Explore the vast range of titles available.

This study investigated the quality of water and its seasonal variation in the Gomal Zam Dam and tributaries, south Waziristan District, Pakistan. For this purpose, water samples were collected from the Gomal Zam Dam and its tributaries in the winter and summer seasons ( n  = 24 in each season). Water samples were analyzed and found within drinking water guidelines set by the World Health Organization (WHO), except turbidity. Water characteristics were evaluated for the water quality index (WQI) and sodium hazards. Based on WQI and sodium hazards, the water of Gomal Zam Dam and its tributaries were observed as good and in permissible levels for drinking and irrigation, respectively. The winter season has slightly poor water quality compared to the summer season due to higher contamination. Gibbs’s and Piper’s models showed that the water quality of Gomal Zam Dam and its tributaries was mainly characterized by the weathering of bedrocks. The studied water is classified as Na-Cl type and Mg-HCO 3 types in the summer and winter seasons, respectively. Statistical analyses revealed that geogenic sources of rock weathering are the dominant factor for controlling the water quality of the area.

Sediments were considered a sink and potential source of heavy metals in the aquatic system. For this purpose, the present study examined surface sediments for spatial and temporal variation of heavy metals pollution and risk indices in the Gomal Zam Dam Basin (GZDB), Pakistan. Sediment samples ( n = 20) were collected from the GZDB, i.e., Gomal Zam Dam, its inlets, and outlets in the winter and summer seasons of 2020, and examined for heavy metals such as zinc (Zn), nickel (Ni), manganese (Mn), lead (Pb), chromium (Cr), copper (Cu), iron (Fe), and cobalt (Co) concentrations. Among GZDB, results showed that the Zhob River Inlet had a higher levels of heavy metals in both seasons. The results revealed that pollution load index values were < 1, observing no pollution in the aquatic system. The risk indices values revealed that sampling sites showed no or very low risk during the summer, 84% of samples showed no or very low risk during the winter, and the rest noted with reasonable risks. Winter season showed higher average values of contamination and risk indices than summer. Statistical analyses revealed that the heavy metals contaminations were mainly due to geogenic sources of rock weathering and ore deposits, with minor contributions from anthropic activities. This study recommends regular monitoring of temporal studies on heavy metals contamination in the water of the GZDB.

Due to their conceptual simplicity and flexibility, non-parametric mixture models are widely used to identify latent clusters in data. However, when it comes to Big Data, such as Landsat imagery, such model fitting is computationally prohibitive. To overcome this issue, we fit Bayesian non-parametric models to pre-smoothed data, thereby reducing the computational time from days to minutes, while disregarding little of the useful information. Tree based clustering is used to partition the clusters into smaller and smaller clusters in order to identify clusters of high, medium and low interest. The tree-based clustering method is applied to Landsat images from the Brisbane region, which were the actual sources of motivation for development of the method. The images are taken as a part of the red imported fire-ant eradication program that was launched in September 2001 and which is funded by all Australian states and territories, along with the federal government. To satisfy budgetary constraints, modelling is performed to estimate the risk of fire-ant incursion in each cluster so that the eradication program focuses on high risk clusters. The likelihood of containment is successfully derived by combining the fieldwork survey data with the results obtained from the proposed method.

In this study, we explore the effects of including noise predictors and noise observations when fitting linear regression models. We present empirical and theoretical results that show that double descent occurs in both cases, albeit with contradictory implications: the implication for noise predictors is that complex models are often better than simple ones, while the implication for noise observations is that simple models are often better than complex ones. We resolve this contradiction by showing that it is not the model complexity but rather the implicit shrinkage by the inclusion of noise in the model that drives the double descent. Specifically, we show how noise predictors or observations shrink the estimators of the regression coefficients and make the test error asymptote, and then how the asymptotes of the test error and the ``condition number anomaly'' ensure that double descent occurs. We also show that including noise observations in the model makes the (usually unbiased) ordinary least squares estimator biased and indicates that the ridge regression estimator may need a negative ridge parameter to avoid over-shrinkage.

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data.

In this paper we present a new redescending M-estimator \"Insha's estimator\" for robust regression and outliers detection that overcomes some drawbacks of other M-estimators for robust regression and outliers detection, such as destruction of the good observations and lack of simplicity in applications. The ?-function associated with the proposed estimator attains more linearity in the central section before it redescends, resulting in enhanced efficiency. Moreover the estimator is continuous everywhere and can be written in closed form without the use of an indictor function. The estimator is also applied to a real world example taken from the literature. For the purpose of comparison with other well-known redescending M-estimators extensive simulation study has been carried out. The example and simulation study show that using this estimator all the outliers can be successfully detected and is not affected by outliers.

Organizations such as government departments and financial institutions provide online service facilities accessible via an increasing number of internet connected devices which make their operational environment vulnerable to cyber attacks. Consequently, there is a need to have mechanisms in place to detect cyber security attacks in a timely manner. A variety of Network Intrusion Detection Systems (NIDS) have been proposed and can be categorized into signature-based NIDS and anomaly-based NIDS. The signature-based NIDS, which identify the misuse through scanning the activity signature against the list of known attack activities, are criticized for their inability to identify new attacks (never-before-seen attacks). Among anomaly-based NIDS, which declare a connection anomalous if it expresses deviation from a trained model, the unsupervised learning algorithms circumvent this issue since they have the ability to identify new attacks. In this study, we use an unsupervised learning algorithm based on principal component analysis to detect cyber attacks. In the training phase, our approach has the advantage of also identifying outliers in the training dataset. In the monitoring phase, our approach first identifies the affected dimensions and then calculates an anomaly score by aggregating across only those components that are affected by the anomalies. We explore the performance of the algorithm via simulations and through two applications, namely to the UNSW-NB15 dataset recently released by the Australian Centre for Cyber Security and to the well-known KDD'99 dataset. The algorithm is scalable to large datasets in both training and monitoring phases, and the results from both the simulated and real datasets show that the method has promise in detecting suspicious network activities.

High-dimensional datasets, where the number of variables ‘p’ is much larger compared to the number of samples ‘n’, are ubiquitous and often render standard classification and regression techniques unreliable due to overfitting. An important research problem is feature selection — ranking of candidate variables based on their relevance to the outcome variable and retaining those that satisfy a chosen criterion. In this article, we propose a computationally efficient variable selection method based on principal component analysis. The method is very simple, accessible, and suitable for the analysis of high-dimensional datasets. It allows to correct for population structure in genome-wide association studies (GWAS) which otherwise would induce spurious associations and is less likely to overfit. We expect our method to accurately identify important features but at the same time reduce the False Discovery Rate (FDR) (the expected proportion of erroneously rejected null hypotheses) through accounting for the correlation between variables and through de-noising data in the training phase, which also make it robust to outliers in the training data. Being almost as fast as univariate filters, our method allows for valid statistical inference. The ability to make such inferences sets this method apart from most of the current multivariate statistical tools designed for today’s high-dimensional data. We demonstrate the superior performance of our method through extensive simulations. A semi-real gene-expression dataset, a challenging childhood acute lymphoblastic leukemia (CALL) gene expression study, and a GWAS that attempts to identify single-nucleotide polymorphisms (SNPs) associated with the rice grain length further demonstrate the usefulness of our method in genomic applications. An integral part of modern statistical research is feature selection, which has claimed various scientific discoveries, especially in the emerging genomics applications such as gene expression and proteomics studies, where data has thousands or tens of thousands of features but a limited number of samples. However, in practice, due to unavailability of suitable multivariate methods, researchers often resort to univariate filters when it comes to deal with a large number of variables. These univariate filters do not take into account the dependencies between variables because they independently assess variables one-by-one. This leads to loss of information, loss of statistical power (the probability of correctly rejecting the null hypothesis) and potentially biased estimates. In our paper, we propose a new variable selection method. Being computationally efficient, our method allows for valid inference. The ability to make such inferences sets this method apart from most of the current multivariate statistical tools designed for today’s high-dimensional data.

Background/Objective: Diabetic nephropathy (DN), a key microvascular complication of type 2 diabetes (T2DM), drives significant morbidity, mortality, and healthcare costs. Vitamin D deficiency has been linked to renal dysfunction, but its role in DN remains unclear. This study assessed the association between vitamin D status and DN versus T2DM without nephropathy. Methods: This cross-sectional hospital-based study included 399 participants (299 DN, 100 T2DM without nephropathy) at a tertiary endocrine clinic. Demographic, clinical, and biochemical data, including serum 25(OH)D, were collected. Chi-square and Mann–Whitney compared categorical and continuous variables, respectively, and multinomial logistic regression assessed the association between vitamin D status and DN (p < 0.05). Results: Patients with DN were older (58.2 ± 7.95 vs. 51.4 ± 9.94 years, p < 0.001), had more advanced CKD (stages 2–3b: 84.6% vs. 20.0%, p < 0.001), and higher albuminuria (moderate: 80.3% vs. 19.0%; severe: 18.4% vs. 0%, p < 0.001). They also showed poorer glycemic control, elevated urea and creatinine, lower serum albumin, dyslipidemia, elevated liver enzymes, and higher uric acid (all p < 0.05). Vitamin D deficiency was more prevalent in DN (37.7% vs. 8.0%, p < 0.001). Unadjusted multinomial regression indicated that T2DM patients without nephropathy had a 91% lower risk of vitamin D deficiency (RRR 0.09; 95% CI 0.04–0.19, p < 0.001) and an 87% lower risk of insufficiency (RRR 0.13; 95% CI 0.05–0.26, p < 0.001) compared with DN patients. After adjusting for age, HbA1c, creatinine, duration of diabetes and eGFR, the reduced risk of deficiency remained significant (RRR 0.04; 95% CI 0.01–0.16, p < 0.001), while the association with insufficiency was no longer significant (p = 0.310). Conclusions: This study shows a significant association between vitamin D deficiency and diabetic nephropathy, though its cross-sectional design precludes causal inference. Reverse causality and residual confounding cannot be excluded. Patients with DN had poorer glycemic control, dyslipidemia, and renal function, along with more frequent vitamin D deficiency. Routine vitamin D monitoring may support early detection and risk stratification in T2DM.

Opioid dependence is a serious worldwide health issue. The available treatments are frequently constrained by side effects and inadequate neuroprotection. New therapeutic drugs that can both address opioid-induced oxidative neurotoxicity and lessen withdrawal symptoms are therefore urgently needed. This study aimed to assess the neurotherapeutic potential of 4-(4-chlorophenyl)-2,4-dihydro-5-(4-pyridyl)-3H-1,2,4-triazole-3-thione (CPTT) in the treatment of morphine withdrawal and related oxidative damage. ADMET analysis was used to evaluate pharmacokinetic and toxicity profiles. The molecular docking and 100-ns molecular dynamics simulations were used to investigate the interaction with the NR2B subunit of the NMDA receptor, a key target in opioid dependency. Male albino mice were made morphine dependent by increasing intraperitoneal dosages over four days, followed by naloxone-induced withdrawal. The impact of CPTT on withdrawal behavior and oxidative stress markers was examined.In silico predictions showed excellent gastrointestinal absorption (89%), moderate solubility and permeability. No interaction was observed with P-glycoprotein, indicating good oral bioavailability. The compound was non-mutagenic and hepatotoxic, though hERG II inhibition flagged a potential cardiotoxicity risk. Metabolism analysis revealed involvement of CYP3A4, with inhibition of CYP1A2, CYP2C19, and CYP3A4, highlighting possible drug-drug interactions. Molecular docking demonstrated strong binding affinity for the NMDA NR2B subunit, and simulation data confirmed the structural stability of the ligand-receptor complex. In vivo, CPTT significantly alleviated naloxone-precipitated withdrawal symptoms. Biochemically, it restored depleted glutathione (GSH) levels, normalized glutathione-S-transferase (GST) and catalase (CAT) activities, and suppressed lipid peroxidation (LPO). It also reduced hippocampal nitrite levels, suggesting inhibition of nitric oxide (NO) production, a key mediator of morphine-induced neuroinflammation and oxidative stress. These findings indicate that CPTT has multi-targeted neuroprotective effects, which are most likely mediated by antioxidant characteristics and glutamatergic transmission regulation. The drug's favorable pharmacokinetic profile, robust NMDA receptor interaction, and in vivo efficacy warrant further preclinical and clinical research as a potential treatment for opioid withdrawal and neurotoxicity.