Perform Data Analysis Research Articles

New Approaches in the Assessment of Pre-Service Teachers' Scientific Research Skills: Multidimensional-Many Facet Rasch Model Application

This survey study aimed to determine pre-service teachers’ skills in conducting and presenting scientific research process and to examine their peer-scoring behaviors. The participants consisted of 36 pre-service teachers and seven peer raters. The analytical rubric developed by the researchers and the performance task were used to collect data. The multidimensional many-facet Rasch measurement model was employed in data analysis. Rasch analysis was carried out with a fully crossed design. The analyses revealed that the least difficult criterion in the dimension of conducting the scientific research process was to perform data analysis while the most challenging criterion was to determine the research model and sample. In addition, the least difficult criterion in the dimension of making an oral presentation was to ensure the interest and participation of the audience, while the most challenging criterion was to make an effective start. The analysis of the rater facet demonstrated that the most severity rater was R3, while the most leniency rater was R4. The study argues that the multidimensional many-facet Rasch model can be used to present reliability and validity evidence in multidimensional performance evaluations.

The Proliferation of Artificial Intelligence in the Forklift Industry—An Analysis for the Case of Romania

This paper investigates the impact of artificial intelligence (AI) on the forklift industry, focusing on logistics and procurement within small and medium-sized enterprises (SMEs) in Romania. Using a mixed-methods approach, including interviews with seven managers from a benchmarked company in the forklift industry (BCFI) and quantitative analysis of operational data, we examine the transformative effects of AI integration. Key findings include a 30% reduction in inventory holding costs due to AI-powered predictive analytics; a 15% decrease in procurement costs through AI-driven supplier evaluation systems; a 25% increase in operational efficiency from AI-optimized route planning; a 40% boost in overall productivity attributed to AI-enabled automation; and a projected 20% reduction in low-skilled labor requirements over the next five years. The study employs environmental, social, and corporate governance (ESG), balanced scorecard (BSC), benchmarking, and activity-based management (ABM) models to analyze risks and implications of AI integration. A case study of a leading Romanian SME in the forklift industry is presented, examining financial strategies using McKinsey’s 7S framework. The paper concludes that while AI offers significant operational benefits, it also presents challenges in workforce transition and ethical considerations that require careful management.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

Maritime Supply Chain Optimisation: A Case Study of Blockchain Integration in Port Logistics Management

This research examines the potential for maritime supply chain optimisation through the integration of blockchain technology in port logistics management. In the era of global digitalisation, the maritime sector faces challenges to improve operational efficiency and transparency. This study uses a case study approach to evaluate the implementation of blockchain in one of Indonesia's major ports. The research methodology involved qualitative and quantitative analyses, including interviews with key stakeholders, direct observation of logistics processes, and analysis of historical port operational data. The blockchain system was implemented over a six-month period, and its performance was compared with conventional logistics management systems. Key challenges identified include the need for a large initial investment, resistance to change from some stakeholders, and the need for industry-wide standardisation of blockchain protocols. This research demonstrates the great potential of blockchain technology in optimising maritime supply chains. Recommendations for further implementation and future research are discussed, with an emphasis on the importance of industry collaboration and regulatory support to accelerate the adoption of this technology in the maritime sector.

Latent factors affecting safer injection practices that can reduce infections and how education can improve them.

The incidence of healthcare-associated infections, particularly injection-related infections, can increase patient comorbidities even in countries with adequate medical resources. Although there are clear guidelines for injection practices to prevent infections, their application in clinical settings is insufficient. Therefore, the objective of this study was to identify factors affecting injection practices associated with reduced infections by conducting surveys targeting practicing healthcare providers involved in administering injections at each healthcare organization and performing data analysis. We administered a survey to healthcare providers responsible for injection practices at each healthcare organization that included items related to infection-safe injection practice guidelines. All survey questions were reviewed by an expert panel of infectious disease and infection control nurses. Survey contents were subjected to exploratory factor analysis (EFA), confirmatory factor analysis, and multivariable robust regression tests to determine the impact of each factor and their correlations. A total of 842 questionnaires were analyzed. Each questionnaire was classified into four factors: reuse and contamination, compliance with aseptic technique, exchange of infusion set, and use of multidose vials. Nurses with higher careers showed more compliance. Education within one year and awareness of each item of the questionnaire had positive associations with proper injection practice. Education is thought to be the most important factor in good injection practices that could reduce infections. Relevant knowledge through timely training is expected to have a positive impact on performance and compliance related to safe injections.

Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the effect of waves. To improve both standards and achieve a more accurate ship performance assessment, this study proposes a new performance prediction model, the multi-input single-output (MISO) system, which assumes that each ship has specific frequency characteristics according to type and size. Based on this new model, in-service navigation data collected from a 176k DWT bulk carrier, which amount to 5.7 million data points, are analyzed to assess the speed performance of the vessel subject to environmental disturbances. The proposed model was validated by comparing its results with ISO 19030 and specifically assessing the speed–power curves and speed reduction measured in operational data with the influence of environmental disturbances removed.

Analysis and Research on Intelligent Logistics Data under Internet of Things and Blockchain

ABSTRACT To explore the security performance of logistics data transmission in an Intelligent Logistics System (ILS) under the Internet of Things (IoT) and Blockchain (BC), IoT is introduced to optimize the logistics transportation process. First, BC is adopted to improve the ILS under IoT, and ILS under IoT combined with BC is established. Next, an analysis of the relationship between the information service provider and the transportation provider in the logistics transaction process is conducted to perform data analysis and to establish an incentive mechanism for logistics transportation under BC. Finally, the simulation analysis of the constructed model is implemented. The results reveal that the constructed model is highly efficient. It can effectively solve the traceability problem of logistics transaction information, and the calculation cost is stable within 1,000 ms. In the comparative analysis of system transmission performance, the model exhibits outstanding results in transmission latency, consistently maintaining an average delay of approximately 350 ms. This stability is primarily attributed to the seamless integration of BC technology, which optimizes data packet transmission paths and mitigates waiting time. Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. As for energy consumption, the model notably reduces overall energy usage within the logistics network by curtailing unnecessary data transmission and computation, particularly in mobile nodes and wireless communication channels. The proposed algorithm excels across various transmission metrics, including transmission delay, data successful acceptance rate, network throughput, and energy consumption are all shown to be the best transmission performance of the proposed algorithm, in which the data successful acceptance rate is close to 1. As a result, the developed ILS model not only guarantees low latency performance but also demonstrates high data transmission security, facilitating more efficient and precise real-time information transmission. In this study, within the ILS framework, the relationship between information service providers and transportation service providers is established through BC, and an incentive mechanism is constructed. However, some shortcomings in this incentive mechanism are recognized. For instance, during the initial stages of BC integration, there are issues of information asymmetry concerning factors such as credit, transportation capacity, and effort level. Moreover, the open access policy of couriers in the model raises concerns, as the access policy itself constitutes sensitive information, which may result in privacy breaches. Hence, as part of future work, the introduction of attribute encryption schemes is planned to obfuscate access policies, thereby safeguarding the privacy of logistics personnel’s access policies.

Comprehensive Analysis of Miscanthus Research: Sustainable Biomass and Bioenergy Through Knowledge Mapping

Miscanthus grass species have gained global research attention in the fields of biomass and bioenergy, witnessing remarkable advancements in the 21st century. This study systematically searched and selected relevant literature on primary research involving Miscanthus species, published between 2007 and 2023, from the China National Knowledge Infrastructure and Web of Science core databases. Utilizing CiteSpace as a bibliometric tool, the study generated quantitative statistics and performed data analysis on the selected articles. The examination covered publication trends, national and author publication outputs, author and journal co-citations, keyword research hotspots, and the temporal evolution of research topics in the Miscanthus research field, offering empirical support for future in-depth investigations and innovation in Miscanthus spp. The research findings reveal the following insights: (1) Publication trends can be divided into two phases (stable growth and fluctuating decline), with recent trends maintaining a relatively high level. (2) A collaborative publication network, led by core authors such as Zili Yi, John Clifton-Brown, and Iris Lewandowski, has been established, characterized by a diverse research focus and close cooperation. (3) Research hotspots encompass several aspects, including biomass production yield, efficiency, and energy products of Miscanthus spp., ecological restoration, genetics, plant physiological research, and chemical components. The primary species investigated are Miscanthus×giganteus, Miscanthus sinensis, Miscanthus floridulus, and Miscanthus lutarioriparius.

Comparison of B Cell Variable Region Gene Segment Characteristics in Neuro-autoantibodies.

Autoimmune pediatric neurologic diseases have variable phenotypes and presentations, making diagnosis challenging. The pathologic mechanisms are also distinct, including cell-mediated and Ab-mediated autoimmunity, paraneoplastic syndromes, and postinfectious processes. In recent years a number of studies have described the characteristics of the autoantibodies involved in a number of these diseases. Some of the described Abs use a restricted set of variable gene segments. We sought to compare the Ab characteristics of autoantibodies related to some of the more common disorders to discover whether specific Ab signatures are universally associated with neuroautoimmune diseases. We initially performed a literature review to summarize the Ab characteristics of autoantibodies related to some of the more common disorders, including N-methyl-d-aspartate receptor (NMDAR) and leucine-rich, glioma-inactivated 1 (LGI-1). Next, we performed data analysis from selected studies that sequenced Ig genes to further characterize NMDAR and LGI-1 autoantibodies including CDR3 length distribution, variable gene sequence usage, and isotype use. We found that CDR3 length of NMDAR autoantibodies was normally distributed whereas the CDR3 length distribution of LGI-1 autoantibodies was skewed, suggesting that there is no global structural restriction on types of autoantibodies that can cause encephalitis. We also found that IgG1-IgG3 were the main NMDAR autoantibody isotypes detected, while IgG4 was the major isotype used in autoantibodies from LGI-1 encephalitis. These findings are useful for our understanding of autoimmune encephalitis and will help facilitate better diagnosis and treatment of these conditions in the future.

The impact of administrative efficiency on SME Growth and Sustainability

enterprises (SMEs). This review examines the relationship between efficient administrative processes and the long-term success of SMEs, emphasizing how streamlined operations, resource management, and decision-making can enhance business performance. SMEs often struggle with limited resources, resulting in operational bottlenecks and inefficiencies that hinder their capacity to scale and sustain growth. By improving administrative efficiency through the adoption of technology, organizational restructuring, and optimized processes, SMEs can achieve greater productivity, reduce costs, and make informed decisions that promote sustainability. The review employs a case study approach, analyzing SMEs across different sectors—manufacturing, retail, and services to demonstrate the tangible benefits of administrative efficiency. The research includes qualitative interviews, operational data analysis, and performance evaluations before and after implementing efficiency-enhancing measures. Key findings highlight improvements in areas such as time management, cost reduction, and customer satisfaction, all contributing to accelerated growth and competitive advantage. Additionally, the study explores how administrative efficiency supports sustainable practices, such as resource optimization and risk management, leading to long-term business viability. Despite these advantages, challenges such as resistance to change, lack of skilled personnel, and financial constraints pose barriers to achieving administrative efficiency in SMEs. The review concludes by offering practical recommendations for overcoming these challenges, including investing in digital tools, employee training, and organizational restructuring. Ultimately, the research underscores the importance of administrative efficiency as a critical factor for SME growth and sustainability, providing insights for policymakers, business owners, and managers aiming to enhance the operational performance of small businesses in a competitive market.

From Disruption to Continuity: Understanding Organizational Resilience Strategies in the Face of Technological Change

In today's rapidly evolving technological landscape, organizations face significant challenges that can disrupt their operations, supply chains, and overall stability. This study examines how organizations manage technological disruptions by exploring the resilience strategies they employ and evaluating their effectiveness across different industries and organizational contexts. Drawing on resilience theory, crisis management literature, and organizational studies, the research adopts a mixed-methods approach that includes in-depth case studies, qualitative interviews with industry experts, and quantitative analysis of organizational performance data. The primary objectives of this study are to identify key resilience strategies, assess their effectiveness in diverse organizational settings, and determine the factors that influence successful implementation. By integrating insights from real-world examples and theoretical frameworks, the research aims to provide a comprehensive understanding of how organizations can better prepare for and respond to technological disruptions. The findings are expected to reveal critical implementation factors such as leadership support, organizational culture, and resource allocation, which are essential for fostering a resilient organizational environment. Ultimately, this study offers actionable recommendations for practitioners seeking to enhance their resilience capabilities. These recommendations will serve as a guide for developing robust resilience strategies, cultivating a culture of adaptability, and overcoming challenges associated with technological disruptions. The study contributes to the broader discourse on organizational resilience, providing valuable insights for both academic research and practical application in an increasingly uncertain technological environment.

AI-Driven FlightOps: Revolutionizing Airline Operations Control with Predictive Analytics and NLP

Abstract: Flight operations control centers (OCCs) are the nerve centers of airline operations, tasked with managing a wide range of activities such as flight scheduling, crew assignments, maintenance coordination, and dealing with disruptions. While traditional approaches have relied heavily on human decision-making and manual analysis of operational data, the increasing complexity of modern airline operations demands more efficient solutions. This paper presents FlightOps AI, a cutting-edge AIpowered system that integrates advanced natural language processing (NLP), deep neural networks (DNNs), reinforcement learning (RL), and computer vision to provide OCC staff with actionable insights and predictive capabilities. The system processes massive amounts of structured and unstructured data in real time, enabling smarter decision-making and automation of routine tasks. By adopting these technologies, FlightOps AI transforms the operational landscape, optimizing airline performance while minimizing human errors

SUPPLY CHAIN DISRUPTION AND SUSTAINABILITY OF PHARMACEUTICAL FIRMS IN ANAMBRA STATE, NIGERIA

<p>The pharmaceutical sector, in Nigeria plays a role in the nations’ healthcare system by supplying medications to the people. However pharmaceutical companies in the Anambra State have been facing obstacles recently due to disruptions in their supply chains. This research investigates how supply chain disruptions impact the sustainability of firms in Anambra State. A mixed-methods approach, involving qualitative interviews with industry experts and quantitative analysis of operational data, identifies the main causes of supply chain disruptions and their effects on this firm’s long-term success. The research reveals that supply chain disruptions result from factors such as delays in customs clearance, inconsistent regulatory policies, currency fluctuations, inadequate transportation infrastructure and issues with supplier reliability. Interviews with industry experts highlighted customs delays as a disruption while quantitative analysis showed a 20% increase in times for imported pharmaceuticals along with a 15% rise in delivery delays and a 10% increase in product returns due to transportation challenges. Moreover, inefficiencies in inventory management were observed, leading to a 5% rise in holding costs and a 3% decrease in turnover rates due to supply chains. Challenges related to suppliers involved issues, with delivery reliability and product quality leading to a 10% extension in supplier lead times and a 5% uptick, in returns. The findings highlighted a direct correlation between these supply chain issues and decreased profitability, operational efficiency, and the ability to meet patient needs, emphasizing the urgent need for improvements to enhance the resilience of the pharmaceutical supply chain in Anambra State.</p>

MATHEMATICAL APPLICATIONS OF GLOBAL WARMING IN SAUDI ARABIA

This study attempts to thoroughly analyze and address the complicated issue of global warming. The questionnaire was distributed to the target group through an online platform, and responses were collected for subsequent analysis. The study utilized statistical package for the social sciences (SPSS) version 20 to perform data analysis. Each multiple linear regression (MLR) model was analyzed separately, allowing for a detailed examination of the relationships between predictor variables and the outcome variable. The stepwise regression method for all applications reveals that three controlled variables chosen using SPSS 20 are , and . (tropical cyclones), (explosions in general) and (transportation by vehicle) were the actual causes of global warming.

Real-time solar PV generation in a building using LSTM-based time series forecasting

The increasing demand for renewable energy has forced the researchers to lay importance on studying the behavior of these sources by monitoring the operational data and performing data analysis. The application of data analytics to renewable energy is one of the most powerful methods to maintain the system's reliability and stability. As the power system becomes more complex the necessity to perform data analytics also increases. When data analytic techniques are applied to solar energy generations through Photovoltaic (PV) dataset, the possible behavior of PV generation performance which is affected by changes in environmental conditions can be predicted and further analytical approaches allow us to detect possible PV panel and inverter failures. This paper is an attempt towards applying the intelligent data analytics approaches to solar PV generation of a real-time photovoltaic plant. The main purpose of the data analytics platform is to analyze the energy yield, assess the PV system performance, and forecast solar PV generation. In this article, Long Short-Term Memory (LSTM) machine learning model is developed to assess and interpret the available information from the gathered data of the PV plant. The proposed model is a sequence-to-sequence regression-based forecasting model which performs time series forecasting in a real-time 100 kW PV plant in the University building of SRM Institute of Science and Technology, Kattankulathur, Tamilnadu.

Enhancing High School Mathematics Education Through School-based Research: Development and Mechanisms

In the context of ongoing educational reforms aimed at fostering critical competencies among high school students, this paper explores the implementation and impact of school-based mathematics research (SBR) as a strategic innovation within the high school curriculum. Adopting mixed research methods, in the study questionnaires were distributed to all students and teachers in the early stage to collect and analyze relevant data and than the study combines quantitative analyses of student performance data with qualitative insights from teacher and student feedback to assess the effects of SBR on teaching practices and learning outcomes. This research is based on the fact that the traditional education model cannot meet the needs of cultivating students' thinking ability, and the new curriculum reform has decentralized teaching research to schools. The study is helpful to explore the effect of SBR on the cultivation of teachers' scientific research ability and the improvement of classroom quality. The results show the powerful role of SBR in promoting teaching practice and student participation. Based on school characteristics, it proposes effective teaching improvement measures to improve the quality of mathematics education.

The Effects of Using Digital Technologies on High School Geography Learning

In recent years, the integration of digital technologies into educational practices has become a focal point for enhancing student learning experiences across various subjects, including Geography. Following this trend, the present study investigates the effects of digital technology use on high school Geography education by comparing traditional teaching methods with modern, digitally enhanced approaches. Thus, the main aim of the paper was to evaluate whether digital tools such as interactive maps, 3D simulations, and augmented reality can improve student engagement and academic performance in geography classes. A quasi-experimental research design was employed, involving quantitative analyses of student performance data before and after the intervention. The study sample consisted of 50 high school students divided into two groups: one taught using traditional methods and another exposed to digital learning environments. Curricular Geography tests were administered to the two groups, along with surveys and interviews, to assess differences in academic outcomes and engagement levels between the two groups. The findings indicate that students in the digital learning group outperformed their peers in the traditional setting, achieving higher test scores. This may be attributed to the use of interactive digital tools, which facilitated a deeper understanding of geographical concepts and promoted active participation in the learning process. However, the study also highlights challenges such as unequal access to technology and the necessity for ongoing teacher training to effectively implement digital resources. These conclusions and insights have led to several important implications for education. The positive impact of digital learning tools suggests that educational systems should consider integrating more technology into the curriculum to enhance learning outcomes.

A Cross-Sectional Study of the A1 Phenotype in the Colombian Caribbean: A Shadow Behind Pulmonary Embolism.

Venous thromboembolic disease (VTE) is an episodic condition of multifactorial origin, commonly manifesting as deep vein thrombosis (DVT) and pulmonaryembolism (PE). VTE is a major cause of morbidity and mortality. As an acute condition, it has the potential for recurrence and is associated with major consequences; this disease poses significant challenges to the healthcare system. VTE is a widespread concern in developed and developing countries; therefore, it is not limited to specific regions or populations. To evaluate the risk factors associated with unprovoked PE in patients in a hospital center in Sincelejo, Colombia. This is an observational, analytical cross-sectional study utilizing retrospective data. From 2010 to 2023, we reviewed 126 medical records of patients who experienced their first unprovoked VTE events and met the inclusion criteria. We performed data analysis using R software version 3.5.1. Of the patients, 36.5% (n = 46) were women; 63.5% (n = 80) were men, with a mean age of 62.22 years (SD = 10.62). About 53% of women presented with PE, compared to 47% of men. The coagulation factor VIII acted as a PE risk factor (p = 0.098). The best model to predict PE development obtained an Akaikeinformationcriterion (AIC) of 176.67, indicating that the A1 positive phenotype is the risk factor with the highest prediction for PE occurrence. High levels of coagulation factor VIII and an A1-positive phenotype are risk factors that may increase PE development. These findings suggest the need for preventive strategies in this risk setting to reduce the incidence and recurrence of PE.

Applications of deep learning in mathematics education: A review

Traditional classroom settings often fail to account for individual differences, leading to disparities in student performance. Deep learning models offer a solution by analyzing performance data to provide targeted interventions and immediate feedback, thereby personalizing educational experiences. This review examines the transformative potential of deep learning in mathematics education through various applications and methodologies. Studies show that deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can analyze complex student data, identify learning patterns, and offer real-time, personalized support. Notable applications include automated classroom feedback systems, intelligent tutoring systems, and predictive models for student performance. Despite the promising results, challenges such as data quality, computational resources, model interpretability, and potential biases remain. Future research should focus on creating inclusive datasets, improving computational efficiency, and enhancing model transparency to fully harness the benefits of deep learning in educational settings.

Sangerbox 2: Enhanced functionalities and update for a comprehensive clinical bioinformatics data analysis platform.

In recent years, development in high-throughput sequencing technologies has experienced an increasing application of statistics, pattern recognition, and machine learning in bioinformatics analyses. SangeBox platform to meet different scientific demands. The new version of Sangs is a widely used tool among many researchers, which encourages us to continuously improve the plerBox 2 (http://vip.sangerbox.com) and extends and optimizes the functions of interactive graphics and analysis of clinical bioinformatics data. We introduced novel analytical tools such as random forests and support vector machines, as well as corresponding plotting functions. At the same time, we also optimized the performance of the platform and fixed known problems to allow users to perform data analyses more quickly and efficiently. SangerBox 2 improved the speed of analysis, reduced resource required for computer performance, and provided more analysis methods, greatly promoting the research efficiency.