Dong Pan¹, Xu Gui¹, Jiayin Xu¹, Yuming Shen¹, Haoran Xu², Yinghao Ma^2,*

Energy Engineering, Vol.121, No.5, pp. 1309-1328, 2024, DOI:10.32604/ee.2024.046464

Abstract With the increasing urgency of the carbon emission reduction task, the generation expansion planning process needs to add carbon emission risk constraints, in addition to considering the level of power adequacy. However, methods for quantifying and assessing carbon emissions and operational risks are lacking. It results in excessive carbon emissions and frequent load-shedding on some days, although meeting annual carbon emission reduction targets. First, in response to the above problems, carbon emission and power balance risk assessment indicators and assessment methods, were proposed to quantify electricity abundance and carbon emission risk level of power planning scenarios, considering power supply regulation… More >

Pengjun Li¹, Qixin Zhao¹, Yingmin Liu¹, Chao Zhong¹, Jinlong Wang^1,*, Zhihan Lyu²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3825-3865, 2024, DOI:10.32604/cmc.2024.046851

Abstract Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order. Amidst the challenges posed by intricate and unpredictable risk factors, knowledge graph technology is effectively driving risk management, leveraging its ability to associate and infer knowledge from diverse sources. This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios. Firstly, employing bibliometric methods, the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge… More >

Sameh Zarif^1,2,*, Eman Morad¹, Khalid Amin¹, Abdullah Alharbi³, Wail S. Elkilani⁴, Shouze Tang⁵

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3565-3583, 2024, DOI:10.32604/cmc.2024.046185

Abstract Due to the exponential growth of video data, aided by rapid advancements in multimedia technologies. It became difficult for the user to obtain information from a large video series. The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization. This method resulted in rapid exploration, indexing, and retrieval of massive video libraries. We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint (BRISK) and bisecting K-means clustering algorithm. The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the… More >

LU ZHANG^*, JINGUO CHU^*, YUSHAN YU

Oncology Research, Vol.32, No.4, pp. 703-716, 2024, DOI:10.32604/or.2023.030988

Abstract Background: Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC). Methods: We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups… More >

Ashraf S. Mashaleh^1,2,*, Noor Farizah Binti Ibrahim¹, Mohammad Alauthman³, Mohammad Almseidin⁴, Amjad Gawanmeh⁵

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2245-2267, 2024, DOI:10.32604/cmc.2023.047323

Abstract Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership functions. This study presents a… More >

Kristi K. Westphaln^1,2,*, Karen Kay Imagawa^2,3, Lorena Espinosa Smith^1,4, Julia Srivastava⁵, Nancy A. Pike^1,5

Congenital Heart Disease, Vol.18, No.6, pp. 657-670, 2023, DOI:10.32604/chd.2023.044179

Abstract Background: Congenital heart disease (CHD) is a chronic medical condition often diagnosed at birth and requires surgical intervention, multiple hospitalizations, and lifelong care. This can put significant stress on the family, leading to altered maternal mental health, bonding and attachment issues, and the potential for child abuse. The purpose of this study is to explore the characteristics of a sample of young children with CHD who experienced hospitalization with concurrent concern for child abuse in a free-standing pediatric tertiary care hospital. Methods: Electronic medical records were reviewed for children aged 0–5 years old who were hospitalized with concern for child… More >

Gustavo Foronda^1,2, Vanessa Ferreira Amorim de Melo^2,3,*, Claudia Regina Pinheiro de Castro Grau⁴, Ingrid Magatti Piva¹, Glaucia Maria Penha Tavares⁴, Ana Cristina Sayuri Tanaka¹, Nana Miura¹

Congenital Heart Disease, Vol.18, No.6, pp. 649-656, 2023, DOI:10.32604/chd.2023.027987

Abstract Background: Children with congenital heart disease (CHD), even after surgical approaches, and especially those who undergo staged procedures in the first months of life, remain vulnerable to readmissions and complications, requiring very close monitoring and differentiated intervention strategies. Methods: Descriptive and exploratory study, of the experience report type, which presents the process of building the high-risk outpatient clinic for complex congenital heart diseases (AAR) at the Instituto do Coração (InCor). Results: Report of the path taken to structure the AAR, demonstrating the organization, interface with the multidisciplinary team, admission and discharge criteria, training, and patient profile. In these five years… More >

Congjun Rao¹, Mengxi Li¹, Tingting Huang^2,*, Feiyu Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898

Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE)… More >

Hong Sun¹, Fangquan Yang², Peiwen Zhang^3,*, Yang Jiao⁴, Yunxiang Zhao⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2549-2569, 2024, DOI:10.32604/cmes.2023.030131

Abstract With the development of the integration of aviation safety and artificial intelligence, research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management, but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry. Therefore, an improved risk assessment algorithm (PS-AE-LSTM) based on long short-term memory network (LSTM) with autoencoder (AE) is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels. Firstly, based on the normal distribution characteristics of… More >

PRIYADHARSHINI THANJAVUR SRIRAMAMOORTHI^1,2, GAYATHRI GOPAL^1,2, SHIBI MURALIDAR^1,2, SAI RAMANAN ESWARAN^1,2, DANUSH NARAYAN PANNEERSELVAM^1,2, BHUVANESWARAN MEIYANATHAN^1,2, SRICHANDRASEKAR THUTHIKKADU INDHUPRAKASH^1,2, SENTHIL VISAGA AMBI^1,2,*

BIOCELL, Vol.47, No.11, pp. 2335-2350, 2023, DOI:10.32604/biocell.2023.029379

Abstract The novel coronavirus pneumonia (COVID-19) is spreading worldwide and threatening people greatly. The routes by which SARS-CoV-2 causes lung injury have grown to be a major concern in the scientific community since patients with new Coronavirus, severe acute respiratory syndrome coronavirus (SARS-CoV-2) have a high likelihood of developing acute respiratory distress syndrome (ARDS) in severe cases. The mortality rate of COVID-19 has increased over the period due to rapid spread, and it becomes crucial to understand the disease epidemiology, pathogenic mechanisms, and suitable treatment strategies. ARDS is a respiratory disorder and is one of the clinical manifestations observed in patients… More > Graphic Abstract