Supply Points Research Articles

Analysis of Microbiological Quality and Antibiotic Resistance Patterns in Milk Supply Chain

The widespread consumption of milk for its nutritional value and health benefits brings the risk of milk-borne diseases due to the presence of various microorganisms, including antibiotic-resistant pathogens. This has led to an increased focus on ensuring the safety of milk products across the supply chain by dairy industries. The study aimed to evaluate microbiological parameters and detect multi-antibiotic-resistant pathogens at three specific supply points and to explore the association between the presence of residual antibiotics and the resistant isolates in milk samples. About 50 milk samples, including raw, soon-after-processed, packaged marketed pasteurized, and UHT milk, were subjected to microbiological analysis. This involved assessing the total bacterial count (TBC) and total coliform count (TCC), conducting antibiotic susceptibility tests through disk and well diffusion assays, detecting virulence genes in multi-antibiotic resistant isolates using gene-specific PCR, and analyzing residual antibiotics by HPLC. The study revealed that the quality of raw milk samples was unacceptable (TBC >4.5x107 CFU/mL and TCC >5.6x104 CFU/mL), while pasteurized samples from processing plants had lower counts than those from retail stores (TBC >5x105 CFU/mL and TCC >1.6x104 CFU/mL) indicating post-pasteurization contamination. About 70.37% of the isolates were Gram-negative, with Escherichia coli (21.4%) and Vibrio (18.8%) being the most prevalent. Resistance to antibiotics was substantial, particularly against ampicillin (86.3%), tetracycline (76%), and ciprofloxacin (58.9%). Gene-specific PCR analysis detected uidA, oprL, and oprI virulence genes in multi-drug-resistant Escherichia coli and Pseudomonas sp. respectively. The study also revealed a direct association between the presence of residual antibiotics and the resistant isolates, emphasizing the need for dairy industry improvements. As high bacterial counts in milk can pose health risks by fostering antibiotic-resistant pathogens, it is essential to mitigate microbiological contamination in the milk supply chain through the implementation of various precautionary measures.
 Bioresearch Commu. 10(1): 1462-1473, 2024 (January)

INVESTIGATING THE COST EFFECTIVENESS OF BIODIESEL DISTRIBUTION IN INDONESIA

The implementation of biodiesel in Indonesia has been regarded as one of the main strategies to improve country’s energy security equally and equitably. However, as the largest archipelago country in the world, the activity in distributing their biodiesel production bears a key problem. The complexity of delivering biodiesel from the supply point to the blending terminal of gasoil might result in cost inefficiency, which in turn will escalate the government funding of biodiesel incentives. This paper aims to both evaluate and formulate the Indonesian biodiesel transportation costs in all routes using either trucking or shipping mode. Based on primary data and information obtained from experts, we found that the most dominant factors that affect the shipping distribution cost are the size of the ship and its activities (such as voyage, ballast, loading, discharge, waiting, and buffer), whilst for the trucking mode, the dominant factors are both its corresponding operational costs and investment costs. Given that fuel prices fluctuate periodically, we also argue that the cost differences between the regulated and formulated reference may arise due to i) the data inaccuracy of the diesel fuel consumption and ii) specific terms outlined in the agreement between the corresponding transporters and suppliers.

Modelling Ca2+ Mobility and Dynamics in Solvent-Free Molten Salt Electrolytes for Calcium Batteries

Multivalent elements based batteries using earth-abundant minerals is a route with promises, not only from a performance but also from a supply and cost point of view; this is not the last true for calcium (Ca) batteries since Ca is the fifth most abundant element in the Earth's crust [1,2,3]. There is, however, no functional electrolyte available yet for Ca batteries, calling for early modelling to be made to map the influence of different Ca-salts and salt concentrations on electrolyte physicochemical properties such as density, viscosity, ionic conductivity, charge carrier dynamics and diffusivity [3, 4]. One particular route little trodden is molten salt electrolytes (MSEs), materials that consist of ions with different valences in direct contact with each other due to the complete absence of any solvent. In practice, MSEs are multicomponent salt mixtures with reduced melting points with respect to the ingoing components and furthermore they also expand the operating temperature range upwards as compared to conventional liquid electrolytes. Moreover, MSEs have high ionic conductivities due to their intrinsically high ionic content and ionic mobility. Additionally, MSEs exhibit wide electrochemical stability windows, non-flammability, and non-toxicity [5, 6]. We have investigated MSEs composed of Ca(FSI)2, LiFSI, NaFSI, and KFSI, by molecular dynamics (MD) simulations. Apart from the basic properties, we focus on the relative dynamical properties of the different cations in the MSEs in order to obtain a fundamental understanding of the systems’ behaviour, both globally and locally, but for natural reasons with special emphasis on the Ca2+ ions.

CROSS-PLATFORM SOFTWARE SYSTEM FOR THE LOGISTICS OF HUMANITARIAN SERVICES

The problem of designing a cross-platform software system for the logistics of humanitarian services is considered. Existing software analogs of international deliveries are considered. A comparison of popular software products in the field of international trade is given. The technologies for developing software services to ensure the cross-platform system for the logistics of humanitarian services are reviewed. The algorithms used in the system for solving the transportation problem, namely the routing problem, are presented, with the help of which the most optimal path between the supply points is selected. The general algorithm of the system operation is proposed and the structural diagram of the application is presented. Keywords: cross-platform, software service, logistics, algorithm, application.

Location and Capacity Planning of Freight Villages: The Case of Türkiye

Freight villages are logistics hubs where distribution and storage related activities meet. Determining their locations and capacities is a strategically important yet a difficult problem due to its complex structure. This paper provides a mixed integer linear programming model for identifying the locations, number, and capacities of freight villages. Objective is to set up a distribution network and minimize its total cost. The proposed model is computationally efficient as the optimal solution can be found within minutes. The model is applied using real life data in Turkey. The application includes major container ports as supply points and all cities as demand points, which are also alternative locations for freight villages. Optimal solution provides a plan and a budget to build the required capacity and number of logistics villages dispersed geographically. Sensitivity analysis is also conducted to investigate the system-wide costs and derive insights when there is a limit on the number of freight villages.

METHODOLOGY OF THE RATIONALE OF AN AMOUNT OF UNMANNED CARGO DELIVERY SYSTEMS

It is possible to reduce human losses during the cargo delivery to units on the battlefield with the use of unmanned systems (UMSs), including logistical unmanned ground vehicles (UGV) of various groups carrying capacity and unmanned aircraft systems (UAS). The urgent task is to determine the required number of unmanned cargo delivery systems in the interests of the mechanized infantry battalion. The method of determining the number of unmanned cargo delivery systems for the logistical means (LM) transportation to the units of the mechanized infantry battalion is described. The optimality criterion of the selected composition of the system is the maximum efficiency-cost ratio (ECR) under the condition of transporting the maximum weight of LM required for the battle day from the combat supply point to the units of the mechanized infantry battalion. In this case, the efficiency indicator is the share of the total weight of LM that must be delivered to the units. In order to calculate this indicator, it is necessary to estimate the probability of successful delivery of LM to different battalion units for each UMSs. The cost indicator is the product of the masses of the cargo and energy costs on the delivery route, purchase costs and total costs for operating the UMSs during the forecasted average life of operation in terms of 1 kg of cargo. Two transport tables are built based on the results of the calculations. The first table includes probability weighting factors, and the second includes weighting factors of the cost of delivering LM from a combat supply point to units by various UMSs. For each of these transport problems, the optimal solution is obtained by linear programming methods, and for the ECR, by methods of optimization of problems with a nonlinear objective function, which is illustrated by an example using the "Solver" add-on in the Excel spreadsheet processor. Keywords: logistic support, unmanned systems, logistical unmanned ground vehicles, unmanned aircraft systems, load capacity, cargo delivery, efficiency-cost, optimization of an amount.

Energy Trading and Power Allocation Strategies for Relay-Assisted Smart Grid Communications: A Three-Stage Game Approach

In smart grid, serious packet loss often occurs in the process of information interaction between the Utilities and customers, which results in supply-demand deviation and further increases the cost of the Utilities. In order to improve the information transmission performance of communication networks, the Utilities purchase relay service from telecom operator to help data aggregator units (DAU) transfer information to gateway (GW), so as to improve communication quality and reduce the cost of the Utilities. Secondly, in order to solve the problem of telecom operators' energy reduction and reduce the cost of purchasing energy, we utilize energy supply point (ESP) to collect the surplus energy of retail customers for energy supply, and telecom operator pays a certain amount of remuneration to ESP in exchange for ESP to continuously supply energy to telecom operator. Then, we establish a three-stage game method and system model between the Utilities, telecom operator and ESP, and propose the relay power allocation and energy transaction pricing strategy. Due to the real-time change of energy demand, we consider two situations of energy oversupply and conservative supply, and use the backward induction method and iterative algorithm to obtain the equilibrium solution of the Stackelberg game, including the unit energy price of ESP, total power of telecom operator, the proportion of transmission power allocated to the relay service, and payment scheme of the Utilities. Simulation results show that the proposed algorithm can quickly and accurately converge to the optimal solution of the problem, and the method can improve the stability of demand-side regulation, reduce the cost of the Utilities and increase the profit of telecom operator.

A new hybrid method for quick and accurate calculation of forest transportation distances

AbstractTransportation is a key forest logistics component and is a large proportion of the overall cost. Often, the transportation cost is determined by contractual agreements and based on the loaded distance from a supply to a demand point. Many alternative routes provide different distances (e.g., shortest route, fastest route, minimum fuel consumption), but these distances are approximate in the contractual agreement; hence, there is a mismatch between approximated costs and actual pay. It is necessary to match supply with demand when planning to use optimization models, as these models must cover many supply and demand points. From this point, many distances need to be established. These can be generated dynamically before optimization or generated a priori as static distance tables. The former can take a long time, whereas the latter needs to use aggregated zones that remain static over time because supply points, such as harvest areas, change continually. To enable fast optimization, distances between zones and demand points can be precomputed; however, they represent only estimated distances between the actual supply point and the demand points. We propose a hybrid method to improve quality and estimate accurate distances in a quick process, using a large case study from a company in Sweden with a standardized system that directly uses computed reference distances as contractually agreed. Results show that many distance estimation approaches give poor cost estimates (1–20%) and increase transportation costs (0.2–0.6%).

Pandemic Economic Crises

The market serves as the convergence point of supply and demand and represents the process through which market relations between economic units materialize. From a global perspective, the focus shifts to the world market, which is the fundamental structure on which the global economy is based. The world economy operates as a very complex ecosystem. When it is exposed to the extremely damaging effects of a global pandemic, the term of a pandemic economic crisis becomes relevant.

Grid supply point operations with TSO-DSO interaction: A comprehensive assessment of operating voltage feasibility

The feasible operating voltage at grid supply point is significantly affected by the high level of distributed energy resources (DERs) integration. However, while the operating voltage is typically defined by the transmission system operator (TSO), often the integration of DERs at specific nodes within the distribution system is not taken into account. This oversight can lead to security issue for the distribution system operator (DSO). Addressing this critical gap, this manuscript offers an in-depth analysis of voltage feasibility at the grid supply point. The TSO-DSO interaction has been utilized to estimate the revised limits of voltage, and the influence of several factors such as the level of DER integration, associated flexibility cost, system's demand level, and regulation of the reactive-to-active power ratio (Q/P) has been highlighted. For this, IEEE 9 bus system for TSO and 33 bus for DSO, are considered to estimate the balance condition between them. The revised voltage limits are estimated on DSO’s 33-bus system. The study shows that DER integration reduces voltage range at the transmission-distribution (T-D) interface. However, high DER integration increases the feasible voltage range by enhancing flexibility. Cost willingness and activation cost widen the voltage range with minimal impact during peak load, while demand levels affect limits throughout the operation window. Furthermore, Q/P regulations have the potential to expand the feasible voltage range.

An adaptive crack inspection method for building surface based on BIM, UAV and edge computing

Unmanned aerial vehicle (UAV) and building information modeling (BIM) have been applied to inspect building surfaces to improve efficiency and reduce labor costs. However, most of the current research focus on the inspection mode of “first collect and then identify” with limited intelligence. Therefore, this study proposes a “global-local” adaptive inspection method for building surfaces by integrating BIM, UAV (with multifunction camera), and edge computing, enabling real-time synchronization of crack data collection and analysis. First, the building surface area to be inspected is extracted from BIM to generate global inspection points using the selected camera's field of view (FOV) and considering coordinates transformation between BIM and reality. Then, A star algorithm integrating with genetic algorithm (GA) is used to optimize the global flight path based on generated inspection points. Finally, the coordinates of the inspection points based on BIM are converted into real-world coordinates, and the flight parameters such as the flight yaw angle (FYA) and the gimbal yaw angle (GYA) are calculated to generate the inspection mission. During global inspection, a local adaptive inspection is proposed for each FOV through real-time local crack identification and flight replanning using edge computing and zoom camera. In addition, multiple battery supply points are automatically added considering the endurance of the UAV. A real building has been used to validate the proposed “global-local” adaptive inspection method. The experiment results show that the inspection time has been significantly reduced compared with the traditional method, and the crack identification of building surfaces can be implemented with high efficiency, precision, quality, and low cost.

Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.

Identification and mapping of surface irrigation potential in the data-scarce Jewuha watershed, Middle Awash River Basin, Ethiopia

Abstract This study was conducted to identify and map the surface irrigation potential in the data-scarce Jewuha watershed of the Awash Basin, Ethiopia. The suitability of the land, based on the soil characteristics and slope, was first assessed by the parametric evaluation technique. The overall suitability of the land was then evaluated considering additional factors including land use land cover, proximity to a water source and road using weighted overlay analysis through the analytic hierarchical process (AHP). Water diversion sites as the source of water supply points were selected based on theoretical site selection criteria with the help of a geographic information system (GIS) and physical observation. The surface water available at the diversion sites was estimated using the Soil and Water Assessment Tool (SWAT) model combined with the spatial proximity regionalization technique. The land suitability analysis revealed that 16.7% (11,359 ha) of the study area is suitable for surface irrigation. Five diversion sites were identified as sources of water supply and the total flows at these sites were 12.92 Mm3. It was found that only 27.3% (3,098 ha) of the suitable land, 5% of the total area of the watershed, can be effectively developed by surface irrigation.

Hierarchical framework for demand prediction and iterative optimization of EV charging network infrastructure under uncertainty with cost and quality-of-service consideration

As electric vehicles (EVs) continue to grow in popularity, the demand for efficient and effective EV charging infrastructure is rapidly increasing. However, the design and deployment of such infrastructure faces numerous challenges, some of which include demand uncertainty, EV network layout, maintenance cost, and quality of service (QoS), and have proven to be particularly challenging in cases where all objectives need to be addressed simultaneously. To address these challenges, this study proposes a hierarchical iterative optimization approach that aims to minimize the demand–supply mismatch at a higher level and considers both cost and QoS under uncertainty at a lower level in a dynamic EV charging network. Specifically, the proposed approach uses a time-series linear regression ensemble to accurately predict future demand at demand points, which is then used to match demand at demand points with supply at supply points. We then model the infrastructure allocation problem as a knapsack problem and use an allocation algorithm to determine the optimal number of charging stations at each supply point. The infrastructure is allocated to minimize total cost and maximize QoS. A framework for EV infrastructure optimization is designed and can be applied to any type of network. The effectiveness of our proposed approach is demonstrated through extensive experimentation on various datasets by comparing with existing state-of-the-art approaches. Simulation results show that the proposed approach is robust to different EV networks, handles uncertainty well, can effectively address the challenges associated with EV infrastructure optimization, and can provide policy makers and infrastructure planners with a practical and efficient tool for designing and deploying charging infrastructure. This study contributes to the field of EV infrastructure network optimization by modeling QoS and proposing a hierarchical optimization framework that effectively addresses the challenges associated with the design and deployment of charging infrastructure.

Automated System for Freight Transportation Optimization on the Transport Network

An automated system for freight traffic optimization on a transport network has been developed, which is realized in the form of a complex computer program with application of the visual design environment of Embarcadero RAD Studio. The program complex consists of the main form from which two subprograms are loaded. The search of optimum routes performed by the routing optimization subprogram is used at transportations of freight on the set transport network based on different schemes (from one vertex (the supplier) to all other vertices (consumers), serially – from each vertex to all other vertices, and from two (or several) set of vertices to all other vertices). Freight delivery between the supply and consumption points was optimized by means of the freight transportation optimization subprogram, taking into account the restrictions on the volume of freight at the points of departure and destination.

How do mountain ecosystem services respond to changes in vegetation and climate? An evidence from the Qinling Mountains, China

Ecosystem services (ESs) have been widely used in the management and protection of regional ecosystems. Among the numerous influencing factors, climate and vegetation are important factors affecting ESs. Nevertheless, unraveling the intricate and non-linear relationship between ESs and the fluctuations in climate and fractional vegetation cover (FVC) remains an elusive task, particularly within the intricate and dynamic context of mountainous terrains. Therefore, the Qinling Mountains (QL) was selected as the study area, four major ESs (habitat quality, water yield, carbon sequestration and soil conservation) were quantified, and two key points (threshold point and critical point of high ESs supply) were determined by elastic analysis and constraint line respectively. The results showed that the influence thresholds of temperature and FVC on ESs were 13.2 °C and 57%, respectively. The results of constraint line showed that ESs is maintained at a high level when the temperature < 11.04 °C, the precipitation > 840 mm, and the FVC > 57%. According to the threshold points and critical points of high ESs supply, sensitive areas and high ESs supply areas were divided respectively. In addition, based on the above two regional divisions, we obtained an ecosystem management planning map of the study area and put forward corresponding practical suggestions for different regions. This study satisfies the docking of the research results and practical decision-making requirements and provides a theoretical scientific basis for the restoration and sustainable management of ecological environments.

Rethinking sustainability in cocoa supply chain in light of SDG disclosure

PurposeThis paper aims to assess how cocoa supply chain companies disclose sustainable development goals (SDGs) information in their sustainability reports. This assessment highlights strategic aspects of sustainable supply chain management and reveals leveraging sustainability points in the cocoa industry.Design/methodology/approachThe two-step qualitative approach relies on text-mining company reports and subsequent content analysis that identifies the topics disclosed and relates them to SDG targets.FindingsThis study distinguishes 18 SDG targets connected to cocoa traders and 30 SDG targets to chocolate manufacturers. The following topics represent the main nexuses of connections: decent labour promotion and gender equity (social), empowering local communities and supply chain monitoring (economic) and agroforestry and climate action (environmental).Practical implicationsBy highlighting the interconnections between the SDGs targeted by companies in the cocoa supply chain, this paper sheds light on the strategic SDGs for this industry and their relationships, which can help to improve sustainability disclosure and transparency. One interesting input for companies is the improvement of climate crisis prevention, focusing on non-renewable sources minimisation, carbon footprint and clear indicators of ecologic materiality.Social implicationsThis study contributes to policymakers to enhance governance and accountability of global supply chains that are submitted to different regulation regimes.Originality/valueTo the best of the authors’ knowledge, no previous study has framed the cocoa industry from a broader SDG perspective. The interconnections identified reveal the key goals of the cocoa supply chain and point to strategic sustainability choices for companies in an important global industry.

Impact of social networks on fertilizer use efficiency in China: Evidence from kiwifruit production in Shaanxi province.

Improving fertilizer use efficiency (FUE) is an effective way to reduce the fertilizer use and non-point source pollution in agriculture production in China. Existing research related to fertilizer use efficiency ignores the impact of famers' social networks, which can significantly affect the fertilizer use and its efficiency. This paper analyzed the impact of social networks on fertilizer use efficiency and the mediator effect of green fertilization technology adoption, based on the calculation of fertilizer use efficiency of 569 kiwifruit farmers in Shaanxi province of China surveyed in 2021 by using stochastic frontier method. The results show that the FUE of kiwifruit production ranged within 0.1027-0.9945, with an average value of 0.333, which showed great variability, and fertilizer input had an average 66.7% reduction potential without reducing output. Social networks and its four dimensions all had a significant positive impact on FUE, in which network trust and network learning had a greater impact on FUE. Farmers' adoption of green fertilization technology played a positive mediator effect in the process of social networks influencing their fertilizer use efficiency, which was mainly promoted by network trust and learning. Farmers' experience in agricultural production, joining the cooperative, the quantity of village agricultural supply points had a significant positive impact on fertilizer use efficiency, while age and gentle of the head of household had a significant negative impact of fertilizer use efficiency. Planting scale had a U-shape impact on FUE. Policies aimed at improving FUE and reducing non-point source pollution should, therefore, focus on improving the social networks of farmers, especially young farmers, strengthening network trust, promoting network learning, network reciprocity and network interaction, strengthening the research and development and extension of green fertilization technology, encouraging farmers to join agricultural cooperative, improving village production conditions, guiding sustainable agricultural production.

Novel Water Pricing Model for Water Network Projects: A Case Study of Jiaodong Water Diversion Project in China

The uneven distribution of water resources in time and space restricts the development of society, which is a universal problem facing the world. To improve the ability to regulate water resources in water diversion projects and alleviate the contradiction between water resources and social development, China has decided to build water network projects (WNPs). A WNP is an integrated water supply system that includes multiple water sources and network water supply lines and integrates the functions of water supply, water delivery, and optimal allocation of water resources into a system that can supply water to many users. An appropriate water pricing mechanism is the key to ensuring the sustainable operation of water network projects. This paper presents a comprehensive water pricing model (WPM) for WNPs. The purpose of this model is to unify the water price measurement standard of WNPs and lay the foundation for water price marketization in the future. By applying the model to the Jiaodong WNP, it is found that the model is expected to improve the unbalanced use of water sources and the large difference in prices at water supply points (WSPs) in the project. This study not only provides a theoretical basis for water pricing reform but also has great potential to improve the efficiency of water resource use.

Water-Supply Potability Status of Bangladesh University of Health Sciences (BUHS)

Water that is free of disease-producing microorganisms and chemical substances harmful to health is called potable water. Drinking water quality has a microbiological and a physicochemical dimension. In public water supply systems, water should be disinfected. This observational study was conducted through a multiple-tube method. And the water supply channel of Bangladesh University of Health Sciences (BUHS) served as the study's sample. Using an aseptic procedure, 105 ml of water from various sources is collected for each sample on a conical flask. The selected sample was from the four different water supply points at BUHS institution. From sample 1, the MPN (Most Probable Number) index we found the combination of positive sets (0-1-0). The MPN value per 100ml water is (1). From sample 2, the MPN index we found the combination of positive sets (1-1-1). The MPN value per 100ml water is (5). From sample 3, the MPN index we found the combination of positive sets (1-2-1). The MPN value per 100ml water is (7). From sample 4, the MPN index we found the combination of positive sets (0-1-0). The MPN value per 100ml water is (1). In Dhaka city, there is a possibility of contamination of the water sources. Bangladesh University of Health Sciences is a renowned institution with a big number of staff. So, it is important to examine the water condition in this institution. Our work and research reflect a little about it. Gladly and hopefully, we found that there is no pathogenic organism in the water of this institution which is used every day and everybody drinks.