Leading with purpose: unraveling the impact of motivating language on work engagement with a focus on psychological meaningfulness in the tourism and hospitality sector.

Purpose: Drawing on speech act theory, this study examined the influence of transformational leadership on employee work engagement and motivating language. Further, this study examines the mediating role of motivating language. In addition, the study examined the moderating role of psychological meaningfulness.

Optimal control analysis to reduce the health complexity of co-infection with COVID-19 and kidney disease.

The COVID-19 pandemic remains a serious health risk, especially with diseases like kidney disease. There is no information in the literature on co-infection of kidney disease with COVID-19. Therefore, the current study introduces a deterministic mathematical model to explore the co-infection dynamics between COVID-19 and kidney disease, intending to offer insightful guidance for effective control strategies.

Optimal sizing of hybrid renewable energy systems relying on the black winged kite algorithm for performance evaluation.

The hybrid photovoltaic (PV) and wind turbine (WT) system combined with a battery (BT) have emerged as an effective renewable energy solution. As the adoption of hybrid systems gain popularity, determining the optimal system size plays a vital role for achieving cost-effectiveness. This study employs a recently developed Black-Winged Kite Algorithm (BKA) to optimize the size of a PV/WT/BT hybrid system to continuously distribute the electrical power to an educational institution sited in Puri, Odisha, India, with the goal of minimizing the total annual cost (TAC) while considering least levelized cost of electricity (LCOE).

Novel control strategies for electric vehicle charging stations using stochastic modeling and queueing analysis.

This study presents a comprehensive analytical framework for modeling electric vehicle (EV) charging infrastructures through a stochastic queueing-theoretic approach that explicitly incorporates critical customer behavioral dynamics. The proposed model addresses key phenomena often overlooked in classical frameworks, including customer impatience (reneging), balking behavior, feedback mechanisms, and state-dependent service threshold policies, within a finite-population, multiple-server environment. These behavioral elements reflect realistic operational scenarios in which users may opt not to join extended queues, abandon the system due to excessive delays, or return for service completion based on prior dissatisfaction.

Real-time human-robot interaction and service provision using hybrid intelligent computing framework.

Human-robot interaction has gained significant attention in various domains, including healthcare, customer service, and industrial automation. High computational cost, inefficient service matching, and elevated failure rates in dynamic service contexts are some primary disadvantages of existing query-processing systems. This research introduces a Hybrid Intelligent Computing Model (HICM) to improve robots' ability to process inquiries autonomously.

Fusing Transformer-XL with bi-directional recurrent networks for cyberbullying detection.

Identifying cyberbullying in languages other than English presents distinct difficulties owing to linguistic subtleties and scarcity of annotated datasets. This article presents a new method for identifying cyberbullying in Bengali text data using the Kaggle dataset. This strategy combines Transformer-Extra Large (XL) with bi-directional recurrent neural networks (BiGRU-BiLSTM).

Enhancing skin lesion classification: a CNN approach with human baseline comparison.

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high-confidence CNN predictions while replacing low-confidence outputs with expert human assessments to enhance diagnostic accuracy. A CNN model utilizing the EfficientNetB3 backbone is trained on datasets from the ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges and evaluated on a 150-image test set.

Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis.

Early and accurate detection of breast cancer is essential for reducing mortality rates and improving clinical outcomes. However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. To address these issues, we propose BreastSwinFedNetX, a federated learning (FL)-enabled ensemble system that combines four hierarchical variants of the Swin Transformer (Tiny, Small, Base, and Large) with a Random Forest (RF) meta-learner.

Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model.

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition that challenges early diagnosis due to its diverse manifestations across different developmental stages. Timely and accurate detection is essential to enable interventions that significantly enhance developmental outcomes. This study introduces a robust and interpretable machine learning framework to diagnose ASD using questionnaire data.

Strategic planning as a catalyst for sustainability: A mediated model of strategic intent and formulation in manufacturing SMEs.

This study examines the influence of Systematic Strategic Planning (SSP) on the Sustainable Performance (SP) of manufacturing Small and Medium Enterprises (SMEs) in Pakistan. Despite SMEs' vital contribution to economic growth, there is limited empirical research on how strategic planning enhances sustainable performance in SMEs operating in emerging economies facing political and economic instability. Drawing on the Triple Bottom Line (TBL) and Resource-Based View (RBV) theories, this study investigates the mediating roles of Strategic Intent (SI) and Strategic Formulation (SF) in the SSP-SP relationship.

TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model.

Mosquitoes, which belong to diverse species, play a significant role in ecological systems and public health. The accurate identification (classification) of mosquito species is essential for a comprehensive understanding of their ecological roles, behaviors, and evolutionary patterns. While numerous studies have attempted to classify the mosquito species based on images, the existing works still have limitations.

CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification.

The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models. This research introduces CancerNet, incorporating convolutional, involutional, and transformer components to extract hierarchical features and capture long-range dependencies from medical imaging data across the channel and spatial domains.

An efficient trustworthy cyberattack defence mechanism system for self guided federated learning framework using attention induced deep convolution neural networks.

As cyberattacks become more advanced, conventional centralized threat intelligence models often fail to keep up with these threats' growing complexity and frequency, highlighting the requirement for innovative approaches to strengthen cybersecurity resilience. Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. The efficiency of FL in enhancing attack intelligence skills emphasizes its probability of driving a novel period of robust and privacy-protecting cybersecurity practices.

Positional accuracy of intraoral scan alignment to a facial scan using structured light scanning and trial denture base with occlusal rim markers in patients with complete maxillary edentulism.

Statement Of Problem: Creating precise 3D virtual head models for patients with complete maxillary edentulism remains challenging owing to the lack of natural landmarks and limitations of current image-matching technologies.

Purpose: The purpose of this study was to evaluate the positional accuracy of intraoral scan alignment to structured light-based facial scans using varying sizes and positions of occlusal rim markers under maxillary edentulous conditions.

Material And Methods: Radiopaque artificial markers of 3 sizes (2, 4, and 6 mm) were made and attached to the maxillary occlusal rim of a completely edentulous patient at different positions (midline-canine [MC], midline [M], canine [C]).

A comprehensive image dataset of Bangladeshi mango variety.

This data article presents a primary dataset collected from various locations in Bangladesh, featuring 10 different mango varieties that are mostly consumed locally. This mango dataset includes the following types: Amrapali, Bari-4, Bari-7, Fazlee, Harivanga, Kanchon Langra, Katimon, Langra, Mollika, and Nilambori. A DSLR camera was used to take high-resolution pictures of every mango variety; as a consequence, 2012 photographs were obtained, although the distribution of images among types is not uniform.

Impact of the COVID-19 Pandemic on Antibiotic Prescriptions at the University Clinical Dentistry Center of Kosovo.

The COVID-19 pandemic led to significant disruptions in healthcare services worldwide, including dental care, resulting in increased reliance on antibiotics as a substitute for in-person dental interventions. This study aimed to examine the prescription of different classes of antibiotics at the University Dental Clinical Center of Kosovo during the COVID-19 pandemic and compare it with prescription trends from the pre-pandemic period. This study analyzed antibiotic prescription patterns at the University Dental Clinical Center of Kosovo (UCDCK) from 2019 to 2022, focusing on dental patients.

Peer-driven task scheduling and resource allocation for enhanced performance in industrial IoT systems.

Peer-to-Peer (P2P) systems in the smart industry, enhanced by the Industrial Internet of Things (IIoT), provide a robust framework for efficiently managing and processing distributed tasks. However, resource sharing and allocation in these systems, often conducted one-to-one, can lead to task stagnancy when multiple resources are required in sequence. This paper proposes a novel Peer-dependent Scheduling and Allocation Scheme (PSAS) that leverages predictive learning to optimize task scheduling and resource allocation to address this limitation.

Comprehensive techno-economic optimization and performance analysis of molten salt concentrated solar power tower plants in Algeria.

This paper presents a comprehensive techno-economic analysis of three molten salt Concentrated Solar Power (CSP) tower plants located in the regions of Mechria, Adrar, and Tindouf in Algeria. The study evaluates the thermal efficiency, economic feasibility, and performance of these CSP using the System Advisor Model (SAM) software, which accurately models Direct Normal Irradiance (DNI), a critical factor influencing plant performance. Key parameters analyzed include Solar Multiple (SM), Thermal energy storage (TES) hours, capacity factor (CF), and the Levelized Cost of Energy (LCOE).

Synergizing NOMA and energy harvesting in full duplex mobile edge computing for optimized energy efficiency.

This research introduces a novel approach to enhance energy efficiency in Mobile-Edge Computing (MEC) systems by integrating Non-Orthogonal Multiple Access (NOMA) with energy harvesting in a full-duplex (FD) environment. The primary objective is to optimize edge computing performance while minimizing energy consumption. A comprehensive resource allocation strategy is proposed, considering CPU frequency, power control for both uplink and downlink, time scheduling, and data offloading.

Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality.

Introduction: Drowsy driving is a significant contributor to accidents, accounting for 35 to 45% of all crashes. Implementation of an internet of things (IoT) system capable of alerting fatigued drivers has the potential to substantially reduce road fatalities and associated issues. Often referred to as the internet of medical things (IoMT), this system leverages a combination of biosensors, actuators, detectors, cloud-based and edge computing, machine intelligence, and communication networks to deliver reliable performance and enhance quality of life in smart societies.

Fuzzy-2 deployment in indirect vector control and hybrid space vector modulation for a two-level inverter fed induction motor drive.

Soft computing based expert systems have a pivotal role to play in designing standalone mobility systems such as unmanned aerial vehicles. The expert systems are designed by learning from functional data of a system, thereby framing rules without relying on apre-defined model. The use of type-2 fuzzy (fuzzy-2) controllers instead of traditional proportional-integral controllers (PICs) enhances the dynamic performance of an indirect vector controlled (IVC) induction motor (IM) in this paper.

Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis.

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification.

Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm.

With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decrease healthcare expenses. The Internet of Things (IoT) presents a promising solution for remote health monitoring by collecting and analyzing vital data like body temperature, ECG, and heart rate, giving real-time insights to medical professionals. However, maintaining effectual monitoring in environments with bandwidth or energy constraints presents crucial threats.

Hybrid adaptive ant lion optimization with traditional controllers for driving and controlling switched reluctance motors to enhance performance.

Switched reluctance motors (SRMs) are favored in industrial applications for their durability, efficiency, and cost-effectiveness, yet face challenges such as torque ripple and nonlinear magnetic behavior that limit their precision in control tasks. To address these issues, this work introduces a novel hybrid adaptive ant lion optimization (HAALO) algorithm, combined with PI and FOPID controllers, to improve SRM performance. The HAALO algorithm enhances traditional ant lion optimization by integrating adaptive mutation and elite preservation techniques for dynamic real-time control, optimizing both torque ripple and speed regulation.