- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Leveraging Industry 4.0 for marketing strategies in the medical device industry of emerging economies.

This research investigates the impact of the technologies through an integrated Fuzzy Analytic Hierarchy Process (F-AHP) and VIKOR model to determine their viability as a marketing strategy for business growth in the Prosthetics and Orthotics (P&O) medical device industry. Based on expert data, the study evaluates the integration of Industry 4.0 technologies and ranks them by priority for marketing performance: "Artificial Intelligence & Machine Learning" (0.

Seasonal occurrence and ecological risk assessment of polycyclic aromatic hydrocarbons in the sediments and water in the left-bank canals of Indus River, Pakistan.

This study investigated a pressing environmental concern: the presence, distribution, sources, and ecological implications of sixteen polycyclic aromatic hydrocarbons (PAHs) in the left-bank canals of Kotri barrage-Akram, Pinyari, and Phuleli of the Indus River in Pakistan. These vital waterways, crucial for industrial, domestic, and agricultural activities, are experiencing contamination threats from anthropogenic sources, particularly PAHs. The study collected three water and two sediment samples from each canal in pre-monsoon and post-monsoon seasons.

Retraction: A novel concept and design for highly efficient photoelectrocatalytic materials with high performance, stability, and charge transport properties: development of an innovative next-generation green technology.

[This retracts the article DOI: 10.1039/D3RA05126A.].

Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical Approaches.

Federated learning (FL) is a decentralized machine learning approach whereby each device is allowed to train local models, eliminating the requirement for centralized data collecting and ensuring data privacy. Unlike typical typical centralized machine learning, collaborative model training in FL involves aggregating updates from various devices without sending raw data. This ensures data privacy and security while collecting a collective learning from distributed data sources.

Rhamnus prinoides leaf extract loaded polycaprolactone-cellulose acetate nanofibrous scaffold as potential wound dressing: An in vitro study.

Rhamnus prinoides leaf contains carbohydrates, saccharides, phenolic acids, and diterpenes with antibacterial, wound-healing, and anti-inflammatory properties. In this study, Rhamnus prinoides leaf extract was successfully incorporated into polycaprolactone-cellulose acetate (PCL-CA) nanofibers through electrospinning technique for the first time. The mats' morphology, diameter, chemical, and crystalline structure were characterized.

Demand side management through energy efficiency measures for the sustainable energy future of Pakistan.

Pakistan is facing energy crises due to localized shortages, market manipulation, infrastructure disruption, rising demand, governance issues, climate and geopolitical events. In this situation Demand Side Management (DSM) is a promising solution to overcome the problem of energy crises. DSM strategy helps to manage consumer demand through energy conservation rather than to addition of new power capacity.

Mechanism of arsenic removal using brown seaweed derived impregnated with iron oxide biochar for batch and column studies.

Water contaminated with arsenic presents serious health risks, necessitating effective and sustainable removal methods. This article proposes a method for removing arsenic from water by impregnating biochar with iron oxide (FeO) from brown seaweed (Sargassum polycystum). After the seaweed biomass was pyrolyzed at 400 °C, iron oxide was added to the biochar to increase its adsorptive sites and surface functional groups, which allowed the binding of arsenic ions.

The nexus between foreign direct investment, economic progress, and quality of institutions in fostering sustainable energy efficiency: Evidence from BRICS economies.

This paper analyzes the relationship between Foreign Direct Investment (FDI), economic growth, and institutional quality to maintain sustainable energy efficiency in BRICS. The objective of our study is to decompose which elements collectively impact the uptake of sustainable energy practices. A comprehensive dataset and an advanced econometric model Data Envelopment Analysis (DEA) are employed to investigate the dynamics at play.

Two-Dimensional MXene-Based Electrocatalysts: Challenges and Opportunities.

MXene, regarded as cutting-edge two-dimensional (2D) materials, have been widely explored in various applications due to their remarkable flexibility, high specific surface area, good mechanical strength, and interesting electrical conductivity. Recently, 2D MXene has served as a ideal platform for the design and development of electrocatalysts with high activity, selectivity, and stability. This review article provides a detailed description of the structural engineering of MXene-based electrocatalysts and summarizes the uses of 2D MXene in hydrogen evolution reactions, nitrogen reduction reactions, oxygen evolution reactions, oxygen reduction reactions, and methanol/ethanol oxidation.

An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture.

Background: Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats.

Memory impacts in hepatitis C: A global analysis of a fractional-order model with an effective treatment.

Background And Objective: Hepatitis virus infections are affecting millions of people worldwide, causing death, disability, and considerable expenditure. Chronic infection with hepatitis C virus (HCV) can cause severe public health problems because of their high prevalence and poor long-term clinical outcomes. Thus a fractional-order epidemic model of the hepatitis C virus involving partial immunity under the influence of memory effect to know the transmission patterns and prevalence of HCV infection is studied.

Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT.

Background: The high mortality rate associated with coronary heart disease has led to state-of-the-art non-invasive methods for cardiac diagnosis including computed tomography and magnetic resonance imaging. However, stenosis computation and clinical assessment of non-calcified plaques has been very challenging due to their ambiguous intensity response in CT i.e.

Turbulence model study for aerodynamic analysis of the leading edge tubercle wing for low Reynolds number flows.

A turbulence model study was performed to analyze the flow around the Tubercle Leading Edge (TLE) wing. Five turbulence models were selected to evaluate aerodynamic force coefficients and flow mechanism by comparing with existing literature results. The selected models are realizable , Shear Stress Transport (SST), ( ) SST model, Transition model and Stress- ω Reynolds Stress Model (RSM).

Assessing the impact of green finance on sustainable tourism development in China.

The escalating urgency to address environmental degradation and promote sustainable development globally has emphasized the critical role of Green Finance (GF) in fostering responsible practices across industries. The tourism sector has drawn significant attention due to its substantial environmental impact, necessitating the implementation of robust financial mechanisms to mitigate its ecological footprint. China, recognized as a key player in the global tourism arena, the convergence of rapid economic expansion and the imperative for environmental conservation presents a distinctive set of challenges and opportunities.

Holistic insight mechanism of ozone-based oxidation process for wastewater treatment.

The world is facing water crises because freshwater scarcity has become a global issue due to rapid population growth, resulting in the need for more industries, agriculture, and domestic sectors. Therefore, it is challenging for scientists and environmental engineers to treat wastewater with cost-effective treatment techniques. As compared to conventional processes (physical, chemical, and biological), advanced oxidation processes (AOP) play an essential role in the removal of wastewater contaminants, with the help of a powerful hydroxyl (OH•) through oxidation reactions.

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis.

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers.

Transition metal-based electrocatalysts for alkaline overall water splitting: advancements, challenges, and perspectives.

Water electrolysis is a promising method for efficiently producing hydrogen and oxygen, crucial for renewable energy conversion and fuel cell technologies. The hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are two key electrocatalytic reactions occurring during water splitting, necessitating the development of active, stable, and low-cost electrocatalysts. Transition metal (TM)-based electrocatalysts, spanning noble metals and TM oxides, phosphides, nitrides, carbides, borides, chalcogenides, and dichalcogenides, have garnered significant attention due to their outstanding characteristics, including high electronic conductivity, tunable valence electron configuration, high stability, and cost-effectiveness.

Effect of banana tree leaves ash as cementitious material on the durability of concrete against sulphate and acid attacks.

The construction industry's rapid growth poses challenges tied to raw material depletion and increased greenhouse gas emissions. To address this, alternative materials like agricultural residues are gaining prominence due to their potential to reduce carbon emissions and waste generation. In this context this research optimizes the use of banana leaves ash as a partial cement substitution, focusing on durability, and identifying the ideal cement-to-ash ratio for sustainable concrete.

Domestic wastewater treatment by Pistia stratiotes in constructed wetland.

The objective of the study was to evaluate the performance of Pistia stratiotes for treatment of domestic wastewater in a free surface water flow constructed wetland. The objective of the study was to evaluate contaminants removal efficiency of the constructed wetland vegetated with P. stratiotes in treatment of domestic wastewater against Hydraulic retention time (HRT) of 10, 20 and 30 days was investigated.

Stochastic and multi-objective design of photonic devices with machine learning.

Compact and highly performing photonic devices are characterized by non-intuitive geometries, a large number of parameters, and multiple figures of merit. Optimization and machine learning techniques have been explored to handle these complex designs, but the existing approaches often overlook stochastic quantities. As an example, random fabrication uncertainties critically determines experimental device performance.