The need for reliable internet connectivity in educational environments is crucial, but is often hampered by inefficient network infrastructure. This study aims to design an optimal Fiber To The Room (FTTR) network topology design based on Gigabit Passive Optical Network (GPON) at SMK NU Ma'arif Kudus, focusing on the efficiency of fiber optic cable installation routes. The research method used is engineering design with a quantitative approach, where the school architectural plan is modeled into a weighted graph. Route optimization is carried out by implementing the Dijkstra Algorithm to find the shortest path from the center node (ODC) to all node termination points (ODP). While node I (ODC) is designated as the starting node because it functions as the network distribution center. The calculation process is carried out by determining the minimum distance from the starting node to all destination nodes (ODP). The calculation results show that the shortest path is divided into two main routes, namely I to C to B to A to D to E and I to F to G to H. The selection of this node is proven to be able to produce a more efficient total distance compared to direct paths in several network segments. Based on these results, it can be concluded that Dijkstra's algorithm is effective in fiber optic network planning because it can optimally determine the path with the minimum distance. The application of this method is expected to assist in decision-making regarding fiber optic network infrastructure planning, making it more efficient and applicable for implementation in school environments.

Anthemideae, with over 111 genera and 1800 species, is one of the largest tribes within the Asteraceae family. Some members of this tribe are recognised for their aromatic and medicinal properties. Central Asia, the Mediterranean region and southern Africa are the main distribution centres for this tribe. The taxonomy and subtribal classification of the Anthemideae remain controversial and unresolved. Pollen micromorphology can be a valuable and informative tool for resolving taxonomic problems at the subtribe, genus and species levels. The present study aims to describe the pollen types within a small subset of Anthemideae tribe from Iran and to investigate the relationships among a small set of subtribes, genera and species using palynological data. The pollen micromorphology of 14 species representing 14 genera of Anthemideae was examined using scanning electron microscopy. Consequently, three pollen types – Anthemis, Artemisia and Handelia – were identified among the studied taxa, with the latter being newly described. Using clustering and ordination analyses – unweighted pair group method with arithmetic mean (UPGMA), principal component analysis (PCA) and principal coordinate analysis (PCoA) – the species from the four subtribes – Anthemidinae, Glebionidinae, Handeliinae and Leucantheminae – could, in most cases, be distinguished from each other. However, some incongruences between pollen features and accepted taxonomy were observed among the Artemisiinae species.

ABSTRACT This study proposes a sustainable joint economic lot size (JELS) model for tuna supply chains under stochastic demand. The model considers a multi-echelon network consisting of a processor, a distribution center, multiple retailers, and end customers. Its novelty lies in integrating battery-constrained electric vehicles and drone-based delivery with stochastic demand and variable lead time, aspects rarely addressed simultaneously in previous studies. Lead time is formulated as a function of production, transportation, loading and unloading, transit, charging, and courier delivery activities. To solve the proposed model, an algorithm is developed and validated through a real-world case study. The results show that electric motorcycles achieve annual cost savings of IDR 8.469 billion (80.61%) compared to drones. Conversely, drones reduce delivery time by 2.32 days (55.42%) but increase carbon emissions by 3.13 t.CO₂e (2.65%). Sensitivity analysis identifies product weight, vehicle load capacity, speed, and transport distance as key factors affecting system performance.

The increasing frequency and severity of disasters highlight the need for responsive humanitarian logistics systems. In post-disaster settings, damaged infrastructure can delay conventional vehicle access, while drones can support timely relief delivery to locations with limited ground accessibility. This study introduces a two-echelon location-routing problem with multiple drones and split deliveries (2E-LRP-MD-SD) for coordinated ground vehicle and drone operations in post-disaster relief distribution. The problem jointly considers distribution centre selection, heterogeneous ground-vehicle deployment, multiple drones per vehicle, synchronised truck-drone routing, and split demand fulfillment. A mixed-integer linear programming formulation is developed to minimise delivery completion time while capturing accessibility asymmetry, capacity restrictions, drone endurance, and temporal synchronisation. To solve larger instances, a three-phase matheuristic, Prescriptive Analytics with Clustering and Optimisation (PACO), is proposed and benchmarked against a Variable Neighbourhood Search (VNS) metaheuristic. A modified subgradient-based lower-bound procedure supports solution-quality assessment. Computational experiments show that 2E-LRP-MD-SD reduces delivery completion time by 15%–42% relative to the ground-vehicle-only system, and that PACO consistently outperforms VNS. The sensitivity analyses show that split deliveries improve efficiency, while drone speed, fleet size, payload–endurance trade-offs, and demand intensity affect delivery completion time. The results inform configuration of truck-drone relief systems under capacity, accessibility, and demand-surge constraints.

ABSTRACT We investigate a centralised distribution network consisting of multiple retail stores and an online store. Each retail location faces unpredictable demand specific to its region, while the online store experiences varying demand across all locations. The warehouse primarily fulfils online orders, but retail stores can also use their inventory to support two omnichannel strategies: Ship-from-Store and Buy-Online-Pickup-in-Store. Before the selling season, retail stores must decide whether to implement one or both strategies, as they require prior preparation and resource allocation. Retail stores replenish their inventory from the distribution centre before the season starts. During the selling season, decision-makers optimise inventory allocation across the network to meet both in-store and online demand, aiming to maximise net profit. This problem is formulated as a two-stage stochastic optimisation model. We apply meta-heuristic techniques, including Simulated Annealing, Tabu Search, and Genetic Algorithm, to solve the model. Computational experiments demonstrate the effectiveness of these methods, achieving high-quality solutions with minimal deviation from the optimal outcome.

The Central-marginal hypothesis predicts that populations occurring at the periphery of a species' geographic distribution experience more adverse environmental conditions, resulting in reduced population density, lower fitness, and potential morphological changes. In insects, morphological traits are strongly associated with ecological performance and resource acquisition, making them useful indicators of how populations respond to environmental gradients. Here, we investigated whether populations of the ant Dinoponera quadriceps differ in activity density and morphofunctional traits between the center and edge of the species' geographic distribution along the Espinhaço Mountain Range, Brazil. Ants were sampled using pitfall traps in two sites approximately 610km apart. Generalized Linear Mixed Models were used to evaluate differences in activity density and trait variation between sites, and a Principal Component Analysis summarized multivariate body size variation. The activity density of D. quadriceps was -higher in the central population in the full dataset but this difference was not robust to the removal of a single outlier trap. A positive correlation between D. quadriceps activity density and richness of other ant species was observed in the full dataset but also disappeared after outlier exclusion. Individuals from the marginal population exhibited significantly smaller overall body size. Additionally, trait-specific differences emerged, with marginal individuals displaying larger cephalic index, longer femora, and larger eyes. These findings suggest that peripheral environments impose energetic constraints that reduce body size while favoring morphological adjustments that enhance locomotor and sensory efficiency, highlighting the importance of intraspecific functional variation in understanding species responses at geographic range limits.

Globally, a significant volume of petroleum products is transported daily through logistic networks to meet diverse regional demands, where unreliable or delayed delivery can lead to serious economic, social, and political consequences. The primary contribution of this research is the development of a comprehensive, robust multi-period mathematical model for the integrated planning of a green multi-modal petroleum product logistic network. This model advances current frameworks by simultaneously integrating pipeline, rail, and road transportation; synchronizing strategic facility development with operational flow allocation under uncertainty; and selecting the optimal network topology from both economic and environmental perspectives. It supports decisions on the location of distribution centers and the construction of pipelines and railways within budget constraints, aligning infrastructure investment with operational efficiency. A real-world case study in central Iran, solved via the Augmented Epsilon Constraint method, validates the approach. Targeted rail and pipeline investments reduce total transportation costs and cut CO₂ emissions by ~ 27.5% compared to the cost-only optimum. Out-of-sample tests across different uncertainty scenarios confirm the robust model's superiority. It achieves 100% feasibility, vs. 50% for the nominal model, lowers average cost by 4.7%, reduces average emissions by 30.4%, and improves uncertainty regret indices by up to ~ 92%. These findings highlight the model's resilience and ability to deliver sustainable, cost-effective petroleum logistics under real-world uncertainty.

Hydrogen supply chains require coordinated planning from upstream production to downstream distribution and end-user delivery; however, significant logistical challenges remain under emerging hydrogen infrastructure constraints. In particular, the transportation sector faces difficulties in achieving efficient distribution while accounting for limited hydrogen refueling availability and vehicle range restrictions. This study evaluates key network design decisions involving distribution center location and fuel cell electric vehicle (FCEV) routing while incorporating hydrogen refueling stations within the transportation system. An integrated framework is proposed by combining K-means clustering for DC location planning with a hydrogen-powered FCEV routing model. Hydrogen refueling stations are incorporated as routing constraints to ensure feasible distribution operations. Next, a case study in Thailand is conducted to validate the proposed model under realistic logistical conditions. The results illustrate how clustering-based allocation improves network coordination, while the integrated FCEV routing approach ensures feasible and efficient delivery under refueling constraints. Comparative analysis further highlights improvements in system performance and provides practical insights for designing coordinated hydrogen logistics systems across integrated supply chain networks.

One of the pivotal strategies for achieving net-zero aviation emissions is the replacement of conventional jet fuel with Sustainable Aviation Fuels (SAF). The very limited availability of SAF necessitates strategic allocation to flight routes to optimize costs and emission reduction. Addressing this challenge, this paper introduces an innovative adaptive robust optimization framework for the distribution of SAF to flight routes in Canada based on a range of domestic production scenarios, fuel transportation costs, and jurisdictional carbon prices. The objective is to identify the optimal location of potential SAF distribution centers and allocate SAF to flight routes over a 25-year period. This complex problem incorporates flight data from the International Civil Aviation Organization (ICAO) and uncertain projections for SAF production. Leveraging a column and constraint generation algorithm, the paper achieves global optimality in solving the problem. The findings reveal that the proposed robust model results in emission cost savings ranging from 7.13% to 18.19% across various distribution center capacities, consistently outperforming the deterministic model. This underscores the effectiveness of the proposed approach in efficiently distributing available SAF under production uncertainties. • Developed a Robust model for SAF distribution to Canadian flight routes over 25 years. • Accounted for uncertainty in SAF production, transport costs, and carbon prices. • Identified ideal SAF distribution centers and allocation strategies using real flight data. • Achieved an 8.06% reduction in emission costs compared to traditional models. • Supports aviation’s transition to net-zero emissions with efficient SAF deployment strategies.

Drones in healthcare logistics: Insights from healthcare professionals' perspective on Zipline delivery services in Ghana.

Spatiotemporal evolution in species richness patterns and future conservation prioritization of Chenopodiaceae in Xinjiang, China under climate change

China is both a major producer and consumer of fresh agricultural products, making cold chain logistics essential for preserving quality and reducing post-harvest loss. However, insufficient pre-cooling capacity in production areas often leads to significant quality deterioration during the first-mile stage, which has not been fully addressed in existing cold chain network design studies. To bridge this gap, this study proposes an integrated optimization framework for designing a first-mile pre-cooling distribution center (DC) network. A multi-objective nonlinear mathematical model is developed to simultaneously minimize total logistics cost and maximize product freshness. To better characterize perishability, a stage-specific freshness decay function captures the nonlinear deterioration of products before and after pre-cooling. Transportation-related carbon emissions are also incorporated to enhance environmental relevance. Given the complexity of the location-routing problem, a genetic algorithm (GA) is used to obtain Pareto-optimal solutions. An empirical case study in Shandong Province, China, is conducted under three scenarios: (1) no pre-cooling, (2) decentralized pre-cooling at origins, and (3) centralized pre-cooling at regional DCs. Results show that the centralized strategy achieves superior performance, reducing total daily cost by 3.79% and producing the lowest freshness loss compared with the no-pre-cooling baseline. In contrast, decentralized origin-side pre-cooling improves freshness preservation but increases total cost by 5.27% due to higher equipment investment and weaker route efficiency. These findings demonstrate that an integrated location-routing perspective can provide more effective first-mile cold chain planning than treating pre-cooling as an isolated facility decision.

Firms increasingly coordinate lot-sizing, distribution center (DC) location, and joint replenishment over time to reduce costs. This paper studies this integrated problem under the (R, S) policy with demand, which stochasticity varies from period to period. We build a model where only the timing of replenishment is the core decision; all else follows from it. To solve efficiently, we design a hybrid differential evolution algorithm with a random neighborhood search. Experiments show our algorithm outperforms eight benchmark methods in solution quality and speed. A sensitivity analysis reveals how key parameters affect the total cost, replenishment frequency, and the number of DCs. Higher ordering costs reduce replenishment frequency, and larger DC setup costs lead to fewer DCs. However, fewer DCs do not always lower the total cost—when dealers are geographically dispersed, more DCs can reduce the overall total system cost. These insights help managers balance the cost components in a supply chain network design.

ABSTRACT This paper tries to investigate remarkable dynamics of retail and distribution centers, examining the impact of demand uncertainties, offered prices, supply disruption, and customer behavior. The main focus is on discriminative pricing within a fiercely competitive distribution system, considering customer behavior during supply disruption. By employing Stackelberg competition, a comprehensive model is constructed that unravels the competition between wholesalers and retailers in the distribution center, providing a profound understanding of the intricate dynamics at play. The model aims to maximize profit and utility for wholesalers and retailers, placing particular emphasis on behavior-based price discrimination at the retail level. To address supply disruption challenges, two resilience strategies are explored: wholesalers holding inventory and establishing supportive contracts with reliable suppliers. These strategies mitigate disruptions and ensure distribution system continuity. To demonstrate practicality, a real-world case problem is implemented and showcases the model’s efficacy. Then, sensitivity analyses provide valuable managerial insights. This study contributes to understanding how retailers and distribution centers optimize operations and decision-making in the face of price discrimination and risk of disruption.

Oceanic islands as cradles for plant diversification: A global phylogeny and biogeography of the Neottopteris clade (Asplenium; Aspleniaceae).

Traditional Value Stream Mapping (VSM) effectively visualizes operational waste but offers limited support for explaining how interdependent inefficiencies reinforce one another and for sequencing improvement actions when multiple wastes co-occur. To address this diagnostic gap, this study develops a structural diagnostic and prioritization artifact that complements VSM by integrating Interpretive Structural Modeling (ISM) and Fuzzy MICMAC into its workflow, enabling the structural analysis of interdependent wastes and supporting prioritization under uncertainty. The artifact was developed and refined through Design Science Research (DSR), then instantiated and evaluated at a Brazilian pharmaceutical distribution center. ISM produced a hierarchical ordering of ten waste mechanisms identified through VSM, while Fuzzy MICMAC classified them into driving, linkage, and dependent roles. These findings were consolidated into a three-step intervention roadmap that incorporates short-term operational feasibility into the sequencing logic. Theoretically, the study extends VSM with a structural reasoning layer that operationalizes causal prioritization without altering its descriptive premise, advancing Lean diagnosis from performance-based prioritization toward systemically grounded prioritization based on influence–dependence relationships. Practically, the artifact offers managers a structured procedure for distinguishing foundational drivers of inefficiency from linkage and dependent manifestations, supporting more transparent and defensible sequencing of improvement actions.

The growing demand for energy and emerging environmental concerns are making it necessary to look for more sustainable alternatives. To address the limitations of first-generation biofuels and reduce dependence on fossil fuels, this study focuses on second-generation bioethanol sourced from non-edible pomegranate waste. This study develops and analyses a supply chain optimization model for the sustainable production of biofuel from pomegranate waste and solves it using a genetic algorithm. The framework assesses key supply chain elements, including collection centres for pomegranate waste, processing plants, bio-refineries for conversion and distribution centres for final bioethanol. The primary objective of the optimization is to reduce the total cost of the biofuel production system and to maximize positive environmental impact through waste valorization. A numerical example validates the framework, and a sensitivity analysis further evaluates the economic viability of the supply chain under fluctuating market conditions, such as variations in the purchasing cost of waste, the production cost of bioethanol and the opening cost of plants. Biofuel production supports the Sustainable Development Goals (SDG-12 and -13) by transforming waste into renewable energy. This study aims to address gaps in biofuel research by focusing on the underutilized area of pomegranate-based biofuel through an integrated supply chain optimization framework. The findings offer practical values for researchers working on renewable energy solutions, policymakers and business leaders.

Intra-abdominal urine leakage can occur during pediatric urologic surgeries such as laparoscopic pyeloplasty. While some patients exhibit postoperative symptoms resembling ileus, the pathophysiological effects of urine exposure on the peritoneum remain unclear. This study aimed to determine whether intraabdominal exposure to sterile urine induces peritonitis or gastrointestinal (GI) dysmotility using a rat model. Four groups were established using 9-week-old female Sprague-Dawley rats: the sham group, the intraperitoneal autologous urine exposure group, the mechanical peritonitis group, and the chemical peritonitis group. One week later, peritoneal and small intestinal tissues were harvested for histological evaluation. Immunohistochemical analysis was performed to assess inflammatory cell infiltration. GI motility was evaluated using orally administered FITC-dextran, and the fluorescence signal distribution was analyzed to calculate the geometric center. In the sham and autologous urine exposure groups, no significant thickening of the peritoneum or infiltration of inflammatory cells was observed, in contrast to the chemical peritonitis group, which exhibited marked histopathological changes. GI motility was preserved in the urine exposure group, as evidenced by a normal FITC distribution and normal geometric center values. In contrast, the peritonitis groups showed delayed intestinal transit and significantly reduced geometric center values. Intraabdominal exposure to sterile urine did not induce histological peritoneal inflammation or significant impairment of gastrointestinal motility in this rat model. These findings suggest that sterile urine alone is unlikely to serve as an independent trigger of postoperative peritoneal inflammation or ileus under controlled experimental conditions.

This paper addresses the two-stage fixed-charge transportation problem with distribution-center (DC) opening costs.We propose adaptive random-key particle swarm optimization with DC-closure local search (ARK-PSO-CLS), a random-key particle swarm optimization (PSO) method with adaptive coefficient scheduling, stagnationtriggered partial restart, and a DC-closure local search, combined with a feasibility-preserving decoder.Experiments are conducted on 149 public benchmark instances from Mendeley Data (50 small, 50 medium, 49 large).For small instances, exact optima are obtained by mixed-integer linear programming (MILP) solved with HiGHS, enabling true optimality gaps; for medium and large instances, gaps are computed relative to the best value found within the compared set.Using swarm size N = 10 and T = 10 iterations, results show statistically significant improvements over greedy construction, random-key PSO (RK-PSO), and random-key genetic algorithm (RK-GA) baselines, while accounting for the additional evaluation cost of local search.Average ranks (lower is better) are 1.75 for ARK-PSO-CLS, 2.28 for RK-PSO, 2.78 for Greedy, and 3.19 for RK-GA.

Federated Learning (FL) has emerged as a prominent paradigm for privacy-preserving distributed training. However, particularly in non-IID and open-participation environments, FL remains highly vulnerable to model poisoning, specifically backdoor attacks. Existing defenses, such as robust aggregation and representation-learning approaches, often struggle against colluding adversaries and adaptive attack strategies, leading to model performance degradation and the propagation of malicious updates. To address these challenges, we propose FLAURA, a robust FL defense framework incorporating adaptive trust evaluation and hybrid aggregation. FLAURA operates within the penultimate-layer representation (PLR) space, integrating a dual-level trust evaluation mechanism. At the global level, it leverages the geometric median of PLRs to robustly estimate the global distribution center, thereby effectively mitigating systematic shifts induced by malicious clusters. At the local level, it employs Maximum Mean Discrepancy (MMD) combined with curvature-based knee point detection to adaptively determine trust boundaries. This design effectively distinguishes benign data heterogeneity from malicious perturbations without requiring prior knowledge of the fraction of adversaries. Furthermore, FLAURA implements a hybrid mechanism of hard filtering and soft weighting to exclude low-trust updates while preserving beneficial model diversity. Extensive experiments on the FMNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that FLAURA significantly outperforms state-of-the-art baselines, reducing attack success rates and target-label confidence while maintaining high accuracy on clean data.