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DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the number of genetic mutations increases, making it necessary to activate DNA repair mechanisms. A crucial factor in the base excision repair process, which helps maintain the stability of the genome, is an enzyme called DNA polymerase $\boldsymbol{\beta}$β (Pol$\boldsymbol{\beta}$β) encoded by the POLB gene. It plays a vital role in the repair of damaged DNA. Additionally, variations known as Single Nucleotide Polymorphisms (SNPs) in the POLB gene can potentially affect the ability to repair DNA. This study uses bioinformatics tools that extract important features from SNPs to construct a feature matrix, which is then used in combination with machine learning algorithms to predict the likelihood of developing cancer associated with a specific mutation. Eight different machine learning algorithms were used to investigate the relationship between POLB gene variations and their potential role in cancer onset. This study not only highlights the complex link between POLB gene SNPs and cancer, but also underscores the effectiveness of machine learning approaches in genomic studies, paving the way for advanced predictive models in genetic and cancer research.

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.A novel prediction method for solar irradiance, targeting both short-term and medium-term forecast horizons, is proposed to improve PV power production. The hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model.Graphical Abstract

Purpose: The purpose of this study is to examine the critical failure factors (CFFs) linked to various types of process improvement (PI) projects such as Kaizen, Lean, Six Sigma, Lean Six Sigma and Agile. Proposing a mitigation framework accordingly is also an aim of this study. Design/methodology/approach: This research undertakes a systematic literature review of 49 papers that were relevant to the scope of the study and that were published in four prominent databases, including Google Scholar, Scopus, Web of Science and EBSCO. Findings: Further analysis identifies 39 factors that contribute to the failure of PI projects. Among these factors, significant emphasis is placed on issues such as “resistance to cultural change,” “insufficient support from top management,” “inadequate training and education,” “poor communication” and “lack of resources,” as primary causes of PI project failures. To address and overcome the PI project failures, the authors propose a framework for failure mitigation based on change management models. The authors present future research directions that aim to enhance both the theoretical understanding and practical aspects of PI project failures. Practical implications: Through this study, researchers and project managers can benefit from well-structured guidelines and invaluable insights that will help them identify and address potential failures, leading to successful implementation and sustainable improvements within organizations. Originality/value: To the best of the author’s knowledge, this paper is the first study of its kind to examine the CFFs of five PI methodologies and introduces a novel approach derived from change management theory as a solution to minimize the risk associated with PI failure.

Purpose: This paper aims to unearth the essential components of Six Sigma for successful deployment and sustainment of service quality in four different organizations in the United Arab Emirates (UAE). More specifically, the paper is intended to determine the motivation to apply Six Sigma, Voice of Customer, Key Performance Indicators (KPIs), Critical-to-Quality (CTQ), readiness factors, Critical Success Factors, sustainment measures, tools and techniques used, challenges/barriers and performance impact on the company. Design/methodology/approach: An exploratory research methodology with multiple case study analyses was adopted to determine the answers to the research objectives. Four case studies from different service processes of four companies were analyzed. The case studies were collated from these companies via a case study protocol with pre-defined criteria. Findings: The analysis shows that service operation improvement projects are primarily dependent on the voice of the internal customer, with return on investment in savings as the KPI of the process. Most organizations prefer cycle time and errors as the CTQs in the Six Sigma projects. Even novice users can effectively apply the Six Sigma methodology with external experts’ assistance, mentoring and interventions. Across the case studies, it is observed that the projects were successfully deployed due to the support of top management leadership, effective communication and cross-functional teams. Employee resistance to change is the common barrier observed during the case study analysis. Eventually, in all the four case studies, Six Sigma is executed with standard tools and techniques within the define, measure, analyze, improve, control (DMAIC) approach. Research limitations/implications: The present study’s findings cannot be generalized due to the limited number of case study analyses in different ecosystems in the UAE. The authors would like to analyse and report more case studies in service quality improvement through the Six Sigma methodology to comprehend and develop a generic roadmap for the deployment of Six Sigma in the UAE service industry. Practical implications: The study’s findings provide insights into commonalities and differences between the essential factors of Six Sigma deployment and sustainability in UAE companies. Originality/value: The study results might help the policymakers and key decision makers in UAE and other countries understand the effectiveness of Six Sigma in service quality improvement with its essential factors for deployment.

AbstractLean is a philosophy that helps organizations develop a continuous improvement culture by eliminating waste and minimizing the use of resources. On the other hand, Industry 4.0 is a complex high-tech model that contributes to flexible organizations using different technologies such as IoT, cloud computing, blockchain, virtual reality, and Artificial Intelligence. The concept of integrating Lean Principles and Industry 4.0 is still very recent and limited, and the literature lacks a bibliometric perspective. The integration is challenging in terms of contentment-wise and is a broad multi-dimensional research area. This study aims to analyze Lean Industry 4.0 or Lean 4.0 and Lean digital transformation literature using bibliometrics, identifying its state of art, benefits, challenges, and scientific gaps. In this study, articles published between 2010 and 2022 from the Scopus database are analyzed to identify significant publications, journals, authors, institutions, and countries based on the number of publications and frequently used keywords and subject areas. This study contributes to developing the state of art of Lean Industry 4.0 and Lean digital transformation and helps researchers and practitioners direct their future studies. The literature performance is measured based on both the number of publications and the number of citations; this bibliometric analysis covers most of the published papers in both research domains.

Green buildings have become broadly adopted in commercial and residential sectors with the objective of minimizing environmental impacts through reductions in energy usage and water usage and, to a lesser extent, minimizing environmental disturbances from the building site. In this paper, we develop and discuss a techno-economic model for a green commercial building that is 100% powered by a photovoltaic (PV) system in stand-alone configuration. A medium-sized office building in El Paso, TX was modelled to rely on a photovoltaic system to supply all of its electricity needs either directly from the PV system or through an energy-storage system (ESS) using Li-ion batteries (LIBs) or reversible fuel cells (RFCs). Cost results show that a 400-kW PV system can generate electricity at a cost of 2.21 cents/kWh in El Paso, TX and the average levelized cost of energy storage (LCOS) using 450-kW RFC is ~31.3 cents/kWh, while this could reach as low as 25.5 cents/kWh using a small LIB ESS. While the RFC provides the flexibility required to meet building-energy demand, LIBs may not be able to meet building needs unless the storage size is increased substantially, which in turn incurs more energy-storage cost, making LIBs less favourable from an economic perspective. Sensitivity analysis revealed that capital cost, discount rate and expected system lifetime play key roles in shaping the LCOS in both systems.A technoeconomic model is developed for a commercial building that is 100 percent powered by a photovoltaic (PV) system in stand-alone configuration. Reversible fuel cells and Li-ion batteries are evaluated for their ability to increase green building resiliency and reduce the cost of energy storage.

Abstract: Recent developments in computing and technology, along with the availability of large amounts of raw data, have contributed to the creation of many effective techniques and algorithms in the fields of pattern recognition and machine learning. The main objectives for developing these algorithms include identifying patterns within the available data or making predictions, or both. Great success has been achieved with many classification techniques in real-life applications. With regard to binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. This study examines rare events (REs) with binary dependent variables containing many more non-events (zeros) than events (ones). These variables are difficult to predict and to explain as has been evidenced in the literature. This research combines rare events corrections to Logistic Regression (LR) with truncated Newton methods and applies these techniques to Kernel Logistic Regression (KLR). The resulting model, Rare Event Weighted Kernel Logistic Regression (RE-WKLR), is a combination of weighting, regularization, approximate numerical methods, kernelization, bias correction, and efficient implementation, all of which are critical to enabling RE-WKLR to be an effective and powerful method for predicting rare events. Comparing RE-WKLR to SVM and TR-KLR, using non-linearly separable, small and large binary rare event datasets, we find that RE-WKLR is as fast as TR-KLR and much faster than SVM. In addition, according to the statistical significance test, RE-WKLR is more accurate than both SVM and TR-KLR. [Copyright &y& Elsevier]

Accurate prediction is important in data mining and data classification. Rare events data, imbalanced or skewed datasets are very important in data mining and classification. However, These types of data are difficult to predict and to explain as has been demonstrated in the literature. The problems arise from various sources. This paper surveys the latest research on such data in the hope of adding further contribution to this important field of data mining.

Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and machine learning perspective and how LR can be applied to imbalanced and rare events data.