Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches.

Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches.

Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.

Predictive Ability of LRINEC Score for Lower Extremity Amputation in Moderate to Severe Diabetic Foot Infection.

OBJECTIVE: To assess the predictive ability of the laboratory risk indicator for necrotising fasciitis (LRINEC) score for lower extremity amputation in patients with moderate to severe diabetic foot infection (DFI). STUDY DESIGN: Observational study. Place and Duration of the Study: Department of General Surgery, Combined Military Hospital, Rawalpindi, Pakistan, from June to September 2023. METHODOLOGY: Patients admitted to the surgical ward with moderate to severe DFI were included by convenience sampling. Patients with severe sepsis, unstable haemodynamics, pressure injuries, and terminal illnesses were excluded. Demographic and clinical data of patients were noted down. LRINEC score was calculated on the day of admission. Final outcome (amputation or otherwise) was recorded on the 30th day the since the day of admission. RESULTS: Two hundred patients with moderate to severe DFI were included. The median age of patients was 56 years (IQR 49-66 years). The median duration of diabetes was 11 years (IQR 4 - 18.75 years). The median LRINEC score at admission was 6 (IQR 3-9). The majority of the patients (65.5%) had some other medical comorbid besides diabetes. Patients who had amputation due to DFI at 30 days post-admission had higher LRINEC scores on admission as compared to those patients who did not have amputation (Median 8 vs. 2, p <0.001). The cut-off point of LRINEC score ≥6.5 at admission had sensitivity of 74% and specificity of 94% in predicting amputation. CONCLUSION: The LRINEC score may be used as an objective scoring system to predict the risk of amputation in patients with moderate to severe DFI in indoor clinical settings. KEY WORDS: Diabetic foot, LRINEC score, Limb loss, Necrotising fasciitis.

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil.

The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.

Serum/plasma biomarkers and the progression of cardiometabolic multimorbidity: a systematic review and meta-analysis.

Background: The role of certain biomarkers in the development of single cardiometabolic disease (CMD) has been intensively investigated. Less is known about the association of biomarkers with multiple CMDs (cardiometabolic multimorbidity, CMM), which is essential for the exploration of molecular targets for the prevention and treatment of CMM. We aimed to systematically synthesize the current evidence on CMM-related biomarkers. Methods: We searched PubMed, Embase, Web of Science, and Ebsco for relevant studies from inception until August 31st, 2022. Studies reported the association of serum/plasma biomarkers with CMM, and relevant effect sizes were included. The outcomes were five progression patterns of CMM: (1) no CMD to CMM; (2) type 2 diabetes mellitus (T2DM) followed by stroke; (3) T2DM followed by coronary heart disease (CHD); (4) T2DM followed by stroke or CHD; and (5) CHD followed by T2DM. Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of the included studies. A meta-analysis was conducted to quantify the association of biomarkers and CMM. Results: A total of 68 biomarkers were identified from 42 studies, which could be categorized into five groups: lipid metabolism, glycometabolism, liver function, immunity, and others. Lipid metabolism biomarkers were most reported to associate with CMM, including TC, TGs, HDL-C, LDL-C, and Lp(a). Fasting plasma glucose was also reported by several studies, and it was particularly associated with coexisting T2DM with vascular diseases. According to the quantitative meta-analysis, HDL-C was negatively associated with CHD risk among patients with T2DM (pooled OR for per 1 mmol/L increase = 0.79, 95% CI = 0.77-0.82), whereas a higher TGs level (pooled OR for higher than 150 mg/dL = 1.39, 95% CI = 1.10-1.75) was positively associated with CHD risk among female patients with T2DM. Conclusion: Certain serum/plasma biomarkers were associated with the progression of CMM, in particular for those related to lipid metabolism, but heterogeneity and inconsistent findings still existed among included studies. There is a need for future research to explore more relevant biomarkers associated with the occurrence and progression of CMM, targeted at which is important for the early identification and prevention of CMM.

Managing Weed-Crop Interactions Enhances Chickpea (Cicer arietinum L.) Chemical Components.

Chickpea (Cicer arietinum L.) is a major pulse crop worldwide, renowned for its nutritional richness and adaptability. Weeds are the main biotic factor deteriorating chickpea yield and nutritional quality, especially Asphodelus tenuifolius Cav. The present study concerns a two-year (2018-19 and 2019-20) field trial aiming at evaluating the effect of weed management on chickpea grain quality. Several weed management practices have been here implemented under a factorial randomized complete block design, including the application of four herbicides [bromoxynil (C7H3Br2NO) + MCPA (Methyl-chlorophenoxyacetic acid) (C9H9ClO3), fluroxypyr + MCPA, fenoxaprop-p-ethyl (C18H16ClNO5), pendimethalin (C13H19N3O4)], the extracts from two allelopathic weeds (Sorghum halepense and Cyperus rotundus), two mulches (wheat straw and eucalyptus leaves), a combination of A. tenuifolius extract and pendimethalin, and an untreated check (control). Chickpea grain quality was measured in terms of nitrogen, crude protein, crude fat, ash, and oil content. The herbicides pendimethalin (Stomp 330 EC (emulsifiable concentrate) in pre-emergence at a rate of 2.5 L ha-1) and fenoxaprop-p-ethyl (Puma Super 7.5 EW (emulsion in water) in post-emergence at a rate of 1.0 L ha-1), thanks to A. tenuifolius control, showed outstanding performance, providing the highest dietary quality of chickpea grain. The herbicides Stomp 330 EC, Buctril Super 40 EC, Starane-M 50 EC, and Puma Super 7.5 EW provided the highest levels of nitrogen. Outstanding increases in crude protein content were observed with all management strategies, particularly with Stomp 330 EC and Puma Super 7.5 EW (+18% on average). Ash content was highly elevated by Stomp 330 EC and Puma Super 7.5 EW, along with wheat straw mulching, reaching levels of 2.96% and 2.94%. Crude fat content experienced consistent elevations across all treatments, with the highest improvements achieved by Stomp 330 EC, Puma Super 7.5 EW, and wheat straw mulching applications. While 2018-19 displayed no significant oil content variations, 2019-20 revealed the highest oil content (5.97% and 5.96%) with herbicides Stomp 330 EC and Puma Super 7.5 EW, respectively, followed by eucalyptus leaves mulching (5.82%). The results here obtained are of key importance in the agricultural and food sector for the sustainable enhancement of chickpea grain's nutritional quality without impacting the environment.

Automation of structural health monitoring (SHM) system of a bridge using BIMification approach and BIM-based finite element model development.

This research focuses on the automation of an existing structural health monitoring system of a bridge using the BIMification approach. This process starts with the Finite Element Analysis (FEA) of an existing bridge for the numerical calculations of static and dynamic parameters. The validation of the FE model and existing SHM system was carried out by the field load testing (Static and dynamic) of the bridge. Further, this study tries to fill the research gap in the area of automatic FE model generation by using a novel methodology that can generate a BIM-based FE model using Visual Programming Language (VPL) scripts. This script can be exported to any FE software to develop the geometry of the FE model. Moreover, the SHM devices are deployed to the Building Information modelling (BIM) model of the bridge to generate the BIM-based sensory model (as per the existing SHM system). In this way, the BIM model is used to manage and monitor the SHM system and control its sensory elements. These sensors are then linked with the self-generated (Internet of Things) IoT platform (coded in Arduino), developing a smart SHM system of the bridge. Resultantly, the system features visualisation and remote accessibility to bridge health monitoring data.

Association of birth and childhood weight with risk of chronic diseases and multimorbidity in adulthood.

BACKGROUND: Little is known about the relationship between early life body size and occurrence of life-course multiple chronic diseases (multimorbidity). We aim to evaluate associations of birth weight, childhood body size, and their changes with the risks of chronic diseases and multimorbidity. METHODS: This prospective cohort study included 246,495 UK Biobank participants (aged 40-69 years) who reported birth weight and childhood body size at 10 years old. Birth weight was categorized into low, normal, and high; childhood body size was reported as being thinner, average, or plumper. Multimorbidity was defined as having two or more of 38 chronic conditions retrieved from inpatient hospital data until 31 December, 2020. The Cox regression and quasi-Poisson mixed effects models were used to estimate the associations. RESULTS: We show that 57,071 (23.2%) participants develop multimorbidity. Low birth weight (hazard ratio [HR] 1.29, 95% confidence interval [CI] 1.26-1.33), high birth weight (HR 1.02, 95% CI > 1.00-1.05), thinner (HR 1.21, 95% CI 1.18-1.23) and plumper body size (HR 1.06, 95% CI 1.04-1.09) are associated with higher risks of multimorbidity. A U-shaped relationship between birth weight and multimorbidity is observed. Changing to be thinner or plumper is associated with multimorbidity and many conditions, compared to changing to be average. CONCLUSIONS: Low birth weight, being thinner and changing to have a thinner body size in childhood are associated with higher risks of developing multimorbidity and many chronic conditions in adulthood. Early monitoring and maintaining a normal body size in childhood could have life-course benefits for preventing multimorbidity above and beyond individual conditions.

Little is known about the relationship between childhood body size and the risk of developing more than one chronic disease later in life. Using data from the UK, we found that low birth weight, high birth weight, and being thinner or plumper than average during childhood were all associated with higher risks of developing more than one chronic disease in adulthood. In addition, changing body shape during childhood to be either thinner or plumper, was associated with being more likely to develop more than one chronic disease later in life. Our results highlight the importance of early monitoring and maintenance of average body size in childhood, as this might prevent the occurrence of chronic diseases later in life.

Breast cancer epidemiology and sociodemographic differences in BRICS-plus countries from 1990 to 2019: An age period cohort analysis.

Background: Breast cancer (BC) is a major health concern in the BRICS-plus, a group of developing nations consisting of Brazil, Russia, India, China, South Africa, and 30 other Asian countries, with nearly half of the world's population. This study aims to identify potential risk factors contributing to the burden of BC by assessing its epidemiological and socio-demographic changes. Methods: Data on BC outcomes were obtained from the 2019 Global Burden of Disease Survey. The age-period-cohort (APC) modeling technique was used to evaluate the nonlinear impacts of age, cohort, and period on BC outcomes and reported risk attributable mortality and disability adjusted life years (DALYs) rate changes between 1990 and 2019. Results: In 2019, there were 0.90 million female BC cases and 0.35 million deaths in the BRICS-plus region, with China and India having the largest proportion of incident cases and deaths, followed by Pakistan. Lesotho experienced the highest annualized rates of change (AROC: 2.61%; 95%UI: 1.99-2.99) in the past three decades. Birth cohorts' impact on BC varies greatly between the BRICS-plus nations, with Pakistan suffering the largest risk increase in the most recent cohort. High body mass index (BMI), high fasting plasma glucose (FPG), and a diet high in red meat contributed to the highest death and DALYs rates in most BRICS-plus nations in 2019, and there was a strong negative link between SDI and death and DALYs rate. Conclusions: The study found that the burden of BC varies significantly between BRICS-plus regions. Thus, BRICS-plus nations should prioritise BC prevention, raise public awareness, and implement screening efficiency measures to reduce the burden of BC in the future, as well as strengthen public health policies and initiatives for important populations based on their characteristics and adaptability.

Enhancing Localization Efficiency and Accuracy in Wireless Sensor Networks.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Analysis Of Inhibitory Effect Of Transcriptional Regulatory Protein (Sty 4289) Of Salmonella Typhi Ct18by Homology Modelling.

BACKGROUND: Salmonella typhi cause typhoid fever which is life threatening disease. It affects approximately 600,000 people per annum around the world. Food and water are the integral components through which this disease is transmitted and becomes base of typhoid. It spreads widely where cleanliness is very poor. Objective was to analyse three-dimensional structure of transcriptional regulator of Salmonella typhi CT18 by homology modelling to inhibit virulent effect of Salmonella typhi. METHODS: Bioinformatics tools and programs like comprehensive Microbial resource (CMR). Interproscan, Basic Local Alignment Search tool (BLAST), Modeller 9.10, Procheck and Prosa were used as bioinformatic tools for effective study of protein. RESULTS: Homology modelling is an appropriate and precise method to find three-dimensional transcriptional regulator to stop its virulency. CONCLUSIONS: Homology modelling is computational and accurate method to find 3D structure of transcriptional regulator to inhibit its virulence effect of causing disease.

Rapidly adapted community health strategies to prevent treatment interruption and improve COVID-19 detection for Syrian refugees and the host population with hypertension and diabetes in Jordan.

BACKGROUND: We evaluated community health volunteer (CHV) strategies to prevent non-communicable disease (NCD) care disruption and promote coronavirus disease 2019 (COVID-19) detection among Syrian refugees and vulnerable Jordanians, as the pandemic started. METHODS: Alongside medication delivery, CHVs called patients monthly to assess stockouts and adherence, provide self-management and psychosocial support, and screen and refer for complications and COVID-19 testing. Cohort analysis was undertaken of stockouts, adherence, complications and suspected COVID-19. Multivariable models of disease control assessed predictors and non-inferiority of the strategy pre-/post-initiation. Cost-efficiency and patient/staff interviews assessed implementation. RESULTS: Overall, 1119 patients were monitored over 8 mo. The mean monthly proportion of stockouts was 4.9%. The monthly proportion non-adherent (past 5/30 d) remained below 5%; 204 (18.1%) patients had complications, with 63 requiring secondary care. Mean systolic blood pressure and random blood glucose remained stable. For hypertensive disease control, age 41-65 y (OR 0.46, 95% CI 0.2 to 0.78) and with diabetes (OR 0.73, 95% CI 0.54 to 0.98) had decreased odds, and with baseline control had increased odds (OR 3.08, 95% CI 2.31 to 4.13). Cumulative suspected COVID-19 incidence (2.3/1000 population) was suggestive of ongoing transmission. While cost-efficient (108 US${\$}$/patient/year), funding secondary care was challenging. CONCLUSIONS: During multiple crises, CHVs prevented care disruption and reinforced COVID-19 detection.

A framework for identification and classification of liver diseases based on machine learning algorithms.

Hepatocellular carcinoma (HCC) is one of the most commonly seen liver disease. Most of HCC patients are diagnosed as Hepatitis B related cirrhosis simultaneously, especially in Asian countries. HCC is the fifth most common cancer and the second most common cause of cancer-related death in the World. HCC incidence rates have been rising in the past 3 decades, and it is expected to be doubled by 2030, if there is no effective means for its early diagnosis and management. The improvement of patient's care, research, and policy is significantly based on accurate medical diagnosis, especially for malignant tumor patients. However, sometimes it is really difficult to get access to advanced and expensive diagnostic tools such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET-CT)., especially for people who resides in poverty-stricken area. Therefore, experts are searching for a framework for predicting of early liver diseases based on basic and simple examinations such as biochemical and routine blood tests, which are easily accessible all around the World. Disease identification and classification has been significantly enhanced by using artificial intelligence (AI) and machine learning (ML) in conjunction with clinical data. The goal of this research is to extract the most significant risk factors or clinical parameters for liver diseases in 525 patients based on clinical experience using machine learning algorithms, such as regularized regression (RR), logistic regression (LR), random forest (RF), decision tree (DT), and extreme gradient boosting (XGBoost). The results showed that RF classier had the best performance (accuracy = 0.762, recall = 0.843, F1-score = 0.775, and AUC = 0.999) among the five ML algorithms. And the important orders of 14 significant risk factors are as follows: Total bilirubin, gamma-glutamyl transferase (GGT), direct bilirubin, hemoglobin, age, platelet, alkaline phosphatase (ALP), aspartate transaminase (AST), creatinine, alanine aminotransferase (ALT), cholesterol, albumin, urea nitrogen, and white blood cells. ML classifiers might aid medical organizations in the early detection and classification of liver disease, which would be beneficial in low-income regions, and the relevance of risk factors would be helpful in the prevention and treatment of liver disease patients.

Biorational Control of Callosobruchus maculatus (Coleoptera: Buchidae) in Stored Grains with Botanical Extracts.

Globally, around 2000 plant species are used against pest control. The utilization of botanicals is considered the most economic and biodegradable methods for the control of stored grains pests. Therefore, the current study was carried out to investigate the repellency potential of five botanicals against Callosbruchus maculatus F. in Haripur, Pakistan. The concentrations of Azadirachta indica L., Nicotiana tabacum L., Melia azedarach L., Nicotiana rustica L., and Thuja orientalis L. were, i.e., 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0% in four replicates to establish contact effects. The data were recorded after 1, 2, 3, 6, 24, 48, 72, and 96 hours. The repellency effect of these plant species against C. maculatus were increased in both the time- and dose-dependent manner, and highest effect was observed at 72 h. In addition, the repellency effect was 91% for A. indica (class: V), 86% M. azedarach, 82%, N. tabacum (class: V), 79% N. rustica (class: IV), and 75% T. orientalis (class: IV) at 3% concentration against C. maculatus. Furthermore, following 96 hours' exposure to treatment the sensitivity response of insects decreases as the time interval increases, i.e., 86% A. indica (class: V) was followed by 71% M. azedarach (class: IV), 65% N. tabacum (class: IV), 61% N. rustica (class: IV), and T. orientalis 57% (class: III) repellency at highest concentration of 3%. The current study concluded that A. indica and M. azedarach can be incorporated for the management of C. maculatus and these plant species might be helpful in the productions of new biopesticides.

The Key Role of microRNAs in Initiation and Progression of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the main type of primary liver malignancy and the fourth leading cause of cancer-related death worldwide. MicroRNAs (miRNAs), a type of non-coding RNA that regulates gene expression mainly on post-transcriptional level has a confirmed and important role in numerous biological process. By regulating specific target genes, miRNA can act as oncogene or tumor suppressor. Recent evidence has indicated that the deregulation of miR-NAs is closely associated with the clinical pathological features of HCC. However, the precise regulatory mechanism of each miRNA and its targets in HCC has yet to be illuminated. This study demonstrates that both oncogenic and tumor suppressive miRNAs are crucial in the formation and development of HCC. miRNAs influence biological behavior including proliferation, invasion, metastasis and apoptosis by targeting critical genes. Here, we summarize current knowledge about the expression profile and function of miRNAs in HCC and discuss the potential for miRNA-based therapy for HCC.

An integrated approach towards marble waste management: GIS, SFA, and recycling options.

Marble units generate an enormous amount of non-biodegradable waste during the processing operations and are considered one of the environmentally unfriendly industrial sectors. This sector has become a global nuisance due to its multi-dimensional damaging nature. Therefore, a multidimensional approach is needed to geographically describe the pollution sources, their waste load, collection mechanism, and their proper disposal or reuse. This article highlights an integrated approach to sorting out the multidimensional issues associated with the marble sector. More than 150 marble processing units (MPUs) are scattered in the study area pouring waste into the environment in the form of slurry. The produced waste roots environmental issues both for fauna and flora of the terrestrial and aquatic segments of the environment. A geospatial-based attempt has been made through geographic information system (GIS) for the identification and description of the pollution sources, MPUs, in the study area. The quantitative assessment has been made through substance flow analysis (SFA) by taking raw marble as the input source and marble product as output. Furthermore, material characterization has been carried out to confirm the chemical composition of the slurry waste for its potential use. Results confirmed that a major part (> 90%) of marble powder is calcium carbonate (CaCO3) which has so many potential uses as raw material. The integrated approach of GIS, SFA, and chemical characterization set forth a model that satisfies multi-dimensional queries regarding pollution sources, pollution load, and sustainable solutions to the problem. The output integrated model provides a digital environmental baseline for the monitoring of MPUs, the amount of waste generated by these MPUs, and its potential reuse options. The proposed model can be utilized worldwide as a decision support tool due to its optimum results.

State-of-the-Art Statistical Approaches for Estimating Flood Events.

Reliable quantile estimates of annual peak flow discharges (APFDs) are needed for the design and operation of major hydraulic infrastructures and for more general flood risk management and planning. In the present study, linear higher order-moments (LH-moments) and nonparametric kernel functions were applied to APFDs at 18 stream gauge stations in Punjab, Pakistan. The main purpose of this study was to evaluate the impacts of different quantile estimation methods towards water resources management and engineering applications by means of comparing the state-of-the-art approaches and their quantile estimates calculated from LH-moments and nonparametric kernel functions. The LH-moments (η = 0, 1, 2) were calculated for the three best-fitted distributions, namely, generalized logistic (GLO), generalized extreme value (GEV), and generalized Pareto (GPA), and the performances of these distributions for each level of LH-moments (η = 0, 1, 2) were compared in terms of Anderson-Darling, Kolmogorov-Smirnov, and Cramér-Von Mises tests and LH-moment ratio diagrams. The findings indicated that GPA and GEV distributions were best fitted for most stations, followed by GLO distribution. The quantile estimates derived from LH-moments (η = 0, 1, 2) had a lower relative absolute error, particularly for higher return periods. However, the Gaussian kernel function provided a close estimate among nonparametric kernel functions for small return periods when compared to LH-moments (η = 0, 1, 2), thus highlighting the importance of using LH-moments (η = 0, 1, 2) and nonparametric kernel functions in water resources management and engineering projects.

COVID-19 epidemiology and changes in health service utilization in Azraq and Zaatari refugee camps in Jordan: A retrospective cohort study.

BACKGROUND: The effects of the Coronavirus Disease 2019 (COVID-19) pandemic in humanitarian contexts are not well understood. Specific vulnerabilities in such settings raised concerns about the ability to respond and maintain essential health services. This study describes the epidemiology of COVID-19 in Azraq and Zaatari refugee camps in Jordan (population: 37,932 and 79,034, respectively) and evaluates changes in routine health services during the COVID-19 pandemic. METHODS AND FINDINGS: We calculate the descriptive statistics of COVID-19 cases in the United Nations High Commissioner for Refugees (UNHCR)'s linelist and adjusted odds ratios (aORs) for selected outcomes. We evaluate the changes in health services using monthly routine data from UNHCR's health information system (HIS; January 2018 to March 2021) and apply interrupted time series analysis with a generalized additive model and negative binomial (NB) distribution, accounting for long-term trends and seasonality, reporting results as incidence rate ratios (IRRs). COVID-19 cases were first reported on September 8 and September 13, 2020 in Azraq and Zaatari camps, respectively, 6 months after the first case in Jordan. Incidence rates (IRs) were lower in camps than neighboring governorates (by 37.6% in Azraq (IRR: 0.624, 95% confidence interval [CI]: [0.584 to 0.666], p-value: <0.001) and 40.2% in Zaatari (IRR: 0.598, 95% CI: [0.570, 0.629], p-value: <0.001)) and lower than Jordan (by 59.7% in Azraq (IRR: 0.403, 95% CI: [0.378 to 0.430], p-value: <0.001) and by 63.3% in Zaatari (IRR: 0.367, 95% CI: [0.350 to 0.385], p-value: <0.001)). Characteristics of cases and risk factors for negative disease outcomes were consistent with increasing COVID-19 evidence. The following health services reported an immediate decline during the first year of COVID-19: healthcare utilization (by 32% in Azraq (IRR: 0.680, 95% CI [0.549 to 0.843], p-value < 0.001) and by 24.2% in Zaatari (IRR: 0.758, 95% CI [0.577 to 0.995], p-value = 0.046)); consultations for respiratory tract infections (RTIs; by 25.1% in Azraq (IRR: 0.749, 95% CI: [0.596 to 0.940], p-value = 0.013 and by 37.5% in Zaatari (IRR: 0.625, 95% CI: [0.461 to 0.849], p-value = 0.003)); and family planning (new and repeat family planning consultations decreased by 47.4% in Azraq (IRR: 0.526, 95% CI: [0.376 to 0.736], p-value = <0.001) and 47.6% in Zaatari (IRR: 0.524, 95% CI: [0.312 to 0.878], p-value = 0.014)). Maternal and child health services as well as noncommunicable diseases did not show major changes compared to pre-COVID-19 period. Conducting interrupted time series analyses in volatile settings such refugee camps can be challenging as it may be difficult to meet some analytical assumptions and to mitigate threats to validity. The main limitation of this study relates therefore to possible unmeasured confounding. CONCLUSIONS: COVID-19 transmission was lower in camps than outside of camps. Refugees may have been affected from external transmission, rather than driving it. Various types of health services were affected differently, but disruptions appear to have been limited in the 2 camps compared to other noncamp settings. These insights into Jordan's refugee camps during the first year of the COVID-19 pandemic set the stage for follow-up research to investigate how infection susceptibility evolved over time, as well as which mitigation strategies were more successful and accepted.

Epidemiological and sociodemographic transitions of female breast cancer incidence, death, case fatality and DALYs in 21 world regions and globally, from 1990 to 2017: An Age-Period-Cohort Analysis.

Introduction: Breast cancer (BC) is the most widely studied disease due to its higher prevalence, heterogeneity and mortality. Objectives: This study aimed to compare female BC trends among 21 world regions and globally over 28 year of data and to assess the association between sociodemographic transitions and female BC risks. Methods: We used Global burden of disease study data and measure the female BC burden according to 21 world regions and sociodemographic indices (SDI). Age-period-cohort (APC) analysis was used to estimate time and cohort trend of BC in different SDI regions. Results: By world regions, age-standardised rate of female BC incidence were high in high-income-North America (ASR, 92.9; (95 %UI, 89.2, 96.6)), Western Europe (84.7; (73.4, 97.2)) and Australia (86; (81.7, 90.2)) in 2017. Whereas this rate was significantly increased by 89.5% between 1990 and 2017 in East Asia. We observed negative association between SDI and death, and DALYs in 25th and below percentiles of death and DALYs for the worldwide regions. Further, there was observed a strong negative correlation between SDI and case fatality percent (r2017 = -0.93; r1990 = -0.92) in both 2017 and 1990 worldwide and highest case fatality percentage was observed in Central Sub-Saharan Africa. Overall, the risk of case-fatality rate tends to decrease most noticeably in high middle SDI countries, and the reduction of the risk of case-fatality rate in the recent cohort was the lowest in the low SDI countries. Conclusions: Remarkable variations exist among various regions in BC burden. There is a need to reduce the health burden from BC in less developed and under developing countries, because under-developed countries are facing higher degree of health-related burden. Public health managers should execute more classified and cost-effective screening and treatment interferences to lessen the deaths caused by BC, predominantly among middle and low SDI countries having inadequate healthcare supplies.