Search Results (141,722)

Multicollinearity and outliers are common challenges in multiple linear regression, often adversely affecting the properties of least squares estimators. To address these issues, several robust estimators have been developed to handle multicollinearity and outliers individually or simultaneously. More recently, the robust Stein estimator (RSE) was introduced, which integrates shrinkage and robustness to effectively mitigate the impact of both multicollinearity and outliers. Despite its theoretical advantages, the finite-sample performance of this approach under multicollinearity and outliers remains underexplored. First, outliers in the y direction have been the main focus of earlier research on the RSE, not considering that leverage points could substantially impact regression results. Second, this study addresses the gap by considering outliers in the y direction and leverage points, providing a more thorough assessment of the RSE robustness. Finally, to extend the limited existing benchmark, we compare and evaluate the RSE performance with a wide range of robust and classical estimators. This extends existing benchmarking, which is limited in the current literature. Several Monte Carlo (MC) simulations were conducted, considering both normal and heavy-tailed error distributions, with sample sizes, multicollinearity levels, and outlier proportions varied. Performance was evaluated using bootstrap estimates of root mean squared error (RMSE) and bias. The MC simulation results indicated that the RSE outperformed other estimators under several scenarios where both multicollinearity and outliers are present. Finally, real data studies confirm the MC simulation results. Full article

Objective: The association between vitamin D status and paediatric fracture risk remains controversial, with inconsistent findings across existing studies. This study aimed to evaluate the relationship between serum 25(OH)D concentrations, vitamin D sufficiency, insufficiency and deficiency, vitamin D supplementation and fracture risk in a large Southeast Asian paediatric cohort. Methods: This retrospective cross-sectional study included children under 18 years whose serum 25(OH)D concentrations were measured between 2014 and 2022. One-way ANOVA determined statistical significance between 25(OH)D concentrations in fracture and non-fracture groups. Prevalence of vitamin D insufficiency, deficiency and supplementation was compared between the two groups. Chi-square tests evaluated the association between 25(OH)D concentrations and supplementation against fracture risk. Results: A total of 4530 children were included (157 fracture cases, 4373 controls). Mean serum 25(OH)D concentration was lower in the fracture group than in the controls (27.44 ± 12.26 vs. 30.75 ± 15.21 ng/mL; p = 0.007). Sub-sufficient vitamin D status (<30 ng/mL) was more prevalent among fracture patients (p = 0.001), and suboptimal (p = 0.001), insufficient (p = 0.001), and deficient (p = 0.014) categories were each significantly associated with fractures. An association between vitamin D supplementation and fracture risk was observed. However, the dataset did not permit the determination of causality and a protective effect cannot be inferred. Conclusions: Higher serum 25(OH)D concentrations were associated with lower fracture risk, suggesting that optimisation of vitamin D status may represent a modifiable factor in paediatric bone health. Healthcare institutions should aim to maintain adequate 25(OH)D concentrations (>30 ng/mL). An association between vitamin D supplementation and fracture risk was observed; however, causality cannot be inferred from this retrospective dataset. Full article

Green infrastructure (GI) provides essential ecosystem services for urban sustainability in the face of urbanization and climate change, including stormwater management, heat mitigation, and reduction in carbon dioxide (CO₂) concentration levels. Existing studies often focus on single-dimensional ecological effects, lacking a systematic investigation of their synergies and trade-offs. This study developed a coupled framework integrating scenario design, model simulation, and multi-indicator evaluation. Fifty-six scenarios, varying by GI combinations, weather conditions, and total annual runoff control rate (RCR), were applied to a high-density industrial district in Nanjing. The results showed that: (1) GI combinations enhanced comprehensive benefits, with the combination including bioretention (BR), permeable pavement (PP), and green roof (GR) performing most effectively. This was followed by the combination of BR and PP, then by BR and GR, while the use of BR alone provided the lowest effectiveness. (2) PP was a key synergistic component, improving heat mitigation and reducing CO₂ concentration levels through the beneficial effects of rainfall events. (3) Exceeding the optimal RCR threshold for some GI combinations diminished tree space and three-dimensional green volume, shifting synergies into trade-offs. (4) Three-dimensional green volume was positively correlated with reductions in Physiological Equivalent Temperature (PET) and CO₂ concentration, confirming its core role. (5) Rainfall boosted carbon sinks, while a significant cooling enhancement required PP. This study elucidates the water–heat–carbon synergy in small-scale GI, supporting multi-objective optimization in high-density urban renewal. Full article

As a key parameter of the Earth’s ecosystem, soil moisture significantly influences land-atmosphere interactions and has important applications in meteorology, hydrology, and agricultural studies. However, existing passive microwave remote sensing products of soil moisture are limited by their discontinuous temporal coverage and relatively coarse spatial resolution (typically 25–55 km), which cannot meet the requirements for fine-scale applications. This study developed and compared four machine learning-based downscaling approaches to improve the spatiotemporal resolution of MCCA SMAP soil moisture products. The methodology involved establishing complex nonlinear relationships between soil moisture and various high-resolution surface parameters including albedo, evapotranspiration, precipitation, and soil properties. High-resolution soil moisture maps were generated by leveraging the scale-invariant characteristics between soil moisture and surface parameters, followed by comprehensive evaluation using in situ ground observations and triple collocation analysis. The results demonstrated that all downscaling models showed excellent consistency with original MCCA SMAP observations (R > 0.93, RMSE < 0.033 m³ m⁻³), while successfully providing enhanced spatial details. The Random Forest (RF) model exhibited superior performance, showing higher correlation coefficients and lower biases when compared with in situ measurements. Uncertainty analysis revealed relatively low uncertainty levels for all models except Backpropagation Neural Network (BPNN) model. The RF-downscaled products accurately tracked temporal variations of soil moisture and showed good responsiveness to precipitation patterns, demonstrating their potential for fine-scale hydrological applications and regional environmental monitoring. Full article

The chlorophyll content of plant leaves measured by the soil plant analysis development (SPAD) is an important indicator for measuring crop growth status and irrigation effect. The rapid, non-destructive and efficient estimation of crop SPAD values is of great significance to the field management of crops. In this study, the canopy hyperspectral reflectance and SPAD values of winter wheat were obtained, and the spectral curve was changed through four spectral processing methods, including first-order differential (FD), second-order differential (SD), multivariate scattering correction (MSC), and Savitzky–Golay smoothing (SG) to improve the correlation between canopy spectral reflectance and SPAD. Furthermore, to investigate and evaluate the performance of various vegetation indices (VIs) in estimating SPAD values for winter wheat, existing published indices were optimized using random band combinations derived from multiple canopy spectral transformations. The optimized vegetation index was used as the input variable of the model, and six machine learning algorithms, including random forest (RF), long short-term memory network (LSTM), multilayer perceptron (MLP), deep recurrent neural network (Deep-RNN), gated recurrent unit (GRU), and convolutional neural network (CNN), were used to construct the winter wheat SPAD values estimation model, and the model was verified. The experimental results demonstrate that, when utilizing an equivalent number of optimized vegetation indices as input, the GRU-based model achieves higher estimation accuracy compared to other models. Specifically, the coefficient of determination (R²) is improved by 0.12 compared to the RF model, by 0.03 compared to the LSTM model, by 0.12 compared to the MLP model, by 0.02 compared to the Deep-RNN model, and by 0.02 compared to the CNN model. At the same time, the GRU model also has a lower root mean square error (RMSE) and relative error (RE) of 7.37 and 24.90%, respectively. This study provides valuable hyperspectral remote sensing technology support for the implementation of winter wheat SPAD values estimation in the field. Full article

Accurate multi-modal traffic demand forecasting is key to optimizing intelligent transportation systems (ITSs). To overcome the shortcomings of existing methods in capturing dynamic high-order correlations between heterogeneous spatial units and decoupling intra- and inter-mode dependencies at multiple time scales, this paper proposes a Dynamic Multi-Relation Learning with Multi-Scale Hypergraph Transformer method (MST-Hype Trans). The model integrates three novel modules. Firstly, the Multi-Scale Temporal Hypergraph Convolutional Network (MSTHCN) achieves collaborative decoupling and captures periodic and cross-modal temporal interactions of transportation demand at multiple granularities, such as time, day, and week, by constructing a multi-scale temporal hypergraph. Secondly, the Dynamic Multi-Relationship Spatial Hypergraph Network (DMRSHN) innovatively integrates geographic proximity, passenger flow similarity, and transportation connectivity to construct structural hyperedges and combines KNN and K-means algorithms to generate dynamic hyperedges, thereby accurately modeling the high-order spatial correlations of dynamic evolution between heterogeneous nodes. Finally, the Conditional Meta Attention Gated Fusion Network (CMAGFN), as a lightweight meta network, introduces a gate control mechanism based on multi-head cross-attention. It can dynamically generate node features based on real-time traffic context and adaptively calibrate the fusion weights of multi-source information, achieving optimal prediction decisions for scene perception. Experiments on three real-world datasets (NYC-Taxi, -Bike, and -Subway) demonstrate that MST-Hyper Trans achieves an average reduction of 7.6% in RMSE and 9.2% in MAE across all modes compared to the strongest baseline, while maintaining interpretability of spatiotemporal interactions. This study not only provides good model interpretability but also offers a reliable solution for multi-modal traffic collaborative management. Full article

As dockless bike-sharing systems rapidly expanded, this study aims to develop a flow-integrated framework for assessing bicycle usage efficiency, which addresses a critical gap in conventional static indicators. Existing studies rely primarily on big data to evaluate location-specific efficiency using Time-to-Booking (ToB). However, ToB ignores network flow effects while bicycles departing from the same location may reach destinations with vastly different ToB values. To overcome this, we propose a flow-integrated ToB (FwToB) index that incorporates the idle time at both the trip origin and destination. Applying this index to central Beijing reveals significant spatial heterogeneity while maintaining the original core-periphery pattern, indicating that most bicycles flow to areas with similar efficiency. Geographically weighted regression further shows that factors like population density, healthcare, shopping facilities, and distance to metro stations influence efficiency with substantial spatial non-stationarity. These findings advance the understanding of bike-sharing efficiency and offer insights for operators and urban planners. Full article

This paper addresses the critical challenge of semantic segmentation for remote sensing images (RSIs) under extremely limited labeled data. A high-quality initial model is paramount for downstream semi-supervised or weakly supervised learning paradigms, as it mitigates error propagation from the outset. We conducted a systematic investigation into self-supervised pretraining to serve this precise need. Within the low-label regime, we identify and tackle two pivotal factors limiting performance: (1) the domain shift between large-scale pretraining data and specific target tasks, and (2) the deficiency in local feature learning caused by large-window masking in visual foundation model (VFM) pretraining. To resolve these issues, we first benchmark various pretraining strategies, demonstrating that a two-phase General-Purpose Pretraining (GPPT) followed by Domain-Adaptive Pretraining (DAPT) framework is optimal, significantly outperforming both single-phase methods and the existing two-phase paradigm initialized from ImageNet. Subsequently, we propose an Edge-Guided Masked Image Modeling (EGMIM) method for the DAPT phase, which explicitly integrates edge priors to guide the masking and reconstruction process, thereby enhancing the model’s capability to capture fine-grained local structures. Extensive experiments on four RSI benchmarks validate the effectiveness of our approach, showing consistent and substantial gains, particularly in extreme low-label scenarios. Beyond empirical results, we provide in-depth mechanistic analyses to explain the synergistic roles of GPPT and DAPT. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

High-resolution marine gravity fields are critical for interpreting seafloor structure, investigating marine geodynamics, and enabling gravity-aided navigation. However, sparse shipborne observations, heterogeneous multi-source geodetic datasets, and the inability of conventional methods to handle nonlinear inversion limit accurate gravity recovery. To overcome these limitations, we propose a spectral physics-informed constraint deep-learning framework based on a multi-channel Swin Transformer to reconstruct high-resolution marine gravity anomaly fields. The model ingests multi-source geodetic inputs organized as 64 × 64 grid patches centered near each computation point and fuses them to predict the target gravity anomaly. We adopt a remove–compute–restore (RCR) strategy that isolates residual gravity signals, which improves numerical stability and accelerates training. Inputs include satellite-altimetry-derived vertical gravity gradients, vertical deflections, mean sea surface height, and topography; the model is trained on over 430,000 shipborne gravity samples from the South China Sea (0–30° N, 105–125° E). To enforce physical consistency, we embed a spectral-domain physics constraint derived from potential-field theory into the loss function; this constraint helps recover short-wavelength gravity signals. We also introduce an adaptive multi-domain multi-scale feature fusion module (AMAMFF) to improve the integration of heterogeneous inputs, and we demonstrate its benefits in experiments across complex terrain. Validation against independent shipborne gravity checkpoints yields an RMS error of 3.09 mGal, indicating a substantial performance advantage over existing deep-learning approaches and conventional gravity-field models. Full article

This paper presents new techniques for electric machine diagnostics that combine advanced signal processing and artificial intelligence (AI)-based techniques using magnetic flux measurements acquired under various operating conditions. Developing an effective electric machine diagnostics tool is paramount for increased industrial productivity and extending the service life of the machine. The existing diagnostic tools face issues, including false indication of faults using classical methods, and the proposed data-driven methods based on machine learning lack transferability of model knowledge on an unseen dataset from different motor types or power ratings due to structural differences. To overcome these diagnostic drawbacks of statistical ML classifiers and classical approaches, innovative feature selection methods were employed in this work to preprocess the measured magnetic flux into a spectrogram image, and the transfer learning (TL) technique was applied to fine-tune convolution neural networks (CNNs) ImageNet pretrained models. The experimental results show the trained statistical ML classifiers and traditional CNN performance on unseen BU data and on the external data, and the performance demonstrated a lack of generalization on external datasets of different power ratings or structures. Models with such drawbacks cannot be used for developing effective diagnostic systems. The TL technique was employed on different deep CNN ImageNet pretrained models with spectrogram images as inputs to the deep CN network. This approach demonstrated an advanced and improved electric machine diagnostic system that addresses the drawbacks of the current ML-based diagnostic systems. The generalized model developed using CNN ResNet50 outperformed other deep CNN ImageNet models in correctly diagnosing faults on both the dataset generated from the authors’ lab and on an external dataset of a different machine from another research lab. Full article

Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems. Full article

At the beginning of life, there are no sex differences in fetal kidney growth, nephron endowment nor in the prevalence of low birth weight. In chronic kidney disease (CKD) in adults, however, significant sex- and gender-specific differences exist in diagnosis, progression, and management of CKD. In adult individuals, CKD is more prevalent in women, but CKD progression is faster in men; nevertheless, women have a higher life expectancy than men. A possible explanation for the enigmatic higher CKD prevalence in women may derive from the Brenner–Luyckx concept of hyperfiltration. Diseases that lead to hyperfiltration will lead to premature nephron loss and to a faster decline in glomerular filtration rate (GFR). This condition is predominantly seen in middle-aged men with a higher GFR, larger hypertrophied kidneys, and a higher prevalence of arterial hypertension, diabetes mellitus, smoking, and hypercholesterolemia compared to women. Thus, a high GFR may not be a good sign if it reflects hyperfiltration. Any GFR must be interpreted against the comorbidities of an individual. An individual may end up with a realistic GFR far below normal once hyperfiltration is stopped, for example, by a Sodium Glucose-Linked Transporter 2 (SGLT2) inhibitor. With regard to the management of CKD, women with CKD receive poorer healthcare compared to men with CKD. Women less frequently receive a CKD diagnosis, are less frequently referred to nephrology for co-management, less frequently undergo eGFR and albuminuria assessments, and are less likely to receive guideline-recommended treatments for CKD, such as angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers, and statins. Cardiovascular risk factors are less rigorously controlled in women with CKD compared to men with CKD. The causes for the poorer CKD care among women are to be found in gender rather than in sex. It is crucial to integrate assessments of sex and gender into both clinical routines and scientific reports. All studies should incorporate sex- and gender-specific analyses, and the evaluation of pre- and postmenopausal women should be conducted separately. The utilization of Gender Scores can help identify the impact of cultural, societal, and psychological factors on observed gender differences in ambulatory healthcare for those with CKD. Guidelines need to be sensitive to gender and emphasize the existing knowledge gaps regarding sex and gender differences in CKD healthcare. Urgent attention is required to substantially improve and ensure equitable healthcare for CKD across sexes and genders. Full article

Mathematical modeling of heat and mass transfer processes in porous media using fractional derivative equations is of great practical importance. Within the framework of such models, obtaining analytical solutions to the corresponding initial–boundary value problems is generally difficult. In this work, we numerically investigate quasilinear parabolic problems involving Caputo time-fractional derivatives. First, the well-posedness and existence of weak solutions are discussed. Then, we construct and implement a finite-difference scheme that is fourth-order accurate in space and second-order accurate in time. Convergence in the maximum norm is proven. Numerical experiments confirm the accuracy and efficiency of the proposed approach. Full article

Naturally Occurring Retirement Communities Supportive Service Programs (NORC-SSPs) are one of the most popular models of aging in place. While the existing NORC literature focuses on the social and service environments of these programs, their built environments remain underexplored, particularly across housing tenures. This study is the first to explore the built environment, social integration, and socio-demographic factors among older people living in NORCs in New York, and their associations with health and well-being. The mixed-methods research included qualitative (interviews with NORC directors) and quantitative (151 resident surveys and an architectural assessment) data on 26 housing developments in New York, collected simultaneously using a convergent parallel design. The findings show that socialization and exercise improve the health and quality of life of NORC residents. The study also revealed that older people living in public housing have different needs than those in cooperative housing, namely a worse perception of their health and dwellings of a poorer physical condition. Therefore, the services offered by NORC programs should vary according to housing type, while management and NORC staff should improve coordination to address maintenance in public housing. Future research should examine interventions to improve the physical environments of NORC residents. Full article