Search Results (218)

Despite the growing popularity of digital payment methods and online shopping environments, little is known about the psychological mechanisms through which they affect consumer buying patterns. Drawing on the Stimulus–Organism–Response (SOR) framework, this study introduces the concept of spendception and examines its dual dimensions: perceived spendception ease (PSE) and perceived spendception control (PSC). These dimensions serve as mechanisms linking digital twin environments (DTEs) to impulse buying. Data were collected from 571 Generation Z consumers engaged in social commerce in Shanghai, China. Structural equation modeling (SEM) and machine learning techniques were employed to test the proposed relationships and evaluate predictive validity. The results reveal that DTE significantly increases impulse buying behavior both directly and indirectly through PSE. Specifically, PSE serves as a significant mediator by reducing the psychological friction associated with spending, thereby encouraging impulse buying decisions. In contrast, PSC acts as a significant moderator that weakens the positive relationship between DTE and impulse buying by enhancing consumers’ perceived ability to regulate their spending behavior. These findings demonstrate that spendception operates through two opposing psychological mechanisms: spending facilitation and spending control. This study contributes to the literature by conceptualizing spendception as a distinct transaction-centered construct and by extending the SOR framework through a dual-mechanism explanation of how digital commerce environments simultaneously encourage and restrain impulsive consumption. Full article

Computer Numerical Control (CNC) machining centers are critical assets in discrete manufacturing, yet many shop floors still rely on periodic expert judgment for machine selection and workload allocation. This practice is unsuitable for high-mix production because machine condition and risk can change rapidly due to tool wear, thermal drift, coolant variation, and alarms. Moreover, decision evidence is fragmented and often incomplete across controller and programmable logic controller signals, production records, and inspection results, making manual evaluation time-consuming and prone to misjudgment. Static rankings can also break down under unforeseen shop-floor disruptions, requiring rapid event-driven re-prioritization and rescheduling. To address these challenges, this research proposes a shop-floor decision intelligence pipeline that executes a rolling-window, uncertainty-aware ranking-and-dispatch loop directly on the shop floor. The industrial compute node continuously collects multi-source operational evidence, normalizes it into a unified event representation, and aggregates rolling-window indicators for each machine. A mapping structure then converts these indicators into neutrosophic triplets that separate performance from evidence credibility. Using this representation, a shop-floor decision procedure continuously updates machine priority scores using a TOPSIS procedure, which are further translated into workload allocation and persistence-confirmed protective action requests. A case study demonstrates end-to-end operation. It shows that the top-ranked machines remain stable under risk-aversion and weight-uncertainty analyses, while the protective logic prevents unsafe dispatching when reject-level conditions persist under reliable evidence. Overall, the proposed pipeline reframes CNC machine selection as a rolling-window, evidence-driven decision process and provides a pathway toward near-real-time and safety-aware shop-floor coordination. Full article

Large retail developments act as strong trip attractors and can substantially alter traffic demand patterns at adjacent urban intersections. This paper analyzes the operational impacts of a major shopping center on two nearby signalized intersections in Novi Sad, Serbia, and evaluates the effects of reconstructing one of them into a turbo roundabout. Traffic data collected before and after the shopping center opening, as well as before and after the intersection reconstruction, were analyzed using calibrated and validated microsimulation models. Results indicate that peak-hour traffic volumes increased by 8.38% and 6.96% at the analyzed intersections following the shopping center opening, leading to increased delays and operational stress under fixed signal control, particularly under unbalanced turning demands. The conversion of the three-leg signalized intersection into a turbo roundabout resulted in substantial reductions in average delay and improvements in level of service under identical traffic demand conditions, mainly due to the elimination of left-turn signal phases and reduced conflict interactions. The findings confirm that turbo roundabouts can provide significant operational benefits in dense urban environments characterized by strong directional flows; however, their effectiveness is highly context-dependent and influenced by traffic composition and geometric constraints. The results are interpreted as representative of typical weekday peak conditions, acknowledging data and temporal limitations. Full article

Rural settlements serve as the core spatial carriers of rural living space, and their spatial evolution and functional transformation reflect the dynamic restructuring of human–land relationships. In karst mountainous areas, complex topography, fragmented land resources, and uneven distribution of public facilities significantly influence settlement patterns and residents’ living spaces. This study aims to quantify the relationship between settlement clustering characteristics and living-space demand and to construct a spatially explicit framework for rural settlement restructuring from a living-space perspective. Taking the Qixingguan District of Bijie City, Guizhou Province—a representative karst mountainous area in Southwest China—as a case study, we develop an integrated analytical framework encompassing spatial identification, demand measurement, and zoning optimization. Settlement clusters were identified using the Nearest Neighbor Index and Kernel Density Analysis, while accessibility to essential services—including education, healthcare, and shopping—was quantified via a Gaussian-based two-step floating catchment area method. Living-space demand was further assessed by integrating accessibility gradients with residential conditions, and restructuring types were classified based on the Living Space Index and the distance from settlements to town centers. The results indicate that (1) rural settlements in Qixingguan District exhibit significant clustering, with high-density zones concentrated around urban peripheries and along transportation corridors; (2) accessibility to living services follows a distance-decay pattern modulated by transportation networks, forming hotspots in suburban and town-center areas and cold spots in peripheral karst mountainous areas; and (3) based on the comprehensive assessment, settlements are categorized into four types—urbanizing villages, central villages, preserved villages, and relocation villages—with corresponding targeted spatial restructuring strategies proposed. This study advances the geographical understanding of rural settlement restructuring in karst mountainous areas and provides empirical evidence for optimizing human–land relationships and promoting more equitable and sustainable spatial development in mountainous regions. Full article

Virtual vitality has become an important complementary dimension for describing urban vitality; however, the identification and formation mechanisms of its stable, high-vitality state during dynamic change remain insufficiently explored. Taking five urban districts of Harbin as the study area, this study uses TikTok short-video data from July to August 2024 (summer) and December 2024 to January 2025 (winter), together with Gaode Map POI data, as the core dataset. Kernel density differences between adjacent weeks are used to measure the dynamic changes in virtual vitality. Bivariate local spatial autocorrelation is applied to identify high-vitality stable zones, and a Random Forest model is employed to examine the nonlinear influence of physical vitality spatial structures. The results show the following: (1) Dynamic change patterns of virtual vitality differ significantly across seasons, and when online attention content points to specific physical spatial structures, a stable high-vitality state is more likely to be maintained. (2) Bivariate local spatial autocorrelation analysis indicates that high-vitality stable zones (HH zones) exhibit significant spatial clustering, with vitality-enhancing zones (LH zones) distributed around them and showing spillover effects, while vitality-declining zones (HL zones) are more scattered. (3) The Random Forest results show that the stable maintenance of high virtual vitality depends more on combinations of spatial structural characteristics with high recognizability, among which distance to activity center (tourism), functional composition dissimilarity (culture), and functional composition dissimilarity (shopping) have the strongest influence. These findings reveal a nonlinear relationship between the stable high-vitality state and the structure of physical vitality space, providing insights for guiding online attention to support physical spatial development. Full article

Food waste prevention and reduction are some of the important initiatives to improve the environmental sustainability of food systems. The global agenda of the United Nations provides a framework of targets and actions against food waste to which the European Union (EU), within the “Farm to Fork” strategy, aims to contribute. In this context, evaluating the impacts of food prevention measures is of great importance for supporting policies. This LCA analyzes the impact of classic lasagna from cradle to grave, through a generic food case study, prepared by food shops in Bologna (Northern Italy). Four scenarios are simulated, comparing the impacts of some end-of-life alternatives for the management of leftover lasagna (landfilling, composting, and redistribution with the digital application of the circular start-up “Squiseat”) versus the ideal scenario where no leftover lasagna is assumed. The results show that the preparation of classic lasagna generates non-negligible impacts on the analyzed LCA categories due to some of its ingredients, such as Bolognese sauce and Parmigiano Reggiano, and their associated production processes. For this reason, it is important to prevent classic lasagna leftovers from being wasted. The comparison of the four scenarios shows that redistribution is the scenario with the lowest impacts in all the investigated impact categories, including global warming (6.24 kg CO₂ eq./kg of lasagna). The impacts are also lower than the ideal scenario due to the assumption of more sustainable means of transport. Normalization of characterized results confirms that Global Warming (GW) is only one of the most relevant impact categories in the life cycle of classic lasagna. The results have practical implications for raising awareness concerning the impacts of food production throughout the whole life cycle and the need for preserving the value of food by avoiding waste. Moreover, this study also shows that a reduction in the impact is a shared outcome that could be achieved by the joint efforts of all the stakeholders involved in the life cycle of food. In this regard, urban centers are confirmed to be important hubs of circular and more sustainable innovation. Finally, the LCA enriches the current research by investigating redistribution through the relationship of the food shop–virtual intermediate–consumer. So far, the prevalent focus of the LCA research allows us to assess the redistribution of collected surplus food from retailers and its delivery to the consumers by means of physical intermediaries and related infrastructures (e.g., food hubs, food banks, and food emporiums). Full article

Accurately reconstructing Mechanical, Electrical and Plumbing (MEP) systems from laser-scanned point clouds is often hindered by structural occlusions, sensor noise, and extreme scale imbalance between large pipes and small fittings. This study presents a hybrid framework, driven by both knowledge and data, for automated pipeline BIM updating. To tackle scale variance, we implement a coarse-to-fine segmentation strategy using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to isolate pipeline instances before segmentation with PointNeXt. Furthermore, a logic-based refinement module integrates geometric and topological priors from the design BIM to correct coordinate deviations in incomplete datasets. Finally, graph isomorphism analysis enables automated topological mapping between unstructured point cloud instances and structured BIM components. Experimental results from a dense shopping center case study demonstrate that the framework achieves a semantic segmentation mIoU of 74.45% and reduces the average spatial coordinate error to within 7 mm. Notably, the automated workflow compressed the modeling time from 3–5 days to approximately 3 h, offering a robust solution for digital twin-oriented facility management. Full article

Shop-floor weld inspection may appear to be a solved problem until a camera is deployed near a galvanized-sheet MIG welding line. The seam reflects light, the texture changes from frame to frame, and the defects of interest are often small and visually subtle. Additionally, the hardware near the line is rarely a data-center GPU. With those constraints in mind, this paper presents YOLO-MIG, a compact detector built on YOLOv10n for weld-seam inspection in practical production conditions. We make three focused changes to the baseline: a C2f-EMSCP backbone block to better preserve weak defect cues with modest parameter growth, a BiFPN neck to keep small-target information alive during feature fusion, and a C2fCIB head to clean up predictions that otherwise get distracted by seam edges and illumination artifacts. On a workshop-collected dataset containing 326 original images, with the training subset expanded through augmentation to 2608 labeled samples in total, YOLO-MIG achieves 98.4% mAP@0.5 and 56.29% mAP@0.5:0.95 on the test set while remaining lightweight (1.83 M parameters, 3.87 MB FP16 weights). Compared with YOLOv10n, the proposed model improves mAP@0.5 by 9.36 points and mAP@0.5:0.95 by 4.89 points, while reducing parameters, GFLOPs, and model size by 43.4%, 19.9%, and 29.9%, respectively. The results suggest that YOLO-MIG is not only accurate but also realistic to deploy at the edge for intelligent weld quality control. Full article

Background/Objectives: Research on children’s eating has primarily focused on parental feeding practices and dietary outcomes, with less attention to how young children themselves understand parental food-related messages and relate them to their own food choices. Recognizing children as active participants in food socialization, this study aimed to examine preschool children’s representations of parental feeding strategies alongside their expressed food-choice considerations. Methods: A qualitative, exploratory, multi-method design was employed within a constructivist framework. Forty kindergarten children aged 4 years 10 months to 5 years 8 months participated in individual, play-based sessions conducted in familiar educational settings. Data were generated using two complementary tools: a doll role-play task eliciting children’s representations of parental feeding strategies and a simulated grocery shopping task eliciting food-choice considerations. All sessions were audio-recorded, transcribed verbatim, and analyzed using reflexive thematic analysis. Results: During role-play, children frequently portrayed parents as emphasizing health-related arguments, control, and negotiation when guiding food intake. Less frequently, they represented strategies such as encouragement to try, deception, or references to body weight. In contrast, during the food-choice task, children’s selections were primarily guided by personal preference, with health considerations mentioned less often. For most participants, the feeding strategies attributed to parents did not closely align with the considerations guiding their own food choices. Conclusions: The findings highlight young children’s active and selective engagement with parental feeding discourse and underscore the contextual nature of food-related meaning-making in early childhood. Rather than reflecting a straightforward transmission of parental messages, children’s food choices appear shaped by situational affordances and perceived autonomy, supporting child-centered approaches to nutrition education and health promotion. Full article

Background: Robotic-assisted navigational bronchoscopy (RNB) using the ION system (Intuitive Surgical, Sunnyvale, CA, USA) combined with cone-beam computed tomography (CBCT) (Cios Spin, Siemens Healthineers, Erlangen, Germany) and tool-in-lesion verification enables precise diagnosis of peripheral pulmonary nodules. Integrating RNB with intraoperative frozen section analysis may allow same-day resection, avoiding delays between diagnosis and treatment. Standard airway management with a single-lumen tube (SLT) limits immediate transition to lung resection, whereas initial double-lumen tube (DLT) placement could streamline workflow and improve safety. This study evaluated the diagnostic performance, procedural efficiency, and feasibility of an integrated ION-guided RNB workflow using either SLT or DLT. Methods: In this single-center retrospective study, 36 consecutive patients undergoing ION-guided RNB for pulmonary nodules between August 2024 and June 2025 were analyzed. Airway management (SLT vs. DLT) was selected based on surgical planning. Lesions were targeted using CBCT or C-arm fluoroscopy, and biopsies were performed via forceps or cryoprobes. Frozen section results guided immediate surgical resection when malignancy was confirmed. Results: Thirty-six patients (mean age 64.9 ± 7.9 years; female/male ratio 16/20) with 42 nodules (mean diameter 1.22 ± 0.76 cm) were included; 76.2% were peripheral. Mean RNB time was 58.3 ± 21.3 min. Overall diagnostic yield was 73.0%, significantly higher with DLT versus SLT (84.2% vs. 50.0%, p = 0.035), with more biopsies per patient (7.9 ± 2.2 vs. 3.2 ± 3.1, p = 0.035). No major complications occurred. Conclusions: ION-guided RNB with CBCT and intraoperative frozen section enables accurate, single-session diagnosis and treatment of pulmonary nodules. Upfront DLT placement facilitates procedural efficiency within a streamlined “one-stop-shop” workflow without compromising diagnostic yield. Full article

Background/Objectives: To compare the clinical outcomes following cataract surgery with implantation of a new partial depth of field (DOFi) intraocular lens (IOL) versus a monofocal IOL of identical material, platform, and haptic design. Methods: Single-center, non-randomized trial including 55 patients randomly assigned to be implanted either with the partial-DOFi IOL Tecnis PureSee (partial-DOFi group, 29 patients) or the Tecnis monofocal IOL DCB00/ZCU (both Johnson & Johnson Surgical Vision) (monofocal group, 26 patients). Monocular visual acuity (VA), refractive, binocular defocus curve, and patient-reported outcomes (QoV and Catquest 9SF questionnaires) were evaluated during a 3-month follow-up. Results: No significant differences between monofocal and partial-DOFi groups were found in monocular postoperative uncorrected- (0.03 ± 0.08 vs. 0.05 ± 0.10, p = 0.419) and corrected-distance VA (−0.03 ± 0.04 vs. −0.03 ± 0.05, p = 0.642). Significantly better distance-corrected intermediate VA was found in the partial-DOFi group (0.29 ± 0.08 vs. 0.10 ± 0.06, p < 0.001). Similarly, postoperative monocular distance-corrected near VA was better in the partial-DOFi group (0.51 ± 0.10 vs. 0.31 ± 0.09, p < 0.001). In the defocus curve, significantly better distance-corrected VAs compared to monofocal were found for all defocus levels from −1.50 to −4.00 D. Minor reports of starbursts were found in both IOL groups. With the Catquest questionnaire, some significant differences were found between groups including reduced difficulty reading newspapers (p < 0.001), improved visibility of prices while shopping (p < 0.001) and enhanced performance of hobbies (p = 0.030) and needlework (p < 0.001). Conclusions: The partial-DOFi IOL evaluated demonstrates superior intermediate and near visual performance compared to a monofocal IOL, while maintaining equivalent distance vision and visual quality. Full article

Background/Objectives: Comparative analysis of antimicrobial resistomes in hospital and community wastewater can provide valuable insights into the diversity and distribution of antimicrobial resistance genes (ARGs), contributing to the advancement of the One Health approach. This study aimed to characterize and compare the resistome profiles of wastewater sources from a hospital and community. Methods: Longitudinal metagenomic analysis was conducted on wastewater samples collected from the National Center for Global Health and Medicine (hospital) and a shopping mall (community) in Tokyo, Japan, between December 2019 and September 2023. ARG abundance was quantified using reads per kilobase per million mapped reads (RPKM) values, and comparative analyses were performed to identify the significantly enriched ARGs in the two sources. Results: A total of 46 monthly wastewater samples from the hospital yielded 825 unique ARGs, with a mean RPKM of 2.5 across all detected genes. In contrast, 333 ARGs were identified in the three shopping mall wastewater samples, with a mean RPKM of 2.1. Among the ARGs significantly enriched in the hospital samples, 23, including genes conferring resistance to aminoglycosides (nine groups) and β-lactam antibiotics (eight groups), exhibited significantly high RPKM values. No ARGs were found to be significantly enriched in the community wastewater samples. Conclusions: This study highlights the higher diversity and abundance of ARGs, particularly those conferring resistance to aminoglycosides and β-lactam antibiotics including carbapenems, in hospital wastewater than in community wastewater. These findings underscore the importance of continuous resistome monitoring of hospital wastewater as part of the integrated One Health surveillance strategy. Full article

The rapid expansion of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) poses significant challenges for the integration of Unmanned Aircraft Systems (UAS) into dense urban environments, where safety risks and population exposure are particularly high. This study proposes and applies a methodology based on probabilistic assessment of both ground and air risk, grounded in the principles of safety management and the use of geospatial data from OpenStreetMap (OSM), official aeronautical charts, and digital urban models. The urban area is discretized into a spatial grid on which independent risks are calculated per cell and later combined through a cumulative probabilistic fusion model. The resulting risk estimates enable the construction of cost matrices compatible with path-search algorithms. The methodology is applied to a case study in Medellín, Colombia, connecting the Oviedo and San Diego shopping centers through Beyond Visual Line of Sight (BVLOS) operations of a DJI FlyCart 30 drone. Results show that planning with the A* algorithm produces safe routes that minimize exposure to critical areas such as hospitals and restricted air corridors, while maintaining operational efficiency metrics. This approach demonstrates a practical bridge between regulatory theory and operational practice in UAM corridor design, offering a replicable solution for risk management in urban scenarios. Full article

The construction of zero-carbon parks has become an urgent priority. Electric load forecasting plays a decisive role in enabling the efficient operation of industrial parks; however, the complexity of electric load features within the parks has limited the accuracy of electric load forecasting. A novel electric load forecasting framework with feature extraction (TPE-AVMD-BiLSTM with feature extraction) is proposed to improve the forecasting accuracy. This framework combines feature extraction, decomposition with TPE optimization, and BiLSTM prediction. Together, these components work to remove the influence of irrelevant or redundant features. To verify the superiority of the proposed model, ablation experiments were carried out. The annual hourly electric load (8760 h) of typical industries was predicted within the park, including a data center, chemical manufacturing company, residence, shopping mall, cement manufacturing plant, and hospital. The results showed that the proposed model achieved high accuracy for all typical industries (R² > 0.9891, E_MAE < 0.3714, E_RMSE < 0.4694), indicating that the forecasting has excellent coverage performance. The performance of the proposed model over the feature-free baseline confirms that incorporating more correlated features enhances prediction stability. The framework presents a viable solution for achieving accurate electric load forecasting within zero-carbon parks. Full article

Rural regions around the world continue to struggle with population decline, underutilized infrastructure, and economic stagnation. While tourism is often promoted as a tool for revitalization, conventional approaches tend to prioritize new construction and external ownership, raising concerns about environmental degradation, cultural dilution, and community exclusion. This study adopts a circular economy perspective to explore how adaptive reuse—repurposing abandoned buildings—can support sustainable rural tourism. Focusing on two rural cases in South Korea, the study examines the role of tourism cooperatives in transforming underused facilities into guesthouses, retail shops, visitor centers, and community hubs. Using a qualitative comparative case study approach combining interviews, observations, and content analysis, this study identified how cooperatives mobilize local resources, preserve cultural and natural assets, and reinvest tourism revenues into community-led initiatives. Findings reveal that cooperative-led adaptive reuse enhances local empowerment, cultural preservation, and economic sustainability. This study concludes that embedding circular economy principles within rural tourism fosters resilience and community-driven revitalization and that tourism cooperatives serve as an effective governance structure for implementing circular economy practices. Full article