bluuebunny/crossref_metadata_2025 huggingface dataset:
President’s Column: SPE@Energy Transition
Authors: Houzé Olivier
Published: October 2024

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Editor’s Note: This is a summary of the October episode of the President’s vodcast. We encourage you to watch the episode to see the full conversation. In this vodcast, Houzé discusses his views on SPE and the energy transition.

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Last month, I presented a to-do list with five main topics: energy transition, finances, governance, membership, and quality.

This month, I will discuss the first topic, energy transition.

1. SPE’s polarized views on energy transition

As I mentioned last month, there are significant geographic contrasts within our industry and SPE regarding how the energy transition is perceived and developed. In some SPE venues and regions, energy transition is the dominant, if not the only subject, and the P in SPE is indeed a major obstacle for support, even from our traditional stakeholders.

In other regions, stakeholders acknowledge the energy transition, but the situation is essentially business as usual. Major investments are underway to sustain production at current levels or even increase hydrocarbon production to record levels, with development plans spanning over several decades. Even in these regions, most, if not all, new projects are designed from the outset to have a low carbon footprint, including mitigating methane emissions. Energy transition is somehow factored in.

So, there are very different narratives, which are reflected in a polite but real polarization within SPE—geographic and, occasionally, demographic. As an SPE President visiting many sections and stakeholders, the contrast is often striking.

But we are engineers operating on the same planet, and there must be a single energy narrative that accounts for the reality of the energy transition in whatever form or definition this takes, locally or globally. To do this, we can try to assess in the most objective way possible how our license to operate may evolve in the short to medium term.

I will start with something you may have seen many times: the three main energy transition scenarios presented by the International Energy Agency (IEA).

These scenarios predict the climate impact by the end of the century based on current and predicted variables, including consumption, population, economics, and technologies. These are just models, but as the statistician George Box pointed out: “All models are wrong, but some are useful.” These IEA models are definitely useful (Fig. 1).
Guest Editorial: Turning Old Data Into New Insights for Better Outcomes
Authors: Cruise Jamie
Published: October 2024

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I recently read about an archeological study at Interamna Lirenas, central Italy, where a very ordinary Roman settlement; thought to be unpromising, ‘a failed backwater,’ revealed new discoveries that have forced historians to rethink what they believed to be true about the final years of the Roman Empire. The study revealed a town that was thriving for centuries after Roman Italy was thought to have been in decline.

The study’s author and project lead said, “There was nothing on the surface, no visible evidence of buildings, just bits of broken pottery. But what we discovered wasn’t a backwater, far from it. We found a thriving town adapting to every challenge thrown at it for 900 years.” The project lead goes on to say that many other average Roman towns in Italy were just as resilient, “It’s just that archaeologists have only recently begun to apply the right techniques and approaches to see this.”

The story reminded me of the enormous variety and volume of historic data that’s been inaccessible for decades in our industry, trapping knowledge and insights of enormous value. This data is old, but it’s not out of date. In truth, it’s a nice problem to have. If we apply new techniques, we can open a world of new information about an asset, operation, or enterprise.

The data includes commercial field development planning, well logs, drilling reports, consulting, unstructured data reports—all the things that combine with the structured technical information that we get from surveys and planning activities.

This treasure trove of data is untapped due to various reasons—outdated infrastructure, fragmented systems, storage in filing cabinets, siloed databases, and forgotten spreadsheets … bits of broken pottery, if you will.

Often, it’s used once and then never referenced again.

Organizations are wrestling with the challenge of bringing this old data into their business. They need to be able to look at the information, to recognize what it is, know what it means, where it should be filed away. They need to be able to bring it together with other relevant information and contextualize it.

The end goal is to have it available when people come to act, to make decisions. For this, it must be reliable, trusted, and discoverable—and at their fingertips. By the time it reaches people, or artificial intelligence (AI), it must be ready to use.

Connecting Data

One challenge companies face is the mechanical issue of connecting the data—getting it out of these siloed and fragmented systems—and into a system where it can be parsed and understood. Thankfully, in recent years a swathe of good technology products for achieving this at scale have been developed.

Data archaeology in our industry is now a realistic prospect.
Black Sea Rising
Authors: Szymczak Pat Davis
Published: October 2024

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Saipem’s flagship vessel Castorone was contracted to start work this summer on Turkey’s offshore deepwater Sakarya gas field while neighboring Romania prepared to enter Phase 2 of its Neptun Deep gas project.

Blessed with some of the same source rock as the hydrocarbon-prolific Caspian Sea (they were one sea in the geologic past), the Black Sea has long been regarded as a frontier basin of great promise, but with unique challenges—earthquakes, sheer slopes plunging to 2200-m depths where sediments are unstable, its bottom-water harbors H2S instead of oxygen, and complex life is nowhere to be found.

Straddling Europe and Asia, the Black Sea is surrounded by Turkey, Russia, and other remnants of the USSR and the czarist empire before it: Romania, Bulgaria, Ukraine, and Georgia. There is also only one way in and one way out, via Istanbul and the Bosphorus Strait whose width varies from 3.7 km (2.3 miles) to 750 m (2,450 ft).

The Bosphorus has long been the southern export route for Russian (and Soviet) oil loaded on tankers at Novorossiysk for transport to the Mediterranean, and over years the Black Sea has become crisscrossed by gas pipelines, most notably the Blue Stream and TurkStream, which seemingly cemented Turkey’s role as a transit state and major consumer of Russian gas.

Times, though, have changed.

With energy security through diversity of supply now top of mind, Romania and Turkey are driving their own exploration and production projects to identify and develop deepwater gas reserves to supply domestic needs as they simultaneously strategize to become future exporters through such infrastructure as the Trans Anatolian Natural Gas Pipeline (TANAP) and liquified natural gas (LNG).

Ukraine and Russia, meanwhile, are the wildcards in the region, considering that the largest deepwater deposits in the Black Sea are believed to lie in the unexplored waters of Ukraine.

Neptun Deep To Position Romania as Largest EU Gas Producer

In June 2023, 50/50 partners Austria’s OMV Petrom and Romanian state-owned Romgaz Black Sea Ltd. took a final investment decision (FID) to proceed with their $4.4-billion Neptun Deep gas project, which data and analytics provider Wood Mackenzie (Woodmac) notes, will make Romania the largest gas producer in the EU.

Neptun Deep is the first deepwater development on the Romanian shelf which is part of the northwestern Black Sea Continental Shelf (Fig. 1). According to Woodmac, it was the EU’s largest upstream FID in 2023.

With an estimated 100 Bcm in reserves, Neptun Deep aims to produce first gas in 2027 from two fields: the deepwater Domino gas field at 1000-m depth and the shallow-water Pelican Sud (South) at 120-m depth. The project’s 10 wells will tie back to an unmanned shallow-water platform and will deliver 8 Bcm/yr (775 Mcf/D) of gas at plateau for about 10 years (Fig. 2).

Neptun Deep’s plateau output will boost OMV Petrom’s production mix to 70% gas from the current 50%, a goal the Austrian operator has set for itself by 2030.
Technology Focus: Offshore Drilling and Completion (October 2024)
Authors: Jayakumar Swathika
Published: October 2024

The evolution of computational technology, coupled with the ability to digitize hardware and engage with end users, has enabled machine learning to enhance operational efficiency, safety, and cost-effectiveness in offshore oil and gas development.

A key takeaway from recent literature is that, while automation tools have been present for years, their widespread adoption has been hindered by a lack of demonstrable financial benefits. To address this, the focus has shifted from mere deployment to achieving real value through significant cost savings, increased safety, and improved operational efficiencies (paper OTC 35137).

Automation efforts in onshore operations prioritize efficiency, while offshore automation must focus on risk reduction and minimizing nonproductive time. For instance, a cutting-edge vessel-motion-prediction tool integrates advanced machine-learning techniques with metocean forecasts to enhance the reliability and safety of offshore drilling operations (paper OTC 35341).

Paper OTC 35418 presents the importance of autonomous inflow-control devices in maximizing the production life of wells in deepwater West Africa, taking into account the various challenging reservoir conditions.

Another critical insight from the research is the role of human intervention in the deployment of automation systems. Successful deployment requires iterative adjustments and feedback from operators to tailor systems to the specific needs of offshore environments. Collaboration among engineers, operators, and decision-makers is essential in refining these technologies to align them with operational goals and safety standards.

Overall, these papers underscore the need for strategic adaptation of automation systems to the specific challenges of offshore environments. The success of predictive machine learning in this context depends on its ability to offer measurable financial benefits, improve safety, and drive operational efficiency.

Recommended additional reading at OnePetro: www.onepetro.org.

SPE 218473 Performance Optimization of Water-Injection Wells Using Autonomous Outflow Control Device Technology in Offshore Norway by G.F. Kvilaas, AkerBP, et al.

SPE 218479 Use of Wired Drill Pipe, Along-String Measurements, and Advanced Logging-While-Drilling Imaging Enhances Wellbore-Condition Understanding and Improves Well Delivery Time, While Telemetry-Optimized Ultradeep Resistivity Measurements Enable Precise Geological Well Placement by Stephen Pink, NOV, et al.

SPE 218360 Autonomous Restriction Navigation Through Wellbore in Wireline and Slickline Operations by S. DiPasquale, SLB, et al.

OTC 35394 Intelligent Optimization of Ultradeepwater Oil-Transmission Risers Using an Enhanced Graph Neural Network by Hong Zhu, China University of Petroleum-Beijing, et al.
WITHDRAWN: Optimal configuration of photovoltaic/photothermal /AA-CAES capacity considering combined cooling, heating and power supply
Authors: Wu Chenxi , You wei , Zheng Jing , Huang Jianbo , He Zhongjie
Published: October 2024

The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Classification Pipeline for Galactose Oxidase Variants Based on a Combined Transition State Molecular Dynamics and Machine Learning approach
Authors: Rosta Edina , Mateeva Teodora , Klaehn Marco , Fan Hao
Published: October 2024

In recent years, the field of Deep Learning has progressed to the point where a lot of protein properties, especially the ones related to structure, can be predicted with high accuracy. However, catalytic properties in mutated enzymes remain notoriously difficult to predict, due to the many dimensions of the problem. Proteins for which there is scarce experimental data are particularly difficult to handle. Here we propose a combination of a physics-based approach and machine learning to try to overcome some of the limitations for experimental datasets of small size, which cannot utilize Deep Learning architectures. Galactose Oxidase is a copper oxidoreductase that enables the catalytic oxidation of D-galactose and has been the subject of a lot of research in the field of directed evolution. The wild type enzyme has been engineered to convert primary and secondary alcohols, considerably expanding the scope of the native catalytic process, which is limited to D-galactose and only a narrow range of other galactose-containing oligosaccharides. The considerably expanded substrate specificity is often accompanied by a reduced catalytic rate in many of the engineered variants. In this work, we propose an approach to predict the effect of mutations and non-native substrates on the catalytic rate of GO variants. We construct a pipeline comprising of Molecular Dynamics (MD) simulations sampling around a TS-like state and tree-based Machine Learning algorithms utilizing features extracted from the MD simulations. This approach can also be translated to other enzymes and does not require a quantum-level description of the active site, which is more time-consuming.
Mountain Communities' Preferences to Adaptation Strategies for Watershed Management in Changing Climate
Authors: Dhungana Nabin , Lee Chun-Hung , Gentle Popular
Published: October 2024

Mountain livelihoods heavily depend on watershed services. Watershed degradation significantly impacts both the environment and community livelihoods. Climate variability further exacerbates these challenges, reducing essential resources like freshwater, forest products, and agricultural productivity, thereby increasing community hardships and limiting adaptive capacity. Thus, there is an urgent need for community-preferred strategies based on community adaptive capacity and preferences to achieve sustainable watershed management. However, empirical, evidence-based adaptation strategies are often lacking. This study evaluated community preferences for adaptation strategies in the Khageri Khola Watershed, Nepal, using a choice experiment focused on adaptation capacity and willingness to participate. Primary data were gathered through in-depth, semi-structured stakeholder interviews (n = 16) and in-person household surveys (n = 440). Among the ten strategies assessed, five— ‘equipping local communities with technology and technology transfer,’ ‘implementing nature-based solutions,’ ‘establishing watershed-level multi-stakeholder institutions,’ ‘policy formulation based on existing practice and learning,’ and ‘empowering local individuals and organizations’—were statistically significant and preferred for adaptation. The results also indicated diverse preferences based on topographic and socio-demographic characteristics between upstream and downstream respondents, underscoring the need to acknowledge stakeholder diversity. The study developed three distinct watershed management scenarios tailored to decentralized policies and frameworks, offering a roadmap for policymakers and practitioners for fostering watershed adaptive management in agrarian regions like Nepal.
Blood Volume Deficit in Postural Orthostatic Tachycardia Syndrome Assessed by Semi-Automated Carbon Monoxide Rebreathing
Authors: Kulapatana Surat , Urechie Vasile , Rigo Stefano , Mohr Abigail , Vance Yuliya A , Okamoto Luis E , Gambao Alfredo , Shibao Cyndya , Biaggioni Italo , Furlan Raffaello , Diedrich André
Published: October 2024

Purpose: Semi-automated carbon monoxide (CO) rebreathing method has been introduced as a non-invasive and radioactive-free blood volume estimation method. We tested whether the semi-automated CO rebreathing method can detect the blood volume deficit in postural orthostatic tachycardia syndrome (POTS). In addition, we explored the relationship between blood volume estimated from the CO rebreathing and body impedance.
Methods: We recruited 53 subjects (21 POTS females, 19 healthy females, 13 healthy males) to record blood volumes and hemodynamic data. Blood volumes were measured by the CO rebreathing and the segmental body impedance. Linear regression models to predict normal volume of red blood cell volume (RBCV), plasma volume (PV), and blood volume (BV) were developed. Percentage deviations from the predicted normal volumes were calculated.
Results: POTS had lower RBCV (25.18±3.95 vs. 28.57±3.68 mL/kg, p=0.010, POTS vs. healthy females), BV (64.53±10.02 vs. 76.78±10.00, p<0.001), and BV deviation (-13.92±10.38% vs. -0.02±10.18%, p<0.001).
POTS had higher supine heart rate (HR) (84±14 vs. 69±11 bpm, p<0.001) and upright HR (123±23 vs. 89±22 bpm, p<0.001).
We found a correlation between BV deviation and upright HR in POTS (r=-0.608, p=0.003), but not in healthy. Volumes from the CO rebreathing and body impedance were well correlated (r=0.629, p<0.001).
Conclusion: The CO rebreathing method can detect BV deficit, as well as the RBCV deficit in POTS. The negative correlation between BV deviation and upright HR indicates that hypovolemia is one of POTS’ pathophysiological causes. Correlations between body impedance and CO rebreathing volume suggest the usefulness for measurements of volume changes.
Radiological and hydraulic durability of alkali-borosilicate radioactive waste immobilization matrix prepared from industrial waste glass grit: Insights into the microstructure alterations
Authors: Sami N.M. , Moamen O.A. Abdel , Hussein E.M. Abou El , Rahman R.O. Abdel
Published: October 2024

The viability of using alkali borosilicate glasses synthesized from industrial waste glass grit for strontium immobilization is preliminary evaluated by examining the immobilization system structure under normal short-term leaching and harsh conditions. The results designate that strontium is immobilized in alkali-borosilicate(ABS) media structure and led to increasing the structural disorder around the trigonal and tetrahedral units in the glass network. The presence of certain elements such as Na+, K+, Ca2+, Mg2+ are worked as glass modifiers that has donated to strontium immobilization in the glass structure. The chemical structure of both ABS and Sr-ABS samples is stable under the studied 80 KGy irradiation dose and there are breakage of some bonds due to the irradiation exposure. Leaching processes contributing to the cumulative leaching fraction of strontium, boron and sodium were quantified and the main governing leaching process for each studied element was identified. Multi-variate procedure was used to get insight into the effect of the leaching temperature, initial pH and leaching time on the final PH of the synthesized simulant waste matrix and the concentration and normalized leaching fraction of the strontium and sodium elements. The attained normalized release rates of strontium (Nr,i) are within the acceptable range of borosilicate glass at all the studied conditions. For simulated Sr-ABS matrix, strontium element is leached via dissolution and first order reaction, while boron and structural alkali Na are mainly leached via first order reaction.
Tooth surface error correction method of hypoid gear based on Morris-LM(Levenberg-Marquard) fusion algorithm
Authors: Li Jun , Wang Zhonghou , Chen Mingzhi , Yuan Chongyue , Gou Zhenglong , Wu Yunlong
Published: October 2024

The accuracy of tooth flank can be affected by errors occurred in adjustment parameters on machine tool in the real processing of hypoid gears. The complex mutual coupling and nonlinear relationship between these makes the improvement of processing accuracy on tooth flank more challengeable. This paper presents a method for correcting tooth flank errors in gears using the Morris-LM (Levenberg-Marquard) fusion algorithm. The Morris algorithm is utilized for global sensitivity analysis of processing parameters, allowing for an intuitive comparison of effects caused by errors. Additionally, a random disturbance amount, obeying a normal distribution, is introduced into the global system to accurately reflect the key processing parameters that greatly impact on tooth flank errors in the actual processing. The complex nonlinear model established with correction of tooth flank errors contains multiple key processing parameters and the evaluation of flank accuracy is conducted by the integration of deviations containing tooth-top, tooth-root, and tooth-mean-square. Then key machining parameters are adjusted by the LM algorithm with a trust-region strategy to enhance the efficiency of tooth flank correction. The multiple measurements experiment on different tooth flanks were conducted after the corrected processing parameters. The results revealed that viewed from the concave flank, the deviations of tooth-top, tooth-root, and tooth-mean-square were respectively decreased by a minimum of 80.34%, 74.23% and 81.24%. Furthermore, viewed from the convex flank, the deviations from the above were also respectively decreased by a minimum of 83,99%,80.33% and 82.35%. These results verified the high accuracy of the proposed algorithm in correcting the tooth flanks of hypoid gears.
Environmental quality and the ebb and flow of urban innovation in China: An Explanation from the Perspective of Human Capital Mobility
Authors: Zhou Jieqi , Liang Wenguang , Qin Xiaolin
Published: October 2024

As urban competition intensifies, city quality increasingly becomes a key factor in determining human capital distribution and fostering urban innovation. Does environmental quality, as a crucial aspect of urban quality, lead to the mobilization of human capital and drive urban innovation? A definitive answer to this question remains unclear. This study begins with urban air pollution and constructs a framework of “environmental quality - human capital mobility - urban innovation efficiency.” It verifies this framework by integrating “Baidu Migration” big data with urban panel data. The findings reveal that: (1) Air pollution significantly inhibits urban innovation efficiency. This conclusion holds even after using instrumental variable techniques to address endogeneity concerns and conducting robustness analyses. (2) Mechanism tests show that reductions in both the quantity and quality of human capital, along with the outflow of high-skilled labor, are key mechanisms underlying the innovation-dampening effects of air pollution. (3) Heterogeneity analysis reveals that the negative effects of air pollution on innovation are more pronounced in inland cities with high-speed rail connections. This implies the reinforcement of China's regional innovation pattern, with lower innovation levels in the western regions and higher levels in the eastern regions. Additionally, enhancing economic agglomeration, providing high-quality public services, fostering cultural diversity, and strengthening digital infrastructure can increase residents' attachment to their local areas, thereby mitigating the negative impact of air pollution.
Foliar application of indole-3-acetic acid drives changes of soil fungal communities in endosphere and rhizosphere of Eucommia ulmoides
Authors: He Zhaoying , Lu Shenglv , Song Keyun , Ran Qingsong , Shao Qiuyu , Dong Chunbo , Zhang Yanwei , Han Yanfeng
Published: October 2024

Aims: Eucommia ulmoides is a valuable tree species endemic to China. However, the limited scale and low prevalence of quality seed resources, coupled with their slow growth and low utilization rate, continue to result in a scenario where supply falls short of demand. Growth hormone plays an important role in the growth and development of plants, notably enhancing the rooting rate of E. ulmoides.
Methods: In this study, based on high-throughput sequencing technology and controlled pot experiments, to assess the influence on the endosphere and rhizosphere fungal communities, the impact of varying concentrations of indole-3-acetic acid (IAA) on the foliar application of E. ulmoides were explored.
Results: The results revealed that the application of IAA significantly altered the composition, diversity and functionality of the fungal communities within the endosphereand rhizosphere of E. ulmoides. The most pronounced effects were observed at an IAA concentration of 1000 µM, with a more substantial impact on the rhizosphere fungi compared to those in the endosphere fungi. Futhermore, the application of IAA significantly increased the abundance of beneficial fungi in the endosphere, which are crucial for the growth and health of E. ulmoides.
Conclusions: This study revealed that the foliar spray of IAA could significantly drived the change of fungal community composition in the rhizosphere and endosphere of E. ulmoides, and increased the beneficial functional microbiota. This study offers novel insights into efficient and sustainable agricultural practices from a microbiological perspective.
Cool islands in a warming landscape: Old-growth beech forests performance in the recent forest crisis in Germany
Authors: Adhikari Yojana , Bachstein Nadine , Gohr Charlotte , Blumröder Janette , Meier Caroline , Ibisch Pierre
Published: October 2024

The climate crisis seriously threatens Central European forests and their ecosystem functions. There are indications that old-growth forests are relatively resilient and efficient in micro-climatic regulation during extreme climatic conditions. This study evaluates five well-protected old beech forests in Germany, part of a UNESCO World Heritage Site. We examined temperature dynamics and vitality in core, buffer, and border zones during hot days from 2017 to 2023, using Landsat 8 and 9 imageries to assess Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), alongside on-site Air Temperature (AT) measurements. Our findings reveal that all five forests were impacted by recent extreme heat events, with core zones remaining cooler and more vital, followed by buffer zones. Temperature-regulating patterns varied with landscape characteristics and the surrounding matrixes. We observed a site-dependent cooling effect of the forest interior that increased with higher LST. Our study highlights the value of old-growth forests and recommends increasing effective protection around mature forests, establishing corridors between isolated patches, and creating mosaics in managed landscapes that include unmanaged areas capable of developing into old-growth ecosystems.
Empirical Study of Ecological Innovation and Occupational Health Hazard among Nigerian Paddy Rice Farmers: Response to Potential Field Contamination and Climate Risks
Authors: Omoyajowo Koleayo , Ogunyebi Amos , Ogunkanmi Adebayo , Njoku Kelechi , Omoyajowo Kolawole , Oludoye Oluseye , Gupta Jyoti , Raimi Morufu
Published: October 2024

This study investigates the adoption of ecological innovations and management of occupational health hazards among Nigerian paddy rice farmers in response to field contamination and climate risks. Conducted across four major rice-producing states—Ekiti, Kwara, Edo, and Ogun—the research employed a semi-structured questionnaire developed from a comprehensive literature review and validated through peer review. Data were collected from 192 farmers through face-to-face interviews and guided dialogues, focusing on ecological innovations, vulnerability assessments, satisfaction with implemented methods, occupational health hazards, and perceptions of sustainable rice cultivation. Findings reveal that only 29.69% of farmers have adopted new measures to address contamination and climate risks, while 70.31% have not. Work-related injuries, such as cuts and abrasions, affected 28% of farmers, with minimal participation (3.1%) in safety training and support from agricultural or government agencies (1%). Correlation analysis demonstrated weak relationships among variables such as farmers' education, adoption of ecological innovations, satisfaction with results, and use of personal protective equipment (PPE). Importantly, farmers who adopted ecological innovations expressed significantly higher satisfaction (odds ratio = 11.613), as did those with prior exposure to pesticides and chemicals (odds ratio = 4.084) regarding safety practices. The study emphasizes the critical need for increased awareness, training, and support to promote sustainable farming practices and improve occupational health outcomes among paddy rice farmers in Nigeria.
New High-Resolution Prototype Versus Standard Spectralis Optical Coherence Tomography in Patients With Central Serous Chorioretinopathy
Authors: Desideri Lorenzo Ferro , Hennenbert Luc , Subhi Yousif , Zinkernagel Martin , Anguita Rodrigo
Published: October 2024

Purpose
To assess the accuracy of High-Resolution OCT in detecting biomarkers associated with central serous chorioretinopathy (CSC) compared to standard OCT.
Methods
We conducted a cross-sectional study involving CSC patients who underwent High-Resolution and standard OCT during the same visit. Using the SPECTRALIS High-Res OCT device (Heidelberg Engineering, Heidelberg, Germany), macular B-scans were obtained and compared with those acquired using a SPECTRALIS HRA + OCT device (Heidelberg Engineering, Heidelberg, Germany). Qualitative assessments were performed, and statistical analyses compared the performance of both OCT modalities.
Results
Thirty-one patients diagnosed with CSC were included with a mean age of 56.3 years (± 10.2). Among them, 29% (n = 9) were classified as acute CSC (aCSC), while 71% (n = 22) had chronic CSC (cCSC). High-Resolution OCT outperformed standard OCT in detecting microstructural changes in the outer retinal layers, including a higher prevalence of disrupted interdigitation zone (IZ) (29% vs. 6%, p = 0.003) and retinal pigment epithelium (RPE) disruption (12% vs. 2%, p = 0.0024). Intergrader agreement was high (Cohen's Kappa = 0.85).
Conclusion
High-Resolution OCT demonstrates promise in identifying critical biomarkers associated with CSC, particularly disruptions in the IZ and RPE. Further validation in larger cohorts is required to confirm their clinical relevance in patients with CSC.
Research on the Elevation Prediction Model of Suspended Continuous Beam Bridge based on PSO-BP Neural Network
Authors: Zhang Feng , Fu MeiZhen , Liang Yuxiong
Published: October 2024

To address the linear prediction problem encountered during the construction of continuous beam bridges with cantilever erection, an elevation prediction model based on PSO-BP combined neural network model was introduced. Model integrates Pearson correlation analysis, Generalized Matrix Inverted Grey Prediction (GM (1,1)) model, Particle Swarm Optimization (PSO) algorithm and Back propagation (BP) Neural Network. Through a literature review on past bridge data and Longxia Taojiang Bridge, predicted elevation values were compared with field measured values. Results demonstrated that predicted values of PSO-BP model are in coincidence with the measured values, meeting accuracy requirements. Therefore, this proposed model can serve as a valuable reference for the cantilever erection of other continuous beam bridges.
Sports Instruction Enhanced by Posture Recognition: A Study Using the Baduanjin as a Case
Authors: Ma Jiewei , Ma Lianzhen , Qi Shilong , Ruan Wenpian
Published: October 2024

This research investigated the effectiveness of posture recognition technology in online Baduanjin instruction. A teaching experiment was conducted with two randomly selected college classes (n = 70) for eight weeks, with participants divided into an experimental group (n = 35) receiving posture recognition-assisted Baduanjin instruction and a control group (n = 35) receiving traditional online instruction. The study assessed the impact on sports learning interest, self-directed learning ability, and Baduanjin skill proficiency using validated questionnaires and expert evaluation. Results demonstrated a significant increase in sports learning interest, particularly in positive learning attitude, sports skill acquisition, extracurricular sports activities, and sports attention, for the experimental group. The experimental group also showed a significant enhancement in self-directed learning ability, especially in learning motivation. Both groups exhibited significant improvement in Baduanjin skill proficiency, with the experimental group demonstrating significantly higher scores than the control group. These findings highlight the potential of posture recognition technology to promote student engagement, enhance self-directed learning, and improve technical proficiency in online sports learning. The study discusses limitations and potential future research directions, emphasizing the need for further exploration of this promising technology in online sports education.
Association between nonalcoholic fatty liver disease and infertility in reproductive-aged females
Authors: Zhao Jun , Liang Ajuan , Li Zhenhua
Published: October 2024

Context: Findings from observational studies indicate association between non-alcoholic fatty liver disease (NAFLD) and polycystic ovary syndrome. However, the relationship between NAFLD and female infertility remains unclear.
Objective: This study aimed to evaluate the associations between NAFLD and female infertility at the phenotypic and genetic levels.
Methods: Cross-sectional analysis using the 2013-2018 and 2017-2020 (pre-pandemic) National Health and Nutrition Examination Survey (NHANES) was performed. NAFLD was defined by utilizing the Hepatic Steatosis Index (HSI), median value of controlled attenuation parameter (CAP)and liver stiffness measurement (LSM). Multivariable logistic regression and subgroup analyses were used to examine the observational associations of NAFLD related indicesand female infertility. Subgroup analyses were used to explore whether the relationship between female infertility and HSI values was influenced by potential confounders. A bidirectional, two-sample Mendelian randomization analysis was used to determine the potential causal relationship between NAFLD and female infertility.
Results: Higher HSI was associated with progressively higher odds of infertility in women. Increasing CAP and LSM value were positively associated with an increased risk of female infertility. There was an interaction between age and NAFLD indices in relation to the prevalence of female infertility. In MR analyses, the IVW method showed no genetically causal relationship between NAFLD and female infertility.
Conclusion: Reproductive-aged women with NAFLD may have higher prevalence of infertility in the U.S. however, findings from MR analysis did not indicate a causal relationship between NAFLD and female infertility.
Effects of sarcopenia and malnutrition on short-term prognosis after minimally invasive pancreaticoduodenectomy
Authors: Xu Zheyu , Chen Jingtao , Mou Yiping , Li Ou , Zhou YuChen
Published: October 2024

Bcakground
Minimally invasive pancreaticoduodenectomy (MIPD) has been routinely performed in major centers, and its feasibility and efficacy in tumor treatment have been highly recognized. However, the resection process of MIPD is still complicated with postoperative morbidities. The effect of sarcopenia and malnutritional status on postoperative clinical outcomes still remains unclear and controversial.
Methods
The clinical data of 207 patients who had consecutively received MIPD between June 2017 and December 2022 were retrospectively analyzed. A multidimensional nutritional assessment was performed before surgery. Multivariate analysis and propensity score– matched (PSM) procedure was performed to identify the effects of sarcopenia and preoperative nutritional status on postoperative short-term prognosis.
Results
In the multivariable analysis, sarcopenia (OR 9.742, 95% CI 3.339–43.025, p = 0.002) was affected postoperative morbidity/mortality, weight Loss % (MD 0.486, 95% CI 0.188–0.784, p = 0.007) and operation time (MD 0.016, 95% CI 0.003–0.029, p = 0.0) were associated with length of hospital stay. Sarcopenia was identified as an independent, prognostic factor for postoperative pancreatic fistulas (POPFs) (OR 8.499, 95% CI 2.558–40.178, p = 0.008) and Clavien Score III IV V (OR 15.211, 95% CI 4.679–71.295, p < 0.001).
Conclusion
Sarcopenia before MIPD can affect postoperative outcomes, especially POPFs. Sarcopenia and nutritional status assessment should be part of routine preoperative procedures to provide early and appropriate nutritional support for prospective MIPD patients.
Association Between Community Violence Exposure and Parental Firearm Ownership: Data from a Nationally Representative Study
Authors: Pelletier Karissa R. , Pizarro Jesenia M. , Royan Regina , Sokol Rebeccah , Cunningham Rebecca M. , Zimmerman Marc A. , Carter Patrick M.
Published: October 2024

Background
Firearm injuries are the leading cause of death for U.S. adolescents. Given the prevalence of firearm ownership in the U.S., particularly among parental figures in homes with children and teens, and the relationship between firearm access and injury outcomes, it is vital to shed light on potential parental motivations for keeping firearms in their homes. The purpose of this analysis was to examine whether exposure to community violence is associated with parental firearm ownership.
Methods
Data from the Firearm Safety Among Children and Teens Consortium's National Survey (6/24/2020-7/24/2020) was examined. The survey sample comprised parents of high-school-age teens (age 14–18). The survey examined various measures, including firearm ownership, storage, community violence exposure, and sociodemographic characteristics. Stepwise logistic regression was used to examine the association between community violence exposure and parental firearm ownership.
Results
Regression models demonstrate that community violence exposure is associated with an increased likelihood of firearm ownership among parents of high-school age teens (OR = 1.08, p < 0.05). Other significant predictors of firearm ownership among parents included parent age (OR = 0.99, p < 0.01), marital status(OR = 1.29, p < 0.05), and educational attainment (OR = 0.60, p < 0.001).
Conclusions
The findings supported the hypothesis that community violence exposure was associated with an increased likelihood of parental firearm ownership, even after adjusting for potential confounders. These findings contribute to the existing literature by shedding light on the possible contributing factors for firearm ownership among parents of teens. Public health interventions focused on raising awareness about the risks of firearm access in households with youths, providing counseling on locked storage practices, and offering resources for accessing secure firearm storage options, such as rapid access storage, may contribute to reducing firearm access among youth. Additionally, community-based initiatives focused on violence prevention and addressing the root causes of community violence can help create safer environments, thereby reducing the perceived need for accessible firearms in the home by parents and caregivers.
Self-supervised learning method of image maskbased on enhanced chunk embedding andthreshold binarization
Authors: Shao Yunxue , Wang Zhiyang , Wang Lingfeng
Published: October 2024

Self-supervised learning is popular for avoiding labeling large-scale datasets to reduce the cost, effectively improving the model's generalization and representation ability . Masking self-supervised learning is the hot spot of self-supervised learning nowadays, most of the existing masking methods are masking the regular chunks after data enhancement chunking, which is not the real sense of random masking, and at the same time, the above masking methods result in a poor local correlation between the chunks of the image. In this paper, we propose a new masking method for self-supervised learning, named TBMP, which masks the image after threshold binarisation before data enhancement, and enhances the chunked embedding between chunks while aggregating the input position information, effectively enhancing the local correlation between image chunks in the model, and solving the problem of mask shape region fixation in the current self-supervised learning. We use the DINO model as a benchmark for which we perform threshold binarisation masking as well as augmented chunk embedding. In this paper, we conduct linear evaluation experiments on several public datasets, and the results show that the TBMP method outperforms all existing mainstream models in self-supervised learning using masks.
The Protective Effect of Icariin on Isoproterenol-Induced LV Remodeling in Mice
Authors: Zeng Fanqun , Huang Dandan , Lin Xiaoying , Li Yeli , Li Yiqi , Huang Bo , Ding Ruixue , Yang Jianwen , Wang Yu , Yang Danli
Published: October 2024

Objective
The objective of this study is to examine the effect of icariin (ICA) on isoproterenol (ISO)-induced left ventricular (LV) remodeling in mice and to investigate potential underlying mechanisms.
Methods
C57BL/6 mice were randomly assigned to the control group, ISO group, as well as low and high dose ICA groups. The ISO group and ICA groups were subjected to ISO (5 mg/kg) subcutaneous injections for a duration of 14 days to induce LV remodeling. Concurrently, mice in the low and high dose ICA groups were orally administered with ICA at doses of 15 mg/kg and 60 mg/kg, respectively, on a daily basis. Following the 14-day treatment period, the ratio of heart weight to body weight was assessed. Morphological changes in the left ventricle were observed by H&E staining, and the occurrence of cardiomyocyte apoptosis in the left ventricle was evaluated by TUNEL staining. ER stress and mitochondrial injury related proteins were examined via western blotting.
Key findings
The findings indicate that icariin at a dosage of 60 mg/kg reduced heart mass index, attenuated inflammatory cell infiltration and myocardial hypertrophy, decreased the ratio of myocardial apoptosis in the left ventricle, and lowered the protein expressions of ER stress and mitochondrial injury related proteins following isoproterenol (ISO) insult.
Conclusion
Icariin mitigates ISO-induced left ventricular remodeling in mice by inhibiting ER stress and mitochondrial injury.
GSCW-YOLO：A Real-Time Lightweight Behavior Recognition Model for Multi- Dairy-Goat
Authors: Wang Xiaobo , Wang Meili , Hu Yufan , Li Mei , Zhao Wenxiao , Mao Rui
Published: October 2024

Livestock behavior serves as a crucial indicator of physiological health. Using deep learning methods to automatically recognize dairy goat behaviors, especially abnormal ones, can aid in the early detection of potential health and environmental issues. This study proposed a multi-scale and lightweight behavior recognition model for dairy goats, known as GSCW-YOLO, which integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features upsampling operator (CARAFE). This integration aims to enhance the YOLOv8n model's attention to behavioral features, mitigate complex background interferences and concurrently enhance the model's capability to discern subtle differences among behaviors. Furthermore, the model includes a small target detection layer and optimization of the Wise-IoU loss function to enhance its adaptability in identifying distant small target behaviors and transient abnormal behaviors in surveillance videos. A new dataset called GoatABRD was also established, encompassing four abnormal behaviors (limping, attacking, death and gnawing) and six common behaviors (standing, lying, eating, drinking, scratching and grooming). The experimental results demonstrate that the precision, recall and mean Average Precision (mAP) of the GSCW-YOLO model reached 93.5%, 94.1% and 97.5%, respectively, marking improvements of 3, 3.1 and 2 percentage points over the YOLOv8n model. Moreover, the GSCW-YOLO model has a considerably smaller size of only 5.9MB, with a frame per second (FPS) of 175. Meanwhile, it outperforms popular models like CenterNet, Efficientdet, and other networks in the YOLO series. GSCW-YOLO provides significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thereby facilitating the modernization of the dairy goat industry.
DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification
Authors: Mu Xuechen , Chen Qiufen , Shi Bocheng , Xu Long , Huang Zhenyu , Xu Ying , Zhang Kai
Published: October 2024

Enhancers are short genomic segments located in non-coding regions in a genome that help to increase the expressions of the target genes. Despite their significance in transcription regulation, effective methods for classifying enhancer categories and regulatory strengths remain limited. To address the issue, we propose a novel end-to-end deep learning architecture named DeepEnhancerPPO. The model integrates ResNet and Transformer modules to extract local, hierarchical, and long-range contextual features. Following feature fusion, we employ the proximal policy optimization (PPO), a reinforcement learning technique, to reduce the dimensionality of the fused features, retaining the most relevant ones for downstream classification. We evaluate the performance of DeepEnhancerPPO from multiple perspectives, including ablation analysis, independent tests, and interpretability of classification results. Each of these modules contributes positively to the model's performance, with ResNet and PPO being the top contributors. Overall, DeepEnhancerPPO exhibits superb performance on independent datasets compared to other models, outperforming the second-best model by 6.7% in accuracy for enhancer category classification. The model also ranks within the top five classifiers out of 25 in enhancer strength classification without the need to re-optimize the hyperparameters, indicating that the DeepEnhancerPPO framework is highly robust for enhancer classification. Additionally, the inclusion of PPO enhances the interpretability of the classification results. The source code is openly accessible at https://github.com/Mxc666/DeepEnhancerPPO.git.
Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging
Authors: Rabiei Leila , Rahmani Farzaneh , Khansari Mohammad , Rajabi Zeinab , Salimi Moein
Published: October 2024

Introduction: Part-of-Speech (POS) Tagging, the process of classifying words into their respective parts of speech (e.g., verb or noun), is essential in various natural language processing applications. POS tagging is a crucial preprocessing task for applications like machine translation, question answering, sentiment analysis, etc. However, existing corpora for POS tagging in Persian mainly consist of formal texts, such as daily news and newspapers. As a result, smart POS tools, machine learning models, and deep learning models trained on these corpora may not perform optimally for processing colloquial text in social network analysis.
Method: This paper introduces a novel corpus, "Colloquial Persian POS" (CPPOS), specifically designed to support colloquial Persian text. The corpus includes formal and informal text collected from various domains such as political, social, and commercial on Telegram, Twitter, and Instagram more than 520K labeled tokens. After collecting posts from these social platforms for one year, special preprocessing steps were conducted, including normalization, sentence tokenizing, and word tokenizing for social text. The tokens and sentences were then manually annotated and verified by a team of linguistic experts. This study also defines a POS tagging guideline for annotating the data and conducting the annotation process.
Results: To evaluate the quality of CPPOS, various deep learning models, such as the RNN family, were trained using the constructed corpus. A comparison with another well-known Persian POS corpus named "Bijankhan" and the Persian Hazm POS tool trained on Bijankhan revealed that our model trained on CPPOS outperforms them. With the new corpus and the BiLSTM deep neural model, we achieved a 14% improvement over the previous dataset.