The antibacterial and anti-biofilm effects of novel synthetized nitroimidazole compounds against methicillin-resistant Staphylococcus aureus and carbapenem-resistant Escherichia coli and Klebsiella pneumonia in vitro and in silico.

The antibiotic resistance and biofilm formation by bacterial pathogens has led to failure in infections elimination. This study aimed to assess the antibacterial and anti-biofilm properties of novel synthesized nitroimidazole compounds (8a-8o). In this study, nitroimidazole compounds were synthesized via the A3 coupling reaction of sample substrates in the presence of copper-doped silica cuprous sulfate (CDSCS).

An artificial intelligence mechanism for detecting cystic lesions on CBCT images using deep learning.

Introduction: The present study aimed to provide and evaluate the efficiency of an artificial intelligence mechanism for detecting cystic lesions on cone beam computed tomography (CBCT) scans.

Method And Materials: The CBCT image dataset consisted of 150 samples, including 50 cases without lesions, 50 dentigerous cysts (DC), and 50 periapical cysts (PC) based on both radiographic and histopathological diagnosis. The dataset was divided into a development set with 70 % of samples for training and validation and a final test set with the other 30 % of samples.

An efficient discrete Chebyshev polynomials strategy for tempered time fractional nonlinear Schrödinger problems.

Introduction: An interesting type of fractional derivatives that has received widespread attention in recent years is the tempered fractional derivatives. These fractional derivatives are a generalization of the well-known fractional derivatives, such as Caputo and Riemann-Liouville. In fact, these derivatives are obtained by multiplying the expressed fractional derivatives by an exponential factor.

Stochastic economic sizing and placement of renewable integrated energy system with combined hydrogen and power technology in the active distribution network.

The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network.

An Innovative Attention-based Triplet Deep Hashing Approach to Retrieve Histopathology Images.

Content-based histopathology image retrieval (CBHIR) can assist in the diagnosis of different diseases. The retrieval procedure can be complex and time-consuming if high-dimensional features are required. Thus, hashing techniques are employed to address these issues by mapping the feature space into binary values of varying lengths.

Mathematical model for highly sensitive photonic crystal fiber sensor based on hyperbolic black holes.

In this paper, the photonic crystal fiber sensor based on the geometry of hyperbolic black holes is proposed. As the hyperbolic black hole concentrates the electromagnetic radiation in its powerful gravitational field, the designed sensor has resulted in the concentration of the most electromagnetic field in the core of fiber. The extraordinary sensitivity of the sensor is due to the topology and exact geometry, which originates from the idea of the black hole.

Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer's diagnosis from EEG signals towards IoMT.

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer's disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis.

Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children.

Heterogeneous biomechanical/mathematical modeling of spatial prediction of glioblastoma progression using magnetic resonance imaging-based finite element method.

Background And Objective: Brain tumors are one of the most common diseases and causes of death in humans. Since the growth of brain tumors has irreparable risks for the patient, predicting the growth of the tumor and knowing its effect on the brain tissue will increase the efficiency of treatment strategies.

Methods: This study examines brain tumor growth using mathematical modeling based on the Reaction-Diffusion equation and the biomechanical model based on continuum mechanics principles.

Cloud-fog architecture-based control of smart island microgrid in master-slave organization using disturbance observer-based hybrid backstepping sliding mode controller.

Distributed control is an effective method to coordinate the microgrid with various components, and also in a smart microgrid, communication graph layouts are essential since changing the topology unexpectedly could disrupt the operation of the distributed controllers, and also an imbalance may occur between the production and load. Hence, reducing the exchanged data between units and system operator is essential in order to reduce the transmitted data volume and computational burden. For this purpose, an islanded microgrid with multiple agents which is using cloud-fog computing is proposed here, in order to reduce the computing burden on the central control unit as well as reducing data exchange among units.

An extended goal programming approach with piecewise penalty functions for uncertain supplier-material selection problem in cardboard box manufacturing systems.

In this study a real case multi-objective material and supplier selection problem in cardboard box production industries is studied. This problem for the first time optimizes the objective functions such as total wastage amounts remained from all raw sheets, total costs of the system including purchasing cost and transportation cost (including fixed and variable costs) of the raw sheets, and total overplus of produced cardboard boxes. To be closer to the real situations, as a novelty, the problem is formulated in belief-degree-based uncertain environment with normal distribution where this type of uncertainty applies the ideas of experts.

Deep-Learning-Based Blood Glucose Detection Device Using Acetone Exhaled Breath Sensing Features of α-FeO-MWCNT Nanocomposites.

Owing to the correlation between acetone in human's exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is important for early diagnosis of diabetes diseases. In this article, a noninvasive blood glucose detection device through acetone sensing in EB, based on an α-FeO-multiwalled carbon nanotube (MWCNT) nanocomposite, was successfully developed. Different amounts of α-FeO were added to the MWCNTs by a simple solution method.

Identifying and Validating Data Elements and Main Characteristics of a Teleconsultation and Televisit System for Patients with Multiple Sclerosis in Iran.

Patients with multiple sclerosis (MS) face barriers and disparities in accessing care for evaluation and treatment. Given the unmet needs and barriers to access to care, teleservices (e.g.

Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images.

Developing advanced systems for 3D brain tissue segmentation from neonatal magnetic resonance (MR) images is vital for newborn structural analysis. However, automatic segmentation of neonatal brain tissues is challenging due to smaller head size and inverted T1/T2 tissue contrast compared to adults. In this work, a subject-specific atlas based technique is presented for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from neonatal MR images.

Technological aspects and recommendations for applying teledentistry in oral medicine: a scoping review.

Background: Teledentistry is applied in oral medicine to help dental practitioners and specialists manage complex oral conditions. This scoping review aims to synthesize the available evidence regarding the technical requirements and the provision of security services, as well as the recommendations on standard oral cavity photography methods for using teledentistry in oral medicine.

Method: The present scoping review was conducted in 2022 according to the Joanna Briggs Institute (JBI) manual.

Resistive Gas Sensors Based on 2D TMDs and MXenes.

ConspectusGas sensors are used in various applications to sense toxic gases, mainly for enhanced safety. Resistive sensors are particularly popular owing to their ability to detect trace amounts of gases, high stability, fast response times, and affordability. Semiconducting metal oxides are commonly employed in the fabrication of resistive gas sensors.

Recent Clinical Implications of FAPI: Imaging and Therapy.

The fibroblast activation protein (FAP) is a biomarker that is selectively overexpressed on cancer-associated fibroblasts (CAFs) in various types of tumoral tissues and some nonmalignant diseases, including fibrosis, arthritis, cardiovascular, and metabolic diseases. FAP plays a critical role in tumor microenvironment through facilitating proliferation, invasion, angiogenesis, immunosuppression, and drug resistance. Recent studies reveal that FAP might be regarded as a promising target for cancer diagnosis and treatment.