Fit accuracy of complete crowns fabricated by generative artificial intelligence design: a comparative clinical study.

Purpose: Designing restorations remains challenging because the process is time-consuming and requires operator skill and experience. This clinical study evaluated the fit accuracy of polymerized complete crowns fabricated using a web-based 3D generative artificial intelligence design (GAID) method compared to crowns fabricated using a conventional computer-aided design (CCAD) method.

Materials And Methods: Sixty-two patients requiring complete crowns in maxillary and mandibular premolars and molars were enrolled.

Enablers and barriers to antimicrobial stewardship for upper respiratory tract infections in Kosovo's primary care: a qualitative study protocol.

Introduction: Upper respiratory tract infections (URTIs) are one of the most common reasons for primary care visits, yet often do not require treatment with antibiotics. Antibiotic use is associated with side effects and the development of antibiotic resistance. Antimicrobial stewardship (AMS) programmes are essential to mitigate this issue.

Neuro-Bridge-X: A Neuro-Symbolic Vision Transformer with Meta-XAI for Interpretable Leukemia Diagnosis from Peripheral Blood Smears.

Acute Lymphoblastic Leukemia (ALL) poses significant diagnostic challenges due to its ambiguous symptoms and the limitations of conventional methods like bone marrow biopsies and flow cytometry, which are invasive, costly, and time-intensive. This study introduces Neuro-Bridge-X, a novel neuro-symbolic hybrid model designed for automated, explainable ALL diagnosis using peripheral blood smear (PBS) images. Leveraging two comprehensive datasets, ALL Image (3256 images from 89 patients) and C-NMC (15,135 images from 118 patients), the model integrates deep morphological feature extraction, vision transformer-based contextual encoding, fuzzy logic-inspired reasoning, and adaptive explainability.

Fabrication and comprehensive experimental evaluation of surfactant-activated PEDOT:PSS/SnO thin films deposited via spin coating for advanced sensing applications.

This research investigates the fabrication of surfactant-mixed tin oxide (SnO) nanostructured thin films on a fluorine-doped tin oxide (FTO) substrate via hydrothermal synthesis, focusing on their structural, morphological, optical, and electrical properties for sensor applications. To examine the effect of surfactant concentration, cetyltrimethylammonium bromide (CTAB) was incorporated at varying weight percentages (0%, 6%, 11%, 16%, and 20%), resulting in five distinct sensor samples, labelled SnO-1, SnO-2, SnO-3, SnO-4, and SnO-5, respectively. X-Ray Diffraction (XRD) analysis confirms a tunable crystallite size from 12.

Applying the defense model to strengthen information security with artificial intelligence in computer networks of the financial services sector.

The increasing digitization of the Financial Services Sector (FSS) has significantly improved operational efficiency but has also exposed institutions to sophisticated Cyber Threat Intelligence (CTI) such as Advanced Persistent Threats (APT), zero-day exploits, and high-volume Denial-of-Service (DoS) attacks. Traditional Intrusion Detection Systems (IDS), including signature-based and anomaly-based approaches, suffer from high False Positive Rates (FPR) and lack the adaptability required for modern threat landscapes. This study aims to develop and evaluate an Artificial Intelligence-Enhanced Defense-in-Depth (AI-E-DiD) designed to provide real-time, adaptive, and scalable cybersecurity prevention for financial networks.

Management of External Root Resorption following Replantation After Accidental Tooth Extraction Using Regenerative Endodontic Procedures: A Case Report.

Accidental extraction of the wrong tooth can lead to complications such as external root resorption (ERR) after replantation. This case report describes the management of an 11-year-old female patient with ERR in a replanted mandibular first molar (#36) following iatrogenic extraction. Regenerative endodontic procedures (REPs) were employed, including chemical disinfection, intracanal calcium hydroxide medication, blood clot induction, and the placement of Biodentine™.

Anti-Bacterial and Anti-Fungal Properties of a Set of Transition Metal Complexes Bearing a Pyridine Moiety and [B(CF)] as a Counter Anion.

Background: Transition metal complexes incorporating fluorinated counter anions represent a significant class of compounds with broad applications in industry, pharmaceuticals, and biomedicine. These fluorinated anions are known to enhance the solubility, stability, and reactivity of the complexes, thereby expanding their functional utility in various chemical and biological contexts.

Methods: A set of metal(II) complexes of the general formula [MPy][B(CF)] where (Py = pyridine, M = Mn (), Fe (), Co (), Ni (), Cu (), Zn ()) have been synthesized by direct reaction of metal halides and pyridine in the presence of Ag[B(CF)].

Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning.

Wearable Sensor (WS)-based monitoring systems detect minute patient movements/ demands and abnormalities through periodic sensing and imaging. Sensor data observed over different intervals is not constant or available based on operating sequences. Due to variations in data sequences, the analysis process becomes complex, resulting in less precise outputs.

A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images.

Multi-Modal Medical Image Fusion (MMMIF) has become increasingly important in clinical applications, as it enables the integration of complementary information from different imaging modalities to support more accurate diagnosis and treatment planning. The primary objective of Medical Image Fusion (MIF) is to generate a fused image that retains the most informative features from the Source Images (SI), thereby enhancing the reliability of clinical decision-making systems. However, due to inherent limitations in individual imaging modalities-such as poor spatial resolution in functional images or low contrast in anatomical scans-fused images can suffer from information degradation or distortion.

Design of a novel and robust 2-DOF PIDA controller based on enzyme action optimizer for ball position regulation in magnetic levitation systems.

Magnetic levitation (maglev) systems are characterized by strong nonlinearities and inherent open-loop instability, making precise position regulation of levitating bodies exceptionally challenging. These systems are highly sensitive to model uncertainties, parameter variations, and external disturbances. This demands advanced control strategies beyond conventional PID techniques.

Surface renewal driven copper recovery by cementation in a stirred reactor with a rotating wiper mechanism.

The rate of Cu cementation on a Zn cylindrical sheet lining the inner wall of a cylindrical batch-stirred reactor was studied, where a U-shaped wiper consisting of two plastic-coated steel rods was used to agitate the solution. The novelty of the reactor lies in the integration of a rotating U-shaped wiper that provides simultaneous mechanical surface renewal and bulk agitation, enhancing copper removal efficiency without the need for additional stirring mechanisms. Variables studied were wiper rotational speed, wiper diameter, pH of the solution, and the initial concentration of CuSO solution.

A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems.

Effective financial risk management in healthcare systems requires intelligent decision-making that balances treatment quality with cost efficiency. This paper proposes a novel hybrid framework that integrates reinforcement learning (RL) with knowledge graph-augmented neural networks to optimize billing decisions while preserving diagnostic accuracy. Patient profiles are encoded using a combination of structured features, deep latent representations, and semantic embeddings derived from a domain-specific knowledge graph.

Multiaxial vibration data for blade fault diagnosis in multirotor unmanned aerial vehicles.

This dataset presents multiaxial vibration signals collected from a multirotor unmanned aerial vehicle (UAV) operating in hover mode for the purpose of blade fault diagnosis. Vibration measurements were recorded at the geometric center of the UAV, where the centerlines of the four rotor arms intersect, using a triaxial accelerometer. The dataset captures variations across the X, Y, and Z axes under different blade fault conditions, including healthy, minor imbalance, severe imbalance, and screw loosening scenarios.

Multilingual sentiment analysis in restaurant reviews using aspect focused learning.

Cross-cultural sentiment analysis in restaurant reviews presents unique challenges due to linguistic and cultural differences across regions. The purpose of this study is to develop a culturally adaptive sentiment analysis model that improves sentiment detection across multilingual restaurant reviews. This paper proposes XLM-RSA, a novel multilingual model based on XLM-RoBERTa with Aspect-Focused Attention, tailored for enhanced sentiment analysis across diverse cultural contexts.

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence.

The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, including distributed denial-of-service (DDoS) attacks. Conventional attack detection methods face significant challenges related to privacy, scalability, and adaptability due to the dynamic nature of IoT environments.

Resilient [Formula: see text] filter design for networked control systems under communication jamming cyberattacks.

Communication jamming across various durations and frequencies leads to unpredictable and time-varying measurement transmission intervals in networked systems. Consequently, the discrete-time models of filtering error systems become time-dependent, thereby complicating the design of filters. This paper addresses the ongoing challenge of [Formula: see text] filtering for networked sampled-data systems subjected to communication jamming attacks.

Fault analysis and performance improvement of grid-connected doubly fed induction generator through an enhanced crowbar protection scheme.

The main problem associated with a doubly fed induction generator (DFIG) during fault is large inrush currents induced in rotor winding, which has detrimental effects on the machine's AC excitation converter. A simple conventional resistance inclusion (crowbar) is employed with a PI controller to protect a DFIG from transient current, but it is observed that this method is not enough to keep transient over-current to an admissible level. In this paper, an effective current limiting technique along with reactive power control is proposed in order to maintain stability, reduce transient current surge to an acceptable level, and enhance the Fault Ride Through capacity of DFIG.

Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources.

The inherent unpredictability and fluctuation of renewable energy systems make it very difficult to precisely estimate power output and manage distribution, which is a major obstacle to their widespread use. Current forecasting techniques often fall short, struggling to effectively handle unexpected spikes or changes in demand, which can lead to inefficiencies and even system instability. To better anticipate short-term demand, optimize the balance between generation and distribution states, and dynamically detect and differentiate inappropriate surges in power distribution, this article proposes the Probabilistic Systematic Processing Method (PSPM), which utilizes reward-based state model learning.

A novel global MPPT method based on sooty tern optimization for photovoltaic systems under complex partial shading.

As the deployment of photovoltaic (PV) systems continues to expand globally, the need for robust and highly efficient Maximum Power Point Tracking (MPPT) algorithms becomes increasingly critical, particularly under complex Partial Shading Conditions (PSC) where multiple local maxima can significantly reduce energy yield. This paper proposes a novel MPPT strategy based on the bio-inspired Sooty Tern Optimization Algorithm (STOA) for Global Maximum Power Point Tracking (GMPPT) in PV arrays subjected to non-uniform irradiance. The STOA algorithm, originally developed for solving complex multimodal optimization problems, is here adapted and optimized for MPPT tasks, demonstrating superior capabilities in terms of convergence speed, tracking accuracy, and dynamic stability.

Solution of optimal power flow with wind power uncertainty using hybridized self-adaptive differential evolution.

The current research presents an optimal power flow (OPF) solution in an electrical system network with the integration of wind power using enhanced self-adaptive differential evolution method with a mixed crossover (ESDE-MC). A model for wind power cost is considered, which contemplates the random nature of wind speed based on Weibull probability density function. The wind energy cost model incorporates the estimated reserve and penalty cost for wind power shortfall and surplus, respectively.

An innovative efficiency of incubator to enhance organization supportive business using machine learning approach.

Many small businesses and startups struggle to adjust their operational plans to quickly changing market and financial situations. Traditional data-driven techniques often miss possibilities and waste resources. Our unique approach, Unified Statistical Association Validation (USAV), allows dynamic and real-time data association and improvement assessment to address this essential issue.

Extraction of carbon powder from pyrolysis of low-density polyethylene plastics and its application in composite laminates.

The escalating environmental concerns associated with plastic waste, particularly Low-Density Polyethylene (LDPE), have spurred research into sustainable recycling strategies. Pyrolysis has been developed as a viable technique for transforming LDPE into appreciated by-products, including carbon powder, which holds potential for advanced material applications. This study investigates the extraction of carbon powder from LDPE via pyrolysis and its subsequent utilization in composite laminates.

A fair dividend approach for aggregating wearable sensor data to improve electronic health records.

Wearable sensor (WS) technology in healthcare is essential because it makes medical diagnosis easier by continuously monitoring important changes in an individual's body. This technology is used to detect aberrant occurrences and predict medical dangers. A central connecting unit is used to stream and send accurate observations to improve the quality of medical diagnosis.

Optimization of cryoprotectants and storage temperatures for preserving viability and probiotic properties of lyophilized bacterial strains from chicken gut.

Probiotics, particularly strains from the genera Bacillus, Lactobacillus, and Staphylococcus, play a vital role in gut health, immune modulation, and pathogen inhibition. However, environmental stressors during storage often compromise their long-term viability and probiotic functionality. By examining how lyophilization affects the viability and probiotic functionality of certain strains of Bacillus, Lactobacillus, and Staphylococcus, this study sought to understand how storage conditions and protective agents affect bacterial survival and important probiotic characteristics.

Enhanced CPCV algorithm for improving power quality in electric vehicle battery charging.

There are several difficulties to overcome when integrating electric vehicles (EVs) into power distribution networks, especially when it comes to preserving power quality (PQ) because of the harmonic distortion produced throughout battery charging. These issues are not sufficiently addressed by conventional charging algorithms like Constant Current Constant Voltage (CCCV), which frequently leads to higher Total Harmonic Distortion (THD), decreased system efficiency, and generally insufficient performance PQ. The Constant Power Constant Voltage (CPCV) charging algorithm, which is a revolutionary approach to addressing these issues, dynamically modifies the charging power according to the battery's state of charge (SoC).