Novel Brain-Inspired Hierarchical Micro-Nanostructured Poly(3,4-ethylenedioxythiophene)/Polydopamine Neural Interface on Titanium Nitride Electrodes for Electrophysiological Signal Recording.

The development of high-performance neural interfaces is critical for advancing brain-machine communication and treating neurological disorders. A major challenge in neural electrode design is achieving a seamless biological-electronic interface with optimized electrochemical properties, mechanical stability, and biocompatibility. In this study, we introduce a hierarchical micronanostructured poly(3,4-ethylenedioxythiophene)-polydopamine (PEDOT-PDA) coating on titanium nitride (TiN) microelectrodes engineered to enhance electrophysiological signal recording and neural integration.

Contextual feedback in object recognition: A biologically inspired computational model and human behavioral study.

Scene context is known to significantly influence visual perception, enhancing object recognition particularly under challenging viewing conditions. Behavioral and neuroimaging studies suggest that high-level scene information modulates activity in object-selective brain areas through top-down mechanisms, yet the underlying mechanism of this process remains unclear. Here, we introduce a biologically inspired context-based computational model (CBM) that integrates scene context into object recognition via an explicit feedback mechanism.

Machine learning-based prediction of drinking water quality index in Western Tehran using KAN, MLP, and traditional models.

In this study, the water quality index (WQI) was calculated using multivariate statistics, incorporating physical, chemical, and microbiological analysis of water samples taken from water supply networks in the western district of Tehran from 2021 to 2024. The principal drinking water parameters such as pH, total hardness, turbidity, lead (Pb), chloride (Cl), fluoride (F), total dissolved solids (TDS), sulfate (SO), nitrate (NO), nitrite (NO), calcium (Ca), magnesium (Mg), arsenic (As), mercury (Hg), cadmium (Cd), fecal coliform and total residual chlorine (Ch) were selected according to Iranian national water standards. The WQI index was predicted using various machine learning algorithms, including multiple linear regression (MLR), support vector machine (SVM) regression, extreme gradient boosting (XGBoost), Random Forest (RF) regression, multilayer perceptron (MLP), and Kolmogorov-Arnold networks (KAN).

Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization.

High-entropy alloys (HEAs) have attracted considerable attention due to their exceptional properties and outstanding performance across various applications. However, the vast compositional space and complex high-dimensional atomic interactions pose significant challenges in uncovering fundamental physical principles and effectively guiding alloy design. Traditional experimental approaches, often reliant on trial-and-error methods, are time-consuming, cost-prohibitive, and inefficient.

Comparison Combination of Autologous Fibroblast Cells Plus Platelet Rich Plasma (PRP) With PRP Alone in Treatment of Atrophic Acne Scars, a Split-Face Pilot Study With Biometric Assessment.

Introduction: Acne scars are a common side effect of acne vulgaris that can negatively impact patients' quality of life and contribute to depression and anxiety. There is no definitive treatment for this condition. Ongoing research aims to discover better therapeutic options for acne scars.

Human Stress Classification Using Cardiovascular and Respiratory Data Based on Machine Learning Techniques.

Background: Stress, a widespread mental health concern, significantly impacts people well-being and performance. This study proposes a novel approach to stress detection by fusing cardiovascular and respiratory data.

Methods: Fifteen participants underwent a mental stress induction task while their electrocardiogram (ECG) and respiration signals were recorded.

Slosh-free feedback stabilization of liquid-propellant satellites with robustness to fuel density.

Fuel sloshing plays a direct and pivotal role in the tracking control tasks of liquid-propellant satellites. To address this problem, existing works have considered some simplifying assumptions on fuel motion, such as fuel rigidity and potential ideal fluid. On the other hand, most studies have used discretized model-based control schemes to stabilize the infinite-dimensional satellite-fuel system, which can cause spillover instability and lead to a significant loss of accuracy.

Enhancing cardiac function assessment: Developing and validating a domain adaptive framework for automating the segmentation of echocardiogram videos.

Background: Accurate segmentation of echocardiographic images is essential for assessing cardiac function, particularly in calculating key metrics such as ejection fraction. However, challenges such as domain discrepancy, noisy data, anatomical variability, and complex imaging conditions often hinder the performance of deep learning models in this domain.

Objective: To propose and validate a domain adaptive segmentation framework for automating the segmentation of echocardiographic images across diverse imaging conditions and modalities.

Multi-metric locality sensitive hashing enhances alignment accuracy of bisulfite sequencing reads: BisHash.

Motivation: Locality-Sensitive Hashing (LSH) is a widely used algorithm for estimating similarity between large datasets in bioinformatics, with applications in genome assembly, sequence alignment, and metagenomics. However, traditional single-metric LSH approaches often lead to inefficiencies, particularly when handling biological data where regions may have diverse evolutionary histories or structural properties. This limitation can reduce accuracy in sequence alignment, variant calling, and functional analysis.

Porosity Modification in MOF Nanocarriers for pH-Responsive Drug Delivery in Cancer Therapy.

Metal-organic frameworks (MOFs) have emerged as promising nanocarriers for targeted drug delivery, particularly in cancer therapy. Introducing structural defects into MOFs significantly enhances their drug-loading capacity and release efficiency. This study explores porosity modification through defect-engineered MOF-808 nanocarriers, synthesized via a mixed-ligand strategy, to enhance the stability, pH-responsiveness, and drug delivery efficiency for cancer therapy.

Factors affecting weld quality in resistance spot welding of advanced high strength steels.

This study investigates the significant factors on weld quality in the resistance spot welding of quench and partitioning advanced high-strength steel. Attention is laid emphasis on nugget formation and growth, weldability range (lobe curve), nugget size and shape (macrostructural features), coating effects, mechanical properties, and fracture behavior of resistance spot welds. As zinc-coated steel sheets are widely applied in body-in-white in automotive industry, coating's effect on weld quality is particularly significant.

Genetic Heterogeneity of Autism Spectrum Disorder: Identification of Five Novel Mutations (RIMS2, FOXG1, AUTS2, ZCCHC17, and SPTBN5) in Iranian Families via Whole-Exome and Whole-Genome Sequencing.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by abnormal social interactions, verbal communication difficulties, and restricted repetitive behaviors. Identifying the underlying genetic factors is crucial because of the complex genetic and environmental etiology. In this study, we performed whole-exome sequencing (WES), whole-genome sequencing (WGS), and array comparative genomic hybridization (aCGH) of four Iranian families with ASD-related conditions to identify novel genomic alterations.

Estimation of accelerometers' bias in decentralized relative localization via the rotating UWB tag.

This paper proposes a novel algorithm for cooperative relative localization of multiple aerial robots. The algorithm estimates acceleration biases and velocities alongside relative positions, leveraging relative range and inertial measurements. A group of quadrotors, consisting of one master and two regular units, collaboratively estimates the relative positions.

Determination of Skeletal Age From Hand Radiographs Using Deep Learning.

Background: Surgeons treating skeletally immature patients use skeletal age to determine appropriate surgical strategies. Traditional bone age estimation methods utilizing hand radiographs are time-consuming.

Purpose: To develop highly accurate/reliable deep learning (DL) models for determination of accurate skeletal age from hand radiographs.

ORR, OER, and HER activity promotion in hierarchical yolk-shell structures based on Co-glycerate@cobalt carbonate hydroxide by dual doping with manganese and boron.

Nowadays, exploring efficient electrocatalysts is crucial for the tri-functional oxygen reduction reaction (ORR), oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). In this study, we designed a novel electrocatalyst based on Mn- and B-doped Co-glycerate@cobalt carbonate hydroxide for the tri-functional OER, HER and ORR under alkaline conditions. Electrochemical measurements demonstrate overpotentials of 318 and 75 mV at a current density of 10 mA cm for the OER and HER, respectively.

Prediction of lower limb joint stiffness and optimization of anthropometric parameters in countermovement jump using an anthropometry-informed neural network.

Background: The Countermovement Jump (CMJ) test, widely used to assess athletes' musculoskeletal and neuromuscular readiness, hinges on the performance of the hip, knee, and ankle joints. Despite extensive research, there is no consensus on which joint is most critical for CMJ performance. This study aims to identify the primary lower limb joint contributing to CMJ execution by analyzing maximum energy production and peak stiffness.

C3-methylation of imidazopyridines via C(sp2)-H activation using magnetic Cu-MOF, and DMSO as solvent and Methyl source.

In this study, we introduce a protocol for the methylation of the C-H bond at the C position in imidazo[1,2-a]pyridine scaffolds, employing magnetic def./Cu-MOF for the activation of C(sp2)-H bonds. The term 'def' refers to strategically induced defects in the Metal-Organic Framework (MOF), which are designed to improve its catalytic efficiency.

Microfluidics study on immiscible displacement mechanisms of oil by hybrid low salinity water and polymer in fractured porous media.

Application of hybrid low salinity water and polymer flooding (LSPF) as an enhanced oil recovery (EOR) method takes advantage from the wettability alteration by low salinity water and enhancement of mobility ratio by addition of polymer. Recently it is shown that polymer affects the possible interactions in oil/brine/rock system e.g.

Differential expression of lncRNAs in lung cancer subgroups.

Purpose: Long non-coding RNAs (lncRNAs) have emerged as promising candidates in lung cancer research. This study aimed to assess the expression of eight autophagy-related lncRNAs in lung cancer tissues compared to matched non-tumor samples and to evaluate their potential as diagnostic biomarkers.

Methods: We examined the expression levels of LINC01963, RAB11B-AS1, AC104083.

Design clues for motility and rheotaxis-based microfluidic chips for sperm sorting.

Microfluidic-based sperm selection is an essential tool recently introduced into assisted reproductive technologies. Conventional approaches such as swim-up and density-gradient centrifugation (DGC) are widely used, however, they lack selectivity and limit the necessary sperm amount in the sample. Moreover, the DGC method has been reported to damage the sperm's DNA, whilst the emerging microfluidic devices offer a soft and flexible way to selectively sort various volumes of raw sperm samples.

Room-temperature ethylene glycol sensor based on cuprous oxide/MXene films.

This study demonstrates a high-performance room-temperature ethylene glycol (EG) gas sensor using CuO/MXene bilayer films on quartz crystal microbalance (QCM) substrates, addressing critical needs for industrial safety and environmental monitoring. The fabricated sensors were systematically characterized by XRD, FTIR, and FESEM, revealing that the CuO/MXene bilayer configuration achieved exceptional performance with an ultra-low detection limit of 381 ppb, high sensitivity of 22.8 Hz/ppm, and excellent selectivity compared to individual CuO, MXene, or their mixture films.

Molecular level characterization of interactions between asphaltene and solid surface for forecasting changes in wettability.

This research presents a new experimental approach designed to investigate the asphaltene-solid surface molecular interactions, and how these interactions affect changes in wettability. The methodology's impact was assessed by altering the calcite surfaces wettability, evaluated through contact angle measurements. Furthermore, the properties of the asphaltene fractions were analyzed using techniques such as Field Emission Scanning Electron Microscopy, elemental analysis, Fourier Transform Infrared Spectroscopy, and zeta potential measurements.

Flow regime transitions in flow blurring injection through a CFD parametric study.

Flow-blurring (FB) is a twin-fluid atomization technique that generates fine sprays through internal turbulent mixing. This study presents a parametric computational investigation of an FB injector operating with air and various liquids at ambient pressure. A validated unsteady two-phase solver based on the Volume of Fluid (VOF) method is used to model the injector at different air-to-liquid mass flow rate ratios (ALRs).

The Relationship between Leisure Activities and Social Health of the Elderly in Iran.

Background: One effective strategy for enhancing the social health of the elderly is the development of leisure activities. The aim of this study was to determine the relationship between leisure activities and social health among elderly residents of Tehran.

Methods: This descriptive-analytical study was conducted in 2018 on 413 elderly individuals aged 60 and above in Tehran.

Adverse effects of maternal and paternal body mass index on assisted reproductive techniques outcomes: A time-lapse study.

Objective: While obesity has been associated with poor reproductive outcomes, the specific factors affecting gametes remain unclear. Our primary objective was to assess the relationship between a couple's pre-pregnancy body mass index (BMI), the morphokinetic characteristics of embryos, and their potential for implantation. We analyzed standard semen parameters, sperm chromatin integrity, and oxidative stress levels in men undergoing assisted reproductive techniques (ART).