EDSSR: a secure and power-aware opportunistic routing scheme for WSNs.

Motivated by the pivotal role of routing in Wireless Sensor Networks (WSNs) and the prevalent security vulnerabilities arising from existing protocols, this research tackles the inherent challenges of securing WSNs. Many current WSN routing protocols prioritize computational efficiency but lack robust security measures, making them susceptible to exploitation by malicious actors. The prevalence of reactive protocols, chosen for their lower bandwidth consumption, exacerbates security concerns, as proactive alternatives require more resources for maintaining network routes.

Large rupture strain cotton ropes hybridized with affordable fiberglass chopped strand mat sheets for enhanced compressive behavior of reinforced concrete columns.

The hybrid confinement system combines various fiber types within a single matrix, allowing for the adjustment of volumetric ratios to optimize confinement performance. Synthetic FRPs are more expensive and have a higher carbon footprint due to significant CO emissions during production. In response, this study presents an innovative hybrid confinement approach using two natural materials: cotton ropes and FSMS (CFS) to improve concrete strength and ductility.

A scientific report on heat transfer analysis due to generated and absorbed heat over an oscillatory stretching sheet: a finite difference-based study.

A non-Newtonian liquid motion across a stretchable surface is relevant to various industrial applications, including extruding plastic sheets and stretching plastic films. In connection with this, the impact of endothermic and exothermic chemical reactions on the flow of rate-type liquid via an oscillatory stretching sheet in the presence of permeable media with the Maxwell liquid model is elucidated in the present study. Scientists and engineers may enhance the efficiency of chemical reactions or heat transmission by optimising system flow and investigating the effect of reactions on flow dynamics.

Supramolecular arrangements in human amyloid tissues using SAXS.

Amyloid diseases are characterized by the accumulation of misfolded protein aggregates in human tissues, pose significant challenges for both diagnosis and treatment. Protein aggregations known as amyloids are linked to several neurodegenerative conditions including Alzheimer's disease, Parkinson's disease, and systemic amyloidosis. The key goal of this research is to employ Small-Angle X-ray Scattering (SAXS) to examine the supramolecular structures of amyloid aggregates in human tissues.

Numerical simulation of three-dimensional seepage field in a tailing pond under multiple operating conditions.

Establishing strong seepage stability for tailings dams is crucial for ensuring their safety and mitigating the risk of failure. This study developed a three-dimensional seepage numerical model using finite element numerical computation for four different elevation conditions (5070 m, 5081 m, 5159 m, and 5213 m) encompassing the pond area and dam body. Seepage calculations were conducted under normal and flooding conditions, and the tailings pond's seepage stability was assessed for various stacking scenarios.

Synchronised removal of nitrogen and sulphate from rubber industrial wastewater by coupling of Sulfammox and sulphide-driven autotrophic denitrification in anaerobic membrane bioreactor.

Global rubber industry, growing 4-6 % annually with 13.76 million Mt of rubber produced in 2019, significantly impacts the economy. This study explores coupling sulfate-dependent ammonium oxidation (Sulfammox) and sulfide-driven autotrophic denitrification (SDAD) within an anaerobic membrane bioreactor (AnMBR) to treat high-strength natural rubber wastewater.

A Comprehensive Review of Plant-Based Cosmetic Oils (Virgin Coconut Oil, Olive Oil, Argan Oil, and Jojoba Oil): Chemical and Biological Properties and Their Cosmeceutical Applications.

Plant-based oils, such as coconut, olive, argan, and jojoba, are abundant in natural emollients and vital fatty acids that hydrate and moisturize the skin. They shield the surface, stop moisture loss, and maintain suppleness of, the skin. They are rich in vitamins, nutrients, and antioxidants that nourish the skin.

Optimizing food waste anaerobic digestion in Kuwait: Experimental insights and empirical modelling using artificial neural networks.

This study investigates, for the first time, the anaerobic digestion of food waste in Kuwait to optimize methane production through a combination of artificial neural network (ANN) modelling and continuous reactor experiments. The ANN model, utilizing eight hidden neurons and a 70-20-10 split for training, validation and testing sets, yielded mean squared error values of 0.0056, 0.

Lump, lump-periodic, lump-soliton and multi soliton solutions for the potential Kadomtsev-Petviashvili type coupled system with variable coefficients.

In this article, the potential Kadomtsev-Petviashvili (pKP) type coupled system with variable coefficients is studied, which have many applications in wave phenomena and soliton interactions in a two-dimensional space with time. In this framework, Hirota bilinear form is applied to acquire diverse types of interaction lump solutions from the foresaid equation. Abundant lump, lump-periodic, lump-soliton and multi soliton solutions to the pKP system are presented by the Hirota bilinear form and a mixture of exponentials and trigonometric functions.

Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique.

The development of cascading hydropower dams in river basins has significantly altered natural flow regimes in recent decades. This study investigates hydrological alterations caused by cascading hydropower dams in the Lancang-Mekong River Basin (LMRB) by integrating the Indicators of Hydrologic Alteration (IHA) method with non-regulated flow predicted using the Random Forest (RF) machine learning (ML) technique. The analysis focuses on four hydrological stations: Chiang Saen, Mukdahan, Pakse, and Stung Treng across pre-impact (1961-1991), transition (1992-2008), and post-impact (2009-2021) periods.

Assessing radiation doses and proposing DRLs for nuclear medicine procedures for pediatric and adult patients in Madinah, Saudi Arabia.

Background: Nuclear medicine diagnostic and treatment procedures represent significant sources of ionizing radiation exposure for both staff and patients. Consequently, assessing and optimizing radiation doses are crucial to minimize potential side effects.

Aim: This study seeks to evaluate the effective radiation doses associated with common diagnostic and treatment procedures, as well as propose diagnostic reference levels (DRLs), within two nuclear medicine centers in Madinah, Saudi Arabia.

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops.

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming.

Passive and Active Exoskeleton Solutions: Sensors, Actuators, Applications, and Recent Trends.

Recent advancements in exoskeleton technology, both passive and active, are driven by the need to enhance human capabilities across various industries as well as the need to provide increased safety for the human worker. This review paper examines the sensors, actuators, mechanisms, design, and applications of passive and active exoskeletons, providing an in-depth analysis of various exoskeleton technologies. The main scope of this paper is to examine the recent developments in the exoskeleton developments and their applications in different fields and identify research opportunities in this field.

A Review of Potential Exoskeletons for the Prevention of Work-Related Musculoskeletal Disorders in Agriculture.

Exoskeletons possess a high potential for assisting the human workforce while eliminating or reducing the risk of Work-Related Musculoskeletal Disorders (WMSDs). However, their usage in agricultural work, where there is a plethora of reported WMSD cases, seems limited. Since agricultural tasks are complex and performed in harsh environments, developing novel exoskeleton-based solutions could be challenging.

Targeting MurG enzyme in Klebsiella pneumoniae: An in silico approach to novel antimicrobial discovery.

Antibiotic resistance poses a global crisis fuelled by widespread antibiotic use, particularly against Gram-negative bacteria like Klebsiella pneumoniae, a leading cause of hospital-acquired infections with high mortality rates. Urgent identification of effective drug targets is imperative, with a focus on metabolic pathways to inhibit bacterial growth. Targeting the crucial metabolic pathways of K.

Impacts of microplastics on ecosystem services and their microbial degradation: a systematic review of the recent state of the art and future prospects.

Microplastics are tiny plastic particles with a usual diameter ranging from ~ 1 μ to 5 µm. Recently, microplastic pollution has raised the attention of the worldwide environmental and human concerns. In human beings, digestive system illness, respiratory system disorders, sleep disturbances, obesity, diabetes, and even cancer have been reported after microplastic exposure either through food, air, or skin.

Therapeutic Potential of Herbal Compounds in Curing Dysmenorrhea Naturally: A Review.

Dysmenorrhea being an effect of uterine contractions in the endometrium is a consequential health concern that may hamper a woman quality of life and disrupt everyday activities. It is described as pain throughout the menstrual cycle which is one of the most prominent causes of pelvic pain in women. Traditional medicine and Ayurveda has for centuries stipulated and employed herbs to treat a variety of illnesses.

Pred-AHCP: Robust Feature Selection-Enabled Sequence-Specific Prediction of Anti-Hepatitis C Peptides via Machine Learning.

Every year, an estimated 1.5 million people worldwide contract Hepatitis C, a significant contributor to liver problems. Although many studies have explored machine learning's potential to predict antiviral peptides, very few have addressed the problem of predicting peptides against specific viruses such as Hepatitis C.

Identification of potential inhibitors for MAP4K4 in glaucoma using meta-dynamics-based dissociation free energy calculation.

Glaucoma, a prevalent eye ailment causing irreversible vision loss, affects over 295 million individuals globally, necessitating the exploration of novel therapeutic avenues. Despite extensive research on targets like the phosphodiesterase enzyme and rho kinase, the potential of MAP4K4 in glaucoma remains untapped. This study aims to identify potent MAP4K4 inhibitors to counteract retinal cell apoptosis and oxidative stress associated with glaucoma.

Active cardboard box with palm wood waste powder and orange oil to prevent browning and quality loss in cabbage: Mode of action and potential for reuse.

Browning, caused by enzymatic activity and storage conditions, affects cabbage during cold storage and is crucial for customer acceptance. This study investigated the effect of cardboard packaging containing low concentrations of nano-orange oil (ONE) at 0.006% in palm wood waste powder for anti-browning and extending the shelf life of cabbage.

Development of lateral acceleration prediction models and consistency evaluation criteria for horizontal curves with gradient on two lane rural highways.

Objectives: The challenge of combined horizontal and vertical highway alignments continues to be a concern in transportation design, particularly regarding truck stability. This study analyses the stability of trucks on horizontal curves combined with ascending or descending gradients using center of gravity (CoG) height and curve geometry as parameters. It also develops models that predict lateral stability of trucks and criteria for evaluating geometric design consistency based on vehicle stability.

Eco-inspired synthesis of MgO-infused g-CN nanocomposites from tulsi seeds for advanced photocatalytic environmental remediation.

This study introduces a novel approach to synthesizing magnesium oxide (MgO) nanoparticles through the use of (tulsi seed) extract combined with the thermal polymerization of MgO-doped graphitic carbon nitride (MgCN) nanocomposites. The nanocomposites were prepared at varying MgO concentrations (0.5 mM, 1.