Andrea De Matthaeis¹, Laura Rehak^2,*, Maria Bianchi³, Rossana Putzulu³, Nicola Piccirillo^3,4, Giulio Maccauro¹

BIOCELL, Vol.50, No.5, 2026, DOI:10.32604/biocell.2026.073783 - 13 May 2026

Abstract For over two decades, mesenchymal stem cells (MSCs) have been recognised as the cornerstone of orthobiologic treatments for musculoskeletal diseases. However, clinical evidence increasingly indicates that MSC engraftment in inflamed tissues is minimal and transient, with effects mainly driven by paracrine and immunomodulatory mechanisms induced by macrophage efferocytosis. This evolving paradigm emphasises the immune system as the central orchestrator of tissue repair. Peripheral blood mononuclear cells (PBMNCs) have emerged as potent effectors of regenerative inflammation, mediating apoptotic cell clearance through efferocytosis, facilitating the transition of macrophages from pro-inflammatory (M1) to reparative (M2) phenotypes, and releasing… More >

Akash Garg^*

Journal on Artificial Intelligence, Vol.8, pp. 273-298, 2026, DOI:10.32604/jai.2026.079401 - 12 May 2026

Abstract As cyber threats continue to evolve in scale and sophistication, the need for intelligent and adaptive security mechanisms has become increasingly urgent. Intrusion Detection Systems (IDS) are critical components in safeguarding computer networks from malicious activities. This review paper presents a comprehensive analysis of recent advancements in deep learning-based IDS, examining various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). The study compares traditional intrusion detection techniques with modern deep learning approaches, highlighting their strengths, limitations, and suitability for real-world deployment. Special attention is given to… More >

Shan Yuan^1,2, Fei Wang^2,*, Jinping Zhang^2,*, Yuxin Jiang², Kaibo Gu², Chenhao Qiao², Yutong Bai², Jie Yu², Quan Chen¹, Dedi Han³

Chalcogenide Letters, Vol.23, No.4, 2026, DOI:10.32604/cl.2026.082805 - 09 May 2026

Abstract Sodium metal batteries stand as a highly promising electrochemical energy storage system; however, their commercialization is severely impeded by challenges such as anode dendrite formation, the shuttle effect of highly reactive intermediates at the cathode, electrode volume expansion, and interfacial instability. Owing to their high electronic conductivity, high theoretical specific capacity, and superior sodiumphilic affinity, tellurium and its tellurides have emerged as pivotal functional materials for enhancing the performance of sodium metal batteries. This study reviews the advancements in their applications within sodium metal batteries, elaborates rational design strategies carbon-based composites, alloying, and heterostructure construction More >

Minhyeok Choi¹, Imran Ahsan², Hyunwook Yu¹, Taeyoung Choe¹, Mucheol Kim^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079504 - 08 May 2026

Abstract Large language models (LLMs) are increasingly integrated into recommender systems to support semantic reasoning, natural language understanding, and user-adaptive personalization. However, their reliance on static parametric knowledge and fixed representations limits robustness in dynamic environments, particularly under long-tail and cold-start conditions. Retrieval-augmented architectures have emerged to address these limitations by grounding LLMs in external, non-parametric knowledge sources. This systematic literature review synthesizes 138 peer-reviewed studies published between 2023 and 2025 in conferences and journals, focusing on retrieval-augmented and LLM-enhanced recommendation. We analyze these works through a three-dimensional framework covering: (i) domain application, (ii) semantic feature… More >

Yutong Guo^1,2, Jianying Yang^1,3, Chao Yang^1,3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077820 - 08 May 2026

Abstract Large language models (LLMs) and related foundation-model workflows are emerging as promising tools for advancing foundry intelligence across the casting value chain. This review examines their applications in material design and property prediction, process parameter optimization and intelligent control, and defect detection and quality tracing in casting environments. The surveyed studies indicate that LLM-enabled systems can help integrate unstructured technical knowledge with multimodal industrial data. This integration supports composition design, simulation-assisted process optimization, diagnostic reasoning, and knowledge-grounded decision support. However, current evidence shows that the transition from pilot demonstrations to robust industrial deployment remains constrained More >

Muhammet Ali Karabulut¹, A. F. M. Shahen Shah², Al-Sakib Khan Pathan^3,*, Phillip G. Bradford⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077625 - 08 May 2026

Abstract Today, technological progress is broad and deep. The next generation networks and systems will integrate features, technologies, and models requiring smooth cooperation between new and old technologies. This survey’s uniqueness is that it considers an integrated, hybrid and heterogeneous future where Internet of Things (IoT), Sixth-Generation (6G) mobile communications technology, and Artificial Intelligence (AI) will work together, providing a smart and connected Intelligent Transportation System (ITS). This smart ITS will give better road safety and optimized travel. Currently, there is a scarcity of surveys focusing particularly on smart ITS that is expected soon. In this More >

Wei-Chen Tang, Xin Chen, Fei Dong^*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.4, 2026, DOI:10.32604/fdmp.2026.079861 - 07 May 2026

Abstract Boiling heat transfer remains a cornerstone of efficient thermal management, with far-reaching implications for energy systems and industrial processes. Advances in this field not only deepen fundamental scientific understanding but also enable transformative improvements in energy efficiency, equipment performance, and operational safety. Contemporary research in this area focuses on accurate parameter prediction, intelligent image analysis, and quantitative characterization of bubble dynamics, collectively advancing both mechanistic insight and engineering optimization. In this context, artificial intelligence (AI), encompassing machine learning and deep learning techniques, has emerged as a powerful paradigm, offering significant advantages in predictive accuracy, data-driven… More >

Gürcan Durmaz^1,*, Gökhan Gürlek²

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2026.075332 - 30 April 2026

Abstract This study examines the evolution of condenser technologies in household refrigerators, focusing on the potential for improving energy efficiency with hot-wall condensers (HWCs). Factors influencing this development, including refrigerant changes, consumer expectations, global regulations, and environmental factors, are evaluated. Design features, advantages, disadvantages, limitations, and comparisons with other condenser types are presented for HWCs. The review identifies key parameters affecting HWC performance: pipe diameter and pitch, outer surface material properties, adhesive tape properties, and contact resistances. The effects of environmental factors such as ambient temperatures and heat transfer coefficients are also considered. The results indicate… More > Graphic Abstract

Matthew Starr¹, Lori Unruh-Snyder^1,*, Luke Gatiboni¹, Koralalage Jayaratne²

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.072898 - 28 April 2026

Abstract Biostimulants, categorized as microbial or non-microbial, including humic substances, seaweed extracts, chitosan, or protein hydrolysates (PHs), have gained significant attention in modern agriculture for their ability to enhance crop productivity, improve nutrient use efficiency, and increase resilience to abiotic and biotic stresses, while reducing dependence on conventional agrochemicals. This review synthesizes the historical development, classification, mechanisms of action, and agronomic benefits of biostimulants, with a particular emphasis on PHs, which are mixtures of amino acids, peptides, and polypeptides derived from plant or animal proteins through enzymatic, chemical, or thermal hydrolysis. The concept of biostimulants has… More >

Curwyn Mapaling^*

Journal of Psychology in Africa, Vol.36, No.2, pp. 309-310, 2026, DOI:10.32604/jpa.2026.083289 - 29 April 2026

Abstract This article has no abstract. More >