The stepped phase change material (PCM) cavity was investigated as a geometry-based passive thermalmanagement strategy for concentrated photovoltaic (CPV) modules. A two dimensional transient CPV-PCM conjugate heat transfer model was developed using the enthalpy-porosity method and the Boussinesq approximation. Four cavity orientations, obtained by combining the step-facing direction with upwardly expanding or upwardly narrowing cavity profiles, and three step widths of 4, 6, and 8 mm were compared with a flat cavity under an identical PCM cross-sectional area. Grid and time-step independence tests were examined, and the numerical framework was validated against published CPV temperature and PCM melting data. The obtained results indicate that the illumination side stepped cavity with an upwardly expanding profile shows the strongest heat transfer enhancement. At a step width of 8 mm, the peak value of the maximum PCM velocity increases by 41.4%, the complete melting time decreases by 14.0%, the maximum reduction in average cell temperature reaches 12.83 degrees C, the heat storage power increases by 8.9%, and the electrical efficiency increases by up to 7.4% relative to the flat cavity. In contrast, the upwardly expanding cavity with steps facing away from the illuminated side provides better temperature uniformity and extends the temperature control duration by up to 36.1%. These results reveal a geometry-induced trade-off between intensified natural convection and cell temperature control performance and provide a numerical basis for the structural screening of compact CPV-PCM thermal-management systems.
The growing adoption of autonomous ground robots and aerial drones for last-mile logistics underscores the urgent need for efficient routing in daily operations. In this context, we investigate a routing problem for autonomous robots, wherein vehicles equipped with different types of containers perform pickup and delivery services subject to stringent time window constraints. Over the planning horizon, each robot may perform multiple trips, returning to the depot to reload or unload as needed. Depot operations are explicitly modeled, including a fixed preparation time per trip and customer-dependent loading and unloading times. To address this problem, we first propose a Branch-and-Price (BP) algorithm that provides exact solutions for small and medium-sized instances. We further develop an efficient Iterated Local Search (ILS) heuristic for large instances. The algorithm employs labeling algorithms embedded in the BP algorithm to optimally determine depot return decisions for a given customer sequence. Extensive computational experiments demonstrate that the BP algorithm produces tight bounds or optimal solutions for instances with up to 40 customers, achieving an average optimality gap below 2.0% while significantly outperforming a state-of-the-art MILP solver. Furthermore, the proposed ILS obtains all optimal solutions confirmed by the BP algorithm and significantly outperforms both a general-purpose MILP solver and the BP algorithm, especially on instances with 100 customers, where it achieves an average gap of over 14.0% relative to the best solutions from exact algorithms. Finally, we conduct sensitivity analyses to assess the effects of customer time window lengths and robot capacity configurations on the solutions, thereby providing managerial insights for last-mile logistics companies.
Let [n]( (k)) be the set of all ordered k-tuples of distinct elements in [n]={1, 2,...,n}. The (n,k,r)-arrangement graph A(n,k,r) with 1 <= r <= k <= n, is the graph with vertex set [n]((k)) and with two k-tuples are adjacent if they are different in exactly r coordinates. Fu-Gang Yin et al. characterized the automorphism group of A(n,k, 1) and proposed an open problem determining the automorphism group of A(n,k,r) with 2 <= r <= k <= n in [J. Graph Theory 98 (2021) 234--254]. In this note, we describe the automorphism groups of A(n,k,k) and A(n,n, 2) from the perspectives of intersecting families and Cayley graphs. (c) 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Major Depressive Disorder (MDD) stands as a foremost contributor to global disability, with a substantial proportion of patients failing to achieve remission through conventional antidepressant therapies. This review positions neuroinflammation as a central pathophysiological mechanism and examines immunotherapy as a promising frontier for a biologically defined subset of patients. We synthesize current evidence detailing how peripheral immune activation, driven by pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α, disrupts neural homeostasis through blood-brain barrier (BBB) compromise, dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, and a shift in tryptophan metabolism toward the neurotoxic kynurenine pathway. We further analyze the heterogeneous immunomodulatory effects of conventional antidepressants—including tricyclic antidepressants and selective serotonin reuptake inhibitors—and critically assess inflammatory cytokines as predictive biomarkers for differential treatment responses. While elevated inflammation is generally linked to resistance to traditional monoaminergic drugs, it may predict enhanced responsiveness to glutamatergic agents such as ketamine or to direct anti-cytokine strategies. Finally, we systematically evaluate the rationale and emerging evidence for cytokine-targeted immunotherapies, including anti-IL-6 receptor agents, TNF-α inhibitors, and NLRP3 inflammasome blockers. We conclude that realizing the full potential of immunotherapy in MDD necessitates a paradigm shift toward biomarker-guided patient stratification, standardized inflammatory profiling, and rigorously designed clinical trials to establish both efficacy and safety.
Human carboxylesterase 1 (hCE1) is a serine hydrolase that is distributed widely in the human liver, intestines, and various tumour tissues. It has now become a biomarker associated with diseases such as cancer. Consequently, the measurement of hCE1 levels in human serum is of significant importance for the diagnosis and assessment of these diseases. The present study involved the preparation of Ag/CQDs/ZIF-8 nanocomposites, which incorporated silver nanoparticles (Ag NPs) and carbon quantum dots (CQDs) into the ZIF-8 framework. The combination of this composite with an enzyme-linked immunosorbent assay (ELISA) and the utilisation of benzidine as the signal molecule resulted in the development of a fluorescence (FL)/surface-enhanced Raman scattering (SERS) dual-mode sensor, which was employed for the sensitive detection of hCE1 in serum. This dual-mode sensor combines the advantages of both FL and SERS technologies, demonstrating excellent selectivity and stability, and enhancing the accuracy and reliability of quantitative measurements. The limits of detection (LOD) for the FL mode and SERS mode were determined to be 0.1 and 0.072 ng·mL− 1, respectively, with the recoveries ranging from 92.44
