Search Results (1,544)

Due to the lack of corn and soybean meal in animal feeding, rice bran meal (RBM) has been proposed as a beneficial substitute for these feedstocks’ ingredients. Its fermentation by using diverse microbes has been adopted as a beneficial technique. In this study, 18 five-month-old finishing pigs (castrated Duroc × Landrace × Large White) were assigned to three dietary groups with six replicates in each group, designated as the control (CON), unfermented RBM (RBM), and fermented RBM (FRBM) groups. RBM was fermented with a mixture of Lactobacillus johnsonii L63 and hydrolytic enzymes at 37 °C and pH 4.8 for 60 h. The results indicated that incorporating 30% fermented or unfermented rice bran meal into the diets of finishing pigs had no significant effect on growth performance. Regarding serum biochemical parameters, most indicators, including alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and triglycerides, showed no significant alterations. However, in both the unfermented and fermented rice bran meal groups, the concentrations of serum total protein, albumin, globulin, cholesterol, and blood urea nitrogen were significantly decreased (p < 0.05), whereas serum nitric oxide levels were significantly increased (p < 0.05). The FRBM group improved intestinal morphology and the digestibility of nutrients (crude protein, ether extract, crude fiber, and gross energy) by altering the mTORC1 pathway and upregulating the relative expression of amino acid and peptide transporter genes in the jejunum. However, the dry matter digestibility decreased compared to the CON group. The RBM group reduced nutrient digestibility, along with alterations in hepatic gene expression related to amino acid metabolism and transport. Therefore, fermented rice bran meal may offer a potential substitute feed ingredient for use in swine diets when conventional ingredients like corn and soybean meal are in short supply. Full article

Cypripedium shanxiense S. C. Chen has high ornamental value; it relies on specific habitats and fungi. Wild C. shanxiense populations need urgent conservation because they are declining rapidly. This study investigated three wild C. shanxiense populations under different canopy densities in the Changbai Mountains, analyzing habitat characteristics and plant morphology. Tissue isolation methods, molecular identification techniques, and metagenomic approaches were applied separately to purify root-colonizing fungi and to investigate the composition and functions of rhizosphere fungi, thereby revealing the diversity of root mycobiome in C. shanxiense. Results revealed that C. shanxiense achieved the best growth when the canopy density was 85.29%, and the lowest growth was under 96.13% canopy density. Soil phosphorus and potassium contents reached their highest levels under 69.33% canopy density, while soil nitrogen and organic matter contents peaked at 85.29%. Soil organic matter and available nitrogen constitute the core nutrient factors for the growth of C. shanxiense. A total of 16 fungal strains were mainly enriched in the roots, all belonging to Ascomycota. Including numerous growth-promoting fungi and pathogenic fungi. The rhizosphere fungi were mainly enriched with Basidiomycota and Ascomycota. Functional genes related to replication, recombination, and repair, and Glycoside Hydrolases. This study clarifies the optimal growth conditions of this species and the dominant rhizosphere and root fungi, providing a scientific basis for the ecological restoration and conservation of rare species. Full article

Background and Objectives: Mechanical alignment (MA) has long been the gold standard in total knee arthroplasty (TKA), aiming for neutral hip–knee–ankle alignment with proven long-term survivorship. However, up to 20% of patients remain dissatisfied, often due to neglect of individual constitutional limb variation and subsequent soft tissue imbalance. This has driven the development of alternative alignment philosophies. This current concepts review aims to determine the various evolving alignment strategies, elucidate their underlying principles, and demonstrate the available clinical outcomes data. Materials and Methods: This review examines MA and the paradigm shift towards personalized alignment techniques, including Kinematic Alignment (KA), restricted Kinematic Alignment (rKA), inverse Kinematic Alignment (iKA), adjusted mechanical alignment (aMA), and the most recent evolution, Functional Alignment (FA). Results: Kinematic alignment and its derivatives (rKA, iKA) seek to better replicate native joint morphology and tension, often reducing the need for soft tissue releases and improving functional outcomes compared to MA. rKA and iKA introduce protective boundaries to avoid extreme phenotypes and possible instability. FA leverages robotic platforms and integrates these principles with real-time gap balancing, demonstrating promise for consistent, personalized outcomes. Some reports, however, advise caution with adjusted Mechanical Alignment (aMA), particularly those that result in phenotypes such as Coronal Plane Alignment of the Knee (CPAK) VII or VIII, which may increase the risk of revision. Conclusions: The philosophy of TKA has evolved from a uniform mechanical target (MA) to a more nuanced, patient-specific strategy. While promising mid- to long-term outcomes and comparable survival data support the viability of KA and its derivatives, critical needs remain, including standardizing nomenclature (especially for FA) and conducting high-quality comparative trials. Future directions involve leveraging high-volume intraoperative data and Artificial Intelligence (AI) to refine decision-making and further personalize alignment strategies, without compromising long-term implant survivorship. Full article

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

This study investigates the use of DFLSS to mitigate customer churn in a prominent telecommunications provider facing challenges from competitive pricing, regulatory changes, and evolving customer expectations. Employing the DMADV methodology, the research developed a proactive retention strategy using techniques such as propensity modeling, customer segmentation, and predictive analytics to identify churn drivers. Targeted interventions, which include future-dated loyalty discounts, outbound retention campaigns, and process optimization through DOEs were implemented and pilot-tested. The pilot involved approximately 5000 high-risk customers per month, resulting in a 6% increase in customers under contract, a 2% improvement in rates, and a 6% reduction in repeat call rates, equating to 2880 fewer calls annually. Financially, the strategy preserved an estimated 10% in revenue over 12 months, while operational enhancements delivered a 2% cost reduction annually through reduced repeat calls. These findings highlight the importance of proactive outreach and continuous improvement in managing churn. Limitations of this study include the narrow market scope and the need for broader validation. The research contributes to the limited literature on LSS in Western telecom markets and provides a replicable model for practitioners. Future work may explore integrating artificial intelligence to enhance churn prediction and retention strategies. Full article

Intangible cultural heritage faces several challenges, including a fragile transmission system, disconnection from modern life, and poor market adaptability. This study takes the Jingsha tenon-and-mortise woodcarving, an important example of Chinese intangible cultural heritage, as a case study to address the issue of the disconnection between traditional craftsmanship and contemporary demands. Methods: A sustainable development model based on user–AIGC–craftsman collaboration is proposed. The research integrates Kano Model and Analytic Hierarchy Process (AHP) based demand analysis, AIGC-generated design solutions, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluation, and Cursor and MCP 3D modeling technologies. The results indicate that this approach reduces design confirmation time from three days to one, minimizes material waste through precise size specifications, and achieves high user satisfaction. The study demonstrates that combining user-centered design with AI-assisted craftsmanship creates a balanced pathway for the sustainability of intangible cultural heritage, while addressing issues of cultural preservation, economic feasibility, and resource efficiency. This tripartite model offers a replicable framework for the sustainable development of traditional crafts globally. Full article

In the last decade, the interdisciplinary field of neuroarchitecture has grown significantly, revealing measurable links between architectural features and human neural processing. This review synthesizes current research at the nexus of neuroscience and architecture, with a focus on how emerging virtual reality (VR) and artificial intelligence (AI) technologies are being utilized to understand and enhance human spatial experience. We systematically reviewed literature from 2015 to 2025, identifying key empirical studies and categorizing advances into three themes: core components of neuroarchitectural research; the use of physiological sensors (e.g., EEG, heart rate variability) and virtual reality to gather data on occupant responses; and the integration of neuroscience with AI-driven analysis. Findings indicate that built environment elements (e.g., geometry, curvature, lighting) influence brain activity in regions governing emotion, stress, and cognition. VR-based experiments combined with neuroimaging and physiological measures enable ecologically valid, fine-grained analysis of these effects, while AI techniques facilitate real-time emotion recognition and large-scale pattern discovery, bridging design features with occupant emotional responses. However, the current evidence base remains nascent, limited by small, homogeneous samples and fragmented data. We propose a four-domain framework (somatic, psychological, emotional, cognitive-“SPEC”) to guide future research. By consolidating methodological advances in VR experimentation, physiological sensing, and AI-based analytics, this review provides an integrative roadmap for replicable and scalable neuroarchitectural studies. Intensified interdisciplinary efforts leveraging AI and VR are needed to build robust, diverse datasets and develop neuro-informed design tools. Such progress will pave the way for evidence-based design practices that promote human well-being and cognitive health in built environments. Full article

Background/Objectives: This article presents a novel approach that combines the Palatal Silicone Key and Injection Molding techniques as a viable alternative for complex anterior cases with high esthetic demands, where layering multiple shades is essential to achieve a natural appearance, rather than using a single monochromatic composite. Methods: The Palatal Silicone Key technique utilizes a silicone index to transfer palatal and incisal anatomy from a diagnostic wax-up, allowing freehand layering of proximal and buccal surfaces with multiple composite shades. The Injection Molding technique provides a simpler and more predictable workflow by using a transparent silicone index to replicate the wax-up. However, the original injection technique relies on a single-shade composite, limiting the esthetic outcomes. In the presented case canines and first premolars were reshaped to replace congenitally missing lateral incisors. Palatal surfaces were built with medium-viscosity enamel shade composite using the silicone key, and dentin anatomy was sculpted freehand with dentin shade composite. Buccal anatomy was restored by injecting enamel shade flowable composite into the transparent index. Results: This combined protocol facilitated the precise transfer of the wax-up, minimizing adjustments, while the use of multiple composite shades reproduced the natural translucency of adjacent teeth, resulting in highly esthetic restorations. Conclusions: Handling traditional composites in complex anterior cases can be time-consuming and technique-sensitive. The presented combination of techniques, while requiring a high level of skill and precision, integrates the strengths of both approaches, enabling a minimally invasive, additive workflow with reduced clinical time and more predictable esthetic outcomes. Full article

The increasing complexity of contemporary urban systems necessitates decision-making frameworks capable of systematically integrating multidimensional sustainability considerations into policy evaluation processes. While existing urban sustainability assessment approaches predominantly focus on isolated environmental or socio-economic indicators, they often lack methodological coherence and direct applicability to operational decision-making. This study proposes a multi-layer sustainability indicator framework explicitly designed to support evidence-based urban decision-making under conditions of uncertainty, institutional constraints, and competing policy objectives. The framework integrates environmental, economic, social, and institutional dimensions of sustainability into a structured decision-support architecture. Methodologically, the study employs a two-stage approach combining expert-based weighting techniques (Analytic Hierarchy Process and Best–Worst Method) with multi-criteria decision-making methods (TOPSIS and VIKOR) to evaluate and rank alternative urban policy scenarios. The proposed framework is empirically validated through an urban case study, demonstrating its capacity to translate abstract sustainability indicators into comparable decision outcomes and policy priorities. The results indicate that the integration of multi-layer indicator systems with formal decision-analysis tools enhances transparency, internal consistency, and strategic coherence in urban governance processes. By bridging the gap between sustainability measurement and decision implementation, the study contributes to the advancement of urban governance scholarship and provides a replicable analytical model applicable to cities facing complex sustainability trade-offs. Full article

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings. Full article

Current research on landslide susceptibility modeling is often constrained by reliance on conventional topographic and geological features, potentially overlooking the discriminative power of surface material properties derived from multi-source remote sensing. This study aims to enhance the accuracy and reliability of susceptibility assessment by innovatively integrating spectral information and advanced machine learning techniques. Focusing on Chongqing, a landslide-prone mountainous region in China, this work conducted three innovative investigations: it (i) introduced 12 spectral features into the feature set; (ii) systematically evaluated spectral features contribution, redundancy, and set completeness through feature engineering; and (iii) implemented a comprehensive Stacking ensemble framework with multiple meta-learners and enhancement strategies (Bagging and Cross-Training) to identify the optimal integration scheme. The key results show that spectral features provided a significant positive impact, boosting the AUC of tree-based ensemble models by up to 4.52%. The optimal model, a Stacking ensemble with Bagging_XGBoost as the meta-learner, achieved a superior test AUC of 0.8611, outperforming all individual base learners. Furthermore, the spatial analysis revealed a concentration of high and very high susceptibility areas in Engineering Geological Zone I, which represents approximately 38% of such areas. This study provides a replicable framework for enhancing landslide susceptibility mapping through the integration of spectral features and ensemble learning, offering a scientific basis for targeted risk management and mitigation planning in complex mountainous terrains. Full article

Background: The acceptance of new vaccines under uncertainty—such as during the COVID-19 pandemic—poses a major public health challenge because efficacy and safety information is still evolving. Methods: We propose an integrative analytical framework that combines a theory-based model of vaccine acceptance—the cognitive–affective–normative (CAN) model—with machine-learning techniques (decision tree regression, random forest, and Extreme Gradient Boosting) and SHapley Additive exPlanations (SHAP) integrated into an importance–performance map (IPM) to prioritize determinants of vaccination intention. Using survey data collected in Spain in September 2020 (N = 600), when the AstraZeneca vaccine had not yet been approved, we examine the roles of perceived efficacy (EF), fear of COVID-19 (FC), fear of the vaccine (FV), and social influence (SI). Results: EF and SI consistently emerged as the most influential determinants across modelling approaches. Ensemble learners (random forest and Extreme Gradient Boosting) achieved stronger out-of-sample predictive performance than the single decision tree, while decision tree regression provided an interpretable, rule-based representation of the main decision pathways. Exploiting the local nature of SHAP values, we also constructed SHAP-based IPMs for the full sample and for the low-acceptance segment, enhancing the policy relevance of the prioritization exercise. Conclusions: By combining theory-driven structural modelling with predictive and explainable machine learning, the proposed framework offers a transparent and replicable tool to support the design of vaccination communication strategies and can be transferred to other settings involving emerging health technologies. Full article

Gold nanoparticles (Au-NP) are frequently used as model nanomaterials to study nanoparticle behavior in plants due to their analytical detectability and negligible natural background in plant tissues. However, the feasibility of visualizing the spatial distribution of foliar-applied Au-NP at low exposure levels using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) remains insufficiently explored. In this study, commercially sourced Au-NP were applied to lentil leaves (Lens culinaris var. Beluga) at a low concentration of 0.5 mg·L⁻¹ using a controlled leaf submersion approach. Leaves were sampled at 1 h, 24 h, and 96 h post-exposure and analyzed by LA-ICP-MS imaging to assess time-dependent changes in gold-associated spatial signals, and to compare elemental distribution patterns with non-exposed controls. Untreated control leaves showed no detectable gold at any sampling time point, confirming negligible native Au background. In treated leaves, LA-ICP-MS imaging revealed an initially localized Au hotspot at 1 h, followed by progressive Au redistribution toward the leaf margins and petiole region by 24 h and 96 h. Gold signals persisted over the full 96 h period, indicating stable association of Au-NP with leaf tissue. Comparative elemental mapping of Ca, Mg, K, P, Fe, Zn, and Cu showed no persistent differences in spatial distribution patterns between treated and control leaves as detectable by LA-ICP-MS. This study demonstrates the feasibility of LA-ICP-MS imaging for visualizing the deposition and temporal spatial redistribution of low-dose foliar-applied nanoparticles in intact leaves. The results provide a methodological reference for future hypothesis-driven studies that apply nanoparticles under more controlled conditions, include increased replication, and combine multiple analytical techniques. Full article

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

Background/Objectives: Despite clonal expansion during a primary immune response, or after subsequent antigen encounters, the frequency of memory B cells (B_mem) specific for an antigen remains low, making their detection difficult. However, unlike serum antibodies, which have a short half-life in vivo and thus require continuous replenishment to maintain stable titers, circulating B_mem are long-lived; they preserve immunological preparedness through their ability to rapidly engage in recall responses and differentiate into antibody-secreting cells (ASCs) upon antigen encounter. To this end, development of assays suited for the reliable detection of rare antigen-specific B_mem is critical and can provide insights into an individual’s antigen exposure history and immune status beyond that offered by traditional serum antibody measurements alone. Methods: ImmunoSpot^® has emerged as a suitable technique for the detection of individual antigen-specific B cells through visualizing their antibody-derived secretory footprints. Here, we report the theoretical and practical foundations for detecting rare antigen-specific B_mem in human peripheral blood mononuclear cells (PBMC). Leveraging the unique availability of verifiably naïve vs. antigen-experienced human samples, we used SARS-CoV-2 Spike (S-) and Nucleocapsid (NCAP) antigens to interrogate the presence of B_mem with these respective specificities. Results: While 100% diagnostic accuracy was achieved for both antigens, detection of NCAP-specific B_mem required reducing the lower detection limit of the standard assay. Specifically, this was achieved by testing a total of 2 million PBMC across multiple replicate assay wells and assessing the cumulative number of secretory footprints detected. Conclusion: The protocols described here should facilitate the reliable detection of ASCs present at varying precursor frequencies and serve as guidance for routine immune monitoring of rare B_mem with specificity for any antigen. Full article