Search Results (50,780)

Unsupervised video anomaly detection (VAD) methods learn from normal data to identify anomalies by capturing pattern deviations. However, they often struggle to model multi-scale features and distinguish between normal and abnormal instances. To address these limitations, we propose a Multi-scale U-shaped Adaptive Clustering Learning (MS-UACL) framework. Built on the U-Net architecture, we redesign it as a 3D-encoder/2D-decoder autoencoder. In the encoder, we introduce a Dual-scale Feature Cascading Module (IDCN), which adopts a pseudo-branch fusion mechanism to systematically model multi-scale spatiotemporal features, thereby enhancing the model’s representational capability. To further enhance the distinction between normal and anomalous patterns, we propose an MLP-based Adaptive Clustering Algorithm (MLP-ACA). Specifically, MLP-ACA employs an initial mapping mechanism to align cluster centers with the underlying normal data distribution, facilitating more accurate feature reconstruction. Additionally, we introduce an adaptive clustering update strategy that optimizes cluster centers by tuning solely the parameters of the MLP. This enables the cluster centers to autonomously converge toward optimal feature representations, thereby accelerating clustering convergence and enhancing pattern separability. Extensive experiments on three benchmark datasets demonstrate that the proposed MS-UACL framework outperforms most existing methods on small- and medium-scale datasets. Full article

While artemisinin and its derivatives demonstrate broad antitumor potential, the stereochemical influence on the bioactivity of higher-order artemisinin assemblies remains inadequately characterized. Herein, we report the synthesis, chromatographic separation, and structural elucidation of four stereoisomeric artemisinin trimers, followed by systematic evaluation of their antitumor efficacy against MCF-7 and MDA-MB-231 breast cancer cell lines. All trimers exhibited potent cytotoxicity against MCF-7 cells (IC₅₀ < 0.09 μM), with trimer 6a (β, β, β) demonstrating robust antitumor activity in both in vitro and in vivo xenograft models. Remarkably, pronounced stereochemistry-dependent activity emerged against MDA-MB-231 cells: 6a displayed approximately 100-fold greater potency than 6b (β, β, α) and 6.6-fold superiority over gemcitabine. Mechanistic investigations revealed that 6a downregulates Cyclin D1, CDK4, and CDK6 expression, thereby inducing G0/G1 phase cell cycle arrest. These findings underscore the pivotal role of stereochemical configuration in modulating artemisinin trimer bioactivity and provide rational guidance for structure-based design of artemisinin-derived anticancer therapeutics. Full article

Background: Diabetic foot ulcer (DFU) is a major complication of type 2 diabetes (T2D), frequently resulting in disability, lower-limb amputation, and substantial healthcare burden. Early identification of patients at high risk of progressing to severe DFU is essential for timely intervention, yet evidence on associated risk factors remains limited in Bangladesh. This study aims to identify demographic, clinical, and behavioral predictors of severe DFU to support early management strategies. Methods: A cross-sectional study was conducted among 159 DFU patients attending the Rajshahi Diabetic Association General Hospital, Bangladesh. Data on demographic characteristics, clinical variables, and behavioral factors were obtained through structured questionnaires and standardized examinations. Severe DFU was defined as Wagner grades 3–5, while grades 0–2 were considered non-severe. Firth’s penalized logistic regression was used to identify determinants of severe DFU. Model performance was assessed using ROC analysis, calibration belt analysis, and decision curve analysis (DCA). Results: Among the 159 participants, 101 (63.5%) presented with severe DFU. Patients with severe DFU had significantly higher BMI (26.1 vs. 23.7 kg/m²), treatment costs (50,000 vs. 20,000 BDT), and were older (57 vs. 54 years). Severe DFU was also associated with higher prevalence of peripheral arterial disease (PAD) (29.7% vs. 3.4%), prior amputation (31.7% vs. 3.4%), peripheral neuropathy (PN) (86.1% vs. 58.6%), and poor glycemic control (71.3% vs. 30.7%) (all p < 0.05). Firth’s regression identified older age (aOR 1.08), poor glycemic control (aOR 3.90), PN (aOR 3.41), PAD (aOR 7.54), and previous amputation (aOR 13.67) as independent predictors of severe DFU. Conclusions: Older age, uncontrolled glycemia, PN, PAD, and prior amputation were significantly associated with severe stages of DFU. Early detection and targeted management of these factors are critical to reducing complications and lowering the healthcare burden. Full article

Background/Objectives: Blood glucose prediction (BGP) for individuals with type 1 diabetes (T1D) is a clinically essential yet highly challenging task in time series forecasting (TSF) and an important problem in personalised medicine. Accurate bespoke BGP is crucial for individualised T1D management, reducing complications, and supporting patient-specific glycaemic risk mitigation. However, the pronounced volatility of glycaemic fluctuations in T1D, combined with the need for mathematical rigor and clinical relevance, hampers reliable prediction. This complexity underscores the demand to explore and enhance more advanced techniques. While adversarial learning is adept at modelling intricate data variability, its potential for BGP remains largely untapped. Methods: This work presents a novel approach for BGP by addressing a key limitation in conventional adversarial learning when applied to this task. Typically, these methods optimise prediction accuracy within a set horizon by minimising adversarial loss. This focus overlooks how predictions align with longer-term patterns, which are critical for clinical relevance in BGP, thereby yielding suboptimal results. To overcome this limitation, we introduce collaborative augmented adversarial learning, designed to improve the model’s temporal awareness. Incorporating collaborative interaction optimisation, this approach enables the model to reflect extended time dependencies beyond the immediate horizon, thereby improving both the clinical reliability of predictions and overall predictive performance. We develop and evaluate four learning systems for BGP: independent learning, adversarial learning, collaborative learning, and adversarial collaborative learning. The proposed systems were evaluated for two clinically relevant prediction horizons, namely 30 min and 60 min ahead. Results: The interdependent collaboratively augmented learning frameworks, validated using the well-established Ohio T1D datasets, demonstrate statistically significant superior performance in both clinical and mathematical evaluations. Conclusions: Beyond advancing BGP accuracy and clinical reliability, the proposed approach supports personalised medicine by improving subject-specific glucose forecasting from CGM data, with potential relevance for more individualised diabetes monitoring and decision support. The proposed approach also opens new avenues for advancements in other complex TSF domains, as outlined in our future work. Full article

Highly deviated wells commonly exhibit large errors in horizon calibration because the logging path follows an inclined borehole trajectory, whereas post-stack seismic processing effectively treats wave propagation as vertical. This mismatch has received limited attention. Here, we performed horizon calibration and velocity-model building for highly deviated wells drilled in the Mahu Sag, Junggar Basin, and obtained three key findings. First, the assumed vertical travel path in post-stack data is the primary cause of the initial mis-tie for highly deviated wells. Second, calibration in the deviated interval requires a strategy distinct from that of vertical wells and may involve substantial stretching or squeezing of the original logs to achieve a consistent time-depth relationship. Third, the map-view projection of a highly deviated well is essentially linear; relative to vertical wells, it provides denser in situ velocity constraints and, with pseudo-well control, supplies 2D velocity information along the well-trajectory plane, thereby improving velocity-field modeling. Validation against drilling data showed that this workflow improved well ties and refined the velocity model, providing practical guidance for geological well planning and reducing drilling risk. Full article

Radiation pneumonitis (RP), one of the most common and severe complications in locally advanced non-small cell lung cancer (LA-NSCLC) patients following thoracic radiotherapy, presents significant challenges in prediction due to the complexity of clinical risk factors, incomplete multimodal data, and unavailable slice-level annotations in pre-radiotherapy CT images. To address these challenges, we propose a multimodal fusion framework based on Dual-Layer Attention-Based Adaptive Bag Embedding Multiple-Instance Learning (DAAE-MIL) for accurate RP prediction. This study retrospectively collected data from 995 LA-NSCLC patients who received thoracic radiotherapy between November 2018 and April 2025. After screening, Subject datasets (n = 670) were allocated for training (n = 535), and the remaining samples (n = 135) were reserved for an independent test set. The proposed framework first extracts pre-radiotherapy CT image features using a fine-tuned C3D network, followed by the DAAE-MIL module to screen critical instances and generate bag-level representations, thereby enhancing the accuracy of deep feature extraction. Subsequently, clinical data, radiomics features, and CT-derived deep features are integrated to construct a multimodal prediction model. The proposed model demonstrates promising RP prediction performance across multiple evaluation metrics, outperforming both state-of-the-art and unimodal RP prediction approaches. On the test set, it achieves an accuracy (ACC) of 0.93 and an area under the curve (AUC) of 0.97. This study validates that the proposed method effectively addresses the limitations of single-modal prediction and the unknown key features in pre-radiotherapy CT images while providing significant clinical value for RP risk assessment. Full article

This study examines whether improvements in quality of life (QOL) require increased household energy consumption (HEC) among low-income households in India by using a comparative analysis of Delhi and Kharagpur. A survey of 879 households (Delhi: n = 539; Kharagpur: n = 340) was conducted, and structural equation modelling (SEM) was applied to analyse the relationships between HEC and key QOL constructs, including residential satisfaction, economic satisfaction, and place attachment. The results indicate that QOL is primarily influenced by socio-psychological and housing-related factors rather than energy consumption alone. In Delhi, QOL is significantly associated with place attachment (β = 0.49, p < 0.001), economic satisfaction (β = 0.33, p < 0.001), and residential satisfaction (β = 0.13, p < 0.05), with the model explaining 42% of the variance (R² = 0.42; RMSEA = 0.048; CFI = 0.94). In Kharagpur, economic (β = 0.61) and residential satisfaction (β = 0.52, p < 0.001) show comparatively stronger effects. Although HEC is strongly associated with appliance ownership and cooling-related practices, it does not show a corresponding relationship with perceived QOL. Descriptive results further show higher well-being in Delhi (M = 3.85 vs. 3.42; d = 0.54). Overall, the findings suggest that differences in QOL between the two cities are more closely linked to socio-economic and residential conditions than to variations in household energy use, highlighting the importance of contextual factors in shaping well-being outcomes. Full article

Short-term load forecasting plays a pivotal role in modern power system operations yet it remains challenging due to the complex spatiotemporal dependencies in load data. This paper proposes a dual-head attention residual network (DARNet) that significantly advances STLF through three key innovations: (1) a hybrid encoder combining 1D-CNN and GRU architectures to simultaneously capture the local load patterns and long-term temporal dependencies, achieving a 28% better locality awareness than that of conventional approaches; (2) a novel dual-head attention mechanism that dynamically models both the inter-temporal relationships and cross-variable dependencies, reducing the feature engineering requirements; and (3) an autocorrelation-adjusted recursive forecasting framework that cuts the multi-step prediction error accumulation by 33% compared to that with standard seq2seq models. Extensive experiments on real-world datasets from three Chinese cities demonstrate DARNet’s superior performance, outperforming six state-of-the-art benchmarks by 21–35% across all of the evaluation metrics (MAPE, SMAPE, MAE, and RRSE) while maintaining robust generalization across different geographical regions and prediction horizons. Full article

Achieving high-level self-reliance in science and technology requires a science and technology finance ecosystem that is aligned with the needs of technological innovation. To overcome bottlenecks in core technologies, firms must accelerate R&D, strengthen their core competitiveness, and pursue innovation-led, quality-oriented development. Using provincial-level data for 2013–2023, this paper constructs an index system for China’s science and technology finance ecosystem from four dimensions: science and technology financial services, science and technology capital markets, science and technology financial organizations, and government guidance for science and technology. We then measure the development level of this ecosystem and employ a panel data model to examine its impact on innovation in Specialized and Sophisticated SMEs. The results show that a more developed science and technology finance ecosystem significantly promotes innovation in these firms, with a stronger effect on substantive innovation than on strategic innovation. These findings remain robust across a series of robustness checks. Further analysis reveals significant heterogeneity across regions and levels of government intervention: the positive effect is stronger in eastern China and in regions with weaker government intervention. Mechanism tests indicate that the science and technology finance ecosystem promotes innovation by facilitating the accumulation of R&D capital and the agglomeration of scientific and technological talent. This study enriches the literature on science and technology finance ecosystems and SME innovation, and provides policy-relevant evidence for ecosystem development and the cultivation of Specialized and Sophisticated SMEs. Full article

Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these limitations, this paper proposes a circular polarization-based quantum encoding framework for image transmission over error-prone channels. In the proposed approach, source images are compressed and source-encoded using standard image coding formats, including the joint photographic experts group (JPEG) standard and the high-efficiency image file format (HEIF), and converted into classical bitstreams. The resulting bitstreams are protected using channel coding and mapped onto quantum states via circular polarization representations, where left- and right-hand circularly polarized states encode binary information. The encoded quantum states are transmitted over noisy quantum channels to model channel impairments. At the receiver, appropriate quantum decoding and channel decoding operations are applied to recover the classical bitstream, followed by source decoding to reconstruct the image. The performance of the proposed framework is evaluated using image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Simulation results demonstrate that the proposed circular polarization-based encoding scheme outperforms existing quantum image encoding techniques, achieving channel SNR gains of 4 dB over state-of-the-art Hadamard-based encoding and 3 dB over frequency-domain quantum encoding methods under severe noise conditions. These results indicate that circular polarization-based quantum encoding provides improved noise robustness and reconstruction fidelity for practical quantum image transmission systems. Full article

This study investigates the impact response and energy absorption performance of additively manufactured PLA–geopolymer hybrid composites for potential application in road safety barriers. Hybrid Charpy specimens were fabricated with three different infill densities (20%, 60%, and 100%), combining a 3D-printed PLA outer shell with a geopolymer core. Charpy impact tests were conducted in accordance with ISO 179-1 and ASTM D6110, and the absorbed energy, specific energy absorption, and mass efficiency were determined experimentally. A phase-based analytical model was also used to estimate elastic energy contributions, while fracture surfaces were examined to identify infill-dependent damage mechanisms. To extend the material-level findings to an engineering-scale application, the observed trends were transferred to a New Jersey-type road safety barrier model and evaluated using ANSYS Explicit Dynamics. The results showed that infill density strongly affects fracture behavior and energy dissipation performance, with 60% infill providing the most balanced response in terms of energy absorption and mass/material efficiency. The originality of the present study lies in going beyond a material-scale investigation of the impact behavior of additively manufactured PLA–geopolymer hybrid structures by integrally correlating the experimental Charpy results with a theoretical energy-based framework, fracture-surface observations, and explicit dynamic finite element analysis of a New Jersey-type road safety barrier model. Full article

Chronic d-galactose (d-gal) treatment is a model to induce accelerated aging-like phenotypes in rodents. However, the sex differences in behavioral and musculoskeletal manifestations of this model are not well understood. Heme oxygenase-1 (HO-1) is a cytoprotective protein that may have anti-aging properties. The goal of this study was to better understand the sex differences in the behavioral and musculoskeletal effects of chronic d-gal treatment in C57BL/6J mice, as well as the role of HO-1 induction or inhibition. Eight-week-old male and female mice received daily saline or d-gal injections (500 mg/kg, s.c.) for 12 weeks. After this time, mice in the d-gal group were randomized into three groups (n = 6/group/sex): d-gal, d-gal + cobalt protoporphyrin (CoPP) (5 mg/kg, s.c. weekly), and d-gal + zinc deutroporphyrin bisglycol (ZnBG) (42 mg/kg, i.p. triweekly) for a period of 4 weeks. Open-field, novel-object recognition, Barnes maze, grip strength, micro-computed tomography (µ-CT), histology, and protein analysis were performed. Chronic d-gal treatment resulted in a sexual dimorphic response, with female mice being more prone to develop deficits in both short- and long-term spatial memory as well as in non-spatial memory. Male mice exhibited deficits only in long-term spatial memory when treated chronically with d-gal. Inhibition of HO-1 was protective in both females and males. Chronic d-gal treatment did not accelerate the development of osteoporosis or sarcopenia in either males or females. Our results demonstrate a sexual dimorphic response to the chronic effects of d-gal treatment on aging, with greater effects in females than in males, which is dependent on HO-1. Full article

Background/Objectives: There is widespread agreement that age is a significant predictor of impaired response to nutritional support. This is generally attributed to anabolic resistance, with impaired absorption considered irrelevant/non-existent. However, animal models demonstrate age-related structural changes in the intestinal mucosa that may reduce absorptive capacity. We aimed to evaluate potential histological changes in the duodenal mucosa associated with aging. Methods: We conducted a single-center observational cross-sectional study. Ambulatory younger (18–45 years) and older (≥70 years) adults referred for upper endoscopy were included and underwent duodenal biopsies. Those biopsies were analyzed and compared for histological/histomorphometric changes, including villus length. Clinical and laboratory data were also recorded. Results: One hundred patients were included (46 men/54 women), 50 aged 18–45 years and 50 aged ≥70 years. There were no duodenal endoscopic changes. The median villus length was 0.35 mm (IQR 0.32–0.41 mm) in older people, lower than in younger adults (0.57 mm; IQR 0.47–0.68 mm) (p < 0.001). In a multivariable regression model including age, sex, and Charlson comorbidity index, age remained inversely associated with villus length (p < 0.001). Older participants also exhibited lower hemoglobin, iron, folate, vitamin B12, albumin and vitamin D levels, despite normal inflammatory markers. Conclusions: Aging is associated with histological changes in the intestinal mucosa, including villus shortening. These findings support the concept of mucosal aging as a distinct biological process. Villus shortening may reflect reduced absorptive surface area and could contribute to age-related nutritional vulnerability, although its functional implications remain to be determined. Full article

Background/Objectives: Reconstruction of orbital floor fractures remains surgically challenging due to limited intraoperative visibility and complex anatomy. Inaccurate implant placement often leads to persistent complications and the need for a revision surgery. This study evaluated the clinical accuracy and re-operation rates of a preoperative 3D-printed model-assisted technique compared to the conventional intraoperative free-hand mesh bending method. Methods: A comparative ambispective study was conducted on 74 patients with isolated orbital floor fractures. The control group (n = 34, retrospective) underwent reconstruction using intraoperatively formed titanium meshes. In the study group (n = 40, prospective), patient-specific 3D-printed models, created by mirroring the healthy contralateral orbit, were used for preoperative mesh adaptation. Primary outcomes included the rate of revision surgery due to implant malposition, changes in orbital volume, and postoperative diplopia. Results: The 3D model group demonstrated a significantly lower rate of revision surgery compared to the control group. In the retrospective group, 5 patients (15%) required reoperation due to implant malposition, whereas no patients (0%) in the prospective 3D group required secondary intervention (p = 0.017). While both techniques effectively restored orbital volume, the 3D group showed greater volumetric precision with less variance. The mean volume difference in the affected orbit was 3078 ± 2204 mm³ in the control group, compared to 2390 ± 1893 mm³ in the study 3D group. At the 6-month follow-up, persistent diplopia was observed in 12% of the control group compared to only 3% in the study group. Conclusions: The use of in-house 3D-printed models for preoperative mesh forming significantly enhances surgical precision and eliminates the need for revision surgery due to implant malposition. This workflow offers a cost-effective, predictable, and accessible alternative to expensive patient-specific implants (PSIs) or intraoperative navigation systems, improving patient safety and long-term clinical outcomes. Full article

Background: Effective anti-aging requires dual strategies to stimulate regeneration and counteract damage. While the combination of hydroxypinacolone retinoate (HPR) and carnosine (CA) holds great promise, their effectiveness is hampered by instability and poor skin penetration. Methods: To overcome these challenges, this study developed HPR and CA co-encapsulated nanoliposomes (HC-NLPs) via high-pressure homogenization as an advanced epidermal/dermal delivery system. Results: HC-NLPs markedly improved skin retention of HPR (58.97%) and CA (111.36%) compared to the free combination (Free-HC). In cellular studies, HC-NLPs displayed excellent biocompatibility and demonstrated a 4.7-fold higher cellular uptake. This led to enhanced proliferative (EdU positive rate increased by 78.32%) and migratory (wound closure improved by 31.5%) capacities. Moreover, HC-NLPs effectively reinforced multiple skin-protective processes associated with aging, including enhanced resistance to oxidative and glycation-induced damage, suppressed inflammatory responses, and strengthened cellular barrier integrity. In 3D skin models, HC-NLPs promoted collagen deposition and improved tissue morphology compared to Free-HC. Their superior in vivo antioxidant and anti-aging effects were further validated in Zebrafish assays. HC-NLPs effectively co-deliver HPR and CA, markedly improving their stability, skin penetration, and cellular internalization. Conclusions: The formulation demonstrates comprehensive pro-regenerative, anti-inflammatory, antioxidative, and anti-glycation effects, representing a promising nano-delivery strategy for advanced anti-aging skincare. Full article