bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maritime decision-makers and cyber security: deck officers’ perception of cyber risks towards IT and OT systems
Authors: Haugli-Sandvik Marie , Lund Mass Soldal , Bjørneseth Frøy Birte 
Published: February 2024

AbstractThrough a quantitative study of deck officers’ cyber risk perceptions towards information (IT) and operational (OT) systems, this paper contributes to substantiate the importance of considering human behaviour within maritime cyber security. Using survey data from 293 deck officers working on offshore vessels, statistical analyses were conducted to measure and predict the participants cyber risk perceptions towards IT and OT systems. Performing a Wilcoxon signed-rank test revealed a significant discrepancy in the levels of cyber risk perception between the system categories. Hierarchical regression analyses were conducted to develop statistical models, considering multiple independent variables, including perceived benefit, cyber security training, experience with cyber-attacks, and trust towards various stakeholders. Key findings revealed distinct results for IT and OT systems, and the regression models varied in both predictive power and significance of the independent variables. Perceived benefit positively predicts deck officers cyber risk perception for both IT and OT systems, while trust, which included measures of social trust and confidence, was not found to be significant. Cyber security training and experience with cyber-attacks only influence deck officers’ perception of cyber risks related to operational technology. Practical implications of this work provide actionable recommendations for the maritime industry, including tailored risk communication tools, training programs, reporting systems, and holistic policies.
Life cycle assessment and techno-economic analysis of sustainable bioenergy production: a review
Authors: Osman Ahmed I. , Fang Bingbing , Zhang Yubing , Liu Yunfei , Yu Jiacheng , Farghali Mohamed , Rashwan Ahmed K. , Chen Zhonghao , Chen Lin , Ihara Ikko , Rooney David W. , Yap Pow-Seng 
Published: February 2024

AbstractThe global expansion of the bioenergy industry raises concerns, emphasizing the need for careful evaluation and sustainable management. To facilitate this, life cycle assessments beyond greenhouse gas emissions and energy balance are essential, along with the standardization of assessment methodologies to enable meaningful comparisons. Here, we review life cycle assessment, chemical aspects, and policy implication of bioenergy production. We discuss life cycle assessment in terms of concepts, methods, impacts, greenhouse gases, land use, water consumption, bioethanol, biodiesel, biogas, and techno-economic analysis. Chemical aspects comprise reaction processes and means to improve efficiency. Concerning policies, tools, and frameworks that encourage sustainable energy production are presented. We found that carbon dioxide removal ranges from 45 to 99% in various bioenergy processes. The review also emphasizes the importance of chemistry in advancing sustainable bioenergy production for a more sustainable and secure energy future.
An Assessment of Sikh Turban’s Head Protection in Bicycle Incident Scenarios
Authors: Yu Xiancheng , Singh Gurpreet , Kaur Amritvir , Ghajari Mazdak 
Published: February 2024

AbstractDue to religious tenets, Sikh population wear turbans and are exempted from wearing helmets in several countries. However, the extent of protection provided by turbans against head injuries during head impacts remains untested. One aim of this study was to provide the first-series data of turbans’ protective performance under impact conditions that are representative of real-world bicycle incidents and compare it with the performance of bicycle helmets. Another aim was to suggest potential ways for improving turban’s protective performance. We tested five different turbans, distinguished by two wrapping styles and two fabric materials with a size variation in one of the styles. A Hybrid III headform fitted with the turban was dropped onto a 45 degrees anvil at 6.3 m/s and head accelerations were measured. We found large difference in the performance of different turbans, with up to 59% difference in peak translational acceleration, 85% in peak rotational acceleration, and 45% in peak rotational velocity between the best and worst performing turbans. For the same turban, impact on the left and right sides of the head produced very different head kinematics, showing the effects of turban layering. Compared to unprotected head impacts, turbans considerably reduce head injury metrics. However, turbans produced higher values of peak linear and rotational accelerations in front and left impacts than bicycle helmets, except from one turban which produced lower peak head kinematics values in left impacts. In addition, turbans produced peak rotational velocities comparable with bicycle helmets, except from one turban which produced higher values. The impact locations tested here were covered with thick layers of turbans and they were impacted against flat anvils. Turbans may not provide much protection if impacts occur at regions covered with limited amount of fabric or if the impact is against non-flat anvils, which remain untested. Our analysis shows that turbans can be easily compressed and bottom out creating spikes in the headform’s translational acceleration. In addition, the high friction between the turban and anvil surface leads to higher tangential force generating more rotational motion. Hence, in addition to improving the coverage of the head, particularly in the crown and rear locations, we propose two directions for turban improvement: (i) adding deformable materials within the turban layers to increase the impact duration and reduce the risk of bottoming out; (ii) reducing the friction between turban layers to reduce the transmission of rotational motion to the head. Overall, the study assessed Turbans’ protection in cyclist head collisions, with a vision that the results of this study can guide further necessary improvements for advanced head protection for the Sikh community.
Effect of basket height and stocking density on production of the sea urchin Tripneustes gratilla: insights and recommendations
Authors: De Vos Bas C. , Cyrus Mark D. , Bolton John J. , Macey Brett M. 
Published: February 2024

AbstractBasket height and stocking density are crucial and related factors for successful commercial sea urchin aquaculture, but these factors have not been definitively determined for production of Tripneustes gratilla. This study investigated the effects of varying basket heights (deep 30 cm vs. shallow 15 cm) and stocking densities (4, 6 and 8 kg.m−2 or 13, 19 and 24% coverage of available basket surface area) on aquacultural production of T. gratilla. Contrary to previous suggestions, reduced consumption was identified as the cause of decreased production in deeper baskets. Consumption was significantly higher in shallow baskets than deep baskets for both the fresh seaweed, Ulva lacinulata, (W = 38, p = 0.026) and formulated feed (W = 76.5, p = 0.007). Consequently, baskets of approximately 15 cm deep are recommended to enhance production of T. gratilla and possibly other urchin species. Two subsequent trials assessed production of T. gratilla at different stocking densities. Trial 1 aimed to maximize urchin size over a three-month grow-out period using fresh U. lacinulata, while trial 2 focused on enhancing gonad production over two months using formulated feed. Although greater stocking density significantly reduced the specific growth rates of individual urchin mass in both trials (p < 0.044), there was no significant impact on mortality, net production or gonad size and quality. The variations in growth rates were attributed to spine loss resulting from negative behavioural interactions (F2, 9 = 9.551; p = 0.005). Based on the objectives of both grow-out and gonad-enhancement phases, we recommend a stocking density of approximately 20% coverage.
Traditional Mexican dietary pattern and cancer risk among women of Mexican descent
Authors: Loroña Nicole C. , Santiago-Torres Margarita , Lopez-Pentecost Melissa , Garcia Lorena , Shadyab Aladdin H. , Sun Yangbo , Kroenke Candyce H. , Snetselaar Linda G. , Stefanick Marcia L. , Neuhouser Marian L. 
Published: February 2024

Abstract
Purpose
To examine the association of a traditional Mexican diet score with risk of total, breast, and colorectal cancer among women of Mexican ethnic descent in the Women’s Health Initiative (WHI).

Methods
Participants were WHI enrollees who self-identified as being of Mexican descent. Data from food frequency questionnaires self-administered at study baseline were used to calculate the MexD score, with higher scores indicating greater adherence to an a priori-defined traditional Mexican diet (high in dietary fiber, vegetables, and legumes). Incident cancers were self-reported by participants from 1993 to 2020 and adjudicated by trained physicians. We used multivariable-adjusted Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).

Results
Among 2,343 Mexican descent women (median baseline age: 59 years), a total of 270 cancers (88 breast, 37 colorectal) occurred during a mean follow-up of 14.4 years. The highest tertile of MexD score was associated with a lower risk of all-cancer incidence (HR: 0.67; 95% CI 0.49–0.91; p-trend: 0.01) and colorectal cancer (HR: 0.38; 95% CI 0.14–0.998; p-trend < 0.05), with each unit increase in the MexD score associated with a 6% lower risk of all-cancer incidence (HR: 0.94; 95% CI 0.88–0.99). There was no statistically significant association with risk of breast cancer.

Conclusion
Consumption of a traditional Mexican diet was associated with a significantly lower risk of all-cancer incidence and colorectal cancer. Confirmation of these findings in future studies is important, given the prevalence of colorectal cancer and a growing U.S. population of women of Mexican descent.
Long-term stability of cellulose membranes in spent deep eutectic solvent used in the recovery of lignin from lignocellulosic biomass
Authors: Ippolitov Vadim , Anugwom Ikenna , Mänttäri Mika , Kallioinen-Mänttäri Mari 
Published: February 2024

AbstractDeep eutectic solvents (DESs) are a novel class of solvents that can be used to fractionate biomass compounds. However, their sustainability depends strongly on their recyclability. In previous research, it was seen that membrane filtration with commercial cellulose membranes (RC70PP and Ultracel 5 kDa) might be a solution for purification of spent deep eutectic solvent (DES) that has been used in lignin extraction (Choline Chloride: Lactic Acid 1:10 molar ratio) from woody biomass. This DES is, however, very acidic (pH 1.3), which can have detrimental effects on the longevity of the membrane. In a previous study, the time that the membranes were exposed to the spent DES was relatively short. This study aims to increase knowledge of how cellulose membranes withstand spent DES over longer time periods of up to 8 weeks. The results show that cellulose membranes are quite stable under exposure to spent DES in terms of pure water flux and PEG retention for up to 4 weeks. After 8 weeks, the RC70PP membrane demonstrated an increase in pure water permeability of 45% and a noticeable decrease in PEG retention. Surface characterization revealed, however, that the chemical structure of the cellulose membranes changed already after 2 weeks of exposure prior to any changes in pure water permeability were observed. Experimentally revealed esterification of cellulose membrane by Lactic Acid of DES led to more negative charge of the exposed samples compared to their references. This esterification was accompanied by hydrolysis that removed amorphous parts and increased the crystallinity of the membrane.
Exact-matching algorithms using administrative health claims database equivalence factors for real-world data analysis based on the target trial emulation framework
Authors: Nishioka Yuichi , Morita Emiri , Takeshita Saki , Tamamoto Sakura , Myojin Tomoya , Noda Tatsuya , Imamura Tomoaki 
Published: February 2024

AbstractReal-world data have become increasingly important in medical science and healthcare. A new, effective, and practically feasible statistical design is needed to unlock the potential of real-world data that decision-makers and practitioners can use to meet people’s healthcare needs. In the first half of the study, we validated our proposed new method by simulation, and in the second half, we conducted a clinical study on actual real-world data. We proposed the “Exact Matching Algorithm Using Administrative Health Claims Database Equivalence Factors (AHCDEFs)” using a target trial emulation framework. The simulation trials were conducted 500 times independently, considering the misclassification and chance errors of all variables and competing events of outcome. Two conventional methods, multivariate and propensity score analyses, were compared. Next, we estimated the effect of specific health guidance provided in Japan on the prevention of diabetes onset and medical expenditures. Our proposed novel method for real-world data returns improved estimates and fewer type I errors (the probability of erroneously determining that there is a difference when, in fact, there is no difference) than conventional methods. We quantitatively demonstrated the effectiveness of specific health guidance in Japan in preventing the onset of diabetes and reducing medical expenditures during five years. We proposed a new method for analyzing real-world data and an exact-matching algorithm using AHCDEFs. The larger the number of patients available for analysis, the more the AHCDEFs that can be matched, thereby removing the influence of confounding factors. This method will generate significant evidence when applied to real-world data.
Contentment and Self-acceptance: Wellbeing Beyond Happiness
Authors: Cordaro Daniel T. , Bai Yang , Bradley Christina M. , Zhu Franklyn , Han Rachel , Keltner Dacher , Gatchpazian Arasteh , Zhao Yitong 
Published: February 2024

AbstractContentment is an emotion felt when the present situation is perceived to be complete as it is. Six studies are presented showing the difference between contentment and other positive emotions, documenting contentment’s unique relationship with self-acceptance, and exploring the positive associations between contentment and wellbeing. First, we demonstrated that contentment is perceived as different from happiness and other positive emotions using an emotion concept mapping methodology (Study 1), a dimensional analysis (Study 2), and a free-response narrative analysis (Study 3). We then demonstrated that both trait (Study 4 and 5) and state levels (Study 6) of contentment are associated with a sense of self-acceptance, and further related to increased wellbeing. We conclude that contentment is a unique positive emotion that is central to wellbeing and life satisfaction, and we discuss important implications for future research.
ON HARDY-TYPE INEQUALITIES AS AN INTELLECTUAL ADVENTURE FOR 100 YEARS
Authors: Persson Lars-Erik , Samko Natasha 
Published: February 2024

AbstractWe describe some chosen ideas and results for more than 100 years prehistory and history of the remarkable development concerning Hardy-type inequalities. In particular, we present a newer convexity approach, which we believe could partly have changed this development if Hardy had discovered it. In order to emphasize the current very active interest in this subject, we finalize by presenting some examples of the recent results, which we believe have potential not only to be of interest for a broad audience from a historical perspective, but also to be useful in various applications.
A Robust Sequential Recommendation Model Based on Multiple Feedback Behavior Denoising and Trusted Neighbors
Authors: Cai Hongyun , Meng Jie , Yuan Shilin , Ren Jichao 
Published: February 2024

AbstractAt present, most of the personalized sequential recommendations utilize users’ implicit positive feedback (such as clicks) to predict user behavior, ignoring the impact of implicit negative feedback and explicit feedback on the accuracy of recommendation results prediction. In this paper, we propose a robust sequence recommendation model based on multi feedback behavior denoising and trusted neighbors, which utilizes multiple feedback behavior data for feature denoising and considers trusted nearest neighbor information to improve model performance. Firstly, by learning the feature representations and interactions of various types of feedback, explicit feedback is used to map and purify implicit feedback with the same and different attributes, resulting in unbiased user performance. Then, we design a filter attention network to identify highly trusted neighbor information. Finally, we integrate pure user interest representations and trusted nearest neighbor representations to improve the accuracy and robustness of the model. The experimental results on two publicly available datasets show that the proposed sequential recommendation model can achieve superior results to baseline methods in both AUC and RelaImpr.
Managing Potable Water in Southeastern Spain, Los Angeles, and Sydney: Transcontinental Approaches to Overcome Water Scarcity
Authors: Bernabé-Crespo Miguel Borja , Loáiciga Hugo 
Published: February 2024

AbstractClimate change and the increase of population pose challenges to ensuring suitable water supply in water-scarce regions. This work presents a comparative analysis of the water-supply approaches adopted in Los Angeles, Southeastern Spain, and Sydney. Results show a decrease in per-capita water use in the period 2000–2020, which reflects an improvement in water conservation. Social factors in the domain of hydropolitics and economic efficiency explain the divergence of water policies adopted. The adaptation to water scarcity and growing population in three regions of developed countries located in different continents sheds light on challenges facing the achievement of water security worldwide.
Positioning and detection of rigid pavement cracks using GNSS data and image processing
Authors: Nasrallah Ahmed A. , Abdelfatah Mohamed A. , Attia Mohamed I. E. , El-Fiky Gamal S. 
Published: February 2024

AbstractModern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. Manual inspection is performed by trained inspectors to assess pavement damages conventionally. This can be cost-intensive, time-consuming, and a source of risk for inspectors. An image-based inspection using a smartphone is adopted to overcome such problems. This paper proposes an automatic crack detection and mapping program for rigid pavement, which can automate the visual inspection process. The program uses Global Navigation Satellite System (GNSS) data recorded by smartphones and various image processing techniques to detect crack lengths and areas in images. The performance of the program was evaluated by a field study. A crack quantification process was performed to compare the manually measured values and crack lengths obtained from the program. The results show that the program can detect other types of distress, such as pop-outs and punch-outs. This method can achieve satisfactory performance compared to the effort and costs spent.
A rapidly converging domain decomposition algorithm for a time delayed parabolic problem with mixed type boundary conditions exhibiting boundary layers
Authors: Aakansha  , Kumar Sunil , Ramos Higinio 
Published: February 2024

AbstractA rapidly converging domain decomposition algorithm is introduced for a time delayed parabolic problem with mixed type boundary conditions exhibiting boundary layers. Firstly, a space-time decomposition of the original problem is considered. Subsequently, an iterative process is proposed, wherein the exchange of information to neighboring subdomains is accomplished through the utilization of piecewise-linear interpolation. It is shown that the algorithm provides uniformly convergent numerical approximations to the solution. Our analysis utilizes some novel auxiliary problems, barrier functions, and a new maximum principle result. More importantly, we showed that only one iteration is needed for small values of the perturbation parameter. Some numerical results supporting the theory and demonstrating the effectiveness of the algorithm are presented.
Investigation of Scalograms with a Deep Feature Fusion Approach for Detection of Parkinson’s Disease
Authors: Cantürk İsmail , Günay Osman 
Published: February 2024

AbstractParkinson’s disease (PD) is a neurological condition that millions of people worldwide suffer from. Early symptoms include a slight sense of weakness and a propensity for involuntary tremulous motion in body limbs, particularly in the arms, hands, and head. PD is diagnosed based on motor symptoms. Additionally, scholars have proposed various remote monitoring tests that offer benefits such as early diagnosis, ease of application, and cost-effectiveness. PD patients often exhibit voice disorders. Speech signals of the patients can be used for early diagnosis of the disease. This study proposed an artificial intelligence–based approach for PD diagnosis using speech signals. Scalogram images, generated through the Continuous Wavelet Transform of the speech signals, were employed in deep learning techniques to detect PD. The scalograms were tested with various deep learning techniques. In the first part of the experiment, AlexNet, GoogleNet, ResNet50, and a majority voting-based hybrid system were used as classifiers. Secondly, a deep feature fusion method based on DenseNet and NasNet was investigated. Several evaluation metrics were employed to assess the performance. The deep feature fusion system achieved an accuracy of 0.95 and an F1 score with stratified 10-fold cross-validation, improving accuracy by 38% over the ablation study. The key contributions of this study include the investigation of scalogram images with a comprehensive analysis of deep learning models and deep feature fusion for PD detection.
Aczel-Alsina power bonferroni aggregation operators for picture fuzzy information and decision analysis
Authors: Ma Lijun , Jabeen Khalida , Karamti Walid , Ullah Kifayat , Khan Qaisar , Garg Harish , Yin Shi 
Published: February 2024

AbstractPicture fuzzy set (PFS) is an expedient mathematical approach for interpreting imprecise and nebulous information, and the power Bonferroni mean (PBM) operator is a crucial generalization of the power average (PA) operator, and the Bonferroni mean (BM) operator. Based on the Aczel-Alsina (AA), operational principles of PFS, we expand the PBM operator to integrate PFVs and develop a few AOs, namely PF Aczel-Alsina PBM (PFAAPBM) operator, weighted PF Aczel-Alsina PBM (WPFAAPBM) operator, PF Aczel-Alsina PGBM (PFAAPGBM) operator, and weighted geometric PF Aczel-Alsina PBM (WGPFAAPBM) operators respectively. These newly suggested PF Aczel-Alsina PBM operators can detect the connections between the membership, abstinence, and non-membership functions, which also maintain the important characteristics of the PBM operator. After that, we analyze a few enticing characteristics along with the particular applications of the suggested operators. Based on our suggested technique, we built an illustrated numerical example for the selection of competent research scientists to cope with MADM issues under the framework of PFVs. Finally, we contrast a few of our suggested methodologies with other prevailing methods to determine the feasibility and legitimacy of our suggested strategies.