Integration of Machine Learning and GAP Analysis for a Data Driven Lecturer Performance Evaluation System

This study addresses the development of lecturers’ performance evaluation system specifically designed for applied universities. It analyzes the existing lecturer’s performance evaluation system in applied universities in Shaanxi Province and highlights several problems based on facts. The current lecturer performance evaluation system in applied universities is very similar to the system used in research universities, and it is found not suitable for the special requirements of applied universities. The indicators used in the performance evaluation system suffer from problems such as excessive quantification, imperfection, rigidity, and dogmas that lack scientific rigor in practical application. Through the literature, the study found that there are not many researches on applied universities. However, the existing research lacks the following three parts. First, there is a lack of performance evaluation policies for applied university lecturers. Second, the performance evaluation indicators are not in line with the talent training goal of applied university. Third, as a feature of application-oriented undergraduate social service research is also very few. Based on this, the main objective of this study is to develop a performance evaluation system for applied university lecturers, taking into account the educational objectives and characteristics of applied university. In this study, semi-structured interviews are used to collect data from research participants to understand the existing performance evaluation system of applied university lecturers from different perspectives and how they think it can be improved. Then form an applied university lecturer performance evaluation system.

The COVID-19 pandemic has sparked a debate on the usefulness of performance evaluations, with employees considering factors like work-from-home and hybrid setups as motivators. Employers aim to maximize employee productivity, and they use performance evaluations to assess performance and emphasize company goals. Ugoani (2020) supports this claim, stating that performance appraisals not only review job performance but also bring attention to the company's goals. Open communication between supervisors and employees is crucial during performance evaluations. However, resistance, lack of motivation, and unclear performance dimensions often lead to questions about performance evaluations. This study aims to determine the effectiveness of performance appraisal practices on employee productivity, as employees today prioritize work-family balance and burnout. The study found that most respondents are not satisfied with their company's performance evaluation system and do not believe it positively affects productivity. They also disagree with the communication of performance results and the effectiveness of supervisors providing feedback. They also disagree with the use of rewards like bonuses or pay raises, which do not motivate employees to work more. They also disagree with the promotion of social activities that do not improve productivity. Additionally, many respondents do not communicate negatively about their work, plan it efficiently, or actively participate in meetings and consultations. They also do not take on extra responsibilities or continuously update their skills and knowledge. The study concludes that the BPO company's performance evaluation practices significantly impact employee productivity.

The study is aimed to calculate an innovative numerical index for bit performance evaluation called Bit Index (BI), applied on a new type of bit database named Formation Drillability Catalogue (FDC). A dedicated research programme (developed by Eni E&P and the University of Bologna) studied a drilling model for bit performance evaluation named BI, derived from data recorded while drilling (bit records, master log, wireline log, etc.) and dull bit evaluation. This index is calculated with data collected inside the FDC, a novel classification of Italian formations aimed to the geotechnical and geomechanical characterization and subdivisions of the formations, called Minimum Interval (MI). FDC was conceived and prepared at Eni E&P Div., and contains a large number of significant drilling parameters. Five wells have been identified inside the FDC and have been tested for bit performance evaluation. The values of BI are calculated for each bit run and are compared with the values of the cost per metre. The case study analyzes bits of the same type, diameters and run in the same formation. The BI methodology implemented on MI classification of FDC can improve consistently the bit performances evaluation, and it helps to identify the best performer bits. Moreover, FDC turned out to be functional to BI, since it discloses and organizes formation details that are not easily detectable or usable from bit records or master logs, allowing for targeted bit performance evaluations. At this stage of development, the BI methodology proved to be economic and reliable. The quality of bit performance analysis obtained with BI seems also more effective than the traditional “quick look” analysis, performed on bit records, or on the pure cost per metre evaluation.

In today's tech landscape, business intelligence (BI) is essential, especially with advanced technologies emerging. This study delves into the transformative potential of artificial intelligence and machine learning in predictive analytics, offering crucial insights into data. The chapter provides an extensive overview, focusing on the future of predictive analytics in BI. It highlights real-time applications, featuring practical work and case studies. Key practices for integrating AI and predictive analytics are introduced, covering data preparation, cleaning, preprocessing, feature engineering, and exploratory data analysis. Integration of ML with BI facilitates visualization, report generation, interactive dashboards, and real-time monitoring. However, manual analysis of complex datasets poses challenges, risking valuable insights. This chapter explores how ML automates the identification of patterns, extracting insights. Key challenges and advantages in integrating machine learning into BI frameworks are also discussed.

Earthen flood control structures such as dams and levees are vital for managing flood risk. In the United States alone, the United States Army Corps of Engineers manages approximately 14,000 mi of levees, with an average age of 59 years. Current inspection standards consist of walking or driving along the levee looking for important performance indicator signs. Manually inspecting levees for such conditions is time consuming, in certain instances possibly dangerous to inspectors, and is susceptible to human subjectivity in some instances with the sheer quantity of levees to inspect. In recent years across the broader civil infrastructure industry, remote inspection technologies, such as unmanned aerial vehicles, have begun seeing use and allowing for systems to be inspected faster and safer than traditional methods. The data obtained from these remote inspection tools can then be utilized with machine learning algorithms to classify and identify both failures, and potential future failure locations. This combination of rapid remote inspection for data collection, and machine learning for data processing, provides a powerful tool to enhance the condition monitoring of levees rapidly, efficiently, and remotely. This paper outlines some current technologies and use cases of these tools for levee inspections and discusses ongoing work related to machine vision and machine learning automation of visual imagery applied to crack detection using convolutional neural networks, where an accuracy of up to 99.76% is achieved for crack detection using a database of 20,800 images of concrete.

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Abstract: This research paper explores the pivotal role of artificial intelligence (AI) in enhancing climate resilience through advanced predictive analytics and data-driven decision-making. As climate change intensifies the frequency and severity of extreme weather events, AI technologies—such as machine learning, deep learning, and neural networks—are being leveraged to model complex environmental systems, forecast climate-related risks, and inform mitigation and adaptation strategies. The paper examines the integration of satellite imagery, remote sensing data, and real-time monitoring with AI algorithms to predict disasters like floods, droughts, and wildfires with greater accuracy. It also investigates AI's contribution to sustainable development by optimizing resource allocation, improving early warning systems, and supporting climate-smart infrastructure. Through case studies and empirical analysis, this study highlights how AI-driven climate intelligence can reduce vulnerabilities, guide global policy, and accelerate the transition to a resilient and sustainable future. Keywords: Artificial intelligence, Climate resilience, Predictive analytics, Sustainable development, Climate risk modeling, Disaster forecasting, Machine learning, Deep learning, Environmental monitoring, Early warning systems, Climate adaptation, Risk reduction, Smart infrastructure, Climate change mitigation, Remote sensing

Glioblastoma (GBM) is an exceptionally aggressive type of brain tumor with a poor prognosis, underscoring the urgent need to identify new molecular targets for therapeutic development. The objective of this research is to clarify the molecular interactions affected by the oncometabolite D-2-hydroxyglutarate (D-2-HG) within the framework of GBM. Differential expression analysis of multi-omics data identified potential target genes linked to GBM pathogenesis. To enhance our understanding of the binding interactions between D-2-HG and the identified target proteins, we utilized an integrated methodology encompassing various machine learning algorithms, network pharmacology techniques, and molecular docking. A sum of 135 genes was recognized as possible targets through which D-2-HG exerts its effects in GBM. The ensuing analysis, utilizing machine learning techniques, identified six crucial genes [eukaryotic translation initiation factor 4E binding protein 1 (EIF4EBP1), fatty acid binding protein 3 (FABP3), potassium voltage-gated channel subfamily Q member 2 (KCNQ2), epithelial cell adhesion molecule (EPCAM), sphingosine-1-phosphate receptor 5 (S1PR5), and metabotropic glutamate receptor 3 (GRM3)] as key regulators. Among these, FABP3, KCNQ2, EPCAM, S1PR5, and GRM3 were significantly downregulated, whereas EIF4EBP1 was markedly upregulated (p < 0.05). Molecular docking simulations indicated a strong binding affinity of D-2-HG towards the target proteins. Our study suggests that D-2-HG plays a significant role in the pathogenesis of GBM by modulating specific genes and signaling pathways. Utilizing machine learning techniques, we identified six essential regulatory genes, and further molecular docking simulations revealed a strong affinity of D-2-HG for these critical targets. Collectively, these results establish a substantial basis for future investigations into the mechanistic role of D-2-HG in GBM oncogenesis.

he pharmaceutical industry demands high standards of equipment reliability to ensure product quality and operational efficiency. This study explores a predictive maintenance framework that integrates machine learning and real-time video analysis to monitor equipment health and prevent failures. The system comprises three main functionalities: training a machine learning model to predict the Remaining Useful Life (RUL) of equipment based on historical sensor data, manual input for RUL prediction, and real-time video monitoring to detect equipment malfunctions. A RandomForestRegressor is employed to model RUL prediction using sensor data such as pressure, temperature, and fan speed, while the YOLO object detection model analyzes video footage to identify anomalies and potential hazards. This approach enables early detection of issues, reduces unplanned downtime, and improves the overall reliability of pharmaceutical manufacturing equipment. The integration of sensor data, predictive algorithms, and visual monitoring forms a robust system aimed at enhancing operational continuity, maintaining product safety, and ensuring regulatory compliance in the pharmaceutical sector. Key Words: Streamlit, Predictive Maintenance, Model Training, Random Forest Regressor, Real-Time Video Monitoring, YOLO Object Detection, RUL Prediction, Machine Learning, Video Upload, Video Processing, Equipment Performance, Data Preprocessing, Model Deployment, Sensor Data, Video Annotation, Preventive Maintenance.

Background Chronic systemic inflammation is a key contributor to cardiometabolic complications in diabetes mellitus (DM) and prediabetes (PreDM). Composite inflammatory indices—including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), platelet-to-hemoglobin ratio (PHR), and aggregate inflammation systemic index (AISI)—have shown prognostic value for mortality. However, their integrated assessment using machine learning and quantification at the population level remain limited. Methods In this retrospective cohort study, 11,304 adults with DM or PreDM from the National Health and Nutrition Examination Survey (NHANES, 2005–2018) were analyzed. The primary outcomes were all-cause and cardiovascular mortality. Associations between inflammatory indices and mortality were evaluated using Cox proportional hazards models. Predictive performance was assessed via Extreme Gradient Boosting (XGBoost), and population attributable fractions (PAFs) estimated the mortality burden related to systemic inflammation. Results NLR, MLR, SIRI, SII, and AISI were independently associated with all-cause and cardiovascular mortality. MLR showed the strongest association (HR: 2.948 and 3.717 for all-cause and CVD mortality, respectively). XGBoost identified SIRI, SII, AISI, MLR, and NLR as key predictors, with SIRI ranked highest for cardiovascular mortality. Inclusion of inflammatory indices improved model discrimination and calibration. PAF analysis suggested that 10–20% of mortality reduction could be attributed to improved inflammatory profiles. Conclusion Systemic inflammatory indices are independent predictors of mortality in individuals with DM or PreDM. Their integration into machine learning models enhances risk prediction and may inform population-level strategies for cardiometabolic risk stratification.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

As Internet technology comes into increasingly wide‐spread use, there is a need for quality control of network service performance to ensure user convenience, such as server access time and response time. Conventional performance evaluations aim at the control of devices at the network layer, but do not consider quality at the application layer. In this paper, the authors propose a method of performance evaluation in end‐point applications to evaluate the quality of service from the viewpoint of user convenience. This method makes possible performance measurement by introducing an observation layer that monitors the behavior of client applications, and by focusing on the request‐response communications between the client and server. A performance evaluation system that uses the principle of the observation layer has been implemented, and it is shown that the performance of an actual running system can be accurately measured, confirming the effectiveness of the performance evaluation system. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 88(2): 71–80, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20083

While achieving economic growth anddevelopment are dependent on the performance of the bureaucracy, there is a growing inability in Pakistan to objectivelyevaluate this performance. In this article, we examine performance evaluations systems that can be applied to developingcountry bureaucracies and find that the best system in the Pakistani context is the SMART performance evaluation system. We then analyze the present Pakistani system and compare it to an example of a SMART system and find that the disconnect between the actual performance of the civil service and the measurement of this performance by the performance evaluation report (PER) in Pakistan, has discouraged optimal performance. The article also looks at the perceptions of senior civil servants themselves on what they perceive as the shortcomings of the performance evaluation management system in Pakistan as well as their opinions on a potential SMART performance evaluations system. The article recommends an immediate re-evaluation of the present performance management where the re-evaluation initiatives must be tempered with the realization that the present system has been established not to optimize bureaucratic performance but rather to sustain the present system of patronage and power.

In the era of the Fourth Industrial Revolution (4IR), vast amounts of data are generated from sources such as IoT, cybersecurity, mobile, business, social media, and healthcare. Artificial intelligence (AI), particularly machine learning (ML), is essential for analyzing this data and developing smart, automated applications. Various machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, are used to enhance application intelligence. Additionally, deep learning, a subset of ML, can process large-scale data effectively. This paper provides an overview of these algorithms and their applications across fields like cybersecurity, smart cities, healthcare, e-commerce, and agriculture. It also discusses the challenges and research opportunities in the field, aiming to be a valuable resource for both academics and industry professionals, as well as decision-makers in various real-world scenarios. Highlights: Post-modern ceramics reflect features and connotations of body language. Research explores theoretical framework, variables, and applications in ceramic sciences. Results reveal key indicators, conclusions, and supporting references. Keywords: Machine learning, Deep learning, Artificial intelligence, Data science, Data-driven decision-making, Predictive analytics, Intelligent applications