that integrates all suppliers and consumers into production chains, thereby eliminating unnecessary intermediaries. (2) Methods. This study employs a comprehensive methodological framework, including systems analysis and mathematical modeling, to develop service algorithms. Object-oriented design and software engineering methods facilitated the development and implementation of a service-oriented architecture for the digital system. (3) Results. The study presents a multi-tier architecture featuring an integration bus based on a service-oriented approach. To implement direct supply-and-demand coupling strategies, the system integrates both internal services (microeconomic indicators) and external services (macroeconomic indicators). Additionally, a recommender system based on neural networks and mathematical models was developed to personalize consumer requests and manage product sales. (4) Conclusions. The software solution is consistent with the AgTech 4.0 concept and enables the creation of a seamless environment for interstate trade. The implementation of the system enhances the transparency of the “product footprint”, facilitates the redistribution of surpluses, and, consequently, contributes to price stabilization. Full article

The demand for large-scale high-performance components with tailored properties in the aerospace and automotive industries has increased interest in multi-material additive manufacturing (AM). Among AM techniques, the Wire Arc Additive Manufacturing (WAAM) process is preferred for bimetallic fabrication due to high deposition rates, low equipment costs, and efficient material utilization. However, differences in metallurgical and thermal properties between dissimilar alloys can cause heat accumulation, leading to thermal stresses, cracking, and weak interfacial bonds. To the best of the authors’ knowledge, no study has reported the fabrication and characterization of a 17-4PH SS/Inconel 625 joint using the large-scale CMT-WAAM Process. To fill this gap, this study characterizes the microstructure and elemental distribution of the joint using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray Microscopy (XRM) and energy dispersive spectroscopy (EDS). Microstructural analysis revealed a martensitic matrix with retained δ-ferrite in the 17-4PH region, a fully austenitic γ-phase in the Inconel 625 region, and a mixed BCC–FCC transition zone at the interface. EDS results demonstrated a Fe–Ni compositional gradient across the interface. Radiographic inspection confirmed a defect-free build, and XRM results showed a porosity of less than 0.003% only in the 17-4PH region. Tensile testing confirmed joint integrity, with fracture occurring in the Inconel 625 region, and average yield and ultimate tensile strengths of 391 ± 7 MPa and 676 ± 9 MPa, respectively. The simplified Johnson-Cook constitutive model successfully predicted the ultimate tensile strength (UTS), with a prediction error of 9.3% compared to the experimental result. Furthermore, a novel 3D-structured light scanner technique was developed and validated with an extensometer to provide insight into localized strain behavior. Full article

Insect farming has rapidly expanded in Europe following regulatory approval of insect-derived proteins in aquaculture feed and increasing interest in the valorization of insect by-products. Insect frass, consisting of excreta and exuviae, is a nutrient-rich material with beneficial microorganisms and potential as a sustainable alternative to conventional fertilizers, although its composition varies with insect species and feedstock. EU legislation requires thermal sanitization prior to market release, yet the effects of the thermal treatment on frass nutrient composition and biofertilizer performance remain poorly understood. Insect frass from black soldier flies (BSFFs) fed on a diet based on dairy industry byproducts was sanitized and mixed with sandy soil and used in two lettuce pot trials under greenhouse conditions. The aim of our study was to determine the effects of thermal sanitization on (1) macro- and micronutrient contents and dynamics (plant N and P uptake); and (2) biofertilizer potential, including plant physiology (chlorophyll, anthocyanins, flavonols, Fv/Fm), plant growth (biomass), and soil microbial activity (dehydrogenase and β-glucosaminidase). BSFF showed a clear potential to induce growth of lettuce plants by increasing chlorophyll content, biomass and microbial activity. Furthermore, the sanitization process did not significantly alter the measured agronomic performance of frass under the tested conditions or reduce its benefits on biomass growth, chlorophyll content, microbial enzyme activity and on nutrient uptake by the lettuce plants. These findings suggest that the mandatory sanitization does not compromise its agronomic functionality, supporting its strong potential within circular agricultural systems under the tested conditions. However, the results are valid under greenhouse conditions and for the specific frass, soil and crop combinations; field validation is needed to confirm these results under large-scale high-value crop production conditions. Full article

In the context of urban regeneration and the redevelopment of existing urban land and built assets, industrial heritage has become a cross-sectoral policy issue involving heritage conservation, spatial reuse, land governance and public cultural uses. Existing studies have primarily examined individual adaptive reuse projects and spatial strategies, whereas the long-term evolution of policy texts has received less systematic attention. Taking Shanghai as a case study, this paper constructs a clause-level corpus of industrial heritage-related policies issued between 2006 and 2025. The corpus comprises 524 clauses extracted from 86 policy documents covering heritage conservation, historic building conservation, cultural and creative industries, land use, planning, urban renewal and industrial tourism. Overall and stage-based Latent Dirichlet Allocation (LDA) models are combined with cross-period topic alignment to identify the structure and evolution of policy themes. The results show that Shanghai’s industrial heritage policies have been shaped not only by heritage conservation concerns, but also by industrial land governance, the transformation of underused industrial land, the regeneration of existing industrial spaces (EIS), industrial culture, tourism and public service provision. Four stages are identified: initial exploration, regulatory consolidation, revitalisation and renewal, and integrated consolidation. Across these stages, four major evolutionary pathways can be observed: industrial land supply and governance, renewal of EIS and old industrial areas (OIA), industrial heritage conservation and value recognition and the expansion of industrial culture, tourism and public services. The paper provides clause-level evidence for understanding industrial heritage governance in China’s urban regeneration context and highlights the need for stronger coordination between heritage, land, planning, industry, culture and tourism policies. Full article

This paper focuses on the analysis of long-term land surface temperature (LST) dynamics and land-use changes in the city of Košice, Slovakia, during the period 1985–2025. The analysis is based on multi-temporal Landsat satellite imagery processed within a geographic information system (GIS) environment. Non-parametric statistical methods, including the Mann–Kendall trend test and the Theil–Sen slope estimator, were applied at the pixel level to identify the direction, magnitude, and statistical significance of long-term trends. Land-use changes were evaluated using CORINE Land Cover data together with the NDVI and NDBI spectral indices. The results revealed a statistically significant increase in land surface temperature across almost the entire urban area, with the mean LST increasing by 5.83 °C between 1985 and 2025. The analysis also confirmed a strong positive correlation between built-up areas and LST values, whereas vegetation cover exhibited a significant cooling effect represented by a strong negative correlation with surface temperature. Spatial analysis identified pronounced warming hotspots concentrated mainly in industrial and newly urbanized areas, while vegetation-stabilized zones showed lower warming intensity or localized cooling trends. The findings highlight the dominant influence of urbanization processes on the city’s thermal regime and emphasize the importance of urban vegetation as a key adaptation element for mitigating the surface urban heat island effect. The study also illustrates the added value of integrating remote sensing data, GIS tools, and pixel-based trend analysis in the assessment of long-term changes in the urban thermal environment of medium-sized Central European cities. The results provide a spatial basis for climate adaptation planning and future assessments of urban thermal comfort and environmental quality. Full article

The textile industry causes significant environmental problems because of its intensive use of water, energy, and chemicals. The stenter machines, which are commonly used in textile finishing processes, release air pollutants such as odor, volatile organic compounds (VOCs), and total organic carbon (TOC) into the atmosphere during drying and fixing processes carried out at high temperatures. The aim of this study was to investigate odor, VOC, and TOC emissions from the stacks of the stenter machines. In this study, odor, VOC, and TOC parameters were examined in samples from the stacks of stenter machines of nine different plants operating in the textile sector in Bursa, Turkey. The samples were analysed in accordance with EN 13725:2022 standard, EN 13649:2014 standard, and EN 12619:2013 standard for odor, VOC, and TOC parameters, respectively. Acetone, carbon tetrachloride, dibromochloromethane, ethylbenzene, tetrachlorethylene, toluene, and p + m-Xylene were the most common components. The TOC concentrations were determined in the range of 13.89–279.23 mg/Nm³. The odor concentrations were determined in the range of 4113–26,627 OU/m³. Full article

To address the low efficiency and high subjectivity of manual inspection of Astragalus slices, as well as the limited fine-grained detection accuracy caused by the visual similarity between the characteristic radial “chrysanthemum heart” texture and minor defects such as insect damage and mold, this study proposes a lightweight intelligent detection model named Astra-YOLO. A dataset consisting of 622 original Astragalus slice images from four categories was divided into training, validation, and test sets at a ratio of 8:1:1. Data augmentation was applied exclusively to the training set, resulting in a total of 3110 images. Based on YOLOv11n, three targeted improvements were introduced: GhostConv lightweight convolution was employed to reduce model parameters and computational cost; the parameter-free SimAM attention mechanism was integrated to suppress interference from complex textures and enhance defect feature representation; and Wise-IoU v3 was adopted to improve bounding box regression for precise localization of small defects. The experimental results demonstrate that Astra-YOLO achieves superior performance with only 2.53 million parameters and 6.20 GFLOPs. The model attains an mAP@0.5 of 92.7%, an mAP@0.5:0.95 of 73.8%, a precision of 92.4%, and a recall of 92.1%. These results indicate that Astra-YOLO effectively balances lightweight design and detection accuracy, outperforming the baseline model and other improved variants, thereby providing reliable technical support for industrial online inspection and automated quality grading of Astragalus slices. Full article

The global sugar industry is facing increasing challenges due to climate variability, sustainability requirements, and the need for improved operational efficiency. These pressures are driving the search for advanced technological solutions to enhance productivity and resource management. Artificial intelligence (AI) has already demonstrated significant potential across various agricultural sectors; however, a comprehensive evaluation of AI applications across the entire sugar industry value chain from crop cultivation to industrial processing and supply chain management remains limited. This review provides a detailed assessment of the current state of AI and internet of things (IoT) implementation in the sugar beet industry. It examines key applications, including precision agriculture for sugarcane and sugar beet cultivation, intelligent monitoring systems for early disease detection, and AI-driven decision support tools for resource optimization. In addition, the study explores the role of AI in sugar manufacturing processes, where machine learning and data-driven models are used to optimize milling operations, improve product quality control, and enable predictive maintenance of industrial equipment. AI technologies are also shown to enhance supply chain efficiency through improved demand forecasting, logistics optimization, and real-time data analytics. Monitoring volatile organic compounds (VOCs) is becoming increasingly important in sugar beet and sugarcane storage. Microbial activity during storage and fermentation can release VOCs such as ethanol, which act as early indicators of crop degradation and spoilage. Detecting these gases using modern gas sensors enables continuous monitoring of storage conditions and crop health. When sensor data is integrated with AI and IoT systems, it can be analyzed in real time to identify early signs of microbial activity, improve storage management, and optimize processing decisions. Such intelligent monitoring systems have the potential to reduce losses and enhance overall efficiency in the sugar production chain. Full article

Heavy metal (HM) contamination in urban soils is a pressing global issue, particularly in rapidly industrializing regions like Kazakhstan, where anthropogenic activities such as transportation, energy production, and manufacturing exacerbate accumulation in ecosystems. In Almaty, the largest city in Kazakhstan, urban expansion and legacy pollution pose risks to soil functions, biodiversity, and public health through bioaccumulation and migration pathways. This study evaluates the spatial distribution and ecological impacts of total heavy metal concentrations (HMs) (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in Almaty’s soils to inform remediation strategies. Soil samples (n = 73) were collected using a systematic grid sampling method across urban, industrial, and peri-urban zones in Almaty. HM concentrations were determined via X-ray fluorescence spectrometry (XRF) following GOST 33850-2016 standards. Pollution indices (contamination factor K_c and integrated pollution index Z_c) were calculated relative to Kazakhstani permissible limits (PDK RK) and Russian approximate permissible concentrations (ODK RF). Statistical analyses included Spearman’s correlation, boxplots, and coefficient of variation. Morphological, physicochemical (pH, humus content), and biological assessments evaluated degradation. Spatial interpolation via GIS mapped the hotspots. HM distributions showed significant variability, with As, Zn, and Ni exceeding norms in >90% of samples (median K_c ≈ 5 for As). Z_c classified >70% of sites as hazardous or extremely hazardous (Z_c > 32), with hotspots in central-eastern districts (Z_c 90–145). Strong correlations (ρ ≥ 0.6) identified a technogenic group (Pb–Zn–Cu–Ni) from traffic and industry, contrasting predominantly geogenic elements with possible anthropogenic contribution (As–Co–Mo–Mn). Pollution induced soil compaction, reduced humus/pH, and disrupting biogeochemical cycles. Local exceedances were noted near TECs, factories, and transport hubs. Almaty’s soils exhibit pervasive technogenic HM pollution, driven by urban sources, leading to ecosystem degradation and health risks. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Prioritized monitoring and phytoremediation in hotspots are recommended to enhance resilience, aligning with UN SDGs for sustainable cities and ecosystems. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Full article

Efficient and unimpeded factor allocation constitutes the cornerstone of urban–rural integrated development. Using panel data from 281 prefecture-level cities in China spanning the period from 2011 to 2023, this study employs a two-way fixed-effects model to empirically investigate the direct effects and transmission channels of the application of data elements on urban–rural integrated development. The findings are as follows: (1) The application of data elements significantly promotes urban–rural integrated development. On average, a 1% increase in the level of data element application is associated with a 0.089% rise in the urban–rural integrated development index. This result remains robust across a battery of robustness tests, with pronounced regional heterogeneity. (2) Mechanism analysis demonstrates that data elements application drives urban–rural integrated development through three primary channels: facilitating labor mobility, mitigating capital misallocation, and elevating total factor productivity (TFP). (3) Heterogeneity analysis based on regional characteristics reveals that this promoting effect is more significant in regions with superior economic growth, more advanced industrial structures, and abundant human capital. (4) Spatial spillover analysis indicates that while data element application facilitates local urban–rural integrated development, it simultaneously exerts a negative spatial spillover effect on neighboring regions. Accordingly, policymakers should prioritize strengthening digital infrastructure in less developed regions and take the lead in establishing cross-jurisdictional data sharing platforms coupled with benefit compensation mechanisms, thereby fostering coordinated advancement and mutually beneficial outcomes in urban–rural integrated development across regions. Full article

Challenging work conditions and a predominantly male workforce have intensified concerns about worker well-being in the U.S. construction industry. Although these issues are increasingly acknowledged, empirical evidence on how management perceives and addresses employees’ psychological needs remains limited. This study seeks to fill that gap by examining managerial awareness and responsiveness to workplace mental health challenges, as well as the implementation of targeted well-being interventions across construction organizations. A mixed-methods approach was employed, integrating quantitative data from a nationwide survey of construction professionals with qualitative insights from open-ended responses. Results indicate that workers experience moderate levels of psychological strain, with notable differences across demographic and occupational groups. Women, non-white professionals, and site supervisors reported higher stress and lower overall well-being, emphasizing the need for more inclusive and tailored interventions. While both senior leaders and frontline supervisors demonstrated moderate awareness of these challenges, supervisors were perceived as less consistent in taking action to address them. Among existing workplace practices, transparent compensation structures and opportunities for skill diversification were identified as the most established supports for well-being. In contrast, mentoring programs, employee recognition, and open feedback mechanisms emerged as promising yet underutilized strategies. Overall, the study contributes by shifting attention from documenting construction-related mental health stressors to understanding how leadership recognition is translated into organizational response. Therefore, the findings provide practical guidance for construction leaders seeking to strengthen workers’ well-being and build a more resilient work environment. Full article