bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Diversity and Inclusion Performance Through the Innovative Application of Technology and Data
Authors: Sarmiento C. , Al-Qubaisi M. , Djibo L. O. , Packham J. 
Published: September 2024

Abstract
An energy technology company with a long-standing commitment to diversity and inclusion (D&I) is acquiring and using data to measure D&I performance and enhance accountability through the implementation of a companywide D&I program. Significant resources were invested to further develop best practices, and the dedicated D&I team adopted a multistakeholder approach.
First, an inclusion index methodology that measures inclusivity using seven elements was implemented and applied to 3 years of data gathered by the company's annual global employee survey. An online application was built to enable human resources (HR) personnel to easily filter the data by specific characteristics. The granularity of the data from the survey has enabled comprehensive analysis to identify where progress has been shown as well as areas for improvement. Then, using this comprehensive data and the inclusion index results, HR personnel are tasked with identifying and prioritizing location- and topic-specific actions to develop improvement plans while addressing cultural or geographical aspects. The action plans are added to a second online application, where their progress can be easily tracked. The purpose of this app is to help each HR manager—who knows their region best and is best qualified to analyze their location-specific results—develop, implement, and monitor action plans that are appropriate for their location and context.
Partnering with internal stakeholders such as global employee resource groups complements local initiatives and brings additional insights to further foster an inclusive workplace. Empowerment at the local level is fundamental to inclusivity because what works in one location may not be appropriate in another.
A case study is presented to demonstrate how one of these action plans was implemented using immersive technology. The technology-based program in an operational location effectively used interactive virtual reality (VR) training to enable employees to better empathize with others and become more aware of their behavior, ultimately helping to support a more inclusive culture. This approach aims to adjust mindsets and ultimately promote positive behavior changes to drive inclusion.
Video Analytics for Workplace Safety
Authors: Corallo Lorenzo , Spingardi Angelo 
Published: September 2024

Abstract
The present paper describes how an artificial intelligence-based video analytics system was implemented to improve workplace safety in a construction site in the Middle East. The system can detect and notify potential HSE violations and provide insights and statistics to support HSE management. The system uses high-resolution cameras that stream videos and images to a local server where the video analytics software is installed. The software uses an AI algorithm that can learn to identify unsafe acts and conditions by analyzing the video data and receiving feedback from the HSE operators. The system also sends real-time notifications to the HSE operators via mobile devices. VAWS detects various types of HSE violations, such as PPE noncompliance, work at height, proximity to heavy machinery, and poor housekeeping. The system's reliability, measured by the ratio of confirmed events to total events, increased from 8% to 30% in six months (from November ’23 to April ’24). The system also learned to reduce false positives by receiving specific comments and images from the VAWS analysis team. Thanks also to this solution is possible to identify the riskiest areas and riskiest activities thus allowing site management to provide feedback and specific training to the workers. The implementation of the system faces challenges such as selecting the appropriate type and position of the cameras, complying with explosive zone requirements where necessary, ensuring the network bandwidth and storage capacity, avoiding glare, shadows, and obstructions. The system also requires human validation and intervention, respecting the privacy and ethical rights of the workers, and managing the cyber security risks of the AI system. Based on available results and further planned developments it can be concluded that VAWS is expected to improve the quality and quantity of HSE supervision at large and remote construction sites, especially for routine tasks, night shifts, and less monitored areas.
Implementation of Artificial Intelligence for Continuous Greenhouse Gas Monitoring and Leak Detection
Authors: Gendron Blake , ElHaggaz Ammar , Al Qassabi Majid 
Published: September 2024

Abstract
In the Oil & Gas industry, fugitive methane emissions account for an estimated 57% of total industry emissions, which combined with upstream flaring comprise nearly 2/3rd of the CO2e across the Oil & Gas upstream/midstream/downstream value chains. While hydrocarbons will likely remain an import (dominant) share of the energy & products mix well into the future, decarbonization efforts have been mobilized to ensure that oil & gas is produced and consumed with a lower carbon intensity. Examples of these decarbonization efforts include the Zero Flaring Initiative that many producers around the world have subscribed to and are working toward. However, fugitive emissions and routine venting/flaring remain a tough-to-abate portion of the industry's emissions, and new technologies around emissions detection are being developed to more accurately (and quickly) measure these "invisible" sources greenhouse gas (GHG) emissions. The combination of governmental mandates (e.g. US EPA Super Emitters Program) and carbon pricing mechanisms are increasing the pressure, and also the incentive, for producers to deploy next-generation emissions detection solutions, rather than relying on conventional solutions and theoretical calculations from emissions factors. Within the bucket of continuous emissions monitoring solutions, certain Artificial Intelligence + Metal Oxide Sensor (AI/MoS-based) technologies show promise in terms of the relative cost effectiveness, and also quality & completeness of site-level emissions data capture. This paper evaluates several new emissions monitoring technology types for the purpose of selection for a specific deployment in Oman. The subsequent six month deployment at multiple sites in Oman, characterized by extreme ambient temperature fluctuations and remoteness of the location, resulted in valuable data supporting the efficacy of AI/MoS-based, continuous emissions monitoring technologies in such applications. As such, this study supports the view that AI/MoS should be considered as part of a wider portfolio approach to emissions monitoring within the Oil & Gas industry, across all segments of the value chain and in all areas & environments.
Comparison of the diagnostic performance of two PIVKA-II assays using the GALAD and ASAP algorithms for detecting hepatocellular carcinoma in Chinese patients
Authors: Hu Yao , Du Jiaying , Gao Quan , Cen Yi , Liu Qin , Chen Yanwen 
Published: September 2024

Abstract

Objectives
Prothrombin induced by vitamin K absence-II (PIVKA-II) is useful for detecting early-stage hepatocellular carcinoma (HCC). Here, we aimed to assess the diagnostic performance of two PIVKA-II assays measured using ARCHITECT and µTASWako for HCC, as well as HCC caused by the hepatitis B virus (HBV), in a Chinese population.
Design and Methods:
The GALAD HCC detection algorithm depends on the µTASWako PIVKA-II assay, while the ASAP algorithm uses the ARCHITECT PIVKA-II assay. These methods were validated and compared using a retrospective cohort of 431 HCC patients and 606 chronic liver disease (CLD) controls from Huashan Hospital between January 2022 and December 2023.
Results
Using receiver operating characteristic curve analyses of the validation cohort, the GALAD algorithm had an area under the curve (AUC) value of 0.896 [95% confidence interval (CI): 0.873–0.919], while the AUC value was 0.894 [95% CI: 0.870–0.918] for the ASAP algorithm. The ASAP algorithm for HBV-associated HCC detection had the highest AUC value (0.950; 95% CI: 0.933–0.967), with a sensitivity of 89.3% and specificity of 81.6%.
Conclusions
Using the GALAD and ASAP algorithms to detect HCC displayed similar favorable accuracy. The ASAP algorithm was more reliable for the detection of HBV-associated HCC in Chinese patients.
Application of natural astaxanthin from Haematococcus Pluvialis
Authors: Chen Tao , Liao Qiang , Huang Yun , Zhu Xun 
Published: September 2024

Abstract

Haematococcus Pluvialis, a freshwater microalgae, is renowned as a significant source of natural astaxanthin, an essential biological resource. This unicellular organism is known to contain over 5% astaxanthin, making it one of the most abundant sources. Astaxanthin levels in H. Pluvialis are elevated under growth conditions stressors such as high salinity, temperature, and light exposure. It is intriguing to observe that all astaxanthin molecules within the microalgae exhibit in a left-handed molecular configuration (3S, 3'S). This paper presented a comprehensive review on H. Pluvialis, focusing on its culture technology, astaxanthin extraction process, and derivative products. The aim is to facilitate and enhance the broader and more in-depth utilization of natural astaxanthin.
Sheltered by trees – long-term yield dynamics in temperate alley cropping agroforestry with changing water availability
Authors: Koch Olef , Moore Jennifer , Hörl Jakob , Cormann Michael , Gayler Sebastian , Lewandowski Iris , Marhan Sven , Munz Sebastian , Pflugfelder Markus , Piepho Hans-Peter , Schneider Julia , von Cossel Moritz , Weinand Tanja , Winkler Bastian , Schweiger Andreas H. 
Published: September 2024

Abstract

As warm season droughts increase in frequency due to climate change, causing severe yield losses especially among cereal crops, European agriculture is in dire need of adaptation. While agroforestry is widely regarded as a key adaptation measure, little is known on how yield performance is influenced by changing water availability. Therefore, we assessed the yield dynamics of five winter crops (winter wheat, triticale, winter barley, winter pea, and rapeseed) during seven growing seasons (2012 to 2023) in a well-established (since 2008) alley cropping agroforestry trial site in Southwestern Germany. The trial integrates three different agroforestry practices in a randomized block design: i) willow short-rotation coppice, ii) walnut trees for nut production, and iii) diverse hedgerows. The relationship between crop yield and climatic water balance was analysed using a linear mixed-model. In this unique long-term comparison, we demonstrate that individual alley cropping practices exhibited distinct yield patterns with increased distance to tree rows. In contrast to the willow short rotation coppice, walnut and hedgerows did not evoke significant winter crop yield declines in their close proximity. While in the walnut plots yields did not significantly vary at all with distance to tree rows, yields adjacent to hedge rows declined significantly towards the alley center. Moreover, inter-annual variation in water availability significantly influenced yield distribution across these distances. While yield response to changing climatic water balance varied with aspect, the tree rows overall contributed to stabilizing crop yields under fluctuating water availability as yields did not vary significantly close to trees. Our results underline the potential of agroforestry to sustain yields in the face of increasingly variable water availability. Therefore, substantiating the contribution of alley cropping agroforestry to resilient farming systems facing increasingly variable weather conditions, informing planning, policy support and agroforestry practice at advancing climate resilient agroforestry in temperate regions.
Robust and generalized deep learning-based approach for mouse bone segmentation from 3D high-resolution μCT imaging data
Authors: Lagzouli Amine , Pivonka Peter , Cooper David , Sansalone Vittorio , Othmani Alice 
Published: September 2024

Abstract

Recent advancements in deep learning have significantly enhanced the segmentation of high-resolution microcomputed tomography (µCT) bone scans. In this paper, we present the Dual-Branch Attention-based Hybrid Network (DBAHNet), a deep learning architecture designed for automatically segmenting the cortical and trabecular compartments in 3D µCT scans of mouse tibiae. DBAHNet’s hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. This evaluation covered variations in resolutions, ages, mouse strains, drug treatments, surgical procedures, and mechanical loading. DBAHNet demonstrated excellent performance, achieving high accuracy, particularly in challenging scenarios with significantly altered bone morphology. The model’s robustness and generalization capabilities were rigorously tested under diverse and unseen conditions, confirming its effectiveness in the automated segmentation of high-resolution µCT mouse tibia scans. Our findings highlight DBAHNet’s potential to provide reliable and accurate 3D µCT mouse tibia segmentation, thereby enhancing and accelerating preclinical bone studies in drug development. The model and code are available at Github.
W-Attention-Residual U-Net Architecture for Massive Brain Tumor Segmentation
Authors: Abas Marwa , Khalaf Ashraf A. M. , Mogahed Hussein , MABROOK M. MOURAD 
Published: September 2024

Abstract

Recently, there has been a notable growth in the utilization of diverse magnetic resonance imaging (MRI) techniques for examining brain tissue. However, the manual examination of each generated image can be a tedious task. UNet and its modifications are among the state-of-the-art models for segmenting medical images that have demonstrated promising MRI performance. This is due to the rapid growth of deep learning and its application to medical imaging challenges. Motivated by UNet's success and its modifications to enhance overall segmentation performance, this paper proposes an architecture linking two basic Attention-Residual UNet, W- AG-Res-U-Net. Each Attention Residual U-Net in the proposed model is integrated with the Atrous Spatial Pyramid Pooling (ASPP) mechanisms. This new architecture connects two Attention U-Net integrated with the two different Atrous Spatial Pyramid Pooling (ASPP) mechanisms. However, using a single Atrous pyramid pooling module, the image contains objects of various sizes in the same class and cannot capture targets that are too large or too small. Therefore, we designed an unsymmetric feature extraction module, the Atrous Spatial Pyramid Module (ASPM1), which integrates the encoder and decoder of the first net at a low atrous dilation convolution rate to capture small objects in the image. In addition, ASPM2 integrates the second net to capture large objects in the image by using the dilation convolution with a more significant atrous rate enhanced residual unit. Moreover, the residual unit in the proposed model is optimized by using a squeeze-and-excitation block to extract adaptive features, suppress wasteful regions, and highlight features significant to certain segmentation tasks. We assessed our brain tumor segmentation model on the Figshare public dataset, achieving an accuracy of 99.8%, a DSC of 97.76%, an IoU of 97.36%, a sensitivity of 93.42%, and a precision of 95.14%. Furthermore, our approach outperforms state-of-the-art techniques in segmentation outcomes.
Enhancing Stock Portfolio Selection with Trapezoidal Bipolar Fuzzy VIKOR Technique: A Multicriteria Decision-Making Approach
Authors: Sharma Sunil Kumar 
Published: September 2024

Abstract

The investors’ main objective to minimize the risks and to get the maximum returns in the random stock market requires to choose the correct mix of the stocks. The traditional portfolio selection methods often struggle with market volatility, leading to less-than-the targeted profits. This paper in an attempt to apply trapezoidal bipolar fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). method to help in decision-making. We combine the fuzzy set theory with the VIKOR method, the trapezoidal bipolar fuzzy with VIKOR (TrBFV) approach offers a comprehensive and flexible system for evaluating investment options. It has been discussed in the paper TrBFV technique and illustrate its application through a real-world example. The analysis has been made with VIKOR method and its integration with trapezoidal bipolar fuzzy sets. The result shows that this novel approach is effective and can help to investors to take decisions in the unpredictable financial market. This study is an attempt to provides investors with a able tool to navigate the challenges of stock market investment.
Validation of a French version of the Empowerment Scale for Mental Health Service Users
Authors: Desvignes Saphir , Boucekine Mohamed , Loubière Sandrine , Leclerc Léa , Auquier Pascal , Tinland Aurélie 
Published: September 2024

Abstract

Background
As the concept of empowerment is increasingly adopted across various mental health care contexts, there is a growing need for standardized measures to assess the effectiveness of empowerment approaches. The Empowerment Scale is widely utilized and translated within the field of mental health, despite its varied psychometric properties. This study aimed to translate the Empowerment Scale into French and assess its internal consistency, validity, and responsiveness.
Methods
This study was part of a larger research project involving 394 participants. The Empowerment Scale was translated into French following cross-cultural adaptation guidelines, with a translation committee consisting of experts and a professional translator. Psychometric properties were assessed using classical test theory. The factor structure was determined through principal component analysis, exploratory factor analysis, and confirmatory factor analysis. Internal consistency was measured using Cronbach's alpha, while validity was evaluated through convergent, discriminant, and concurrent validity analyses. Responsiveness was assessed by comparing empowerment scores to changes in recovery rates.
Results
The factor analyses supported a four-factor, 18-item model, showing good fit indices (CFI = 0.97, TLI = 0.97, AGFI = 0.97, SRMR = 0.07, RMSEA = 0.07). Internal consistency was acceptable for the overall scale (alpha = 0.84) and the "self-esteem-self-efficacy" dimension (alpha = 0.88) but lower for the other dimensions. The scale demonstrated moderate correlations with recovery (r = 0.47) and quality of life (r = 0.28). The Empowerment Scale demonstrated low or insignificant responsiveness, except for the "self-esteem-self-efficacy" dimension, which showed moderate responsiveness.
Conclusions
The French version of the Empowerment Scale has a good factor structure with 4 factors and 18 items. The “self-esteem” dimension demonstrates good concurrent validity and reliability, and moderate responsiveness, while other dimensions require additional validation.
CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs for Reduced hERG Liability
Authors: Kyro Gregory W. , Martin Matthew T. , Watt Eric D. , Batista Victor S. 
Published: September 2024

Abstract

The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, NaV1.5 and CaV1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source at https://github.com/gregory-kyro/CardioGenAI to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows.
Semantic-enhanced Heterogeneous Graph Contrastive Learning for Recommendation
Authors: Sang Lei , Song Feiran , Wang Yu , Zhang Yi , Zhang Yiwen 
Published: September 2024

Abstract

Heterogeneous Graph Neural Network (HGNN) has become increasingly indispensable in recommender systems due to its capability to integrate multi-source heterogeneous information, thereby modeling real-world recommendation scenarios effectively. Concurrently, contrastive learning has achieved significant success in graph learning. However, most existing contrastive learning methods utilize random perturbations to enhance user/item embedding representations. This unreliable data augmentation strategy often leads to the loss of crucial graph structure and introduces misleading supervision signals, thereby hindering the effective capture of rich semantic information within heterogeneous graphs. To address these issues, we propose a novel model named Semantic-enhanced Heterogeneous Graph Contrastive Learning for recommendation (SeHGCL). SeHGCL designs a generative reconstructed data augmentation method with Masked Auto- Encoder (MAE) to capture the intrinsic semantic connections within the graph structure, thus obtaining the semantic-enhanced embeddings and addressing the unreliability of traditional data augmentation methods. Besides, the supervision signals derived from this generative reconstructed data augmentation are then utilized to aid the learning of the primary task’s representation through an enhanced dual-view contrastive learning method. Experiments conducted on three real-world datasets demonstrate that SeHGCL can effectively enhance the semantic representation with respectable performance over state-of-the-art baselines. Codes are available at https://github.com/jerrica0409/SeHGCL