Timely and continuous drug availability is a key indicator of hospital service quality; however, drug stagnation (overstocking) and stockouts persist as critical challenges in the Pharmacy Unit of North Buton Regional Hospital. This qualitative case study aimed to analyze the implementation of drug logistics management and its contribution to these problems, involving in-depth interviews with drug logistics managers, the head of the pharmacy unit, and hospital management, complemented by document review and field observations. Data were thematically analyzed using NVivo and benchmarked against the indicators stipulated in the Indonesian Minister of Health Regulation No. 72 of 2016. The findings reveal that most stages of logistics management planning, procurement, receiving, storage, distribution, and control were suboptimally implemented. Drug stagnation was primarily driven by inaccurate demand forecasting and uneven distribution, whereas stockouts resulted from supply delays, weak procurement coordination, and the limited effectiveness of the Pharmacy and Therapeutics Committee. Overall, deficiencies in the logistics information system, constrained human and financial resources, and poor inter-unit integration emerged as the main underlying factors, indicating the urgent need for strengthening data-driven demand planning, optimizing the role of the Drug Logistics Unit (KFT), and developing an integrated logistics information system to improve drug availability and service performance at the hospital.

Purpose This study uses cybernetic theory to examine the integration of predictive artificial intelligence (AI) and the ship-then-shop model in fashion supply chains. The study examines how predictive systems can enhance adaptability, reduce waste and promote sustainability by incorporating real-time feedback loops into operational and consumer-facing processes. Design/methodology/approach A structured literature review was conducted across peer-reviewed sources and supported by a thematic coding matrix. This matrix classifies insights into five key phases of the supply chain: sourcing, production, warehousing, distribution and reverse logistics. The review also incorporates cybernetic and systems theory to conceptualize AI as a control mechanism within adaptive feedback systems. Findings AI can enhance the sustainability and efficiency of the fashion supply chain by enabling dynamic decision-making. The ship-then-shop model decouples production from immediate purchases, aligns output with demand and reduces overproduction. However, the review also identifies gaps in empirical validation and ethical risks, including algorithmic opacity, labour displacement and consumer surveillance. Originality/value This study makes an original contribution by framing AI as a cybernetic enabler of system-level transformation in fashion logistics. Unlike existing literature, it synthesizes predictive intelligence, consumer behaviour and adaptive operations within an integrated conceptual model that highlights opportunities and ethical challenges for sustainable innovation.

Abstract The Maos Railway Bridge over the Serayu River, located in Maos Village, Cilacap Regency, Central Java, is a strategic infrastructure asset along the southern railway corridor of Java Island, operating since 1894. As part of the Trans-Java railway network constructed by the Staats Spoorwegen (SS), the bridge connects the main Yogyakarta–Cilacap railway line and plays a vital role in regional logistics distribution and passenger mobility. However, this 130-year-old structure is currently threatened by local scouring around its piers, which poses a significant risk of structural failure. This study aims to identify the potential scouring risks, analyze the influence of river flow on the morphology and depth of scour holes, and propose effective mitigation strategies. The methodology involves hydrological analysis, hydraulic modelling using HEC-RAS, and a comprehensive risk assessment encompassing physical impacts of a potential bridge collapse. Simulation results indicate that scour depths may reach up to -9.01 meters in piers and -17.95 in abutments. The bridge’s location on a river bend further intensifies scour due to secondary currents and velocity redistribution, highlighting the need for effective scour protection measures to ensure structural stability under extreme flow events.

Cyproflanilide, a novel meta-diamide insecticide, overcomes multiple insecticide resistance in Cnaphalocrocis medinalis: Evidence from comparative toxicity assays and genome-edited Drosophila.

This paper examines the conceptual, structural, and functional relationships between distribution logistics and supply logistics, two domains that are often perceived as synonymous despite their fundamentally different roles within the logistics chain. The study clarifies frequently used terms, defines their correct positioning within distribution and supply logistics, and analyses their interconnections in the context of material, information, and financial flows. Based on established scientific literature, professional standards, and practical project experience, the paper highlights the distinctions between distribution, physical distribution, physical supply, and distribution logistics as a system. It further explains the functions of distribution in value creation, harmonisation of supply and demand, and execution of essential operational activities, including transport, storage, regrouping, and customer service. The research emphasises the systemic integration of distribution and supply as inseparable parts of a single chain, driven by the potential difference between supply and demand. The paper also outlines the structural role of distribution channels, logistics channels, and acquisition and logistics distribution subsystems, illustrating how coordinated strategic and operational decisions influence the performance of the entire logistics system. By defining key terms and illustrating their relationships, the study contributes to a clearer conceptual framework for logistics professionals and supports more accurate use of logistics terminology in academic and practical environments. The proposed framework supports consistent terminology usage in logistics education, research, and practical system design.

NetLogo is widely used for agent-based simulation of transport and logistics because it is open, flexible, and fast to prototype. This review synthesizes peer-reviewed studies on NetLogo across four application streams: (a) Intelligent Transport Systems (ITS) and flow optimisation; (b) Public-transport operations and evacuation management; (c) Traffic-control, energy use, emissions analysis; and (d) Driver/vehicle behaviors and connected/autonomous vehicle modelling. We map how agent rules, interaction topologies, and calibration choices shape material, information, and human flows across networks and terminals, and how they inform transport operations, distribution logistics, and supply chain decisions. A thematic synthesis highlights five recurring gaps: (1) Oversimplified agent size/geometry; (2) Limited behavioral realism for drivers, passengers, and controllers; (3) Weak mesoscopic linking between macro demand and micro operations; (4) Short time horizons for dynamics of congestion and energy; and (5) Incomplete calibration-verification-validation. We propose a logistics-oriented research agenda: multi-scale coupling (macro–meso–micro), behavior learning with ML, real-time digital-twin use cases, standardized CV&V protocols, and energy-emissions co-metrics. The review clarifies where NetLogo adds value to transport logistics - rapid what-if testing of control policies, routing, and terminal operations - while outlining steps needed for rigorous, decision-grade models.

The subject of this article is the processes of organization and optimization of the distribution logistics of technical defense products under wartime conditions, in particular, the development of a network logistics structure considering reverse flows, risks, and limited resources. The purpose of the study is to improve the efficiency of distribution logistics of technical defense products in wartime by constructing an adaptive structure of logistics flows and developing a mathematical optimization model that accounts for risks and resource constraints. The article addresses the following tasks: analysis of the specific features of distribution logistics for technical defense products in combat conditions; development of a structural model of logistics flows that considers direct and reverse links as well as the reuse of components; construction of a mathematical model for optimizing logistics processes with consideration of time costs, delay risks, and resource constraints; justification of the application of scenario analysis and adaptive planning to enhance the resilience and continuity of supply under uncertainty. The following methods are applied: a systems approach, structural decomposition of logistics processes, risk-based approach, constrained mathematical modeling, scenario analysis, and adaptive planning. The results obtained include the construction of a structural model of logistics flows for supplying the Armed Forces of Ukraine, which takes into account direct and reverse flows as well as component reuse; the development of a mathematical model for optimization of distribution logistics with an objective function of minimizing time and constraints on supply volume, total risk, and the number of routes. It is demonstrated that risk consideration significantly affects the choice of optimal routes and strategies. The feasibility of using scenario analysis and adaptive planning to ensure continuity of supply in combat conditions is substantiated. Conclusions. The proposed structural and mathematical models make it possible to reduce the logistics cycle time, increase the resilience of the logistics system to risks, and ensure rapid response to changing circumstances. The use of reverse logistics and component reuse helps reduce costs and mitigate resource shortages. Scenario analysis and adaptive planning are effective tools for managing the distribution logistics of technical products under uncertainty and wartime risks.

This paper proposes a representative point-based bootstrap (RP-bootstrap) to improve confidence interval estimation for the logistic distribution. The method replaces the traditional empirical distribution with a smoothed approximation constructed from statistically optimal representative points (RPs), leading to a more stable resampling distribution. We integrate the RP-bootstrap with the bootstrap-t, percentile, and BCa methods to construct intervals for the location and scale parameters. Its performance is compared to the classical nonparametric bootstrap via comprehensive Monte Carlo simulations and two real-data applications. The results show that the RP-bootstrap delivers noticeable improved finite-sample performance, particularly for small samples where standard bootstrapping often under-covers. It achieves recognizably higher coverage probabilities while maintaining shorter or comparable expected interval lengths. These improvements are strongest for the bootstrap-t interval and are consistent for both parameters, though more marked for the location. In conclusion, the RP-bootstrap is a computationally efficient and reliable alternative for logistic inference, offering enhanced accuracy, especially in small-sample scenarios. Purpose: Construction of confidence intervals under small sample size is frequently encountered in statistical inference, such as estimating some treatment effect in medical research with limited number of patients. Traditional nonparametric bootstrap methods often suffer from undercoverage in such settings. To address this limitation, we propose the RP-bootstrap—a resampling procedure that draws samples from an approximated distribution formed by representative points (RPs) of the logistic distribution. Methods: The RP-bootstrap is developed for constructing confidence intervals for the mean and variance of the logistic distribution. The algorithm generates weighted samples from the estimated RPs. The RP-bootstrap method is applied to construction of different types of confidence intervals (CIs) like the bootstrap-t, percentile, and {\rm BCa} CIs. Its performance and comparison with the traditional nonparametric bootstrap are evaluated through Monte Carlo simulation and real-data application. Results: Based on the Monte Carlo study under a set of small sample sizes, the RP-bootstrap achieves noticeable higher empirical coverage probability and competitive or shorter expected interval lengths compared with the nonparametric bootstrap. The improvements are much noticeable for small sample sizes like n<30 and for the bootstrap-t confidence intervals, where the nonparametric bootstrap frequently shows undercoverage of the true population parameter. Contribution: This study demonstrates that representative points provide a stable and efficient alternative to resampling methods from logistic models. The RP-bootstrap offers a practical method for reliable small-sample inference and yields confidence intervals with improved accuracy and reduced variability relative to the traditional nonparametric bootstrap method.

We have introduced Modified Logistic Lomax (MLL) Distribution Modifying the Logistic Lomax model. Modification of the model makes it to give different shaped density curve and hazard rate curves enabling model more flexible and suitable for various type of the datasets. Study of various statistical properties and Simulation study is performed. The MLL model is more useful for real datasets where classical probability models do not analyze the dataset appropriately. Maximum likelihood estimation (MLE), Least square estimation (LSE) and Cramer-von Mises methods (CVM) are used for parameter estimation and consistent results of the parameters areseen. Information criterion and graphical approach demonstrate the superiority of the model over some probability models. The MLL model may be helpful for the researcher to know the formulation of the new probability model and its application for real data analysis. All the computation analysis is performed using R programming.

ABSTRACT In this study, daily maximum temperature data from seven synoptic stations in Ardabil Province were analyzed using statistical probability distributions to estimate extreme temperature values for 2–100-year return periods. Using daily data from seven stations, this study applies a cumulative frequency model with CumFreq software to estimate extreme maximum temperatures for 2–100-year return periods, fitting the best statistical distribution at each station to reflect local variability. The Mann-Kendall test revealed significant maximum temperatures increases at Parsabad, Khalkhal, and Sareyn, with non-significant changes elsewhere and a slight decline at Airport Ardabil. The Logistic distribution best captures maximum temperatures in Ardabil (absolute difference: 2.37%), while Parsabad is well-modeled by the Fisher-Tippet T2 mirrored distribution, yielding the lowest discrepancy (1.58%). Additionally, the GEV distribution, showing a best-fit at Sareyn (4.23%), exemplifies the need for station-specific models, as each location’s unique climate is best represented by different distribution types. The findings reveal marked temperature differences across stations; for example, Bilesavar exhibits the highest temperatures, reaching 44.85 °C at the 100-year return period, while Khalkhal’s maximum reaches only 37.01 °C. These results highlight the need for site-specific temperature estimates to guide tailored climate adaptation strategies and address regional climate variability in Ardabil Province.

The Suramadu National Road is a strategic corridor connecting Madura Island with the Surabaya metropolitan area, playing a crucial role in supporting public mobility and logistics distribution. The high traffic intensity and vital function of this road section require compliance with safety and roadworthiness standards. However, initial observations indicated several issues, such as malfunctioning street lighting, unauthorized U-turns, and the presence of hazardous objects on the roadside, which could potentially increase the risk of accidents. This highlighted the need for a Roadworthiness Test (ULFJ) evaluation to ensure that this road section met technical and operational requirements. This study aimed to analyze the roadworthiness level of the Suramadu–Bangkalan National Road (Sta 0+100 to Sta 3+400), a 3,392-meter section. The star rating approach, as stipulated in Minister of Public Works and Public Housing Regulation No. 4 of 2023 and Circular Letter 11/SE/Db/2024, was used. Data were obtained through field surveys at 100-meter intervals across 68 assessment segments. The assessment resulted in a Star Rating Score of 10.71, corresponding to a 3-star rating. According to PUPR Ministerial Regulation No. 4 of 2023, the road still met operational requirements and was classified as a safe road based on Circular Letter 11/SE/Db/2024. However, the 3-star rating indicated that several safety aspects only met minimum standards. Therefore, the prepared technical recommendations need to be implemented immediately to upgrade this road section to a safer category with a target rating of 4 or 5 stars.

Micro, small, and medium enterprises (MSMEs) are important economic drivers for Indonesia, especially in labor-intensive sectors like footwear manufacturing. MSMEs, though, face acute logistical problems because of heterogeneous customer demand, limited production capacity, and ever-increasing transportation costs. Few existing works have focused on monthly logistics planning for MSMEs in developing countries with realistic costing and demand structures. To develop and analyze a Genetic Algorithm (GA) optimization model to maximize profit within a constrained monthly footwear profit distribution network. To achieve this, we needed to assess how multi-retailer product allocation balance could be achieved with minimum operational constraints such as production caps, cost-efficient logistics, and streamlined processes. This study employed a quantitative experimental design approach and implemented a GA with real-valued chromosome representation, tournament selection, single-point crossover, and Gaussian mutation. The model was built using real data from a footwear MSME operating in the Lamongan and Tulungagung regions of Indonesia. The algorithm was implemented using Python and tested for reliability with 10 executed validations for independence. Within 60 generations, the GA maintained consistent convergence and achieved a final fitness value with a coefficient of variation of 0.24%. The optimized allocation achieved a net profit margin of 15.22% while utilizing the available production capacity (600 units/month). Because of increased profit contribution, greater-distance wholesale customers were served first despite incurring higher transport costs. The model had no constraint violation and reduced transportation costs to 1.45% of total revenue. Using GA to address multi-objective distribution challenges in the context of MSMEs appeared to have positive results, confirming the effectiveness of this approach. The proposed approach helps frame and guide critical allocation and routing decisions, which can be made within the boundaries of operational constraints. Further work is needed to incorporate stochastic demand modelling and multi-objective problem extensions and seek real-time application to bolster support for decision-making in dynamic scenarios.

In the implementation of natural disaster management operations, one of the primary considerations is the process of distributing logistical aid to disaster-struck locations. For logistics management to be beneficial and in accordance with needs, standardization of disaster management logistics is necessary. One of the natural disaster management operations involving Air Squadron 32 was the response to the 2019 Lombok earthquake. In the logistics distribution process, Air Squadron 32 still encountered various problems and obstacles related to the implementation of BNPB Regulation Number 23 of 2014 concerning the Standardization of Disaster Management Logistics. The problem phenomenon that emerged was the weather factor, which could hamper the flight of the C-130 aircraft, resulting in a longer distribution time. The purpose of this study is 1) To describe and analyze the implementation of the BNPB Regulation Number 23 of 2014 concerning Standardization of Disaster Management Logistics in the distribution of logistics for the Lombok Island Earthquake Disaster in the Air Squadron 32 area. 2) To describe and analyze the obstacles and supporters in the implementation of the BNPB Regulation Number 23 of 2014 concerning Standardization of Disaster Management Logistics in the distribution of logistics for the Lombok Island Earthquake Disaster in the Air Squadron 32 area. The data analysis technique uses three components, namely 1) reducing data by summarizing or selecting the main points from the data obtained, 2) the reduced data is then presented in the form of a brief description, and 3) after presenting the data, the author concludes. To test the validity of the data, the author employs triangulation by using different data collection techniques to obtain data from the same source.

SummaryHybridization between closely related species is increasingly recognized as a major source of biodiversity. Yet, whether it can create advantageous trait combinations while purging harmful alleles remains unknown. To address this question, we studied Arabis nemorensis and Arabis sagittata, two endangered species that currently hybridize in a single hotspot.We chose two representative individuals originating from the hotspot, generated high‐quality annotated genome sequences, crossed them to form an F2 population, quantified segregation distortion along the genome, measured 22 phenotypic traits and mapped their genetic basis.Two genomic regions showed strong segregation distortion favoring A. sagittata alleles in the F2 and potentially accelerating their introgression. Fifty‐eight quantitative trait loci (QTLs) were identified for 20 traits, with additive and dominance effects best fitting Gaussian and logistic distributions, respectively. It was found that 48% of QTLs were unlinked to reduced fitness or segregation distortion. A major QTL affecting flowering time implicated Terminal-Flower 1 (TFL1) as a candidate gene for life‐history adaptation. QTLs did not overlap with recent selective sweeps, except for those controlling rosette size.Our findings offer unique insights into both the potential for adaptive trait combinations and the constraints imposed by hybrid fitness loss during incipient stages of hybridization.

Based on the air-ground collaborative logistics distribution model using UAVs and vehicles, this paper addresses the logistics scenario of delivering before picking up. Considering factors such as customer priority, vehicle cost, and UAV cost, with the objective of minimizing the total travel cost, we propose the Air-ground Collaborative Logistics Transportation Scheduling Problem with Pickup and Delivery Considering Customer Priority (ALTSPPDCP). Based on the characteristics of the model, the use of heuristic algorithms can efficiently solve such optimization problems, avoiding the time-consuming exhaustive search and improving the quality of the solutions. This paper designs a multi-layer, multi-stage encoding and decoding strategy based on the Three-Learning Strategy Particle Swarm Optimization algorithm, integrating the ascending order sorting method and dynamic segmentation method to transform the particle space into the model space. An intelligence optimization algorithm for solving ALTSPPDCP is proposed. Finally, In the 50-node scenario of the model comparison, the vehicle-UAV schema achieved a total cost that was 14.86% lower than that of the vehicle-only schema. In the algorithm comparison experiment, the optimal solution obtained by TSLPSO reduced costs by 39.99% and 27.94% compared to PSO and RPSO, respectively.

This study investigates the storage and distribution logistics associated with cruise ship outfitting, concentrating on the continuous location-routing optimization problem in the presence of obstacles. The objective is to determine the optimal placement of a distribution centre within a continuous planar region and optimize the corresponding material distribution routes. A mixed-integer nonlinear programming model was developed, combining continuous area location optimization with discrete location-routing optimization. A two-stage hybrid algorithm was proposed. To handle obstacles' impact on distances, a novel weighted distance calculation incorporating obstacle area and distance factors was introduced, replacing standard Euclidean distance for path selection. Tailored coding and heuristic rules were also designed. 14 distinct datasets of varying scales were developed for experimental purposes, reflecting real-world construction scenarios and encompassing up to 120 delivery points. The effectiveness and novelty of the proposed method were substantiated through experimental validation and case study analyses.

To explore patterns of medical service and medication use for mental health in women with breast cancer (BC) pre- and post-diagnosis. Population-based South Australian Cancer Registry linked with medical services and medication prescriptions data for all women with BC diagnosed between April/2013 and March/2014. The patterns of medication and medical service use for mental health were examined in 1-yearly intervals between 1-year before and 5-years after BC diagnosis. Generalised estimating equations models with binary logistic distribution were used to examine characteristics associated with medication and medical service use for mental health. Data of 1143 women were analysed. The proportion of people who had a prescribed medication for mental health increased from 23% 1-year pre-diagnosis to 29% in the first year of diagnosis and remained stable at 29%-30% throughout 5 years post-diagnosis. The prevalence of medical service use for mental health increased from 7% 1-year pre-diagnosis to 13% in the first year of diagnosis, although the percentage decreased gradually over time post-diagnosis. A higher burden of comorbidity and time period after BC diagnosis were associated with increased likelihood for medication and medical service use for mental health, while older age, residing outside of major cities and receipt of radiotherapy were associated with decreased likelihood of mental health service use. There is an apparent increase in medical service and medication use for mental health in the first year of BC diagnosis. These findings support the importance of early mental health screening to identify at-risk individuals for targeted intervention.

This study aims to examine the existing literature related to the efficiency of general cargo transportation systems as a critical element in strengthening national maritime connectivity. Through this approach, a more comprehensive understanding is expected regarding the contributions, constraints, and factors influencing the effectiveness of general cargo transportation in supporting the smooth distribution of maritime logistics across regions in Indonesia. This research adopts a qualitative descriptive approach through a literature review. The focus of the analysis is directed toward achieving a comprehensive understanding of the efficiency of general cargo transportation in supporting national maritime connectivity. The reviewed literature is categorized based on several key aspects affecting efficiency, including infrastructure, operational processes, policy, and technology. The findings indicate that the high volume of domestic sea freight, reaching hundreds of millions of tons, demonstrates that maritime transport remains the most dominant mode for large-scale general cargo logistics in Indonesia. The growth in sea freight further reflects an increasing dependence on maritime transportation as the primary mode of inter-island goods distribution. Enhancing national maritime effectiveness can be achieved through several key indicators, including the efficiency of customs services, competitive pricing structures, the provision of high-quality logistics services, increased port infrastructure capacity, service diversification, and accelerated service completion times.

Background: The food sector plays a critical role in promoting population well-being and contributes significantly to economic, social, and environmental development. However, inefficiencies in distribution logistics often result in elevated operational costs, potentially compromising the viability of enterprises in this sector. This study focuses on evaluating the economic feasibility of a fresh beef and pork distribution center in the southern region of Minas Gerais, Brazil. Methods: A case study methodology with a quantitative approach was adopted. Methodological triangulation was applied by combining a traditional Economic Feasibility Analysis (EFA) with a Monte Carlo Simulation to incorporate uncertainty in key input variables. This approach enabled a comprehensive assessment of project viability under both deterministic and probabilistic conditions. Results: The results indicated that distribution price per kilogram, market share, population growth, and per capita meat consumption had a positive correlation with profitability. The economic analysis confirmed the viability of the proposed distribution center, with high expected profitability and a short payback period. The Monte Carlo Simulation revealed that market share, unit price, and consumption levels are the most influential drivers of financial performance, while logistics costs represent the main limiting factor. Conclusions: This study provides a robust, data-driven framework for investment decision-making in food logistics infrastructure. It demonstrates the value of integrating deterministic and probabilistic analyses to improve risk management and strategic planning in the food distribution sector.

This study investigated the impact of a Yard Management System (YMS) implemented at a third-party logistics distribution center in the United States. Five years of operational data (2018–2022), including 72 monthly observations of inbound and outbound freight performance (measured in pounds) and detention occurrences (measured in US dollars), were analyzed using one-way ANOVA to assess pre- and post-implementation performance. The results indicated that the YMS significantly improved inbound and outbound freight volume, reduced detention occurrences, and enhanced operational efficiency within the third-party logistics distribution center. These findings suggest that YMS can be an effective tool for enhancing yard-level operational efficiency, reducing delays, and supporting broader supply chain optimization strategies in third-party logistics environments.