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This research investigates the strategic interplay between pricing, delivery promises, and handling strategies for service-level returns—products returned by consumers due to operational issues like late delivery rather than product defects. In a vertical decentralized supply chain with a manufacturer and an e-tailer, a shorter promised delivery lead time (PDL) attracts more customers but also increases the risk of late delivery, making products more return-prone. Modeling the return rate as an endogenous variable dependent on the e-tailer’s PDL decision, we develop a Manufacturer-Stackelberg (MS) game-theoretic model to examine whether service-level returns should be handled by the manufacturer (Buy-Back strategy) or the e-tailer (No-Returns strategy). The results suggest that the optimal handling strategy depends on the e-tailer’s reselling ratio—a measure of its efficiency in extracting value from returns. A win-win situation is achieved when the reselling ratio is smaller than a threshold, as the manufacturer’s decision to buy back these returns also benefits the e-tailer. Surprisingly, when the manufacturer leaves the e-tailer to handle FFRs, a higher reselling ratio is not necessarily profitable for the e-tailer. Extending the analysis to a retailer-Stackelberg (RS) scenario reveals that the supply chain’s power structure is a fundamental determinant of the optimal returns handling strategy, shifting the equilibrium from a counterintuitive, power-distorted outcome in a MS system to an intuitive, profit-driven one in a RS system.

This study proposes a multimodal deep learning framework for modeling the interplay between biological, biomechanical, and environmental factors in children’s social skill development. Using the SAADSC (Self-Attentive Adversarial Deep Subspace Clustering) model, we integrate neurobiological data (e.g., cortisol levels, heart rate variability), biomechanical indicators (e.g., postural control, gait symmetry), and family function variables (e.g., cohesion, stress) to uncover latent clusters reflecting developmental profiles. The model leverages self-attention mechanisms, adversarial training, and residual modules to extract meaningful representations from high-dimensional, heterogeneous data. Five benchmark datasets and real-world social-behavioral data were used to validate the model’s accuracy and robustness, with performance evaluated via ACC and NMI. Results show that our model outperforms traditional clustering and GAN-based baselines, with notable gains from the self-representation and attention modules. The findings support the feasibility of integrating neurophysiological, behavioral, and contextual data through explainable deep clustering, offering novel insights for early identification of social developmental risk and personalized intervention design. While standard benchmark datasets such as MNIST, Fashion-MNIST, Yale B, COIL-20, and USPS were employed to validate the technical stability and robustness of the proposed clustering framework, we also integrated real-world multimodal behavioral, biological, and environmental data to demonstrate the model’s practical relevance for children’s social skill development.

Photocatalyst is the core of photocatalysis and directly determines photocatalytic performance. However, low quantum efficiency and low utilization of solar energy are important technical problems in the application of photocatalysis. In this work, a series of polyoxometalates (POMs) [H3PW12O40] (PW12)-doped titanium dioxide (TiO2) nanofibers modified with various amount of silver (Ag) nanoparticles (NPs) were prepared by utilizing electrospinning/photoreduction strategy, and were labelled as x wt% Ag/PW12/TiO2 (abbr. x% Ag/PT, x = 5, 10, and 15, respectively). The as-prepared materials were characterized with a series of techniques and exhibited remarkable catalytic activities for visible-light degradation tetracycline (TC), enrofloxacin (ENR), and methyl orange (MO). Particularly, the 10% Ag/PT catalyst with a specific surface area of 155.09 m2/g and an average aperture of 4.61 nm possessed the optimal photodegradation performance, with efficiencies reaching 78.19% for TC, 93.65% for ENR, and 99.29% for MO, which were significantly higher than those of PW12-free Ag/TiO2 and PT nanofibers. Additionally, various parameters (the pH of the solution, catalyst usage, and TC concentration) influencing the degradation process were investigated in detail. The optimal conditions are as follows: catalyst usage: 20 mg; TC: 20 mL of 20 ppm; pH = 7. Furthermore, the photodegradation intermediates and pathways were demonstrated by HPLC-MS measurement. We also investigated the toxicity of products generated during TC removal by employing quantitative structure-activity relationship (QSAR) prediction through a toxicity estimation software tool (T.E.S.T. Version 5.1.2.). The mechanism study showed that the doping of PW12 and the modification of Ag NPs on TiO2 broadened the visible-light absorption, accelerating the effective separation of photogenerated carriers, therefore resulting in an enhanced photocatalytic performance. The research provided some new thoughts for exploiting efficient and durable photocatalysts for environmental remediation.

Windows are the least energy efficient part of the buildings, as building accounts for 40% of global energy consumption. Traditional smart windows can only regulate solar transmission, while all the solar energy on the window is wasted. Here, for the first time, the authors demonstrate an energy saving and energy generation integrated smart window (ESEG smart window) in a simple way by combining louver structure solar cell, thermotropic hydrogel, and indium tin oxides (ITO) glass. The ESEG smart window can achieve excellent optical properties with ≈90% luminous transmission and ≈54% solar modulation, which endows excellent energy saving performance. The outstanding photoelectric conversion efficiency (18.24%) of silicon solar cells with louver structure gives the smart window excellent energy generation ability, which is more than 100% higher than previously reported energy generation smart window. In addition, the solar cell can provide electricity to for ITO glass to turn the transmittance of hydrogel actively, as well as the effect of antifreezing. This work offers an insight into the design and preparation together with a disruptive strategy of easy fabrication, good uniformity, and scalability, which opens a new avenue to realize energy storage, energy saving, active control, and antifreezing integration in one device. The authors develop a revolutionary smart window with a multi‐layer louver structure, containing a silicon solar cell, thermotropic hydrogel, and ITO active layer, which combine both an energy saving and energy generation ability (ESEG smart window) with leverages high solar energy modulation together with high photoelectric conversion efficiency (PCE).

The appearance of Allium L. seeds is very similar, and it is difficult to achieve fast and accurate classification using traditional seed classification methods, which may cause damage to the seeds. Therefore, finding a quick and nondestructive classification method is very important to solve the problem of seed confounding in actual production. In this study, hyperspectral imaging technology was combined with a variety of data preprocessing and classification models to achieve rapid and nondestructive classification of Welsh onion, onion, and Chinese chives seeds. In this paper, 1050 Welsh onion, onion, and Chinese chives seeds were used as materials, and their 400–1000 nm spectral images were collected for processing. Standard Normal Variable (SNV), Multivariate Scattering Correction (MSC), First-order Differential (FD), and Second-order Differential (SD) were used to denoise the spectral data. Then the dimensionality was reduced by Principal Component Analysis (PCA). Four classification models, Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (KNN), were used to classify seeds quickly and accurately. The results show that the prediction accuracies of the Original-PLS-DA model, Original-Linear SVM model, and FD-Linear SVM model are the highest, reaching 98%, while the accuracy, recall rate, and F1 score all reach 96%. This study provides a new idea for rapid and nondestructive classification of Allium L. seeds in practical production.

Soil contamination by heavy metals represents a critical environmental challenge, demanding reliable assessment methods. While biotoxicity assays are widely employed, the selection of sensitive biomarkers for heavy-metal contamination remains poorly defined. This study systematically assessed the sensitivity of biological indicators by analyzing 17 peer-reviewed studies (2003–2024) from various databases. The results revealed significant changes in the physiological and biochemical indicators of soil organisms exposed to heavy metals. Specifically, compared to control groups, the experimental groups showed 180%, 150%, and 145% catalase (CAT), peroxidase (POD), and malondialdehyde (MDA) concentrations, respectively. Meta-regression analysis indicated that biomarker responses are shaped by metal type, concentration, exposure duration, soil organism species, and soil variables. Cadmium exposure significantly increased CAT activity (+2.26), SOD activity (+3.46), POD activity (+3.44), and MDA content (+2.80). While CAT activity exhibited significant publication bias, POD and MDA remain promising biomarkers, with applicability varying across species and environmental conditions. This study presents a decision framework for biomarker selection based on metal speciation and soil properties, aiming to standardize ecological risk assessments and strengthen regulatory monitoring of heavy-metal impacts on soil health.

To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system between the RGB-D camera and 3D calibration field is transformed using 3D corresponding points. Second, the inliers are obtained using the Bayes SAmple Consensus (BaySAC) algorithm to eliminate gross errors (i.e., outliers). Third, the parameters of the 3D registration model are calculated by the iteration method with variable weights that can further control the error. Fourth, three systematic error compensation models are established and solved by the stepwise regression method. Finally, the optimal model is selected to calibrate the RGB-D camera. The experimental results show the following: (1) the BaySAC algorithm can effectively eliminate gross errors; (2) the iteration method with variable weights could better control slightly larger accidental errors; and (3) the 3D compensation method can compensate 91.19% and 61.58% of the systematic error of the RGB-D camera in the depth and 3D directions, respectively, in the 3D control field, which is superior to the 2D compensation method. The proposed method can control three types of errors (i.e., gross errors, accidental errors and systematic errors) and model errors and can effectively improve the accuracy of depth data.

Mendelian Susceptibility to mycobacterial disease (MSMD) is a rare inherited immunodeficiency disorder characterized by increased susceptibility to atypical mycobacterial infections induced by defective IFN-γ pathway. We report three patients from a family presenting with multiple osteolytic lesions and cutaneous granulomas due to Mycobacterium marinum infections. Functional studies, including Western blotting and immunofluorescence, assessed phosphorylation and nuclear translocation of the mutant STAT1-Ile707Thr in eukaryotic overexpression systems. A luciferase reporter assay evaluated its transcriptional activity. Additionally, structural analysis using AlphaFold3 predicted the variant's functional impact. A novel STAT1 variant (c.2120T>C, p.Ile707Thr) was identified. The STAT1-Ile707Thr mutant exhibited reduced phosphorylation and impaired nuclear translocation compared to wild-type STAT1. The luciferase assay confirmed decreased transcriptional activity. AlphaFold3-based cluster analysis supported a loss-of-function effect of the mutant. This study expands the spectrum of STAT1 variants and microbial pathogens associated with MSMD.

Background: Traumatic brain injury (TBI) is a common brain injury with a high morbidity and mortality. The complex injury cascade triggered by TBI can result in permanent neurological dysfunction such as cognitive impairment. In order to provide new insights for elucidating the underlying molecular mechanisms of TBI, this study systematically analyzed the transcriptome data of the rat hippocampus in the subacute phase of TBI. Methods: Two datasets (GSE111452 and GSE173975) were downloaded from the Gene Expression Omnibus (GEO) database. Systematic bioinformatics analyses were performed, including differentially expressed genes (DEGs) analysis, gene set enrichment analysis (GSEA), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network construction, and hub gene identification. In addition, hematoxylin and eosin (HE), Nissl, and immunohistochemical staining were performed to assess the injured hippocampus in a TBI rat model. The hub genes identified by bioinformatics analyses were verified at the mRNA expression level. Results: A total of 56 DEGs were shared in the two datasets. GSEA results suggested significant enrichment in the MAPK and PI3K/Akt pathways, focal adhesion, and cellular senescence. GO and KEGG analyses showed that the common DEGs were predominantly related to immune and inflammatory processes, including antigen processing and presentation, leukocyte-mediated immunity, adaptive immune response, lymphocyte-mediated immunity, phagosome, lysosome, and complement and coagulation cascades. A PPI network of the common DEGs was constructed, and 15 hub genes were identified. In the shared DEGs, we identified two transcription co-factors and 15 immune-related genes. The results of GO analysis indicated that these immune-related DEGs were mainly enriched in biological processes associated with the activation of multiple cells such as microglia, astrocytes, and macrophages. HE and Nissl staining results demonstrated overt hippocampal neuronal damage. Immunohistochemical staining revealed a marked increase in the number of Iba1-positive cells in the injured hippocampus. The mRNA expression levels of the hub genes were consistent with the transcriptome data. Conclusions: This study highlighted the potential pathological processes in TBI-related hippocampal impairment. The crucial genes identified in this study may serve as novel biomarkers and therapeutic targets, accelerating the pace of developing effective treatments for TBI-related hippocampal impairment.

Akabane disease is an arthropod-borne disease caused by Akabane virus (AKAV), which is characterized by abortion, premature birth, stillbirth, congenital arthrosis, and hydrocephalic anencephalic syndrome in pregnant cattle and sheep. The occurrence of AKAV was proved by RT-PCR amplification based on AKAV S fragment, virus isolation, cells inoculation, cytopathy, transmission electron microscopy, and gene sequencing. The PCR amplicon was approximately 850 bp and was sequenced, and molecular identification of AKAV was conducted through phylogenetic analysis of S gene sequence. The results indicated that AKAV isolated from cattle in this study was genetically close to the strain isolated from Rhizomys pruinosus in China in 2016. However, the outbreak in bamboo rats may have been a sporadic event. The probability that Akabane virus (AKAV) can spread in rodents and mammals is still uncertain and requires further investigation. Using transmission electron microscopy (TEM), AKAV particles displayed the typical morphology associated with bunyaviruses reported previously. In brief, the AKAV infection in cattle has been confirmed. This case report highlights the necessity for enhanced surveillance and preventive measures to mitigate the potential impact on livestock health and productivity.