Search Results (5,082)

Health, social, and ethical considerations highlight the need for new male contraceptives. Pimozide is an FDA approved drug known to block T-type calcium channels and which shares structural similarities with mibefradil, a proven antagonist of the CatSper channel. In this study, we examined the effect of pimozide on CatSper, a key target for non-hormonal male contraception. Molecular docking and molecular dynamics simulations were carried out to assess how pimozide binds within the channel pore, and binding energies were estimated using MM-GBSA. To determine its impact on sperm function, we evaluated hyperactivation, the acrosome reaction, and CatSper activity. Our computational analyses indicate that pimozide functions as a pore blocker of the CatSper channel. Experimental findings further support this, showing that pimozide inhibits CatSper activity, and impairs hyperactivation and the acrosome reaction in human spermatozoa. Overall, these results identify pimozide as a novel CatSper antagonist and propose a binding mode, offering a basis for the rational design of reversible, non-hormonal male contraceptives that target the CatSper channel. Full article

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

Geopolitical risks—including export controls, entity listings, and end-use restrictions—have become a major source of disruptions in semiconductor supply chains. The impact of such disruptions depends not only on the policy trigger itself but also on the vulnerability of cross-regional partnerships between supply chain partners. Specifically, under the same policy regime, firms with weak partnerships suffer far greater disruption than those with strong partnerships. Apart from risk propagation, this vulnerability also propagates through the supply chain: when an upstream supply channel has weak partnerships, its downstream stages also become more exposed to disruptions. We call this phenomenon vulnerability propagation. Existing Bayesian Network (BN) frameworks portray risk propagation through fixed parameters that do not reflect partnership vulnerability and cannot capture vulnerability propagation. To fill this gap, we propose a Dependency-Robust Bayesian Network (DeRBN) that conditions risk propagation parameters on the partnership vulnerability. A robust worst-case oriented evaluation method is developed to assess the disruption risk under data scarcity. Computational experiments on a typical semiconductor supply chain network show that (i) moving from all-strong to all-weak partnerships increases the worst-case risk by approximately 24%, (ii) the dependency-induced risk amplification is unevenly distributed across supply channels, with the most influential channel contributing approximately 2.2 times the marginal risk of the least influential one, and (iii) the relative ranking of vulnerability profiles remains perfectly stable under varying levels of data uncertainty. These results suggest that DeRBN has the potential to serve not only as a risk assessment tool but also as a diagnostic instrument for identifying and prioritizing the most vulnerable supply channels for targeted risk mitigation. Full article

Using panel data for 135 Chinese prefecture-level cities from 2007 to 2023, this study investigates the impact of new digital infrastructure on green finance development. The new digital infrastructure indicator is constructed based on the proportion of relevant keywords appearing in government work reports, while the green finance index is reconstructed using the entropy-weighting method across seven dimensions. The estimation results indicate that new digital infrastructure exerts a significant positive effect on green finance development. This conclusion remains robust after a series of robustness checks, including alternative variable measurements, winsorization treatment, and instrumental-variable estimation. Mechanism analysis reveals that industrial structure upgrading, particularly the advancement of industrial structure, serves as an important transmission channel. Further heterogeneity analysis shows that the promoting effect is more pronounced in cities with larger economic scale, those located outside major urban agglomerations, and cities with higher levels of financial resource aggregation. These findings provide empirical evidence for the role of digital infrastructure in fostering green finance and facilitating sustainable regional development. Full article

Rivers have always been essential to humankind. They are used for many purposes and, as a result, have been heavily modified. Human impacts, many of them still poorly understood, interfere with river ecosystems, making them vulnerable to disturbances. Amongst these, mega events along riverbanks are listed. We studied the effects of the “FM4 Frequency Festival,” which attracted more than 200,000 visitors, on microalgae in the channelized section of the River Traisen in St. Pölten, the capital of Lower Austria. During the festival, phosphorus, dissolved organic carbon, and chloride increased significantly during the whole study period compared with before and after. Although the overall epilithic biomass remained unchanged during the festival period, the phytobenthos community experienced an increase in taxonomic richness downstream of the festival area. Both the Shannon diversity (mean ± SD = 2.89 ± 0.34) and Pielou’s evenness (mean ± SD = 0.73 ± 0.08) did not differ significantly between the sampling dates before, during, and after the festival. We found a shift towards Achnanthidium minutissimum as the dominant species during the festival. Diatoma ehrenbergii, which is sensitive to nutrient enrichment and organic pollution, disappeared during the event. Overall, the biofilm shifted towards a community dominated by heterotrophs during the festival, likely due to high organic loading. Pelagic microalgae experienced a rise in the total taxa number during the festival, which was partly caused by resuspension of phytobenthos. Our results reflect significant impacts from visitors to the Traisen ecosystem. Not only short-term changes in the hydrochemical environment but also mechanical disturbances of the phytobenthos caused by visitors were demonstrated. We suggest continuous monitoring to verify that such events will not have long-term impacts on the system. Full article

Emotion recognition plays a crucial role in human–computer interaction, health monitoring, and affective computing by analysing physiological signals. Despite recent advancements, current research still faces challenges, including the lack of effective fusion strategies for diverse physiological modalities, difficulties in handling high-dimensional feature representations, and limited use of efficient temporal modelling techniques to capture complex emotional patterns. This study proposes a deep learning-based approach that fuses multiple physiological modalities, including Electroencephalography (EEG), Electrooculography (EOG), Electromyography (EMG), Galvanic Skin Response (GSR), Respiratory Rate (RR), Skin Temperature (SKT), and Photoplethysmography (PPG), to improve emotion recognition. Arousal and valence ratings were binarized into two classes (low/high) using a threshold of 4.5, formulating a binary classification problem. In addition to utilising Bidirectional Long Short-Term Memory (Bi-LSTM), the study employs Temporal Convolutional Networks (TCN), a widely used approach for time-series analysis, to efficiently capture temporal dependencies. The proposed model optimises feature selection through channel-wise strategies, incorporates advanced learning rate scheduling, and reduces computational overhead. Furthermore, window-wise, block-wise, and trial-wise evaluation protocols were investigated to assess the impact of temporal information leakage on emotion recognition performance. Using the DEAP dataset for validation, the proposed TCN-based approach achieved classification accuracies of 88.42% for valence and 86.35% for arousal under an overlapping block-wise evaluation protocol, demonstrating improved performance in binary emotion recognition and highlighting the importance of leakage-aware model assessment. Full article

Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework, where structured pruning and mixed-precision quantization-aware training (QAT) are jointly optimized with TensorRT–DeepStream for efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured pruning strategy that applies asymmetric channel pruning to convolutional and feature-fusion modules while compressing the Swin Transformer prediction heads and bottleneck stacks, thereby reducing parameters and computation with limited impact on multi-scale representation capability. Second, we develop a hardware-aware mixed-precision QAT scheme that maps computation-intensive backbone layers to INT8 while preserving the Transformer-related modules in FP16, improving inference efficiency while mitigating the accuracy loss caused by uniform low-bit quantization. Third, we compile the optimized network with TensorRT and integrate the resulting inference engine into a DeepStream-based asynchronous video pipeline on the edge platform, enabling end-to-end acceleration by reducing decoding, preprocessing, and memory-transfer overheads. Experimental results on the VisDrone2019-DET dataset and the NVIDIA Jetson Orin NX demonstrate that DUST-YOLO achieves 43.7% mAP@0.5 accuracy with an end-to-end latency of 36.3 ms and a throughput of 27.5 FPS. Compared with the state of the art, DUST-YOLO reduces end-to-end latency by 56.9% and improves end-to-end video throughput by $2.31\times$. Full article

Transient receptor potential vanilloid 5 (TRPV5) is a calcium- and pH-sensitive ion channel. It plays a role in tumor biology and cellular calcium homeostasis. Due to the inverse pH gradient in solid tumors (extracellular acidosis and increased intracellular pH), TRPV5 is interesting as a signaling molecule in tumors, as the altered pH in the tumor microenvironment (TME) impacts tumor growth and metastasis. This is the first study to analyze the expression of TRPV5 in common skin cancers, i.e., basal cell carcinomas (BCC), squamous cell carcinomas (SCC), malignant melanomas (MM) and melanocytic nevi (MCN). The results showed a significantly lower expression of TRPV5 in BCC than in all other tumor entities analyzed. While less than half of the BCC were positive for TRPV5, SCC, MM, and MCN exhibited a high level of positive staining results. These results suggest that TRPV5 may especially help as a novel marker in the differentiation of SCC from BCC. The low expression of TRPV5 in BCC, a rarely metastatic tumor, may also point to a role of TRPV5 in the progression of epithelial skin tumors. Further functional studies, however, are needed to clarify the exact role of TRPV5 in skin tumors. Full article

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios. Full article

Coastal reclamation reshapes tidal hydrodynamics in semi-enclosed bays by removing intertidal storage, modifying channel conveyance, and redistributing tidal exchange among connected sub-regions. This study quantifies the multi-decadal cumulative impacts of reclamation on tidal currents and tidal prism in Yueqing Bay, China, using shoreline and bathymetric reconstructions for 1978, 2002, 2013, and 2020; hydrological observations; and a two-dimensional MIKE21 FM tidal hydrodynamic model. Characteristic cross-sections were used to estimate bay-wide and sub-regional tidal prisms, and representative stations were used to diagnose current-speed responses. The bay-wide tidal prism decreased from 15.235 × 10⁸ m³ in 1978 to 12.316 × 10⁸ m³ in 2020, corresponding to a reduction of 2.919 × 10⁸ m³ (19.16%). The strongest loss occurred during 1978–2002, when large-scale reclamation and closure of the Xuanmen Channel removed tidal storage and redirected flow into the remaining main-channel system. Although reclamation intensity weakened after 2013, mean current speed still changed by −0.050 to 0.033 m/s and sub-regional tidal-prism shares continued to adjust, indicating delayed hydrodynamic reorganization rather than immediate stabilization. These results show that reclamation impacts cannot be explained by reclaimed area alone; they depend on project timing, spatial layout, and the connectivity with key tidal pathways. The findings support staged assessment and pathway-sensitive shoreline management in reclaimed semi-enclosed bays. Full article

The 2008 financial crisis pushed policymakers around the world to rethink how banks could manage risk, leading to the implementation of stricter regulations, including capital buffers and bail-in mechanisms, aimed at making the financial system more resilient. This study examines how three key regulations under Basel III, namely, the Countercyclical Capital Buffer (CCyB), the Capital Conservation Buffer (CCB), and the Capital Surcharge (CS), shape lending patterns in Indonesian banks. The effectiveness of the bail-in policy in helping banks strengthen their capital base is also examined. This study uses difference-in-differences analysis on panel data from 97 banks between 2010 and 2024 to examine the impact of stricter capital regulations on banks’ ability to channel credit to the public and business sectors. Basel III aims to strengthen the resilience of banks; however, this policy could impact credit access and banking stability in Indonesia. This study found a positive impact on LDR of large banks after the treatment, which indicates the banks’ efforts to use the funds collected through credit distribution. This study empirically examines the impact of capital buffer regulation and the bail-in instrument in Indonesia as an emerging-market country with a segmented banking sector and banks’ classification by ownership and core capital value. Full article

Daily hydropower regulation and mainstem backwater generate complex hydrodynamic conditions in the Min River estuary, posing significant challenges to navigation safety. To analyze the impact of mainstem backwater on tributary navigation safety, this study focuses on the lower Min River reach affected by backwater from the Jinsha River. A depth-averaged 2D hydrodynamic model is established, and a water level difference parameter is used to construct the stage–discharge relationship at the estuary based on long-term measured water level and discharge data. Indicators including backwater distance, water surface slope, hydrodynamic axis migration, flow velocity, and cross-flow are used to delineate navigation risk zones. The results indicate the following: (1) The backwater intensity and extent are primarily governed by the mainstem and tributary discharges and by the distance from the estuary. High discharge and water levels produce significant backwater effects and reduced flow velocity. Empirical formulas for backwater length under various discharge conditions are established to support navigation decision-making, with RMSE values ranging from 0.42 km to 0.92 km. (2) Variations in estuarine water level induce oscillations in the hydrodynamic axis. When the upstream discharge is 900 m³/s and the estuarine water level is 258.4 m, the maximum oscillation amplitude reaches 20.33 m. (3) During periods of medium and low water, the reach exhibits significant navigation-obstructing behavior, with high-risk zones concentrated in Tongluowan, Yangjiaoshi, and other shoals 5–8 km being found from the estuary. (4) Under the design discharge condition, the minimum estuarine water level required to ensure adequate channel depth, appropriate flow velocity, and manageable ship resistance for safe navigation is 267.96 m. This study provides a scientific basis for navigation safety and channel regulation in the Min River estuary and similar reaches affected by mainstem backwater, thereby supporting sustainable waterborne transport. Full article

The circular economy (CE) is increasingly recognized as a strategic approach that enables firms to address environmental challenges while enhancing competitiveness and long-term value creation. However, evidence regarding its impact on business performance remains fragmented across sectors, performance dimensions, and organizational contexts. This study presents a systematic literature review conducted in accordance with the PRISMA 2020 guidelines to examine how CE practices influence business performance. The review synthesizes evidence from 79 peer-reviewed publications published between 2015 and 2025. The findings identify five major channels through which CE practices affect business performance: (1) economic, environmental, and social performance, (2) operational and supply chain performance, (3) competitive advantage and strategic positioning, (4) financial and environmental performance, and (5) barriers and performance in SMEs. Across these dimensions, CE practices are frequently associated with improved resource efficiency, cost reduction, innovation capacity, supply chain resilience, and enhanced environmental outcomes, including waste reduction and lower emissions. The review suggests that the performance effects of CE are contingent upon contextual factors such as firm size, ownership structure, industry characteristics, regulatory environment, and digital capabilities. While large firms often benefit from greater resources and organizational capacity, SMEs face significant barriers related to finance, technology, and governance, although these can be mitigated through collaboration networks and digitalization. The study contributes to the Strategic Environmental Management literature by indicating that CE practices may function not only as environmental initiatives but also as strategic capabilities that support competitiveness, resilience, and sustainability transitions. The findings provide implications for managers seeking to integrate circularity into business strategy and for policymakers designing institutional conditions that enable circular business transformation. Full article

This paper develops a dynamic multidimensional ecosystem service value index for 280 prefecture-level cities in China from 2000 to 2023. The index is constructed by integrating remote sensing, GIS, and ecological–economic indicators, with machine learning used as a data-driven tool to aggregate multidimensional ecological information. Building on this measurement framework, the paper applies a staggered Difference-in-Differences (DID) model to evaluate the impact of the ecological conservation redline policy on regional ecosystem service value. The results show that the policy significantly increases urban ecosystem service value and that the effect is cumulative over time. Mechanism analysis suggests that the policy mainly works through three channels: ecological benefit improvement, ecological spatial reconstruction, and community public participation. Heterogeneity analysis further shows that the effect is stronger in early pilot cities and in high-ecological-function zones. In addition, policy coordination and local governance capacity significantly strengthen policy effectiveness. By combining multidimensional ecosystem service measurement with causal policy evaluation, this study extends existing research on ecological conservation redline and provides empirical evidence for improving land spatial governance and ecological protection policy design in China. Full article

Against the backdrop of escalating global climate risks, reconciling economic expansion with ecological sustainability has emerged as a core challenge for urban sustainable development worldwide. This study leverages China’s Climate-Adaptive City Pilot Policy as a quasi-natural experiment and employs staggered difference-in-differences (DID) estimation on panel data covering 280 Chinese cities from 2006 to 2024 to evaluate the policy’s causal effect on urban green total factor productivity (GTFP). The empirical results yield three key findings. First, climate-adaptive urban construction delivers a significant improvement in GTFP, with a pronounced time-lagged effect: the policy exerts no statistically significant impact in the short term but generates substantial positive outcomes in the long run, verifying the dynamic implications of the strong Porter hypothesis. Second, mechanism analysis reveals two valid transmission channels through which the policy boosts GTFP, namely the expansion of firm entry (particularly the entry of non-polluting enterprises) and the agglomeration of high-skilled talents. Notably, the talent agglomeration channel is only effective in cities with advanced economic development. Dynamic tests further confirm that both firm entry and talent agglomeration responses to the policy follow consistent short-term insignificant and long-term significant patterns. Third, heterogeneous analysis demonstrates that the policy’s green growth dividends are more prominent in southern cities, non-resource-based cities, and national transportation hub cities. This study extends the existing literature on the green efficiency effects of climate adaptation policies and provides empirical evidence and differentiated policy insights for optimizing urban green transformation governance in the new era. Full article