Wenjie Zhou^#, Jiawei Ding^#, Danfeng Xu^*

Oncology Research, Vol.33, No.11, pp. 3493-3522, 2025, DOI:10.32604/or.2025.066783 - 22 October 2025

Abstract Objectives: Despite the fact that prostate cancer is one of the most common tumors in men, this study intends to evaluate the predictive significance of immune and metabolic genes in prostate cancer using multi-omics data and experimental validation. Methods: The research developed and validated a prognostic model utilizing The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, integrating immune and metabolic gene sets. Additionally, the prognostic gene Adenylate Kinase 5 (AK5) was analyzed in prostate cancer tissue microarrays from Ruijin Hospital. The functional role of the AK5 gene was validated through knockdown and… More >

Jeyapaul Angel Ida Chellam¹, Bright Brailson Mansingh², Daniel Stalin Alex³, Joseph Selvi Binoj^4,*

Journal of Polymer Materials, Vol.42, No.3, pp. 773-794, 2025, DOI:10.32604/jpm.2025.069295 - 30 September 2025

Abstract Machine learning models can predict material properties quickly and accurately at a low computational cost. This study generated novel hybridized nanocomposites with unsaturated polyester resin as the matrix and Areca fruit husk fiber (AFHF), tamarind fruit fiber (TFF), and nano-sized coconut shell powder (NCSP). It is challenging to determine the optimal proportion of raw materials in this composite to achieve maximum mechanical properties. This task was accomplished with the help of ML techniques in this study. The tensile strength of the hybridized nanocomposite was increased by 134.06% compared to the neat unsaturated polyester resin at… More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

J. S. V. Siva Kumar¹, Mahmad Mustafa², Sk. M. Unnisha Begum³, Badugu Suresh⁴, Rajanand Patnaik Narasipuram^5,*

Energy Engineering, Vol.122, No.10, pp. 3891-3904, 2025, DOI:10.32604/ee.2025.070052 - 30 September 2025

Abstract Electric vehicle (EV) monitoring systems commonly depend on IoT-based sensor measurements to track key performance parameters such as vehicle speed, state of charge (SoC), battery temperature, power consumption, motor RPM, and regenerative braking. While these systems enable real-time data acquisition, they are often hindered by sensor noise, communication delays, and measurement uncertainties, which compromise their reliability for critical decision-making. To overcome these limitations, this study introduces a comparative framework that integrates reference signals, a digital twin model emulating ideal system behavior, and real-time IoT measurements. The digital twin provides a predictive and noise-resilient representation of More >

Tayfun Abut^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4023-4042, 2025, DOI:10.32604/cmc.2025.066920 - 23 September 2025

Abstract In this study, a dynamic model for an inverted pendulum system (IPS) attached to a car is created, and two different control methods are applied to control the system. The designed control algorithms aim to stabilize the pendulum arms in the upright position and the car to reach the equilibrium position. Grey Wolf Optimization-based Linear Quadratic Regulator (GWO-LQR) and GWO-based Fuzzy LQR (FLQR) control algorithms are used in the control process. To improve the performance of the LQR and FLQR methods, the optimum values of the coefficients corresponding to the foot points of the membership… More >

Yun Wang^1,2,#, Xin-Yu Li^1,3,#, Sheng-Li Wu^1,3, Pianchou Gongpan¹, Da-Hong Li², Chang-An Geng^1,3,*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2563-2574, 2025, DOI:10.32604/phyton.2025.068185 - 29 August 2025

Abstract Twelve lignans (1–12) isolated from Amomum tsao-ko leaves were evaluated for the inhibitory effects against α-glucosidase and PTP1B. Compounds 1−4 and 10 showed inhibition on α-glucosidase with inhibitory ratios ranging from 53.8% to 90.0%, while compound 10 demonstrated 56.1% inhibition on PTP1B at 200 μM. Notably, erythro-5-methoxy-dadahol A (2) and threo-5-methoxy-dadahol A (3) displayed obvious inhibition on α-glucosidase with IC₅₀ values of 33.3 μM and 22.1 μM, significantly outperforming acarbose (IC₅₀ = 344.0 μM). Kinetic study revealed that compound 3 maintained a mixed-type mode, engaging with both free enzyme and enzyme-substrate complex via non-competitive and uncompetitive mechanisms. Molecular docking simulations further clarified its More >

Wenda Ma, Qizhi Tang^*, Huang Lei, Longfei Chang, Chen Wang

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1265-1286, 2025, DOI:10.32604/sdhm.2025.065294 - 05 September 2025

Abstract Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring (SHM). However, traditional methods struggle to address the reconstruction of acceleration responses with complex features, resulting in a lower reconstruction accuracy. This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks (FCN) to achieve precise reconstruction of acceleration responses. In the designed network architecture, the incorporation of skip connections preserves low-level details of the network, greatly facilitating the flow of information and improving training efficiency and accuracy. Dropout techniques are employed to reduce… More >

Rwaa Omer Ali Ahmed¹, Salaheldin Farah Attallah Bakhiet^2,*, Ayman Mohamed Taha Abdelaziz Ahmed¹, FadlAlMawla AbdulRadi¹, Ismael Salamah Albursan³

Journal of Psychology in Africa, Vol.35, No.4, pp. 431-439, 2025, DOI:10.32604/jpa.2025.070057 - 17 August 2025

Abstract The study aims to determine the validity and reliability of the Wechsler Preschool and Primary Scale of Intelligence–Third Edition (WPPSI-III) scores in a sample of kindergarten and lower primary pupils from Khartoum State, Sudan. It also aims to examine whether test’s factor structure in this sample replicated that of the original WPPSI-III. The study sample consisted of 384 kindergarten and primary school children in Khartoum State (females = 50% mean age = 4.14, SD = 1.37), selected using stratified random sampling across its seven localities: Khartoum, Jebel Awliya, Khartoum Bahri, East Nile, Omdurman, Ombada, Karari.… More >

Mahmood Alsaadi, Mohammed E. Seno^*, Mohammed I. Khalaf

Intelligent Automation & Soft Computing, Vol.40, pp. 29-52, 2025, DOI:10.32604/iasc.2024.060390 - 10 January 2025

Abstract The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, More >

Tabassum Kanwal¹, Saif Ur Rehman^1,*, Azhar Imran², Haitham A. Mahmoud³

Computer Systems Science and Engineering, Vol.49, pp. 185-212, 2025, DOI:10.32604/csse.2024.056535 - 10 January 2025

Abstract This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70%… More >