bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Approach to Map Heavy Oil Viscosity from Standard Mud Gas – A Field Case from the Peregrino Field
Authors: Bravo M. C. , Cely A. , Xavier D. , Masutti L. , Souza M. , Donnadieu S. , Yang T.
Published: September 2023

Peregrino is a heavy oil field in the Campos basin offshore Brazil. The field started production in 2011, and Phase 2 was on stream in 2022. More development drilling is ongoing. The oil viscosity in Phase 2 has large variations from limited exploration wells, ranging from 30 to 180 cp. Any changes in the viscosity in the reservoirs can lead to a large difference in oil recovery. Therefore, updating the viscosity distribution in the reservoirs along with the development drilling is important.

Different methods can acquire reservoir oil viscosity, including downhole logging and sampling, mud gas, extracts from cuttings, and surface oil sampling. Our previous studies demonstrate that mud gas provides real-time and continuous reservoir oil properties. Due to the use of water-based mud in the development drilling and relatively high degasser temperature, the standard mud gas data has been of good quality come to light gas components C1 to C3. Standard mud gas logging is part of the mud logging service, and there is no additional cost to acquire the data. Can we develop a simple approach to predict the oil viscosity based on the "free" standard mud gas data?

A thorough study has been performed based on the reservoir fluid database from the Peregrino field. The results show the methane/ethane and methane/propane ratios are the best parameters correlated to reservoir oil viscosity. Before adopting the new method using standard mud gas, we extensively compared results with the measurement of PVT (Pressure Volume Temperature) samples and pressure points. The comparison shows that the simple approach based on standard mud gas provides an oil viscosity classification that distinguishes between high and low-viscosity fluids along a given well. The threshold for the two categories is identified from the reservoir fluid database, and the results from the mud gas method are in excellent agreement with the experimental results from the PVT analysis. The latter ones are regarded as the ground truth answer. Therefore, we deployed a simple approach based on standard mud gas data to map oil viscosity for future wells.

The new approach using standard mud gas data provides a real-time method to identify the continuous reservoir oil viscosity following the well path without data acquisition cost. Along with drilling more wells, we will soon achieve a detailed and accurate reservoir oil viscosity distribution in different reservoirs. The viscosity mapping of the reservoirs lays the ground for further optimizing the drilling target and well placement and improving the oil recovery.
Stuckpipe Prediction Based on Artificial Intelligence and Real-Time Sub/Surface Parameters Indicators Measurements
Authors: Mohammed A. Malki , Mahmoud F Abughaban , Thiago T Guimaraes , Albara’ A Alshawabkeh
Published: September 2023

Drilling operations should be as conservative as possible. These operations will become more critical in extensively depleted reservoirs. The drilling crew have to manage the allowable surface parameters in order to ensure smooth operations and can no longer rely on traditional operations either balanced or overbalanced drilling. This paper presents a model to predict stuckpipe incidents based on supervised machine-learning and real-time feature-engineering integrated with rig physical parameters. It avails of the real-time drilling data collection, fixes data issues, and analyzes the patterns within these parameters values to identify the warning signs of stuckpipe incidents. It has been tested on both types of wells either stuck or non-stuck incidents. The model was able to give alarms of downhole incidents early before the stuckpipe incidents happen to allow for time window and help the rig crew go for proactive actions to mitigate these risks.
Adopting the Data Mesh – An approach to unlock business value
Authors: Ramchandani D. , Mackinnon D.
Published: September 2023

This paper presents a case study on the adoption of Data Mesh, a novel approach to managing data in organisations, and its impact on unlocking business value. The study explores the implementation of Data Mesh principles, including the establishment of decentralised data ownership and domain-oriented data teams. The results demonstrate the effectiveness of Data Mesh in improving data accessibility, data quality, and accelerating data-driven decision-making processes. This paper provides insights and practical recommendations for organisations looking to adopt Data Mesh to harness the true potential of their data assets.
Mitigating Global Warming with Underground Hydrogen Storage: Impacts of H2S Generation
Authors: Rosman A. , Hamdi Z. , Ali M. , Bataee M.
Published: September 2023

In the last few years, the share of renewable energy as a supply source for electrical energy has increased. However, the renewable energy sector is faced with the problem of storing the excess energy produced due to fluctuation of energy demand due to seasonality. Storing hydrogen in subsurface formation has become a viable option in recent years as more hydrocarbon reservoirs are depleted. A review of the literature revealed that there are limited modelling efforts for underground hydrogen storage in depleted gas reservoirs owing to a lack of field implementation for calibration. Modelling of such processes is important for operators to be able to design as Underground Hydrogen Storage (UHS) properly for HSE reasons especially considering some reservoirs may be prone to reservoir souring due to biochemical reactions with hydrogen injection. The simulation model used in this study and simulation cases were run using Eclipse to evaluate the magnitude of hydrogen losses due to sulfate-reduction reactions. In addition, H2S generation from this reaction will also be evaluated to see the potential impact on overall UHS operations. Furthermore, the H2S quantity will be limited by the available amount of CO2, thus, the solubility of CO2 in the formation water will be highlighted.

From this modelling study, it is observed that depending on the estimated reaction rate and sulfate concentration, H2S generated and produced can be of considerable amount which can pose serious HSE problems. It also means that less hydrogen will be recovered for power generation because some hydrogen will be converted to CO2 and H2S in the reservoir, rendering UHS uneconomical and not a viable option for hydrogen storage. Selection of depleted hydrocarbon reservoirs for UHS must then take into account rock minerals, formation water pH, reservoir pressure and temperature among others to ensure maximum hydrogen is recovered.
A Feasibility Study of Geothermal Energy Extraction in the Baram Basin, Sarawak Using Numerical Reservoir Simulation Based on Analogue Data
Authors: Bataee M. , Carter R. , Hamdi Z. , Bennour Z. , Mahmud H. K. B.
Published: September 2023

Renewable energy sources are becoming favorable choices nowadays, due to Regulators’ desire to respond to the challenges of climate action and meet the need for global reduction of CO2. Geothermal energy is a recognized source of clean, low carbon renewable energy, however, selecting an optimized location for extracting this heat energy from the subsurface depends on many factors, and there are always risks involved with obtaining accurate and realistic values for the key parameters. In this project, the feasibility of geothermal energy extraction was studied using reservoir simulation, based on publicly available analogue data. The investigation focused on the Baram Basin, located in the state of Sarawak, Malaysia. Published data from existing fields and recent exploration and appraisal activities are used for estimating the likely range for both surface and subsurface parameters. Different geothermal development scenarios were run with and without injection wells to investigate the effect of formation pressure on sustainable water production. Results of this study indicate that optimum well spacing of 300m was predicted for maximum sustainable heat extraction. Results of the work compare the amount of enthalpy derived from production with estimates of Levelized Cost of Electricity. Simplified economics were run to explore commercial competitiveness. Sensitivity analysis has explored the key risk factors in this study and reveals the likely ranges for recoverable thermal energy for a profitable project. Geothermal energy sets an example for other energy providers by offering renewable baseload power, reducing emissions, enhancing energy independence, creating jobs, utilizing heat for various applications, and emphasizing long-term resource availability, inspiring greater sustainability, reliability, and environmental stewardship in energy generation practices.
Porthos – CO2 Storage in Highly-Depleted Gas Fields
Authors: Schiferli W.
Published: September 2023

In two planned large-scale CCS projects in the Netherlands – Porthos and Aramis – depleted gas fields will be used for CO2 storage. These fields are characterized by low reservoir pressures. For example, the Porthos project is planned to inject into a field with a reservoir pressure below 20 bar. Project design and operational philosophy need to be specifically tailored to the storage reservoir properties in order to avoid excessively low temperatures when injecting into such fields. This paper describes how these challenges were addressed for the Porthos project.

In most CCS projects, a CO2 mixture is transported in a surface network at high pressure and ambient temperature and injected into an aquifer. At the high reservoir pressure typical of aquifer storage the CO2 stream remains in dense phase or supercritical conditions in the entire system. This dense phase transport strategy is not feasible for the P18 field since the bottomhole pressure (BHP) is around 25 bar at the required injection rates. At this low pressure, CO2 will exist in two-phase conditions which results in very low temperatures of −10 °C. These low temperatures are unacceptable since they may result in hydrate formation in the reservoir and well integrity issues.

A specific operating philosophy and project design was developed to avoid unacceptably low temperatures. At a reservoir pressure below 50 bar, CO2 is injected in gas phase in the pipeline and wells. Once the reservoir reaches a pressure of 50 bar the pipeline pressure is increased to 85 bar to achieve dense phase conditions. The well is operated in two-phase conditions but due to the higher BHP well temperatures are now acceptable. However, if CO2 is transported at ambient temperature the injection flow range per well is very narrow and the required project injection range cannot be met. This is addressed by using the heat of compression to heat the CO2 stream and insulating the pipeline to achieve elevated arrival temperature. Without these specific choices, safe injection into the P18 field would not have been possible.
Dynamic-Geomechanics Coupled Modelling Helps Improve Understanding of the Reservoir and Improve Storage Capacity Estimate of Depleted Carbonate Gas Reservoir for Bulk Carbon Dioxide Sequestration
Authors: Chidambaram P. , Mohd Ali S. , Tewari R. D. , Tan C. , Mazeli A. H. , Das D. P. , Tiwari P. K. , Widyanita A.
Published: September 2023

A depleted gas reservoir in Central Luconia field, located offshore Sarawak, was evaluated for future carbon dioxide (CO2) storage. This carbonate reservoir has experienced seafloor subsidence during producing life. When coupled dynamic-geomechanics model was history matched, it was discovered that earlier understanding of reservoir performance along with aquifer extent and size were incomplete. This new understanding has led to a significant change in forecasted CO2 storage capacity of the reservoir.

In this reservoir, as production rate declined, reservoir pressure decline started reversing. During the producing life of the reservoir, standalone history matched dynamic model was used to forecast reservoir performance. This model sufficiently explained production performance and in fact production forecasts made from the model were reliable. After the reservoir had ceased to produce, it was evaluated for CO2 storage. As part of the CO2 storage studies, a coupled dynamic-geomechanics model was built. This was when it was noted that seafloor continued to subside even as reservoir pressure was increasing post cessation of production. This highlighted the fact that something other than hydrocarbon reservoir section was compacting. Further studies revealed that regional aquifer that was not included in the standalone dynamic model was undergoing compaction post-production.

This reservoir has a bottom acting aquifer, which has a barrier/baffle zone at the bottom of the aquifer that separates it from an extended/regional aquifer. Standalone dynamic model was truncated at the barrier/baffle zone. This aquifer was sufficient to explain the pressure support received but was insufficient to explain sea floor subsidence post-production. Further studies revealed that the hydrocarbon section was in communication with extended aquifer. And the baffle zone has a major role to play in understanding CO2 storage. Using dynamic-geomechanics coupled model with extended aquifer, it was discovered that baffle zone undergoes pore collapse during production. This translates to significantly lower aquifer efflux across the baffle zone during CO2 injection compared to aquifer influx during production. This directly impacts the storage capacity of the reservoir since the invaded aquifer could not be efficiently displaced by injected CO2. Storage capacity estimate from coupled dynamic-geomechanics model is about 50% lower compared to standalone dynamic model.
Projecting Petrophysical Logs at the Bit through Multi-Well Data Analysis with Machine Learning
Authors: Sharma A. , Burak T. , Nygaard R. , Hoel E. , Kristiansen T. , Hellvik S. , Welmer M.
Published: September 2023

The vertical distance from logging while drilling (LWD) sensors to the bit is often more than 30m (98 ft), which leads to difficulty in performing real-time comparison of LWD and drilling data. This study aims to predict the petrophysical data at the drill bit with the objective of determining the best supervised machine learning algorithm to incorporate to reduce the sensor offset problem. The bulk density and porosity logs are predicted at the bit in this paper using petrophysical and drilling parameters. The results of the model will be used to perform lithology identification in real-time that can be used in real-time drilling analysis.

To predict the bulk density and porosity logs at the bit, data from four different wells located in the Norwegian continental shelf in the North Sea was used as a training dataset. The data from a fifth well from the same field was used as a validation dataset. The prediction was based on input variables of the Gamma ray (GR) log data recorded close to the bit, along with other drilling parameters measured at the bit using Measurement while Drilling (MWD) sensors. The five regression models used for prediction and comparative analysis were: Multi-linear regression (MLR), K-nearest neighbor (KNN) regression, Random forest regression (RFR), Support vector machine (SVM) regression and Artificial neural network (ANN).

All five models were tested for their accuracy in predicting porosity and bulk density, and it was determined that the KNN model was more effective for predicting both porosity and bulk density. The coefficient of determination (R2) value for the KNN model for porosity and bulk density predictions were 86% and 74% respectively with the least mean square error (MSE) calculated on the blind dataset (data from a well not included in model training). SVM was found to be the least effective model for predicting both porosity and bulk density, as it had the highest MSE value.

Prediction of porosity and bulk density logs at the bit using multiple machine learning techniques to eliminate the sensor offset problem have not been performed extensively in the past. The developed machine learning model will improve real-time drilling analysis.
Field Experience Using Flow Positioned Ditch Magnet Systems - Contribution to Efficient Drilling
Authors: Ånesbug G. O. , Pallin J. E. , Aase B. , Andresen O. I. , Sandvik M. , Khalili P. , Saasen A.
Published: September 2023

The presence of magnetic debris in the drilling fluid has a detrimental effect on the drilling process. The BHA compass becomes shielded and most Logging While Drilling operations are affected. Magnetic particles will be present whenever no efficient method to remove these are used. The following text shows how removal of such contamination contributed to successfully drill 6624m horizontal 8 ½" section of in one run on with high average ROP on a North Sea field.

Benchmarking of the flow positioned magnets with simpler systems has shown that it may be several times as effective in removal of magnetic debris than competing systems. Furthermore, this removal significantly eased the directional control during drilling. For the particular well the removal of magnetic debris was very effective. It was stated that due to the improved directional control they did not observe the severe whirl typically expected in long tangent sections. Directional control is known to be affected by the magnetic particle content in the drilling fluid. Neither was there any need for changing any logging tools during drilling. Such changing of logging tools is not uncommon because of agglomeration of metal on the measurement ports. Also, the lifetime of the turbine in the MWD increased partly because of less metallic debris in the drilling fluid. In the article it is summarised the experience of operating the ditch magnets along with an outline of the drilling performance. The amount of removed steel is quantified. The effect on directional drilling is assessed. At the same time, the workload to clean the magnets properly is shown as well as the need for the crew to operate the system properly.
Constructing Three-Phase Envelopes Using a Trust-Region-Based Algorithm
Authors: Xu Lingfei , Chen Zhuo , Li Sirui , Li Huazhou
Published: September 2023

We develop a new trust-region (TR)-based algorithm to construct complete three-phase envelopes for reservoir fluid mixtures. The new algorithm is developed based on a basic algorithm for two-phase envelope constructions (Xu and Li 2023). A state-of-the-art TR method with a realistic exact subproblem solver is implemented in the algorithm, and an integrated strategy is adopted to construct complete phase envelopes with two-phase and three-phase branches. We test the performance of the TR-based algorithm by constructing multiphase envelopes for a total of 15 fluid mixtures that include hydrocarbons-CO2, hydrocarbons-asphaltene, and hydrocarbons-water mixtures. Comparison against the conventional Newton-based algorithm indicates that the TR-based algorithm leads to a much higher computational efficiency and an enhanced robustness.
Diversity of bacterial symbionts associated with the tropical plant bug Monalonion velezangeli (Hemiptera: Miridae) revealed by high-throughput 16SrRNA sequencing.
Authors: Navarro-Escalante Lucio , Benavides Pablo , Acevedo Flor Edith
Published: September 2023

Insects and microbes have developed complex symbiotic relationships that evolutively and ecologically play beneficial roles for both, the symbiont and the host. In most Hemiptera insects, bacterial symbionts offer mainly nutritional, defense and reproductive roles and have promoted the adaptive radiation of several hemipteran phytophagous lineages. The tropical plant bug Monalonion velezangeli (Hemiptera: Miridae) is a polyphagous herbivore considered an important insect pest for several economically-relevant tropical crops; however, information about the composition of its bacterial microbiota was missing. In this study, we describe the diversity and structure of the bacterial microbiota in the nymph and adult life stages of M. velezangeli using Illumina high-throughput sequencing of 16S ribosomal RNA gene amplicons (meta-barcoding). We found that both insect life stages share a similar microbiota in terms of bacterial diversity and community structure. The intracellular symbiont Wolbachia (~92%) dominated the overall microbiome composition in these life stages, and along with bacteria genera Romboutsia, Ignavibacterium, Clostridium, Allobaculum, Paracoccus, Methylobacterium, Faecalibacterium, Collinsella, Rothia, Sphingomonas and other 4 undetermined genera, were found as members of the core microbiota. Based on PCR screening and DNA sequencing of wsp gene, Wolbachia infection was confirmed in almost 80% of samples, represented by two different isolates or strains within the supergroup B. This data opens new questions and avenues to better understand the contribution of symbiotic bacteria in the biological performance of this insect pest and provide bases to explore other insect control methods.
Enhancing SPARQL Query Performance with Recurrent Neural Networks
Authors: Chen YiHui , Lu Eric Jui-Lin , Lin Jin-De
Published: September 2023

DBpedia is one of the most resourceful link databases today,
and to access information in DBpedia databases, we need to use
query syntax (e.g., SPARQL). However, not all users know SPARQL, so
we must use a natural language query system to translate the user's
query into the corresponding query syntax. It is costly and
time-consuming for the query system to generate query syntax.
Therefore, this paper proposes generating query syntax from natural
language. Two multi-label learning methods are used for question
transformation: Binary Relevance (BR) and Classifier Chains (CC).
To predict all the labels that match the query intentions, we use
Recurrent Neural Networks (RNNs) to build a multi-label classifier
for generating RDF triples. To better consider the relationship
between RDF triples, the Binary Relevance is integrated into an
ensemble learning approach to propose an Ensemble BR. The
experiments perform better than the other research to improve the
query accuracy. (This article is accepted by IEEE Access with
Digital Object Identifier: 10.1109/ACCESS.2023.3308691 entitled
Enhancing SPARQL Query Performance with Recurrent Neural
Networks)
Association between testosterone and cancers risk in women: a two-sample Mendelian randomization study
Authors: Li Zhizhou , Wang Maoyu , Hua Meimian , Wang Ziwei , Ying Yidie , Zhang Zhensheng , Zeng Shuxiong , Wang Huiqing , Xu Chuanliang
Published: September 2023

Objective:Previous observational studies have explored the correlation between testosterone and cancer risk. However, the causal association between testosterone and various cancer types in women remains inconclusive. The objective of this Mendelian randomization study is to evaluate the causal links between total testosterone (TT) and bioavailable testosterone (BT) with cancer risk in females.Methods:Initially, a rigorous quality control process was used to identify suitable instrumental single nucleotide polymorphisms (SNPs) linked with the exposure under investigation that exhibited a significant association. The genetic causal relationship between female testosterone levels and the risk of developing cancers was examined via two-sample Mendelian randomization. A variety of analytical methods, including inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode, were employed in the investigation. Key findings were primarily based on the results obtained via IVW (random effects), and sensitivity analyses were conducted to assess the reliability of the obtained results. Moreover, maximum likelihood, penalized weighted median, and IVW (fixed effects) methods were utilized in order to further validate the robustness of the results.Results:Based on the results of IVW analysis, our study indicated a positive causal relationship between BT and breast cancer (OR = 1.1184, 95%CI: 1.0448-1.1971, P = 0.0083) and endometrial cancer (OR = 1.4995, 95%CI: 1.3179-1.7061, P = 9.94E-09). Moreover, our findings also showed a positive causal association between TT and breast cancer (OR = 1.1403, 95%CI: 1.0574-1.2298, P = 0.0043), cervical cancer (OR = 1.0017, 95%CI: 1.0006-1.0028, P =0.0122), and endometrial cancer (OR = 1.5046, 95%CI: 1.3103-1.7277, P = 9.06E-08). However, no causal relationship was found with BT and TT on other types of cancer (corrected P> 0.05).Conclusions:This study elucidates the role of testosterone in the development of breast cancer, endometrial cancer, and cervical cancer, while also indicating a potential tenuous link between testosterone and bladder cancer as well as skin cancer. Nonetheless, no statistically meaningful relationship between testosterone and various other types of cancer in females was observed.
Efficacy of IL-23 inhibitors and IL-12/23 inhibitors in the induction treatment of Crohn's disease: a meta-analysis based on randomized controlled trials
Authors: Guo Wenying , Shentu Haojie , Sha Suyong , Wang Dongying , Chen Xi , Huang Zhengwei , Dong Nan , Lai Haijia , Chen Meiling , Xu Jianying , Huang Jianing , Zhong Ziwei
Published: September 2023

Background
A growing number of randomized controlled trials (RCTs) have demonstrated interleukin (IL)-23 and IL-12/23 inhibitors effectiveness in treating Crohn's disease (CD). This study evaluated the efficacy of IL-23 and IL-12/23 inhibitors in the induction phase for the treatment of CD.
Methods
We searched the following databases from inception until December, 2022: Medline, Embase, Web of Science and the Cochrane Library. The primary outcome was the proportion of CD patients who achieved clinical remission at the end of the induction therapy period. Secondary outcomes included clinical response, endoscopic remission, endoscopic response and normalized C-reactive protein (CRP).
Results
After screening, 7 RCTs were included in our study. The meta-analysis showed that, in the induction period, more patients treated with IL-23 inhibitors and IL-12/23 inhibitors achieved clinical remission than patients with placebo therapy (RR = 2.11; 95%CI: 1.83–2.44; RR = 1.94; 95%CI: 1.64–2.29; respectively). The IL-23 inhibitor group and the IL-12/23 inhibitor group showed higher clinical response rates than the placebo group (RR = 1.92; 95%CI: 1.74-2,11; RR = 1.83; 95%CI: 1.61–2.09; respectively). In addition, IL-23 inhibitor group had higher endoscopic remission rate and endoscopic response rate than placebo group, the corresponding pooled RR were 3.40 (95%CI: 2.57–4.50) and 2.65 (95%CI: 2.65–3.12), respectively.
Conclusions
IL-23 and IL-12/23 inhibitors were efficiency methods in the induction treatment of CD.
A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records
Authors: Li Qiuying , Li Jiaxin , Chen Jiansong , Zhao Xu , Zhuang Jian , Zhong Guoping , Song Yamin , Lei Liming
Published: September 2023

Background: Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients.
Methods: The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (logistic regression, support vector machine, K-nearest neighbors, Naïve Bayes classifier, perceptron, decision tree classifier, and random forest classifier) were used to develop delirium prediction models under full (n=32) and simple (n=20) feature sets, respectively.
Result: The area under the receiver operating characteristic curve (AUC) was higher under the full feature set (ranging from 0.61 to 0.85) than under the simple feature set (ranging from 0.31 to 0.76). Among all machine learning methods, the random forest classifier showed excellent potential for predicting delirium in patients using the full or simple feature set.
Conclusions: We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.
The effect of macroscopic herd inputs on individual investment behaviour
Authors: Nielsen Kristian Roed , Kaiser Micha , Glückstad Fumiko Kano
Published: September 2023

The influence of others' behaviour on our own is pervasive and is often argued to result from an information cascade, where decisions in a sequential setting are influenced by the early decisions of others. However, the degree to which individuals modify behaviour through the integration of social information (i.e. other people’s decisions) varies significantly between individuals. We propose that the scale and longevity of the herd itself at the time when an individual decides, defined as macroscopic herd inputs, significantly influences whether individuals integrate social information. To observe the influence of macroscopic herd inputs on behaviour we employ data from a social trading platform finding that it exerts a strong linear influence on individual investment decisions. The effect of information cascades on individual behaviour is therefore dependent not only on personal factors but also on macroscopic herd inputs that shape the degree to which an individual integrates social information.
How ESG Reporting Effects on Green Credit , Green Investment : Moderating Role of Growth Sales
Authors: Rauf Fawad , Wanqui Wang , Naveed Khwaja , Qadri Syed Usman , Ali Muhammad Sibt E
Published: September 2023

Scholars and authorities are becoming more interested in environmental challenges, and experts have revealed their organizational consequences. At the same time, ESG reporting is regarded as a critical component of financial performance in developed and developing nations. In light of this, this study aims to investigate how China's manufacturing industry sales growth is impacted by ESG reporting, green innovation performance, green credit, green investment, and R&D intensity. Non-financial trade manufacturing companies listed on the Shanghai and Shenzhen stock exchanges between 2015 and 2020 were selected for this study. For data estimation, panel regression estimations using OLS, fixed effects models have been used. The results demonstrate a significant positive correlation between manufacturing industry growth sales in China and ESG reporting, green innovation performance, green credit, green investment, and R&D intensity. Lastly, several practical takeaways are offered to boost green innovation performance among ESG reporting enterprises and increase the effectiveness of R&D intensity. These findings, including Policy recommendations, will benefit all parties involved.
Healthcare efficiency scenario: A structured literature review, implications and research agenda
Authors: Rella Angela , Raimo Nicola , Vitolla Filippo , Esposito Paolo
Published: September 2023

Background Despite the great number of studies and interests in this field, research in this area is still scarce, and some aspects don’t emerge. For this reason, it is important to conduct an international healthcare system debate by showing the findings of SLR putting attention to healthcare efficiency, and organizing the articles in a more systematic and holistic way.Methods The present study provides a comprehensive structured literature review (SLR) of the papers dealing with efficiency in the healthcare sector from an international perspective. For this purpose, this study examines articles published in journals until 2023, by extracting crucial insights about healthcare efficiency, in terms of structures and services.Results A sample of 292 articles extracted from Scopus is preliminarily submitted for bibliometric analysis, then 126 articles are examined with bibliographic coupling and content analysis to identify the different areas of topic categorizations. In light of this, the study through the PRISMA framework and, then the content analysis provides the areas most affected by academic literature, and consequentially, the areas that have not yet been explored systematically.Conclusions Due to this, the research agenda underlines the unexplored areas and lays the foundation for future academic studies.
Pre-pregnancy weight loss associations with prenatal and postpartum mental health conditions: A retrospective cohort study
Authors: Ferber Megan , Chrusciel Timothy , Cantwell Sophia , Salas Joanne , Christopher Kara M.
Published: September 2023

Background: Obesity is a risk factor for adverse outcomes during and following pregnancy. Most women are advised to lose weight prior to becoming pregnant, to help alleviate complications including prenatal and postpartum depression and anxiety. Yet, no studies have examined how the process of losing weight prior to pregnancy interacts with the development of prenatal and postpartum mental health disorders.The objective of the study was to determine if women with pre-pregnancy weight loss (≥10%) vs. those who do not, in the two years prior to pregnancy, have a lower risk for new onset prenatal and postpartum mental health conditions
Methods: This retrospective cohort study used data from the Virtual Data Warehouse of a large Midwestern, U.S. based hospital system. The final sample consisted of 6,085 female patients of reproductive age that had given birth between 10/1/2011-6/30/2020 and had two recorded weights in the year prior to conception. Univariate analysis between weight loss and outcome variables (pre-natal and post-partum depression and anxiety) and multivariate analysis using logistic regression was conducted for variable significant on univariate analysis.
Results: On univariate analysis, women with pre-pregnancy weight loss had increased odds of post-partum depression (OR=1.47, 95%CI=1.03-2.10), though decreased odds of prenatal anxiety (OR=0.59, 95% CI 0.33-0.90). After controlling for confounders in the multivariate analysis, there was not a significant difference in the odds of post-partum depression; however, women who lost weight had approximately half the odds of having prenatal anxiety than those who did not lose weight (OR=0.54, 95%CI=0.33-0.90).
Discussion: The experience of achieving weight loss prior to pregnancy may foster a sense of agency within pregnant women, helping them to reduce their experience of pre-natal anxiety. Providers could engage in patient conversations around weight loss and mental health management in a strengths-based framework to continue to foster this sense of agency.
Conclusion: Anxiety and depression were uniquely related to pre-pregnancy weight loss. Pre-pregnancy weight loss was associated with lower odds of prenatal anxiety and higher odds of postpartum depression. These results highlight the need for real world examination of pre-conception treatment recommendations and their association with non-physical health-based outcomes.
Improving bridge effect to overcome interspecific hybrid sterility by pyramiding hybrid sterile loci from Oryza glaberrima
Authors: Li Jing , Zhou Jiawu , Xu Peng , Ying Yang , Deng Xianneng , Deng Wei , Zhang Yu , Lv Yonggang , Pu Qiuhong , Tao Dayun
Published: September 2023

In order to evaluate the hybrid sterile effect and the bridging effect caused by the hybrid sterile locus, total nine NILs carrying one, two and three alleles of O. glaberrima were developed and used as test lines to cross with O. sativa and O. glaberrima accessions. The results showed that the degree of pollen sterility of F1 hybrids between the NILs and Dianjingyou 1 has deepened with the increase of the number of alleles from O. glaberrima at the hybrid sterile loci. Under the action of three hybrid sterile loci, F1 hybrids between the two cultivated rice species are almost completely sterile. On the other hand, the NILs carrying a single allele of O. glaberrima had limited effect on improving pollen fertility of interspecific hybrid. The pyramided lines containing multiple sterile alleles of O. glaberrima showed increased pollen fertility when crossing with O. glaberrima accessions. Further the backcrossing can be used to improve pollen and spikelet fertility of interspecific hybrids after the first cross. The report has potential for understanding the nature of interspecific hybrid sterility, and overcoming the interspecific hybrid F1 pollen grain sterility between O. sativa and O. glaberrima.
Endoscopic main duct stenting in refractory postoperative pancreatic fistula after distal pancreatectomy – a friend or a foe?
Authors: Linder Stefan , Holmberg Marcus , Agopian-Dahlenmark Louiza , Zhao Helena , Åkerström Johan Hardvig , Sparrelid Ernesto , Ghorbani Poya
Published: September 2023

Background
Clinically relevant (CR) postoperative pancreatic fistula (POPF) after distal pancreatectomy (DP) are common. Endoscopic treatment (ET) has only scarcely been explored. The aim of this study was to evaluate risk factors for CR POPF after DP and the efficacy of ET in adjunct to standard therapy.
Methods
Consecutive patients without previous pancreatic surgery who underwent DP between 2011 and 2020 were evaluated, analyzing risk factors for CR POPF. The choice and performance of ET, main pancreatic duct (MPD) stenting, was not standardized. Healing time and complications after ET were registered.
Results
406 patients underwent DP, CR POPF occurred in 29.6%. ET was performed in 17 patients 27 days (median) after index surgery. Risk for CR POPF was increased in ASA-PS 1–2 patients, MPD ≤ 3mm, procedure time ≥ 3h, and CRP ≥ 180 on postoperative day 3. POPF resolved with standard treatment after 32 days and 59 days in the ET group (p < 0.001). There was one mortality in the ET-group (not procedure related). Mild post-ERCP pancreatitis occurred in three patients.
Conclusions
CR POPF is common after DP. Long operating time, a narrow MPD, low ASA score, and high postoperative CRP were risk factors for CR POPF. ET was not beneficial but proper evaluation was not possible due to few patients and non-standardized treatment. Complications after ET appeared mild.
The Brazilian national prospective active surveillance (AS) cohort of patients with low-risk prostate cancer in the public health system: vigiaSUS study protocol
Authors: Basso Jeziel , de Lima Juliana Beust , Bessel Marina , Leitão Santiago Alonso Tobar , Baptista Thais Machado , Roithmann Sergio , Carvalhal Eduardo Franco , Schmitt Caio , Pedrollo Ivan , Schuch Alice , Hartmann Antonio Atalibio , Estivallet Carmen , Ribeiro Guilherme Behrend Silva , Zordan Ricardo Andre , Velho Pedro Henrique Isaacsson
Published: September 2023

Background
Prostate cancer exhibits a very diverse behaviour, with some patients dying from the disease and others never needing treatment. Active surveillance (AS) consists of periodic PSA assessment (prostate-specific antigen), DRE (digital rectal examination) and periodic prostate biopsies. According to the main guidelines, AS is the preferred strategy for low-risk patients, to avoid or delay definitive treatment. However, concerns remain regarding its applicability in certain patient subgroups, such as African American men, who were underrepresented in the main cohorts. Brazil has a very racially diverse population, with 56.1% self-reporting as brown or black. The aim of this study is to evaluate and validate the AS strategy in low-risk prostate cancer patients following an AS protocol in the Brazilian public health system.
Methods
This is a multicentre AS prospective cohort study that will include 200 patients from all regions of Brazil in the public health system. Patients with prostate adenocarcinoma and low-risk criteria, defined as clinical staging T1–T2a, Gleason score ≤ 6, and PSA < 10 ng/ml, will be enrolled. Archival prostate cancer tissue will be centrally reviewed. Patients enrolled in the study will follow the AS strategy, which involves PSA and physical examination every 6 months as well as multiparametric MRI (mpMRI) and prostate biopsy annually. The primary objective is to evaluate the reclassification rate, and secondary objectives include determining the treatment-free survival rate, metastasis-free survival, and specific and overall survival. Exploratory objectives include the evaluation of quality of life and anxiety, the impact of PTEN loss and the economic impact of AS on the Brazilian public health system.
Discussion
This is the first Brazilian prospective study of patients with low-risk prostate cancer under AS. To our knowledge, this is the largest AS study cohort with a majority of nonwhite patients. We believe that this study is an opportunity to better understand the outcomes of AS in populations underrepresented in studies. Based on these data, an AS national clinical guideline will be developed, which may have a beneficial impact on the quality of life of patients and on public health.
Trial registration:
Clinicaltrials registration is 56352622.8.1001.5330
Study on clinical application of 3D printing model in the extraction of donor tooth for autotransplantation
Authors: Hou Rui , Hui Xiaoyong , Xu Guangjie , Li Yongqing , Yang Xia , Xu Jie , Liu Yanli , Zhu Minghui , Zhu Qinglin , Sun Yu
Published: September 2023

Objective To clarify whether the 3D printing model has auxiliary functions in toto extraction of donor tooth (DT) for autotransplantation of teeth (ATT).Methods A questionnaire survey was designed to investigate the auxiliary function of 3D model in extraction. Then, a clinical study was designed on intra-group comparison between the predicted and actual donor teeth situation in different cases. Finally, the consistent rate for the predicted results and the actual results were compared with the two groupsResults The overall difficulty of general practitioners was significantly higher than surgeons (p = 0,031). General practitioners believed that the model is more beneficial than the surgeon in the complete extraction of donor teeth, and the difference was statistically in the evaluation of the root number and root curvature of donor teeth. A remarkable difference was observed between the predicted results and the actual results of tooth positions and root numbers in group without model (p < 0,05). The consistency rate of the model group (94.62%) was significantly higher than that of non 3D model group (86.15%) (p = 0.034).Conclusion The 3D printing model of DT is helpful for dentists to predict the accuracy of toto extraction in donor teeth.
Synchronization of Two Symmetric Bones of the Human Body for Control of Bone Cancer
Authors: Abadi Ali Soltani Sharif , Rafeeyan Mansour , Abootalebi Vahid
Published: September 2023

The human body has symmetric bones. This paper uses control engineering concepts to design a suitable controller to synchronize two symmetric bones of the human body to control and treat bone cancer. A Nonsingular Terminal Sliding Mode Control (NTSMC) method will be employed to design the proposed control inputs. The control inputs can be the chemical drugs that can be used to treat bone cancer. The dynamical equations of bone cancer will be used to apply the designed control method and test it. For testing the designed controller, Simulink/MATLAB software will be used. The proposed controller is chattering-free, robust against uncertainties and external disturbances, and finite-time stable in the control engineering view. Bone cancer will be treated for almost one year using the proposed control method.
Artificial Intelligence Carbon Neutrality Strategy in Sports Event Management based on STIRPAT-GRU and Transfer Learning
Authors: Zhang Ying
Published: September 2023

With the increasing concern about carbon emissions and climate change, carbon neutrality has become a crucial issue in various domains, including sports event management. Artificial intelligence has shown great potential in addressing environmental challenges and improving sustainability. To develop AI-powered carbon neutrality strategies for sports event managementthe ,this paper proposed a method based on STIRPAT-GRU and transfer learning. The primary objective of this research is to analyze the impact of population, wealth, and technology on carbon emissions in sports event management and to predict future trends and patterns of carbon emissions. Firstly, the STIRPAT model is used to identify the key drivers of carbon emissions in sports events. Then, the GRU neural network is then used to develop a predictive model to forecast future trends and patterns of carbon emissions. Finally, transfer learning is used to improve the accuracy and robustness of the model by leveraging knowledge and experience gained from other domains.The experimental results show that the proposed approach outperforms other methods, demonstrating its effectiveness in predicting carbon emissions in sports events and developing a carbon neutrality strategy. The significance of this research lies in its potential to provide sports event managers with a data-driven approach to manage carbon emissions, leading to a more sustainable and environmentally friendly sports industry.
A Multicenter Study: Demographic, Immunological and Clinical Characteristics in Systemic Sclerosis Patients in Latvia.
Authors: Ivanova Kristine , Ribakova Olga , Mihailova Anna , Mozeitovica Evelina , Kadisa Anda , Zepa Julija , Kenina Viktorija , Kurjane Natalja , Bulina Inita
Published: September 2023

Background
Systemic sclerosis (SSc) is considered by many to be one of the most severe autoimmune rheumatic diseases with lower prevalence observed in Northern Europe. No previous studies on the prevalence of SSc in Latvia have been conducted and the aim was to study the demographic and clinical data of patients with SSc in northeastern Europe country.
Methods
This study was conducted in two main Latvian hospitals for adults and includes patients with SSc who were consulted between 2016 and 2021.
Results
During the study period, 159 patients with SSc were consulted. The point prevalence on 1 January 2021 was 84.0 per million. Antinuclear antibodies were present in 82.58% of patients, equally in females (82.81%) and in males (81.41%). Centromere pattern was more frequently observed in females than in males (40.19% vs 19.04%), while speckled pattern was the most frequently observed pattern in both genders (50.98% vs 57.14%). At disease onset females tended to be younger (46.51 ± 13.52) than males (50.5 ± 16.64). More than half of patients received treatment with glucocorticoids at any point of the disease (68.31%).
Conclusions
Systemic sclerosis is less common in Latvia than in other countries and regions. Due to its location, the data from Latvia are consistent with a north-south gradient in Europe. ANA presence did not differ between genders, but in females centromere pattern was much more likely to be present. More than half of patients received treatment with GCs at any point of the disease.