bluuebunny/crossref_metadata_2025 huggingface dataset:
Out-Plane Buckling of Arches with Variable Cross-Section
Authors: Jiang Angfeng , Deng Deyuan , Dai Wei , You Xiuwen , Lu Hanwen
Published: November 2023

The variable cross-section arch is widely used in practical engineering because of its beautiful arc and excellent mechanical properties. However, there is still no systematic and comprehensive study on the out-plane buckling of variable cross-section arches. In view of this, this paper is focused on the elastic analytical research of out-plane buckling of arches with variable cross-sections under a uniformly distributed radial local load. The pre-buckling and out-plane buckling behavior of a variable cross-sectional arch under an external load is quite different from that of an arch with a uniform cross-section. Castigliano’s second theorem is used to establish pre-buckling force method equilibrium equations for variable cross-sectional arches under a uniformly distributed radial local load, and corresponding analytical solutions of normal stress, axial compression, and the bending moments are obtained. Based on the energy method and the Ritz method, analytical solutions of the critical load for the elastic out-plane buckling of arches with variable cross-sections are derived. Comparisons with ANSYS results indicated that the analytical solutions are able to accurately predict the pre-buckling internal forces and critical out-plane buckling load of variable cross-section arches subjected to a uniformly distributed radial local load. It is found that the internal forces and the out-plane buckling load of an arch are significantly affected by the variation of cross-sectional height. As the ratio of the arch’s cross-sectional height increases, the bending moment decreases, and the axial force and critical out-plane buckling load increase. Analytical solutions of pre-buckling internal force and critical out-plane buckling load problems for arches with variable cross-sections have a wider significance since they can provide an effective explicit analytic method for the optimal design of arch structures.
Current Trends in Surgical Management of Hepatocellular Carcinoma
Authors: Angeli-Pahim Isabella , Chambers Anastasia , Duarte Sergio , Zarrinpar Ali
Published: November 2023

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. Surgical management, including hepatic resection, liver transplantation, and ablation, offers the greatest potential for a curative approach. This review aims to discuss recent advancements in HCC surgery and identify unresolved issues in the field. Treatment selection relies on the BCLC staging system, with surgical therapies primarily recommended for early-stage disease. Recent studies have shown that patients previously considered unresectable, such as those with portal vein tumor thrombus and uncomplicated portal hypertension, may benefit from hepatic resection. Minimally invasive surgery and improved visualization techniques are also explored, alongside new techniques for optimizing future liver remnant, ex vivo resection, and advancements in hemorrhage control. Liver transplantation criteria, particularly the long-standing Milan criteria, are critically examined. Alternative criteria proposed and tested in specific regions are presented. In the context of organ shortage, bridging therapy plays a critical role in preventing tumor progression and maintaining patients eligible for transplantation. Lastly, we explore emerging ablation modalities, comparing them with the current standard, radiofrequency ablation. In conclusion, this comprehensive review provides insights into recent trends and future prospects in the surgical management of HCC, highlighting areas that require further investigation.
The Response of the Replication Apparatus to Leading Template Strand Blocks
Authors: Bellani Marina A. , Shaik Althaf , Majumdar Ishani , Ling Chen , Seidman Michael M.
Published: November 2023

Duplication of the genome requires the replication apparatus to overcome a variety of impediments, including covalent DNA adducts, the most challenging of which is on the leading template strand. Replisomes consist of two functional units, a helicase to unwind DNA and polymerases to synthesize it. The helicase is a multi-protein complex that encircles the leading template strand and makes the first contact with a leading strand adduct. The size of the channel in the helicase would appear to preclude transit by large adducts such as DNA: protein complexes (DPC). Here we discuss some of the extensively studied pathways that support replication restart after replisome encounters with leading template strand adducts. We also call attention to recent work that highlights the tolerance of the helicase for adducts ostensibly too large to pass through the central channel.
Clinical Outcomes of COVID-19 Infection among Patients with Chronic Obstructive Pulmonary Disease: Findings from the Philippine CORONA Study
Authors: Jamora Roland Dominic G. , Albay Albert B. , Ditching Mary Bianca Doreen F. , Sy Marie Charmaine C. , Villanueva Emilio Q. , Espiritu Adrian I. , Anlacan Veeda Michelle M.
Published: November 2023

Background: The global pandemic caused by the coronavirus disease 2019 (COVID-19) resulted in many deaths from fulminant respiratory failure. Chronic obstructive pulmonary disease (COPD) is the leading cause of morbidity and mortality worldwide. There has been great concern regarding the impact of COPD on the COVID-19 illness. Methods: Data from the Philippine CORONA study were analyzed to determine the association of COPD and COVID-19 in terms of mortality, disease severity, respiratory failure, mechanical ventilation, and lengths of stay in the intensive care unit (ICU) and hospital. Results: A total of 10,881 patients were included in this study, and 156 (1.4%) patients had been diagnosed with COPD. A majority of COVID-19 patients with COPD had other existing comorbidities: hypertension, diabetes mellitus, chronic cardiac disease, and chronic kidney disease. COPD patients were 2.0× more likely to present with severe to critical COVID-19 disease. COVID-19 patients with COPD in our study have a 1.7× increased mortality, 1.6× increased respiratory failure, and 2.0× increased risk for ICU admission. Smokers with COVID-19 were 1.8× more likely to present with more severe disease and have a 1.9× increased mortality. Conclusion: Our study supports the growing evidence that COPD among COVID-19 patients is a risk factor for higher mortality, more severe form of COVID-19, higher ICU admission, and higher respiratory failure needing ventilatory support.
DephosNet: A Novel Transfer Learning Approach for Dephosphorylation Site Prediction
Authors: Yang Qing , Wang Xun , Zheng Pan
Published: November 2023

Protein dephosphorylation is the process of removing phosphate groups from protein molecules, which plays a vital role in regulating various cellular processes and intricate protein signaling networks. The identification and prediction of dephosphorylation sites are crucial for this process. Previously, there was a lack of effective deep learning models for predicting these sites, often resulting in suboptimal outcomes. In this study, we introduce a deep learning framework known as “DephosNet”, which leverages transfer learning to enhance dephosphorylation site prediction. DephosNet employs dual-window sequential inputs that are embedded and subsequently processed through a series of network architectures, including ResBlock, Multi-Head Attention, and BiGRU layers. It generates predictions for both dephosphorylation and phosphorylation site probabilities. DephosNet is pre-trained on a phosphorylation dataset and then fine-tuned on the parameters with a dephosphorylation dataset. Notably, transfer learning significantly enhances DephosNet’s performance on the same dataset. Experimental results demonstrate that, when compared with other state-of-the-art models, DephosNet outperforms them on both the independent test sets for phosphorylation and dephosphorylation.
Moving towards a Mutant-Based Testing Tool for Verifying Behavior Maintenance in Test Code Refactorings
Authors: Teixeira Tiago Samuel Rodrigues , Silveira Fábio Fagundes , Guerra Eduardo Martins
Published: November 2023

Evaluating mutation testing behavior can help decide whether refactoring successfully maintains the expected initial test results. Moreover, manually performing this analytical work is both time-consuming and prone to errors. This paper extends an approach to assess test code behavior and proposes a tool called MeteoR. This tool comprises an IDE plugin to detect issues that may arise during test code refactoring, reducing the effort required to perform evaluations. A preliminary assessment was conducted to validate the tool and ensure the proposed test code refactoring approach is adequate. By analyzing not only the mutation score but also the generated mutants in the pre- and post-refactoring process, results show that the approach is capable of checking whether the behavior of the mutants remains unchanged throughout the refactoring process. This proposal represents one more step toward the practice of test code refactoring. It can improve overall software quality, allowing developers and testers to safely refactor the test code in a scalable and automated way.
A Literature Review of Racial Disparities in Prostate Cancer Research
Authors: Vermeille Matthieu , Koster Kira-Lee , Benzaquen David , Champion Ambroise , Taussky Daniel , Kaulanjan Kevin , Früh Martin
Published: November 2023

Background: Despite recent awareness of institutional racism, there are still important racial disparities in prostate cancer medical research. We investigated the historical development of research on racial disparities and bias. Methods: PubMed was searched for the term ‘prostate cancer race’ and added key terms associated with racial disparity. As an indicator of scientific interest in the topic, we analyzed whether the number of publications increased linearly as an indicator of growing interest. The linearity is expressed as R2. Results: The general search term “prostate cancer race” yielded 4507 publications. More specific search terms with ≥12 publications showing a higher scientific interest were found after 2005. The terms with the most publications when added to the general term were “genetic” (n = 1011), “PSA” (n = 995), and “detection” (n = 861). There was a linear increase in publications for “prostate cancer race” (R2 = 0.75) since 1980. Specific terms added to the general terms with a high linear increase (R2 ≥ 0.7) were “screening” (R2 = 0.82), “detection” (R2 = 0.72), “treatment access” (R2 = 0.71), and “trial underrepresentation” (R2 = 0.71). However, only a few studies have investigated its association with sexual activity. A combination with “sexual” showed 157 publications but only two years with ≥12 publications/year. Conclusion: The terms “genetic”, “PSA”, and “detection” have been the focus of recent research on racial differences in prostate cancer. We found that old stereotypes are still being mentioned but seem to find little interest in the current literature. Further research interest was found in “treatment access”. Recently, interest in socioeconomic factors has decreased.
Advanced Technology (Continuous Glucose Monitoring and Advanced Hybrid Closed-Loop Systems) in Diabetes from the Perspective of Gender Differences
Authors: Nuzzo Maria Grazia , Schettino Marciano
Published: November 2023

An ever-growing body of evidence suggests that sex and gender influence the pathophysiology, incidence, prevalence, clinical manifestations, course, and response to therapy of diabetes. Sex and gender differences are particularly evident in type 1 diabetes, especially in patients using advanced technologies (CGM and AHCL), as they are factors that interact with each other and have an impact on adherence to therapy, which affects not only metabolic compensation, but also, therefore, the prevention of complications and quality of life.
A MicroRNA-Based Method for High-Viremia Detection—A New Approach on a Romanian Lot of Chronically Infected Patients with Hepatitis B Virus
Authors: Manea Marina , Apostol Dimitri , Constantinescu Ileana
Published: November 2023

The HBV (hepatitis B virus) infection is intended for elimination, but evaluating patients is both costly and insufficiently applied in several countries. An expensive analysis in Romania is HBV-DNA quantification, with a limited prognostic potential. Our study intended to find new predictors for high viremia in HBV patients, using molecules involved in the multiple assessment of various HBV complications, such as microRNAs. A total of 61 subjects (48 patients with chronic HBV infection and 13 healthy subjects) were generally evaluated. Using a RT-PCR method, with a 2−ΔΔCT algorithm, we detected the expressions of miR-122 and miR-146a in 33 subjects. MiR-21 was the internal control. The results were analyzed with the R 4.2.2. software. Kruskal–Wallis’s comparisons, Spearman correlations, and several logistic regression methods were applied. The median age of the patients was over 40 years. Without microRNAs, we could not obtain a good prediction formula. The combination of miR-122 and age proved to be the best prediction method for high viremia, with an AUC of 0.827, and a sensitivity of 89.5%. This is the first study which included age and miR-122 as independent predictors for high viremia in Romanian HBV-positive patients. MiR-122 is a new potential biomarker in the evaluation of Romanian patients.
High Sensitivity Singlet Oxygen Luminescence Sensor Using Computational Spectroscopy and Solid-State Detector
Authors: Yu Tiffany C. , Davis Steve J. , Scimone Mark T. , Grimble John , Maguluri Gopi , Anand Sanjay , Cheng Cheng-En , Maytin Edward , Cao Xu , Pogue Brian W. , Zhao Youbo
Published: November 2023

This paper presents a technique for high sensitivity measurement of singlet oxygen luminescence generated during photodynamic therapy (PDT) and ultraviolet (UV) irradiation on skin. The high measurement sensitivity is achieved by using a computational spectroscopy (CS) approach that provides improved photon detection efficiency compared to spectral filtering methodology. A solid-state InGaAs photodiode is used as the CS detector, which significantly reduces system cost and improves robustness compared to photomultiplier tubes. The spectral resolution enables high-accuracy determination and subtraction of photosensitizer fluorescence baseline without the need for time-gating. This allows for high sensitivity detection of singlet oxygen luminescence emission generated by continuous wave light sources, such as solar simulator sources and those commonly used in PDT clinics. The value of the technology is demonstrated during in vivo and ex vivo experiments that show the correlation of measured singlet oxygen with PDT treatment efficacy and the illumination intensity on the skin. These results demonstrate the potential use of the technology as a dosimeter to guide PDT treatment and as an analytical tool supporting the development of improved sunscreen products for skin cancer prevention.
Distributed Formation Control of Multi-Robot Systems with Path Navigation via Complex Laplacian
Authors: Wu Xiru , Wu Rili , Zhang Yuchong , Peng Jiansheng
Published: November 2023

This paper focuses on the formation control of multi-robot systems with leader–follower network structure in directed topology to guide a system composed of multiple mobile robot agents to achieve global path navigation with a desired formation. A distributed linear formation control strategy based on the complex Laplacian matrix is employed, which enables the robot agents to converge into a similar formation of the desired formation, and the size and orientation of the formation are determined by the positions of two leaders. Additionally, in order to ensure that all robot agents in the formation move at a common velocity, the distributed control approach also includes a velocity consensus component. Based on the realization of similar formation control of a multi-robot system, the path navigation algorithm is combined with it to realize the global navigation of the system as a whole. Furthermore, a controller enabling the scalability of the formation size is introduced to enhance the overall maneuverability of the system in specific scenarios like narrow corridors. The simulation results demonstrate the feasibility of the proposed approach.
Dimensionless Groups by Entropic Similarity: II—Wave Phenomena and Information-Theoretic Flow Regimes
Authors: Niven Robert K.
Published: November 2023

The aim of this study is to explore the insights of the information-theoretic definition of similarity for a multitude of flow systems with wave propagation. This provides dimensionless groups of the form Πinfo=U/c, where U is a characteristic flow velocity and c is a signal velocity or wave celerity, to distinguish different information-theoretic flow regimes. Traditionally, dimensionless groups in science and engineering are defined by geometric similarity, based on ratios of length scales; kinematic similarity, based on ratios of velocities or accelerations; and dynamic similarity, based on ratios of forces. In Part I, an additional category of entropic similarity was proposed based on ratios of (i) entropy production terms; (ii) entropy flow rates or fluxes; or (iii) information flow rates or fluxes. In this Part II, the information-theoretic definition is applied to a number of flow systems with wave phenomena, including acoustic waves, blast waves, pressure waves, surface or internal gravity waves, capillary waves, inertial waves and electromagnetic waves. These are used to define the appropriate Mach, Euler, Froude, Rossby or other dimensionless number(s)—including new groups for internal gravity, inertial and electromagnetic waves—to classify their flow regimes. For flows with wave dispersion, the coexistence of different celerities for individual waves and wave groups—each with a distinct information-theoretic group—is shown to imply the existence of more than two information-theoretic flow regimes, including for some acoustic wave systems (subsonic/mesosonic/supersonic flow) and most systems with gravity, capillary or inertial waves (subcritical/mesocritical/supercritical flow). For electromagnetic wave systems, the additional vacuum celerity implies the existence of four regimes (subluminal/mesoluminal/transluminal/superluminal flow). In addition, entropic analyses are shown to provide a more complete understanding of frictional behavior and sharp transitions in compressible and open channel flows, as well as the transport of entropy by electromagnetic radiation. The analyses significantly extend the applications of entropic similarity for the analysis of flow systems with wave propagation.
Beyond Geospatial Inquiry—How Can We Integrate the Latest Technological Advances into Geography Education?
Authors: Lee Jongwon
Published: November 2023

In geography education, geospatial technologies have been actively researched and widely used in combination with classroom inquiry and fieldwork. Recently, emerging data sources and technologies, such as open data, big data, and Artificial Intelligence (AI), are now gaining attention for their educational potential. In this study, both existing and emerging data and technologies are combined and grouped into four categories: (1) Geospatial Web service, (2) open data and big data, (3) fieldwork using Geographic Information Systems (GIS), (4) AI and coding. And based on these, geospatial inquiry activities for learning high school Korean Geography were developed. Learning to use these data sources and technologies is expected to equip students with essential 21st century skills.
E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors
Authors: Zhang Chenglong , Ahn Hyunchul
Published: November 2023

This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk prediction results needed to be provided to the instructor every week, four representative time points during the semester were chosen to assess the predictions. Furthermore, we used eight single and three integrated machine-learning algorithms to compare the prediction results. The results show that using the same semester code course data for training saved prediction calculation time and improved the prediction accuracy at all time points. In week 16, predictions using the algorithms with the voting classifier method showed higher prediction accuracy and were more stable than predictions using a single algorithm. The prediction accuracy of this model reached 81.2% for the midterm predictions and 84% for the end-of-semester predictions. Finally, the study used the Shapley additive explanation values to explore the main predictor variables of the prediction model.
Multi-View Stereo Network Based on Attention Mechanism and Neural Volume Rendering
Authors: Zhu Daixian , Kong Haoran , Qiu Qiang , Ruan Xiaoman , Liu Shulin
Published: November 2023

Due to the presence of regions with weak textures or non-Lambertian surfaces, feature matching in learning-based Multi-View Stereo (MVS) algorithms often leads to incorrect matches, resulting in the construction of the flawed cost volume and incomplete scene reconstruction. In response to this limitation, this paper introduces the MVS network based on attention mechanism and neural volume rendering. Firstly, we employ a multi-scale feature extraction module based on dilated convolution and attention mechanism. This module enables the network to accurately model inter-pixel dependencies, focusing on crucial information for robust feature matching. Secondly, to mitigate the impact of the flawed cost volume, we establish a neural volume rendering network based on multi-view semantic features and neural encoding volume. By introducing the rendering reference view loss, we infer 3D geometric scenes, enabling the network to learn scene geometry information beyond the cost volume representation. Additionally, we apply the depth consistency loss to maintain geometric consistency across networks. The experimental results indicate that on the DTU dataset, compared to the CasMVSNet method, the completeness of reconstructions improved by 23.1%, and the Overall increased by 7.3%. On the intermediate subset of the Tanks and Temples dataset, the average F-score for reconstructions is 58.00, which outperforms other networks, demonstrating superior reconstruction performance and strong generalization capability.
Arc_EffNet: A Novel Series Arc Fault Detection Method Based on Lightweight Neural Network
Authors: Ning Xin , Sheng Dejie , Zhou Jiawang , Liu Yuying , Wang Yao , Zhang Hua , Lei Xiao
Published: November 2023

Arc faults can cause a severe electric fire, especially series arc faults. Artificial intelligence (AI)-based arc fault detection methods can have a boasted detection accuracy. However, the complexity and large parameter quantity of the AI-based algorithm will hinder its real-time performance for detecting series arc faults. This paper proposes a lightweight arc fault detection method based on the EffNet module, which can make the algorithm less complex with the same detection accuracy level. An arc fault test platform was constructed to collect arc current data, covering eight types of loads required by the IEC 62606 standard. The raw arc current data are used directly as an input for the proposed algorithm, reducing the module’s complexity. According to features of arc current mainly represented in the time domain, the first and last convolution layers of the EffNet module can be improved. Additionally, the spatially separable convolution is well-tuned and trimmed to achieve a more lightweight and better-performance architecture for arc fault detection called Arc_EffNet. Remarkably, this model achieves an impressive arc detection accuracy of 99.75%. An arc fault detection prototype was built using the Raspberry Pi 4B to evaluate the real-time detecting performance of the proposed method. The experimental results show that the prototype takes a time of about 72 ms to respond to a series arc fault, which can fulfill the requirement of real-time detection for arc faults.
The Optimal Color Space for Realistic Color Reproduction in Virtual Reality Content Design
Authors: Kim Hyun-Suh , Kim Eun Joung , Kim JungYoon
Published: November 2023

In the emerging era of the Metaverse in which virtual reality (VR) is used for various purposes such as product demonstration, marketing, and online commerce, it becomes essential to reproduce colors accurately not only for gaming but also for brand recognition and product representation. In this regard, this study investigated the optimal color space to minimize the difference between the intended colors for the VR device and the colors selected on the designers’ monitor during the development process. To this end, this study conducted measurements and provided technical demonstrations to highlight the color differences between three different color spaces of sRGB, AdobeRGB, and DCI-P3. Through this approach, we discovered that designing the VR content using the DCI-P3 color gamut yields the most ideal results currently available.
The Integration of Tools for the Techno-Economic Evaluation of Fixed and Floating Tidal Energy Deployment in the Irish Sea
Authors: O’Connell Ross , Kamidelivand Mitra , Polydoros Ioannis , Wright Christopher , Bonar Paul , Williams Alison J. , Murphy Jimmy
Published: November 2023

Marine renewable energy (MRE) development will be crucial to achieve worldwide energy decarbonization. In Europe, 1 GW and 40 GW of ocean energy are set to be developed by 2030 and 2050, respectively. Support is essential if wave and tidal stream arrays are to become more economically viable than they currently are. Four recently developed open-access software tools are used in this study to investigate the critical and expensive elements of potential demonstration and commercial scale tidal projects. The tools have been designed and built to assist users with array configurations, foundation and mooring (F&M) design, operation and maintenance (O&M) strategies, and techno-economic analysis. Demonstration of their use is performed in this study to model scenarios for 2 MW, 10 MW, 40 MW, and 100 MW tidal energy projects employing typical 500 kW fixed and 2 MW floating turbines at the West Anglesey Tidal Demonstration Zone in the Irish Sea. The following metrics are examined: the power output and wake losses of staggered and line configurations; the design and costs of simple gravity-based foundations, gravity-based anchors and the four-chain catenary mooring system of a single turbine; the mean O&M costs and farm availability over the project life; and the breakdown of levelized cost of energy (LCoE) for all eight scenarios to ultimately reveal minimum values of 173 EUR/MWh and 147 EUR/MWh for fixed and floating tidal energy technologies, respectively. The thorough analysis facilitated within these four tools to forecast realistic situations in a specific location can help users design a tidal energy project for an area with considerable potential for commercial scale projects, and thus assist the ocean energy community in promoting and nurturing the sector in the years and decades ahead.
Data-Driven Modeling of Appliance Energy Usage
Authors: Assadian Cameron Francis , Assadian Francis
Published: November 2023

Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several challenges remain to be addressed. Therefore, providing highly accurate and optimized models has become the primary research goal of many studies. This paper analyzes appliance energy consumption through a variety of machine learning-based strategies. Utilizing data recorded from a single-family home, input variables comprised internal temperatures and humidities, lighting consumption, and outdoor conditions including wind speed, visibility, and pressure. Various models were trained and evaluated: (a) multiple linear regression, (b) support vector regression, (c) random forest, (d) gradient boosting, (e) xgboost, and (f) the extra trees regressor. Both feature engineering and hyperparameter tuning methodologies were applied to not only extend existing features but also create new ones that provided improved model performance across all metrics: root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best model (extra trees) was able to explain 99% of the variance in the training set and 66% in the testing set when using all the predictors. The results were compared with those obtained using a similar methodology. The objective of performing these actions was to show a unique perspective in simulating building performance through data-driven models, identifying how to maximize predictive performance through the use of machine learning-based strategies, as well as understanding the potential benefits of utilizing different models.
Compositional Simulation of CO2 Huff-n-Puff Processes in Tight Oil Reservoirs with Complex Fractures Based on EDFM Technology Considering the Threshold Pressure Gradient
Authors: Zheng Jiayu , Jiang Tianhao , Chen Xiaoxia , Cui Zhengpan , Jiang Shan , Song Fangxin , Wen Zhigang , Wang Lei
Published: November 2023

Although tight oil reservoirs have abundant resources, their recovery efficiency is generally low. In recent years, CO2 injection huff-n-puff has become an effective method for improving oil recovery on the basis of depleted production of volume-fracturing horizontal wells in tight oil reservoirs. In order to study the effects of CO2 huff-n-puff (CO2-HnP) on production, a compositional numerical simulation study of CO2 huff-n-puff (CO2-HnP) was conducted in tight oil reservoirs with complex fractures. Embedded discrete fracture model technology was used in the simulations to characterize complex fractures. The process of CO2 huff-n-puff (CO2-HnP) was simulated, which consists of CO2 injection, CO2 soaking, and CO2 production. Taking into account the threshold pressure gradient and stress sensitivity in the model, we conducted a series of numerical simulations with different production condition parameters, such as bottom-hole pressure, CO2 injection rate, injection time, soaking time, and the number of cycles of CO2 huff-n-puff (CO2-HnP). Then, the effects of these sensitivity parameters on the cumulative oil production (COP) were studied. The results indicate that the threshold pressure gradient and rock stress sensitivity factors greatly affect the pressure field of tight reservoirs and the cumulative oil production (COP) of multistage-fracturing horizontal wells. The production parameters all have an impact on the COP. The injection rate and circulation number both have optimal values, and the injection time and soak time tend to have less significant effects on the growth of cumulative oil production over time. According to the numerical simulation, the optimal solution is 5 × 104 m3/day injection rate per cycle, 25 days of injection time, 35 days of soaking time, three cycles, and production for 5 years, which can obtain the optimal cumulative oil production.
Policies and Energy Efficiency of Heat Recovery Ventilators in South Korea
Authors: Jeon Byung-Heon , Yang Su-Kwang , Ahn Young-Chull , Kang Yul-Ho
Published: November 2023

To reduce national greenhouse gas emissions, the South Korean government has encouraged new energy businesses and implemented policies to reduce energy consumption in buildings, and aims to construct all new buildings as zero-energy buildings by 2025. According to the promotion of policies on passive houses and zero-energy buildings, the thermal insulation and airtight performance of new buildings have been further enhanced. However, to enhance indoor comfort and air quality in new airtight buildings, it is critical to secure an adequate amount of ventilation. Heat recovery ventilators (HRVs) in South Korea have been used for more than 20 years as high-efficiency energy equipment; however, the high-efficiency standard 20 years ago (cooling efficiency 45%, heating efficiency 70%) is still being employed without any change. Most HRVs in the Korean market either meet or exceed this standard. This study examined HRV performance changes from 2010 to 2020 based on the data of 847 HRV performance certifications given by a certification agency. It also analyzed how institutional strategies and related laws contributed to the enhancement of such performance. As HRVs in South Korea are only required to satisfy the pre-defined efficiency criteria, the development and use of HRVs focus more on cost reduction rather than efficiency enhancement. Under such market conditions, it is challenging to research and design highly efficient HRVs along with customer satisfaction. If better market conditions are offered that would welcome HRVs with higher efficiency, the development of better HRVs, as compared to those analyzed in this research study, would be possible.
Optimization of Offshore Wind Power Generation in Response to the 2022 Extreme Drought in Portugal
Authors: Camilo Fernando M. , Santos Paulo J. , Lobato Pedro J. , Moreira Sandrina B.
Published: November 2023

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, as extreme weather periods are occurring in a cyclical manner with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing the existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of the offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where the hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. This study presents an optimization model adapted to combat the unpredictability of extreme weather conditions. This strategic framework significantly increases the resilience and reliability of offshore wind energy production, marking a significant advance in the management of renewable energy under the pressure of climate variability. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.
Credibility Theory-Based Information Gap Decision Theory to Improve Robustness of Electricity Trading under Uncertainties
Authors: Zhao Xin , Wang Peng , Li Qiushuang , Li Yan , Liu Zhifan , Feng Liang , Chen Jiajia
Published: November 2023

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment.
Special Issue “Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems”
Authors: Kamal Tariq , Hassan Syed Zulqadar
Published: November 2023

The increasing environmental damage caused by adversarial factors, a growing need for energy, the continued reliance on fossil fuels, which comes with rising costs, and the global push for net-zero emissions targets have drawn significant focus on the global promotion of renewable energy sources [...]
Mi-CARE: Comparing Three Evidence-Based Interventions to Promote Colorectal Cancer Screening among Ethnic Minorities within Three Different Clinical Contexts
Authors: Watson Karriem S. , Tossas Katherine Y. , San Miguel Yazmin , Gastala Nicole , San Miguel Liliana G. , Grumeretz Scott , Henderson Vida , Winn Robert , Jimbo Masahito , Naylor Keith B. , Gregory Megan E. , Molina Yamilé , Hughes Ashley M.
Published: November 2023

Multiple evidence-based interventions (EBIs) have been developed to improve the completion of colorectal cancer (CRC) screening within Federally Qualified Health Centers (FQHCs) and other safety net settings in marginalized communities. Little effort has been made, however, to evaluate their relative effectiveness across different clinical contexts and populations. To this end, we tested the relative effectiveness of three EBIs (mailed birthday cards, lay navigation, and provider-delivered education) among a convenience sample of 1252 patients (aged 50–75 years old, who were due for CRC screening and scheduled for a visit at one of three clinics within a network of Federally Qualified Health Centers (FQHCs) in the United States. To be eligible for the study, patients had to identify as African American (AA) or Latino American (LA). We compared the effects of the three EBIs on CRC screening completion using logistic regression. Overall, 20% of the study population, an increase from a baseline of 13%, completed CRC screening. Clinical demographics appeared to influence the effectiveness of the EBIs. Mailed birthday reminders appeared to be the most effective within the multi-ethnic clinic (p = 0.03), provider-delivered education within the predominantly LA clinic (p = 0.02), and lay navigation within the predominantly AA clinic (p = 0.03). These findings highlight the importance of understanding clinical context when selecting which evidence-based interventions to deploy.
A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China
Authors: Liu Shuang , Tan Nengzhi , Liu Rui
Published: November 2023

Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Predicting, mapping, and evaluating the flood inundation risk (FIR) is important for coastal tourism. This study developed a spatial tourism-aimed framework by integrating a Weighted k Nearest Neighbors (WkNN) algorithm, geographic information systems, and environmental indexes, such as precipitation and soil. These model inputs were standardized and weighted using inverse distance calculation and integrated into WkNN to infer the regional probability and distribution of the FIR. Zhejiang province, China, was selected as a case study. The evaluation results were mapped to denote the likelihood of an FIR, which was then validated by the historical Maximum Inundation Extent (MIE) extracted from the World Environment Situation Room. The results indicated that 80.59% of the WkNN results reasonably confirmed the MIE. Among the matched areas, 80.14%, 90.13%, 65.50%, and 84.14% of the predicted categories using WkNN perfectly coincided with MIE at high, medium, low, and very low risks, respectively. For the entire study area, approximately 2.85%, 64.83%, 10.8%, and 21.51% are covered by a high, medium, low, and very low risk of flood inundation. Precipitation and elevation negatively contribute to a high-medium risk. Drainage systems positively alleviate the regional stress of the FIR. The results of the evaluation illustrate that in most inland areas, some tourism facilities are located in high-medium areas of the FIR. However, most tourism facilities in coastal cities are at low or very low risk, especially from Hangzhou-centered northern coastal areas to southern Wenzhou areas. The results can help policymakers make appropriate strategies to protect coastal tourism from flood inundation. Moreover, the evaluation accuracy of WkNN is higher than that of kNN in FIR. The WkNN-based framework provides a reasonable method to yield reliable results for assessing FIR. The framework can also be extended to other risk-related research under climate change.
The Role of Sirtuin-1 (SIRT1) in the Physiology and Pathophysiology of the Human Placenta
Authors: Wątroba Mateusz , Szewczyk Grzegorz , Szukiewicz Dariusz
Published: November 2023

Sirtuins, especially SIRT1, play a significant role in regulating inflammatory response, autophagy, and cell response to oxidative stress. Since their discovery, sirtuins have been regarded as anti-ageing and longevity-promoting enzymes. Sirtuin-regulated processes seem to participate in the most prevalent placental pathologies, such as pre-eclampsia. Furthermore, more and more research studies indicate that SIRT1 may prevent pre-eclampsia development or at least alleviate its manifestations. Having considered this, we reviewed recent studies on the role of sirtuins, especially SIRT1, in processes determining normal or abnormal development and functioning of the placenta.
Adult-Onset Transcriptomic Effects of Developmental Exposure to Benzene in Zebrafish (Danio rerio): Evaluating a Volatile Organic Compound of Concern
Authors: Connell Mackenzie L. , Wu Chia-Chen , Blount Jessica R. , Haimbaugh Alex , Kintzele Emily K. , Banerjee Dayita , Baker Bridget B. , Baker Tracie R.
Published: November 2023

Urban environments are afflicted by mixtures of anthropogenic volatile organic compounds (VOCs). VOC sources that drive human exposure include vehicle exhaust, industrial emissions, and oil spillage. The highly volatile VOC benzene has been linked to adverse health outcomes. However, few studies have focused on the later-in-life effects of low-level benzene exposure during the susceptible window of early development. Transcriptomic responses during embryogenesis have potential long-term consequences at levels equal to or lower than 1 ppm, therefore justifying the analysis of adult zebrafish that were exposed during early development. Previously, we identified transcriptomic alteration following controlled VOC exposures to 0.1 or 1 ppm benzene during the first five days of embryogenesis using a zebrafish model. In this study, we evaluated the adult-onset transcriptomic responses to this low-level benzene embryogenesis exposure (n = 20/treatment). We identified key genes, including col1a2 and evi5b, that were differentially expressed in adult zebrafish in both concentrations. Some DEGs overlapped at the larval and adult stages, specifically nfkbiaa, mecr, and reep1. The observed transcriptomic results suggest dose- and sex-dependent changes, with the highest impact of benzene exposure to be on cancer outcomes, endocrine system disorders, reproductive success, neurodevelopment, neurological disease, and associated pathways. Due to molecular pathways being highly conserved between zebrafish and mammals, developmentally exposed adult zebrafish transcriptomics is an important endpoint for providing insight into the long term-effects of VOCs on human health and disease.
Increase in hnRNPA1 Expression Suffices to Kill Motor Neurons in Transgenic Rats
Authors: Huang Bo , Liu Xionghao , Zhang Tingting , Wu Qinxue , Huang Cao , Xia Xu-Gang , Zhou Hongxia
Published: November 2023

A dominant mutation in hnRNPA1 causes amyotrophic lateral sclerosis (ALS), but it is not known whether this mutation leads to motor neuron death through increased or decreased function. To elucidate the relationship between pathogenic hnRNPA1 mutation and its native function, we created novel transgenic rats that overexpressed wildtype rat hnRNPA1 exclusively in motor neurons. This targeted expression of wildtype hnRNPA1 caused severe motor neuron loss and subsequent denervation muscle atrophy in transgenic rats that recapitulated the characteristics of ALS. These findings demonstrate that the augmentation of hnRNPA1 expression suffices to trigger motor neuron degeneration and the manifestation of ALS-like phenotypes. It is reasonable to infer that an amplification of an as-yet undetermined hnRNPA1 function plays a pivotal role in the pathogenesis of familial ALS caused by pathogenic hnRNPA1 mutation.
Transcriptomic Analysis of the Reduction in Seed Oil Content through Increased Nitrogen Application Rate in Rapeseed (Brassica napus L.)
Authors: Hao Pengfei , Ren Yun , Lin Baogang , Yi Kaige , Huang Lan , Li Xi , Jiang Lixi , Hua Shuijin
Published: November 2023

Nitrogen is essential for improving the seed oil yield of rapeseed (Brassica napus L.). However, the molecular mechanism by which increased nitrogen rates impact seed oil content is largely unknown. Therefore, a field experiment was conducted to determine how three nitrogen application rates (120, 240, and 360 kg ha−1) regulated seed oil content via transcriptomic analysis. The results showed that the seed yield and the protein and total N contents increased from N1 to N3, with average increases of 57.2%, 16.9%, and 79.5%, respectively. However, the seed oil content significantly decreased from N1 to N3, with an average decrease of 8.6%. These results were repeated over a number of years. The quantity of oil protein bodies observed under a transmission electron microscope was in accordance with the ultimate seed oil and protein contents. As the nitrogen application rate increased, a substantial number of genes involved in the photosynthesis, glycolysis, and phenylpropanoid biosynthesis pathways were up-regulated, as were TF families, such as AP2/ERF, MYB, and NAC. The newly identified genes were mainly involved in carbohydrate, lipid, and amino acid metabolism. Metabolic flux analysis showed that most of the genes involved in glycolysis and fatty acid biosynthesis had higher transcript levels in the early development stages. Our results provide new insights into the molecular regulation of rapeseed seed oil content through increased nitrogen application rates.