Search Results (659)

Conservation subdivisions cluster residences in smaller lots compared to conventional subdivisions in order to protect common open spaces (COS) with the assumption that clustering and open space preservation will provide increased ecosystem services. However, the guidelines and regulations for conservation subdivisions typically lack direction on how COS should be designed and maintained. The primary objective of our study was to examine the landscape structure of COS across 54 conservation subdivisions developed in Waukesha County, Wisconsin, USA, and our secondary objective was to evaluate whether COS landscape structure is associated with bird occurrence. We selected seven FRAGSTATS landscape metrics relevant for assessing biophysical processes in urbanized areas in the categories of area/size (PLAND, MPS, LPI), edge/shape (ED, PD), and fragmentation/isolation/aggregation (LSI, IJI). Our k-means clustering and non-metric multidimensional scaling analysis found that the landscape structure of COS varies across the conservation subdivisions and that there are three distinct categories of COS (i.e., intact large-patch COS, fragmented medium-patch COS, and small, dispersed COS) in these subdivisions. Analysis of bird occurrences using eBird data did not show conclusive results but suggested that future systematic wildlife observation in the three groups of conservation subdivisions may provide evidence of the importance of COS composition and configuration, as COS landscape structure may influence ecosystem services. Full article

This paper focuses on the binary task of parking-slot occupancy detection (vacant vs. occupied) from UAV aerial imagery. Accurate parking-slot detection from UAV imagery is challenged by small target sizes, highly regular rectangular shapes, large-scale variations, complex backgrounds, and perspective distortions. Compared with fixed surveillance cameras, UAV-based detection offers flexible deployment, wide-area coverage, and no requirement for pre-installed infrastructure, making it especially suitable for large open-air parking lots and temporary parking scenarios. To address this, this study proposes a task-specific framework for UAV-based parking-slot detection, with improvements in backbone design, attention modeling, and bounding-box regression. A lightweight MnasNet-inspired backbone is used to improve multi-scale feature extraction at low computational cost. An enhanced EMA module with adaptive grouping, FFT-based frequency enhancement, and gated fusion is introduced to better model the structured patterns of parking lot scenes. In addition, a UIoU+ loss tailored to rectangular geometry is proposed to improve localization quality. Sensitivity analysis and repeated experiments show that the method is stable and statistically reliable. All main metrics are evaluated on an independent held-out test set to ensure generalization. Extensive experiments demonstrate that each component brings consistent performance gains. The proposed model achieves 99.44 ± 0.12% mAP@0.5, 90.31 ± 0.27% mAP@0.5:0.95, 99.27 ± 0.15% precision, and 99.00 ± 0.18% recall on the self-built UAV Parking Lot dataset. Its mAP@0.5:0.95 is 26.01 percentage points higher than the YOLOv11n baseline. Consistent performance improvements are also validated on two additional public benchmarks (CARPK and PKLot), confirming the generalization of the proposed method beyond the self-built dataset. Most importantly, the method supports real-time inference on embedded UAV platforms and achieves state-of-the-art performance among lightweight detectors, making it an ideal solution for practical intelligent parking management. Ablation studies further confirm the complementary synergy between the proposed backbone, attention module, and loss function. Full implementation code, pre-trained weights, and detailed reproduction guidelines are publicly available to ensure research reproducibility. Full article

Background/Objectives: Opportunities for pre-service midwifery students to manage postpartum hemorrhage (PPH), a high-risk, infrequent emergency, are limited. Virtual reality (VR) can provide contextual decision-making practice, whereas mannequin training provides tactile psychomotor practice. Evidence on combining these modalities and their instructional sequence remains limited. This exploratory pilot study examined implementation and preliminary outcomes of a blended lecture–VR–mannequin program. Methods: Ten final-year midwifery students were allocated by drawing lots to lecture–VR–mannequin (L–VR–M; n = 5) or lecture–mannequin–VR (L–M–VR; n = 5). Performance was assessed using an Objective Structured Clinical Examination (OSCE) before and after the program; motivation was assessed using the Course Interest Survey after the program. Analyses were exploratory, with effect sizes and Hodges–Lehmann confidence intervals reported alongside p values. Results: No statistically significant between-sequence differences were found. Across all participants, the median OSCE score increased from 34.5 (IQR, 31.0–35.0) to 44.0 (IQR, 41.0–45.0); the paired median difference was 10.5 points (95% CI, 9.0–15.5; p = 0.005; r = 0.89). This change cannot be attributed solely to the program because there was no no-intervention control and the same scenario was repeated. Uterine fundal massage showed a relatively large exploratory effect favoring L–VR–M (r = 0.54), but the confidence interval included no difference. Conclusions: The program was deliverable within one approximately 4 h session and was associated with higher post-program OSCE scores in this small cohort. The design does not establish effectiveness or an optimal sequence because of the very small sample, baseline imbalance, non-blinded assessors, and possible testing and practice effects. A larger controlled study using alternate OSCE scenarios, blinded assessors, and delayed follow-up is required. Full article

Group-buying platforms require the joint treatment of individual user relevance and hard operational constraints, including minimum group size, lot capacity, spending limits, product availability, and simultaneous participation limits. This paper presents Preference-Aware Hybrid Combinatorial Group Optimization (PA-HCGO), a prototype-calibrated decision framework that integrates implicit feedback preference learning with constrained user–lot assignment. The contribution is not a new recommender architecture or a new integer programming solver, but a system-level integration of learned user–lot utility and hard group-buying feasibility constraints inside the SmartBuy Connect prototype. The empirical part uses an anonymized prototype dataset containing 200 products, 150 users, 150 lots, 4000 user events, and 500 orders. The evaluation is explicitly divided into three tracks: observed temporal recommendation testing, a calibrated counterfactual pre-activation scenario, and synthetic scalability tests generated from prototype-calibrated distributions. In the observed transactional test subset, which contains 40 held-out transactional items from 36 users, the PA-PREF preference layer achieved Recall@10 = 0.3611 and NDCG@10 = 0.1319 using user–category profiles available at the end of the training interval. Although PA-PREF obtained the highest point estimate, its advantage over the content-only baseline was not statistically separable at the 95% level on this small transactional subset. The result is therefore treated as preliminary. For general engagement, the popularity baseline remained stronger, indicating that the proposed preference model is more useful for transactional intent prediction than for all activity types. In the calibrated counterfactual pre-activation scenario, PA-HCGO activated 117 of 150 lots, compared with 85 lots under independent greedy assignment. This scenario is a constructed pre-activation setting rather than an observed historical platform state. In synthetic scalability tests, the method remained computationally feasible up to 5000 users and 1000 lots, with a mean solve time of 4.7174 s in the prototype implementation. The results suggest that learned user–lot utility can improve constrained group formation, but the evidence should be interpreted as prototype-calibrated rather than as proof of industrial-scale business effectiveness. Full article

Predicting the remaining useful life (RUL) of lithium-ion batteries is essential as it relates to the safety, reliability, and maintenance of both electric vehicles and energy storage systems. While machine learning algorithms have greatly enhanced the accuracy of prediction, a lot of the models available today still have some major flaws including redundant features, poor performance due to non-optimized model settings, and the necessity of the complete degradation process of the system. In this study, we develop a machine learning framework using particle swarm optimization (PSO) to perform hyperparameter tuning and feature optimization to improve lithium-ion battery RUL prediction. The framework allows for the automatic determination of the key degradation indicators, resulting in the optimization of the learning model and enhancement of both the accuracy and interpretability of the predictions. Furthermore, the framework is designed to be employed in an early-cycle learning environment wherein only the first 30% of battery discharge cycles are utilized in the model training. This method is intended to mimic the constraints of real-world applications in which complete lifecycle data is unavailable. Under a properly nested evaluation protocol, the results indicate that the model achieves a mean R² of 0.354 ± 0.157 and RMSE of 19.13 ± 1.39 using only 30% of degradation cycles, compared to a mean R² of 0.922 ± 0.014 and RMSE of 18.10 ± 1.11 under full-cycle training across five independent evaluations. While early-cycle prediction shows reduced and more variable explanatory power due to the smaller sample size, the model maintains reasonable error magnitude, supporting its potential utility for early diagnosis of lithium-ion batteries. The effectiveness of the framework is confirmed by feature importance analysis, evaluation of optimization convergence and multi-metric error assessment. In conclusion, the framework offers a highly interpretable and effective solution for predicting lithium-ion battery RUL, in alignment with the emerging data-driven approaches to battery management systems. Full article

Parcel area sits at the intersection of urban planning, land administration and land surveying. It underpins development intensity, floor area allocation, minimum lot thresholds, land readjustment and value capture mechanisms. Yet discrepancies between modern measurements and ownership titles are usually evaluated through fixed tolerance formulas rather than quantified confidence intervals. While coordinate precision is routinely specified, the uncertainty of the derived parcel area is seldom expressed explicitly, limiting the traceability of planning calculations based on cadastral geometry. This paper presents a variance-based formulation for estimating parcel area uncertainty from boundary coordinates. Using the Gauss area function and first-order propagation, vertex precision is translated into parcel-level confidence intervals based on horizontal RMS parameters commonly reported in cadastral practice, including documented transformation accuracy. The Greek cadastre provides an illustrative case combining a national GNSS infrastructure, a unified reference system and formula-based area screening embedded in statutory workflows. Illustrative examples show how area uncertainty varies with parcel geometry and measurement origin. Absolute uncertainty increases with parcel size and boundary elongation, while relative uncertainty decreases with parcel size. A Monte Carlo analysis of the error-correlation structure shows that the diagonal, independent model is not a universal bound: depending on the structure of the transformation error and on parcel geometry it may either overstate or understate the true area uncertainty, by factors between about 0.4 and 3.5 in the cases examined. The results clarify how coordinate precision propagates into regulatory-relevant area values and support more transparent interpretation of area discrepancies in planning and land administration contexts. Full article

Single-machine, single-product inventory models with generalized interarrival times have lacked a fully rigorous and computationally efficient optimization framework because a sign error in earlier derivations produced ad hoc feasibility restrictions and overly broad search domains. This study presents a corrected derivation that proves the strict convexity of the minimum-cost objective and establishes the existence and uniqueness of an interior optimum without auxiliary conditions, therefore consolidating previously fragmented results into a single theorem. Building on these structural properties, the maximum-profit formulation is reduced to a one-dimensional program with natural finite bounds that tightly bracket the optimizer, replacing earlier paired bounds defined on an effectively unbounded domain. Numerical results for a canonical benchmark show that the tightened admissible interval recovers the same optimum with fewer function evaluations, thereby improving computational efficiency and implementation robustness. The paper therefore contributes a corrected optimality theory, a problem-native one-dimensional reformulation of the profit model, and a more reproducible computational procedure for capacity-constrained single-machine systems under generalized interarrival times. Full article

Grid trading is a rule-based Forex execution scheme that places a ladder of buy and sell limit orders at fixed price increments, profiting from price oscillation without a directional forecast. To accelerate recovery, the scheme is commonly paired with Martingale lot scaling, a gambling strategy—distinct from the stochastic-process notion of the same name—that increases the lot size at each adverse price level. This combination achieves a high short-term hit rate but retains a non-zero probability of catastrophic drawdown, a pattern equivalent to the Gambler’s Ruin problem. Attempts to augment grid trading with supervised machine learning face a label-misalignment problem: the usual label “did the price rise or fall at horizon $h$?” does not capture the path-dependent payoff of a grid that may still profit after an initially adverse move. This paper presents the ML-Augmented High-Frequency Grid Trading System (AHFGTS) and reports three contributions. (i) Strategy-Embedded Labeling (SEL) derives each binary training label from a full forward simulation of the deployed grid over a 15 bar H1 (one-hour) horizon, so the training objective matches the execution objective. (ii) Soft Martingale execution replaces classical 2× doubling with ten sub-linearly scaled lot multipliers generated by linspace(1, 5, 10), cutting four-level cumulative exposure by 63% relative to 2× doubling. (iii) The Drawdown Dichotomy Ratio (DDR = Maximum Equity Drawdown/Maximum Balance Drawdown) is introduced as a scalar risk metric that, to our knowledge, is the first such metric for the gap between floating and realized risk in Martingale-family systems. A twelve-month out-of-sample evaluation on EUR/USD H1 (38 million ticks, 99% modeling quality, 1:500 leverage) produced 444 trades, a 65.77% win rate (z = 6.65, p < 0.0001; Cohen’s $h$ = 0.321), Profit Factor 2.85 (a 90–138% improvement over unfiltered grid baselines), and 442.6% net annual return; DDR was 5.72× (maximum equity drawdown, MED, of 79.97% vs. maximum balance drawdown, MBD, of 13.98%), quantifying the structural Martingale risk that persists after ML augmentation. The study evaluates a single currency pair under 1:500 offshore leverage and should be read as a methodological demonstration of SEL, Soft Martingale, and DDR rather than a universal performance claim. Full article

Shared residential yards constitute a substantial yet often overlooked component of open space in dense cities. While their ecological significance has been demonstrated locally, less is known about how their morphological characteristics relate to estimated vegetated potential across metropolitan residential fabrics and how this potential is affected by urban renewal. This study addresses this gap through a typological analysis of shared residential yards in the Tel Aviv metropolitan area, Israel. Building on prior field-based research in Givatayim, the analysis was extended to five additional cities. Using GIS, residential environments were classified by building type and lot size category, and analyzed in relation to prevalence, Potentially Vegetated Area (PVA), chronological development, spatial clustering, and maintenance regimes. PVA is used here as a proxy for vegetated potential and as an indicator of spatial conditions associated with plant diversity and species richness. The findings show that the typological framework captures most shared residential areas. Several typologies account for a substantial share of estimated PVA, indicating higher vegetated potential and spatial conditions associated with biodiversity-supporting functions. Yet many buildings with relatively high estimated vegetated potential are older than 45 years and thus more likely to be exposed to redevelopment pressure. Estimated vegetated potential varies in relation to typological distribution, spatial clustering, management dynamics, and chronological exposure to renewal. Thus, we argue that shared residential yards should be recognized as a distributed ecological resource whose transformation cannot be addressed effectively through parcel-based planning alone. Full article

Firms increasingly coordinate lot-sizing, distribution center (DC) location, and joint replenishment over time to reduce costs. This paper studies this integrated problem under the (R, S) policy with demand, which stochasticity varies from period to period. We build a model where only the timing of replenishment is the core decision; all else follows from it. To solve efficiently, we design a hybrid differential evolution algorithm with a random neighborhood search. Experiments show our algorithm outperforms eight benchmark methods in solution quality and speed. A sensitivity analysis reveals how key parameters affect the total cost, replenishment frequency, and the number of DCs. Higher ordering costs reduce replenishment frequency, and larger DC setup costs lead to fewer DCs. However, fewer DCs do not always lower the total cost—when dealers are geographically dispersed, more DCs can reduce the overall total system cost. These insights help managers balance the cost components in a supply chain network design. Full article

Upstream textile small and medium-sized enterprises (SMEs) frequently exhibit constrained supply chain resilience owing to persistent information latency and structural dependence on downstream orders. To address these challenges, this study develops and validates a customer-to-manufacturer (C2M) intelligence framework that enables data-driven production planning using publicly available e-commerce data. The framework incorporates ethically compliant acquisition of consumer demand signals, semantic translation of unstructured market data into textile engineering attributes, machine-learning-based demand forecasting, and human-centric decision support. Utilizing 3.87 million consumer comments from 127,846 product listings, a Neural Boosted Tree model with entity embeddings for textile attributes was constructed. This model achieved a mean R² of 0.921 in cross-validation, surpassing benchmark methods. Consumer comment volume was validated as a proxy for sales activity, facilitating demand estimation. Forecasts were translated into production guidance using Monte Carlo simulation and a decision dashboard. In a 12-month field study at a Taiwanese dyeing SME, implementation resulted in a 28% reduction in inventory value, a 31% decrease in dye lot changeovers, and a 16% increase in capacity utilization. This research extends the C2M paradigm from downstream retail contexts to upstream textile SMEs, proposes an integrated and operationally feasible intelligence framework for resource-constrained manufacturers, and demonstrates how digital intelligence can enhance supply chain resilience while supporting, rather than replacing, human decision-making. The results indicate that upstream textile SMEs can leverage publicly visible e-commerce signals to enhance production planning responsiveness, minimize inventory exposure and dye-lot disruptions, and strengthen resilience to demand uncertainty through planner-centered digital decision support. Full article

This study details the design and development of an ultra-sensitive microwave sensor for non-invasive blood glucose monitoring, achieved by analyzing variations in the response of a split-ring resonator (SRR) through advanced engineering methodologies. There were three design phases in the development process. In the first phase, a standard SRR design was used. It had a resonant frequency of 2.975 GHz in S₂₁ and a sensitivity of only 0.0032 dB/(mg/dL). In the second phase, an interdigital capacitor (IDC) was added to the SRR structure. This made it work better and made it more sensitive, with a sensitivity of 0.015 dB/(mg/dL) at 4.1 GHz. The third phase was to use a fourth-order Moore fractal geometry to improve the resonance properties of the design a lot. From the obtained S₁₁, the maximum sensitivity was 0.042 dB/(mg/dL), which was a huge improvement in sensing efficiency compared to earlier designs. Several resonant frequencies were recorded between 4.84 and 7.56 GHz. The addition of the fractal structure made the electromagnetic field stronger in the resonant space and made the waves interact more with small changes in the biological medium, all without changing the sensor’s size (80 mm × 40 mm). These results show that fractal architecture is a promising way to create non-invasive, accurate, and easily integrated sensors in biological systems that can continuously measure blood glucose levels. Full article

Unmanned Aerial Vehicle (UAV) fertilization is important for precision agriculture. However, multi-rotor UAVs show a lot of inconsistencies in homogeneity and unclear deposition patterns when they spread granular fertilizer in different operational situations. This study utilized the DJI T40 UAV to measure discharge rates and create a correlation model. An orthogonal design combined DEM simulation with field experiments to look at how flight height and disc speed affected spreading uniformity and effective swath for single and overlapping flight paths. The discharge rate has a strong linear relationship with control parameters (R² > 0.94), which means that it is very easy to predict for all particle sizes. Single-pass deposition shows an “M-shaped” bimodal profile with particles of different sizes arranged in a radial pattern. The best values for H and n were found to be 7 m and 1200 rpm, respectively, and gave a 10 m effective swath width and a coefficient of variation (CV) of 13.79%. Deposition patterns change nonlinearly with flight height and disc speed. Particle size consistency is critical for distribution stability, with flight height being the key quality determinant and particle size variation the primary source of instability. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

Cold supply chains require coordinated inventory and storage decisions to preserve product quality while managing high energy consumption. This paper develops a joint economic lot-sizing model for a two-echelon cold supply chain that explicitly integrates time–temperature-dependent quality degradation with energy consumption in refrigerated warehouses. Unlike traditional approaches, energy is modeled as an endogenous function of warehouse filling level and warehouse temperature, allowing the interaction between inventory volume, energy efficiency, and quality preservation to be captured. The model is formulated under three coordination policies—Lot-for-Lot, traditional agreement, and consignment stock—and solved under joint decision making. Numerical results for chilled and frozen products show that neglecting energy and quality costs can lead to sub-optimal policies with total cost penalties exceeding 300% compared to the proposed integrated optimization. Results further indicate that a consignment stock agreement can reduce total system costs by up to 9% relative to traditional policies, while the optimal lot size is highly sensitive to energy prices, product value, and warehouse temperature. These findings highlight the critical role of jointly optimizing inventory, energy, and quality decisions in cold supply chains and provide actionable insights for designing more sustainable and energy-efficient production inventory systems. Full article