Search Results (9,134)

Inflation, recognized as a social determinant of health (SDOH), significantly affects the daily lives of individuals through the rising costs of food, housing, and other basic needs, all of which are public health concerns. Since the COVID-19 pandemic, inflation has become a prominent concern in the U.S. and has been linked to increased stress and poor mental health among adults. While data on inflation is tracked routinely, how it is discussed publicly is understudied. Social media platforms provide insights into how inflation is framed and experienced by the public, and these assessments may be used to determine public health needs and policy advocacy. In this study, we conducted a time-bound, platform-specific case study of inflation-related discourse on X (formerly Twitter). Analysis revealed a predominance of negative sentiments (68.5%) including frustration and distrust. Posts primarily concerned monetary policy/government spending (31.6%), Federal Reserve interest rates/financial markets (24.5%), and U.S. presidential politics (12.9%). The users did not explicitly discuss personal-level hardships, and the discussions largely focused on macro-level issues framed in polarized political perspectives. These patterns matter for public health because institutional trust shapes support for social and health policies. Our study findings suggest a fragmented social environment that may exacerbate community-wide anxiety and challenge health promotion efforts and the need for public health surveillance through surveys or personal interviews to identify and address the psychological burden of inflation. Full article

Background/Objectives: Andrographolide (ADG) is a plant-derived compound with promising anticancer properties, but its medical use is limited due to poor water solubility and low bioavailability. This study proposes developing a gold-based nanocomposite drug delivery system, using a simplified synthesis method, to improve ADG’s hydrophilicity and enhance its delivery efficiency. Methods: A one-step method was used to synthesize gold nanocomposites with carbon quantum dots (CBQDs) and doped CBQDs acting as reducing and stabilizing agents. These nanocomposites were then conjugated with ADG and thoroughly characterized using multiple structural and spectroscopic techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), ultraviolet–visible spectroscopy (UV–Vis), transmission electron microscopy (TEM), Raman spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy. Hydrophilicity enhancement was evaluated using NMR-based log P measurements. Biological assessment involved cell viability assays and confocal microscopy studies in PC3 prostate cancer cells, along with the morphological evaluation of human red blood cells. Results: XRD confirmed the formation of crystalline, face-centered cubic gold nanoparticles, while spectroscopic analyses verified successful nanocomposite formation and ADG conjugation. NMR results showed enhanced hydrophilicity of ADG. Biological tests demonstrated that the nanocomposites were compatible with cells. Conclusions: This study presents a straightforward strategy for synthesizing gold-based nanocomposites that enhance the hydrophilicity and delivery potential of andrographolide, supporting their applicability as nanocarrier platforms for anticancer drug delivery. Full article

Maritime aerial imaging is strongly affected by rapid illumination variations induced by dynamic sea conditions, which often cause conventional exposure control approaches to misinterpret intrinsic scene brightness as overexposure resulting from elevated camera settings. To overcome this issue, an adaptive exposure control framework based on a Glare-Aware Attention Network is proposed, implemented within an end-to-end dual-branch architecture. The framework utilizes an Exposure State Encoding (ESE) module to encode the current frame’s exposure parameters as conditional vectors, thereby resolving physical ambiguities in scene understanding. A Glare-Aware Spatial Attention (GASA) mechanism is further introduced, incorporating a glare prior map (GPM) generated using a “high-luminance, low-texture” heuristic to explicitly suppress sun glint effects. A Scene Difficulty-Adaptive Loss Weighting (SDAW) scheme is designed to adaptively regulate loss weights, and region-aware evaluation metrics, KREA and ISR, are defined. On a self-collected maritime aerial imaging dataset, the proposed approach significantly outperforms both traditional and deep learning-based methods in terms of full-frame and region-level performance metrics. Compared with the multi-task CNN baseline that has the closest parameter count, it achieves a 1.7 dB gain in PSNR. Cross-dataset validation on SeaDronesSee, temporal consistency analysis, and embedded platform testing further support the generalization and real-time feasibility of the proposed solution. Offering a high-accuracy, region-aware exposure control solution for aerial cameras in complex sea surface scenarios. Full article

Background/Objectives: Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cancer, characterized by frequent metastasis and poor prognosis. Epithelial–mesenchymal transition (EMT) plays a pivotal role in tumor progression. Protocadherin 9 (PCDH9) has emerged as a potential tumor suppressor, but its relationship with EMT markers in ccRCC remains unclear. This study aimed to investigate the expression patterns and prognostic significance of PCDH9, β-catenin (CTNNB1), Snail (SNAI1), and Vimentin (VIM) in ccRCC. Methods: Immunofluorescence analysis was performed on formalin-fixed paraffin-embedded tissue sections from 48 ccRCC patients (31 low-grade, 17 high-grade) and adjacent normal renal cortex. Findings were validated using The Cancer Genome Atlas (TCGA-KIRC) dataset via GEPIA2/GEPIA3 platforms, including differential expression, correlation, and survival analyses. Results:PCDH9 mRNA was significantly downregulated in ccRCC tumors (TCGA-KIRC), while VIM was upregulated at the transcriptomic level. Tissue-level immunofluorescence quantification revealed discordant patterns, highlighting the influence of cellular heterogeneity on bulk protein assessment. The strong positive correlation between PCDH9 and CDH1 observed in normal kidney was completely lost in tumor tissue. Unexpectedly, PCDH9 showed positive correlations with EMT transcription factors (ZEB1, SNAI1) in tumors. In univariate survival analysis, high PCDH9 and CTNNB1 expression were associated with improved overall survival. Multivariate Cox regression revealed endpoint-specific prognostic signatures: VIM independently predicted disease progression, while SNAI1 predicted overall mortality. CTNNB1 was consistently protective across both endpoints. Conclusions: Our findings support a tumor-suppressive role for PCDH9 in ccRCC and reveal disruption of epithelial adhesion molecule co-regulation during tumorigenesis. The identification of endpoint-specific prognostic signatures has implications for patient stratification and suggests that ccRCC exhibits a partial EMT phenotype rather than classical EMT. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for remote dimensional inspection in transportation monitoring and infrastructure control. In such applications, measurement results are often interpreted relative to regulatory thresholds, making the reliability of inspection decisions strongly dependent on measurement uncertainty. This study presents a probabilistic framework for evaluating measurement uncertainty and decision risk in UAV-based dimensional inspection tasks. A measurement model describing uncertainty scaling with observation geometry is formulated, and the probability of exceedance relative to a regulatory limit is derived. The framework integrates probabilistic measurement modeling with a risk-based decision formulation that accounts for false-positive and false-negative inspection outcomes. The resulting integral inspection risk is analyzed for representative sensing modalities commonly used in UAV platforms, including vision-based systems, LiDAR, and radar sensors. The results demonstrate that uncertainty scaling with flight altitude significantly influences exceedance probability and decision reliability. Sensors with lower intrinsic dispersion maintain sharper threshold transitions and therefore provide more stable regulatory decisions. Sensitivity analysis further confirms that moderate variations in measurement uncertainty can substantially affect inspection risk. The proposed framework provides a quantitative tool for evaluating sensing technologies in UAV-based inspection missions and supports the design of reliable drone-assisted dimensional compliance monitoring systems. Full article

Soil monitoring is fundamental for maintaining global soil health, ensuring food security, and achieving sustainable development. While satellite platforms provide invaluable tools for this purpose, the accuracy of soil monitoring heavily relies on the appropriate design of their remote sensing payload parameters. This study focuses on enhancing the accuracy of satellite-based global soil monitoring. Key physicochemical soil parameters—including total nitrogen (TN), soil organic matter (SOM), total salt content (TSS), moisture content (MC), and clay fraction (Clay)—were analyzed. A full-chain analytical validation model integrating “instrument–radiative transfer–soil parameter inversion” was developed. Using spectral measurements and soil sample analyses from the black soil region of Northeast China, the spectral response characteristics of core soil parameters were simulated and cross-validated under varying spectral resolutions and integration times. Results indicate that, under specific parameter configurations, the ‘Linshi-1’ satellite achieved robust TN inversion accuracy with R² > 0.65. SOM consistently exhibited good inversion performance, with RMSE ranging between 5.04 and 5.76 g/kg across various spectral treatments (all < 6 g/kg). TSS inversion demonstrated strong stability, maintaining an RMSE of approximately 0.43–0.44 g/kg at resampled spectral resolutions≥10 nm (corresponding to an SNR > 263). MC inversion accuracy was sensitive to both spectral resolution and regional variations, requiring a resampled resolution below 10 nm for consistently high accuracy. Clay inversion required the highest resolution, achieving an RMSE of less than 6 g/kg only at resampled resolutions of 1 nm or 2 nm (SNR approximately 150–210). These findings guided the design of the ‘Linshi-1’ black soil monitoring satellite system and its hyperspectral payload prototype. This effort establishes a solid theoretical and methodological foundation for future deployment, providing crucial space-based support for China’s black soil resource management and sustainable utilization. Full article

Background: Youth soccer players are exposed to repeated unilateral loading during a period of rapid growth and neuromuscular maturation. These demands may contribute to postural deviations and inter-limb functional asymmetries that can influence movement control and mechanical efficiency. This study protocol aims to establish a standardized multimodal framework for assessing postural alignment, postural control, lower limb mechanical output, ankle dorsiflexion strength, support-limb neuromuscular activation, and contextual training and recovery variables in licensed youth soccer players aged 13 to 17 years. Methods: This prospective observational study will include 75 male youth soccer players recruited from S.C. Fotbal Club Ripensia Timișoara S.A. The primary outcome is the inter-limb asymmetry index derived from unilateral countermovement jump performance. Secondary outcomes include postural alignment, balance, bilateral jump performance, ankle dorsiflexion strength, and support limb electromyographic activity during the instep kick. Participants will complete a clinical evaluation questionnaire, including demographic, training, and recovery variables. Assessments will be conducted using the GaitON system, Kinvent K-Delta force platforms, K-Myo surface electromyography, and K-Pull dynamometry, before and after a regular training session. Biological maturation will be estimated using the Mirwald maturity offset method. Expected Results: The protocol will allow characterization of inter-limb asymmetries across postural, balance, jump, and electromyographic parameters. Conclusions: This protocol aims to provide a practical and standardized model for functional screening in youth soccer players. Full article

This paper develops an accountability-aware fractional control framework for embodied intelligent systems in shared human environments. The approach combines a Caputo fractional-order stabilizing law, an intent-evidence realization with softmax belief reconstruction, and a conditional proxemic safety layer. Sufficient conditions are established for local Mittag-Leffler stability of the augmented error dynamics and forward invariance of the safe set. Numerical results are presented as a theorem-validation benchmark. For the base case with $\alpha =0.9$, the augmented error norm decays from $1.2359$ to $9.90\times {10}^{-3}$ while the safety margin remains strictly positive, and the robustness condition is satisfied with a margin of $1.8641$. An $\alpha$-sweep and a step-size convergence study further show that the fractional order induces a systematic safety–performance trade-off and that the reported behaviors are numerically stable. Additional simulations with four intent classes, bounded observation noise, and Monte Carlo uncertainty stress tests are included to strengthen the numerical evidence beyond the two-intent theorem-validation case. The manuscript also clarifies the quantitative interpretation of the accountability index, the conditional nature of the safety theorem, and an implementable sampled safety-filter realization for concrete robotic platforms. The results support the proposed framework as a mathematically consistent tool for shaping the balance between regulation and proxemic safety. Full article

The rapid expansion of artificial intelligence and cloud computing is straining terrestrial data center infrastructure, motivating exploration of space-based data centers (SBDCs) as a scalable and energy-efficient alternative. While orbital platforms offer unique advantages, including continuous solar energy, radiative cooling, and global coverage, their practical deployment is constrained by unresolved reliability challenges across the mission lifecycle. This study presents a lifecycle-oriented reliability and risk assessment for SBDCs spanning launch, orbital operation, maintenance, and end-of-life phases, using a structured systems-level analysis of failure modes and operational dependencies. This paper focuses on compute-centric SBDC architectures, treating storage solely as a supporting resource. We identify and classify space-environment-specific risks, including launch-induced mechanical stress, radiation-driven degradation, thermal extremes, and single points of failure in power and communication subsystems. By integrating engineering constraints with economic considerations, we develop a unified risk-chain framework that shows how reliability limitations propagate from component design to system cost and operational viability. The analysis reveals a critical trade-off: achieving terrestrial-grade reliability in orbit requires substantial redundancy and radiation hardening, increasing mass and cost and reducing economic feasibility, whereas lower-reliability designs introduce operational and financial risks that challenge sustainability. These findings establish reliability as the central determinant of SBDC viability, providing an applied foundation for fault-tolerant, modular, and lifecycle-aware design strategies essential for transitioning orbital cloud infrastructure from concept to scalable reality. Full article

A TiO₂/hydrazine system was investigated as a proof-of-concept platform for coupling chemical CO₂ capture with light-driven H₂ evolution under UV irradiation. Hydrazine served as the CO₂ capture agent, leading to the formation of carbamate-type intermediates, while TiO₂ acted as the photoresponsive solid. FT-IR, UV-Vis, and mass spectrometry analyses supported carbamate formation after CO₂ uptake and confirmed H₂ generation during irradiation, reaching a maximum of 33.2 μmol under the conditions evaluated. Deuterated experiments showed no detectable HD or D₂, indicating that H₂ evolution predominantly proceeded via hydrazine dehydrogenation rather than direct water splitting. On the basis of the available spectroscopic evidence, a tentative pathway involving carbamate intermediates and nitrogen-containing oxidation products is proposed. However, key control experiments required to confirm a strictly photocatalytic origin of H₂ evolution were not performed in the present exploratory study. Therefore, the observed behavior is more appropriately interpreted as preliminary photoinduced reactivity in a TiO₂/hydrazine/CO₂ system rather than definitive proof of a fully established photocatalytic mechanism. Overall, the results establish a preliminary proof of concept, while the limitations related to control experiments, product identification, quantification, and reproducibility are recognized. Full article

Urban planning increasingly depends on methods capable of capturing citizen perspectives in forms that are both inclusive and analytically useful for decision-making. Conventional participation mechanisms, such as public meetings, paper questionnaires, and online platforms, often suffer from low reach, strong self-selection effects, and weak suitability for structured comparative analysis. This paper presents XRCity, a decision support system that combines extended reality, conversational artificial intelligence, and a planner-side backend to support participatory urban planning in public spaces. The system is centered on Olivia, a life-sized virtual assistant deployed on outdoor interactive screens, and on a backend environment that enables planners to prepare knowledge resources, configure interaction scripts, validate conversational behavior, process transcripts, and analyze elicited opinions. The contribution of the paper is not just the presentation of an XR interface, but the description and validation of a complete decision-support pipeline that connects campaign design, citizen interaction, opinion structuring, and planner-side analytics. The system was validated through real-world deployment in Torres Vedras, Portugal. Across more than 250 interactions and over 740 min of conversation, 191 usable sessions were analyzed, showing an average of 6.7 messages per user and 2.8 min per interaction. Of these sessions, 14.7% produced at least one structured response to an urban planning question, exceeding the project target of 10%. These results indicate the operational feasibility of using public-space conversational XR to elicit analyzable planning input, while a formal validation of the opinion-matching step remains future work. Full article

Polyethylene (PE) microplastics are increasingly recognized as a critical environmental and food-safety concern; however, routine monitoring remains limited by conventional methods that are labor-intensive, time-consuming, and difficult to translate into rapid, on-site screening. Here, we report a machine learning-guided electrochemical fingerprinting platform for rapid PE microplastic detection using a chitosan–PE interfacial film coupled with electrochemical impedance spectroscopy (EIS) and coulometry. The platform generated concentration-dependent electrical fingerprints in artificial ocean water, captured through Bode, Nyquist, and charge–time responses. Quantification was achieved across 1–256 ng/mL with strong linearity (R² = 0.976) and an ultralow LoD of 0.1 ng/mL, demonstrating high analytical sensitivity. Practical applicability was validated through spike–recovery in ocean water (R² = 0.967) and shrimp-derived matrices with matrix-matched normalization, yielding recoveries of 90–105% across low, mid, and high spike levels. Under the tested particle set, PE produced stronger responses than non-target polypropylene (PP) and polystyrene (PS), supporting empirical polymer discrimination. Machine learning classification using impedance-derived features achieved an AUC = 0.98, with 100% correct identification of Low and 95.24% correct identification of High samples. Overall, this electrochemical–ML framework enables rapid, sensitive, and matrix-tolerant PE microplastic screening in environmental water and seafood-related matrices, offering a promising pathway toward portable microplastic monitoring. Full article

Protected horticulture moves fragile pots, plug trays, seedlings, harvested products, and carriers through narrow, humid, and crowded spaces. Transport robots must therefore integrate locomotion, perception, localization, handling, placement, scheduling, and human–robot interaction rather than operate as simple carts. This structured narrative review reorganizes evidence from seedling transplanting, nursery operations, harvest support, manipulation, perception, and autonomous navigation around the complete transport chain: target recognition, pickup, loading, loaded navigation, docking, unloading or placement, payload protection, and workflow feedback. The synthesis covers mobile platforms, payload support, perception and localization, motion control, gentle handling, digital support, and fleet coordination. Three barriers remain: short laboratory tests rarely provide season-long evidence; many prototypes are too specialized for variable workflows; and benchmarks seldom combine motion accuracy, handling reliability, payload quality, and resilience. Progress will require modular platforms, robust sensing, payload-safe control, standardized interfaces, and closer co-design between robotics and horticultural operations. Full article

The growing diversity of wireless standards and complex real-world channel effects render automatic modulation classification (AMC) increasingly challenging for spectrum monitoring and edge intelligence. However, most competitive deep-learning-based AMC networks still require ${10}^5$–${10}^6$ parameters, exceeding the memory available on resource-constrained edge platforms. We propose LGP-Net, a lightweight gated-fusion network that pairs a physics-informed expert branch with a compact temporal encoder built from depthwise separable convolution (DSConv), squeeze-and-excitation (SE) attention, and a single-layer gated recurrent unit (GRU). Specifically, unlike other dual-branch structures that directly concatenate the outputs of both pathways, this work designs a lightweight gating unit that requires no external signal-to-noise ratio (SNR) labels and adaptively reweights the two pathways according to signal-quality degradation. With fewer than 40 K parameters, a peak activation footprint of 26.00 KB and an amortised inference latency of 9.7 $\mathsf{\mu }$s per sample under GPU acceleration, LGP-Net attains 65.00% overall accuracy on RadioML 2016.10B (91.48% at 0 dB) and 62.76% on RadioML 2016.10A, placing it in a competitive accuracy–efficiency regime relative to architectures consuming 5× to 500× more parameters. These characteristics support deployment-oriented feasibility under memory-constrained edge settings and high-throughput spectrum-monitoring pipelines. Full article

Background: Vision is a key sensory system for postural regulation; however, the effects of degraded visual input and complete visual occlusion on static balance are not fully understood. The aim of the present study was to compare postural control during single-leg stance under two reduced-vision conditions (eyes open in darkness vs. complete visual occlusion) in healthy young adults and examine the potential influence of sex and mild visual deficits. Materials and Methods: This within-subject laboratory study included 42 healthy young adults (21 males, 21 females; mean age 20.67 ± 0.48 years). Participants performed three valid 20 s single-leg stance trials on a force platform under two visual conditions: eyes open in darkness and complete visual occlusion using an opaque mask. The order of conditions was randomized and counterbalanced, and the mean value of the three valid trials under each condition was used for analysis. Postural sway outcome variables included CoP Area, Oscillation Width, Oscillation Height, Total Displacement, and Mean Velocity. A two-way mixed-design ANOVA examined the effects of visual condition and sex. Additional mixed ANCOVA analyses were performed using body weight as a covariate to verify whether the sex-related findings remained after adjustment for body weight. Exploratory subgroup analyses based on mild visual deficits were performed using independent-samples t-tests. Results: No significant overall main effect of visual condition was observed for any postural sway variable (all p > 0.05). However, a significant condition × sex interaction was found for CoP Area (F(1,40) = 9.910, p = 0.003, η²p = 0.199), indicating different response patterns between males and females across conditions. Significant main effects of sex were also found for Total Displacement (F(1,40) = 9.212, p = 0.004, η²p = 0.187) and Mean Velocity (F(1,40) = 9.090, p = 0.004, η²p = 0.185), with males showing higher values overall. The sex-related findings for CoP Area, Total Displacement, and Mean Velocity remained significant after adjustment for body weight. No significant sex effects were found for Oscillation Width or Oscillation Height, and no significant differences were observed between participants with and without mild visual deficits in either condition (all p > 0.05). Conclusions: Altered visual input did not produce a uniform overall effect on postural sway during single-leg stance in healthy young adults. Instead, the findings indicate a more differentiated pattern, with a sex-specific response for CoP Area and overall sex-related differences in Total Displacement and Mean Velocity that were not explained by body weight. Mild visual deficits were not associated with significant balance alterations under the present experimental conditions. These findings support a more nuanced interpretation of postural regulation under reduced visual input and highlight the importance of considering individual characteristics, particularly sex, in balance assessment. Full article