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This case study aims to investigate the causes of low efficiency in a carton production line by calculating Overall Equipment Effectiveness (OEE). A typical carton factory has been chosen for this study, which has two production lines, namely finished goods and corrugated boards. The define, measure, analyze, improve, and control approach is applied while implementing Six Sigma principles such as Kaizen, fishbone diagrams, and 5W+1H as its systematic procedure. The analysis involves estimating four machines’ average OEE across 12 shifts. A cost-effective method is applied to resolve the problems that cause the lines to be less efficient. By applying the proposed method, the OEE becomes more efficient by 29% for finished goods and 9% for corrugated boards. Value stream mapping has been used to track the improvements. It is found that emphasizing higher OEE values enhances operational performance, leading to better efficiency, power usage, cycle time, and equipment repair.

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation.

Malachite green (MG) dye is a common environmental pollutant that threatens human health and the integrity of the Earth’s ecosystem. The aim of this study was to investigate the potential biodegradation of MG dye by actinomycetes species isolated from planted soil near an industrial water effluent in Cairo, Egypt. The Streptomyces isolate St 45 was selected according to its high efficiency for laccase production. It was identified as S. exfoliatus based on phenotype and 16S rRNA molecular analysis and was deposited in the NCBI GenBank with the gene accession number OL720220. Its growth kinetics were studied during an incubation time of 144 h, during which the growth rate was 0.4232 (µ/h), the duplication time (td) was 1.64 d, and multiplication rate (MR) was 0.61 h, with an MG decolorization value of 96% after 120 h of incubation at 25 °C. Eleven physical and nutritional factors (mannitol, frying oil waste, MgSO4, NH4NO3, NH4Cl, dye concentration, pH, agitation, temperature, inoculum size, and incubation time) were screened for significance in the biodegradation of MG by S. exfoliatus using PBD. Out of the eleven factors screened in PBD, five (dye concentration, frying oil waste, MgSO4, inoculum size, and pH) were shown to be significant in the decolorization process. Central composite design (CCD) was applied to optimize the biodegradation of MG. Maximum decolorization was attained using the following optimal conditions: food oil waste, 7.5 mL/L; MgSO4, 0.35 g/L; dye concentration, 0.04 g/L; pH, 4.0; and inoculum size, 12.5%. The products from the degradation of MG by S. exfoliatus were characterized using high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). The results revealed the presence of several compounds, including leuco-malachite green, di(tert-butyl)(2-phenylethoxy) silane, 1,3-benzenedicarboxylic acid, bis(2-ethylhexyl) ester, 1,4-benzenedicarboxylic acid, bis(2-ethylhexyl) ester, 1,2-benzenedicarboxylic acid, di-n-octyl phthalate, and 1,2-benzenedicarboxylic acid, dioctyl ester. Moreover, the phytotoxicity, microbial toxicity, and cytotoxicity tests confirmed that the byproducts of MG degradation were not toxic to plants, microbes, or human cells. The results of this work implicate S. exfoliatus as a novel strain for MG biodegradation in different environments.

Stroke poses significant challenges to affected individuals, their families, and healthcare systems, with adherence to medications being a pivotal determinant of health outcomes. In this study, we aim to evaluate the medication adherence of stroke patients living in Iraq, and explore how patients' demographic and clinical details relate to their adherence levels. Furthermore, we seek to assess the self-care practices used by stroke patients and their adherence to them. We carried out a cross-sectional correlational study conducted from November 2022 to April 2023, stroke patients diagnosed in seven hospitals across Baghdad and Al-Mothanna governorate were recruited, with diagnoses confirmed by physicians and senior neurologists using MRI and/or CT scans. Patients' adherence to medications, demographic data, clinical characteristics, and self-care activities were analyzed using descriptive statistics and regression analyses. Of the 200 participants, mean age was 58.27 years, with males constituting 53.5%. About 40.5% had a hemorrhagic stroke, and 59.5% an ischemic stroke. The mean adherence score was 13.36 (SD= 4.658) out of a possible 28. Factors significantly correlated with medication adherence included age, monthly income, time since having a stroke, and education level. Adherence was also significantly linked to having diabetes mellitus or high blood pressure. The assessment of participants' self-care activities and medication adherence revealed that responses to questions about healthcare habits varied, with \"None\" being the most common response for most items. Notably, we found no significant association between adherence and factors such as gender, marital status, living place, and smoking status. Medication adherence remains suboptimal among stroke patients in Iraq. Various demographic and clinical factors play a role in influencing adherence. The conformity to medication regimens and factors associated with it among individuals who have suffered a stroke in Iraq is vital.

Aim The purpose of this study was to investigate the prevalence of poststroke depression (PSD) in Saudi Arabia and its association with socio‐demographic and clinical factors. Design A predictive correlational cross‐sectional study. Methods The study adopted a non‐probability convenience sampling method to recruit 211 stroke survivors between April and October 2021 from the neurology outpatient departments of two main governmental hospitals in Saudi Arabia. PSD was measured using a self‐assessment reliable and valid scale (The Hospital Anxiety and Depression Scale [HADS]). Results More than two‐thirds (70.6%) of the study sample (Mean age = 53 years, SD = 8.5, 51.2% were males) experienced some degree of depression (Score ≥8); of these, approximately half (48.8%) were in severe depression. The final prediction model was statistically significant (χ2 [15] = 31.39, p ˂ .01). PSD is a statistically significant health issue and requires immediate attention by healthcare providers to improve the health outcomes of stroke survivors.

It is important to understand the nature of the relationship between revenue, government spending and economic growth for any given country. Thus, the main objective of this research is to assess the relationship among non-oil revenue, government spending and economic growth in Bahrain. The study used annual time series data for the period from 1990 to 2020 collected from the Arab Monetary Fund (AMF) and Central Bank of Bahrain (CBB). This paper used time-series Vector Error Correction Model (VECM) approach of stationarity test, cointegration test, stability test and Granger causality test. Moreover, Impulse Response Function (IRF) has also been generated to explain the response to shock between the variables. The overall findings showed that government spending appears to be the main source for economic growth in Bahrain, therefore, in order to stabilize economic growth in Bahrain, government spending management needs reforming and income sources diversity is certainly required. On the other hand, the findings also revealed that the contribution of non-oil revenue had a greater effect on the shocks of economic growth. The findings of this study will be valuable and extremely useful to the policymakers to conduct a suitable fiscal reform in Bahrain.

A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables. T2D is regarded as a silent illness that can advance for years before being diagnosed. Finding genetic markers for T2D and metabolic syndrome in groups with similar environmental exposure is therefore essential to understanding the mechanism of such complex characteristic illnesses. So herein, we demonstrated the exclusive use of the collaborative cross (CC) mouse reference population to identify novel quantitative trait loci (QTL) and, subsequently, suggested genes associated with host glucose tolerance in response to a high-fat diet. In this study, we used 539 mice from 60 different CC lines. The diabetogenic effect in response to high-fat dietary challenge was measured by the three-hour intraperitoneal glucose tolerance test (IPGTT) test after 12 weeks of dietary challenge. Data analysis was performed using a statistical software package IBM SPSS Statistic 23. Afterward, blood glucose concentration at the specific and between different time points during the IPGTT assay and the total area under the curve (AUC0-180) of the glucose clearance was computed and utilized as a marker for the presence and severity of diabetes. The observed AUC0-180 averages for males and females were 51,267.5 and 36,537.5 mg/dL, respectively, representing a 1.4-fold difference in favor of females with lower AUC0-180 indicating adequate glucose clearance. The AUC0-180 mean differences between the sexes within each specific CC line varied widely within the CC population. A total of 46 QTL associated with the different studied phenotypes, designated as T2DSL and its number, for Type 2 Diabetes Specific Locus and its number, were identified during our study, among which 19 QTL were not previously mapped. The genomic interval of the remaining 27 QTL previously reported, were fine mapped in our study. The genomic positions of 40 of the mapped QTL overlapped (clustered) on 11 different peaks or close genomic positions, while the remaining 6 QTL were unique. Further, our study showed a complex pattern of haplotype effects of the founders, with the wild-derived strains (mainly PWK) playing a significant role in the increase of AUC values.

In this paper, we present a novel family of soft sets named \"finite soft-open sets\". The purpose of investigating this kind of soft sets is to offer a new tool to structure topological concepts that are stronger than their existing counterparts produced by soft-open sets and their well-known extensions, as well as to provide an environment that preserves some topological characteristics that have been lost in the structures generated by celebrated extensions of soft-open sets, such as the distributive property of a soft union and intersection for soft closure and interior operators, respectively. We delve into a study of the properties of this family and explore its connections with other known generalizations of soft-open sets. We demonstrate that this family strictly lies between the families of soft-clopen and soft-open sets and derive under which conditions they are equivalent. One of the unique features of this family that we introduce is that it constitutes an infra soft topology and fails to be a supra soft topology. Then, we make use of this family to exhibit some operators in soft settings, i.e., soft fo -interior, fo -closure, fo -boundary, and fo -derived. In addition, we formulate three types of soft continuity and look at their main properties and how they behave under decomposition theorems. Transition of these types between realms of soft topologies and classical topologies is examined with the help of counterexamples. On this point, we bring to light the role of extended soft topologies to validate the properties of soft topologies by exploring them for classical topologies and vice-versa.

Obesity and its attendant conditions have become major health problems worldwide, and obesity is currently ranked as the fifth most common cause of death globally. Complex environmental and genetic factors are causes of the current obesity epidemic. Diet, lifestyle, chemical exposure, and other confounding factors are difficult to manage in humans. The mice model is helpful in researching genetic BW gain because genetic and environmental risk factors can be controlled in mice. Studies in mouse strains with various genetic backgrounds and established genetic structures provide unparalleled opportunities to find and analyze trait-related genomic loci. In this study, we used the Collaborative Cross (CC), a large panel of recombinant inbred mouse strains, to present a predictive study using heterozygous Smad4 knockout profiles of CC mice to understand and effectively identify predispositions to body weight gain. Male C57Bl/6J Smad4+/− mice were mated with female mice from 10 different CC lines to create F1 mice (Smad4+/−x CC). Body weight (BW) was measured weekly until week 16 and then monthly until the end of the study (week 48). The heritability (H2) of the assessed traits was estimated and presented. Comparative analysis of various machine learning algorithms for predicting the BW changes and genotype of mice was conducted. Our data showed that the body weight records of F1 mice with different CC lines differed between wild-type and mutant Smad4 mice during the experiment. Genetic background affects weight gain and some lines gained more weight in the presence of heterozygous Smad4 knockout, while others gained less, but, in general, the mutation caused overweight mice, except for a few lines. In both control and mutant groups, female %BW had a higher heritability (H2) value than males. Additionally, both sexes with wild-type genotypes showed higher heritability values than the mutant group. Logistic regression provides the most accurate mouse genotype predictions using machine learning. We plan to validate the proposed method on more CC lines and mice per line to expand the literature on machine learning for BW prediction.

Juvenile polyposis syndrome (JPS) is a rare autosomal dominant disorder characterized by multiple juvenile polyps in the gastrointestinal tract, often associated with mutations in genes such as Smad4 and BMPR1A. This study explores the impact of Smad4 knock-out on the development of intestinal polyps using collaborative cross (CC) mice, a genetically diverse model. Our results reveal a significant increase in intestinal polyps in Smad4 knock-out mice across the entire population, emphasizing the broad influence of Smad4 on polyposis. Sex-specific analyses demonstrate higher polyp counts in knock-out males and females compared to their WT counterparts, with distinct correlation patterns. Line-specific effects highlight the nuanced response to Smad4 knock-out, underscoring the importance of genetic variability. Multimorbidity heat maps offer insights into complex relationships between polyp counts, locations, and sizes. Heritability analysis reveals a significant genetic basis for polyp counts and sizes, while machine learning models, including k-nearest neighbors and linear regression, identify key predictors, enhancing our understanding of juvenile polyposis genetics. Overall, this study provides new information on understanding the intricate genetic interplay in the context of Smad4 knock-out, offering valuable insights that could inform the identification of potential therapeutic targets for juvenile polyposis and related diseases.