Perform Data Analysis Research Articles

Impact of Automation on Retail Logistics: AI-Powered Solutions for Efficient Supply Chains

This research explores the transformative impact of automation on retail logistics, focusing specifically on AI-powered solutions that enhance supply chain efficiency. As e-commerce continues to grow, retailers face increasing pressure to streamline logistics processes to meet rising consumer expectations for fast, reliable deliveries and cost-effective operations. AI technologies, such as machine learning, predictive analytics, and autonomous systems, have emerged as critical tools in addressing these challenges by improving various logistics functions, including demand forecasting, inventory management, route planning, and warehouse automation. This study investigates the role of AI-powered solutions in enhancing operational performance, reducing costs, and boosting customer satisfaction across retail logistics networks. A mixed-methods approach is employed, combining quantitative analysis of logistics performance data and qualitative case studies from retail companies that have integrated AI technologies into their supply chains. The quantitative analysis examines key performance indicators such as delivery time, cost per order, inventory turnover, and customer satisfaction before and after the adoption of AI solutions. The qualitative analysis draws on case studies from companies like Amazon, Walmart, and JD.com to explore the real-world applications and challenges of AI in retail logistics. The results indicate that AI automation improves forecasting accuracy, reduces operational costs, enhances inventory management, and improves delivery efficiency. However, challenges such as system integration, data quality, and workforce adaptation are also identified. This study contributes to understanding the current state of AI applications in retail logistics and offers practical insights for industry leaders aiming to optimize their supply chains. Keywords: Automation, Artificial Intelligence, Retail Logistics, Supply Chain Efficiency, Machine Learning, Inventory Management, Predictive Analytics, E-commerce, Cost Reduction, Customer Satisfaction

Effects of plant active substances in rheumatoid arthritis—a systematic review and network meta-analysis

BackgroundPlant active substances are extensively utilized in treating rheumatoid arthritis (RA). Despite numerous experimental and clinical studies on plant active substances their efficacy remains largely unsubstantiated. The widespread use of these extracts as therapeutic measures for RA is problematic due to the lack of compelling evidence.ObjectiveOur research aims to assess the impact of plant active substances on RA by conducting a network meta-analysis.MethodsWe systematically searched four electronic databases—PubMed, EMBASE, the Cochrane Central Register of Controlled Trials, and Web of Science—from their inception to August 2024. The main focus was on assessing primary outcomes, including the Visual Analogue Scale (VAS), inflammatory markers, Swollen Joint Count (SJC), Tender Joint Count (TJC), and Disease Activity Score on 28 joints (DAS28). We performed data analysis using StataMP 15.1 software and ranked the therapeutic effects based on the Surface Under the Cumulative Ranking Curve (SUCRA) probability values.ResultsBased on screening procedures, 18 eligible studies were incorporated into the analysis. These studies encompassed a total of 1,674 RA patients and investigated 10 different plant active substance therapies. Specifically, 10 studies included VAS indicators, 17 studies included inflammatory marker indicators, 14 studies included DAS28 indicators, 13 studies included SJC indicators, and 13 studies included TJC indicators. Based on SUCRA values, quercetin appeared to be the most effective treatment for decreasing serum VAS levels (67.3%). Furthermore, curcumin emerged as the most promising option for reducing inflammatory marker levels (72.3%), SJC (75.6%), and TJC (76.2%). Lastly, with respect to DAS28, resveratrol emerged as the optimal choice (74.3%).ConclusionAccording to the network meta-analysis (NMA), curcumin exhibited superior efficacy compared to placebo in decreasing SJC and TJC. Additionally, curcumin demonstrated greater effectiveness in reducing inflammatory markers. Quercetin was more effective in reducing VAS, and resveratrol was more effective in reducing DAS28. Patients with RA may benefit from these findings. Insightful information from this study is helpful for RA patients to consider using plant active substance therapies. For their efficacy and safety to be confirmed, more proof is needed.

BiolutoxR: An R-Shiny package for easy performing data analysis of a toxicity test based on bacterial bioluminescence inhibition

biolutoxR: An R-Shiny package for easy performing data analysis of a toxicity test based on bacterial bioluminescence inhibition

Implementation of Pre-Cleaner as An Effort to Preventive Engine Lacks Power Bulldozer D85ess-2 Support Area Rom PT Putra Perkasa Abadi Site Mip Lahat

Bulldozer D85ESS-2 operating in the ROM Support Area of PT Putra Perkasa Abadi Site MIP Lahat often experiences engine lacks power problem, which disrupts productivity and increases maintenance costs. This problem is generally caused by air contamination entering the engine system, resulting in decreased combustion efficiency and component damage. This study aims to analyse the effectiveness of pre-cleaner implementation as a preventive measure in overcoming this problem. The method used was a quantitative approach with the collection and analysis of operational data from the D85ESS-2 bulldozer before and after the installation of the pre-cleaner. The data analysed included the frequency of occurrence of the engine lacks power problem, downtime duration, and fuel usage. The results showed that the implementation of a pre-cleaner on the D85ESS-2 bulldozer at PT Putra Perkasa Abadi successfully resolved the problem of engine lacks power by reducing the accumulation of coal dust in the air cleaner. The test results show significant improvements in engine performance, with most parameters meeting the set standards. Thus, the use of a pre-cleaner improves combustion efficiency, reduces maintenance costs, and extends engine life.

Protocol to identify small-molecule inhibitors against cancer drug resistance.

Drug resistance has emerged as a critical challenge in clinical cancer treatment. Here, we present a high-throughput screening protocol to identify therapeutic small-molecule inhibitors against drug-resistant cancer cells. We detail the steps for constructing drug-resistant cell models, executing the chemical screening process, and performing data analysis and validation. This protocol facilitates the rapid identification of therapeutic strategies for different types of drug-resistant cancers and aids in studying mechanisms. For complete details on the use and execution of this protocol, please refer to Zhang etal.1.

The Role of Microfinance Institutions in Micro, Small and Medium Enterprises (MSMES): a Case Study of Budi Gadai

This study examines the role of microfinance institutions in supporting the development of Micro, Small, and Medium Enterprises (MSMEs), with a case study on Budi Gadai, located on Jalan Pancing. The objective of this research is to analyze how microfinance, particularly Budi Gadai, contributes to the growth and sustainability of MSMEs through accessible financing and financial management guidance. The research employs a qualitative approach with in-depth interviews and analysis of business performance data from MSME clients of Budi Gadai. The findings indicate that Budi Gadai provides a significant impact on MSME growth by offering simplified loan access, which helps business owners overcome financing barriers. The majority of MSMEs reported increased revenue and business expansion after obtaining funding from Budi Gadai. However, challenges remain, such as high interest rates and limited loan amounts. Nevertheless, Budi Gadai plays a critical role in the economic resilience of MSMEs, especially during periods of financial crisis. This study concludes that microfinance institutions like Budi Gadai are essential for the sustainability and growth of MSMEs, particularly in urban areas.

Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach

Abstract. Blade erosion of wind turbines causes significant performance degradation, impairs aerodynamic efficiency, and reduces power production. However, traditional monitoring systems based on supervisory control and data acquisition (SCADA) data, which rely on operational data from turbines, lack effectiveness at early detection and quantification of these losses. This research builds on an established turbine performance integral (TPI) method with a sensor-augmented aeroelastic modelling approach to enhance wind turbine performance assessment, focusing on blade erosion. Applying this approach to a distinct multi-megawatt turbine model, the study integrates multibody aeroelastic simulations and real-world operational data analysis. The study identified readily available sensors that were sensitive to blade surface roughness changes caused by erosion. Operational data analysis of offshore wind turbines validated the initial sensor selection and approach. Refined simulations using further virtual sensors quantified the effect size of these sensors' output under different turbulence levels and blade states, employing Cohen's d – a dimensionless metric measuring the standardised difference between two means. For the turbine investigated, findings indicate that sensors such as blade tip torsion, blade root flap moment, shaft moment, and tower moments, especially under lower turbulence intensities, are particularly sensitive to erosion. This confirms the need for turbine-specific, controller-informed sensor selection and emphasises the limitations of generic solutions. This research provides an approach for bridging simulation insights with operational data for turbine-specific performance assessment, contributing to the development of condition monitoring systems (CMSs), resilient turbine designs, and maintenance strategies tailored to specific operating conditions.

Deep mining of megawatt large wind turbine actual operating data: exploration of Bayesian parameter fusion modeling

Abstract Real-time operation data analysis and condition monitoring of large wind turbines are crucial for efficient and safe operation of wind farms. In response to this, an accurate prediction model architecture based on the multivariate linear regression algorithm is proposed for a deeper understanding of the actual operation of large wind turbines. Comparing different prediction variables, we have confirmed that the average wind direction and average wind speed play a core role in predicting active power. The display has significantly better approximation than the traditional multiple linear regression (MLR), especially in the peak region of period-like variation. The quasi-periodic peak variation characteristics of the active power near the observed series 2000, 2500 and 3000 are realized, and the peak prediction results of B-MLR are significantly better than those of traditional MLR. Meanwhile, for RMSE and R-squared, B-MLR is significantly better than the traditional MLR algorithm in the active power prediction of wind turbines. However, the data complexity in actual applications poses a challenge to the effectiveness of the Bayesian fusion algorithm. Further optimization of the algorithm is needed to cope with the complex and variable real data environment. This study provides scientific evidence for the efficient operation, precise maintenance, and environmentally friendly design of wind turbines, promoting the continuous progress and development of wind power generation technology.

Quantitative study on the environmental impact of Beijing’s urban rail transit based on carbon emission reduction

Urban rail transit, as an efficient and eco-friendly mode of transportation, plays a pivotal role in mitigating traffic congestion and lowering urban carbon emissions. Despite the significant contributions by scholars in this area, debates surrounding the quantification of carbon emissions during the operational phase of urban rail transit persist, particularly in assessing its impact on reducing ground traffic congestion. This study examines the passenger flow during Beijing’s morning and evening peak hours, assuming that all passengers initially using urban rail transit switch to buses and taxis during these periods. A traffic congestion prediction model is developed based on the analysis of actual traffic operation data under this assumption. Through this model, the study calculates the potential congestion times across various scenarios, employing a bottom-up approach to carbon emission estimation to analyze the impact on carbon emissions. Results spanning 2015 to 2021 suggest that substituting urban rail transit with buses could increase congestion by 37–92 min and 46–59 min during morning and evening peaks, respectively, leading to a 24-82% and 27-56% surge in carbon emissions. The conversion of all these vehicles to taxis would result in a direct paralysis of Beijing’s road transport network, with a corresponding increase in carbon emissions of between 289% and 556% and 333% and 614%, respectively.These outcomes emphasize the substantial efficacy of urban rail transit in curbing traffic congestion and carbon emissions.

The Effect of PCC Strength and Other Parameters on the Performance of PCC Pavements

This study presents preliminary findings from an ongoing Federal Highway Administration project investigating the impact of increased Portland Cement Concrete (PCC) strength, among other properties, on the performance of PCC pavements. Traditionally, PCC pavement specifications have prioritized aggregate gradation, flexural strength, compressive strength, and air content as indicators of quality. However, this research suggests that other PCC properties could also serve as significant predictors of long-term pavement performance. Through a detailed literature review and preliminary analysis of the Long-Term Pavement Performance data, this paper aims to (1) identify high-strength PCC properties associated with superior pavement performance, particularly in terms of durability near joints and edges; (2) identify testing methods to quantify these properties; and (3) develop revised mix design procedures to produce pavements with these beneficial properties while still satisfying economic and construction requirements. The study observes general trends in PCC strength across different climates and explores their implications for pavement performance, including distress modes such as spalling, faulting, and overall roughness. Initial models for predicting these distresses are presented, highlighting the complex interplay between PCC properties, environmental conditions, and pavement performance. These findings aim to guide the development of more durable and predictable PCC pavements, with further research needed to validate the observed trends and refine predictive models. (Abstract generated by AI tool ChatGPT 4)

Analysis of genetic and performance data in dairy herds

Herd performance is a function of genetics and management. Understanding the environment and genotype is therefore essential when providing proactive herd health advice. This review explains how genetic data are calculated and used, and offers insight into assessing genetic expression by analysing performance data. Optimal management enables cows to express their genetic potential. Where genetics are not being expressed, vets and consultants should provide advice to improve management.

Interconnected Supply Chain Management and Logistics: Key to Driving Business Success

Introduction: Supply Chain Management (SCM) and logistics are crucial elements in the success of any business, particularly in today's interconnected global economy. Effective integration of these functions enhances operational efficiency, reduces costs, and creates a competitive advantage.Objective: This paper explores the role of interconnection in supply chain management and logistics, highlighting the importance of seamless collaboration among all stakeholders. With the rise of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), the connectivity between various parts of the supply chain is improving, enabling real-time data sharing and greater responsiveness.Methodology: The Kaggle kernel "gelarerouzbahani/data-analysis-supply-chain-optimization" performs data analysis with a focus on optimizing supply chain processes.Result: By examining case studies and industry trends, this research emphasizes how interconnected supply chains are key to business growth and resilience in a dynamic marketplace.Conclusion: The analysis of the supply chain dataset has provided valuable insights into sales performance, revenue distribution, stock levels, and shipping costs

The Role of Artificial Intelligence in Enhancing the Decision-Making Process "A Field Study on Faculty Members at the College of Technical Sciences, Derna"

The study aims to identify the role of artificial intelligence in improving the decision-making process from the perspective of faculty members at the Faculty of Technical Sciences in Derna. To achieve the study's objectives, a descriptive analytical method was employed, relying on some previous studies. The study population consisted of faculty members at the college, with a sample size of (35) faculty members from various departments within the college. For data analysis, a computer was utilized along with a statistical program from the software services provided in (SPSS), leading to several findings, the most notable of which is that artificial intelligence plays an important and effective role in enhancing the decision-making process within the college. This study aims to explore the role of artificial intelligence in improving strategic decision-making processes in educational institutions. By analyzing performance data and providing recommendations based on artificial intelligence, senior management can make more accurate and effective decisions, such as expanding academic programs or improving operational efficiency. Additionally, the study presented a set of recommendations that are hoped to be followed within the institution. Key words: Artificial Intelligence, Decision-Making Process, The employees, faculty members.

Sistem Informasi Monitoring dan Evaluasi Kinerja Service Center Menggunakan Performance Dashboard

This research focuses on the development of a performance dashboard application to measure Key Performance Indicators (KPIs) such as the Customer Satisfaction Index (CSI) and Net Promoter Score (NPS). The main issue is the inaccurate measurement of customer satisfaction, making it difficult for management to make the right decisions in designing business strategies. The system development methodology is the Rapid Application Development (RAD) approach, which allows for quick and efficient application development. This system enables the company to effectively monitor and analyze performance data that can aid in better decision-making. With the information provided by this system, the company can adjust its business strategies to improve customer satisfaction, increase loyalty, and achieve its business goals. The research findings show that this dashboard system can provide more accurate insights into customer satisfaction and loyalty, which in turn helps the company improve service performance, address weak areas, and plan more measurable strategic steps to effectively achieve business goals

Reducing the CO2 footprint at an LNG asset with replicate trains using operational data-driven analysis. A case study on end flash vessels

Abstract A liquefied natural gas (LNG) facility often incorporates replicate liquefaction trains. The performance of equivalent units across trains, designed using common numerical models, might be expected to be similar. In this article, we discuss statistical analysis of real plant data to validate this assumption. Analysis of operational data for end flash vessels from a pair of replicate trains at an LNG facility indicates that one train produces 2.8%–6.4% more end flash gas than the other. We then develop statistical models for train operation, facilitating reduced flaring and hence a reduction of up to 45% in CO2 equivalent flaring emissions, noting that flaring emissions for a typical LNG facility account for ~4%–8% of the overall facility emissions. We recommend that operational data-driven models be considered generally to improve the performance of LNG facilities and reduce their CO2 footprint, particularly when replica units are present.

Data Analysis and Prediction of Student Academic Performance

Introduction: Predicting student performance across datasets with varying distributions remains a complex challenge in educational analytics. This study presents a novel approach to address this issue by utilizing transfer learning techniques to improve prediction accuracy. Objective: The research leverages a comprehensive dataset from Kaggle, encompassing demographic details, social factors, and academic performance indicators, to uncover significant patterns and relationships that influence student outcomes. By analyzing these factors, the study provides valuable insights that enable students to assess their academic progress, refine their learning strategies, and enhance overall efficiency.Method: The proposed methodology not only improves predictive accuracy but also bridges existing gaps in understanding student performance across diverse educational contexts. Result: These findings can be applied to develop personalized support systems, empowering students with actionable recommendations tailored to their individual needs.Conclusion: By addressing these challenges, the study contributes to a deeper understanding of student performance dynamics and highlights the potential of advanced predictive techniques to drive meaningful educational interventions.

Battery-Assisted Trolleybuses: Effect of Battery Energy Utilization Ratio on Overall Traction Energy Consumption

Urban public transport companies worldwide are introducing environmentally friendly bus solutions, and in cities with an existing trolleybus network, battery-assisted trolleybuses are an efficient alternative. Equipped with a traction battery, these vehicles allow a short range outside of the trolley lines and on-board recuperation, and they combine the advantages of electric buses and trolleybuses without the need for costly infrastructure expansions. Two similar battery-assisted trolleybuses are in operation in Žilina, where the unitary traction energy consumption has been observed to decrease as a function of the battery-powered and on-trolley-line vehicle run ratio. This theory was confirmed by statistical regression analysis of real operational data for one year of operation in different situations. This research also includes an analysis of battery-assisted trolleybuses’ operation on conventional trolleybus lines, which brought surprising findings, as well as an analysis of other selected indicators that can be dependent on the battery run ratio. These findings can contribute to reducing traction energy consumption by simply modifying the operating technologies without the need for extensive investment in infrastructure or vehicle equipment. This research is pilot research ready for in-depth research.

A Web Intelligent Hotel Management Framework Based on IoT and Generative AI

The hotel industry has faced numerous opportunities and challenges due to the advent of the artificial intelligence (AI) and the data-driven era. To address this, a novel augmented online performance analysis model is proposed for hotel management operations. This model seamlessly integrates the Internet of Things (IoT), generative AI technologies, and web engineering, allowing for the collection and analysis of multifaceted operational data. Consequently, real-time insights pertaining to room reservations, occupancy rates, and revenue streams are derived, serving as the basis for data-driven optimization strategies. Moreover, by incorporating generative AI technologies, the proposed model demonstrates the ability to dynamically generate predictive models, simulate scenarios, synthesize actionable insights, and adapt to evolving trends. As a result, it offers adaptive solutions for complex hotel management scenarios that were previously beyond the reach of traditional methods.

PENGEMBANGAN SISTEM INFORMASI MANAJAMEN UNTUK MENINGKATKAN EFISIENSI OPERASINAL UMKM

This Activity aims to analyze the impact of the Management Information System (MIS) on the operational efficiency of UMKM Kedai Atiya. The study was conducted through interviews with the owner of Kedai Atiya and operational data analysis. The results indicate that the implementation of MIS can enhance the operational efficiency of UMKM through reduced processing time, improved data accuracy, and better decision-making development. However, several obstacles were also identified, such as resource limitations and awareness of the importance of MIS. This Activity contributes to the development of MIS strategies for UMKM and provides recommendations to improve operational efficiency. Kata kunci : Sistem Informasi Manajemen, Efisiensi Operasional, UMKM, Kedai Atiya, Pengembangan Strategi. Abstract This research aims to analyze the impact of the ManagementInformation System (MIS) on the operational efficiency of UMKM Kedai Atiya. The study was conducted through interviews with theowner of Kedai Atiya and operational data analysis. The resultsindicate that the implementation of MIS can enhance theoperational efficiency of UMKM through reduced processingtime, improved data accuracy, and better decision-making development. However, several obstacles were also identified, such as resource limitations and awareness of the importance ofMIS. This research contributes to the development of MIS strategies for UMKM and provides recommendations to improveoperational efficiency. Keywords: : Management Information System, OperationalEfficiency, MSMEs, Atiya Store, Strategy Development.

Inclusive Education: Enhancing Social Integration and Academic Achievement for Students With and Without Learning Disabilities Aged 5 to 14

Inclusive education is recognized as essential for fostering the holistic development of children, irrespective of their abilities or limitations. This paper examines the effects of inclusive education on students with learning disabilities (SWLD) and students without learning disabilities (SWOLD) in Southern Vietnam, focusing on its impact on social integration and academic achievement. The study employs a mixed-methods research design, combining qualitative interviews with educators and quantitative analysis of student performance data. There are 487 participants, consisting of 132 SWLD and 355 SWOLD. These participants were drawn from inclusive and non-inclusive schools across various educational levels in southern Vietnam. Results indicate that inclusive practices significantly enhance social integration, empathy, and understanding among students. For SWLD, inclusive education leads to improved academic outcomes, particularly in the early education stages, while SWOLD reports favourable or neutral effects on academic achievements. However, at the secondary education level, the impact tends to be neutral or adverse for SWOLD. The research also identifies challenges in Southern Vietnam, including insufficient funding, inadequate teacher training and limited policy support, which hinder the effective implementation of inclusive education. Addressing these barriers is crucial for creating an equitable learning environment for all students in Vietnam.