Search Results (44)

Gigacasting is emerging as a disruptive manufacturing route for automotive body-in-white structures, especially for electric vehicles, by enabling large aluminium alloy components to replace assemblies traditionally produced from stamped and joined sheet-metal parts. This paper presents a technological assessment of aluminium gigacasting against conventional multi-material mix sheet-metal manufacturing. The comparison addresses product architecture, structural performance, manufacturability, factory organisation, cost, repairability, supply chain implications, and sustainability. Gigacasting offers benefits in part consolidation, reduced joining operations, shorter process chains, and potentially lower non-material manufacturing costs, making it attractive for high-volume, low-variant EV platforms and greenfield production. However, these advantages are counterbalanced by challenges, including high capital investment, limited die life, defect sensitivity, dimensional distortion, mechanical-property variation, and reduced repairability. Recent benchmark data also indicate that total part cost and production-phase CO₂ emissions may remain higher than conventional solutions when aluminium material cost, component mass, and aluminium carbon intensity are considered. Conventional sheet-metal architectures retain advantages in modularity, repairability, quality control, tooling flexibility, and lower-risk implementation in brownfield plants. The analysis concludes that gigacasting should not be regarded as a universal replacement for sheet-metal multi-material Body-in-White (BIW) manufacturing but as a platform-dependent technology whose success requires defect control, low-carbon aluminium supply, process-aware simulation and validation, and high and stable production volumes. Full article

Due to the increasing heterogeneity of consumer needs and preferences, manufacturing companies are forced to expand their product range to maintain market share while avoiding cost increases. However, increasing product variety increases the complexity of assembly systems and complicates planning, design, and production management. The quantification of manufacturing complexity and its impact on key performance indicators remains a subject of debate. To examine the relationship between assembly complexity, assembly line balance, and productivity from an operator-oriented perspective, an empirical complexity indicator for mixed-model assembly workstations is proposed. This indicator is based on experimentally collected data and analysis of working time variability. The proposed indicator is evaluated through controlled experimental case studies conducted in a learning factory environment. The results indicate that the relationship between complexity and productivity is not linear. Instead, within the investigated experimental boundaries, the observed trend suggests a turning point beyond which further increases in complexity are associated with decreased productivity, while line balance continues to improve. This finding suggests that integrating the proposed complexity indicator into production planning and management may support decision-making related to assembly line balancing and complexity management in manual assembly systems. Full article

Non-suicidal self-injury (NSSI) is a major concern in adolescent mental health, yet the psychometric properties of the Inventory of Statements About Self-Injury (ISAS) have not previously been examined in a Greek adolescent inpatient sample. This preliminary study evaluated the internal consistency, factorial structure, and construct validity evidence of the ISAS in 95 Greek adolescents receiving inpatient psychiatric care (mean age = 14.69 years, SD = 1.30; 86.3% female). Data were obtained retrospectively from clinical records. Psychometric evaluation included Cronbach’s alpha coefficients, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and correlations with the Youth Self-Report (YSR). The ISAS showed satisfactory internal consistency at the higher-order factor level, with alpha coefficients of 0.82 for the interpersonal factor and 0.85 for the intrapersonal factor, although Autonomy and Interpersonal Boundaries showed weak reliability. EFA supported a broad two-factor intrapersonal–interpersonal structure. CFA findings were exploratory and mixed. The original CFA models showed inadequate fit, whereas exploratory modified and post hoc sensitivity models showed improved but non-definitive fit. Convergent validity evidence was modest and was supported by associations between the original broad ISAS intrapersonal score and YSR self-harm behavior, suicidal ideation, internalizing-related dimensions, and Total Problems. Discriminant-pattern evidence was limited. Overall, the findings provide preliminary support for the clinical usefulness of the ISAS as an adjunctive assessment tool in this population, but they do not constitute definitive validation. Further prospective validation in larger, more diverse, and independent samples is needed. Full article

The present study examined whether offensive play-type indicators in professional basketball reflect broader latent playing-style dimensions and whether play-type usage or efficiency is more strongly associated with competitive success. Data were obtained from the official NBA statistics website and included 6400 games across five seasons (2019–2020 to 2023–2024), comprising 5979 regular-season games and 421 playoff games. For each offensive play type, two indicators were analysed separately: usage percentage and efficiency, operationalised as points per possession (PPP). Principal component analyses were conducted independently for regular-season and playoff data, and for usage and efficiency variables. In addition, linear mixed-effects models were used to examine the relationship between play-type indicators and competitive success while accounting for games nested within teams. Only regular-season efficiency variables showed adequate sampling adequacy for factorial analysis (KMO = 0.774), yielding a four-component solution that explained 58.85% of the total variance. In the mixed-effects models, usage variables were not significantly associated with success, whereas efficiency indicators showed greater explanatory value. Specifically, pick-and-roll ball handler PPP and spot-up PPP emerged as the strongest positive predictors of success, with smaller effects observed for roll-man PPP and cut PPP. The efficiency-only model improved model fit relative to the frequency-only model (marginal R² = 0.799 vs. 0.755), whereas adding usage variables to efficiency provided only a negligible additional contribution (marginal R² = 0.803). These findings suggest that, in the NBA, competitive success is more closely related to the effectiveness with which offensive actions are executed than to the relative frequency with which they are used. From an applied perspective, play-type efficiency appears to provide more actionable information than usage-based summaries for performance analysis and tactical decision-making. Full article

The strategic decision to choose in-house production and outsourcing (make and buy) is one of the enormous research questions of international business studies. However, the dynamics of offshore mixed sourcing involving suppliers with varying capabilities remain under-explored. This study intends to elucidate how a firm optimizes the division of labour between an affiliated offshore factory and heterogeneous contract manufacture. We adopt a single-case study design to analyse a Japanese apparel firm operating in China. The empirical analysis using the plant-level data of both in-house and outsourcing Chinese factories reveals a clear strategic distinction: the affiliated factory specializes in High-Mix Low Volume (HMLV) production to manage market volatility, whereas outsourcing partners are utilized for volume production, segmented by their quality capabilities. This study contributes to the literature by demonstrating that mixed sourcing is not merely a cost-saving tactic but a mechanism to manage supply chain heterogeneity. Full article

The transition period in dairy cows involves profound metabolic adaptations that challenge energy balance and liver function. This study evaluated the effects of rumen-protected methionine (RPM) and choline (RPC) on hepatic and skeletal muscle metabolism. Twenty-one multiparous Holstein cows from a 2 × 2 factorial design (CON, RPM, RPC) underwent liver and semitendinosus biopsies at −10, +7, and +20 d relative to parturition. Untargeted LC-MS metabolomics detected 2288 and 1454 molecular features in liver and muscle. Data were analyzed using mixed-model ANOVA (FDR ≤ 0.05), complemented by multivariate approaches including sparse PLS-DA and PERMANOVA to assess global metabolic variation. Metabolite annotation was performed using HMDB (±0.005 Da). Dietary supplementation significantly affected 105 hepatic metabolites, whereas time influenced 552 metabolites, generally reflecting increases or decreases in concentration from the prepartum to early postpartum periods. Network analysis identified nine hepatic co-expression modules associated with RPM and RPC. Hub metabolites included glucose-6-phosphate, mannose-6-phosphate, and sphingomyelins, indicating modulation of carbohydrate and lipid metabolism. In muscle, treatment effects were modest, with PERMANOVA and PLS-DA confirming limited discrimination among groups and a predominant temporal effect. Overall, RPM and, to a lesser extent, RPC modulated key hepatic metabolic pathways, supporting energy and redox homeostasis during early lactation. These findings highlight the potential of methyl-donor supplementation to enhance metabolic resilience at the tissue level in transition cows. Full article

Morocco is increasingly vulnerable to climate change, as reflected by recurrent droughts and rising soil and groundwater salinization, which threaten staple crops and rural livelihoods. In this context, the introduction of drought- and salinity-tolerant crops such as quinoa represents a strategic option for enhancing agricultural resilience and supporting sustainable rural development. This study analyzes quinoa adoption in two contrasting Moroccan regions, Rehamna and the Oriental, with the aim of determining key socio-economic, institutional, and environmental drivers. Field surveys were conducted to collect data on farmers’ personal characteristics, farm attributes, and access to resources related to quinoa cultivation, including water, information, and credit. Data analysis combined descriptive statistics, a binary logistic regression model (Logit), Factorial Analysis for Mixed Data (FAMD), and Hierarchical Cluster Analysis (HCPC) to identify adoption determinants and explore heterogeneity among farmers. The results reveal both common factors and region-specific dynamics shaping quinoa adoption. Cooperative membership emerges as a central determinant in both regions, facilitating access to information, collective learning, and market integration, with a stronger effect observed in the Oriental region. Water scarcity appears as a critical constraint, particularly in Rehamna. Adoption pathways also differ across regions, with a higher prevalence of direct adoption among farmers in the Oriental. Interpreted through the lens of innovation diffusion and multidimensional sustainability, the findings show that quinoa adoption is not merely a technical choice but a socio-economic adaptation strategy. Quinoa should therefore be considered a complementary crop within diversified farming systems, contributing to environmental resilience, income diversification, and social inclusion. These results provide relevant insights for the design of policies aimed at promoting sustainable agricultural innovation in marginal environments. Full article

Large density fluctuations near the QCD critical point can be probed via intermittency analysis, which involves measuring scaled factorial moments (SFMs) of multiplicity distributions in relativistic heavy-ion collisions. Intermittency reflects the emergence of scale invariance and self-similar structures, which are closely related to symmetry principles and their breaking near a second-order phase transition. We present a systematic model study of intermittency for charged hadrons in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 7.7, 11.5, 19.6, 27, 39, 62.4, and 200 $\mathrm{GeV}$. Using the cascade UrQMD model, we demonstrate that non-critical background effects can produce sizable SFMs and a large scaling exponent if they are not properly removed using the mixed-event subtraction method. To estimate the possible critical intermittency signal in experimental data, we employ a hybrid UrQMD+CMC model, in which fractal critical fluctuations are embedded into the UrQMD background. A direct comparison of the second-order SFM between the model and STAR experimental data suggests that a critical intermittency signal on the order of approximately $1.8\%$ could be present in the most central Au+Au collisions at RHIC energies. This study provides practical guidance for evaluating background contributions in intermittency measurements and offers a quantitative estimate for the critical signal fraction present in the STAR data. Full article

The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, such as convolutional neural networks (CNNs), reinforcement learning (RL), and federated learning (FL), within Taiwan’s advanced manufacturing sectors, including semiconductor fabrication, smart assembly, and industrial energy optimization. The present study draws on patent data and industrial case studies from leading firms, such as TSMC, Foxconn, and Delta Electronics, to trace the evolution from classical optimization to hybrid, data-driven frameworks. A critical analysis of key challenges is provided, including data heterogeneity, limited model interpretability, and integration with legacy systems. A comprehensive framework is proposed to address these issues, incorporating data-centric learning, explainable artificial intelligence (XAI), and cyber–physical architectures. These components align with industrial standards, including the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). The paper concludes by outlining prospective research directions, with a focus on cross-factory learning, causal inference, and scalable industrial AI deployment. This work provides an in-depth examination of the potential of machine learning to transform manufacturing into a more transparent, resilient, and responsive ecosystem. Additionally, this review highlights Taiwan’s distinctive position in the global high-tech manufacturing landscape and provides an in-depth analysis of patent trends from 2015 to 2025. Notably, this study adopts a patent-centered perspective to capture practical innovation trends and technological maturity specific to Taiwan’s globally competitive high-tech sector. Full article

This study explores the chemical composition of different macrophyte species and infers their potential in extracting nutrients and some heavy metals from water as well as the use of macrophytes’ biomass as natural fertilizers. It used a dataset obtained from a previous study composed of 445 samples of chemical concentrations in the dried biomass of 16 macrophyte species collected from the Santana Reservoir in Rio de Janeiro, Brazil. Correlation tests, analysis of variance, and factor analysis of mixed data were performed to infer correspondences between the macrophyte species. The results showed that the macrophyte species can be grouped into three different clusters with significantly different profiles of chemical element concentrations (N, P, K⁺, Ca²⁺, Mg²⁺, S, B, Cu²⁺, Fe²⁺, Mn²⁺, Zn²⁺, Cr³⁺, Cd²⁺, Ni²⁺, Pb²⁺) in their biomass (factorial map from PCA). Most marginal macrophytes have a lower concentration of chemical elements (ANOVA p-value < 0.05). Submerged and floating macrophyte species presented a higher concentration of metallic and non-metallic chemical elements in their biomass (ANOVA p-value < 0.05), revealing their potential in phytoremediation and the removal of toxic compounds (such as heavy metal molecules) from water. A cluster of macrophyte species also exhibited high concentrations of macronutrients and micronutrients (ANOVA p-value < 0.05), indicating their potential for use as soil fertilizers. These results reveal that the plant’s location in the reservoir (marginal, floating, or submerged) is a relevant feature associated with macrophytes’ ability to remove chemical components from the water. The obtained results can contribute to planning the management of macrophyte species in large water reservoirs. Full article

The objective of this experiment was to determine whether compound microbial inoculants could enhance the fermentation of oat and common vetch silage that were stored in the Northwest Sichuan Plateau for 60 days under extremely low temperatures. Oat and common vetch harvested from single and mixed artificially planted grassland of oat and common vetch were chopped into 2–3 cm (oat, S1; common vetch, S2; oat–common vetch = 2:1, S3), then sterile water (T1), Zhuang Lemei IV silage additive (T2), and Fu Zhengxing silage additive (T3) were added to the feed and ensiled at the local outdoor environment for 60 days. Data were analyzed as a 3 × 3 factorial arrangement of treatments with the main effects of the materials, additives, and their interaction. Interactions between the materials and additives significantly affected the fermentation quality and the content of DM, WSC, and NDF and the number of yeasts in forages. Treatments with S3 have significantly higher contents of lactic acid, acetic acid, and lactic acid bacteria in the feed than those in the S1 and S2 treatments, while the contents of AN/TN and propionic acid were significantly lower compared with the S1 and S2 treatments (p < 0.05). Concentrations of lactic acid, acetic acid, and propionic acid were significantly increased and the content of neutral detergent fiber in the T2-treated silage decreased compared with the T1 treatment (p < 0.05). The T3 treatment significantly reduced the number of yeasts in the silage but the compound lactic acid bacteria additive treatment (T1, T2) significantly decreased the butyric acid content and pH of the feed and increased the acid detergent fiber content and the number of lactic acid bacteria in the feed compared with the T1 treatment. Among them, the butyric acid content of the T3 treatment decreased by 63.64–86.05%, while that of the T2 treatment decreased by 36.36–83.33% (p < 0.05). The comprehensive analysis of the membership function revealed that the silage quality was the best after the S3T2 treatment, so the implementation of the S3T2 combination in the Northwest Sichuan Plateau can provide guarantees for the production of local high-quality forage grass and alleviate the shortage of forage grass. Full article

The province of Sankuru, located within the Democratic Republic of Congo, is distinguished by its extensive rubber plantations, which have a long history in the region. These plantations have had a considerable impact on the region’s agrarian landscape over time. In addition to the exploitation of latex, for which the conditions are currently very limited, these plantations provide goods and services to the local population and are dominated by rural communities that are highly dependent on these natural resources. This study aimed to characterize the socio-demographic and agrarian profile of historical rubber plantations while assessing the occurrence of the ecosystem services (ESs) they provide. Particular attention will be paid to the farmers’ perceptions of these services, an essential element for the rational management of natural resources. This study used a mixed methodological approach, integrating semi-structured interviews, focus groups, and statistical analyses including chi-square testing and multiple correspondence factorial analysis (MCAFA) to obtain and analyze the data comprehensively. The results indicate that historical rubber plantations in Sankuru provide 21 ESs, which are grouped into four categories: eleven provisioning services, four regulating services, four cultural services, and two supporting services. It has been observed that local communities attach significant importance to the provision of services including the provision of firewood (96.67%) and the utilization of forest resources for traditional pharmacopoeia (91.33%). These plantations have come to be regarded as valuable cultural heritage by local communities over time. The younger generation evinces a greater interest in utility services than the older generation, which displays a preference for cultural services. However, older people demonstrate a more profound understanding of cultural and regulatory services. By emphasizing the species that contribute to ESs and recognizing plantations as cultural heritage, the study enhances the comprehension of the significance of local ecosystems. These findings provide a crucial foundation for directing local policy toward integrated management of historic rubber plantations in Sankuru. By considering the perceptions of local people, the study contributes to the sustainable conservation of these plantations for the present and future generations. Full article

This study investigates the feasibility of using mineralized collector-drainage water (CDW) for irrigating maize crops on light gray soils in the Syrdarya region of Uzbekistan, an area facing severe water scarcity and soil salinity challenges. The research is particularly novel as it explores maize production in marginalized soils, a subject previously unexamined in this context. The experiment was designed as a three-factor factorial study with three replications, following the guidelines of the Uzbekistan Cotton Scientific Research Institute. Five irrigation treatments (Fresh Water, Fresh Water 70% vs. CDW 30%, Complex Method (Mixing with Specific Rules), CDW 70% vs. Fresh Water 30% (Mixing) and only CDW) were evaluated using an Alternate Furrow Irrigation system, incorporating various mixtures of fresh water and CDW to determine their effects on soil salinity, crop health and yield. The amount of irrigation water required was determined using a soil moisture balance model, with soil samples collected at multiple depths (0–100 cm) to monitor changes in moisture content and salinity. Salinity levels and soil health parameters such as alkalinity, chloride, sulfate and cation/anion balances were measured at different stages of crop growth. Data were collected over three growing seasons (3 years). An analysis of the data revealed that using CDW, even in mixtures with fresh water, can sustain crop production while managing soil salinity. Notably, irrigation methods such as Mixing 70–30 and the Complex Mixing Method effectively reduced freshwater dependency and maintained the crop yield without significantly increasing salinity. The results suggest that CDW could be a viable alternative water source in regions where traditional water resources are limited. The findings have significant implications for improving water use efficiency and agricultural productivity in areas facing similar environmental challenges. This research not only contributes to the broader understanding of sustainable irrigation practices in arid regions but also provides a scientific basis for the wider adoption of CDW in Uzbekistan, potentially enhancing food security and supporting long-term agricultural sustainability in the region. Full article

Climate change is an aspect in our lives that presents urgent challenges requiring innovative approaches and collaborative efforts across diverse fields. Our research investigates the growth and thematic structure of the intersection between climate change research and machine learning (ML). Employing a mixed-methods approach, we analyzed 7521 open-access publications from the Web of Science Core Collection (2004–2024), leveraging both R and Python for data processing and advanced statistical analysis. The results reveal a striking 37.39% annual growth in publications, indicating the rapidly expanding and increasingly significant role of ML in climate research. This growth is accompanied by increased international collaborations, highlighting a global effort to address this urgent challenge. Our approach integrates bibliometrics, text mining (including word clouds, knowledge graphs with Node2Vec and K-Means, factorial analysis, thematic map, and topic modeling via Latent Dirichlet Allocation (LDA)), and visualization techniques to uncover key trends and themes. Thematic analysis using LDA revealed seven key topic areas, reflecting the multidisciplinary nature of this research field: hydrology, agriculture, biodiversity, forestry, oceanography, forecasts, and models. These findings contribute to an in-depth understanding of this rapidly evolving area and inform future research directions and resource allocation strategies by identifying both established and emerging research themes along with areas requiring further investigation. Full article

Objective: This study aimed to establish a correlation between fecal calprotectin levels (FC) and intestinal inflammation in patients with spondyloarthritis without inflammatory bowel disease. Methods: A total of 180 SpA patients were included in the study of them 20.6% required Digital chromoendoscopy (DCE). FC, C-reactive protein (CRP), HLA-B*27 and clinical indices were assessed. Results: Positive fecal calprotectin (PFC) and high fecal calprotectin (HFC) levels were observed in 27.0% and 16.0% of patients, respectively. HFC correlated with a Bath Ankylosing Spondylitis Functional Index (BASFI) score > 4.0 (p = 0.036) and a Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score > 4.0 (p = 0.047). Loss of vascular pattern in the ileum (LVPI) was observed in approximately 70.0% of patients (p = 0.005), which was associated with PFC and abdominal bloating (p = 0.020). LVPI was also linked to microscopic inflammation (p = 0.012) and PFC with abdominal pain (p = 0.007). HFC was significantly associated with alterations in the ileal mucosa (p = 0.009) and LVPI (p = 0.001). Additionally, HFC and diarrhea were associated with LVPI in 27.3% of patients (p = 0.037) and with erosions in the ileum (p = 0.031). Chronic ileal inflammation correlated with HFC (p = 0.015), ASDAS-CRP > 2.1 (p = 0.09), LVPI (p = 0.001), and villous atrophy (p = 0.014). Factorial analysis of mixed data (FAMD) identified significant associations between micro/macroscopic changes in chronic inflammation and HFC (CC = 0.837); increased levels of CRP and microscopic acute inflammation (CC = 0.792); and clinical activity scores of ASDAS-CRP and BASDAI (CC = 0.914). Conlusions: FC levels were significantly elevated in patients with SpA, particularly those with LVPI, suggesting their potential as a valuable biomarker for managing SpA when joint manifestations coincide with ileal villous atrophy. This indicates a shared immune pathway linked to chronic gut damage. Full article