Low Cost Research Articles

Point-of-care Testing HbA1c screening for type 2 diabetes in urban and rural areas of China: a cost-effectiveness analysis

BackgroundPoint-of-care Testing (POCT) glycosylated hemoglobin (HbA1c) is a convenient, cheap, effective and accessible screening method for type 2 diabetes in rural areas and community settings that is widely used in the European region and Japan, but not yet widespread in China. The study is the first to evaluate the cost-effectiveness of POCT HbA1c, fasting capillary glucose (FCG), and venous blood HbA1c to screen for type 2 diabetes in urban and rural areas of China, and to identify the best socio-economically beneficial screening strategy.MethodsBased on urban and rural areas in China, economic models for type 2 diabetes screening were constructed from a social perspective. The subjects of this study were adults aged 18–80 years with undiagnosed type 2 diabetes. Three screening strategies were established for venous blood HbA1c, FCG and POCT HbA1c, and cost-effectiveness analysis was performed by Markov models. One-way sensitivity analysis and probabilistic sensitivity analysis were performed on all parameters of the model to verify the stability of the results.ResultsCompared with FCG, POCT HbA1c was cost-effective with an incremental cost-utility ratio (ICUR) of $500.06/quality-adjusted life year (QALY) in urban areas and an ICUR of $185.10/QALY in rural areas, within the willingness-to-pay threshold (WTP = $37,653). POCT HbA1c was cost-effective with lower cost and higher utility compared with venous blood HbA1c in both urban and rural areas. In the comparison of venous blood HbA1c and FCG, venous blood HbA1c was cost-effective (ICUR = $20,833/QALY) in urban areas but not in rural areas (ICUR = $41,858/QALY). Sensitivity analyses showed that the results of the study were stable and credible.ConclusionsPOCT HbA1c was cost-effective for type 2 diabetes screening in both urban and rural areas of China, which could be considered for future clinical practice in China. Factors such as geographic location, local financial situation and resident compliance needed to be considered when making the choice of venous blood HbA1c or FCG.

Validation of an web-based dietary assessment tool (RiksmatenFlex) against doubly labelled water and 24 h dietary recalls in pregnant women

IntroductionDigital technologies have enabled new possibilities to assess dietary intake and have shown promise in terms of decreased participant burden, improved accuracy and lower costs. However, their potential and validity in pregnant populations are scarcely explored.ObjectivesThis study aimed to (a) validate energy intakes obtained from a web-based dietary recall method developed for national surveys (RiksmatenFlex) against total energy expenditure (TEE) by means of the doubly labelled water (DLW) method, and (b) to compare intakes of macronutrients, key unhealthy and healthy foods as well as adherence to food-based dietary guidelines between RiksmatenFlex and repeated 24 h telephone dietary recalls in healthy Swedish pregnant women.MethodsThis study was conducted as a nested validation within the HealthyMoms trial. Intakes of foods, macronutrients and energy were assessed during three days through RiksmatenFlex and 24 h telephone dietary recalls, and Swedish Healthy Eating Index (SHEI) scores were also calculated for both methods (n = 52). For 24 women, TEE was also assessed through the DLW method. Paired Samples T-tests and Wilcoxon Signed Ranks Tests were used to identify differences between means for foods, macronutrients, energy and SHEI scores. Pearson correlation coefficient or Spearman’s rho were performed to identify relationships between variables. To compare energy intake (RiksmatenFlex) with TEE (DLW method) and 24 h telephone dietary recalls, Bland and Altman plots were constructed.ResultsAverage energy intake from RiksmatenFlex (10,015 [SD 2004] kJ) was not statistically different from TEE (10,252 [SD 1197] kJ) (p = 0.596) (mean difference: -237 kJ/24 h). Correspondingly, there were small mean differences between average intakes of key unhealthy and healthy foods and average SHEI scores between RiksmatenFlex and 24 h telephone dietary recalls. However, the Bland and Altman plots showed wide limits of agreement for all dietary variables (e.g., for energy intake using RiksmatenFlex versus TEE: ±4239 kJ/24 h). High correlations between the investigated dietary variables for the two dietary methods were observed (r = 0.751 to 0.931; all p < 0.001).ConclusionRiksmatenFlex captured average intakes of energy, unhealthy and healthy food groups and adherence to food-based dietary guidelines in a comparable way to 24 h telephone dietary recalls and the DLW method. Our results support the validity of RiksmatenFlex as a web-based dietary assessment method for future use in pregnancy for intervention studies and national dietary surveys.

A knowledge graph algorithm enabled deep recommendation system

Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy.

Jellyfish-Inspired Soft Robot Driven by Pneumatic Bistable Actuators.

Soft actuators offer numerous potential applications; however, challenges persist in achieving a high driving force and fast response speed. In this work, we present the design, fabrication, and analysis of a soft pneumatic bistable actuator (PBA) mimicking jellyfish subumbrellar muscle motion for waterjet propulsion. Drawing inspiration from the jellyfish jet propulsion and the characteristics of bistable structure, we develop an elastic band stretch prebending PBA with a simple structure, low inflation cost, exceptional driving performance, and stable driving force output. Through a bionic analysis of jellyfish body structure and motion, we integrate the PBA into a jellyfish-like prototype, enabling it to achieve jet propulsion. To enhance the swimming performance, we introduce a skin-like structure for connecting the soft actuator to the jellyfish-like soft robot prototype. This skin-like structure optimizes the fluid dynamics during jet propulsion, resulting in improved efficiency and maneuverability. Our study further analyzes the swimming performance of the jellyfish-like prototype, demonstrating a swimming speed of 3.8 cm/s (0.32 body length/s, BL/s) for the tethered prototype and 4.7 cm/s (0.38 BL/s) for the untethered prototype. Moreover, we showcase the jellyfish-like prototype's notable load-bearing capacity and fast-forward swimming performance compared to other driving methods for underwater biomimetic robots. This work provides valuable insights for the development of highly agile and fast responsive soft robots that imitate the subumbrellar muscle of jellyfish for efficient water-jet propulsion, utilizing skin-like structures to enhance swimming performance.

Optimal allocation of wind power hybrid energy storage capacity based on ant colony optimization algorithm

Renewable energy sources, such as wind power, face challenges owing to their erratic construction and intermittent nature, leading to the emergence of energy storage strategies to achieve grid stability and reliable electricity supplies. Determination of the correct size of energy storage devices for wind power plants is complicated. In this study, the ant colony optimization (ACO) algorithm is proposed for the best distribution/sizing of wind-generated hybrid storage capacity. Ants’ foraging habits motivate the development of wind turbines with high dependability, high efficiency and low energy storage costs. A study is conducted to confirm the practicality of the proposed method, using real data from a wind power plant to calculate the contrast between wind energy production and load consumption features, to influence the appropriate quantity for energy storage organization. The study demonstrates that the ACO approach provides precise and efficient solutions for determining the optimal energy distribution in wind power plants.

Tests for market failures and overcoming transaction costs barriers to market participation in Afghanistan

AbstractSimilar to other fragile poor countries, the majority of the rural population in Afghanistan depend directly or indirectly on small-scale farming for their livelihoods, and poor infrastructure and other barriers restrict access to markets and services, making improving access to local input markets a significant challenge for policymakers aiming to increase productivity. This paper offers empirical evidence addressing two important issues for smallholder participation in input markets: are rural markets incomplete or failing and how do transaction costs affect participation in factor markets? Pooled cross-sectional data from three waves of nationally representative surveys between 2011 and 2016 with a sample of nearly 21,000 households are used. The analysis first tests and rejects the separability hypothesis that household production and consumption decisions are independent which can be interpreted as evidence of market failures. A control function approach with instrumental variables to address endogeneity is employed to estimate household input market participation decisions. Ownership of or access to information and communication technology and transport equipment are used as proxies for transaction costs. Results confirm that lower transaction costs increase input market participation and expenditure on inputs by farm households. Additionally, households with better access to roads and district or provincial market centres have higher market participation and expenditure on input. Reducing transaction costs plays an important role in input use, potentially increasing productivity and incomes.

Day-Ahead Dynamic Economic Emission Dispatch Considering Photovoltaic Predicted Output Using Sailfish Optimizer and Simulated Annealing

The Indonesian government continues to focuson boosting equitable access to environmentally friendlyelectricity to all levels of Indonesian society. This can be shownthrough the achievement of an electrification ratio of 99.40 percent in the third quarter of 2021 coupled with promisinggrowth in the capacity of Renewable Energy (RE) powerplants. Economic Dispatch (ED) aims to optimize the outputpower of generating units to minimize the total cost of powersystem operations. With the application of ED, the minimumgeneration cost will be obtained for the production of electricpower generated by generating units in an electricity systemwhile still paying attention to constraints. For hybrid plantssuch as the Selayar Solar Power Plant, the challenge toperforming ED is the intermittency of solar energy. So thatbefore the ED is carried out, solar radiation forecasting iscarried out using LSTM. To measure the accuracy offorecasting, RMSE, MSE MAE, and MAPE are used withaverage results of 6.311, 40.11, 4.61, and 4.87. With theapplication of ED, the minimum generation cost is obtained forthe production of electric power generated while stillconsidering the constraints. Through ED, fuel allocation can beoptimized, helping to reduce production costs and carbonemissions, and making a positive contribution toenvironmental sustainability. To improve ED results in thesailfish optimizer, the use of simulated annealing is added sothat the results have lower total cost and emission values thanED without simulated annealing. Rp 128.024 and 398 kg,respectively.

Environmental-Friendly, Long-Lastingly Moist Phase Change Gel with Tunable Cross-Linking Time for Effective Borehole Sealing.

Flexible environmental phase change gel (EPCG) is a promising and attractive material for borehole sealing due to its relatively lower usage, tunable cross-linking time, and potential degradable properties. However, conventional borehole sealing materials lack a lower manufacturing cost, lower reaction temperature, remarkable antimold function, and admirable hole sealing performance for long-term high-performance borehole sealing. Hence, it remains a critical challenge to realize a flexible phase-change gel featuring tunable cross-linking time, antimold ability and degradability, and long-lasting moist performance for high-performance borehole sealing. Herein, we illustrate a facile approach to fabricate the borehole sealing materials based on environmentally friendly, antimold, long-lasting moist composite phase-change gel fabricated from a modified cellulose gel polymer network. The prepared phase change gel presents adjustable cross-linking time with the initial setting time of 30-60 min, which could be used to seal holes with different drilling parameters. Furthermore, the fabricated gel illustrates reliable water retentiveness performance and antimold ability, assuring the long-period borehole sealing effect. Furthermore, EPCG has been applied for borehole sealing and shows great sealing ability and gas extraction performance. Therefore, this work paves the way to fabricate a flexible phase-change gel featuring tunable cross-linking time, antimold ability, and long-lasting moist performance for high performance borehole sealing.

2FAKA-C/S: A Robust Two-Factor Authentication and Key Agreement Protocol for C/S Data Transmission in Federated Learning

As a hot technology trend, the federated learning (FL) cleverly combines data utilization and privacy protection by processing data locally on the client and only sharing model parameters with the server, embodying an efficient and secure collaborative learning model between clients and aggregated Servers. During the process of uploading parameters in FL models, there is susceptibility to unauthorized access threats, which can result in training data leakage. To ensure data security during transmission, the Authentication and Key Agreement (AKA) protocols are proposed to authenticate legitimate users and safeguard training data. However, existing AKA protocols for client–server (C/S) architecture show security deficiencies, such as lack of user anonymity and susceptibility to password guessing attacks. In this paper, we propose a robust 2FAKA-C/S protocol based on ECC and Hash-chain technology. Our security analysis shows that the proposed protocol ensures the session keys are semantically secure and can effectively resist various attacks. The performance analysis indicates that the proposed protocol achieves a total running time of 62.644 ms and requires only 800 bits of communication overhead, showing superior computational efficiency and lower communication costs compared to existing protocols. In conclusion, the proposed protocol securely protects the training parameters in a federated learning environment and provides a reliable guarantee for data transmission.

Rivaroxaban versus vitamin K antagonist treatment on the progression of coronary calcification: the IRIVASC-trial

Vitamin K antagonists (VKA) remain the only option of anticoagulation for people with mechanical valve replacement and due to their wider availability and lower acquisition costs, VKA’s remain widely used in low- and middle-income countries. It has been suggested that prolonged use of VKAs can increase the development of vascular and valvular calcification, though this effect has not been examined in larger randomized prospective trials. This investigator-initiated multicenter, prospective, randomized, open-label interventional trial randomized patients with baseline coronary or valvular calcification and an indication for prolonged oral anticoagulation therapy to Marcumar or Rivaroxaban. Patients were followed-up through repeat coronary computed tomographies to measure the progression of coronary and valvular calcification for up to 24 months. 192 patients were randomized between 2013 and 2018 to receive either Rivaroxaban or Marcumar and followed for up to 24 months. Coronary calcification significantly increased over time although there was no significant difference in progression between the groups after 12 and 24 months as measured by the Agatston score [360.7 (90.2; 1075.3) vs 380.4 (136.4; 1546.9) p = 0.69], the volume score [295.8 (93.0; 995.3) vs 335.5 (128.7; 1316.9) p = 0.95] and the mass score [58.5 (15.9; 172.0) vs 71.1 (24.8; 257.3) p = 0.5]. Dephosphorylated, uncarboxylated matrix Gla Protein (Dp-ucMGP) significantly decreased in the VKA group [Δ dp-uc MGP – 95.2 (− 554.1; 156.0) vs 231.3 (− 59.7; 388.1) p < 0.001]. There does not appear to be a relevant effect of vitamin K inhibition by the vitamin K antagonist marcumar upon coronary calcification.

A Comparative Study of Two Oroxylum indicum (L.) Kurz. Phenotypes Based on Phytochemicals and Antioxidant Effects, and the Anti-Inflammatory Activity of Leaf and Pod Extracts

Indian trumpet tree Oroxylum indicum (L.) Kurz exhibits a wide range of biological activities in all plant parts, including anti-inflammation, antioxidant, and wound-healing activities. In Thailand, there are tall- and short-stem phenotypes. The latter are preferred for commercial cultivation due to their fast growth and lower harvesting cost. This study aimed to compare the chemical profiles and antioxidant effects of leaves and young pods between two phenotypes using principal component analysis (PCA) and then to evaluate the anti-inflammatory potential of the selected phenotype’s plant parts. The biomarker contents were quantified by HPLC. The antioxidants were determined using the DPPH, ABTS, and FRAP models. Nitric oxide (NO) production assays in LPS-induced RAW264.7 macrophages were performed to determine the anti-inflammatory property of the extracts. The PCA revealed that there were no differences in total phenolic content, total flavonoid content, or antioxidant activities between short- and tall-stem phenotypes. Higher potency of the NO-inhibitory effect was achieved from the leaf extract than the pod extract. These results support using the short-stem phenotypes for utilizing the leaf and pod of O. indicum, and suggest choosing the leaf part for further anti-inflammatory product development.

E-Commerce Growth and Digital Payments in the Philippines

Purpose: To aim of the study was to analyze the e-commerce growth and digital payments in the Philippines. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The growth of e-commerce and digital payments in the Philippines has been significantly influenced by factors such as perceived security, ease of use, and government policies. Studies show that digital payments have increased consumer spending and boosted sales for SMEs, especially during the COVID-19 pandemic. However, barriers like financial literacy and trust issues still hinder widespread adoption, particularly among low-income groups. Government initiatives and improved digital infrastructure have played crucial roles in promoting e-commerce growth. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), diffusion of innovations theory &amp; unified theory of acceptance and use of technology (UTAUT) may be used to anchor future studies on e-commerce growth and digital payments in the Philippines. Digital payment providers should invest in comprehensive educational campaigns to improve financial literacy and build consumer trust. Policymakers should prioritize the development of digital infrastructure, particularly in rural and underserved areas, to ensure equitable access to e-commerce and digital payment systems.

Reinforcement-Learning-Assisted Service Function Chain Embedding Algorithm in Edge Computing Networks

Edge computing networks are critical infrastructures for processing massive data and providing instantaneous services. However, how to efficiently allocate resources in edge computing networks to meet the embedding requirements of service function chains has become an urgent problem. In this paper, we model the resource allocation problem in edge computing networks as a service function chain embedding problem model, aiming to optimize the resource allocation through reinforcement learning algorithms to achieve the goals of low cost, high revenue, and high embedding rate. In this paper, the basic concepts of edge computing network and service function chain are elaborated, and the resource allocation problem is transformed into a service function chain embedding problem by establishing a mathematical model, which provides a foundation for the subsequent algorithm design. In this paper, a service function chain embedding algorithm based on reinforcement learning is designed to gradually optimize the resource allocation decision by simulating the learning process. In order to verify the effectiveness of the algorithm, a series of simulation experiments are conducted in this paper and compared with other algorithms. The experimental results show that the service function chain embedding algorithm based on reinforcement learning proposed in this paper exhibits superior performance in resource allocation. Compared with traditional resource allocation methods, the algorithm achieves significant improvement in terms of low cost, high revenue, and high embedding rate.

Evaluating the Effectiveness of 5G Technology in Enhancing Mobile Network Performance in South Korea

Purpose: The aim of the study was to evaluate the effectiveness of 5G technology in enhancing mobile network performance in South Korea. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: South Korea's adoption of 5G technology has significantly enhanced mobile network performance. The deployment of 5G has resulted in vastly improved download and upload speeds, reduced latency, and better network reliability compared to previous technologies. This advancement has enabled smoother and more reliable mobile internet experiences, facilitating new applications such as augmented reality (AR), virtual reality (VR), and Internet of Things (IoT) devices. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) &amp; resource-based view (RBV) theory may be used to anchor future studies on evaluating the effectiveness of 5G technology in enhancing mobile network performance in South Korea. Develop practical guidelines for telecom operators on the deployment and optimization of 5G networks. These guidelines should address best practices for infrastructure development, spectrum management, and network configuration to maximize performance. Advocate for the establishment of comprehensive regulatory frameworks that support the widespread deployment of 5G technology.

Influence of Machine Learning on Predictive Maintenance in Manufacturing in Kenya

Purpose: The aim of the study was to evaluate the influence of machine learning on predictive maintenance in manufacturing in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Machine learning has significantly enhanced predictive maintenance in Kenya's manufacturing sector. By analyzing large datasets from equipment sensors, ML algorithms can predict equipment failures before they occur, minimizing downtime and maintenance costs. This proactive approach improves operational efficiency and extends equipment lifespan, benefiting manufacturers by reducing unplanned disruptions and enhancing overall productivity. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, resource-based view (RBV) &amp; sociotechnical systems theory may be used to anchor future studies on the influence of machine learning on predictive maintenance in manufacturing in Kenya. Manufacturing firms should invest in comprehensive training programs to equip their workforce with the necessary skills to manage and interpret ML-driven maintenance systems. Governments and industry regulators should create incentives for the adoption of ML technologies in manufacturing.

Role of Augmented Reality in Improving Remote Technical Support in Nigeria

Purpose: The aim of the study was to evaluate the role of augmented reality in improving remote technical support in Nigeria. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The integration of Augmented Reality (AR) in remote technical support in Nigeria has significantly enhanced troubleshooting efficiency by allowing technicians to visualize issues in real-time, providing immediate and interactive guidance. This technology has improved customer satisfaction by reducing downtime and accelerating resolution processes, making support more accessible, especially in remote areas. Additionally, AR has contributed to cost reduction by minimizing the need for physical technician visits and extensive in-person training sessions. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), media richness theory (MRT) &amp; cognitive load theory (CLT) may be used to anchor future studies on role of augmented reality in improving remote technical support in Nigeria. Implement AR solutions to provide remote technicians with detailed, real-time visual overlays that guide diagnostic and repair processes. Formulate policies that establish standards for AR technology in remote technical support. This includes guidelines for data security, privacy, and interoperability to ensure that AR solutions are safe, reliable, and can work seamlessly with other technologies.

Impact of 5GTechnology on Mobile Internet Usage in Japan

Purpose: To aim of the study was to analyze the impact of 5G technology on mobile internet usage in Japan. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The rollout of 5G technology in Japan has led to a 35% increase in mobile internet usage, with users enjoying faster speeds and improved online experiences. This enhancement supports high-bandwidth applications like video streaming and gaming, and benefits businesses by facilitating remote work and innovative services such as augmented reality (AR) and the Internet of Things (IoT). However, substantial infrastructure investments are needed to ensure widespread coverage and reliability. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) &amp; network externalities theory may be used to anchor future studies on impact of 5G technology on mobile internet usage in Japan. Practically, businesses and mobile service providers in Japan can leverage 5G to develop and deploy innovative services such as augmented reality (AR) applications, real-time gaming experiences, and immersive virtual communication platforms. Policymakers can develop frameworks that facilitate fair competition among telecom operators while promoting investment in 5G infrastructure.

The Effect of Smart Grid Technology on Energy Consumption Management in Canada

Purpose: The aim of the study was to evaluate the effect of smart grid technology on energy consumption management in Canada. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Smart grid technology has significantly enhanced energy consumption management in Canada. By integrating advanced metering infrastructure and real-time data analytics, smart grids optimize energy distribution, reduce peak demand, and enhance grid reliability. This technology enables better demand-response programs and promotes renewable energy integration, leading to more efficient energy use and reduced environmental impact. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) &amp; the theory of planned behavior (TPB) may be used to anchor future studies on the effect of smart grid technology on energy consumption management in Canada. Smart grid technology enables more efficient energy usage by providing consumers with real-time data and insights into their consumption patterns. The vast amounts of data generated by smart grids necessitate robust policies to protect consumer privacy and ensure data security. Policymakers should establish clear regulations governing the collection, storage, and use of smart grid data.

Impact of Artificial Intelligence on Supply Chain Efficiency in Turkey

Purpose: The aim of the study was to evaluate the impact of artificial intelligence on supply chain efficiency in Turkey. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) has significantly enhanced supply chain efficiency in Turkey. AI applications such as predictive analytics, demand forecasting, and optimization algorithms have streamlined operations, reduced costs, and improved decision-making. Automation of repetitive tasks through AI has increased productivity and accuracy in inventory management and logistics. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV) theory, dynamic capabilities theory &amp; technology acceptance model (TAM) may be used to anchor future studies on the impact of artificial intelligence on supply chain efficiency in Turkey. Invest in continuous training and development programs for supply chain professionals to effectively utilize AI tools. Develop industry standards and regulatory frameworks for the ethical use of AI in supply chains. These policies should address data privacy, security, and the responsible use of AI technologies to protect stakeholders.

Food Distribution Models to Determine Distribution Routes, Distances and Costs

The agricultural sector is one of the sectors that has an important role in supporting the Indonesian economy. The agricultural sector in Indonesia needs to continue to be developed in line with population growth and technological developments to increase agricultural production. The problem that occurs is handling so that deficit areas will get supplies from surplus areas, so good food distribution is needed to aggregate and distribute these supplies as needed so that the basic needs of the community are met fairly and equitably. To overcome this, a food distribution model is needed to be able to accumulate the process of distributing food products, especially rice from commodity warehouses to 9 warehouses spread across Serang Regency, as a solution to the problems that occur and aims to achieve low cost efficiency by determining efficient and optimal routes, distances, and distribution costs using the Saving Matrix method and Nearest Neighbor. Based on this method, 3 routes were formed with a total distribution distance of 294.5 km. The allocation of vehicles used is 3 units of tronton trucks with a capacity of 45 tons. The distribution cost needed is Rp.1.934.866,- every day or Rp.58.045.980,- per month in other words the cost requirement every year is Rp.696.551.760,- Keywords: Agricultural Sector, Distribution Model, Saving Matrix and Nearest Neighbor