Dongjie Tan¹, Xiaoyu Yang², Xinxin Cao^3,*

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081510 - 24 August 2026

Abstract Interfacial adhesion failure is the primary limiting factor in the long-term durability of epoxy-emulsified asphalt micro-surfacing pavements. However, while digital image correlation (DIC) has been extensively applied to evaluate the bulk fatigue of traditional hot-mix asphalt and concrete, its specific application to the complex bi-material interface between rigid concrete substrates and cold-mixed, thermosetting epoxy-asphalt overlays remains limited. Consequently, current research lacks real-time data on full-field strain evolution and the transitional damage localisation mechanisms during dynamic fatigue processes under extreme temperature gradients. To this goal, three-point bending fatigue tests were performed at various temperatures (ranging from… More >

Xinzhan Li¹, Haixin Sun², Li Li^1,3,*, Zongjin Li³, Guangming Xie⁴, Guangzhao Li⁴

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081014 - 24 August 2026

Abstract To utilize waste clay bricks and reduce carbon emissions, recycled brick powder (RBP) was prepared from waste brick-concrete structures and used to produce alkali-activated slag-recycled brick powder foam concrete (ASRFC). This paper evaluated the impact of RBP replacement rates and water-binder ratio on the physical and mechanical properties of foam concrete, including its thermal conductivity, strength, and pore structure. The results demonstrated that the addition of 10% RBP resulted in decreases in the apparent density and thermal conductivity of ASRFC, while flexural strength and the flexural-compressive strength ratio exhibited significant increases. These phenomena can all… More >

Crystal Su¹, Barrett Carley¹, Casandra Gardner¹, Andrea Shepherd¹, Adrian Wagg^2,*

Canadian Journal of Urology, Vol.33, No.4, pp. 893-902, 2026, DOI:10.32604/cju.2026.079016 - 21 August 2026

Abstract Backgrounds: Urinary incontinence (UI) is increasingly prevalent, particularly in older adults. UI management is frequently under-resourced and is associated with the development of potentially avoidable complications, placing a significant burden on the patient and on the healthcare system. This study aimed to estimate the cost of UI-related complications in Canada. Methods: We conducted a comprehensive literature review using PubMed, Canadian Institute for Health Information (CIHI) data and citation mining to identify the prevalence of UI, UI-related complications, and associated treatment costs across each of acute care, long-term care (LTC), homecare and self/family care. UI-related complications… More >

Xiaokai Shi, Li Zuo^*

Canadian Journal of Urology, Vol.33, No.4, pp. 851-864, 2026, DOI:10.32604/cju.2026.074350 - 21 August 2026

Abstract Background: Interstitial cystitis (IC) is a chronic condition characterized by frequent urination, urgency, and pelvic pain. While its pathogenesis remains incompletely understood, the prevailing epithelial theory suggests that a defective glycosaminoglycan (GAG) layer increases bladder permeability, allowing urinary toxins to chronically stimulate the detrusor muscle and activate mast cells. Current therapies, such as intravesical hyaluronic acid instillation, have demonstrated limited efficacy. This study aimed to evaluate the feasibility of sterile recombinant human collagen type III (rHCIII) in mitigating bladder injury and its histological effects on urothelial integrity. Method: An IC rat model was established using… More >

Merve Sinem Karahan^*, Mehmet Karaköse

Journal of Quantum Computing, Vol.8, pp. 101-121, 2026, DOI:10.32604/jqc.2026.078969 - 21 August 2026

Abstract Accurate prediction of carbon emissions is essential for developing sustainable environmental policies and mitigating global warming. Road transportation represents one of the major sources of global CO₂ emissions due to its dependence on fossil fuels. This study presents a comparative framework that evaluates classical machine learning models alongside a hybrid quantum–classical learning architecture for vehicle-based CO₂ emission prediction. A large-scale vehicle emissions dataset containing 7385 samples collected over approximately seven years was obtained from the official open-data platform of the Government of Canada. Key vehicle characteristics, including engine size, fuel consumption, transmission type, and vehicle class,… More >

Bala Thripura Akasam^*

Journal of Cyber Security, Vol.8, pp. 525-539, 2026, DOI:10.32604/jcs.2026.085438 - 21 August 2026

Abstract The widespread adoption of Artificial Intelligence (AI) coding assistants across enterprise software development teams has accelerated delivery velocity while simultaneously introducing a persistent and empirically documented security quality gap in the code these tools produce. Vulnerability classes including insecure output handling, prompt injection constructs, sensitive information disclosure patterns, and cryptographic misuse appear at elevated rates in AI-generated output regardless of model advancement, while organizational governance frameworks have failed to keep pace with the speed of AI tool deployment, creating conditions in which vulnerable code reaches production through informal risk acceptance rather than accountable remediation processes.… More >

Praise Emeka Nze^*, Adeniran Kolade Ademuwagun, Muktar Bello, Fortune Daberechi Ifeanyi, Samaila Musa Abdullahi, John Tighil

Journal of Cyber Security, Vol.8, pp. 469-486, 2026, DOI:10.32604/jcs.2026.083943 - 21 August 2026

Abstract The rapid evolution of adversarial cyber threats demands proactive, scalable security testing methodologies capable of producing realistic, organization-specific attack scenarios. Conventional approaches, including manual red-teaming, scripted Breach and Attack Simulation (BAS) platforms, and tabletop exercises, are constrained by high expert dependency, limited scenario variability, and an inability to dynamically adapt to an organization’s unique threat profile. This paper proposes and evaluates a Large Language Model (LLM)-Assisted Threat-Driven Testing System that integrates the MITRE Adversarial Tactics, Techniques, and Common Knowledge (MITRE ATT&CK) framework v14, a structured knowledge base of adversarial tactics, techniques, and procedures (TTPs), with… More >

Darlington Okeke^*

Journal of Cyber Security, Vol.8, pp. 487-523, 2026, DOI:10.32604/jcs.2026.080111 - 21 August 2026

Abstract Background: Novel cyber threats to organizations have greatly escalated due to the high digitalization rates of organizations, and cybersecurity readiness is a critical capability of organizations and not a technical issue. Although there is increased awareness, most organizations are ill-equipped due to weaknesses in human behavior, governance, leadership commitment, technology infrastructure, and incident response mechanisms. This paper discusses organizational factors that play a major role in cybersecurity readiness. Methods: Primary data from 230 participants were collected using a questionnaire approach and analyzed using IBM SPSS software. The quantitative methods adopted include descriptive statistics, Cronbach’s reliability More >

Hantian Zhang¹, Wentai Wu^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086343 - 13 August 2026

Abstract Large language models (LLMs) are increasingly used in retrieval-augmented generation (RAG) systems, where they are expected to answer questions based on retrieved evidence. In many cases, however, the right behavior is not to answer. A model should abstain when the evidence is insufficient, irrelevant, or contradictory. Existing evaluations mainly focus on final-answer accuracy, and they often pay less attention to whether models can recognize evidence quality before responding. To study this problem, we propose the Evidence Sufficiency Benchmark, a five-level benchmark for evaluating answer-abstention calibration. The benchmark covers evidence conditions from L1 Full Support to… More >

Yuqing Cao, Xiliang Chen^*, Legui Zhang^*, Jun Lai, Haoyang Dong, Xuefei Sun, Xiaoyan Wang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086261 - 13 August 2026

Abstract The design of reward functions is crucial to the success of reinforcement learning, yet the process often relies on expert experience and is difficult to debug. Although large language models (LLMs) offer new opportunities for automated reward design, existing methods still face challenges such as poor interpretability, inability to reuse knowledge, and optimization blindness. To address these issues, this paper proposes a method for structural decoupling and knowledge reuse evolution, referred to as SD-KRE. Its core lies in treating the reward function as a composition of multiple structured units with clear semantics and functionally decoupled… More >