Jalpa Thakkar¹, Siddharth Shankar Mishra², V. Shanmugapriya³, Mohan Kolhe^4,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.078267 - 18 June 2026

Abstract The big challenge in developing wind energy over the past century, which has focused on environmentally friendly production methods to meet the requirements of modern power systems, is the need for holistic architectures that can cope with variability, connection issues, and sector coupling far beyond conventional electricity-only models. This review offers a critically synthesized, process-level overview of progressive wind–hydrogen hybrids, offering a collective view of advancements in electrical layouts, hydrogen-driven conversion routes, and AI-driven control schemes. In contrast to previous surveys that consider these areas in isolation, we provide an explicit examination of the technical… More >

Raquel Sanchez-Baltasar¹, Nerea Castañeda-Fernández¹, Jorge Olivares-Arancibia², Carlos Torres-Villar^3,4, Julio Plaza-Diaz^5,6,7,8,*, Lourdes Herrera-Quintana^1,*

Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.081924 - 16 June 2026

Abstract Breast cancer (BC) is the most frequently diagnosed malignancy in women worldwide and remains one of the leading causes of cancer-related mortality, with substantial international disparities in incidence, stage at diagnosis, access to treatment, and survival. In recent years, BC management has evolved rapidly through advances in molecular characterization, imaging, pathology, targeted therapies, immunotherapy, and survivorship care. Nevertheless, important gaps persist in early and accurate detection, biomarker standardization, equitable access to care, and patient-specific treatment selection. These advances require timely, evidence-based, and context-specific clinical frameworks to support appropriate implementation, and to avoid the use of… More >

Zhihao Zhang^1,#, Zhuodong Liu^1,#, Xiangyu Li², Lei Zhang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081922 - 15 June 2026

Abstract Financial fraud detection across institutions faces a fundamental tension between the need for diverse training data and regulatory prohibitions on sharing sensitive records. Existing federated learning approaches suffer from performance degradation under non-IID distributions and substantial utility losses when uniform differential privacy is applied to inherently sparse fraud signals. To this end, this paper proposes HiFraud, a hierarchical federated framework featuring three key components: fraud-aware dynamic clustering with complementarity regularization to group institutions by fraud pattern similarity while preserving rare-type representation; star-chain knowledge transfer augmented by not-true-class distillation to propagate novel fraud patterns rapidly within… More >

Geunhwi Park¹, Juneyoung Park^2,*, Chunjoo Yoon³, Jaehong Park³

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081652 - 15 June 2026

Abstract Urban flooding caused by extreme rainfall events disrupts transportation systems, yet generating realistic flood-traffic scenarios for disaster preparedness remains a labor-intensive manual process. This study proposes a Knowledge Graph (KG)-driven pipeline that automatically generates domain-specific training data for fine-tuning small language models (sLLMs) to synthesize urban flood-traffic scenarios. A domain KG comprising 58 entities and 285 relationships was constructed for Jinju City, South Korea, integrating empirical flood data from 112 local documents with quantitative rainfall-traffic impact values from 14 international studies. Nine domain constraint rules, including a novel spatial consistency rule, ensure the physical plausibility… More >

Akmalbek Abdusalomov¹, Kudratjon Zohirov², Sojida Ochilova², Jakhongir Oramov³, Zafar Ruziyev³, Malika Rustamova⁴, Gulrukh Sherboboyeva⁵, Komil Tashev^6,7, Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081292 - 15 June 2026

Abstract Unmanned aerial vehicles (UAVs) are also increasingly becoming more often in the transportation infrastructure of smart cities, so that they can successfully achieve real-time observation of traffic, emergency coordination, and two-way communication relaying. However, the security and privacy risks arising in open, highly mobile intelligent transportation systems (ITS) enabled by UAVs are critical, as they pose threats of impersonation, replay, Sybil, and tracking attacks. Secondly, standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity. To address these challenges, this study introduces a Generative-AI-assisted… More >

Sheyun Zhang, Ruichun Gu^*, Chaofeng Li, Zhijian Dong, Hefei Wang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081065 - 15 June 2026

Abstract Federated learning (FL) enables collaborative model training without sharing raw data. However, in real-world applications, clients often exhibit statistical heterogeneity, missing classes, and long-tailed distributions, which can substantially degrade the generalization performance of conventional parameter aggregation and some personalization approaches. Moreover, distillation or alignment-based methods may suffer from unstable supervision and difficult optimization under highly heterogeneous settings. To this end, this paper proposes a novel method called FKD-RTM (Heterogeneous Federated Knowledge Distillation Based on Residual-Enhanced Tree-to-MLP Knowledge Transfer). The key idea is to decouple local teaching from globally aggregatable student learning: we introduce a Gradient… More >

Bhanu Talwar^1,*, Puneet Thapar¹, Tahani Alsubait², Mai Alduailij³, Ateeq Ur Rehman^4,*, Salil Bharany⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080977 - 15 June 2026

Abstract Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates… More >

Meihuizi Jia^1,*, Hongyan Ran¹, Shanshan Li²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080333 - 15 June 2026

Abstract Large language models (LLMs) have demonstrated considerable ability in solving various tasks via Chain-of-Thought (CoT) prompting, which has precipitated extensive research into their application for complex mathematical reasoning problems. However, current research on mathematical reasoning with CoT predominantly focuses on textual mathematical tasks, such as math word problems, while paying limited attention to multimodal geometric scenarios. To bridge this gap, we propose KG-HoT, a model that harnesses the generative and comprehension capabilities of Multimodal large language models (MLLMs) to enhance complex geometric problem-solving in multimodal systems. Our knowledge-grounded approach enables MLLMs to generate hybrid chains-of-thought More >

Huaixuan Yan, Yan Gong^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080178 - 15 June 2026

Abstract To correctly diagnose faults in farm machinery, we need to know a lot about the field and have experience with maintenance. However, most of this important information is stored in old, unstructured documents like technical manuals and expert logs. These documents don’t have a standard way to be represented digitally, which makes it very hard to build automated diagnosis systems. There are three main technical problems with getting structured knowledge out of this kind of text: noise from optical character recognition (OCR) during digitization, the extreme lack of labeled samples in specialized fields (low-resource constraints),… More >

Isam Bahaa Aldallal¹, Saadaldeen Rashid Ahmed^2,3, Abdullahi Abdu Ibrahim¹, Oguz Bayat⁴, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078742 - 15 June 2026

Abstract Urban parking problems worsen traffic jams, gas use, and pollution. Old parking systems often lack up-to-date space information, which annoys drivers and wastes their time. This research presents a smart IoT-enabled real-time space monitoring and booking system applicable to both urban parking management and Smart Manufacturing logistics environments, including loading bay coordination and Automated Guided Vehicle (AGV) docking station management. The system employs ultrasonic and IR sensors, managed by an Arduino UNO, to identify vehicles and track space availability. A servo-motor regulates entry. Slot data is presented on a Liquid Crystal Display screen and accessible More >