bluuebunny/crossref_metadata_2025 huggingface dataset:
Moving with the Times: Incorporating Emerging Health Threats to Progress Industry Leading Health Performance Indicators
Authors: Patterson Joan , Walker Kirsty , Poore Wendy , Diaz Guillermo Gomez
Published: September 2024

The joint IOGP-Ipieca Health Committee published an update of Health Performance Indicators – a guide for the oil and gas industry in 2023. As part of the revision process, the tools used to collect industry leading performance indicator data from participating Member Companies were combined and modernized. This paper describes the factors used in the update process and the results of the subsequent data collections.

The IOGP-Ipieca Health Committee's Health Performance Indicators Subcommittee reviewed the technical content of the leading health performance indicators to better reflect evolving industry health risks. This included a response to emerging infectious diseases, the importance of mental health and well-being in the workplace, fitness for duty, and improvements in health surveillance and fatigue management. These updates aim to improve workforce health performance by focusing on preventative actions that can be taken by organizations to enhance the worker well-being.

The online data collection tool, developed in 2023, combines granular scoring for health management system elements related to process maturity and coverage within an organization. The scoring system has been revised to enable companies to show more incremental improvements, and also includes the provision of work hours in the data collection process to allow better industry level scoping. The framework for health performance indicators in relation to elements and individual statements has been expanded to include additional health management aspects and lessons learned post global pandemic.

Health leading indicator data has been collected from the IOGP and Ipieca Member Companies, using the revised scoring system, for 2022 and 2023. Insights from this data collection are presented in this paper and compared with the previous data collections from 2011-2021.

The IOGP-Ipieca Health Committee leading health performance indicator database is the only source of industry level insight into health management across oil and gas organizations. Analysis of the data is used to inform and direct the work of the Health Committee to continually improve worker well-being across the industry.
Safety Critical Task Analysis (SCTA): Identifying & Reducing Opportunities for Human Error
Authors: Layton S. , Garner T. , Karim M. Abdul
Published: September 2024

It is commonly known that human errors whilst undertaking safety critical tasks (SCTs) has been a significant contributory factor in a number of major incidents in the O&G industry (e.g. Deepwater Horizon, Texas City, Piper Alpha, etc). This paper will discuss the Safety Critical Task Analysis (SCTA) methodology as a way to proactively identify and review the human factors affecting safe and effective completion of SCTs.

The methodology involves determining which tasks are safety critical, developing a priority listing of SCTs for analysis, applying guidewords to establish which human action could contribute to an incident (or make a barrier less effective or fail), identifying the performance influencing factors (PIFs) that contribute to the potential for a human to make an error/mistake, and establishing mitigation measures to reduce the likelihood or consequence of human error.

The paper will discuss how the approach will vary depending on the application scenario, for example during Projects (i.e. FEED and Detailed Design), or during facility operations.

SCTA is focused on tasks that have the potential to lead to major accident hazards (MAHs) not occupational risks. SCTA provides a structured approach for assessing the whole working environment and the socio-technical system, and provides an enhanced awareness of the individual safety critical steps in a SCT.

The paper will discuss how existing safety reports (e.g. bowtie analysis) can be used to identify the focus for the SCTA in support of the barrier management process, and the range of human influencing shaping factors (PIFs) that contribute to the occurrence of human error (i.e. Job Factors, Person Factors and Organisational Factors).

The paper will describe the adaptations made to the methodology depending upon the focus of the SCTA (i.e. during Projects, input to safety case/reports, or when changes occur to SCTs on-site), and how SCTA is becoming embedded as an ongoing facility activity, building STCA capability and awareness into frontline operations as part of the overall safety management system (SMS).
Understanding Nature Related Challenges and Opportunities in O&G Company
Authors: Kawaguchi N.
Published: September 2024

It is increasingly recognized that companies need to accelerate actions to halt and reverse the loss of nature and contribute to nature positive. In 2022, the "Kunming-Montreal Global Biodiversity Framework (GBF)" was adopted during the fifteenth meeting of the Conference of the Parties (COP15), and nature-related initiatives such as the TNFD Framework have been published.

INPEX recognizes the importance of addressing the increasingly diverse global environmental issues connected to our business activities, including climate change, biodiversity, water, and waste management. In INPEX, six of the seven core sustainability themes in ISO 26000 were identified as areas of high importance for INPEX and our stakeholders, and these form our Material Issues. One of the Material Issues is biodiversity conservation and water management. Recognizing biodiversity conservation as a significant global environmental issue, the "Policy and Commitments on Biodiversity Conservation" was established and published in December 2022, following a resolution by the Board of Directors.

The type and degree of impacts that our operations have on biodiversity differ depending on the scale, activities, and location of each project. Accordingly, the biodiversity conservation efforts required for each project also differ. Therefore, we assess the importance of biodiversity in the areas in which we operate, and the risks and impacts on biodiversity that each project brings. We then apply a mitigation hierarchy to plan strategies for avoiding, mitigating, and compensating for those risks and impacts, and to conserve biodiversity, in environmentally sensitive areas that are particularly important.

This paper presents the challenges and opportunities that INPEX has identified through the internal nature readiness assessment based on WBCSD's assessment tool (2021), and the internal assessment trial using the TNFD Framework "LEAP Approach".

The internal self-assessment using WBCSD's nature readiness assessment tool (2021) revealed several company-wide challenges and opportunities in INPEX's nature readiness. Some progressing areas were the establishment and publication of nature related policies and commitments, and the implementation of actions based on mitigation hierarchy in operations. On the contrary, setting quantitative targets and KPIs on material topics and evaluating dependencies have been identified as challenges.

An internal assessment trial of TNFD Framework "LEAP Approach" was implemented for INPEX's operational sites in Japan. Through the assessment, the practicality and challenges of applying the LEAP Approach were identified. The LEAP Approach provides the concepts and guidance to collect data and assess location-specific impacts, dependencies, risks, and opportunities. Some key challenges identified were the scoping of the assessment, prioritization of material locations, availability and quality of location-specific data, and selection of appropriate metrics and indicators.
Empowering Sustainable Energy: A collaborative Initiative in Solar Power Installation in Algeria
Authors: Medjitena Mehdi , Kelkouli Mohamed , Abderrahmane Mohamed Said Si
Published: September 2024

This paper describes a collaborative effort between a global energy technology company and a local startup business which specializes in solar energy technology. The aim was to facilitate the installation of solar panels at two of the company's facilities, an office and an operational base. This project was undertaken in alignment with the company's commitment to decarbonization. The project marks a significant step towards the use of renewable energy sources and also highlights the company's dedication to fostering local entrepreneurs and startup businesses.

Solar panels with a total capacity of 136 KWc (kilowatt peak) were installed at a large operational base in Algeria. The installation is projected to annually yield approximately 185,000 KWh (kilowatt hours), reducing CO2 emissions by 74 tonnes per year. Sustainable practices have also been implemented at a large office facility, in the same country, to minimize energy and water utilization, involving the installation of solar water heaters and infrared taps. These initiatives collectively contribute to approximately 10% of the entire building's energy consumption being converted to energy from solar systems.

Through a turnkey solution, the company has supported the local startup business in delivering three solar energy projects since 2021, with a total production capacity of 340 KWc. This collaboration has not only contributed to the company's sustainability goals but also has played a pivotal role in the growth of a local startup business. Over three consecutive years, the local startup's success in delivering projects has paved the way for further collaborations with other stakeholders, including multi-national companies, and national authorities. The company's collaboration and support of this local enterprise have been instrumental in promoting local talent and fostering business growth in the country's solar energy sector.

This initiative sets a precedent for the company's activities in the country by actively promoting sustainable energy solutions through collaborations with local enterprises. The success of this local startup business in securing and delivering these projects highlights the potential for similar collaborations to drive positive change in the industry, which benefits local communities by building a sustainable ecosystem as well as achieving decarbonization objectives for company facilities.
Sustainable Operations through Collaborative Initiatives with Local Indigenous Communities: Case Studies in North America
Authors: Dodds Denise , LeFevre Deidre
Published: September 2024

A global energy technology company has empowered its local teams to prioritize environmental and social initiatives that bring the most benefit to local stakeholders. This paper describes case studies from North American collaborative initiatives with Indigenous communities to encourage participation in the workforce and develop inclusive environmental, sustainability, and educational programs.

Strengthening collaborative relationships with Indigenous communities has increased employment and opened business development opportunities for those communities, provided a more culturally diverse workforce for the energy technology company, and assisted the community in achieving their priorities using digital technology.

The global energy technology company's local teams set objectives and developed programs with local Indigenous communities. In the United States, a customized training and workforce development program was created to provide local candidates with firsthand experience of working on the Alaska North Slope. In Canada, business development opportunities and Indigenous needs were analyzed to create unique business synergies.

An environmental recycling project in Canada has provided business development opportunities to a local area Indigenous community. Rubber components that were once sent to landfills are now donated to an Indigenous-owned business to be recycled into new rubber industrial items to be sold locally.

Another initiative described in the paper focuses on finding a solution for a community's inability to consistently access clean water and used digital technology to solve water imbalance issues in the community's water treatment plant. The energy technology company provided educational scholarships for the community's water treatment plant employees, in addition to software licenses and training to use company process simulation software and 3D modeling technology. This collaborative effort has helped the community achieve its priority to obtain consistent access to clean drinking water through its water treatment facility. The initiative has been presented at local water conferences.
CO2 Estimation Tool – Carbon Footprint of Offshore EPCI Projects in the Digital Era
Authors: Spingardi A. , Nunzi P. , Negoi R. , Zignani T.
Published: September 2024

In the world of today, the offshore industry is still of central importance in the global energy sector, yet it faces significant challenges in managing its carbon footprint. As the world shifts towards more sustainable energy practices, the importance of accurately estimating and reducing carbon emissions in offshore activities has never been more critical.

The Offshore Carbon Estimation (SOCE), an internal tool designed to address this challenge, is providing a cost effective, user friendly and reliable framework for estimation of the carbon footprint for Engineering Procurement Construction and Installation (EPCI) Projects in the offshore energy sector. Made available to users either through an excel file or an online Power Apps Platform, the SOCE tool, it is enabling transparency, collaboration, and independence in conducting carbon footprint analysis for tenders or projects assess to understand the impact of different strategic solutions and the emission reduction potential for the measures identified.
Building Effective Bowties Through Various VAP Cycle Stages to Operationalize Them for Frontliners for Effective Barrier Management
Authors: Lodha R. V. , Nambi R. , Selveraju J.
Published: September 2024

Process safety excellence is achieved when design integrity, technical integrity, and operational integrity are demonstrated for various barriers throughout the lifecycle of an asset. The Bowtie diagram is instrumental in visualizing these barriers and understanding the causal relationships between threats and consequences.

The Bowtie diagram and its analysis play a significant role in managing Major Accident Hazards (MAH) under the Control of Major Accident Hazards (COMAH) regulations in the oil and gas industry. It is a highly effective tool when developed comprehensively and tailored to specific facility or plant operations. The Bowtie diagram not only provides a visual representation of the causes and potential consequences of hazardous material releases or events but also serves as an effective tool in demonstrating technical and operational integrity for robust process safety governance.

Building comprehensive, location-specific Bowties requires meticulous effort and time, but it can deliver effective barrier management in terms of the 3Ps: Plant (hardware and systems), Processes (critical processes and procedures), and People (critical positions and competencies). The substantial effort and time needed to develop these diagrams pose a key challenge in comprehensive Bowtie development and analysis.
Understanding Psychological Safety to Enhance a Better Working Environment
Authors: Al Riyami Salim
Published: September 2024

Collaboration, creativity, and agility are essential in today's business. Within this dynamic environment, psychological safety - the perception of being free from negative repercussions when taking risks, sharing ideas, and admitting errors - emerges as a critical determinant of success. This paper explores the multifaceted benefits of psychological safety within the work environment, examining its impact on both individual well-being and organizational performance.

This paper explores the methods and techniques that were used by the team in bp Oman which was set up to help foster psychological safety. The use of formal face-to-face listening sessions is one important strategy. These meetings gave staff members a designated area to have open and honest conversations with management. Leaders actively listened to concerns and suggestions, which demonstrated a genuine commitment to receive employee feedback. Such interactions helped foster trust and a sense of psychological safety, as employees perceived their voices as valued and heard.

Another method of collecting feedback and concerns was through an online feedback system along with the face-to-face sessions, which found a useful tool for evaluating psychological safety and promoting ongoing improvement. Employees have a safe and practical way to anonymously express their opinions through anonymous surveys or suggestion boxes. This gives people the opportunity to offer insightful comments, even if they would be reluctant to do so in person. Organizations can pinpoint areas in which psychological safety needs to be strengthened and create focused treatments by examining official online feedback.

There are several benefits to developing psychological safety in the workplace. Psychological well-being is enhanced when workers are allowed to express themselves freely in the workplace. There's a greater chance that workers will feel less stressed and anxious, which will enhance their mental well-being and job satisfaction.

In conclusion, this study recognizes the significance of continuous efforts to assess and enhance psychological safety in businesses. Through the use of formal Face-to-Face Listening Sessions and formal Online Feedback in conjunction with leadership development efforts, the company was able to identify lots of areas for improvement and come up with a rigorous action plan to address all the challenges and implement the identified improvements. The intention is to foster a safe workplace where workers feel empowered, respected, and inspired to give their all.

The journey towards fostering psychological safety is an iterative process, and the rewards are immense, leading to a culture of innovation, collaboration, and ultimately, organizational success.
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

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

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

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

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

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

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

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

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

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

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

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

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
Determinants of the willingness to reduce or quit cocaine use among nightlife attendees
Authors: de Jonge Martha Charlotta , Bukman Andrea J. , Blankers Matthijs , Onrust Simone A. , Spronk Desirée B. , Laar Margriet van , Kleinjan Marloes
Published: September 2024

Background Worldwide, cocaine use and availability is very high by historical standards. People who use cocaine are exposed to physical and mental health risks. Prevention activities for this group traditionally focus on harm reduction, but research suggests there are opportunities for the deployment of interventions aimed at the reduction or cessation of cocaine use.
Objectives This study aims to improve our understanding of the characteristics of people who are willing to reduce or quit their cocaine use, as well as the determinants of motivation to reduce or quit cocaine use.
Methods We did a secondary data analysis on data collected for a Dutch cross-sectional study of Night live attendees (N=4824). Specifically, we selected the n=317 participants who had used cocaine at least once per month in the previous year. Logistic regression analyses were performed to investigate associations between willingness to reduce or quit cocaine use, and variables including risk perception, social norms regarding cocaine use, and mental health.
Results We found that Personal acceptance of your own use was associated with both willingness to reduce and to quit cocaine use. A higher frequency of cocaine use was associated with willingness to quit cocaine use. Associations were also found for Perceived harmfulness and several other determinants.
Conclusions Identifying determinants of motivation to reduce or quit cocaine use is crucial for developing interventions aimed at reduction or cessation of cocaine use.
Analyzing the Role of Artificial Emotional Intelligence in Personalizing Human Brand Interactions: A Mixed-Methods Approach
Authors: Asiabar Mojtaba Ghorbani , Asiabar Morteza Ghorbani , Asiabar Alireza Ghorbani
Published: September 2024

Objective: This study investigates the role of artificial emotional intelligence in personalizing human brand interactions.
Methods: A mixed-methods approach was employed, combining quantitative and qualitative data analysis. In the quantitative phase, online interaction data from 500 human brands with their audiences were collected over 6 months and analyzed using machine learning algorithms. The qualitative phase involved in-depth interviews with 25 branding experts and 50 consumers.
Results: Quantitative findings revealed that the use of artificial emotional intelligence led to a 37% increase in engagement rates and a 28% increase in audience satisfaction (p < 0.001). Thematic analysis of qualitative data showed that artificial emotional intelligence strengthens the emotional connection between human brands and their audiences by creating personalized interactions.
Conclusions: This research contributes to existing literature by presenting a novel conceptual model for integrating artificial emotional intelligence into personal branding strategies. It provides valuable guidance for professionals in leveraging emerging technologies to create more effective communications with audiences.
The Capacity of Lower Level Health Centers To Manage Non-Communicable Diseases In Uganda
Authors: Turyamureeba Robert
Published: September 2024

With funding from the Danish Civil Society Fund (CISU), and supported by the Danish NCD Alliance (DNCDA) and Uganda Non Communicable Diseases Alliance (UNDCA), the study focused on assessing the capacity of lower-level Health Centres (HC III & HC IV) to manage Non-Communicable Diseases (NCDs) in Uganda. Specifically, the survey assessed the capacity of HC III and HC IV units to: (i) detect the main NCDs (cancer, diabetes and heart disease); (ii) offer clinical care, including timely referral of patients with NCDs; and (iii) raise awareness, through health education and health promotion, on major risk factors to NCDs. The survey focused mainly on Cancer, Diabetes and Heart Diseases as the most prevalent NCDs. Using a cross-sectional survey design that embraced both qualitative and quantitative methods, the study found that HCs III and IV had inadequate technology, medicines, facilities, policies, record-keeping, human resource capacity to manage NCDs and conduct effective health education and promotion. As such, the study recommended that: the staffing norms of all levels of HCs should be upgraded; funding should be increased at the district level; the salaries and general working conditions of HC workers should be improved; and all HC health personnel should be trained in NCD management.
A Bibliometric Taxonomy of Virtual Reality in Education: Global Trends and Key Challenges Across Multiple Indices and Leading Publishers
Authors: Samala Agariadne Dwinggo , Rawas Soha , Rahmadika Sandi , Criollo-C Santiago , Fikri Ryan , Sandra Randi Proska
Published: September 2024

Technology integration in education has driven significant innovation, with Virtual Reality (VR) emerging as a powerful tool to enhance learning experiences. VR enables students to engage in immersive, interactive environments that go beyond traditional teaching methods, supporting visualization of complex concepts and experiential learning. While VR has demonstrated success in disciplines like medicine, engineering, and design, its application across other fields remains underexplored. This study addresses this gap by conducting a comprehensive bibliometric analysis of VR in education, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, utilizing multiple indices within the Web of Science Core Collection and data from leading publishers. The analysis uncovers key trends, such as Springer Nature's dominance in publishing VR-related research and the geographic distribution of VR studies, with the USA, China, and the UK being prominent contributors. Despite VR's potential, challenges such as high hardware costs, integration difficulties, and the need for educator training hinder its widespread adoption. The study also identifies potential solutions, including the development of affordable VR tools and better curriculum integration. These findings offer valuable insights for policymakers, educators, and technology developers, helping to guide more effective implementation and wider adoption of VR in diverse educational contexts.