bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study Investigates Seismic Monitoring for Carbon Storage Leak Detection
Authors: Carpenter Chris 
Published: February 2024

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22980, “Carbon Storage Leak Detection Through Seismic FWI and RTM: Different Survey Analyses,” by Sajjad Amani, Kyoto University. The paper has not been peer reviewed. Copyright 2023 International Petroleum Technology Conference. Reproduced by permission.

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In the complete paper, marine seismic data processing is investigated as a tool for monitoring possible leakages in geological carbon storage. Because of the great importance of storage permanence, a precise leakage-monitoring strategy is crucial. The proficiency of seismic monitoring solutions for leakage monitoring can be affected by shallower layers as a result of structure, seismic wave attenuation, and leak size. The authors explore two popular seismic monitoring methods used in this application in different scenarios: full waveform inversion (FWI) and reverse-time migration (RTM).

Introduction
Among the various carbon capture and storage (CCS) options, underground storage in saline aquifers is the best-understood solution. To assure storage consistency and permanence, finding the best strategy to precisely detect possible carbon leaks is essential. A perfect method must demonstrate the difference between stored CO2 and injected CO2 to detect potential fast- and slow-leakage areas. The seismic monitoring technique is the most efficient approach in this respect.
Two popular tools for seismic monitoring are FWI and RTM. Several studies of their use in this application have been conducted. However, previous research did not analyze different survey arrays for carbon-leak detection using FWI and RTM. The current research aims to investigate quantitative aspects of CCS monitoring to conduct sensitivity analysis of the three different survey arrays [vertical seismic profile (VSP), crosswell, and surface] for different amounts of CO2-storage leakage in a saline aquifer reservoir. The capability of seismic-imaging methods for small amounts of leakage was tested. Comparison of these three arrays using monitoring methods such as RTM and FWI reveals their pros and cons in providing detailed information about the reservoir.
In this research, a simple synthetic model was built that closely fits actual reservoirs characterized by suitable physical features such as velocity and density. Subsequently, elastic wave propagation simulation was implemented by use of a finite-difference scheme over a physical model of the reservoir. Then, FWI was applied to enhance the accuracy of the model parameters. With an efficient forward-modeling and inversion scheme, RTM was used as a powerful imaging tool that provides final high-resolution results for monitoring CO2 migration and possible leakage. This process examined the synthetic model for different amounts of CO2 leakage in saline aquifers to evaluate the performance of CO2 leakage monitoring using the seismic method. The results of the different receiver and source arrays also were compared to establish their effectiveness.
Development of novel resistivity-chargeability statistical relationships for subsurface characterization at Langkawi, Kedah.
Authors: BALA GABRIEL ABRAHAM , Bery Andy Anderson , Gnapragasan Joseph , Akingboye Adedibu Sunny 
Published: February 2024

Abstract
The significance of resistivity-chargeability relationships has been acknowledged and applied in various geologic terrains and different environmental conditions. However, there remains an underexplored opportunity to fully utilize these methods in complex geological terrains with a mixture of granitic and sedimentary rocks, where empirical relationships have not been established. Such discoveries are crucial for accurately delineating petrophysical and geomechanical properties, which are essential in addressing urgent environmental concerns like landslides, foundation collapse, groundwater shortages, and pollution. To address this research gap, a novel approach was employed, resistivity-chargeability data with simple linear regression modeling. The study focused on developing resistivity-chargeability relationships specifically tailored for tropical granitic environments, using a typical example from Kedah Langkawi, Malaysia. The regions are characterized by complex geological features, ruggedness, and irregular progressive weathering and fracturing of subsurface strata, making the task challenging. Despite these complexities, the study successfully derived an efficient resistivity-chargeability empirical relation that correlates resistivity and chargeability. The derived empirical relationship exhibited high accuracy, surpassing 87%, in predicting chargeability from resistivity datasets or vice versa. This achievement holds great promise in promptly and accurately addressing environmental issues specific to the target region under study. By utilizing this novel resistivity-chargeability relationship, geoscientists, engineers and environmental practitioners can make informed decisions and effectively manage environmental challenges in these regions, especially during the pre-development stage.
Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder
Authors: Desrivières Sylvane , Zhang Zuo , Robinson Lauren , Whelan Robert , Jollans Lee , Wang Zijian , Nees Frauke , Chu Congying , Bobou Marina , Du Dongping , Cristea Ilinca , Banaschewski Tobias , Barker Gareth , Bokde Arun , Grigis Antoine , Garavan Hugh , Heinz Andreas , Bruhl Rudiger , Martinot Jean-Luc , Martinot Marie-Laure Paillère , Artiges Eric , Orfanos Dimitri Papadopoulos , Poustka Luise , Hohmann Sarah , Millenet Sabina , Fröhner Juliane , Smolka Michael , Vaidya Nilakshi , Walter Henrik , Winterer Jeanne , Broulidakis M. , van Noort Betteke , Stringaris Argyris , Penttilä Jani , Grimmer Yvonne , Insensee Corinna , Becker Andreas , Zhang Yuning , King Sinead , Sinclair Julia , Schumann Gunter , Schmidt Ulrike 
Published: February 2024

Abstract

This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.
Factors Influencing Family Planning Decisions in Saudi Arabia
Authors: Miskeen Elhadi , Korkoman Sultana , Alhassoun Norah Khaled , Aljuhani Rafeef Fahad , Alqahtani Rawan Ali Hamad , Alwabari Salsabeel Salman , Alamri Mohannad S. , Alshahrani Abdullah M. , Alhalafi Abdullah Hassan , Almalki Shehab , Fifi Jabir Al , Elnour Suaad , ALmuqrin Farah Fahad , Ali Yaqin Ali Al , Karkaman Mohammed jalwi , Al-Shahrani Abdullah M. 
Published: February 2024

Abstract
Background: Family planning (FP) is an essential intervention to improve the health and well-being of women and their children. Understanding the determinants of FP decisions among Saudi women is critical to the success of FP efforts. This study aims to assess socio-cultural factors related to FP decisions in Saudi Arabia (SA).
Method: A cross-sectional survey-based survey examined a convenient sample of 1489 ever-married women living in SA. A structured online questionnaire was developed to assess the personal and socio-demographic characteristics of the sample as well as their fertility and FP preferences, practices, and experiences. Participants were approached through different social media platforms.
Results: Women's reported ever use of contraceptive method was 64.1%. Challenges women face in trying to use contraceptive methods were side-effects of the method (40%) and partner opposition (19%). Women's ever use of contraception was significantly different according to their age group, residence, occupation, income level, duration of marriage, number of children, reaching their desired number of children, having challenges in using contraception (p<0.001), their career goals (p=0.005) and access to FP (p=0.030). Logistic regression results for the factors influencing the decision toward family planning showed the significant factors: marital status (odd 1.28, p-value = 0.042, 95 % CI 1.02-1.61), having a supportive husband (odds 1.35, p-value = 0.018, 95% CI 1.08-1.69), and family and societal expectations (odds 1.32, p-value = 0.034 and 95% CI 1.04-1.68).
Conclusion: This study contributes essential insights into the socio-cultural factors influencing FP decisions among Saudi women. The identified determinants, including economic considerations, spousal support, and societal expectations, provide a foundation for tailored interventions to promote informed and autonomous FP choices. These findings ultimately contributed to women's and their children's health and well-being in SA.
Integrated Insulin-Iron Nanoparticless: A Multi-Modal Approach for Receptor-Specific Bioimaging, Reactive Oxygen Species Scavenging, and Wound Healing
Authors: Attri Komal , Chudasama Bhupendra , Mahajan Roop L. , Choudhury Diptiman 
Published: February 2024

Abstract
Metallic nanoparticles have emerged as a promising option for various biological applications, owing to their distinct characteristics such as small size, optical properties, and ability to exhibit luminescence. In this study, we have successfully employed a one-pot method to synthesize multifunctional insulin-protected iron [Fe(II)] nanoparticles denoted as [IFe(II)NPs]. The formation of IFe(II)NPs is confirmed by the presence of FTIR bonds at 447.47 and 798.28 cm-1, corresponding to Fe–O and Fe–N bonds, respectively. Detailed analysis of the HR-TEM-EDS-SAED data reveals that the particles are spherical in shape, partially amorphous in nature, and have a diameter of 28.6 ± 5.2 nm. Additionally, Metal Ion Binding (MIB) and Protein Data Bank (PDB) analyses affirm the binding of iron ions to the insulin hexamer. Our findings underscore the potential of IFe(II)NPs as a promising new platform for a variety of biomedical applications due to their high signal-to-noise ratio, and minimal background fluorescence. The particles are highly luminescent, biocompatible, and have a significant quantum yield (0.632). Exemplar applications covered in this paper include insulin receptor recognition and protection against reactive oxygen species (ROS), harmful molecules known to inflict damage on cells and DNA. The IFe(II)NPs effectively mitigate ROS-induced inflammation, which is a hinderance to wound recovery, thereby facilitating enhanced wound recovery.
Hypoxia drives progression of multiple sclerosis by enhancing the inflammasome activation in macrophages with Porphyromonas gingivalis infection.
Authors: Okano Tokuju , Ashida Hiroshi , Tsukasaki Masayuki , Iida Tamako , Yamamoto Masahiro , Takayanagi Hiroshi , Sakamoto Takeharu , Suzuki Toshihiko 
Published: February 2024

Abstract

Porphyromonas gingivalis (P. gingivalis), a gram-negative anaerobic bacterium, is the most common cause of periodontitis. P. gingivalis secretes several virulence factors, such as gingipains, LPS, and OMVs, which potentially induce the release of inflammatory cytokines such as IL-1β, TNFα, and IL-6 through inflammasome activation and TLRs. Previous reports have suggested that the host inflammatory response to P. gingivalis infection induces the progression of autoimmune diseases, including multiple sclerosis. We demonstrated enhancement of NLRP3 inflammasome activation in macrophages by P. gingivalis infection under hypoxia with inducing ROS. We also found that HIF-1α regulated by TRIF triggered exacerbation of inflammasome activation by P. gingivalis under hypoxia. Moreover, HIF-1α deficiency in myeloid cells recovered the progression of neurological symptoms in a mouse model of multiple sclerosis with P. gingivalis infection, including IL-1β and IL-17 production in the spleen. In this study, we showed that hypoxia controls inflammasome activation induced by periodontitis-associated bacterial infection, resulting in the progression of autoimmune diseases.
Automated Detection of Selected Tea Leaf Diseases by Digital Image Processing Using Convolutional Neural Network (CNN): Bangladesh Perspective
Authors: Rahman Hafijur , Ahmad Iftekhar , Jon Parvej Hasan , Rabbi Md Forhad , Salam Abdus 
Published: February 2024

Abstract
Globally, tea production and its quality fundamentally depend on tea leaves which are susceptible to invasion from pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element to prevent and control spreading of diseases that hinder yield and quality. Image processing techniques are a sophisticated tool that is rapidly gaining traction in the agricultural sector for the detection of a wide range of diseases with excellent accuracy. This study focuses on a pragmatic approach for automatically detecting selected tea foliage diseases based on convolutional neural network (CNN). A large dataset of 3,330 images has been created by collecting samples from different regions of Sylhet division, the tea capital of Bangladesh. The proposed CNN model is developed based on tea leaves affected with red rust, brown blight, grey blight and healthy leaves. Afterward, the model’s prediction was validated with laboratory tests that included microbial culture media and microscopic analysis. The accuracy of this model was found to be 96.65%. Chiefly, the proposed model was developed in the context of the Bangladesh tea industry.
The role of mixing and crystallization in the differentiation of magmatic systems
Authors: Ostorero Lea , Morgavi Daniele , Arzilli Fabio , Boudon Georges , Petrelli Maurizio , Pisello Alessandro , Perugini Diego , Franciosi Luigi , Balcone-Boissard Hélène 
Published: February 2024

Abstract
Understanding magma differentiation and formation of eruptible magmas is one of the key issues in Earth sciences. Many studies have either focused on mixing or crystallization, but none have studied these two processes simultaneously. Here, we conduct an innovative experimental study investigating the simultaneous occurrence of crystallization and dynamic mixing, using basaltic and dacitic end members at sub-liquidus conditions. We reproduce the injection of mafic magma into felsic magma and their mixing while crystallization occurs. Our results indicate that crystallization of basaltic magmas occurs faster than mixing between basalt and dacite leading to the formation of crystal-rich mafic enclaves within a felsic magma and the development of basaltic andesitic to andesitic melts. Then, convection promotes stretching and folding that favor chemical and physical magma mixing, disaggregation of enclaves and formation of clusters of crystals in disequilibrium with the surrounding melt. Magma mixing is the predominant process after the initial crystallization event. Our results provide insights into pre-eruptive dynamics, which is crucial for improving volcanic hazard assessment.
Numerical Simulation Study of Shield Tunnel Construction Under Canal in Water-Rich Sand Layer
Authors: Jianhua Liu , Liliuyang Bai , Chen Xing , Ke Liu 
Published: February 2024

Abstract
Focusing on the impact of tunnel shield construction on the surface and surrounding environment in water-rich sand layers beneath a canal in the paper, based on indoor geotechnical experiments, the FLAC3D method is used to establish the numerical model for the shield construction of the Nantong City Metro Line 1 in Jiangsu Province, passing through the water-rich sand layer beneath the Tonglv Canal. The study explores the effects of shield tunneling on the surface and surrounding environment through numerical simulation, thereby guiding in-site construction. The simulation results indicated that there is no significant difference in the surface settlement values between single-line and double-line synchronous tunneling, but in general, the latter has a larger surface settlement and strata deformation. The thrust force at the tunnel face of the shield construction significantly disturbs the deformation of the strata, and excessive or insufficient thrust force can lead to bulging deformation of the soil in front of the excavation face. The excavation surface and the soil behind it will be affected by the thrust of the palm face to produce extrusion deformation in the opposite direction to the digging direction, and extrusion deformation may occur with the increase of the thrust force. Therefore, the palm surface thrust should be slightly larger than the value of the static soil pressure and should be adjusted timely according to the construction section of the shield tunneling. It is recommended to adjust the grouting pressure to about 0.2 MPa when passing through the middle of the canals on both sides of the water-dividing island and to appropriately increase it when passing through the water-dividing island or the riverbank. The research results provide a reference value for the application of the earth pressure balance shield tunneling method in the construction of water-rich sand layers.
An abnormal course of the left phrenic nerve: A case report of anatomical variation
Authors: Lang Tai-Zhi , Wang Ze-Yu , Wang Shi-Hao , Sun Ye , Li Wan-Yao , Yang Jing-Han , Yang Yi-Ran , Qin Xiang-Zheng 
Published: February 2024

Abstract
Background
The phrenic nerve is an important efferent nerve of the cervical plexus, and its anatomical structural variations have clinical reference value.
Methods
Adult neck specimens were obtained through the Morphology Experimental Center and dissected under a surgical microscope.
Results
The phrenic nerve trunk courses along the fascia, situated above the anterior and middle scalene muscles. It then courses anteriorly and inferiorly before bifurcating into two branches - internal and external branch - approximately 1.5 mm later. Inside the thoracic cavity, the internal and lateral branches reunite to form the phrenic nerve.
Conclusions
This report on the anatomical variations of the phrenic nerve has certain clinical value
The Effects of Microburst Nonlinear Varying Loads on Aircraft Using a Multi-Point Approach
Authors: Pourmoradi Ali , Sabzehparvar Mehdi 
Published: February 2024

Abstract
Microburst is an atmospheric phenomenon that poses a dangerous and unavoidable threat to flying aircraft during takeoff and landing. The dynamic response of the aircraft encountered to the microburst was studied. Research on the interaction between microburst and aircraft motion is needed to assess the effects of aerodynamic forces and momentum. Unlike recent studies using conventional integrated aerodynamic models, the multi-point loading approach allows microburst nonlinear wind loading at any point on the wing and tail combination. In this method, a general reconstruction of aerodynamic forces and moments is calculated by microburst integration with different non-uniformly distributed loads functions on each surface of the aircraft. This algorithm solves the nonlinear equations of motion of a plane with six degrees of freedom and updates the plane's dynamic parameters as input to calculate time- and space-varying microburst effects for each element. The computational complexity of this method is therefore much reduced compared to CFD analysis and at the same time the details of the flight characteristics are taken into account without simplification in the simulation, which takes about 90 seconds of simulation time. We simulate and compare the results of a new and existing micro-race modeling approach. Comparing the two models shows significant differences in flight conditions such as aircraft flight path, angle of attack and angular velocity.
The Minha Casa Minha Vida social housing programme and leprosy in Brazil: An analysis of the 100 Million Brazilian Cohort (2010-2015)
Authors: Teixeira Camila Silveira Silva , Pescarini Júlia Moreira , Sanchez Mauro Niskier , Ferreira Andrêa Jacqueline Fortes , Fiaccone Rosemeire Leovigildo , Ichihara Maria Yury , Ortiz Renzo Flores , Brickley Elizabeth B. , Craig Peter , Leyland Alastair H. , Katikireddi Srinivasa Vittal , Penna Maria Lucia F. , Penna Gerson O. , Ribeiro-Silva Rita de Cássia , Barreto Mauricio L. 
Published: February 2024

Abstract
Background
Ensuring housing interventions can contribute to improved living conditions which are strong socioeconomic determinants of leprosy. We estimated the association between the social housing programme Minha Casa Minha Vida (MCMVP) and leprosy new cases.
Methods
We followed families registered in the 100 Million Brazilian Cohort linked with MCMVP receipt and nationwide registries of leprosy between 2010 and 2015. We used Cox regression weighted by stabilized inverse probability of treatment weighting (IPTW) to assess the hazard ratio (HR) for the effect of MCMVP on leprosy. Weights were obtained by propensity score using demographic and socioeconomic covariates at baseline. Sensitivity analyses were done considering potential delays to receiving MCMVP, municipality of residence population size and by controlling by the baseline risk of leprosy among potential recipients.
Results
We followed up 24584768 individuals, of which 618883 (2.5%) were MCMVP recipients, and detected 8,874 new leprosy cases during the study period. Leprosy incidence was higher among MCMVP recipients (13.32/100,000 pyr; 95%CI = 11.45–15.49) compared to non-recipients (11.72/100,000 pyr; 95%CI = 11.47–11.97). MCMVP recipients had higher leprosy incidence (HR = 1.66; 95%CI = 1.34–2.06), compared to non-recipients. Point estimates were lower when considering a delay of 6 or 12 months to moving into the new household (HR = 1.53; 95%CI = 1.20–1.95 and HR = 1.37; 95%CI = 1.05–1.78, respectively), in small/medium municipalities (≤ 300,000 inhabitants) (HR = 1.95; 95%CI = 1.51–2.52), and higher among individuals who subsequently became MCMVP beneficiaries before receiving the benefit (HR = 2.29; 95%CI = 1.93–2.72).
Conclusions
This study found a higher risk of leprosy associated with MCMVP that may reflect reverse causality. Our findings suggest the programme is, in fact, reaching the most vulnerable individuals, as intended in its objectives. Besides, the higher risk of leprosy among MCMVP beneficiaries even before receiving the benefit observed in sensitivity analysis may reflect residual confounding factors related to structural poverty.