IoT based intelligent pest management system for precision agriculture.

Despite seemingly inexorable imminent risks of food insecurity that hang over the world, especially in developing countries like Pakistan where traditional agricultural methods are being followed, there still are opportunities created by technology that can help us steer clear of food crisis threats in upcoming years. At present, the agricultural sector worldwide is rapidly pacing towards technology-driven Precision Agriculture (PA) approaches for enhancing crop protection and boosting productivity. Literature highlights the limitations of traditional approaches such as chances of human error in recognizing and counting pests, and require trained labor.

Csec-net: a novel deep features fusion and entropy-controlled firefly feature selection framework for leukemia classification.

Leukemia, a life-threatening form of cancer, poses a significant global health challenge affecting individuals of all age groups, including both children and adults. Currently, the diagnostic process relies on manual analysis of microscopic images of blood samples. In recent years, machine learning employing deep learning approaches has emerged as cutting-edge solutions for image classification problems.

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.

Highly selective and reusable nanoadsorbent based on FeO-embedded sodium alginate-based hydrogel for cationic dye adsorption: Adsorption interpretation using multiscale modeling.

This study aims to develop a stable and efficient magnetic nanocomposite hydrogel (MNCH) for selective removal of methylene blue (MB) and crystal violet (CV). MNCHs with different FeO contents (0-9 wt%) were synthesized following graft co-polymerization method using sodium alginate, acrylamide, itaconic acid, ammonium persulfate and N,N-methylene bisacrylamide. Among them, MNCH, with 5 wt% FeO, showed highest removal efficiency (>95 %).

PHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning.

With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, it has limited resources and cannot process bigger tasks.

Electrophoretic deposition of polyvinyl alcohol, C-H NRs along with moringa on an SS substrate for orthopedic implant applications.

Metals are commonly used in bone implants due to their durability and load-bearing capabilities, yet they often suffer from biofilm growth and corrosion. To overcome these challenges, implants with enhanced biocompatibility, bioactivity, and antimicrobial properties are preferred. Stainless steel (SS) implants are widely favored in orthopedics for their mechanical strength and cost-effectiveness.

Long-range polymer ordering by directional coating to remarkably enhance the charge carrier mobility in PCDTPT-based organic field-effect transistors.

With the wide potential of organic field-effect transistors in all the modern electronic circuitries, researchers are grappling with the challenge of poor charge transport and hence lower mobility in organic polymers. Low-charge carrier mobility is mainly due to disorder in the molecular packing of organic semiconductors along with other factors, such as impurities, defects and interactions between molecules. The current research work has been conducted to align the molecular chains of poly[4-(4,4-dihexadecyl-4H-cyclopenta[1,2-‌‌‌b:5,4-‌b']‌dithiophen-2-yl)-alt-[1,2,5]thiadiazolo-[3,4-c]pyridine] (PCDTPT) using directional coating techniques such as dip coating and brush coating on nano-grooved substrates.

Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately.

An electrochemical sensing potential of cobalt oxide nanoparticles towards citric acid integrated with computational approach in food and biological media.

Although citric acid (CA) has antioxidant, antibacterial, and acidulating properties, chronic ingestion of CA can cause urolithiasis, hypocalcemia, and duodenal cancer, emphasizing the need for early detection. There are very few documented electrochemical-based sensing methods for CA detection due to the challenging behavior of electrode fouling caused by reactive oxidation products. In this study, a novel, non-enzymatic, and economical electrochemical sensor based on cobalt oxide nanoparticles (CoONPs) is successfully reported for detection CA.

Comparative investment analysis between crypto and conventional financial assets amid heightened geopolitical risk.

This empirical research study aims to investigate the asymmetric spillovers among crypto and key financial assets such as gold, equity, bonds, and the dollar-to-ruble exchange rate volatility, focusing on new developments during the Russia-Ukraine conflict in 2022. Utilizing time- and frequency-domain methodologies, this study conducts an in-depth analysis employing daily frequency data from January 01, 2018, to May 30, 2023. The study employs value at risk and conditional value at risk estimations to assess potential losses in the portfolio during the crisis.

Investigation of hybrid ultrasonic machining process of Nomex honeycomb composite using a toothed disc cutter.

Nomex honeycomb composite (NHC) has been increasingly used in the automotive, aerospace and defence applications due to its excellent thermal and mechanical properties. Its complex cellular hexagonal thin-walled configuration along with heterogeneous, soft and brittle nature pose substantial processing defects such as burr formation, tearing of walls, surface roughness, dimensional inaccuracy and low machining quality during conventional machining (CM). These surface defects have a substantial influence on the operating life and functional performance of its sandwiched structural members.

Retraction: Influence of chemical composition on the amount of second phases precipitates and transformation temperatures of TiNiPdCu shape memory alloys prepared through novel powder metallurgy route.

[This retracts the article DOI: 10.1039/D3RA05513B.].

Numerical and artificial intelligence based investigation on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling.

This article presents a numerical and artificial intelligence (AI) based investigation on the web crippling performance of pultruded glass fiber reinforced polymers' (GFRP) rectangular hollow section (RHS) profiles subjected to interior-one-flange (IOF) loading conditions. To achieve the desired research objectives, a finite element based computational model was developed using one of the popular simulating software ABAQUS CAE. This model was then validated by utilizing the results reported in experimental investigation-based article of Chen and Wang.

Tailoring NiCoCu layered double hydroxide with Ag-citrate/polyaniline/functionalized SWCNTs nanocomposites for supercapacitor applications.

Supercapacitors have substantially altered the landscape of sophisticated energy storage devices with their exceptional power density along with prolonged cyclic stability. On the contrary, their energy density remains low, requiring research to compete with conventional battery storage devices. This study addresses the disparities between energy and power densities in energy storage technologies by exploring the integration of layered double hydroxides (LDH) and highly conductive materials to develop an innovative energy storage system.

Comparative analysis of the equilibrium, kinetics, and characterization of the mechanism of rapid adsorption of Congo red on nano-biosorbents based on agricultural waste in industrial effluents.

This study aims to remove Congo red dye from industrial effluent using economical agriculturally-based nano-biosorbents like magnetic orange peel, peanut shells, and tea waste. The nano-biosorbents were characterized by various analytical techniques like SEM, FT-IR, BET and XRD. The highest adsorption capacity was obtained under the following ideal conditions: pH = 6 (orange peel and peanut shells), pH = 3 (tea waste), and dosages of nano-biosorbents with varying timeframes of 50 min for tea waste and peanut shells and 30 min for orange peel.

Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach.

Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamethoxazole (SMX) and total organic carbon (TOC) under varying levels of HO, Fe concentration, pH, and temperature using statistical and artificial intelligence techniques including Multiple Regression Analysis (MRA), Support Vector Regression (SVR) and Artificial Neural Network (ANN). In statistical metrics, the ANN model demonstrated superior predictive accuracy compared to its counterparts, with lowest RMSE values of 0.

Magnesium Magic: State-of-the-Art Nanocrystalline Materials Paving the Way for Hydrogen Storage.

Hydrogen has been regarded as a promising alternative to traditional fossil fuels, presenting itself as a viable and environmentally friendly energy choice. The design and fabrication of highly efficient hydrogen storage materials is crucial to the wide utilization of hydrogen-based technologies. Magnesium-based nanocrystalline materials have received significant interest in the field of hydrogen storage due to their remarkable hydrogen storage capabilities and release efficiency.

Real-time navigation of mecanum wheel-based mobile robot in a dynamic environment.

Path planning and control of a mobile robot, in a dynamic environment, has been an important research topic for many years. In this paper an algorithm for autonomous motion of a mobile robot is proposed, with mecanum wheels, to reach a goal while avoiding obstacles through the shortest path in a dynamic environment. The proposed method uses a hybrid and a velocity obstacle algorithms for path planning and obstacle avoidance.

Electrochemical sensing of B-complex vitamins: current challenges and future prospects with microfluidic integration.

Vitamins are crucial micronutrients found in limited quantities in food, living organisms, and soil. Since most vitamins are not produced within the human body, a lack of these essential nutrients can result in various physiological disorders. Analyzing vitamins typically involves costly, time-consuming methods, requiring skilled personnel, automated equipment, and dedicated laboratory setups.

Proposed novel classification of circumscribed Lower-Grade Gliomas (cLGG) vs. infiltrating Lower-Grade Gliomas (iLGG): Correlations of radiological features and clinical outcomes.

Purpose: We hypothesize that lower grade gliomas (LGG) can be identified and classified into two distinct subtypes: radiologically circumscribed Lower-Grade Gliomas (cLGG) and infiltrating Lower-Grade Gliomas (iLGG) based on radiological parameters and that these two different subtypes behave differently in terms of clinical outcomes.

Methods: We conducted a retrospective cohort study on surgical patients diagnosed with lower grade glioma over five years. Patient records and MRIs were reviewed, and neurosurgeons classified tumors into cLGG and iLGG groups.

Integrating DFT and machine learning for the design and optimization of sodium alginate-based hydrogel adsorbents: Efficient removal of pollutants from wastewater.

This study presents a novel approach to design and optimize a sodium alginate-based hydrogel (SAH) for efficient adsorption of the model water pollutant methylene blue (MB) dye. Utilizing density functional theory (DFT) calculations, sodium alginate-g-poly (acrylamide-co-itaconic acid) was identified with the lowest adsorption energy (E) for MB dye among 14 different clusters. SAHs were prepared using selected monomers and sodium alginate combinations through graft co-polymerization, and swelling studies were conducted to optimize grafting conditions.

Enhancing supercapacitor performance of Ni-Co-Mn metal-organic frameworks by compositing it with polyaniline and reduced graphene oxide.

Metal-organic frameworks (MOFs) are one of the most sought-after materials in the domain of supercapacitors and can be tailored to accommodate diverse compositions, making them amenable to facile functionalization. However, their intrinsic specific capacitance as well as energy density is minimal, which hinders their usage for advanced energy storage applications. Therefore, herein, we have prepared six electrodes, , Ni-Co-Mn MOFs, polyaniline (PANI), and reduced graphene oxide (rGO) along with their novel nanocomposites, , C, C, and C, comprising MOFs : PANI : rGO in a mass ratio of 100 : 1 : 0.

Expression of concern: Influence of chemical composition on the amount of second phases precipitates and transformation temperatures of TiNiPdCu shape memory alloys prepared through novel powder metallurgy route.

Expression of concern for 'Influence of chemical composition on the amount of second phases precipitates and transformation temperatures of TiNiPdCu shape memory alloys prepared through novel powder metallurgy route' by Abid Hussain , RSC , 2023, , 29376-29392, https://doi.org/10.1039/D3RA05513B.

Sustainable wastewater purification with crab shell-derived biochar: Advanced machine learning modeling & experimental analysis.

Detoxifying ecologically persistent dyes is vital for environmental and human well-being. Herein, crabshell waste is transformed into porous carbon (CB900) through pyrolysis, achieving a remarkable removal rate of 90.5% (CR-RR) and adsorption capacity (∼256.