Search Results (477)

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

This study examines the determinants of apartment prices in 17 post-WWII multi-family housing estates in Ljubljana, Slovenia, constructed between 1947 and 1986. Using 1973 verified transactions from 2020 to 2025, the analysis evaluates spatial, structural, environmental, and accessibility-related variables through a combination of statistical and machine learning techniques. A hedonic price model based on ordinary least squares (OLS) demonstrates modest explanatory power (R² = 0.171), identifying local market reference prices, floor level, noise exposure, and window renovation as significant predictors. In contrast, seven machine learning models—Random Forest, XGBoost, and Gradient Boosting Machines (GBMs), including optimized versions—achieve notably higher predictive accuracy. The best-performing model, GBM with Randomized Search CV, explains 59.6% of price variability (R² = 0.5957), with minimal prediction error (MAE = 0.03). Feature importance analysis confirms the dominant role of localized price references and structural indicators, while environmental and accessibility variables contribute variably. In addition, three clustering methods (Ward, k-means, and HDBSCAN) are employed to identify typological groups of neighborhoods. While Ward’s and k-means methods consistently identify four robust clusters, HDBSCAN captures greater internal heterogeneity, suggesting five distinct groups and detecting outlier neighborhoods. The integrated approach enhances understanding of spatial housing price dynamics and supports data-driven valuation, urban policy, and regeneration strategies for post-WWII housing estates in Central and Eastern European contexts. Full article

Urban vitality acts as a key driver of sustainable urban development, while the built environment serves as its physical foundation. However, spatial heterogeneity in urban landscapes leads to imbalanced impacts of economic, social, and environmental factors on vitality. Therefore, it is essential to investigate the underlying principles governing vitality impacts imposed by diverse components of the built environment at the spatial level. This study synthesized multi-source remote sensing data alongside geospatial datasets aiming to quantify vitality and built environment indicators across Shenyang, China. We applied Ordinary Least Squares (OLS) regression for collinearity diagnosis and Multi-scale Geographically Weighted Regression (MGWR) to model spatial heterogeneity impacts at the planning-unit level. The regression factor analysis yielded three primary conclusions: (1) Functional Mixture Degree, Bus Stop Density, and Subway Station Density demonstrated a statistically significant positive correlation with urban vitality. (2) FAR (Floor Area Ratio), Vegetation Coverage, Commercial Facility Density, and Road Density exhibited differentiated effects in core areas versus peripheral areas. (3) Public Facility Density and Bus Stop Density showed a negative correlation trend with vitality levels in Industrial Functional Zones. We propose a geospatial analysis framework that leverages remote sensing to decode spatially heterogeneous built environment–vitality linkages. This approach supports precision urban renewal planning by identifying location-specific interventions. Geospatial big data and MGWR offer replicable tools for analyzing urban sustainability. Future work should integrate real-time sensor data to track vitality dynamics. Full article

Through their open space layout, rich green configuration and low floor area ratio (FAR), low-density commercial blocks show significant advantages in creating high-quality outdoor thermal comfort (Universal Thermal Climate Index, UTCI) environment, reducing regional energy consumption load (building energy consumption, BEC) potential, providing pleasant public space experience and enhancing environmental resilience, which are different from traditional high-density business models. This study proposes a workflow for morphological design of low-density commercial blocks based on parametric modeling via the Grasshopper platform and the NSGA-II algorithm, which aims to balance environmental benefits (UTCI, BEC) and spatial efficiency (FAR). This study employs EnergyPlus, Wallacei and other relevant tools, along with the NSGA-II algorithm, to perform numerical simulations and multi-objective optimization, thus obtaining the Pareto optimal solution set. It also clarifies the correlation between morphological parameters and target variables. The results show the following: (1) The multi-objective optimization model is effective in optimizing the three objectives for block buildings. When compared to the extreme inferior solution, the optimal solution that is closest to the ideal point brings about a 33.2% reduction in BEC and a 1.3 °C drop in UTCI, while achieving a 102.8% increase in FAR. (2) The impact of design variables varies across the three optimization objectives. Among them, the number of floors of slab buildings has the most significant impact on BEC, UTCI and FAR. (3) There is a significant correlation between urban morphological parameters–energy efficiency correlation index, and BEC, UTCI, and FAR. Full article

The storage conditions of multi-floor grain warehouses change frequently during grain circulation. This paper investigates the effects of various storage conditions on the seismic performance of multi-floor grain warehouses. The numerical results indicate that the higher the storage material distribution position, the greater the damping ratio of the structural model and the more obvious the contribution of storage material movement to the damping of the structure. The intensity of earthquake action and the spatial height of the floor where the storage material is located are negatively correlated with the acceleration response of the structure. Under full-silo conditions, when the peak ground acceleration (PGA) is 0.4 g, the acceleration amplification factor at the top of the structure is 69.7% of the corresponding parameter at 0.1 g. The discontinuity in the storage space of the structure results in a torsional effect on the structure. When PGA = 0.22 g, the peak inter-story displacement angle of the first floor differs by nearly 1.7 times under different operating conditions, and the peak inter-story displacement angle of the second floor during an earthquake with PGA = 0.40 g differs by about 1.5 times under different operating conditions. The lateral pressure of the silo wall at different burial depths under earthquake action shows a highly nonlinear distribution trend, and the overpressure coefficient at the same burial depth of the warehouse wall is proportional to the PGA of the earthquake action. During 0.1 g, 0.22 g, and 0.40 g earthquakes, the maximum overpressure coefficients at the bottom of the warehouse wall on different floors are 1.13, 1.21, and 1.66, respectively. Full article

This study presents a multi-objective optimization framework for enhancing environmental performance in high-density residential complexes, addressing the critical balance between sunlight access, visual openness, and ground-level privacy. Applied to Helio City Phase 3 in Seoul—a challenging case with 2026 units surrounded by adjacent blocks—the research developed a sequential three-stage optimization strategy using computational design tools. The methodology employs Ladybug simulations for solar analysis, Galapagos genetic algorithms for view optimization, and parametric modeling for privacy assessment. Through grid-based layout reconfiguration, tower form modulation, and strategic conversion of vulnerable ground-floor units to public spaces, the optimized design achieved 100% sunlight standard compliance (improving from 64.31%), increased average visual openness to 66.31% (from 39.48%), and eliminated all privacy conflicts while adding 30 residential units. These results demonstrate that computational optimization can significantly surpass conventional planning approaches in addressing complex environmental trade-offs. The framework provides a replicable methodology for performance-driven residential design, offering quantitative tools for achieving regulatory compliance while enhancing residents’ experiential comfort in dense urban environments. Full article

Compound fertilizer is generally applied to alleviate multi-nutrient deficiency problems in forest stands, but research on the effect of fertilizer application on soil CO₂ efflux (Rs) processes has focused on the role of single-nitrogen (N) application. This study evaluates the effects of N addition in compound fertilizer on the rates in Pinus densiflora S. et Z. (Korean red pine) stands. Compound fertilizer with N (N₃P₄K₁ = 113:150:37 kg ha⁻¹ yr⁻¹) and without N (P₄K₁ = 150:37 kg ha⁻¹ yr⁻¹) was applied on the forest floor for three years. Rs rates were measured for four years, from April 2011 to March 2015. The mean annual Rs rates during the study period were 3.10 µmol m⁻² s⁻¹ in the N₃P₄K₁, 3.08 µmol m⁻² s⁻¹ in the P₄K₁, and 3.08 µmol m⁻² s⁻¹ in the control treatment. The rates in all treatments were significantly lower in 2013 (2.73 µmol m⁻² s⁻¹) than in other sampling years (3.03–3.58 µmol m⁻² s⁻¹) when the mean soil water content was the lowest (15.7%) during the four sampling years (other sampling years: 23.0–24.1%). The exponential relationships between Rs and the soil temperature were slightly more significant in the fertilized (N₃P₄K₁: R² = 0.72–0.80; P₄K₁: R² = 0.70–0.81) treatments compared to the control (R² = 0.62–0.74) treatment. The mean Q₁₀ values for the four years were similar between the N₃P₄K₁ treatment (4.19), the control (4.23) treatment, and the P₄K₁ (4.24) treatment. The results demonstrate that mean annual Rs rates in Korean red pine stands were not affected by the increased N availability in compound fertilizer, whereas decreases in mean annual Rs rates may be strongly attributed to the soil water content. Full article

Dimensional stability is a key trait for structural wood applications such as flooring, yet its genetic basis in Eucalyptus pellita F.Muell. and its hybrids remain poorly understood. Addressing this gap is essential for improving processing efficiency and product quality through targeted breeding. This study assessed variation in shrinkage and density, their relationships with growth and chemical traits, and associated genetic markers. Wood samples from E. pellita, E. pellita × E. urophylla S.T.Blake, and E. pellita × E. brassiana S.T.Blake were collected from two plantation sites in northern Australia. Radial and tangential shrinkage and density were measured alongside growth and chemical traits. SNP genotyping was conducted to identify markers linked to these physical properties. Significant differences were observed among hybrid types. E. pellita × E. urophylla recorded the lowest tangential unit shrinkage (0.06%), while E. pellita × E. brassiana had the highest basic density (651 kg/m³). Shrinkage and density showed moderate to strong correlations with growth and chemical traits. Several SNPs were associated with these properties; all were located in the intergenic region near Eucgr.A00211. Among these, only one SNP exceeded the −log₁₀(p) significance threshold. These results provide early genetic insights and potential candidate markers for improving wood quality in Eucalyptus breeding programs. This exploratory study, constrained by a small sample size (n = 58), identifies putative SNPs for future validation in broader, multi-environment trials. Full article

Accurate semantic segmentation of indoor architectural elements, such as staircases and elevators, is critical for safe and efficient robotic navigation, particularly in complex multi-floor environments. Traditional fusion methods struggle with occlusions, reflections, and low-contrast regions. In this paper, we propose a novel feature fusion module, Skip-Connection Fusion (SCF), that dynamically integrates RGB (Red, Green, Blue) and depth features through an adaptive weighting mechanism and skip-connection integration. This approach enables the model to selectively emphasize informative regions while suppressing noise, effectively addressing challenging conditions such as partially blocked staircases, glossy elevator doors, and dimly lit stair edges, which improves obstacle detection and supports reliable human–robot interaction in complex environments. Extensive experiments on a newly collected dataset demonstrate that SCF consistently outperforms state-of-the-art methods, including PSPNet and DeepLabv3, in both overall mIoU (mean Intersection over Union) and challenging-case performance. Specifically, our SCF module improves segmentation accuracy by 5.23% in the top 10% of challenging samples, highlighting its robustness in real-world conditions. Furthermore, we conduct a sensitivity analysis on the learnable weights, demonstrating their impact on segmentation quality across varying scene complexities. Our work provides a strong foundation for real-world applications in autonomous navigation, assistive robotics, and smart surveillance. Full article

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person’s plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints using a topological graph and regions by semantic labels. This is incorporated into an MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario and a waypoint planner that generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the-art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to new and unseen environments. Full article

A steel modular building is a highly prefabricated form of steel construction. It offers rapid assembly, a high degree of industrialization, and an environmentally friendly construction site. To promote the application of multi-story steel modular buildings in earthquake fortification zones, it is imperative to conduct in-depth research on their seismic behavior. In this study, a seven-story modular steel building is investigated using shaking table tests. Three seismic waves (artificial ground motion, Tohoku wave, and Tianjin wave) are selected and scaled to four intensity levels (PGA = 0.035 g, 0.1 g, 0.22 g, 0.31 g). It is found that no residual deformation of the structure is observed after tests, and its stiffness degradation ratio is 7.65%. The largest strains observed during the tests are 540 × 10⁻⁶ in beams, 1538 × 10⁻⁶ in columns, and 669 × 10⁻⁶ in joint regions, all remaining below a threshold value of 1690 × 10⁻⁶. Amplitudes and frequency characteristics of the acceleration responses are significantly affected by the characteristics of the seismic waves. However, the acceleration responses at higher floors are predominantly governed by the structure’s low-order modes (first-mode and second-mode), with the corresponding spectra containing only a single peak. When the predominant frequency of the input ground motion is close to the fundamental natural frequency of the modular steel structure, the acceleration responses will be significantly amplified. Overall, the structure demonstrates favorable seismic resistance. Full article

Background/Objectives: Pelvic organ prolapse (POP) is a common condition that significantly impacts quality of life. Research has focused largely on older women, while experiences of younger women remain relatively underexplored despite challenges unique to this population. Informed by the biopsychosocial model of illness, this study aims to assess the symptom burden, treatment goals, and information needs of younger women complaining of prolapse by analyzing questionnaire responses from an existing electronic Personal Assessment Questionnaire—Pelvic Floor (ePAQ-PF) dataset. Methods: Mixed-methods content analysis was conducted using free-text data from an anonymized multi-site ePAQ-PF dataset of 5717 responses collected across eight UK NHS trusts (2018–2022). A quantitative, deductive approach was first used to identify younger women (≤50 years old) with self-reported prolapse. ePAQ-PF scores for younger women with prolapse were compared with those aged >50 years, using Mann–Whitney tests. Free-text response data were analyzed inductively to qualitatively explore younger women’s symptom burden, treatment goals, and information needs. Results: Of the 1473 women with prolapse identified, 399 were aged ≤50 years. ePAQ-PF scores of the younger cohort demonstrated significantly greater symptom severity and bother than those aged >50, particularly in bowel, prolapse, vaginal, body image, and sexual health domains (p < adjusted threshold). Qualitative analysis undertaken to understand women’s concerns and priorities produced five health-related themes (physical health; functionality; psychosocial and emotional wellbeing; reproductive and sexual health; and healthcare journeys) and a sixth intersecting theme representing information needs. Conclusions: The findings highlight the substantial symptom burden of younger women with prolapse, as well as treatment goals and information needs specific to this population. The development of age-specific resources is identified as a requirement to support this group. Full article

The growing urban population has made densification a key focus of urban development. It is crucial to create an urban planning strategy that understands the environmental, social, and economic effects of densification at both the district and city levels. In Switzerland, densification is a legally binding aim to foster housing and jobs within urban boundaries. The challenge is to accommodate population growth while maintaining a high quality of life. Zurich exemplifies this situation, necessitating the accommodation of approximately 25% of the anticipated increase in both the resident population and associated workplaces, as of 2016. This study examined the effects of urban densification on urban forms and microclimates in the Altstetten–Albisrieden district. It developed five densification scenarios based on current urban initiatives and assessed their impacts. Results showed that the current Building and Zoning Plan provides sufficient capacity to accommodate growth. Strategies such as densifying parcels older than fifty years and adding floors to newer buildings were found to minimally impact existing urban forms. Using the SOLWEIG model in the Urban Multi-scale Environmental Predictor (UMEP), this study simulated mean radiant temperature (Tmrt) in the selected urban areas. The results demonstrated that densification reduced daytime average temperatures by 0.60 °C and diurnal averages by 0.23 °C, but increased average nighttime temperatures by 0.38 °C. This highlights the importance of addressing warm nights. The study concludes that well-planned densification can significantly contribute to urban liveability, emphasising the need for thoughtful building design to improve outdoor thermal comfort. Full article

Assessing energy consumption in existing old residential buildings is key for urban energy conservation and decarbonization. Previous studies on old residential building energy assessment face challenges due to data limitations and inadequate prediction methods. This study develops a novel approach integrating building energy simulation and machine learning to predict large-scale old residential building energy use using multi-source datasets. Using Guangzhou as a case study, open-source building data was collected to identify 31,209 old residential buildings based on age thresholds and areas of interest (AOIs). Key building form parameters (i.e., long side, short side, number of floors) were then classified to identify residential archetypes. Building energy consumption data for each prototype was generated using EnergyPlus (V23.2.0) simulations. Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. Results indicated that five representative prototypes exhibited cooling energy use ranging from 17.32 to 21.05 kWh/m², while annual electricity consumption ranged from 60.10 to 66.53 kWh/m². The XGBoost model demonstrated strong predictive performance (R² = 0.667). SHAP (Shapley Additive Explanations) analysis identified the Building Shape Coefficient (BSC) as the most significant positive predictor of energy consumption (SHAP value = 0.79). This framework enables city-level energy assessment for old residential buildings, providing critical support for retrofitting strategies in sustainable urban renewal planning. Full article

The behavior of floor-raised chickens is closely linked to their health status and environmental comfort. As a type of broiler chicken with special behaviors, understanding the daily actions of yellow-feathered chickens is crucial for accurately checking their health and improving breeding practices. Addressing the challenges of high computational complexity and insufficient detection accuracy in existing floor-raised chicken behavior recognition models, a lightweight behavior recognition model was proposed for floor-raised yellow-feathered chickens, based on a Dual-Backbone Heterogeneous YOLO Network. Firstly, DualHet-YOLO enhances the feature extraction capability of floor-raised chicken images through a dual-path feature map extraction architecture and optimizes the localization and classification of multi-scale targets using a TriAxis Unified Detection Head. Secondly, a Proportional Scale IoU loss function is introduced that improves regression accuracy. Finally, a lightweight structure Eff-HetKConv was designed, significantly reducing model parameters and computational complexity. Experiments on a private floor-raised chicken behavior dataset show that, compared with the baseline YOLOv11 model, the DualHet-YOLO model increases the mAP for recognizing five behaviors—pecking, resting, walking, dead, and inactive—from 77.5% to 84.1%. Meanwhile, it reduces model parameters by 14.6% and computational complexity by 29.2%, achieving a synergistic optimization of accuracy and efficiency. This approach provides an effective solution for lightweight object detection in poultry behavior recognition. Full article