Search Results (26,547)

In recent years, blockchain has been increasingly used in shipping supply chains, enabling supply chain members to track the production process of shipping products, thereby increasing visibility for firms and boosting their competitiveness. When firms decide whether to invest in blockchain, they crucially consider the cost of development and fairness of profit distribution along the supply chain, with a particular focus on non-linear production cost and fairness concern. We build a Stackelberg game model for four scenarios utilizing a two-echelon supply chain made up of a single shipping company and a single freight forwarder, taking into account fairness concern and non-linear production cost. We analyze how participants in the shipping supply chain make decisions when the shipping company has non-linear production cost and the freight forwarder has fairness concern. The findings suggest that the interaction between the non-linear production cost of the shipping company and the level of fairness concern of the freight forwarder affects the managerial decisions of both the freight forwarder and the shipping company. In the presence of economies of scale or diseconomies of scale, fairness concern can effectively help the freight forwarder to increase its share of profits within the supply chain, while the shipping company changes in the opposite direction. Furthermore, when the freight forwarder takes fairness concern into account, its profit and utility do not always rise in direct proportion to the fairness concern degree. Interestingly, there is always an inverse relationship between the shipping company’s profit and the degree of fairness concern, regardless of whether there are economies of scale or diseconomies of scale. This paper provides management insights for companies considering blockchain in their plans, highlighting the importance of combining non-linear production cost and fairness concern to achieve profit goals. Full article

Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and robustness across diverse environments remains difficult. In this paper, we propose DAR-MDE, a novel framework that combines an autoencoder backbone with a Multi-Scale Feature Aggregation (MSFA) module and a Refining Attention Network (RAN). The MSFA module enables the model to capture geometric details across multiple resolutions, while the RAN enhances depth predictions by attending to structurally important regions guided by depth-feature similarity. We also introduce a multi-scale loss based on curvilinear saliency to improve edge-aware supervision and depth continuity. The proposed model achieves robust and accurate depth estimation across varying object scales, cluttered scenes, and weak-texture regions. We evaluated DAR-MDE on the NYU Depth v2, SUN RGB-D, and Make3D datasets, demonstrating competitive accuracy and real-time inference speeds (19 ms per image) without relying on auxiliary sensors. Our method achieves a $\delta$ < 1.25 accuracy of 87.25% and a relative error of 0.113 on NYU Depth v2, outperforming several recent state-of-the-art models. Our approach highlights the potential of lightweight RGB-only depth estimation models for real-world deployment in robotics and scene understanding. Full article

Surrogate models are widely recognized as effective, data-driven predictive tools for storm surge risk assessment. For such applications, surrogate models (referenced also as emulators or metamodels) are typically developed using existing databases of synthetic storm simulations, and once calibrated can provide fast-to-compute approximations of the storm surge for a variety of downstream analyses. The storm surge predictions need to be established for different geographic locations of interest, typically corresponding to the computational nodes of the original numerical model. A number of inland nodes will remain dry for some of the database storm scenarios, requiring an imputation for them to estimate the so-called pseudo-surge in support of the surrogate model development. Past work has examined the adoption of kNN (k-nearest neighbor) spatial interpolation for this imputation. The enhancement of kNN with hydraulic connectivity information, using the grid or mesh of the original numerical model, was also previously considered. In this enhancement, neighboring nodes are considered connected only if they are connected within the grid. This work revisits the imputation of peak storm surge within a surrogate modeling context and examines three distinct advancements. First, a response-based correlation concept is considered for the hydraulic connectivity, replacing the previous notion of connectivity using the numerical model grid. Second, a Gaussian Process interpolation (GPI) is examined as alternative spatial imputation strategy, integrating a recently established adaptive covariance tapering scheme for accommodating an efficient implementation for large datasets (large number of nodes). Third, a data completion approach is examined for imputation, treating dry instances as missing data and establishing imputation using probabilistic principal component analysis (PPCA). The combination of spatial imputation with PPCA is also examined. In this instance, spatial imputation is first deployed, followed by PPCA for the nodes that were misclassified in the first stage. Misclassification corresponds to the instances for which imputation provides surge estimates higher than ground elevation, creating the illusion that the node is inundated even though the original predictions correspond to the node being dry. In the illustrative case study, different imputation variants established based on the aforementioned advancements are compared, with comparison metrics corresponding to the predictive accuracy of the surrogate models developed using the imputed databases. Results show that incorporating hydraulic connectivity based on response similarity into kNN enhances the predictive performance, that GPI provides a competitive (to kNN) spatial interpolation approach, and that the combination of data completion and spatial interpolation emerges as the recommended approach. Full article

The paper aims to identify areas and forms of cooperation between manufacturing companies and their business-to-business (B2B) stakeholders in CE. The paper includes a theoretical part (the literature review) and an empirical part (a survey of 200 manufacturing enterprises in Poland, in 2024). The study found that (1) companies that declared the inclusion of CE in their business strategy were statistically more likely to declare cooperation in the scopes analysed; (2) cooperation of business partners in one area intensifies and encourages action in other areas, increasing the chances of achieving jointly agreed goals; and (3) the implementation of CE in companies requires appropriate management of B2B relationship capital, which is a strong catalyst for the development of innovation in supply chains. Involving suppliers in the co-creation of CE innovations through partnerships in different areas has a positive impact on the value and competitive advantage of these actors. In Poland, there are publications available on the general condition of the SME sector and studies on the principles and examples of CE implementation—including in the SME sector—but there is a lack of targeted studies and reports on the activities of these entities in the field of CE—the proposed study fills this research gap. Full article

The present study investigates passive microdroplet generation in a T-junction microchannel using microscopic observations, microscale particle image velocimetry (Micro-PIV) visualization, and lattice Boltzmann simulations. The key flow regimes, i.e., dripping, threading, and parallel flow, are characterized by analyzing the balance between hydrodynamic forces and surface tension, revealing the critical role of the flow rate ratio of the continuous to dispersed fluids in regime transitions. Micro-PIV visualizes velocity fields and vortex structures during droplet formation, while a lattice Boltzmann model with wetting boundary conditions captures interface deformation and flow dynamics, showing good agreement with experiments in the dripping and threading regimes but discrepancies in the parallel flow regime due to neglected surface roughness. The present experimental results highlight non-monotonic trends in the maximum head interface and breakup positions of the dispersed fluid under various flow rates, reflecting the competition between the squeezing and shearing forces of the continuous fluid and the hydrodynamic and surface tension forces of the dispersed fluid. Quantitative analysis shows that the droplet size increases with the flow rate of continuous fluid but decreases with the flow rate of dispersed fluid, while generation frequency rises monotonically with the flow rate of dispersed fluid. The dimensionless droplet length correlates with the flow rate ratio, enabling tunable control over droplet size and flow regimes. This work enhances understanding of T-junction microdroplet generation mechanisms, offering insights for applications in precision biology, material fabrication, and drug delivery. Full article

Water is an essential resource for life and is also a necessary resource for the sustainable economic competitiveness of any country. In recent decades, climate change, economic development, and rising population have led to water scarcity in certain regions. In response, new technologies and water management techniques have been researched and developed, which are now incorporated into the concept of smart cities. These innovations, called smart water systems, aim to enhance water management by monitoring consumption, quality, reservoir levels, leaks, and asset conditions, and optimizing water processes to maximize water system resilience. The first systems were based on smart meters and have advanced to so-called digital twins for water systems. This review aims to present a comprehensive review of smart water system developments, the geographic distribution of the works, their technological readiness level, and their implementation challenges. Full article

Skeletons serve as the body’s fundamental structure and perform multiple vital functions, and thus even subtle modifications can lead to functional changes. However, studies on sexual shape dimorphism in the appendicular skeletons of anurans remain scarce, especially for fossorial species. Herein, we investigated the sexual differences in the external morphology and internal skeletal shape of girdles and limbs of fossorial Kaloula rugifera during the breeding season. The results reveal the following: (1) Males exhibit significantly longer forelimbs and hindlimbs, suggesting advantages in male‒male competition and amplexus. (2) Males possess more curved and dilated coracoids and broader pubis-ischium regions, which may enhance forelimb contraction efficiency and hindlimb locomotor performance. (3) Males feature a more developed crista ventralis, likely enhancing forelimb flexion for tightly grasping females during mating. In contrast, females display a torsioned femur and robust hindlimb morphology, suggesting adaptations for digging and load-bearing. These findings provide insights into how anurans optimize reproductive fitness and ecological adaptations through morphological specialization. To our knowledge, this study presents the first examination of sexual shape dimorphism in anuran appendicular skeletons utilizing three-dimensional geometric morphometrics. Full article

Peanut cultivation currently plays a minor role in Portuguese agriculture, despite the country’s favorable soil and climatic conditions. In the Azores archipelago, where agriculture is a key economic activity, peanut production has recently sparked interest among rural producers. Weeds pose a major threat to crop development, particularly for short-cycle species like peanuts. This study aimed to determine the period prior to weed interference (PPI) in peanut crops under two row spacings (40 cm and 60 cm) on São Miguel Island, Azores. Eight treatments were established—0–15, 0–30, 0–45, 0–60, 0–75, 0–90 days after emergence (DAE), full-season coexistence, and a weed-free control—to represent increasing periods of weed competition. A randomized block design with four replicates was used for each spacing. The weed community included eight species, with Cyperus spp., Digitaria spp., Amaranthus blitum, and Portulaca oleracea being the most prevalent. Weed interference throughout the entire cycle led to yield losses exceeding 81% and 86% at 40 cm and 60 cm row spacings, respectively. The PPI was defined at a 5% yield reduction threshold, which is a commonly accepted benchmark in weed science to determine the beginning of the critical period of weed interference. Full article

Background: Regulating sensations of fatigue and discomfort while performing maximal endurance exercise becomes essential for making informed decisions about persistence and/or failure during intense exercise. Athletes with a higher effort capacity have competitive advantages over those with a lower one. The microbiota–brain axis is a considered the sixth sense and a modulator of the host’s emotional stability and physical well-being. Objectives: This narrative review aims to explore and evaluate the potential mechanisms involved in regulating perceptions during endurance exercise, with a focus on the possible relationship between the gut microbiota balance and the neural system as an adaptive response to high fatigue chronic exposure. Methods: Electronic databases (PubMed, Web of Science, Google Scholar, and Scopus) were used to identify studies and hypotheses that had documented predefined search terms related to endurance exercise, gut microbiota, the central nervous system, pain, discomfort, fatigue, and tolerance to effort. Results: This narrative review shifts the focus concerning the symbiotic relationship between the gut microbiota, the vagus nerve, the central/enteric nervous system, and the regulation of afferences from different organs and systems to manage discomfort and fatigue perceptions during maximal physical effort. Consequently, the chronicity supporting fatigued exercise and nutritional stimuli could specifically adapt the microbiota–brain connection through chronic efferences and afferences. The present hypothesis could represent a new focus to be considered, analysing individual differences in tolerating fatigue and discomfort in athletes supporting conditions of intense endurance exercise. Conclusions: A growing body of evidence suggests that the gut microbiota has rapid adaptations to afferences from the brain axis, with a possible relationship to the management of fatigue, pain, and discomfort. Therefore, the host–microbiota relationship could determine predisposition to endurance performance by increasing thresholds of sensitive afferences perceived and tolerated. A richer and more diverse GM of athletes in comparison with sedentary subjects can improve the bacteria-producing metabolites connected to brain activity related with fatigue. The increase in fatigue thresholds directly improves exercise performance, and the gut–brain axis may contribute through the equilibrium of metabolites produced for the microbiota. Full article

This two-part study assesses the impact of biogas inhibition on large-scale waste-to-energy anaerobic digestion (WtE-AD) plants through techno-economic and life cycle assessment approaches. The first part addresses technical and economic aspects. An anaerobic co-digestion system using vegetable waste (FVW) and meat waste (MW) was operated at laboratory scale in a semi-continuous regime with daily feeding to establish a stable process and induce programmed failures causing methanogenic inhibition, achieved by removing MW from the reactor feed and drastically reducing the protein content. Experimental data, combined with bioprocess scale-up models and cost engineering methods, were then used to evaluate the effect of inhibition periods on the profitability of large-scale WtE-AD processes. In the experimental stage, the stable process achieved a yield of 521.5 ± 21 mL CH₄ g⁻¹ volatile solids (VS) and a biogas productivity of 0.965 ± 0.04 L L⁻¹ d⁻¹ (volume of biogas generated per reactor volume per day), with no failure risk detected, as indicated by the volatile fatty acids/total alkalinity ratio (VFA/TA, mg VFA L⁻¹/mg L⁻¹) and the VFA/productivity ratio (mg VFA L⁻¹/L L⁻¹ d⁻¹), both recognized as effective early warning indicators. However, during the inhibition period, productivity decreased by 64.26 ± 11.81% due to VFA accumulation and gradual TA loss. With the progressive reintroduction of the FVW:MW management and the addition of fresh inoculum to the reaction medium, productivity recovered to 96.7 ± 1.70% of its pre-inhibition level. In WtE-AD plants processing 60 t d⁻¹ of waste, inhibition events can reduce net present value (NPV) by up to 40.2% (from 0.98 M USD to 0.55 M USD) if occurring once per year. Increasing plant capacity (200 t d⁻¹), combined with higher revenues from waste management fees (99.5 USD t⁻¹) and favorable electricity markets allowing higher selling prices (up to 0.23 USD kWh⁻¹), can enhance resilience and offset inhibition impacts without significantly compromising profitability. These findings provide policymakers and industry stakeholders with key insights into the economic drivers influencing the competitiveness and sustainability of WtE-AD systems. Full article

This paper investigates federated learning (FL) for cross-site scripting (XSS) detection under out-of-distribution (OOD) drift. Real-world XSS traffic involves fragmented attacks, heterogeneous benign inputs, and client imbalance, which erode conventional detectors. To simulate this, we construct two structurally divergent datasets: one with obfuscated, mixed-structure samples and another with syntactically regular examples, inducing structural OOD in both classes. We evaluate GloVe, GraphCodeBERT, and CodeT5 in both centralised and federated settings, tracking embedding drift and client variance. FL consistently improves OOD robustness by averaging decision boundaries from cleaner clients. Under FL scenarios, CodeT5 achieves the best aggregated performance (97.6% accuracy, 3.5% FPR), followed by GraphCodeBERT (96.8%, 4.7%), but is more stable on convergence. GloVe reaches a competitive final accuracy (96.2%) but exhibits a high instability across rounds, with a higher false positive rate (5.5%) and pronounced variance under FedProx. These results highlight the value and limits of structure-aware embeddings and support FL as a practical, privacy-preserving defence within OOD XSS scenarios. Full article

Elite athletes are at an increased risk of infections due to behavioral and social factors and frequent travel. Furthermore, heavy physical exercise may induce immunosuppression. Most infections in athletes are acute respiratory illnesses (ARIs) with various viral etiologies. Although athletes, as young, healthy adults, are not at risk for severe infections, a prolonged ARI may ruin a training season or a significant competition or may spread within a sports team. Many common infections are vaccine-preventable. This Opinion advocates for more active vaccination among athletes, although some of the vaccines are not officially recommended for young adults. New respiratory syncytial virus (RSV) protein vaccines are effective and well-tolerated. Yearly influenza and COVID-19 vaccinations are strongly recommended. Conjugated polyvalent pneumococcal vaccines are recommended because they may also induce protection against respiratory viral infections. Pertussis and measles outbreaks are occurring globally. The history of measles vaccination should be reviewed, and consideration should be given to a pertussis booster vaccination (Tdap). A recombinant vaccine can effectively prevent herpes zoster. The vaccination of elite athletes is a cost-effective and powerful tool, but it is currently underused. The sports medicine community can address vaccine hesitancy among athletes by listening to their concerns and giving accurate information. Full article

The explosive growth of tasks on crowdsourcing platforms has intensified information overload, making it difficult for workers to spot lucrative bids; yet mainstream recommenders inherit a user-independence assumption from e-commerce and therefore overlook the real-time competition among workers, which degrades ranking stability and accuracy. To bridge this gap, we propose the Adaptive Parameter Exponential Decay Algorithm (APAED), which first produces base relevance scores with an offline neural model and then injects a competition-aware exponential decay whose strength is jointly determined by the interquartile range of each worker’s score list (global factor) and the live bid distribution of every task (local factor). This model-agnostic adjustment explicitly quantifies competitive intensity without handcrafted features and can be paired with any backbone recommender. Experiments on a real-world dataset comprising 25,643 tasks and 19,735 workers show that APAED cuts the residual RMSE of HR@10 from $9.575\times {10}^{-4}$ to $5.939\times {10}^{-4}$ (−38%) and that of MRR from $2.920\times {10}^{-4}$ to $0.736\times {10}^{-4}$ (−75%), substantially reducing score fluctuations across epochs and consistently outperforming four strong neural baselines. These results confirm that explicitly modeling worker competition yields more accurate and stable task recommendations in crowdsourcing environments. Full article

The aim of this study was to investigate the metabolites in Aspergillus subramanianii 1901NT-1.40.2 extract using UPLC-MS, isolate and elucidate the structure of individual compounds, and study the antimicrobial and cytotoxic activities of the isolated compounds. The structures of two previously unreported ergostane triterpenoid aspersubrin A (1) and pyrazine alkaloid ochramide E (2) were established using NMR and HR ESI-MS. The absolute configuration of 1 was determined using quantum chemical calculations. Moreover, the known polyketides sclerolide (3) and sclerin (4); the indolediterpene alkaloid 10,23-dihydro-24,25-dehydroaflavinine (5); the bis-indolyl benzenoid alkaloids kumbicin D (6), asterriquinol D dimethyl ether (7), petromurin C (8); and the cyclopentenedione asterredione (9) were isolated. The effects of compounds 3-9 on the growth and biofilm formation of the yeast-like fungus Candida albicans and the bacteria Staphylococcus aureus and Escherichia coli were investigated. Compounds 5 and 6 inhibited C. albicans growth and biofilm formation at an IC₅₀ of 7–10 µM. Moreover, the effects of compounds 3-9 on non-cancerous H9c2 cardiomyocytes, HaCaT keratinocytes, MCF-10A breast epithelial cells, and breast cancer MCF-7 and MDA-MB-231 cells were also investigated. Compound 8 (10 µM) significantly decreased the viability of MCF-7 cells, inhibited colony formation, and arrested cell cycle progression and proliferation in monolayer culture. Moreover, 8 significantly decreased the area of MCF-7 3D spheroids by approximately 30%. A competitive test with 4-hydroxytamoxyfen and molecular docking showed that estrogen receptors (ERβ more than ERα) were involved in the anticancer effect of petromurin C (8). Full article

Background: Insulin-degrading enzyme (IDE) has become an essential target for the clinical treatment of various important diseases, including type 2 diabetes, Alzheimer’s disease, and breast cancer, owing to its diverse substrate specificity. Particularly in cancer therapy, IDE inhibitors have received significant attention. Methods: We evaluated the in vitro inhibitory activity (IC₅₀) of ethyl 2-(3,5-dioxo-2-p-tolyl-1,2,4-thiadiazolidin-4-yl) acetate (1) against wild-type IDE. The mechanism of action was investigated using Lineweaver–Burk double reciprocal plots and molecular docking analyses. Additionally, we examined the structure–activity relationship, cytotoxicity, selectivity, and effects on cell migration to assess its potential druggability. Based on molecular docking results, we prepared the mutant protein T142A and compared its inhibitory effects with those of the wild-type and mutant proteins. Results: Compound 1 exhibited an inhibitory effect on IDE (IC₅₀ = 3.60 μM). This compound exerts its inhibitory effect through competitive binding to the catalytic site of IDE. Compound 1 demonstrated selective cytotoxicity toward cancer cells compared to normal cells, effectively inhibiting IDE at concentrations ≤ 10 μM. At a concentration of 3.6 μM, the inhibitory effect of the compound on cancer cell migration was significantly stronger than that observed in normal cells. Although the T142A mutant retained catalytic hydrolysis activity with a similar K_m value, its reaction rate was markedly lower than that of the wild-type enzyme. Conclusions: Compound 1 exhibits a competitive inhibitory effect on IDE, selectively targeting IDE with greater toxicity toward cancer cells compared to normal cells. It also inhibits cancer cell migration. Notably, 1 demonstrates significantly stronger inhibitory activity against the T142A mutant than the wild-type IDE, indicating that the Thr142 residue plays a crucial role in the interaction between the IDE hydrophobic pocket and 1. These findings suggest that 1 holds potential as a chemotherapeutic agent for treating IDE-related cancers, including breast, prostate, and pancreatic cancers. Full article