Browse Articles

Comparative analysis of different biophysical techniques for exosome characterization

A low-complexity compact dual-polarized patch antenna with high isolation and low cross-polarization for in-band full-duplex applications

Abstract This paper proposes a simple design of a dual-polarized microstrip patch antenna featuring compact dimensions, low cross-polarization, and high port isolation, specifically designed for full-duplex wireless applications. The antenna is implemented on a single-layer substrate, and size reduction is achieved through a capacitive loading technique. Additionally, capacitive loading also contributes to increasing polarization purity by suppressing cross-polarization radiation. A fabricated prototype with an overall size of $$0.18\lambda \times 0.18\lambda \times 0.03\lambda$$ at 5.6 GHz demonstrates stable performance across the frequency range of 5.53–5.62 GHz. Within this band, the antenna attains an isolation level exceeding 38 dB, a maximum broadside gain of approximately 4.3 dBi, and low cross-polarization radiation of less than $$-40$$ dB. Compared to other related compact dual-polarized designs, the proposed antenna exhibits the best performance in terms of isolation and cross-polarization suppression, featuring a compact and simple structure.

Regional tissue oxygenation during high-intensity exercise following voluntary isocapnic hyperpnea versus inspiratory threshold loading in endurance–trained individuals: a randomized controlled trial

Bond of ribbed and threaded steel reinforcement bars post-installed in concrete considering bonded length and adhesive type

Exploring the importance of shape on dynamic recognition of self-face or friend-face

Numerical study on the performance of a forced ventilation system under hydrogen leakage in an underground hydrogen equipment room

A hybrid machine learning approach for predicting the flexural strength of concrete reinforced with waste aluminium fibres

Scenario based traffic optimization in Egypt performance gains through simulation modeling

Abstract Rapid urbanization and the growing number of vehicles in Mansoura, Egypt, have exacerbated traffic congestion, underscoring the need for efficient urban traffic management solutions. This study evaluates traffic control strategies at Umm Kulthum Square using the SUMO (Simulation of Urban Mobility) software. Two scenarios were analyzed: Scenario 1 represents existing traffic conditions, with road geometry and lane counts adjusted to real-world dimensions derived from satellite imagery, while Scenario 2 applies optimized lane connections, turning movements, and traffic signal configurations, including signal locations, phase durations, and signal states. Simulations were conducted for 999 s and 3599 s to evaluate traffic performance, queuing behavior, emissions, fuel consumption, and noise levels. Compared with Scenario 1, Scenario 2 reduced average departure delay from 6.02 to 5.54 s at 999 s and from 32.63 to 7.08 s at 3599 s. Average travel duration decreased from 414.19 to 349.25 s and from 388.60 to 246.15 s, while waiting time was reduced from 139.90 to 100.90 s and from 288.21 to 143.70 s, respectively. The number of completed trips increased from 262 to 317 vehicles at 999 s and from 672 to 715 vehicles at 3599 s. Environmental performance also improved, with notable reductions in CO₂, CO, HC, NOx, and PMx emissions, as well as fuel consumption and noise levels. Additionally, queuing time and queue lengths decreased, indicating smoother traffic flow. These results demonstrate that traffic signal optimization and intersection design improvements can significantly enhance traffic efficiency and reduce environmental impacts at congested urban intersections in Mansoura.

A three-level discretization framework for dynamic behaviors of graphene-coated rotational blades with preset-angle sandwich structure under complex loads

The oncogenic effect and mechanism of LINC00520/miR-372/SLC7A11 axis in renal clear cell carcinoma

Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection

Confidence-calibrated federated graph attention for internet of things agents under latency SLOs

Structure elucidation and evaluation of the antimicrobial and antitumor activities of 5-methylthiazole-based Schiff base and its metal chelates

Abstract The current thiazole Schiff base ligand was obtained upon reacting 2-amino-5-methylthiazole with 2,4-dihydroxybenzaldehyde ( H 2 L ). The transition metal chelates then were presented by reacting the ligand with Mn(II), Cu(II), and Zr(IV) salts in 1:1 molar ratio forming chelates, 1 , 2 and 3 , respectively. The reaction of the ligand with Cd(CH₃COO)₂ afforded Cd(II) complex, 4 , in the molar ratio 2L:1 M. The type of contact that occurs between metal ions and the thiazole ligand was investigated by the assistance of magnetic susceptibility, FTIR, mass, UV–Visible and 1 H NMR spectral and micro-elemental analyses. The resulting data demonstrated that the ligand–metal interaction happened via the phenolic O-atom ortho -position and the imine group N- atom which is dehydrogenated to form the metal complexes. Solvent molecules attached to the metal ions in complexes, whether present or absent were suggested using TGA in addition to thermodynamic activation parameters for the thermal decomposition steps were computed by the Eyring equation. Magnetic moment and UV–Visible measurements indicated formation of square planar Cu(II) chelate and octahedral Zr(IV) chelate, while the Mn(II) and Cd(II) ions formed tetrahedral metal chelates. XRD and TEM used to reveal structural microcrystalline data of both the organic compound ( H 2 L ) and inspected complexes. The patterns of X‐ray diffraction introduced the crystalline nature of each of the free ligand, complexes 1 , 3 and 4 , whereas complex 3 was relatively amorphous without an even dispersion of the solid constituents throughout the precipitation procedure. The study rigorously analyzed molecular structures using Density Functional Theory (DFT) calculations, revealing significant variations in ligand bond lengths and angles upon complexation, and demonstrating the electron-donating and accepting properties of HOMO and LUMO orbitals. Biological activity for the prepared compounds was accomplished. They exhibited high activity, especially after chelation. The Antimicrobial activity afforded very auspicious data upon comparison with the applied reference antibiotic. The greatest anti-cancer activity has been achieved by Cu(II)-complex 2 which showed IC 50 value = 16.89 µg/ml against MCF-7 cell lines which is greater than the IC 50 value of scaled drug applied (IC 50 of 5-flurouracil = 28.0 µg/ml). Promising bioactive compounds’ binding affinities with HepG-2 and MCF-7 DNA helices were estimated to apply molecular docking.

Effects of intramuscular quadratus lumborum block on post-cesarean pain: a randomized controlled trial

Comprehensive evaluation of the environmental performance and durability of biochar-incorporated concrete

Biological effects of 5G-modulated 700 MHz RF-EMF exposure on neuronal and glial cell models under isothermal conditions

UTR-DynaPro: a CNN–transformer multimodal language model for decoding 5′UTR regulatory mechanisms

In-situ magnetic field-controlled synthesis of SnO2-SnO nanoparticle films for enhanced photovoltaic self-cleaning and anti-soiling

An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification

Abstract Chest radiography remains an essential diagnostic tool for thoracic diseases, yet interpreting overlapping anatomical structures is particularly challenging when multiple pathologies co-occur a common clinical scenario often oversimplified in deep learning approaches. This study presents an optimized EfficientNetB0 framework designed explicitly for multi-label classification of chest X-rays using the NIH dataset, integrating CLAHE-based contrast enhancement, strategic class balancing, and a comparative transfer learning strategy that preserves the dataset’s inherent multi-label complexity. The proposed model achieved superior diagnostic performance with a macro-average AUC of 0.906 and recall of 0.824, outperforming DenseNet121 and MobileNetV2, and demonstrated strong per-class discrimination, especially for Pneumonia (AUC = 0.950) and Cardiomegaly (AUC = 0.946). These results confirm that the framework effectively balances learning capacity and generalization in a realistic multi-label clinical setting, offering a robust, interpretable solution suitable for computer-aided diagnosis where accurate detection of co-occurring thoracic pathologies is critical.

Analysis of water-permeable fractured zone in weakly cemented overburden considering rock strain-softening