bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast Evaluation of Reservoir Connectivity via a New Deep Learning Approach: Attention-Based Graph Neural Network for Fusion Model
Authors: Saihood Tariq , Saihood Ahmed , Al-Shaher Mohamed Adel , Ehlig-Economides Christine , Zargar Zeinab
Published: September 2024

The goal is to estimate the injector-to-producer connectivity from injection-production history data by implementing an attention-based graph neural network for fusion model (AGFM). The AGFM can identify the complex relationships between the injectors and producers, ensuring the spatially dense estimated injector-to-producer connectivity. The model is trained and tested on a dataset containing two types of injecting fluids: carbon dioxide (CO2) and water. The AGFM model correlates the relationships between every injector and all producers concerning produced oil, water, and gas. AGFM involves constructing a graph where nodes represent wells and edges represent their spatial and operational relationships. Node features include injection and production rates, while edge weights are based on physical proximity and connectivity among wells. The model can also optimize the water alternating gas ratio (WAG ratio) by alternating the injection of CO2 and water. The model can benefit from optimizing the WAG parameters to improve sweep efficiency and reduce gas channeling. We assessed the AGFM model through three scenarios of experiments. The first scenario uses CO2 as the injection fluid, the second uses water as the injection fluid, and the third uses CO2 and water alternately. We aligned the actual produced gas, oil, and water in each scenario for the methods with the model's predictions. We also compared the results with some selected state-of-the-art in terms of accuracy and mean squared error (MSE) (more quantitative and qualitative can be presented). The significant finding of the AGFM model was its ability to identify long-range dependencies between the injector and producer wells correctly. The AGFM model also correctly identified the immediate connections between each injector and all producer wells. Overall, the observational results support the conclusion that the AGFM model is a promising new approach for injector-producer connectivity estimation.
Enhancing Management Insight into Mergers & Acquisitions Using Probabilistic Financial Analysis
Authors: Hughson L. , Klimley B. J. , Orlando M. J.
Published: September 2024

Between 2011 and 2022 greater than 50% of global E&P merger and acquisition (M&A) transactions destroyed shareholder value with only 25% generating approximately 60% of total excess returns, per a 2024 paper from McKinsey & Co.1 Upgrading the financial analysis approach to provide better insights for M&A decision-making professionals is therefore increasingly important to enhance their probability of success. This paper analyzes the 2021 merger of Cabot Oil & Gas with Cimarex Energy (the Transaction) to understand the distinct advantages gained from applying a probabilistic approach to the financial analysis of mergers, acquisitions and divestitures. Traditional deterministic valuation and transaction analyses, which rely on a static set of assumptions, are compared to a probabilistic approach based on Monte Carlo simulation using a readily available Excel add-in. This paper reviews and utilizes the additional insights from the probabilistic output, generated from the input probability distribution functions and correlation coefficients chosen, unavailable from a traditional, deterministic financial approach. The probabilistic approach provides an enhanced transaction analysis process that produces the critical metrics company Boards and shareholders increasingly require in determining if a transaction should be undertaken. This paper highlights how a probabilistic approach to the M&A process: (i) is straightforward to implement; (ii) facilitates a greater use of real-world data; (iii) generates outcome probabilities for key transaction metrics unavailable with a deterministic approach; and (iv) provides unique risk insights into those transaction metrics. This probabilistic approach to the M&A process represents a significant, practical improvement to management's ability to evaluate specific transactions and enhance their likelihood of success.
A Producer-Based Capacitance Resistance Model (CRM) to Characterize Oil Reservoirs with Natural Water Influx: a Realistic Application
Authors: Koroglu B. , Onur M. , Li Y. , Alpak F. O.
Published: September 2024

In water-drive reservoirs, predicting variable water influx to individual wells poses significant challenges due to geological heterogeneity and diverse production strategies. This study introduces a novel capacitance-resistance model (CRM) that robustly incorporates natural water influx into oil reservoirs from aquifer systems, eliminating the need for average reservoir and aquifer pressure versus time data. The proposed aquifer-integrated CRM addresses the pressure change equation between the aquifer and reservoir systems using fundamental CRM parameters including aquifer pore volume and productivity index. The development process focuses on the CRM with producer representation (CRMP) and reformulates it to calculate instantaneous water influx based on reservoir and aquifer pressure differences, naming this model CRMPAQ. The history matching algorithm utilizes Sequential Least-Squares Programming (SLSQP) to manage both inequality and equality constraints, employing linear inequality constraints to ensure that the summation of aquifer-producer connectivity and well-to-well connectivity does not exceed unity. Additionally, a linear equality constraint ensures that the summation of producer pore volumes equals the total reservoir pore volume. The CRMPAQ model is designed to match total production rates and bottomhole pressures (BHPs), if available. Numerical experiments conducted using CRMPAQ on both 3D synthetic and realistic field datasets demonstrate its effectiveness. Comparisons with results from the commercial simulator ECLIPSE, which accounts for saturation dependency on well productivity, further validate the model. The realistic field dataset comprises three wells from a heterogeneous reservoir with natural water influx, frequent shut-ins, and variable skin effects, with daily records of production rates and BHPs. The results indicate that CRMPAQ yields comparable outcomes and effectively emulates the behavior of high-fidelity reservoir simulators in terms of total liquid production rates and BHP data. CRMPAQ is significantly more computationally efficient; two to three orders of magnitude faster than high-fidelity simulator history matching. Furthermore, when matching BHP data using flow rate history, CRMPAQ accurately accounts for the effects of frequent shut-ins as well as the variable productivity indices on parameter estimation. The model was tested on synthetic datasets and validated with a realistic field dataset (used in SPE-210102-PA). The realistic dataset considers three wells produced from a heterogeneous reservoir with natural water influx, frequent shut-ins, and variable skin effects. The wells' production rates and BHP data are recorded at daily time intervals.
Dynamics of the 1873 CE “Breccia De Fiore” phreatic eruption at Vulcano (Aeolian Islands, Italy) through historical chronicles, physical volcanology, and numerical modelling
Authors: Giansante Silvia , Ongaro Tomaso Esposti , Cioni Raffaello , Pistolesi Marco , Vitturi Mattia de' Michieli
Published: September 2024

Phreatic events may represent precursors of magmatic eruptions, but they can also occur as single or multiple episodes punctuating the activity of volcanoes characterised by active hydrothermal systems. The Breccia De Fiore deposit represents evidence of the phreatic activity preceding by fifteen years the onset of the 1888–1890 magmatic eruption at La Fossa di Vulcano. This study integrates historical chronicles, sedimentological and physical descriptions of the deposit, and 3D numerical simulations to reconstruct eruption dynamics. Results indicate that this deposit was the product of multiple, shallow explosions of low magnitude (< 5×104 m3 cumulative volume), possibly occurred during a short time interval at the beginning of the phreatic sequence in 1873. The explosions produced pyroclastic density currents and ballistic fallout, affecting the slopes of the La Fossa cone. The asymmetry of the deposit, outcropping on the north-western flank of the cone, is well reproduced by 3D simulations as the product of explosions from an inclined vent driven by shallow (i.e., < 150 m deep) pressure build-up (up to 5 MPa) from accumulating magmatic fluids. Simulations of explosion dynamics and the distribution of eruptive products allows us to put some constraints on the two main controlling parameters of phreatic scenarios: erupted total mass and specific energy. This study also highlights the potential impact of such relatively small phreatic explosions on the Vulcano island and the need for quantifying their hazards, especially given the proximity to populated sites and the growing tourism in this active volcanic area.
Interpeduncular GABAergic neuron function controls threat processing and innate defensive adaptive learning
Authors: Molas Susanna , Williams Elora , Snively Leshia , O'Meara Benjamin , Jacobs Hannah , Kolb Miranda , Zhao-Shea Rubing , Baratta Michael , Tapper Andrew
Published: September 2024

The selection of appropriate defensive behaviors in the face of potential threat is fundamental to survival. However, after repeated exposures to threatening stimuli that did not signal real danger, an animal must learn to adjust and optimize defensive behaviors. Despite extensive research on innate threat processing, little is known how individuals change their defensive behaviors when presented with recurrent threat exposures without evidence of a real risk. Insight into this process is critical as its dysregulation may contribute to neuropsychiatric conditions, such as anxiety disorders. Here, we used the visual looming stimulus (VLS) paradigm in mice to investigate innate threat processing and adaptive defensive learning. Repeated exposure to VLS over consecutive sessions reduced immediate freezing responses and time spent inside a sheltered area upon VLS events, leading to an increase in foraging behaviors. Fiber photometry recordings and optogenetic manipulations revealed that VLS innate adaptive defensive learning is associated with reduced recruitment of the midbrain interpeduncular nucleus (IPN), a structure associated with fear and anxiety-related behaviors. Functional circuit-mapping identified a role for select IPN projections to the laterodorsal tegmental nucleus in gating defensive learning. Finally, we uncovered a subpopulation of IPN neurons that express the neuropeptide somatostatin and encode safety- and avoidance signals in response to VLS. These results identify critical behavioral signatures of innate defensive responses and a circuit that regulates the essential features of threat processing.
How a Moment of Great Intensity Exposed Grave Structural Violence: COVID-19’s Gendered Effect on Subjective Wellbeing in MENA Countries
Authors: Barsoum Ghada , Majbouri Mahdi
Published: September 2024

The COVID-19 pandemic has been a critical juncture that exposed many existing inequities in facing this global threat. Building on Galtung’s conceptualization of positive peace as the absence of structural violence and institutionalized inequality, we study the gendered effect of the COVID-19 pandemic on reported subjective wellbeing (SWB) in four countries in the Middle East. Data from mobile phone panel surveys, with a total sample of 12,614 observations collected during this critical juncture, show that women consistently reported a lower level of SWB than men in all four countries. Women experienced higher increases in unemployment rates than men in all four countries, despite their already higher rates prior to the pandemic. Controlling for individual characteristics and geographic-time fixed effects, the main factor associated with lower SWB was the decline in household income, reflecting the intersectionality of poverty and gender. In addition, reported increase in the time spent on housework negatively affected women’s SWB. The pandemic has further accentuated gender inequality in all four countries and exposed the inherent structural violence experienced by women in these contexts.
Evaluation of grain yield, quality characteristics and high molecular weight glutenin subunits of bread wheat cultivars in different agro-ecological regions of Türkiye
Authors: Aktaş Bekir , GÖKDERE Halil İbrahim
Published: September 2024

The current study aimed to determine and compare the grain yield, some quality characteristics and high-molecular-weight glutenin subunits (HMW-GS) of bread wheat cultivars cultivated in several agro-ecological regions using a total of 46 registered bread wheat cultivars in the 2016–2017 and 2017–2018 growing seasons. The results determined that the environment had the largest share in the total variation (Genotype + Environment + Genotype×Environment Interaction). The Mediterranean region had the highest average grain yield with 8137 kg ha− 1, while the Central Anatolia region (under the rainfed conditions) had the lowest average with 4260 kg ha− 1. The average thousand kernel weight of the cultivars was 35.3–39.9 g, test weight 77.2–79.2 kg hL− 1, protein content 13.4–14.7%, Zeleny sedimentation 39.2–53.3 mL, and alveograph energy value varied between 191.2-276.4 10− 4 J. Regarding the HMW-GS, 18 of the 46 cultivars scored 10, and one scored 5. The highest mean protein content and alveograph energy value was determined in cultivars with 9 Glu-1 score. In Zeleny sedimentation, cultivars with 10 Glu-1 score showed the highest mean value. The most common subunits in loci; it is 2* in Glu-A1, 7 + 8 and 7 + 9 in Glu-B1, and 5 + 10 in Glu-D1. The fact that registered cultivars predominantly carry these subunits at Glu-1 loci, which could be the result of yield and quality-oriented selection in the breeding process. It was concluded that high quality new varieties could be developed by HMW-GS oriented crosses and selections in wheat breeding programs.
How to Solve Systematical Problem Such as Ecosystems Involving Nonlinear Dynamics and Complexity by Systematic Solution Through Modeling and Simulation
Authors: Cha Deok soo , il Kim Kyoung
Published: September 2024

This study is provided to third-party scientists other than physicists and mathematicians. This study revealed that nonlinear dynamics and complex systems that have not been solved for the past 300 years are systematic problems caused by dynamic systems, such as ecosystems; they also provide a systematic solution completed through [modeling-simulation-verification-return] using mathematical tools (system analysis theory) developed in the 20th century. Therefore, this is an innovative solution that no one could have imagined. Nevertheless, the systematic solution does not overlap or conflict with chaos theory in statistical physics. Therefore, we can freely use both solutions and provide them to third-party scientists without any conditions, because third-party scientists have no reason to avoid or reject them. Therefore, this study should be implemented immediately without peer review. If, at that stage, errors are found in the solution or the user wants to stick to the existing chaos theory, the existing chaos theory can be used; there is no problem with this. Therefore, this research will be established as a solution to solve the nonlinear dynamics and complexity exhibited by dynamic systems with self-regulating functions.
Fetal Kidney Transplantation into In Utero Fetuses
Authors: Yokoo Takashi , Morimoto Keita , Yamanaka Shuichiro , Matsui Kenji , Kinoshita Yoshitaka , Inage Yuka , Yamamoto Shutaro , Koda Nagisa , Matsumoto Naoto , Saito Yatsumu , Takamura Tsuyoshi , Fujimoto Toshinari , Fukunaga Shohei , Tajiri Susumu , Matsumoto Kei , Ozawa Katsusuke , Wada Seiji , Kobayashi Eiji
Published: September 2024

Potter sequence consists of various symptoms associated with renal dysplasia. For bilateral renal agenesis, there is no hope of survival. As a novel therapeutic approach for Potter sequence, we developed a unique approach of “transplantation of fetal kidneys from a different species during the fetal period.” In this study, we first validated the approach using allogeneic transplantation. Fetal kidneys with bladders from green fluorescent protein-expressing rats (embryonic day 14.0–16.5) were subcutaneously transplanted into allogeneic rat fetuses in utero (embryonic day 18.0–18.5). After birth, the transplanted fetal kidneys were confirmed to have urine production capability. Furthermore, long-term (up to 150 days) urine production was sustained. Next, we performed xenotransplantation. The transplantation of mouse fetal kidneys into rat fetuses in utero led to the maturation of renal tissue structures. We demonstrated organ transplantation into in utero fetuses using fetal kidneys as donor organs for fetal therapy.
Exploration of the relationship between SDGs and CSR reports with text mining techniques for stock exchange companies over Taiwan
Authors: yu Tai-Yi , Horng Jeou-Shyan , Chang I-Cheng , Yu Tai-Kuei , Liu Chih-Hsing , Chou Sheng-Fang
Published: September 2024

This research collects corporate social responsibility (CSR) reports from stock exchange companies in Taiwan and employs text mining technologies to analyze the relationship and document similarity between CSR reports from various industries and the Sustainable Development Goals (SDGs). The methods used include natural language processing (NLP), TF-IDF weighting, principal component analysis (PCA), and document similarity assessment. The study applies sub-items of selected SDG terms to screen feature words, build the TF-IDF matrix, analyze the CSR report contents using PCA, and utilize cosine similarity to compare the similarity between CSR reports and SDG sub-items. A total of 225 feature words were identified based on SDG sub-items, with the top 60 feature words (26.7%) accounting for 77.9% of the total TF-IDF weights, aligning with the Pareto principle. Analyzing 370 CSR reports from selected stock exchange companies (0050 ETF), unique and representative feature words and explained variations were identified. Each rotated principal component allowed the identification of corresponding SDG sub-items through specific feature words. The high diversity of feature words resulted in low and unique explained variance for each rotated principal component. Document similarity comparisons between CSR reports and SDG sub-items revealed confidence levels indicating the degree of alignment between CSR reports and SDG sub-items. For the natural language segmentation process and automatic document classification of CSR reports, the assistance of domain experts is recommended to ensure accurate and consistent segmentation and classification results.
Does institutional quality matter for renewable energy consumption and CO2 emissions? Evidence from a panel of BRICS countries
Authors: Ummalla Mallesh , Goyari Phanindra
Published: September 2024

The main purpose of the present study is to explore the impact of institutional quality (IQ) on renewable energy consumption (REC) and CO2 emissions (CO2e) in a panel of BRICS countries, spanning the period 1996Q4-2016Q4. To accomplish this, we use several panel econometric techniques on targeted models. The results of the Westerlund panel cointegration test show that there is a significant long-run equilibrium association among the variables. Furthermore, Augmented Mean Group (AMG) estimator outcomes reveal that IQ has a significant positive impact on REC. Non-renewable energy consumption (NREC) has a positive association with CO2e, while IQ and REC significantly reduce CO2e. Finally, we found no causal nexus between IQ, REC, and CO2e in BRICS countries. Our novel study findings not only provide new empirical evidence to the existing literature but also help policymakers strengthen the IQ to promote the uses of renewable energy and mitigate CO2e.
E-learning benefits for medical students: the longitudinal use of an online learning experience at an international medical school
Authors: Zavala-Cerna Maria G , Caballero Augusto E. , Verbeeck-Mendez Steffi , Parker Michael J.
Published: September 2024

The COVID-19 pandemic forced schools worldwide to change the delivery of the medical curriculum to an exclusively online format. Some important findings after this experience highlight the possibility of continuing with the online form of curriculum delivery, at least partially, to reinforce the learning experience of medical students. Our research goal was to analyze the benefits that medical students can gain after exposure to high-quality e-learning. Our primary aim was to analyze the gain of knowledge, as well as self-rated benefits and understanding, after exposure to high-quality e-learning. We conducted a retrospective study to report on the outcomes achieved after 3 years of experience with the online delivery of basic biomedical science courses. We included results from 1,546 students and 2,613 course enrollments in two different medical programs. Here, we report on students’ benefits after online courses in terms of knowledge gain and satisfaction. The information presented here could be used to make decisions about the incorporation of e-learning into the medical curriculum.
Application of a neural network for modeling and identification in a wastewater treatment plant
Authors: Carrillo Daniel Muñoz , Árcega Anilú Franco , Morales Virgilio López , García-Islas Luis Heriberto , Martines Hilario , Misses Manuel Alejandro Ojeda
Published: September 2024

This work presents the modeling of a wastewater treatment plant using nonlinear ordinary differential equations that allow dissolved oxygen to be monitored. The plant configuration is based on an anoxic bioreactor, an aerobic bioreactor and a secondary settler connected in cascade. The mathematical modeling of the plant based on the ASM model No. 1. On the other hand, it is possible to model the system by neural model of the plant using hierarchical neural networks. The objective of which is to identify some reactions that occur in bioreactors. The implementation of the identification of reactions is carried out through numerical simulations that allow validating the effectiveness of the proposed model and also obtaining relevant results to be able to apply them in future work in neural control.
ADHD Rating Scale Adaptation in Saudi Arabia: Factor Structure, Measurement Invariance, and Normative Data
Authors: Pierce Corey D. , Alhossein Abdulkarim , Lambert Matthew C. , Aldabas Rashed , Bakhietb Salaheldin Farah Attallah , Fitzpatrick Heather
Published: September 2024

The ADHD Rating Scale is widely used to assess ADHD in the United States, and psychometrically sound. However, when assessments are translated into other languages, it is necessary to re-examine psychometrics. This study aimed to analyze the factor structure, test measurement invariance, and provide normative data for an adaptation of the ADHD Rating Scale in Saudi Arabia using parent ratings of 3,127 youth and teacher ratings of 2,595 students. Outcomes corroborate the validity of the two-factor correlated model for both parent and teacher ratings, and the measurement invariance among age and gender groups. The adapted ADHD Rating Scale is suitable for assessing the ADHD symptomology of youth in Saudi Arabia using the normative standards developed in this study.
Adaptive Visual-Inertial Odometry Using SuperGlue and Dynamic EKF with Information-Based Confidence Estimation
Authors: ASIL Ufuk , Nasibov Efendi
Published: September 2024

This study presents an innovative Visual-Inertial Odometry (VIO) approach for Unmanned Aerial Vehicles (UAVs). The proposed system integrates the modern deep learning-based SuperGlue algorithm with an information based adaptive Extended Kalman Filter (EKF). The system establishes a dynamic confidence estimation mechanism using image entropy, intensity variation, and motion blur metrics, thereby providing robust pose estimation even in challenging environmental conditions. Thanks to the superior performance of advanced transformer-based feature matching methods like SuperGlue, the proposed loosely-coupled sensor fusion technique approaches the accuracy of traditional tightly-coupled approaches, potentially offering an alternative to these methods. Comprehensive experiments conducted on the EuRoC MAV dataset demonstrate that the proposed method provides significant improvements compared to conventional approaches. Particularly in challenging scenarios, an improvement of approximately 50% was observed in the estimation of quaternions and Euler angles. This study reveals that loosely-coupled sensor fusion, when combined with advanced feature matching techniques and adaptive filtering strategies, can offer a robust alternative to tightly-coupled approaches. The results highlight the potential applications of the proposed method in fields such as robotic navigation, autonomous vehicles, and augmented reality. Additionally, the Python code associated with our study has been shared as open-source on GitHub for use in other academic studies at "\href{https://github.com/ufukasia/Adaptive-VIO-Odometry}{https://github.com/ufukasia/Adaptive-VIO-Odometry}".
GPER1/ACACB are potential target genes associated with intracranial aneurysm and vascular endothelial cell senescence
Authors: Zeng Lang , Lu Xuanzhen , Huang Yuzhen , Tu Qin , He Yongqi , Fang Ziwei , Nie Shuyi , Huang Yi , Yu Maling , Min Xiaoli , Zhang Cuntai , Yu Jiasheng , Zhang Le
Published: September 2024

The incidence of intracranial aneurysm (IA) is significantly higher in postmenopausal women than in men and premenopausal women, a disparity traditionally attributed to the decline in estrogen levels. However, the function and expression of estrogen receptors (ERs), including ERα, ERβ, and GPER1, in vascular tissues suggests the involvement of estrogen-independent mechanisms in vascular aging and related pathologies. To investigate this hypothesis and to elucidate the pathophysiology of IA from the perspective of vascular endothelial cell senescence (VECS), a comprehensive bioinformatics analysis was performed using three IA datasets (GSE75436, GSE122897, GSE54083) and two VECS datasets (GSE214476, GSE102397) from the GEO database. This study identified 452 genes that were significantly downregulated and may share common pathogenic molecular mechanisms in both IA and VECS. Notably, GPER1 was the only ER that was consistently downregulated in both IA and VECS. Further weighted gene co-expression network analysis and module clustering identified ACACB as highly co-expressed with GPER1 and negatively correlated with both IA and VECS. In vitro validation confirmed that reduced GPER1 expression during VECS may exacerbate endothelial senescence, suggesting a potential estrogen-independent protective role of GPER1 in preventing IA and VECS. In addition, pharmacological predictive analysis identified PD0325901, SCH772984 and selumetinib as potential therapeutic agents targeting both GPER1 and ACACB, offering a novel strategy for the treatment of IA and VECS-related pathologies, highlighting the therapeutic potential of targeting these molecules in these conditions.
Cu(β-diketonato)2 Bathochromic shifts from the Ultraviolet towards the Visible region
Authors: Conradie Marrigje M
Published: September 2024

The DFT calculated Ultra-Violet/Visible properties of 11 different Cu(β-diketonato)2 complexes are presented. The selected β-diketonato ligands on the Cu complex contains none, one or two aromatic rings. Experimentally measured absorbance maxima range of the Ultra-Violet/Visible is observed at 295–390 nm and the calculated range is 302–425 nm, for the 11 complexes in this study. More aromatic rings on the ligand lead to Bathochromic shifts of the experimentally measured absorbance maxima from the of the Ultra-Violet towards the Visible region. Absorbance maxima of the Cu(β-diketonato)2 complexes with no aromatic rings on the ligand are found to be predominantly ligand-to-metal charge transfer excitations. Whereas introducing one or two aromatic rings shifts the excitations to predominantly ligand-to-ligand charge transfer.
Exploring Trust In Artificial Intelligence Among Primary Care Stakeholders: A Mixed-Methods Study
Authors: Sides Teresa , Kbaier Dhouha , Farrell Tracie , Third Aisling
Published: September 2024

Background:
Artificial intelligence (AI) in healthcare has grown rapidly in recent years. The United Kingdom government recognises AI’s potential to enhance National Health Services with increased funding available. Despite this, AI acceptance presents unique challenges in primary care (PC), characterised by fragmented structures and multiple government departments. The organisational levels within PC are categorised as macro, meso, and micro levels. Many existing studies focus on micro-level stakeholders.
Methods:
This study investigates the factors affecting trust in artificial intelligence (AI) within PC settings through a mixed-methods approach. An online survey addressed this research gap by encompassing stakeholder perspectives at all organisational levels. To validate the results in-depth semi-structured interviews were conducted with some survey participants enabling triangulation of the data.
Results:
The results demonstrate the crucial role of meso-level stakeholders in facilitating trust in and acceptance of AI. Thematic analysis identified key barriers which include: a requirement for skills development, concerns about job displacement and factors associated with resistance to change. The study also highlighted disparities in care and potential inequities arising from varied AI usage rates. Public perception, leadership approval and media influence were identified as critical factors needing careful management to ensure successful AI integration in healthcare.
Conclusion:
The key contribution to the research field is the data from all stakeholder levels on the perceptions of AI for PC. Despite the study's robustness, limitations such as self-selection bias and low interview participation were noted. The findings underscore the necessity for ethical AI systems, targeted stakeholder engagement, and strategies to ensure equitable and effective AI implementation in PC. Further research in the relationship between trust and equity of care would be beneficial to the important research in the field of AI for PC.
Synthesis and characterization of starch-g-polyacrylamide-co-polylactic acid hydrogel for the potential wound dressing application
Authors: Mondal Md. Ibrahim H. , Kadri H. Jahan , Ahmed Firoz , Rahman Md. Hasinur
Published: September 2024

A starch-based hydrogel was synthesised by direct grafting of polylactic acid (PLA) and acrylamide onto starch. Potassium persulphate (K2S2O8) was used as an initiator and citric acid (CA) was used as an eco-friendly cross-linker. The purpose of the graft was to test an alternative anti-microbial wound dressing material. The FTIR, XRD, SEM and EDX data confirmed that the copolymerisation and cross-linking of the hydrogel was successful. Tests, with chemical reactions; yielded TGA data, which confirmed the enhanced thermal and mechanical properties of the augmented hydrogel. The hydrogel swelled up to 481% at pH 7.20 and exhibited a water vapour transmission rate of 148 g/m2 per day. The hydrogel also showed anti-microbial activity against both gram-positive (S. aureus) and gram-negative (E. coli) bacteria. Its zone of inhibition was 21 mm and 19 mm with a mild anti-oxidant property. This synthesised hydrogel is completely non-toxic and bio-degradable, making it especially suitable as a wound dressing material.
Automatic Modulation Recognition Method Basedon Multimodal I/Q-FRFT Fusion
Authors: Ning Meng , Zhou Fan , Wang Wei , Wang Yang , Fei Shunchao , Wang Jian
Published: September 2024

Automatic modulation recognition (AMR) is a key technology in the domain of cognitive radio communications. Accurately identifying the modulation schemes of signals is crucial for enhancing the robustness and reliability of communication systems. The existing methods usually use deep learning technology to intelligently model the single modality such as in-phase quadrature (I/Q). However, single modality has the disadvantages of low recognition rate and limited feature expression ability. In this paper, we propose an innovative automatic modulation recognition method based on multimodal fusion of I and Fractional Fourier Transform (FRFT). Specifically, we first design the entire multimodal approach as an intermediate fusion mode, that is, performing the unimodal representation first and then multimodal fusion later. In the multimodal fusion stage, we introduce a Multimodal Processing Unit (MPU), which realizes the enhancement and aggregation of different modes by learning the complementary features between and within modalities. Furthermore, we employ contrast learning to categorize signals of different modulation into positive and negative samples to enhance the robustness of the multimodal features. In order to verify the effectiveness of the proposed method, we conduct experiments on two public datasets, RML2016.10a and HisarMod2019.1. The experimental results indicate that the multimodal approach consistently outperforms the unimodal methods significantly, achieving the state-of-the-art.
The effect of high-quality voice on observing employees’ helping behavior: A moderated mediation model
Authors: Liang Liang , Li Zhongqiu , Shang Hangbiao , Zhao Feng , Liu Sitong
Published: September 2024

Background: Grounding on social information processing theory and theory of cooperation and competitiveness, this study aims to explore the impact of voicers’ high-quality voice on observing employees’ helping behavior while considering the mediator of perceptions of benefit to resources, along with the moderating role of goal competitiveness.
Method: This study employed a two-wave study to examine the theoretical model. A total of 226 employees in China were used to test our hypotheses by adopting hierarchical multiple regression method. SPSS and MPLUS were used to analyze data.
Results: The findings revealed that voicer’s high-quality voice positively predicted observing employees’ helping behavior, and perceptions of benefit to resources mediated this relationship. Furthermore, goal competitiveness weakened the positive relationship between voicers’ high-quality voice and observing employees’ perceptions of benefit to resources as well as the positive indirect effects of voicers’ high-quality voice on observing employees’ helping behavior via perceptions of benefit to resources.
Conclusion: Voice quality is vital for organizational effectiveness but related research is limited. Hence, scholars encourage future research to shift attention from voice frequency to voice quality. Our research contributes to the emerging literature on employee voice quality by revealing the positive effect of high-quality voice on observing employees’ helping behavior through perceptions of benefit to resources and exploring the boundary condition (i.e., goal competitiveness) of this relationship. Our research is among the first to explore the influence of high-quality voice in observing employees’ helping behavior, enriching the literature on voice quality.
Identification of $tqg$ flavor-changing neutral current interactions using machine learning techniques
Authors: Ko Byeonghak , Heo Jeewon , Jang Woojin , Lee Jason Sang Hun , Roh Youn Jung , Watson Ian James , Yang Seungjin
Published: September 2024

Flavor-changing neutral currents (FCNCs) are forbidden at tree level in the Standard Model (SM), but they can be enhanced in physics Beyond the Standard Model (BSM) scenarios.To test these potential enhancements, we investigate the production of a top quark and an associated parton through the $tqg$ FCNC process, which originates from the $tug$ and $tcg$ vertices.The $tqg$ FCNC events can be produced with a top quark and either an associated gluon or quark, while SM only has events with a top quark and an associated quark. We apply machine learning techniques to distinguish the $tqg$ FCNC events from the SM backgrounds, including $qg$-discrimination variables.We use the Boosted Decision Tree (BDT) method as a baseline classifier, assuming that the leading jet originates from the associated parton.We compare with a Transformer-based deep learning method known as the Self-Attention for Jet-parton Assignment (SaJa) network, which allows us to include information from all jets in the event, regardless of their number, eliminating the necessity to match the associated parton to the leading jet.The SaJa network with qg-discrimination variables has the best performance, giving expected upper limits on the branching ratios $\Br(t \to qg)$ that are 25--35\% lower than those from the BDT method.
GCD-SPKAU:A Model-based Meta-learning with Sharing Prior Knowledge Among User-clusters classified through Graph Community Detection for Cold-Start Recommendation
Authors: Dai Yang , Wang Heyu , Xu Fulin
Published: September 2024

The cold-start problem has consistently been a significant challenge in recommender systems, particularly in the initial stage for new users or new items. This is due to the lack of sufficient historical interaction data, which presents a significant obstacle to the development of effective personalized recommendations. Nowadays, a considerable number of researchers and scholars have investigated the issue of cold-start problems, resulting in the emergence of a multitude of sophisticated methodologies aimed at addressing this challenge. Among these, meta-learning has emerged as a particularly promising avenue of enquiry. Most existing meta-learning algorithms assume that a priori knowledge can be shared globally among all users. However, a problem arises that information sharing among users with widely different interests is usually ineffective, which leads to suboptimal solutions. Therefore, it is important to improve the meta-learning model. As a result, this paper proposes a new, improved meta-learning model idea, which first invokes the clustering algorithm of Graph Community Detection to classify different categories of user clusters, and then obtains the unique prior knowledge belonging to themselves in each user cluster, and finally enables the prior knowledge to be shared among the clusters of those categories that have the most effective effects. We compare the performance advantages and disadvantages of the new model and the traditional model in cold-start recommendation, verify the effectiveness of our new idea in improving the performance of cold-start recommendation, and discuss its potential applications and future research directions.
Porcine milk small extracellular vesicles modulate peripheral blood mononuclear cell proteome in vitro
Authors: Avila Gabriela , Bonnet Muriel , Viala Didier , Dejean Sebastian , Agazzi Alessandro , Lecchi Cristina , Ceciliani Fabrizio
Published: September 2024

Small extracellular vesicles (EVs) are a subtype of nano-sized extracellular vesicles that mediate intercellular communication. EVs can be found in different body fluids, including milk. Monocytes internalize porcine milk EVs and modulate immune functions in vitro by decreasing their phagocytosis and chemotaxis while increasing their oxidative burst This study aimed to assess the impact of porcine milk EVs on the porcine peripheral blood mononuclear cells (PBMC) proteome. Porcine PBMC were incubated with porcine milk EVs or medium as a control. Extracted proteins were then analyzed using nano-LC-MS/MS. A total of 1584 proteins were identified. The supervised multivariate statistical analysis, sparse variant partial least squares – discriminant analysis (sPLS-DA) for paired data identified discriminant proteins (DP) that contributed to a clear separation between the porcine milk EV treated cells and control groups. A total of 384 DP from both components were selected. Gene Ontology (GO) enrichment analysis with ProteINSIDE provided the evidence that the DP with a higher abundance in porcine milk EVs, like TLR2, APOE, CD36, MFGE8, were mainly involved in innate immunity and EV uptake processes. These results provide a proteomics background to the immunomodulatory activity of porcine milk EVs and to the potential mechanisms used by immune cells to internalize them.
Radiological evaluation of pituitary lesions by CT, plain radiography and MRI
Authors: Arian Maha Mejwal , khattab Khalid
Published: September 2024

Background: The pituitary is an important endocrine gland located at the base of the brain. It is involved in nearly all processes of homeostasis, as well as growth and development. This gland commonly develops pathology, which may result in a mass effect on adjacent intracranial structures or be hormonally active.
Methods and materials: Study location: Al-Mouwasat University Hospital (Endocrine + Surgery + Radiology Departments)., Study type: A retrospective cross-sectional study, Study duration: From February 2020 to December 2023, focusing on cases that underwent MRI of the pituitary gland and radiological images.
Results: Number of patients who underwent lateral radiographs of the skull with a focus on the sella turcica: 250 patients: normal images (normal sella turcica) 82%, abnormal sella turcica 18%, enlargement of the sella turcica 42%, calcifications within the sella turcica 17%, destruction and erosion of the anterior clinical processes 5%, destruction and erosion of the posterior clinical processes 7%, destruction and erosion of the anterior and posterior clinical processes 22%, deformity of the sella turcica 13%, calcifications around the sella turcica 12%. We found that the tumor size of 1-1.5 cm was 40%, the tumor size of 1.5-2 cm was 42%, the tumor size of 2-3 cm was 18%, the rate of pituitary dwarfism was 18%, the small adenoma with low density appeared in 10%, and the large adenoma with high density appeared in 14%.
Conclusion: Standard views of the pituitary gland can be obtained by image reconstruction and formatting to standard coronal, sagittal, and axial anatomic landmarks. Since MRI can acquire imaging data in multiple planes, patient positioning is also less critical.
Discovering Maternal Minds: Exploring Maternal Mind-Mindedness for Preschoolers
Authors: Ambreen Mominah
Published: September 2024

Most studies on mind-mindedness (MM) and its role in child development have been conducted predominantly in Western countries. To address this gap, we conducted the first-ever qualitative study in Pakistan with a sample of working and nonworking mothers (N = 30) of preschoolers (aged 2–6 years). Our aim was to assess their ability to describe their children in a mind-directed manner. Each mother was asked to describe her child. The transcripts of these four- to five-minute speech samples were coded for (i) mental attributes (comments that refer to or describe the child's mental life), (ii) behavioral attributes (comments that refer to the child's behavior), (iii) physical attributes (physical appearance, age, position in the family), (iii) general attributes (any vague comment), and (iv) self-referential attributes (comments in which the primary reference is self-focused rather than describing the child). The results showed that 19 out of 30 mothers could appropriately describe their child in the context of their mental characteristics, as calculated by the index score given in the coding manual (Meins & Fernyhough, 2015). In addition, variations in mothers' responses on the basis of their occupation status and the child's birth order were also assessed qualitatively. Working mothers displayed less mind-mindedness than nonworking mothers did, and mothers with only one child were also found to be less mind-minded. This is the latest and entirely new study conducted in Pakistan and therefore offers a breakthrough in research. Potential limitations include a lack of generalizability and possible bias in mothers’ descriptions to appear socially desirable.
EBSD mapping of Cu-Fe-sulfides reveal microstructures enriched in critical/precious metals and resolve deformation histories
Authors: King Samuel A. , Cook Nigel J. , Ciobanu Cristiana L. , Ehrig Kathy , Rodriguez Yuri Tatiana Campo , Gilbert Sarah , Basak Animesh
Published: September 2024

Chalcopyrite (CuFeS2) and bornite (Cu5FeS4) from the Olympic Dam Cu-U-Au-Ag deposit (South Australia) are characterized using electron backscatter diffraction (EBSD) to identify microstructures and their correlations with trace element concentrations measured by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Natural chalcopyrite is shown to be a rich source of micro-structural and -textural information, preserving <110> and <001> crystallographic preferred orientations (CPO), {110} and {112} twin systems, grain boundary migration, foam textures and subgrain boundaries. Selected examples of chalcopyrite illustrate different aspects of its behavior and relationship with bornite across the mineralogically zoned deposit. The oriented stress imposed by brecciation and/or fluid fluxes at Olympic Dam, alongside temperature, pressure and strain rate, are shown to induce a variety of microstructures preserved in chalcopyrite. Microstructures can therefore be used to elucidate sequential stages of low to medium temperature (<300 °C) ore evolution. Although pyrite microstructures are already routinely used to understand higher temperature ore evolution, complementary microstructural study of chalcopyrite coexisting with pyrite has the potential to reveal deformational events across a more complete range of temperatures. In ore systems that lack obvious evidence for post-mineralization deformation, chalcopyrite is particularly well suited to unravel episodes of low-to medium-temperature overprinting.

EBSD mapping reveals what appears as single grains of chalcopyrite in reflected light is in fact aggregates composed of ~100 individual grains. In contrast, analyzed bornite displays overwhelming crystallographic homogeneity. Rare instances of misorientation in bornite are all associated with replacement and, if correlated with EBSD analysis of coexisting chalcopyrite and its inclusions (e.g., cobaltite), can be used to discern the origin and evolution of different bornite associations. LA-ICP-MS trace element mapping of chalcopyrite aggregates indicate that grain boundaries host concentrations of Pb, Bi, Ag and Sb, with twin boundaries displaying a weaker concentration of the same elements. Bornite grain boundaries are also enriched in Pb. These observations confirm the critical role played by microstructures in Cu-(Fe)-sulfides as traps for Pb, a non-target contaminant in copper concentrates, as well as new evidence for the physical state of Ag and potential value-add critical metals like Bi and Sb. The preferential occurrence of Pb, Bi, Ag and Sb along permeable grain boundaries may incentivize efforts to remove contaminants and/or recover by-products via leaching.
Investigating the influence of Australia Day and Christmas Day on water demand in the Greater Sydney region
Authors: Ghannam Safa , Hussain Farookh
Published: September 2024

The objective of this research is to investigate whether particular occasions, such as Australia Day and Christmas Day, have a notable impact on water demand in the Greater Sydney region. By examining water demand during these events, the study aims to enhance understanding of water consumption patterns and contribute to the development of effective demand management strategies. Multivariate time series data from several water plants in the Greater Sydney region were analyzed using three methods: correlation heatmaps, t-tests, and descriptive statistics. The findings indicate that neither Australia Day nor Christmas Day has a significant impact on water demand at different water plants in the Greater Sydney region. These results suggest that public holidays may not need to be a critical factor in short-term water demand forecasting models for this area.