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Nonalcoholic fatty liver disease (NAFLD) affects around a quarter of the global population, paralleling worldwide increases in obesity and metabolic syndrome. NAFLD arises in the context of systemic metabolic dysfunction that concomitantly amplifies the risk of cardiovascular disease and diabetes. These interrelated conditions have long been recognized to have a heritable component, and advances using unbiased association studies followed by functional characterization have created a paradigm for unravelling the genetic architecture of these conditions. A novel perspective is to characterize the shared genetic basis of NAFLD and other related disorders. This information on shared genetic risks and their biological overlap should in future enable the development of precision medicine approaches through better patient stratification, and enable the identification of preventive and therapeutic strategies. In this Review, we discuss current knowledge of the genetic basis of NAFLD and of possible pleiotropy between NAFLD and other liver diseases as well as other related metabolic disorders. We also discuss evidence of causality in NAFLD and other related diseases and the translational significance of such evidence, and future challenges from the study of genetic pleiotropy.

Construction and demolition waste treatment has become an increasingly pressing economic, social, and environmental concern across the world. This study employs a science mapping approach to provide a thorough and systematic examination of the literature on waste management research. This study identifies the most significant journals, authors, publications, keywords, and active countries using bibliometric and scientometric analysis. The search retrieved 895 publications from the Scopus database between 2001 and 2021. The findings reveal that the annual number of publications has risen from less than 15 in 2006 to more than 100 in 2020 and 2021. The results declare that the papers originated in 80 countries and were published in 213 journals. Review, urbanization, resource recovery, waste recycling, and environmental assessment are the top five keywords. Estimation and quantification, comprehensive analysis and assessment, environmental impacts, performance and behavior tests, management plan, diversion practices, and emerging technologies are the key emerging research topics. To identify research gaps and propose a framework for future research studies, an in-depth qualitative analysis is performed. This study serves as a multi-disciplinary reference for researchers and practitioners to relate current study areas to future trends by presenting a broad picture of the latest research in this field.

NAFLD is one of the most important causes of liver disease worldwide and will probably emerge as the leading cause of end-stage liver disease in the coming decades, with the disease affecting both adults and children. The epidemiology and demographic characteristics of NAFLD vary worldwide, usually parallel to the prevalence of obesity, but a substantial proportion of patients are lean. The large number of patients with NAFLD with potential for progressive liver disease creates challenges for screening, as the diagnosis of NASH necessitates invasive liver biopsy. Furthermore, individuals with NAFLD have a high frequency of metabolic comorbidities and could place a growing strain on health-care systems from their need for management. While awaiting the development effective therapies, this disease warrants the attention of primary care physicians, specialists and health policy makers.

Metabolic associated fatty liver disease (MAFLD) is the principal worldwide cause of liver disease and affects nearly a quarter of the global population. The objective of this work was to present the clinical practice guidelines of the Asian Pacific Association for the Study of the Liver (APASL) on MAFLD. The guidelines cover various aspects of MAFLD including its epidemiology, diagnosis, screening, assessment, and treatment. The document is intended for practical use and for setting the stage for advancing clinical practice, knowledge, and research of MAFLD in adults, with specific reference to special groups as necessary. The guidelines also seek to improve patient care and awareness of the disease and assist stakeholders in the decision-making process by providing evidence-based data. The guidelines take into consideration the burden of clinical management for the healthcare sector.

The clinical manifestations of NAFLD (both hepatic and extrahepatic) depend on the outcome of complex interactions between its primary drivers including poor lifestyle habits and diet, a dysfunctional microbiota, genetic predisposition, and environmental cues that result in metabolic dysfunction and liver disease. Importantly, MAFLD brings the liver disease into closer alignment with our current understanding of obesity, metabolic syndrome, and systems biology. [...]for clinicians, the definition simplifies the diagnostic process by using “positive” criteria rather than exclusionary ones and establishes a conceptual framework grounded in science for considering other etiologies that might contribute to fatty liver diseases. In other words, it captures the full spectrum of the disease. [...]cryptogenic cirrhosis” and lean MAFLD can now be diagnosed by physiological and metabolic criteria rather than being viewed as completely separate entities.

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

Heritage buildings are significant historical and architecture added value, which requires deep and precise preliminary brainstorming when considering upgrading or retrofitting these valuable buildings. In this study, we opted to highlight some passive design architecture interventions to improve the thermal comfort and the required cooling energy for buildings. The Murabba Palace in Riyadh was selected as a case study. DesignBuilder software was used to evaluate the energy performance of ten passive architectural design alternatives throughout different seasons in an attempt to improve the energy performance and thermal comfort of heritage buildings. The ten passive design scenarios encompassed double low-E glass, double reflected glass, double low-E glass and double wall with an air gap, double low-E glass and double wall with thermal insulation, double low-E glass and double wall with lightweight thermal insulation, double low-E glass and double wall with sprayed foam insulation, double reflected glass and double wall with an air gap, double reflected glass and double wall with thermal insulation, double reflected glass and double wall with lightweight thermal insulation, and double reflected glass and double wall with sprayed foam insulation. The results show that using double low-E glass and applying a double wall with polystyrene thermal insulation can enhance the thermal comfort inside the building and reduce the energy performance and CO2 emissions to 17% and 9%, respectively.

The significant energy consumption associated with the built environment demands comprehensive energy prediction modelling. Leveraging their ability to capture intricate patterns without extensive domain knowledge, supervised data-driven approaches present a marked advantage in adaptability over traditional physical-based building energy models. This study employs various machine learning models to predict energy consumption for an office building in Berkeley, California. To enhance the accuracy of these predictions, different feature selection techniques, including principal component analysis (PCA), decision tree regression (DTR), and Pearson correlation analysis, were adopted to identify key attributes of energy consumption and address collinearity. The analyses yielded nine influential attributes: heating, ventilation, and air conditioning (HVAC) system operating parameters, indoor and outdoor environmental parameters, and occupancy. To overcome missing occupancy data in the datasets, we investigated the possibility of occupancy-based Wi-Fi prediction using different machine learning algorithms. The results of the occupancy prediction modelling indicate that Wi-Fi can be used with acceptable accuracy in predicting occupancy count, which can be leveraged to analyze occupant comfort and enhance the accuracy of building energy models. Six machine learning models were tested for energy prediction using two different datasets: one before and one after occupancy prediction. Using a 10-fold cross-validation with an 8:2 training-to-testing ratio, the Random Forest algorithm emerged superior, exhibiting the highest R2 value of 0.92 and the lowest RMSE of 3.78 when occupancy data were included. Additionally, an error propagation analysis was conducted to assess the impact of the occupancy-based Wi-Fi prediction model’s error on the energy prediction model. The results indicated that Wi-Fi-based occupancy prediction can improve the data inputs for building energy models, leading to more accurate energy consumption predictions. The findings underscore the potential of integrating the developed energy prediction models with fault detection systems, model predictive controllers, and energy load shape analysis, ultimately enhancing energy management practices.

Bridges are generally acknowledged as one of the vital structures of transportation systems. Meanwhile, they are prone to time-variant damage and deterioration mechanisms over their life span. With that in mind, this research study aims to explore state-of-the-art work in relation to deterioration models and related critical factors of reinforced concrete bridges. Particularly, this study presents a mixed review methodology (scientometric and systematic) that reviews over 300 publications in Scopus and Web of Science databases over the period 1985–2023. The study scrutinized and categorized the wide spectrum of deterioration factors in reinforced concrete bridges with the help of deterioration models. Results manifested that implicating deterioration factors can be grouped into seven main clusters, namely chemical, material properties, design & construction, physical, operational, environmental, and force majeure. In addition, it is noted that hitherto, there has been a lack of sufficient research efforts on non-destructive evaluation-based deterioration models.