Single Precursor-Derived Sub-1 nm MoCo Bimetallic Particles Decorated on Phosphide-Carbon Nitride Framework for Sustainable Hydrogen Generation.

The strategic design and fabrication of efficient electrocatalysts are pivotal for advancing the field of electrochemical water splitting (EWS). To enhance EWS performance, integrating non-noble transition metal catalysts through a cooperative double metal incorporation strategy is important and offers a compelling alternative to conventional precious metal-based materials. This study introduces a novel, straightforward, single-step process for fabricating a bimetallic MoCo catalyst integrated within a three-dimensional (3D) nanoporous network of N, P-doped carbon nitride derived from a self-contained precursor.

Noether and partial Noether approach for the nonlinear (3+1)-dimensional elastic wave equations.

The Lie group method is a powerful technique for obtaining analytical solutions for various nonlinear differential equations. This study aimed to explore the behavior of nonlinear elastic wave equations and their underlying physical properties using Lie group invariants. We derived eight-dimensional symmetry algebra for the (3+1)-dimensional nonlinear elastic wave equation, which was used to obtain the optimal system.

A comparative analysis of L-moments, maximum likelihood, and maximum product of spacing methods for the four-parameter kappa distribution in extreme value analysis.

The study has investigated the implications of three estimation methods, namely L-moments, Maximum Likelihood, and Maximum Product of Spacing (MPS), for fitting the four-parameter Kappa Distribution (KAPD) in extreme value analysis using Monte Carlo simulations. The accuracy of the estimates has been evaluated using root mean square error (RMSE) and bias. The paper also includes an analysis of the effect of the estimation method on the estimated quantiles considering a real-life example of annual maximum peak flows and the Generalized Normal Distribution as the error distribution.

AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.

Visual analysis of peripheral blood smear slides using medical image analysis is required to diagnose red blood cell (RBC) morphological deformities caused by anemia. The absence of a complete anaemic RBC dataset has hindered the training and testing of deep convolutional neural networks (CNNs) for computer-aided analysis of RBC morphology. We introduce a benchmark RBC image dataset named Anemic RBC (AneRBC) to overcome this problem.

Probiotic significance of strains: a comprehensive review on health impacts, research gaps, and future prospects.

A rising corpus of research has shown the beneficial effects of probiotic on human health, contributing to the growing popularity of these microorganisms in recent decades. The gastrointestinal and urinary tracts are home to these bacteria, which play a vital role in the microbial flora of both humans and animals. The probiotic, i.

Exploring the role of irisin as a potential biomarker in adolescents and young adults with polycystic ovarian syndrome.

Background: Irisin is a myokine potentially linked to insulin sensitivity. Polycystic ovarian syndrome (PCOS) is a prevalent hormonal condition defined by insulin resistance. Previous studies have reported elevated circulating irisin levels in adult females with PCOS.

Biochemical Screening, In-vitro and In-silico Characterization of Citrullus colocynthis Fruit Extracts: A Combined Experimental and Computation Study.

Background: Several medicinal plants are identified as therapeutic agents for the world's most deadly disease cancer. A member of the "Cucurbitaceae" family of medicinal plants, Citrullus colocynthis (C. colocynthis) has various pharmacological actions.

Molecular engineering on tyrian puprle natural dye as TiO based fined tuned photovoltaic dye material: DFT molecular analysis.

In this research, molecular modification is employed to see the enhancement in the efficiency of Tyrian Purple (TP), a natural dye, for organic photovoltaic materials. By using Density Functional Theory (DFT) based molecular modeling, seven new structures are designed with pi spacer to extend electron donor moieties. Teheir Frontier Molecular Orbital (FMO) analysis demonstartes their charges with a similar pattern of distributions over their Highest Occupied and Lowed Unocuupied Molecular Orbitals (HOMO/lUMO).

Evaluating and optimizing NBR-modified bituminous mixes: a rheological and RSM-based study.

Bitumen shows visco-elastic behavior, exhibiting both elastic and viscous properties as predicted by dynamic response and phase angle. Modern asphalt bituminous pavements face issues such as early-stage fatigue cracks, rutting, and permanent deformations due to low-temperature cracking, high-temperature deformation, moisture susceptibility, and overloading. These pavement distresses result in the formation of potholes, alligator cracks, and various deformations, which accelerate the need for rehabilitation and maintenance.

Synthesis and analysis of a novel thermal interface material for DC-DC boost converter.

The rapid evolution of power electronics has triggered an intensified focus on thermal management within electronics circuits, stemming from the critical necessity to mitigate thermal-related failure rates. Thermal management in power electronics circuits relies heavily on efficient heat transfer to prevent overheating of components and ensure their reliable operation, optimal performance, and safety. To facilitate the effective heat transfer, a thermal interface material (TIM) is utilized between switching components such as MOSFETs and heat sinks to improve surface contact, which increases heat transfer.

Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI).

The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including Multi Expression Programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc.

Alcohol-gating femtosecond laser-induced micro/nano-structured membranes with reversible switching wettability and breathability.

A reversible liquid gating membrane with the ability to regulate gas/liquid transport is critical for many fields, such as biological applications, multiphase separation, and sewerage treatment. Numerous membranes can respond to external stimuli and dynamically control gas/liquid fluid transport; however, simultaneously achieving regulated gas/liquid transport membranes through simple manufacturing remains a challenge. In this work, we investigated an alcohol-regulation gating membrane femtosecond laser one-step processing, allowing dynamically controllable gas/liquid transfer.

Assessing the impact of jigsaw technique for cooperative learning in undergraduate medical education: merits, challenges, and forward prospects.

Background: Jigsaw method is a structured cooperative-learning technique that lays the groundwork towards achieving collective competence, which forms the core of effective clinical practice. It promotes deep learning and effectively enhances team-work among students, hence creating a more inclusive environment.

Objective: Present study was designed to introduce jigsaw model of cooperative learning to early-year undergraduate medical students, measure its effectiveness on their academic performance, and evaluate the perspectives of both students and faculty members regarding the same.

Triple-0: Zero-shot denoising and dereverberation on an end-to-end frozen anechoic speech separation network.

Speech enhancement is crucial both for human and machine listening applications. Over the last decade, the use of deep learning for speech enhancement has resulted in tremendous improvement over the classical signal processing and machine learning methods. However, training a deep neural network is not only time-consuming; it also requires extensive computational resources and a large training dataset.

Forecasting residual mechanical properties of hybrid fibre-reinforced self-compacting concrete (HFR-SCC) exposed to elevated temperatures.

The use of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) has escalated recently due to its significant advantages in contrast to normal concrete such as increased ductility, crack resistance, and eliminating the need for compaction etc. The process of determining residual strength properties of HFR-SCC after a fire event requires rigorous experimental work and extensive resources. Thus, this study presents a novel approach to develop equations for reliable prediction of compressive strength (cs) and flexural strength (fs) of HFR-SCC using gene expression programming (GEP) algorithm.

Analyzing injury severity of three-wheeler motorized rickshaws: A correlated random parameters approach with heterogeneity in means.

Traffic crashes involving three-wheeler motorized rickshaw (3-WMR) are alarming public health and socioeconomic concerns in developing countries. While most of the earlier studies have dealt with safety analysis of four- and two-wheelers, there is a noticeable gap in understanding the safety dynamics, especially the risk factors affecting the crashes involving 3-WMR. The present study aims to address this gap by exploring potential risk factors influencing 3-WMR crashes, utilizing a correlated random parameters multinomial logit model with heterogeneity in means (CRPMNLMHM).

Development and Validation of Novel HPLC Methods for Quantitative Determination of Vitamin D3 in Tablet Dosage Form.

In the present work, an efficient isocratic HPLC method was developed for the precise and accurate estimation of vitamin D in tablet form. The chromatographic conditions comprised an L3 silica column (5 µm in particle size, 4.6 mm × 250 mm) with a mobile phase n-hexane/ethyl acetate (85:15 /) with a flow rate of 2.

Investigating Potential Cancer Therapeutics: Insight into Histone Deacetylases (HDACs) Inhibitions.

Histone deacetylases (HDACs) are enzymes that remove acetyl groups from ɛ-amino of histone, and their involvement in the development and progression of cancer disorders makes them an interesting therapeutic target. This study seeks to discover new inhibitors that selectively inhibit HDAC enzymes which are linked to deadly disorders like T-cell lymphoma, childhood neuroblastoma, and colon cancer. MOE was used to dock libraries of ZINC database molecules within the catalytic active pocket of target HDACs.

DPI_CDF: druggable protein identifier using cascade deep forest.

Background: Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.

Toxicological profile and potential health concerns through metals and trace elements exposure in brick kiln workers from Lahore, Pakistan.

This study examined levels of lead (Pb), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), and arsenic (As) in blood, hair, and nails of 18 brick kiln workers from three brick kiln units located around a metropolitan city, Lahore, Pakistan. All the trace elements except Hg and As were detected in the studied matrices of Brick kiln workers. In general, brick kiln workers reflect the highest concentration of Pb, followed by Cd, Cr, and Cu.

An effective tellurium surface modification strategy to enhance the capacity and rate capability of Ni-rich LiNiCoMnO cathode material.

LiNiCoMnO (NCM) layered oxide is contemplated as an auspicious cathode candidate for commercialized lithium-ion batteries. Regardless, the successful commercial utilization of these materials is impeded by technical issues like structural degradation and poor cyclability. Elemental doping is among the most viable strategies for enhancing electrochemical performance.