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.
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
Q355E high-strength structural steel is widely used in thick-plate welding applications for construction machinery. While high-heat-input metal active gas (MAG) welding enhances welding efficiency, it tends to induce coarse grain structures and degraded mechanical properties in welded joints. To date, there remains a lack of systematic research into the coupled effects of welding current on the microstructure, texture, and mechanical properties of Q355E steel under high heat input conditions. This study investigated the effects of varying welding currents on the microstructure, EBSD characteristics, texture, and mechanical properties of Q355E high-strength structural steel during MAG welding under high heat input conditions. Combined with coupled thermomechanical simulation analysis of stress-strain distribution, the research aimed to determine optimal welding parameters. Results indicated: The ferrite content within the weld joint decreased with increasing welding current; EBSD analysis revealed a disordered, interwoven network structure in the weld core zone, exhibiting high average KAM values and high dislocation density; Extensive dynamic recrystallisation occurred in both the coarse grain zone and fine grain zone of the joint, with the coarse grain zone showing the highest recrystallisation rate (74.7
