Chitosan-based hydrogels: A comprehensive review of transdermal drug delivery.

This article provides a comprehensive review of chitosan-based hydrogels for transdermal drug delivery. It covers various aspects including the chemical structure of chitosan and its derivatives, crosslinking agents, hydrogel morphology, and drug loading and release behaviors. The review draws on 16 studies sourced from Scopus, focusing on how the composition and structure of hydrogels influence drug release.

Highly sensitive split ring resonator-based sensor for quality monitoring of edible oils.

This research presents the design and analysis of a compact metamaterial (MTM)-based star-shaped split-ring resonator (SRR) enclosed in a square, constructed on a cost-effective substrate for liquid chemical sensing applications. The designed structure has dimensions of 10 × 10 mm and is optimized for detecting adulteration in edible oils. When the sample holder is filled with different percentages of oil samples, the resonance frequency of the MTM-based SRR sensor shift significantly.

AllianceBlockchain in the Governance Innovation of Internet Hospitals.

The rise of Internet hospitals has significant issues associated with data security and governance in managing sensitive patient data. This paper discusses an alliance blockchain (i.e.

Solution Casting Effect of PMMA-Based Polymer Electrolyte on the Performances of Solid-State Electrochromic Devices.

Electrochromic devices (ECDs) are devices that change their optical properties in response to a low applied voltage. These devices typically consist of an electrochromic layer, a transparent conducting substrate, and an electrolyte. The advancement in solid-state ECDs has been driven by the need for improved durability, optical performance, and energy efficiency.

Ultra-compact quintuple-band terahertz metamaterial biosensor for enhanced blood cancer diagnostics.

Cancer and its diverse variations pose one of the most significant threats to human health and well-being. One of the most aggressive forms is blood cancer, originating from bone marrow cells and disrupting the production of normal blood cells. The incidence of blood cancer is steadily increasing, driven by both genetic and environmental factors.

Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation.

Artificial intelligence and robotics are revolutionizing surgical practices by enhancing precision, efficiency, and patient outcomes. With global healthcare systems increasingly adopting AI-driven technologies, the integration of robotics in surgery addresses critical challenges such as surgical accuracy, minimally invasive techniques, and healthcare accessibility. However, disparities in access and ethical concerns regarding automation persist globally, necessitating a balanced discourse on these advancements.

Investigation of electrochromic performances of multicolor VO devices fabricated at low processing temperature.

In recent decades, poorly insulated windows have increased the energy consumption of heating and cooling systems, thus contributing to excessive carbon dioxide emissions and other related pollution issues. From this perspective, the electrochromic (EC) windows could be a tangible solution as the indoor conditions are highly controllable by these smart devices even at a low applied voltage. Literally, vanadium pentoxide (VO) is a renowned candidate for the EC application due to its multicolor appearance and substantial lithium insertion capacity.

LBF-MI: Limited Boolean Functions and Mutual Information to Infer a Gene Regulatory Network from Time-Series Gene Expression Data.

Background: In the realm of system biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time-series gene expression dataset. However, most of these techniques pose scalability concerns given their capability to consider only two to three regulatory genes over a specific target gene.

3D highly isolated 6-port tri-band MIMO antenna system with 360° coverage for 5G IoT applications based machine learning verification.

A multipurpose antenna system that can handle a broad area of frequencies is crucial in the effort to build up widespread 5G Internet-of-Things (IoT) networks. For fifth-generation Internet-of-things applications, this research introduces a new multi-band antenna that can operate in the sub-6 GHz band (2-7 GHz), Ku-band (13-17.5 GHz), and millimeter wave band (25-39 GHz).

Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics.

This study presents the design and analysis of a compact 28 GHz MIMO antenna for 5G wireless networks, incorporating simulations, measurements, and machine learning (ML) techniques to optimize its performance. With dimensions of 3.19 λ₀ × 3.

Intrusion detection system for V2X communication in VANET networks using machine learning-based cryptographic protocols.

Vehicle-to-everything (V2X) communication has many benefits. It improves fuel efficiency, road safety, and traffic management. But it raises privacy and security concerns.

Calcium-atom-modified boron phosphide (BP) biphenylene as an efficient hydrogen storage material.

Porous nanosheets have attracted significant attention as viable options for energy storage materials because of their exceptionally large specific surface areas. A recent study (, 2024, , 33-39) has demonstrated that Li/Na-metalized inorganic BP-biphenylene (b-BP) and graphenylene (g-BP) analogues possess suitable functionalities for hydrogen (H) storage. Herein, we evaluate the H storage performance of alkaline earth metal (AEM = Be, Mg, Ca)-decorated b-BP and g-BP structures based on first-principles density functional theory (DFT) calculations.

Automated programming approaches to enhance computer-aided translation accuracy.

With the continued development of information technology and increased global cultural exchanges, translation has gained significant attention. Traditional manual translation relies heavily on dictionaries or personal experience, translating word by word. While this method ensures high translation quality, it is often too slow to meet the demands of today's fast-paced environment.

Computational approach for counting of SISH amplification signals for HER2 status assessment.

The human epidermal growth factor receptor 2 (HER2) gene is a critical biomarker for determining amplification status and targeting clinical therapies in breast cancer treatment. This study introduces a computer-aided method that automatically measures and scores HER2 gene status from invasive tissue regions of breast cancer using whole slide images (WSI) through silver hybridization (SISH) staining. Image processing and deep learning techniques are employed to isolate untruncated and non-overlapping single nuclei from cancer regions.

4'-(2-Meth-oxy-phen-yl)-2,2':6',2''-terpyridine.

In the title compound, CHNO, the N atoms of the pyridine rings exhibit a typical - arrangement: the dihedral angles between the central pyridine ring and the peripheral rings are 22.24 (4) and 2.38 (4)°.

2-Amino-5-oxo-4-(thio-phen-2-yl)-5,6,7,8-tetra-hydro-4-chromene-3-carbo-nitrile.

The crystal structure of the title compound, CHNOS, reveals two symmetrically independent mol-ecules within the asymmetric unit. Each mol-ecule contains a chromenone core attached to a 2-thio-phene ring, cyano, and amino groups. The 2-thio-phene ring of one of the two mol-ecules in the asymmetric unit was found to be disordered over two positions, with the major component having a site occupancy factor of 0.

Bayesian optimization driven strategy for detecting credit card fraud with Extremely Randomized Trees.

Credit card usage has surged, heightening concerns about fraud. To address this, advanced credit card fraud detection (CCFD) technology employs machine learning algorithms to analyze transaction behavior. Credit card data's complexity and imbalance can cause overfitting in conventional models.

Radio frequency-based human activity dataset collected using ESP32 microcontroller in line-of-sight and non-line-of-sight indoor experiment setups.

This study presents the "ESP32 Dataset," a dataset of radio frequency (RF) data intended for human activity detection. This dataset comprises 10 activities carried out by 8 volunteers in three different indoor floor plan experiment setups. Line-of-sight (LOS) scenarios are represented by the first two experiment setups, and non-line-of-sight (NLOS) scenarios are simulated in the third experiment setup.

A fact based analysis of decision trees for improving reliability in cloud computing.

The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms: AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree).

Busy independent directors and IPO company earnings management: Evidence from China.

This study investigates the effect of busy independent directors on earnings management in Chinese initial public offering (IPO) companies from 2010 to 2020. Using various measures of busy independent directors, the results indicate that directors in IPO companies play a significant role in supervision and governance, thereby significantly mitigating earnings management. Busy independent directors, serving on multiple boards, may face time constraints, but, intriguingly, companies with such directors show a lower inclination for earnings manipulation.

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images.

Viruses are submicroscopic agents that can infect other lifeforms and use their hosts' cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses are highly adaptable, resilient, and capable of causing severe complications in their hosts' bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological threat both animal and plant species face.

Optoelectronic performance prediction of HgCdTe homojunction photodetector in long wave infrared spectral region using traditional simulations and machine learning models.

This research explores the design of an infrared (IR) photodetector using mercury cadmium telluride (HgCdTe). It proposes two- and three-dimensional homojunction models based on p-HgCdTe/n-HgCdTe, focusing on applications in the long-wavelength infrared range. The photodetector's performance is analyzed using Silvaco ATLAS TCAD software and compared with analytical calculations based on drift-diffusion, tunneling, and Chu's approximation techniques.