Browse Articles

Nutritional quality and heavy metal in invasive stone moroko (Pseudorasbora parva) from natural waters and aquaculture ponds

A double-blind, randomized controlled pilot study comparing the safety of intracameral levofloxacin and intracameral cefazolin in patients undergoing cataract surgery

Cancer cell dynamics on silica fibers

Systematic review and meta-analysis of antioxidants with or without exercise training improving muscle condition in older adults

Understanding what really drives trust in the workplace and the importance of trustor characteristics as predictors of co-worker trust

Impact of the tumor microenvironment on survival in anaplastic thyroid carcinoma

Connecting chemical and protein sequence space to predict biocatalytic reactions

Numerical investigation of the influence of air layer on the melting behavior of RT42 PCM in a multi-hexagonal cell

Six journal rejections and a major rethink: why I’m happy to admit to my research failures, and you should too

Intelligent-based AT-SHAPF control for enhanced power quality in SOFC-driven hybrid system

China’s research hospitals push for prominence

Continuous once-through electrooxidation treatment using mixed metal oxide anode for bacterial disinfection

A social network graph partitioning algorithm based on double deep Q-Network

Abstract With the rapid expansion of social networks, efficiently mining and analyzing massive graph data has become a fundamental challenge in social network research. Graph partitioning plays a pivotal role in enhancing the performance of such analyses. However, conventional graph partitioning methods predominantly rely on local structural information and often overlook the rich attribute information associated with vertices in social network graphs. To overcome this limitation, this paper introduces GP-DQN (Graph Partitioning via Double Deep Q-Network), a large-scale graph partitioning algorithm that jointly considers structural correlations, attribute disparities among user vertices, and partition load balancing. GP-DQN encodes partition load metrics and vertex attributes into vector representations and employs a Graph Convolutional Network (GCN) to aggregate both vertex features and neighborhood structures, thereby improving the accuracy and scalability of the partitioning process. A tailored reward function is designed to guide partitioning actions, where a Double Deep Q-Network (DDQN) predicts the expected partitioning rewards based on GCN-extracted features for assigning each vertex to different partitions. The partitioning strategy is iteratively optimized using both immediate and expected rewards, ultimately achieving balanced load distribution while minimizing the number of edge cuts. Experimental results demonstrate that GP-DQN produces well-balanced partitions with significantly fewer edge cuts, leading to enhanced computational efficiency within each partition.

Spin squeezing in an ensemble of nitrogen–vacancy centres in diamond

Exploration of isolated actives from Coleus amboinicus leaves as anticancer agents: in vitro testing, network pharmacology studies, and molecular docking

Thickness configuration optimization of B4C/UHMWPE composite armor under varying impact velocities and areal densities through numerical and experimental study

Biofilm production and virulence traits among extensively drug-resistant and methicillin-resistant Staphylococcus aureus from buffalo subclinical mastitis in Bangladesh

Abstract Methicillin-resistant Staphylococcus aureus (MRSA) is a critical pathogen implicated in subclinical mastitis (SCM), a hidden threat to dairy productivity. This study investigated the prevalence, antibiotic resistance profiles, and virulence traits of MRSA from SCM-affected riverine buffaloes in Jamalpur, Bangladesh. A total of 344 milk samples were screened using the California Mastitis Test (CMT) and Modified Whiteside Test (MWST). Among the milk samples, 46.5% were positive for SCM by CMT. Culture, biochemical tests, and PCR confirmed 73 (21.2%) Staphylococcus spp., of which 30 (41.1%) were identified as S. aureus and 43 (58.9%) as non-aureus staphylococci (NAS). Among the 30 S. aureus -positive isolates, 10 (33.3%) were identified as methicillin-resistant S. aureus (MRSA), corresponding to a prevalence of 2.9% among the total milk samples. The MRSA isolates exhibited high multidrug resistance, especially to tetracycline (80%) and cefoxitin (80%), and commonly harbored resistance genes such as tetA (80%), aac(3)-iv (70%), and sul1 (50%). Virulence genes hla (66.7%) and sea (50%) were frequently detected, while icaA was found in 23.3% of MRSA. Notably, 60% of MRSA isolates were categorized as XDR based on international standard definitions, while 60% were biofilm producers with high MARI values up to 0.92, indicating severe resistance potential. These findings underscore a significant burden of MDR/XDR MRSA with virulence potential in buffalo SCM, posing serious risks to animal and public health.

Cellular automata based key distribution for lightweight hybrid image encryption with elliptic curve cryptography

Comparative study of the chemical profile, cytotoxic activity, and molecular docking of Serenoa repens extract and a pharmaceutical product

An aeroelastic wind energy harvester with continuous orbiting motion and no friction components

Abstract A continuous-movement aeroelastic energy harvester with no friction parts is presented. Different from the commonly used vortex-induced vibration or galloping devices, the proposed energy harvesting system is constructed with a circular arc airfoil mounted to a flexible beam that follows a closed trajectory rather than oscillating linearly. The continuous motion of the airfoil results in the flow being fully attached, resulting in a greater efficiency than that of conventional oscillating wind energy harvesters. Experimental and numerical investigation has been conducted with the efficiency of energy conversion by the main element, the blade, measured to reach 3.5%.