Search Results (2,230)

Supplier selection is one of the critical processes that entail multiple complex deliberations. The selection of an appropriate alternative supplier is a highly intricate process, primarily due to there being multiple criteria which are exceptionally subjective. This paper aims to develop a practical framework for choosing a suitable precast supplier by integrating the Value Engineering (VE) concept, Stepwise Weight Assessment Ratio Analysis (SWARA), and the Weighted Aggregated Sum Product Assessment (WASPAS) technique. This paper introduces a novel method to estimate the quality weights of alternative suppliers’ criteria (CQW) by linking factory capacity with the coefficients of the nine significant criteria, computed using principal component analysis (PCA). None of the formal studies make this link directly. The framework’s findings were validated by comparing its results with an expert assessment of five Saudi supplier alternatives. The results revealed that the framework’s results agree with the expert’s judgment. The method of payment criterion received the highest weight, indicating that it was considered the most important of the nine criteria identified. Combining PCA and VE with the WASPAS technique resulted in an unprecedentedly effective selection tool for precast suppliers. Full article

Small and medium-sized enterprises (SMEs) in the manufacturing sector often struggle to make effective use of production data due to fragmented systems and limited digital infrastructure. This paper presents a case study of implementing an AI-enhanced digital twin in a custom sportswear manufacturing SME developed under the AI and Robotics Estonia (AIRE) initiative. The solution integrates real-time production data collection using the Digital Manufacturing Support Application (DIMUSA); data processing and control; clustering-based data analysis; and virtual simulation for evaluating improvement scenarios. The framework was applied in a live production environment to analyze workstation-level performance, identify recurring bottlenecks, and provide interpretable visual insights for decision-makers. K-means clustering and DBSCAN were used to group operational states and detect process anomalies, while simulation was employed to model production flow and assess potential interventions. The results demonstrate how even a lightweight AI-driven system can support human-centered decision-making, improve process transparency, and serve as a scalable foundation for Industry 5.0-aligned digital transformation in SMEs. Full article

This study aims to validate the Online Sexual Harassment Perpetration among Peers (OSHP-P) instrument for assessing online sexual harassment among adolescents in two different countries, Spain and Italy, considering both new forms of online sexual harassment and gender differences. The instrument was validated by means of a Confirmatory Factor Analysis (CFA) with a sample of 1041 Spanish (M_age = 15.0, SD = 0.88) and 1385 Italian (M_age = 14.8, SD = 0.87) adolescents, demonstrating factorial invariance across both country and gender. The best-fitting model was two-dimensional, with ambiguous and direct Sexual Cyber Perpetration (SCP) and Non-Consensual Sharing Perpetration (NCSP) factors. Co-involvement (i.e., involvement in both types of aggression) rates were 10.3% in Spain and 7.8% in Italy. No significant gender differences were found for involvement in either the overall scale (46.4% for girls, 44.1% for boys) or the NCSP subscale (3.0% girls vs. 2.2% boys), although significantly higher co-involvement was found among boys (7.7% girls vs. 10.1% boys). This study contributes to the existing body of research on online sexual harassment among peers in adolescence by presenting a new assessment tool that has been shown to be invariant between Spanish and Italian adolescents, as well as between boys and girls. Full article

Mungbean is grown as a summer crop in subtropical climates globally. The global demand for mungbean is increasing, and opportunities exist to expand production regions to more marginal environments, such as southern Australia, as an opportunistic summer crop to help meet the growing global demand. Mungbean has the potential to be an opportunistic summer crop when an appropriate sowing window coincides with sufficient soil water. This expansion from subtropical to temperate climates will pose challenges, including low temperatures, a longer day length and a low and variable water supply. To assess mungbean suitability to temperate, southern Australian summer rainfall patterns and soil water availability, we conducted field experiments applying a range of water treatments across four locations with contrasting rainfall patterns within the state of Victoria (in southern Australia) in 2020–2021 and 2021–2022. The water treatments were applied prior to sowing (60 mm), the vegetative stage (40 mm) and the reproductive stage (40 mm) in a factorial combination at each location. Two commercial cultivars, Celera II-AU and Jade-AU, were used. Water scarcity during flowering and the pod-filling stages were important factors constraining yield. Analysis of yield components showed that increasing water availability at critical growth stages, viz. the vegetative and reproductive stages, of mungbean was associated with increases in total biomass, HI and grain number in addition to increased water use and water use efficiency (WUE). Average WUEs ranged from 1.3 to 7.6 kg·ha⁻¹·mm⁻¹. The maximum potential WUE values were 6.4 and 5.1 kg·ha⁻¹·mm⁻¹ for Celera II-AU and Jade-AU across the sites, with the estimated soil evaporation values (x-intercept) of 83 and 74 mm, respectively. Nitrogen fixation was variable, with %Ndfa values ranging from 9.6 to 76.8%, and was significantly affected by soil water availability. This study emphasises the importance of water availability during the reproductive phase for mungbean yield. The high rainfall zones within Victoria have the potential to grow mungbean as an opportunistic summer crop. Full article

This study investigates the influence of divalent metals—(Mg(II), Co(II), and Ni(II)) in layered double hydroxides (LDHs), with a constant trivalent Fe(III) component—on the decoloration of crystal violet and methyl blue dyes via a Fenton-type oxidation reaction. The catalysts, synthesized by co-precipitation and hydrothermal treatment, were tested in both hydroxide and oxide forms under varying agitation conditions (0 and 280 rpm). A 2² × 3 factorial design was used to analyze the effect of the divalent metal type, catalyst phase, and stirring. The Mg/Fe oxide, with the highest BET surface area (144 m²/g) and crystallite size (59.7 nm), showed superior performance—achieving up to 98% decoloration of crystal violet and 97% of methyl blue within 1 h. The kinetic analysis revealed pseudo-second-order and pseudo-first-order fits for crystal violet and methyl blue, respectively. These findings suggest that LDH-based catalysts provide a fast, low-cost, and effective option for dye removal in aqueous systems. 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

Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I²C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes. Full article

Understanding the influence of the sheep breed and roughage source on the composition of rumen bacteria and methanogens is essential for optimizing roughage efficiency. The experiment employed a 2 × 2 factorial design. Twenty-four Dumont and Mongolian sheep (initial body weight of 18.94 ± 1.01 kg) were randomly assigned by breed to two dietary treatment groups (AH: alfalfa hay; CS: corn straw); the experiment lasted 90 days. The results showed that sheep fed alfalfa hay diets had a higher feed intake and weight gain, and Dumont sheep had a higher feed intake than Mongolian sheep (p < 0.05). The diversity and composition of ruminal bacteria and methanogens differed between Dumont and Mongolian sheep fed either AH or CS diets. The taxonomic analysis revealed a distinct clustering pattern based on the roughage source, but not on the breed. When fed a corn straw diet, the bacterial Chao1 index of Dumont sheep increased (p < 0.05), while the diversity and richness of methanogens in Mongolian sheep increased (p < 0.05). Additionally, we have identified unique biomarkers for the rumen bacteria and methanogens of Dumont and Mongolian sheep in response to different roughage sources. The results suggest that the differences in the microbiota of the sheep were associated with the roughage source and breed. The higher growth performance of Dumont sheep might be attributed to the increase in bacterial diversity and the decrease in methanogenic bacteria diversity. Full article

The oxidative degradation of anthocyanins in red wine was investigated under controlled conditions using hydroxyl radicals generated in the presence of Cu (II) as a catalyst. A full factorial experimental design with 23 replicates was used to evaluate the effects of hydrogen peroxide concentration, catalyst dosage, and reaction temperature on anthocyanin degradation over a fixed time. Statistical analysis (ANOVA and multiple regression) showed that all three variables and the main interactions significantly affected anthocyanin loss, with temperature identified as the most influential factor. The combined effects were described by a first-order polynomial model. The activation energies for degradation ranged from 56.62 kJ/mol (cyanidin-3-O-glucoside) to 40.58 kJ/mol (peonidin-3-O-glucoside acetate). Increasing the temperature from 30 °C to 40 °C accelerated the degradation kinetics, almost doubled the rate constants and shortened the half-life of the pigments. At 40 °C, the half-lives ranged from 62.3 min to 154.0 min, depending on the anthocyanin structure. These results contribute to a deeper understanding of the stability of anthocyanins in red wine under oxidative stress and provide insights into the chemical behavior of derived pigments. The results are of practical importance for both oenology and viticulture and support efforts to improve the color stability of wine and extend the shelf life of grape-based products. Full article

Social desirability is a widely studied phenomenon due to its impact on the validity of self-reported data. It refers to the tendency of individuals to respond to questions in a manner that they believe is socially acceptable or favorable rather than providing truthful or accurate answers. This study evaluated the psychometric properties of the Brief Social Desirability Scale (BSSD-4) in Ecuadorian youth, analyzing its reliability, factorial and convergent validity, and measurement invariance by sex, age group, and experiences of dating violence. An instrumental study was conducted with a non-probabilistic convenience sample of 836 participants (aged 14–26). Reliability was adequate (Ω = 0.75, α = 0.81, CR = 0.759). Confirmatory factor analysis showed good fit indices (CFI = 0.98, TLI = 0.97, RMSEA = 0.056, SRMR = 0.037). Convergent validity was acceptable (AVE = 0.50, VIF < 2.01). A network analysis confirmed the unidimensionality of the scale and structural differences between groups. Measurement invariance by sex and age was verified, but differences in the network structure were found based on victimization and perpetration of violence. The BSSD-4 is a valid and reliable instrument for assessing social desirability in Ecuadorian youth, useful for population studies and intergroup comparisons. Further research is recommended to explore its invariance in populations with a history of violence, as differences in scalar invariance were observed. Full article

A 14-day study was conducted to evaluate the effect of litter type (dirty litter, DL; fresh litter, FL) and Salmonella Enteritidis SE challenge (no challenge, NC; challenge, SE) on the growth performance and cecal microbial composition of neonate chicks. Day-old chicks (n = 240, Ross 708 male) were allocated to a 2 × 2 factorial design consisting of four treatments: chicks raised on dirty litter (CONDL), chicks raised on fresh litter (CONFL); and chicks raised on litter types similar to CONDL and CONFL but inoculated with 7.46 × 108 CFU SE/mL at d 1 (CONDLSE and CONFLSE). The performance indices measured included body weight (BW), body weight gain (BWG), feed intake (FI), mortality, and feed conversion ratio (FCR). Cecal SE concentration was assessed on d 3 and 14, and ceca were collected from chicks on day 14 for DNA extraction. The Illumina Miseq platform was used for microbiome analysis of the V3–V4 region of the 16S rRNA gene. The interaction of litter type and SE influenced FCR and FI. CONDL recorded the poorest FCR (1.832). FI was highest and similar in CONFLSE, CONDL, and CONDLSE (0.655, 0.692, and 0.677, respectively). Cecal SE concentration was significantly reduced in CONDLSE at d 3 and 14. Alpha diversity was higher (p < 0.05) in the DL compared to that in NC. Beta diversity showed a separation (p < 0.05) between the DL and the FL. Comparative tree analysis revealed 21 differential significant genera, with 14 prevalent in the DL and 7 in the FL, specifically, bacteria genera such as Lactobacillus, Clostridia_vadinBB60_group, Lachnospira, Oscillospiraceae UCG_005, and Marvinbryantia, which play significant roles relating to improved growth performance, metabolic homeostasis within the gut, energy metabolism, and short-chain fatty acid (SCFA) utilization. Our results concluded that litter management regimen differentially alters the microbiome of chicks, which accounts for the improved performance and exclusion of pathogens in the study. Full article

This study focuses on enhancing crop yield prediction in plant factory environments through precise crop canopy image capture and background interference removal. This method achieves highly accurate recognition of the crop canopy projection area (CCPA), with a coefficient of determination (R²) of 0.98. A spatial resolution of 0.078 mm/pixel was derived by referencing a scale ruler and processing pixel counts, eliminating outliers in the data. Image post-processing focused on extracting the canopy boundary and calculating the crop canopy area. By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R², prediction speed, training time, and model size. Among them, the Wide Neural Network model emerged as the most optimal. It demonstrated remarkable predictive accuracy with an R² of 0.95, RMSE of 27.15 g, and MAPE of 11.74%. Furthermore, the model achieved a high prediction speed of 60,234.9 observations per second, and its compact size of 7039 bytes makes it suitable for efficient, real-time deployment in practical applications. This model offers substantial support for managing crop growth, providing a solid foundation for refining cultivation processes and enhancing crop yields. Full article

Greenhouse horticulture plays an important role globally by producing nutritious fruits and vegetables with high resource use efficiency. Modern greenhouses are large-scale high-tech production factories that are increasingly data-driven, and where climate and irrigation control are gradually becoming more autonomous. This is enabled by technological developments and driven by shortages in skilled labor and the demand for improved resource use efficiency. In the Autonomous Greenhouse Challenge, it has been shown that controlling greenhouse cultivation can be done efficiently with intelligent algorithms. For an optimal strategy, however, it is essential that control algorithms properly account for crop responses, which requires appropriate sensors, reliable data, and accurate models. This paper presents the results of the 4th Autonomous Greenhouse Challenge, in which international teams developed six intelligent algorithms that fully controlled a dwarf tomato cultivation, a crop that is well-suited for robotic harvesting, but for which little prior cultivation data exists. Nevertheless, the analysis of the experiment showed that all teams managed to obtain a profitable strategy, and the best algorithm resulted a production equivalent to 45 kg/m²/year, higher than in the commercial practice of high-wire cherry tomato growing. The predominant factor was found to be the much higher plant density that can be achieved in the applied growing system. More difficult challenges were found to be related to measuring crop status to determine the harvest moment. Finally, this experiment shows the potential for novel greenhouse cultivation systems that are inherently well-suited for autonomous control, and results in a unique and rich dataset to support future research. Full article

The Values in Action Inventory (VIA) is one of the most widely used measures for assessing character strengths. While the original version includes 240 items, shorter versions such as the VIA-72 have been developed to enhance its applicability. Psychometric studies of the VIA-72 in Spanish are still limited. This study examined the factorial structure, reliability, and convergent validity of the Spanish VIA-72 in a sample of 470 adults. Three alternative models—comprising three, five, and six factors—were tested using confirmatory factor analysis. All models showed acceptable fit, but the three-factor solution—Caring, Self-Control, and Inquisitiveness—showed the best performance in terms of parsimony, fit indices, and conceptual clarity. Internal consistency for the three-factor model was high across dimensions and comparable to previous studies. Convergent validity was supported through meaningful correlations with personality traits, particularly with conscientiousness. The factorial structure largely replicated findings obtained with both VIA-72 and VIA-240. These results support the Spanish VIA-72 as a reliable and valid instrument for assessing character strengths, offering a concise, theory-based alternative for Spanish-speaking populations. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article