bluuebunny/crossref_metadata_2025 huggingface dataset:
The Risk of Atrial Fibrillation and Previous Ischemic Stroke in Cognitive Decline
Authors: Pal Tunde , Baba Dragos-Florin , Preg Zoltan , Nemes-Nagy Eniko , Nyulas Kinga-Ilona , German-Sallo Marta
Published: July 2024

Objectives: Our study investigated the inverse relationship between cognitive decline (CD) and the presence of documented atrial fibrillation (AFib), ischemic stroke, heart failure, lower extremity peripheral artery disease, and diabetes mellitus. Methods: We conducted a retrospective cross-sectional study between December 2016 and November 2019. A total of 469 patients were enrolled who underwent cognitive evaluation with three cognitive tests (Montreal Cognitive Assessment—MOCA, Mini-Mental State Examination—MMSE, and General Practitioner Assessment of Cognition—GPCOG). We used the standard cut-off values, and the optimal thresholds were obtained from the receiver operating characteristic curves. Results: The standard cut-off level of the MOCA (<26 points) was associated with the presence of AFib (OR: 1.83, 95% CI: 1.11–3.01) and the optimal cut-off level with <23 points with ischemic stroke (OR: 2.64, 95% CI: 1.47–4.74; p = 0.0011). The optimal cut-off value of the MMSE (<28 points) was associated with the presence of ischemic stroke (OR: 3.07, 95% CI: 1.56–6.07; p = 0.0012), AFib (OR: 1.65, 95% CI: 1.05–2.60; p = 0.0287), and peripheral artery disease (OR: 2.72, 95% CI: 1.38–5.36; p = 0.0039). GPCOG < 8 points were associated with ischemic stroke (OR: 2.18, 95% CI: 1.14–4.14; p = 0.0176) and heart failure (OR: 1.49, 95% CI: 1.01–2.21; p = 0.0430). Conclusions: Our research highlighted the broader utility of cognitive assessment. The MOCA and MMSE scores proved to be associated with documented AFib. Higher cognitive test results than the standard threshold for CD of the MMSE, GPCOG, and lower MOCA scores represented risk factors for the presence of previous ischemic stroke.
Fully Automated Measurement of Cobb Angles in Coronal Plane Spine Radiographs
Authors: Chen Kenneth , Stotter Christoph , Klestil Thomas , Mitterer Jennyfer A. , Lepenik Christopher , Nehrer Stefan
Published: July 2024

Background/Objectives: scoliosis is a three-dimensional structural deformity characterized by lateral and rotational curvature of the spine. The current gold-standard method to assess scoliosis is the measurement of lateral curvature of the spine using the Cobb angle in coronal plane radiographs. The interrater variability for Cobb angle measurements reaches up to 10°. The purpose of this study was to describe and assess the performance of a fully automated method for measuring Cobb angles using a commercially available artificial intelligence (AI) model trained on over 17,000 images, and investigate its interrater/intrarater agreement with a reference standard. Methods: in total, 196 AP/PA full-spine radiographs were included in this study. A reference standard was established by four radiologists, defined as the median of their Cobb angle measurements. Independently, an AI-based software, IB Lab SQUIRREL (version 1.0), also performed Cobb angle measurements on the same radiographs. Results: after comparing the readers’ Cobb angle end vertebrae selection to the AI’s outputs, 194 curvatures were considered valid for performance assessment, displaying an accuracy of 88.58% in end vertebrae selection. The AI’s performance showed very low absolute bias, with a mean difference and standard deviation of differences from the reference standard of 0.16° ± 0.35° in the Cobb angle measurements. The ICC comparing the reference standard and the AI’s measurements was 0.97. Conclusions: the AI model demonstrated good results in the determination of end vertebrae and excellent results in automated Cobb angle measurements compared to radiologists and could serve as a reliable tool in clinical practice and research.
Enhancing Ovarian Tumor Diagnosis: Performance of Convolutional Neural Networks in Classifying Ovarian Masses Using Ultrasound Images
Authors: Giourga Maria , Petropoulos Ioannis , Stavros Sofoklis , Potiris Anastasios , Gerede Angeliki , Sapantzoglou Ioakeim , Fanaki Maria , Papamattheou Eleni , Karasmani Christina , Karampitsakos Theodoros , Topis Spyridon , Zikopoulos Athanasios , Daskalakis Georgios , Domali Ekaterini
Published: July 2024

Background/Objectives: This study aims to create a strong binary classifier and evaluate the performance of pre-trained convolutional neural networks (CNNs) to effectively distinguish between benign and malignant ovarian tumors from still ultrasound images. Methods: The dataset consisted of 3510 ultrasound images from 585 women with ovarian tumors, 390 benign and 195 malignant, that were classified by experts and verified by histopathology. A 20% to80% split for training and validation was applied within a k-fold cross-validation framework, ensuring comprehensive utilization of the dataset. The final classifier was an aggregate of three pre-trained CNNs (VGG16, ResNet50, and InceptionNet), with experimentation focusing on the aggregation weights and decision threshold probability for the classification of each mass. Results: The aggregate model outperformed all individual models, achieving an average sensitivity of 96.5% and specificity of 88.1% compared to the subjective assessment’s (SA) 95.9% sensitivity and 93.9% specificity. All the above results were calculated at a decision threshold probability of 0.2. Notably, misclassifications made by the model were similar to those made by SA. Conclusions: CNNs and AI-assisted image analysis can enhance the diagnosis and aid ultrasonographers with less experience by minimizing errors. Further research is needed to fine-tune CNNs and validate their performance in diverse clinical settings, potentially leading to even higher sensitivity and overall accuracy.
Contemporary Role of Positron Emission Tomography (PET) in Endocarditis: A Narrative Review
Authors: Sammartino Antonio Maria , Bonfioli Giovanni Battista , Dondi Francesco , Riccardi Mauro , Bertagna Francesco , Metra Marco , Vizzardi Enrico
Published: July 2024

Endocarditis, a serious infectious disease, remains a diagnostic challenge in contemporary clinical practice. The advent of advanced imaging modalities has contributed significantly to the improved understanding and management of this complex disease. 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) imaging has shown remarkable potential in improving the diagnostic accuracy of endocarditis. In the update of the Modified Duke Criteria, in 2023, The International Society for Cardiovascular Infectious Diseases (ISCVID) Working Group recognized specific 18F-FDG PET/CT findings as a major diagnostic criterion, particularly in patient with prosthetic valve endocarditis. The ability of PET to visualize metabolic activity allows for the identification of infective foci and could differentiate between infective and non-infective processes. This review examines the clinical utility of PET in differentiating infective endocarditis from other cardiovascular pathologies, highlighting its sensitivity and specificity in detecting native and prosthetic valve infections, including patients with transcatheter aortic valve implantation (TAVI), cardiac implantable devices (CIEDs), and left ventricular assistance devices (LVAD). Also, practical aspects and indications are illustrated to optimize the quality of imaging and reduce potential false positive results. In conclusion, the current use of PET in endocarditis has become a valuable diagnostic tool; as technological advances continue, PET will play an increasingly important role in the multidisciplinary approach to the management of endocarditis.
The Brain–Gut Axis, an Important Player in Alzheimer and Parkinson Disease: A Narrative Review
Authors: Caradonna Eugenio , Nemni Raffaello , Bifone Angelo , Gandolfo Patrizia , Costantino Lucy , Giordano Luca , Mormone Elisabetta , Macula Anna , Cuomo Mariarosa , Difruscolo Rossana , Vanoli Camilla , Vanoli Emilio , Ferrara Fulvio
Published: July 2024

Neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), are severe age-related disorders with complex and multifactorial causes. Recent research suggests a critical link between neurodegeneration and the gut microbiome, via the gut–brain communication pathway. This review examines the role of trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, in the development of AD and PD, and investigates its interaction with microRNAs (miRNAs) along this bidirectional pathway. TMAO, which is produced from dietary metabolites like choline and carnitine, has been linked to increased neuroinflammation, protein misfolding, and cognitive decline. In AD, elevated TMAO levels are associated with amyloid-beta and tau pathologies, blood–brain barrier disruption, and neuronal death. TMAO can cross the blood–brain barrier and promote the aggregation of amyloid and tau proteins. Similarly, TMAO affects alpha-synuclein conformation and aggregation, a hallmark of PD. TMAO also activates pro-inflammatory pathways such as NF-kB signaling, exacerbating neuroinflammation further. Moreover, TMAO modulates the expression of various miRNAs that are involved in neurodegenerative processes. Thus, the gut microbiome–miRNA–brain axis represents a newly discovered mechanistic link between gut dysbiosis and neurodegeneration. MiRNAs regulate the key pathways involved in neuroinflammation, oxidative stress, and neuronal death, contributing to disease progression. As a direct consequence, specific miRNA signatures may serve as potential biomarkers for the early detection and monitoring of AD and PD progression. This review aims to elucidate the complex interrelationships between the gut microbiota, trimethylamine-N-oxide (TMAO), microRNAs (miRNAs), and the central nervous system, and the implications of these connections in neurodegenerative diseases. In this context, an overview of the current neuroradiology techniques available for studying neuroinflammation and of the animal models used to investigate these intricate pathologies will also be provided. In summary, a bulk of evidence supports the concept that modulating the gut–brain communication pathway through dietary changes, the manipulation of the microbiome, and/or miRNA-based therapies may offer novel approaches for implementing the treatment of debilitating neurological disorders.
Goal Setting for Participatory Person-Centered Geriatric Rehabilitation—From Function-Centered Rehabilitation towards Digitally Supported Personalized and Integrated Care for Older People
Authors: Skoumal Martin , Lindner-Rabl Sonja , Honegger Martina , Pertinatsch Christoph , Kadane Christof , Neubacher Britta , Herzog Carolin , Roller-Wirnsberger Regina
Published: July 2024

As chronic illness is common among older people, self-care practices for older people are needed to control health status, to prevent possible complications and to ensure optimal quality of life. The literature has demonstrated that integrated care approaches are one key success factor for delivering person-centered and sustainable care for older people, with rehabilitation being a cornerstone in tertiary care prevention for older citizens. The current paper addresses the state of the literature for person-centered geriatric rehabilitation (GR) and the importance of personalized and participatory goal setting. In accordance with the bio–psycho–social model of the International Classification of Functioning, Disability and Health (ICF), social participation and the related goals are of particular importance for the entire rehabilitation process. The social participation of individuals enrolled into GR is therefore one of the milestones to be achieved during GR. Personalized goal setting during the entire rehabilitation process, Comprehensive Geriatric Assessment (CGA) and shared decision making allow a comprehensive care approach separate from solely function-based rehabilitation. The review also focusses on recent developments in digitalization in healthcare and delivers insights into how healthcare professionals’ collaborative practice supports sustainable rehabilitation results in patients of advanced chronological age.
Neuron-Specific Enolase as a Predictor of Neurologic Outcomes in Extracorporeal Cardiopulmonary Resuscitation Patients
Authors: Jeong Yong Ho , Lim Suk Kyung , Cho Yongil , Kim Yun Jin , Jang Hyo Jun , Cho Yang Hyun , Shin Yonghoon , Jung Jae Seung , Kang Jin Kook , Cho Sung-Min , Lee Jun Ho
Published: July 2024

Background: Neuron-specific enolase (NSE) has traditionally been used as a biomarker to predict neurologic outcomes after cardiac arrest. This study aimed to evaluate the utility of NSE in predicting neurologic outcomes in patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR). Methods: This observational cohort study included 47 consecutive adult ECPR patients (median age, 59.0 years; 74.5% males) treated between January 2018 and December 2021 at a tertiary extracorporeal life support center. The primary outcome was a poor neurologic outcome, defined as a Cerebral Performance Category score of 3–5 at hospital discharge. Results: Twelve (25.5%) patients had abnormal findings on computed tomography of the brain. A poor neurologic outcome was demonstrated in 22 (46.8%) patients. The NSE level at 72 h after ECPR showed the best prediction power for a poor neurologic outcome compared with NSE at 24 and 48 h. A cutoff value exceeding 61.9 μg/L for NSE at 72 h yielded an area under the curve (AUC) of 0.791 for predicting poor neurologic outcomes and exceeding 62.1 μg/L with an AUC of 0.838 for 30-day mortality. Conclusions: NSE levels at 72 h after ECPR appear to be a reliable biomarker for predicting poor neurologic outcomes and 30-day mortality in ECPR patients.
The Effectiveness of an Exercise Program on Muscle Strength and Range of Motion on Upper Limbs, Functional Ability and Depression at Early Stage of Dementia
Authors: Papamichail Panagiotis , Sagredaki Maria-Louiza , Bouzineki Christina , Kanellopoulou Sophia , Lyros Epameinondas , Christakou Anna
Published: July 2024

Background: Dementia involves the loss of cognitive abilities and represents a decline from the prior level of function, which impairs functional abilities in day-to-day life. The purpose of the present study is to examine the effectiveness of an exercise program on the muscle strength and range of motion of the upper limbs, the functional status, and the depression of elderly people with early stage dementia. Methods: The sample consisted of 60 elderly people with early stage dementia who were randomly divided into a control and an experimental group of 30 participants each. The experimental group received a 12-week Otago exercise program with 45 min duration of each session. The control group received usual care without doing exercise. The outcome measures of muscle strength and range of motion of the upper limbs, the functional status, and the depression were assessed by valid instruments and tests at the beginning and at the end of the intervention program. Repeated measures one-way ANOVA and Mann–Whitney tests examined the differences between the two groups at the end of the 12-week exercise program. Results: Statistically significant differences were found between the experimental and control groups in the entire outcome measures (functional ability F = 9.35 p < 0.05; muscle strength right hand F = 32.33, p < 0.05 left hand U = 95.50 p < 0.01; e.g., range of motion shoulder extension U = 104.00 p < 0.01), except depression. Conclusions: Both muscle strength and range of motion of the upper limbs, as well as the functional ability, were improved by the exercise program. Further research is needed to investigate the present results, in particular to explore the long-term cognitive, behavioral, and functional status outcomes of exercise in the early stages of dementia.
Polymer Microspheres Carrying Schiff-Base Ligands for Metal Ion Adsorption Obtained via Pickering Emulsion Polymerization
Authors: Honciuc Andrei , Negru Oana-Iuliana , Honciuc Mirela , Solonaru Ana-Maria
Published: July 2024

Several traditional methods for producing polymer microparticle adsorbents for metal ions exist, such as bulk polymerization followed by milling and crushing the material to micron-size particles, precipitation from organic solvents, and suspension polymerization utilizing surfactants. Alternative methods that are easily scalable and are environmentally friendly are in high demand. This study employs Pickering Emulsion Polymerization Technology (PEmPTech) to synthesize nanostructured polymer microspheres that incorporate Schiff-base ligands, which can be utilized for metal ion adsorption, and specifically Cu(II) ions. Our innovative approach makes use of nanoparticle-stabilized, surfactant-free emulsions/suspensions, enabling the straightforward production of ligand-bearing microspheres while allowing for the precise modulation of the polymer matrix chemistry to maximize adsorption capacities. Through this method, we demonstrate notable enhancements in Cu(II) ion adsorption, which correlates with both the polarity of the monomers used and the concentration of Schiff-base ligands within the microspheres. Notably, our results offer insights into the structure–activity relationships essential for designing tailored adsorbents. This work provides a scalable method to produce high-performance adsorbents and also contributes to sustainable methodologies by excluding harmful surfactants and solvents.
Effect of Excessive Clamping Force on Bolted CFRP Composite Plates
Authors: El-Sisi Alaa , Salim Hani , Alshalal Iqbal , Nawar Mahmoud , El-Feky Mohamed H.
Published: July 2024

Friction-type bolted joints are widely used in both the civil and aerospace industries. Uncontrolled excessive bolt clamping force can cause damage to the laminated fiber-reinforced polymeric (FRP) composite through the thickness and damage the joint before applying the service loads. The effect of the friction coefficient (between 0 and 0.3), bolt clearance, joint type, and other parameters on failure modes and the maximum bolt clamping force of the carbon FRP lapped joint is studied. A three-dimensional finite element (FE) model consisting of a bolt, a washer, a laminate FRP composite plate, and steel plates was developed for the simulation of the double- (3DD) and single (3DS)-lapped bolted joint. The FE model was validated by using experimental results and was able to predict the experimental results by a difference of between 2.2 and 6.7%. The joint capacity of the clamping force was found to be greatly increased by adopting the double lap technique, which involves placing an FRP composite plate between two steel plates. Also, it was recommended to use an internal washer diameter less than or equal to the FRP composite plate hole diameter since a larger washer clearance can produce higher contact pressure and reduce the resistance by 22%. In addition, reducing the bolt head diameter can lead to a 65% reduction in the 3DS joint clamping strength.
Few-Shot Conditional Learning: Automatic and Reliable Device Classification for Medical Test Equipment
Authors: Pachetti Eva , Del Corso Giulio , Bardelli Serena , Colantonio Sara
Published: July 2024

The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResNet integrated with an encoder as a backbone to encode conditional shape information for the classification of neonatal resuscitation equipment from less than 100 natural images. The model is also strengthened by incorporating a reliability score, which enriches the prediction with an estimation of classification reliability. The model, whose performance is cross-validated, reached a median accuracy performance of over 99% (and a lower limit of 73.4% for the least accurate model/fold) using only 87 meta-training images. During the test phase on complex natural images, performance was slightly degraded due to a sub-optimal segmentation strategy (FastSAM) required to maintain the real-time inference phase (median accuracy 87.25%). This methodology proves to be excellent for applying complex classification models to contexts (such as neonatal resuscitation) that are not available in public databases. Improvements to the automatic segmentation strategy prior to the extraction of conditional information will allow a natural application in simulation and hospital settings.
Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images
Authors: Gharaibeh Nasr Y. , De Fazio Roberto , Al-Naami Bassam , Al-Hinnawi Abdel-Razzak , Visconti Paolo
Published: July 2024

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.
A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer
Authors: Guo Wei , Xu Li , Zhao Danyang , Zhou Dianqiang , Wang Tian , Tang Xujing
Published: July 2024

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.
Decarbonizing Maritime Transport through Green Fuel-Powered Vessel Retrofitting: A Game-Theoretic Approach
Authors: Liang Chengji , Sun Weiwei , Shi Jian , Wang Kailai , Zhang Yue , Lim Gino
Published: July 2024

Addressing the urgent global challenge of man-made greenhouse gas emissions and climate change necessitates collaborative action between shipping lines and government regulatory agencies. Aligning with the International Maritime Organization’s emissions reduction strategy, this paper presents a novel bi-level programming model that unifies these stakeholders. On the upper level of the proposed bi-level model, a number of shipping lines optimize retrofitting plans for their vessels to maximize economic benefits. On the lower level, the regulatory agency responds to the carbon reduction efforts by setting retrofitting subsidies and emission penalty rates. This framework represents a multi-leader–single-follower game involving shipping lines and the regulatory agency, and its equilibrium is determined through an equilibrium problem with equilibrium constraints (EPEC). The EPEC comprises multiple single-leader–follower problems, each of which can be formulated as a mathematical program with equilibrium constraints (MPEC). The diagonalization algorithm (DM) is employed for its solution. Simulation studies performed based on a ten-year planning period show that the proposed approach can effectively promote vessel retrofitting and the use of green fuels, which leads to an annual emission reduction of over 50%.
GFRENet: An Efficient Network for Underwater Image Enhancement with Gated Linear Units and Fast Fourier Convolution
Authors: Zhang Bingxian , Fang Jiahao , Li Yujie , Wang Yue , Zhou Qinglong , Wang Xing
Published: July 2024

Underwater image enhancement is critical for a variety of marine applications such as exploration, navigation, and biological research. However, underwater images often suffer from quality degradation due to factors such as light absorption, scattering, and color distortion. Although current deep learning methods have achieved better performance, it is difficult to balance the enhancement performance and computational efficiency in practical applications, and some methods tend to cause performance degradation on high-resolution large-size input images. To alleviate the above points, this paper proposes an efficient network GFRENet for underwater image enhancement utilizing gated linear units (GLUs) and fast Fourier convolution (FFC). GLUs help to selectively retain the most relevant features, thus improving the overall enhancement performance. FFC enables efficient and robust frequency domain processing to effectively address the unique challenges posed by the underwater environment. Extensive experiments on benchmark datasets show that our approach significantly outperforms existing state-of-the-art techniques in both qualitative and quantitative metrics. The proposed network provides a promising solution for real-time underwater image enhancement, making it suitable for practical deployment in various underwater applications.
Recent Progress on Built-in Wave Energy Converters: A Review
Authors: Wang Hao , Sun Jiajing , Xi Ziyue , Dai Shu , Xing Fuzhen , Xu Minyi
Published: July 2024

A built-in wave energy converter (BI-WEC) is a type of WEC that is fully encapsulated within a floating body that is easy to integrate and promotes reliability. Significant advantages in integration and reliability make BI-WECs a promising pathway to achieve an in situ power supply for massive distributed marine equipment (such as ships, buoys, or USVs). A comprehensive review of the recent advances in built-in wave energy converters can help address the most relevant issues in BI-WEC development. This study enumerates recent progress on BI-WECs (energy capture, power take-off, and control) and summarizes the characteristics of various designs. Different design philosophies and technical pathways can be better understood through the classification and analysis offered by this study. This review helps to form a basic understanding of BI-WEC development to achieve in situ power sustainability for a large amount of distributed marine equipment in long-term sustained marine operations.
A YOLOv7-Based Method for Ship Detection in Videos of Drones
Authors: Wang Quanzheng , Wang Jingheng , Wang Xiaoyuan , Wu Luyao , Feng Kai , Wang Gang
Published: July 2024

With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7.
Method for Collaborative Layout Optimization of Ship Equipment and Pipe Based on Improved Multi-Agent Reinforcement Learning and Artificial Fish Swarm Algorithm
Authors: Zhang Hongshuo , Yu Yanyun , Song Zelin , Han Yanzhao , Yang Zhiyao , Ti Lang
Published: July 2024

The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategies. However, the engine room environment is complex, involving two significantly different challenges: equipment layout and pipe layout. Traditional methods fail to achieve optimal collaborative layout objectives. To address this research gap, this paper proposes a collaborative layout method that combines improved reinforcement learning and heuristic algorithms. For equipment layout, the engine room space is first discretized into a grid, and a Markov decision process (MDP) framework suitable for equipment layout is proposed, including state space, action space, and reward mechanisms suitable for equipment layout. An improved adaptive guided multi-agent Q-learning (AGMAQL) algorithm is employed to train the layout model in a centralized manner, with enhancements made to the agent’s exploration state, exploration action, and learning strategy. For pipe layout, this paper proposes an improved adaptive trajectory artificial fish swarm algorithm (ATAFSA). This algorithm incorporates a hybrid encoding method, adaptive strategy, scouting strategy, and parallel optimization strategy, resulting in enhanced stability, accuracy, and problem adaptability. Subsequently, by comprehensively considering layout objectives and engine room attributes, a collaborative layout method incorporating hierarchical and adaptive weight strategies is proposed. This method optimizes in phases according to the layout objectives and priorities of different stages, achieving multi-level optimal layouts and providing designers with various reference schemes with different focuses. Finally, based on a typical real-world engine room engineering case, various leading algorithms and strategies are tested and compared. The results show that the proposed AGMAQL-ATAFSA (AGMAQL-ATA) exhibits robustness, efficiency, and engineering practicality. Compared to previous research methods and algorithms, the final layout quality improved overall: equipment layout effectiveness increased by over 4.0%, pipe optimization efficiency improved by over 40.4%, and collaborative layout effectiveness enhanced by over 2.2%.
Novel Somay’s GLUCAR Index Efficiently Predicts Survival Outcomes in Locally Advanced Pancreas Cancer Patients Receiving Definitive Chemoradiotherapy: A Propensity-Score-Matched Cohort Analysis
Authors: Topkan Erkan , Senyurek Sukran , Kılic Durankus Nulifer , Ozturk Duriye , Selek Ugur
Published: July 2024

Background: Propensity score matching (PSM) was used to investigate the prognostic value of a novel GLUCAR index [Glucose × (C-reactive protein ÷ albumin)] in unresectable locally advanced pancreatic cancer (LA-NPC) patients who received definitive concurrent chemoradiotherapy (CCRT). Methods: The PSM analysis comprised 142 LA-PAC patients subjected to definitive CCRT. Receiver operating characteristic (ROC) curve analysis was utilized to identify relevant pre-CCRT cutoffs that could effectively stratify survival results. The primary and secondary objectives were the correlations between the pre-CCRT GLUCAR measures and overall survival (OS) and progression-free survival (PFS). Results: The ROC analysis revealed significance at 43.3 for PFS [area under the curve (AUC): 85.1%; sensitivity: 76.8%; specificity: 74.2%; J-index: 0.510)] and 42.8 for OS (AUC: 81.8%; sensitivity: 74.2%; specificity: 71.7%; J-index: 0.459). Given that these cutoff points were close, the standard cutoff point, 42.8, was selected for further analysis. Comparative survival analyses showed that pre-CCRT GLUCAR ≥ 42.8 (n = 71) measures were associated with significantly shorter median PFS (4.7 vs. 15.8 months; p < 0.001) and OS (10.1 vs. 25.4 months; p < 0.001) durations compared to GLUCAR < 42.8 measures (n = 71). The multivariate analysis results confirmed the independent significance of the GLUCAR index on PFS (p < 0.001) and OS (p < 0.001) outcomes. Conclusions: Elevated pre-CCRT GLUCAR levels are robustly and independently linked to significantly poorer PFS and OS outcomes in unresectable LA-PAC patients treated with definitive CCRT.
Rates of Vaccination against COVID-19 in Psychiatric Outpatients
Authors: Bosnjak Mina Cvjetkovic , Kuljancic Dusan , Vejnovic Ana-Marija , Hinic Darko , Knezevic Vladimir , Ratkovic Dragana , Bosic Vanja , Vasic Vesna , Sakic Branislav , Segan Darja , Savic Predrag , Abazovic Minja , Comic Masa , Siladji Djendji , Simic-Panic Dusica , Ivetic Poledica Olga
Published: July 2024

Background: The aim of this study was to compare the rates of vaccination against COVID-19 infection in psychiatric outpatients and the general population, as well as rates of infected patients. In addition, the level and type of anxiety due to the pandemic were observed in patients with psychotic, anxiety, and depressive disorders. Materials and Methods: In the present study, 171 patients with pre-existing mental disorders completed the questionnaire about the doses and types of vaccination against COVID-19. During 2021–2023, patients with different mental disorders, aged from 18 to 80, were included. All patients filled in a self-reported questionnaire including general information (age, sex, marriage, education, working status, comorbid conditions) as well as questions about mental health, receiving vaccination, and the course of COVID-19 infection if it was present. All patients gave informed consent for the interview. Results: Patients with pre-existing mental disorders were more likely to be vaccinated against COVID-19 compared with the general population. The Sinopharm vaccine was most frequently applied. In the observed patients, 46.8% were infected, but just 7% had a medium or serious form of infection and were not vaccinated. Conclusions: In our study, the percentage of vaccinated psychiatric patients was greater than that in the general population, except in psychotic patients, who were mostly limited by fear. Such results can be explained by the high percentage of somatic comorbidities in this population and perhaps insufficient information about the positive effects of vaccination.
Modeling and Forecasting Historical Volatility Using Econometric and Deep Learning Approaches: Evidence from the Moroccan and Bahraini Stock Markets
Authors: Boudri Imane , El Bouhadi Abdelhamid
Published: July 2024

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.
Impact of Board Committee Characteristics on Social Sustainability Reporting in Sub-Saharan Africa: The Moderating Role of Institutional Ownership
Authors: Blay Marshall Wellington , Hoeyi Prosper Kweku , Badu Ebenezer Agyemang , Jibril Abdul Bashiru
Published: July 2024

The corporate strategic planning of businesses in sub-Saharan Africa (SSA) largely focuses on immediate financial performance with minimal credence to social sustainability. Thus, studies on the linkage between corporate governance (CG) and sustainability reporting have focused on developed economies. This study therefore investigated the role of institutional ownership in the impact of board committee characteristics on social sustainability reporting. This study involved strongly balanced panel data with 1969 observations of 275 publicly listed non-financial firms in SSA within the timeframe of 2012 to 2021. Data were analyzed using STATA 14.1. The hypotheses were tested using the two-step system of the generalized method of moment (GMM) using the Arellano–Bond dynamic panel data estimation method. The rate of social sustainability reporting was 39.4%. Relatively, Mauritian and South African firms had the most effective board committee characteristics and higher levels of social sustainability reporting. Although institutional ownership had no significant effect on social sustainability reporting, it moderated the effect of sustainability committee independence and sustainability committee gender diversity on social sustainability reporting. This paper presents a new perspective on the corporate governance and social sustainability literature by examining the effect of institutional ownership on board committee characteristics and social sustainability reporting in SSA. In terms of policy implication, there is the need for mandatory regulatory and legal CG framework that is regularly updated at national and regional levels in SSA to motivate listed firms to establish sustainability committees with efficient characteristics to promote social sustainability reporting.
Financial Risk, Debt, and Efficiency in Indonesia’s Construction Industry: A Comparative Study of SOEs and Private Companies
Authors: Wibowo Febrianto Arif , Satria Arif , Gaol Sahala Lumban , Indrawan Dikky
Published: July 2024

This study aims to evaluate the financial risk, debt, and efficiency of state-owned enterprises (SOEs) in Indonesia’s construction industry and compare these aspects with those of private companies through financial ratio analysis and efficiency analysis approaches. Four SOEs from the construction sector were evaluated and compared to five private companies with financial data ranging from 2015 to 2022. Financial ratio analysis was applied to assess debt and financial risk, while efficiency analysis utilized data envelopment analysis (DEA) and paired t-tests to validate differences between the two groups of companies. This study reveals that the financial ratio performance of state-owned companies is relatively poor, with low profitability, critical liquidity, and a high debt ratio. Debt, as a source of capital in financing construction projects, causes companies to face a greater debt risk. This study also validates that SOEs have lower efficiency compared to private companies. In response to current challenges, SOEs should prioritize enhancing liquidity through faster receivable collections, debt restructuring, capital infusions, and divestment, reducing non-essential investments, focusing on asset recycling, and improving project efficiency.
Mapping Corporate Sustainability and Firm Performance Research: A Scientometric and Bibliometric Examination
Authors: Chopra Akshat , Singh Ashima , Debnath Rajarshi , Quttainah Majdi Anwar
Published: July 2024

Corporate sustainability has garnered increasing attention within the business community as corporations communicate to influence their stakeholders to build sustainable relationships. There has been a surge in research exploring its connection to firm performance, but existing studies lack a cohesive and concentrated approach. The aim of this study is to explore the trends of growth of publications; gauge the annual growth rate, annual ratio of growth, relative growth rate, doubling time, and scientific production index; predict future production levels; and look at the relationship between corporate sustainability and firm performance by analysing the literature as well as identifying clusters and links with the Sustainable Development Goals (SDGs). The top countries contributing to the research were China, India, and the United States, accounting for over 45% of the global publications. The study analysed a focused corpus of 65 documents from the Scopus database on specific subfields of corporate sustainability and firm performance, identifying five main thematic clusters related to environmental performance, financial performance, corporate sustainability reporting, corporate social performance, and green supply chain management, with significant citations related to 17 SDGs. The annual growth rate (AGR) of publications was found to be −2.88%, with an average of 4.06 publications per year. The relative growth rate (RGR) decreased from 0.69 in 2010 to 0.36 in 2023, and the doubling time (Dt.) increased from 1.00 in 2010 to 1.93 in 2023. Employing structured methods and the PRISMA protocol, this scientifically rigorous study points towards identification of research themes linking sustainability practices to firm performance. Exponential smoothing (Holt’s linear trend model) is employed to project future research output within the field. The significant trends include an increase in publication frequency since 2017, indicating a growth phase in the research field. The findings highlight the need for greater investigation from developing countries and the importance of integrating sustainability considerations into business strategies.
Quantifying the Wild: Public Acceptance and Challenges of Urban Wildscapes in Chiba, Japan
Authors: Su Daer , Chen Jie , Li Hongyu , Luo Shixian , Xie Jing , Wang Huixin , Sun Fuhao , Ren Wanyue , Kinoshita Takeshi
Published: July 2024

People’s reduced connection with nature has led to many health problems. In the NBS framework, urban wildscapes (UWSs) are considered an important solution. They can contribute to improving the health of residents and ecosystems within the city. However, overly wild green spaces may also be offensive to residents. It is necessary to understand the public’s acceptance of UWSs. Current studies on the acceptance of UWSs have used vague terms to generalize the “wildness degree”. In this study, we attempted to quantify the degree of wildness using plant height and plant abundance in Japan and analyzed the results through mediated effect analysis. We discovered the following: 1. Japanese residents have low acceptance of UWSs in vacant lots. 2. The use of “height” and “abundance” may be a more objective way to quantify wildness. 3. The negative effects of abundance can be minimized. The negative effect of height cannot be controlled. This study addresses a gap in the study of Asian cities and proposes a novel approach to quantifying “wildness.” It improves the health benefits of UWSs. Furthermore, it can offer guidance on the management and construction of urban green spaces.
Policy-Driven Vegetation Restoration in Qinghai Province: Spatiotemporal Analysis and Policy Evaluation
Authors: Zhang Yuchen , Zhu Jianghong , Wang Lin , Wang Ke , Zhang Jianjun
Published: July 2024

The Chinese government has implemented numerous ecological policies in Qinghai Province aimed at protecting and restoring the natural ecosystem. Yet, amid global climate change, the precise effects of these policies on ecological improvement remain ambiguous. There is an urgent need for a comprehensive evaluation of the effects of these policies at a regional scale and an analysis of the changes in policy implementation stages to optimize the strategic direction of regional ecological policies. In this study, using mathematical statistics and spatial analysis, we analysed the spatial and temporal characteristics of the Normalized Difference Vegetation Index (NDVI) in Qinghai Province from 2000 to 2023. Further, by systematically reviewing ten major ecological policies currently or previously implemented in the region, we explored the response of vegetation restoration to these policies through both horizontal and vertical evaluations by reasonably delineating the policy study sub-zones. The study identified distinct stages of policy implementation—regreening, stabilizing, and natural recovery—and correlated these stages with the efficacy of policy impacts. Our findings indicate significant vegetation coverage improvements across Qinghai Province over the past two decades, with all ecological policies positively influencing the environment. The main contribution of this study is that it comprehensively evaluates the impact of multiple ecological policies on vegetation restoration at the regional scale, providing a reference for the formulation and adjustment of subsequent ecological policies.