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.

Awareness of Melanoma and Skin Self-Examination at a Tertiary Care Hospital in Pakistan: A Study of Knowledge, Attitudes, and Practices.

Background Melanoma, the most lethal form of skin cancer, contributes to a majority of skin cancer fatalities globally. It carries a grim prognosis with a high propensity for early metastasis. People are not aware of moles, their natural course of progression, and skin self-examination.

Exploring nonlinear chaotic systems with applications in stochastic processes.

This manuscript explores the stability theory of several stochastic/random models. It delves into analyzing the stability of equilibrium states in systems influenced by standard Brownian motion and exhibit random variable coefficients. By constructing appropriate Lyapunov functions, various types of stability are identified, each associated with distinct stability conditions.

Salinity Stress Responses and Adaptation Mechanisms of : A Comprehensive Study on Growth, Water Relations, Ion Balance, Photosynthesis, and Antioxidant Defense.

(Decne.) is a leaf succulent C perennial found in arid saline areas of southern Pakistan and neighboring countries, where it is utilized as herbal medicine. This study investigated how growth, water relations, ion content, chlorophyll fluorescence, and antioxidant system of change as salinity levels increase (0, 150, 300, 600, and 900 mM NaCl).

Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning.

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA's defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data's integrity.

IoT-based automated system for water-related disease prediction.

Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million people die from diseases caused by water each year, and 1.

Simultaneous alleviation of antimony toxicity in rice and in-Vitro bio-accessibility by using biochar and seaweed based fertilizer blend.

Antimony (Sb) toxicity is a significant threat to crop production and humans. Its concentration is increasing in soil and water due to human activities which needs dire attention to address this challenge. Biochar is a promising amendment to remediate polluted soils, however, its role in mitigating the toxic impacts of Sb on plants is still unclear.

Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks.

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data.

Synergetic Modulation of Electronic Properties of Cobalt Oxide via "Tb" Single Atom for Uphill Urea and Water Electrolysis.

Exploring single-atom (SA) catalysts in hybrid urea-assisted water electrolysis offers a viable alternative to both Hydrogen (H) generation and polluted water treatment. However, the unfavorable electronic stabilization, low SA content, intrinsically slow kinetics, and imbalanced adsorption-desorption steps are the bottleneck for its scale-up implementation. Herein, a rare-earth Terbium single atom (Tb) is topologically stabilized on defect-rich CoO (Tb@d-CoO) by Tb─O co-ordination for urea oxidation reaction (UOR) and H evolution reaction (HER).

GABA (γ-aminobutyric acid) enrichment and detection methods in cereals: Unlocking sustainable health benefits.

Gamma-aminobutyric acid (GABA), a non-protein amino acid primarily biosynthesized in cereals, is vital to the human neurological system. As the primary neurotransmitter, it promotes relaxation, inhibits depression and sleeplessness, and regulates synaptic transmission and neuronal development. GABA also protects the liver, kidneys, and intestines, reduces blood pressure, and fights cancer and inflammation.

Offset curves: An application in road simulation.

Routable Digital Maps represent a recent advancement driven by the growth of Intelligent Transportation System applications. Efforts have been made to develop and utilize maps within computer systems that can autonomously identify optimal routes. Typically, online map visualizations lack essential information such as traffic patterns, construction zones, or lane counts, which are crucial for designing accurate and realistic routes.

Improving crop production using an agro-deep learning framework in precision agriculture.

Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets.

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms-Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure.

Multifaceted roles of zinc nanoparticles in alleviating heavy metal toxicity in plants: a comprehensive review and future perspectives.

Heavy metal (HM) toxicity is a serious concern across the globe owing to their harmful impacts on plants, animals, and humans. Zinc oxide nanoparticles (ZnO-NPs) have gained appreciable attention in mitigating the adverse effects of abiotic stresses. The exogenous application of ZnO-NPs induces tolerance against HMs by improving plant physiological, metabolic, and molecular responses.

Factors Contributing to Lack of Adherence to Antihypertensive Medications Among Patients at Nishtar Hospital in Multan, Pakistan.

Background: Hypertension affects 26.7% of Pakistan's population, with only 6% achieving control. This study investigates antihypertensive medication adherence in Multan, focusing on socioeconomic and patient-related factors influencing non-adherence to study the lack of adherence to antihypertensive medications in hypertensive patients and its associated factors at Nishtar Hospital in Multan, Pakistan.

Development and evaluation of silver-infused Biopolymer coated cotton wound dressings as possible wound healing material.

Wound management is a critical clinical issue, with substantial economic and social implications. Traditional dressings often lead to poor healing, impacting the effective wound repair is an ongoing challenge. So, this research aimed to investigate the development of a novel medical textile auxiliary in the form of a padded cotton bandage coated with a blend of agricultural biopolymers (xanthan gum and gum arabic) containing AgNPs with an emphasis on environmental friendliness and sustainability.

Biowaste rice husk derived cellulosic hydrogel incorporating industrial cotton waste nonwoven for wound dressing.

Bio-wastes are organic materials achieved through biological sources. The rice crop produces a substantial amount of biowaste in the form of rice husk, which is rich in cellulose. In this research, cellulose was extracted from rice husk by alkalization and bleaching process.

Tetra aniline-based polymers ameliorate BPA-induced cardiotoxicity in Sprague Dawley rats, in silico and in vivo analysis.

Aims: Bisphenol A (BPA), xenoestrogen, is an environmental toxicant, that generates oxidative stress leading to cardiotoxicity. The oxidative stress can be neutralized by natural and synthetic antioxidants. The present study elucidates the highly selective antioxidative potential of synthetic tetra aniline polymers Es-37 and L-37 against Bisphenol A-induced cardiac cellular impairments and the role of miRNA-15a-5p in the regulation of different apoptotic proteins.

Development of sustainable flame-retardant bio-based hydrogel composites from hemp/wool nonwovens with chitosan-banana sap hydrogel.

Flame retardant (FR) finishing is crucial for developing protective textiles, traditionally relying on halogen, phosphorus, and phosphorus-nitrogen chemistries, which have limitations like toxicity and fabric stiffening. Innovative approaches such as nanotechnology, plasma treatments, and natural resource-based finishes are being explored to achieve sustainable FR textiles. This study presents the development and comprehensive characterization of hydrogel composites made from nonwoven fabrics composed of various hemp/wool blends (70/30, 80/20, and 90/10).

Hydroentangled waste cotton non-woven based alginate hydrogel wound dressing for high wound exudates.

Hydrogels are used in modern wound dressings due to their ability to provide comfort with quick healing. However, poor mechanical properties of hydrogels limit their availability in commercial wound dressings. Nonwovens are highly porous, strong, and flexible structures that can provide support to hydrogels without compromising their properties.

Identification and characterization of human GDF15 knockouts.

Growth differentiation factor 15 (GDF15) is a secreted protein that regulates food intake, body weight and stress responses in pre-clinical models. The physiological function of GDF15 in humans remains unclear. Pharmacologically, GDF15 agonism in humans causes nausea without accompanying weight loss, and GDF15 antagonism is being tested in clinical trials to treat cachexia and anorexia.