bluuebunny/crossref_metadata_2025 huggingface dataset:
A Scoping Review of Determinants of Drinking and Driving Behavior among Young Adult College Students in the US
Authors: Bonsu Laurencia , Grigsby Timothy J. , Johansen Christopher , Awan Asma , Kapukotuwa Sidath , Sharma Manoj
Published: May 2024

College students are a primary population for risky alcohol use behaviors, with one of every eleven students grappling with severe alcohol-related issues. The objective of this scoping review was to synthesize the existing literature to identify factors influencing the prevalence of drinking and driving (DAD) behaviors among college students. A scoping review was conducted using Medline (PubMed), ERIC, The American Journal on Addictions, and the NCHA databases. Criteria for article selection included being published in English and focused on DAD behaviors among college students. Articles excluded from the review were systematic reviews and discussion pieces without empirical findings related to college DAD. Of the included studies (n = 23), most identified a range of factors as being influential in college students’ DAD behavior including a family history of alcohol misuse, the use of other substances such as marijuana, age of initial alcohol consumption, place of residence, propensity for sensation seeking, affiliation with sorority/fraternity groups, and the perception of associated risks. Effective strategies may include education on the risks of combined alcohol and substance use, screening and brief interventions tailored to at-risk students, and the implementation of campus policies that promote responsible alcohol consumption and deter DAD.
A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data
Authors: Li Zukun , Wei Daoming , Zhang Xuefeng , Gao Yaoting , Zhang Dianjun
Published: May 2024

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.
Fault Kinematics of the 2023 Mw 6.0 Jishishan Earthquake, China, Characterized by Interferometric Synthetic Aperture Radar Observations
Authors: Huang Xing , Li Yanchuan , Shan Xinjian , Zhong Meijiao , Wang Xuening , Gao Zhiyu
Published: May 2024

Characterizing the coseismic slip behaviors of earthquakes could offer a better understanding of regional crustal deformation and future seismic potential assessments. On 18 December 2023, an Mw 6.0 earthquake occurred on the Lajishan–Jishishan fault system (LJFS) in the northeastern Tibetan Plateau, causing serious damage and casualties. The seismogenic fault hosting this earthquake is not well constrained, as no surface rupture was identified in the field. To address this issue, in this study, we use Interferometric Synthetic Aperture Radar (InSAR) data to investigate the coseismic surface deformation of this earthquake and invert both ascending and descending line-of-sight observations to probe the seismogenic fault and its slip characteristics. The InSAR observations show up to ~6 cm surface uplift caused by the Jishishan earthquake, which is consistent with the thrust-dominated focal mechanism. A Bayesian-based dislocation modeling indicates that two fault models, with eastern and western dip orientations, could reasonably fit the InSAR observations. By calculating the coseismic Coulomb failure stress changes (∆CFS) induced by both fault models, we find that the east-dipping fault scenario could reasonably explain the aftershock distributions under the framework of stress triggering, while the west-dipping fault scenario produced a negative ∆CFS in the region of dense aftershocks. Integrating regional geological structures, we suggest that the seismogenic fault of the Jishishan earthquake, which strikes NNE with a dip of 56° to the east, may be either the Jishishan western margin fault or a secondary buried branch. The optimal finite-fault slip modeling shows that the coseismic slip was dominated by reverse slip and confined to a depth range between ~5 and 15 km. The released seismic moment is 1.61 × 1018 N·m, which is equivalent to an Mw 6.07 earthquake. While the Jishishan earthquake ruptured a fault segment of approximately 20 km, it only released a small part of the seismic moment that was accumulated along the 220 km long Lajishan–Jishishan fault system. The remaining segments of the Lajishan–Jishishan fault system still have the capability to generate moderate-to-large earthquakes in the future.
Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series
Authors: Fonseca Alejandro , Marshall Michael Thomas , Salama Suhyb
Published: May 2024

Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapajós River Basin of Brazil—an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests “ensemble of ensembles” technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning.
Estimating the Intensity of Tropical Cyclones from Spiral Signatures Acquired by Spaceborne SAR
Authors: Yurchak Boris S.
Published: May 2024

Accurate estimates of tropical cyclone (TC) intensity are important for improving forecasts as well as studying ocean dynamics during such extreme events. Since most cyclone life occurs over the open ocean, remote sensing techniques play an important role in obtaining the necessary data. The possibility of using the configuration of spiral signatures of mature tropical cyclones (TCs) observed in synthetic aperture radar (SAR) images to estimate the maximum wind speed of a TC is considered. This study assessed the intensity of 14 TCs in the Atlantic and Pacific Oceans using radar images obtained by the Radarsat Hurricane Application Project. TC intensity was estimated using the hyperbolic-logarithmic approximation of TC spiral signatures (HLS approximation). Additionally, the edges of the spiral signatures were partially fitted using a logarithmic spiral to improve the reliability of the HLS approximation. For the first time, a physical model of changing the crossing angle of the logarithmic portion of the edges was proposed and tested on SAR images of the TC. HLS maximum wind speed estimates were compared with Best Track estimates. The results showed the closeness of both estimates with a correlation of 0.95 and a standard deviation of 2.9 m s−1. The results indicate the possibility of using the HLS approximation to estimate the intensity of mature TCs from SAR data.
Comprehensive Review of RF MEMS Switches in Satellite Communications
Authors: Shao Bingqian , Lu Chengjian , Xiang Yinjie , Li Feixiong , Song Mingxin
Published: May 2024

The miniaturization and low power consumption characteristics of RF MEMS (Radio Frequency Microelectromechanical System) switches provide new possibilities for the development of microsatellites and nanosatellites, which will play an increasingly important role in future space missions. This paper provides a comprehensive review of RF MEMS switches in satellite communication, detailing their working mechanisms, performance optimization strategies, and applications in reconfigurable antennas. It explores various driving mechanisms (electrostatic, piezoelectric, electromagnetic, thermoelectric) and contact mechanisms (capacitive, ohmic), highlighting their advantages, challenges, and advancements. The paper emphasizes strategies to enhance switch reliability and RF performance, including minimizing the impact of shocks, reducing driving voltage, improving contacts, and appropriate packaging. Finally, it discusses the enormous potential of RF MEMS switches in future satellite communications, addressing their technical advantages, challenges, and the necessity for further research to optimize design and manufacturing for broader applications and increased efficiency in space missions. The research findings of this review can serve as a reference for further design and improvement of RF MEMS switches, which are expected to play a more important role in future aerospace communication systems.
Differential BroadBand (1–16 GHz) MMIC GaAs mHEMT Low-Noise Amplifier for Radio Astronomy Applications and Sensing
Authors: Jimenez-Martin Jose Luis , Gonzalez-Posadas Vicente , Parra-Cerrada Angel , Espinosa-Adams David , Segovia-Vargas Daniel , Hernandez Wilmar
Published: May 2024

A broadband differential-MMIC low-noise amplifier (DLNA) using metamorphic high-electron-mobility transistors of 70 nm in Gallium Arsenide (70 nm GaAs mHEMT technology) is presented. The design and results of the performance measurements of the DLNA in the frequency band from 1 to 16 GHz are shown, with a high dynamic range, and a noise figure (NF) below 1.3 dB is obtained. In this work, two low-noise amplifiers (LNAs) were designed and manufactured in the OMMIC foundry: a dual LNA, which we call balanced, and a differential LNA, which we call DLNA. However, the paper focuses primarily on DLNA because of its differential architecture. Both use a 70 nm GaAs mHEMT space-qualified technology with a cutoff frequency of 300 GHz. With a low power bias Vbias/Ibias (5 V/40.5 mA), NF < 1.07 dB “on wafer” was achieved, from 2 to 16 GHz; while with the measurements made “on jig”, NF = 1.1 dB, from 1 to 10 GHz. Furthermore, it was obtained that NF < 1.5 dB, from 1 to 16 GHz, with a figure of merit equal to 145.5 GHz/mW. Finally, with the proposed topology, several LNAs were designed and manufactured, both in the OMMIC process and in other foundries with other processes, such as UMS. The experimental results showed that the NF of the DLNA MMIC with multioctave bandwidth that was built in the frequency range of the L-, S-, C-, and X-bands was satisfactory.
Investigating a Machine Learning Approach to Predicting White Pixel Defects in Wafers—A Case Study of Wafer Fabrication Plant F
Authors: Shih Dong-Her , Yang Cheng-Yu , Wu Ting-Wei , Shih Ming-Hung
Published: May 2024

CMOS image sensor (CIS) semiconductor products are integral to mobile phones and photographic devices, necessitating ongoing enhancements in efficiency and quality for superior photographic outcomes. The presence of white pixels serves as a crucial metric for assessing CIS product performance, primarily arising from metal impurity contamination during the wafer production process or from defects introduced by the grinding blade process. While immediately addressing metal impurity contamination during production presents challenges, refining the handling of defects attributed to grinding blade processing can notably mitigate white pixel issues in CIS products. This study zeroes in on silicon wafer manufacturers in Taiwan, analyzing white pixel defects reported by customers and leveraging machine learning to pinpoint and predict key factors leading to white pixel defects from grinding blade operations. Such pioneering practical studies are rare. The findings reveal that the classification and regression tree (CART) and random forest (RF) models deliver the most accurate predictions (95.18%) of white pixel defects caused by grinding blade operations in a default parameter setting. The analysis further elucidates critical factors like grinding load and torque, vital for the genesis of white pixel defects. The insights garnered from this study aim to arm operators with proactive measures to diminish the potential for customer complaints.
Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation
Authors: Amyan Ayman , Abboush Mohammad , Knieke Christoph , Rausch Andreas
Published: May 2024

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.
Spectroscopic Techniques and Hydrogen-Sensitive Compounds: A New Horizon in Hydrogen Detection
Authors: Si Bu , Hu Yan , Yao Longchao , Jin Qiwen , Zheng Chenghang , Wu Yingchun , Wu Xuecheng , Gao Xiang
Published: May 2024

Detecting hydrogen leaks remains a pivotal challenge demanding robust solutions. Among diverse detection techniques, the fiber-optic method distinguishes itself through unique benefits, such as its distributed measurement properties. The adoption of hydrogen-sensitive materials coated on fibers has gained significant traction in research circles, credited to its operational simplicity and exceptional adaptability across varied conditions. This manuscript offers an exhaustive investigation into hydrogen-sensitive materials and their incorporation into fiber-optic hydrogen sensors. The research profoundly analyzes the sensor architectures, performance indicators, and the spectrum of sensing materials. A detailed understanding of these sensors’ potentials and constraints emerges through rigorous examination, juxtaposition, and holistic discourse. Furthermore, this analysis judiciously assesses the inherent challenges tied to these systems, simultaneously highlighting potential pathways for future innovation. By spotlighting the hurdles and opportunities, this paper furnishes a view on hydrogen sensing technology, particularly related to optical fiber-based applications.
Multivariate and Matrix-Variate Logistic Models in the Real and Complex Domains
Authors: Mathai A. M.
Published: May 2024

Several extensions of the basic scalar variable logistic density to the multivariate and matrix-variate cases, in the real and complex domains, are given where the extended forms end up in extended zeta functions. Several cases of multivariate and matrix-variate Bayesian procedures, in the real and complex domains, are also given. It is pointed out that there are a range of applications of Gaussian and Wishart-based matrix-variate distributions in the complex domain in multi-look data from radar and sonar. It is hoped that the distributions derived in this paper will be highly useful in such applications in physics, engineering, statistics and communication problems, because, in the real scalar case, a logistic model is seen to be more appropriate compared to a Gaussian model in many industrial applications. Hence, logistic-based multivariate and matrix-variate distributions, especially in the complex domain, are expected to perform better where Gaussian and Wishart-based distributions are currently used.
Locoregional vs. General Anaesthesia for Minimally Invasive Video-Assisted Parathyroidectomy (MIVAP) Using Propensity Score Matching Analysis: A Feasibility Study
Authors: Pennestrì Francesco , Procopio Priscilla Francesca , Prioli Francesca , Gallucci Pierpaolo , Sessa Luca , Martullo Annamaria , Laurino Antonio , Revelli Luca , Modesti Cristina , De Crea Carmela , Raffaelli Marco
Published: May 2024

Focused parathyroidectomy is the preferred surgical method for treating primary hyperparathyroidism (pHPT) sustained by the pre-operatively well-localized parathyroid adenoma. We aimed to compare the effectiveness, safety, and short-term clinical outcome of minimally invasive video-assisted parathyroidectomy (MIVAP) in locoregional anaesthesia (LA) vs. general anaesthesia (GA) by means of propensity score matching (PSM) analysis. Retrospective research of patients who underwent MIVAP between January 2014 and December 2022 was carried out. Patients were divided into two groups based on the anaesthesiologic procedure (LA vs. GA). Overall, 553 patients underwent MIVAP. After PSM, 115 patients in the LA group and 230 patients in the GA group were included. MIVAP under LA was associated with shorter median operative time (16 vs. 35 min, p < 0.001), shorter median operative room occupation time (44 vs. 73 min, p < 0.001), and lesser median post-operative visual analogue scale pain, with comparable post-operative hospital stay and complication rate. MIVAP under LA is a safe and feasible procedure with significant advantages over GA in terms of post-operative pain and operative room occupation time. This last step can finally result in more efficient utilisation of the operative room and the health care system’s resources.
Investigating Factors Affecting the Purchase Intention in Petroleum Stations Implementing Sustainable Practices: A Pro-Environmental Behavior Approach with a Consideration of Sustainable Initiatives Knowledge
Authors: Rebualos Rogel Angelo A. , Prasetyo Yogi Tri , Cahigas Maela Madel L. , Nadlifatin Reny , Gumasing Ma. Janice J. , Ayuwati Irene Dyah
Published: May 2024

The application of sustainability practices has become one of the key drivers to gaining a favorable stand in the market. However, petroleum companies are hesitant with its implementation due to the perceived negative financial impact. This study was conducted to determine the purchase intentions of consumers from petroleum stations implementing sustainability practices by utilizing the pro-environmental planned behavior (PEPB) framework. The research utilized an online questionnaire with 400 respondents who have been a petrol station customer. The data were examined with a higher-order construct using partial least squares structural equation modelling (PLS-SEM). The findings showed a positive relationship between variables and revealed that economic concern, subjective norms, perceived behavioral control, economic factors, and sustainable knowledge significantly influenced customers’ intention to purchase goods and services from a petrol station adopting sustainability practices, while attitude was found to have no direct significant impact on customers’ intention. The results of this study adds value to the potential increase in PEPB understanding and consumer behavior and may be beneficial for petroleum companies as the basis for managerial decisions regarding the implementation of sustainability practices or initiatives towards adopting the concept of “green stations” and consumer preferences to attract purchase intentions.
Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening
Authors: Liu Xuan , Wang Ruirui , Shi Wei , Wang Xiaoyan , Yang Yaoyao
Published: May 2024

The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman’s Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R2 of 0.85 and an MSE of 10.74 on the training set and an R2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening.
Estimation of Centrifugal Pump Efficiency at Variable Frequency for Irrigation Systems
Authors: Bordeasu Dorin , Dragan Florin , Filip Ioan , Szeidert Iosif , Tirian Gelu Ovidiu
Published: May 2024

The sustainability of the food production achieved with the help of irrigation systems and the sustainability of their energy consumption are major challenges of the current century. Pumping systems currently account for approximately 30% of global electrical energy consumption. As electricity prices rise, there is a growing need for technological advancements to enhance energy efficiency and reduce consumption costs effectively. This study focuses on operating centrifugal pumps at variable frequency as an effective means of achieving this goal. Most centrifugal pump manufacturers/providers traditionally assume that pump efficiency remains constant across various operating frequencies, often equating the efficiency at various frequencies to that at the standard frequency (50/60 Hz). In contrast, this paper introduces a new formula for estimating pump efficiency, crucial to precise power consumption determination, particularly in variable-frequency scenarios. The formula parameters are identified by using experimental data acquired from an existing pumping system. The tests and results presented in this paper demonstrate that the proposed formula outperforms the formulas of the current industry standards in accuracy. Practically, the new relation assures enhanced accuracy in estimating pump efficiency and absorbed power, allowing for the design of a more precise model used for control systems synthesis required for operating centrifugal pumps at variable frequency. This research offers a new way of calculating pump efficiency, which could be very useful for industry practitioners seeking to optimize energy consumption in pumping systems.
A Low-Cost Energy Monitoring System with Universal Compatibility and Real-Time Visualization for Enhanced Accessibility and Power Savings
Authors: Khan Hashim Raza , Kazmi Majida , Lubaba  , Khalid Muhammad Hashir Bin , Alam Urooj , Arshad Kamran , Assaleh Khaled , Qazi Saad Ahmed
Published: May 2024

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform.
Eco-Friendly Smart Car Parking Management System with Enhanced Sustainability
Authors: Sakib Nazmus , Bakibillah A. S. M. , Susilawati Susilawati , Kamal Md Abdus Samad , Yamada Kou
Published: May 2024

Efficient car parking management systems that minimize environmental impacts while maximizing user comfort are highly demanding for a future sustainable society. Using electric or gasoline vehicle-type information, emerging computation and communication technologies open the opportunity to provide practical solutions to achieve such goals. This paper proposes an eco-friendly smart parking management system that optimally allocates the incoming vehicles to reduce overall emissions in closed parking facilities while providing comfort incentives to the users of electric vehicles (EVs). Specifically, upon arrival of a car, the most suitable parking spot is determined by minimizing an adaptive objective function that indirectly reflects anticipatory operation for the overall performance maximization of the parking facility using electric or gasoline vehicle-type information. The adaptive objective function includes a trade-off factor that tunes driving and walking distances, relating emissions and comfort to treat incoming vehicles appropriately. The proposed system is simulated for managing a model car parking facility in a shopping complex in Japan, and the aspects related to fuel consumption, CO2 emissions, and user comfort are evaluated and benchmarked with other standard parking management systems. The proposed system reduces CO2 emissions and fuel consumption and improves parking efficiency compared to the current parking management systems, while also prioritizing user comfort.
Returning Cropland to Grassland as a Potential Method for Increasing Carbon Storage in Dry-Hot Valley Areas
Authors: He Yakai , Kou Weili , Chen Yue , Lai Hongyan , Zhao Kaifu
Published: May 2024

A key aspect of mitigating global climate warming is enhancing the carbon storage capacity of terrestrial ecosystems. China’s Grain for Green Program (GFGP) is the largest ecological restoration project in the world, which is closely associated with land use change. A systematic assessment of the GFGP’s impact on regional carbon storage is of great significance for promoting regional development and maintaining ecosystem stability. Therefore, this study selects a typical dry-hot valley area—Yanjin County—as the study area, which serves as an ecological protection barrier in Southwest China. We employed the InVEST model and Geo-detector model based on land use data from three periods (2000, 2014, and 2019), combined with static overlay analysis methods, in order to evaluate the impact of the implementation of GFGP on the spatial and temporal distribution of carbon storage. We also explored the driving factors of the spatial differentiation of carbon storage. The results indicate that, since the implementation of the GFGP, a total of 180.03 km2 of cropland has been converted to forestland, increasing the forest cover rate from 81.83% to 83.37%. The project has contributed 5.88 × 105 t to regional carbon storage, effectively offsetting carbon emissions caused by human activities such as urban expansion while also promoting the growth of regional carbon storage. The implementation of the GFGP has led to changes in three types of land use. Among them, converting cropland to forestland (3262 t/km2) is the most effective carbon sequestration method, and converting cropland to grassland (2530 t/km2) has shown great potential in carbon sequestration. Additionally, the study found that elevation (0.038–0.059) is the main factor affecting the spatial differentiation of carbon storage, and the interaction between elevation and other factors can effectively enhance the carbon sequestration capacity of regional ecosystems. Overall, the GFGP not only plays a significant role in combating climate warming but also makes an important contribution to improving the stability and sustainability of regional ecosystems.
Peru’s National Policy on Financial Inclusion and Its Alignment with Sustainable Development Goal I
Authors: Ticona Machaca Alejandro , Gutiérrez Castillo Félix Henry , Turpo Aliaga Bertelly , Cano Ccoa Dominga Micaela , Yucra Quispe Roger , Cahuana Sánchez John Herbert , Duran Ttito Corina Nanci , Malaga Yllpa Yasser , Silva Flores Lourdes Janet , Callupe Cueva Paulo César
Published: May 2024

This article analyzes the implementation of the National Financial Inclusion Strategy (NFIS) and its alignment with Sustainable Development Goal (SDG) I: Eradicate poverty. Despite the progress achieved, structural gaps persist and substantially limit the NFIS’s contribution to poverty reduction, especially among rural, indigenous, extreme poverty, and other vulnerable groups. The article employs a mixed methods approach combining qualitative and quantitative techniques. On the qualitative side, a hermeneutic documentary analysis of the NFIS and related regulations was carried out, with a critical textual interpretation supported by specialized software. Quantitatively, descriptive statistical techniques were applied to analyze official financial inclusion indicators—methodological integration was achieved through analytical triangulation under a concurrent mixed methods approach. The progress of initiatives under the NFIS was evaluated, identifying limitations such as the digital divide in rural areas, limited financing for Micro and Small Enterprises, gaps in financial education, and growing exposure to digital fraud. Although the NFIS promotes greater access to financial services, it does not explicitly focus on the poorest and most excluded populations. Therefore, it is recommended that the objectives and indicators of the NFIS be reformulated to focus on universal access and effective use of financial services by the population in extreme poverty and chronic exclusion. In addition, a strategic articulation with social protection policies is necessary, as well as promoting culturally relevant microfinance and inclusive finance models, strengthening consumer protection, and consolidating public–private alliances in high-poverty areas. Finally, strong monitoring and accountability are also key.
Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas
Authors: Meng Chang , Hong Mei , Hu Yuncai , Li Fei
Published: May 2024

Soil copper (Cu) contamination in mining areas poses a serious threat to the surrounding environment and human health. Timely determination of Cu concentrations is crucial for the ecological protection of mining areas. Hyperspectral remote sensing technology, with its non-destructive monitoring advantages, is essential for monitoring soil Cu pollution and achieving sustainable agricultural development. Using the hyperspectral technique for assessing soil Cu concentration, four machine learning models (support vector regression (SVR), random forest (RF), partial least squares regression (PLSR), and artificial neural network (ANN)), combined with three types of input variables (the full-band, sensitive bands, and optimized spectral indices (Opt-TBIs)) were employed. The hyperspectral reflectance of 647 soil samples from an abandoned tailings mine in western Inner Mongolia, China was collected. The sensitive bands were extracted using the successive projections algorithms (SPA), and 12 Opt-TBIs were selected. Results showed that the regions with higher soil Cu concentration extracted by SPA and Opt-TBIs were concentrated in the red edge and near-infrared regions. Compared with the full spectrum and SPA-sensitive bands, models based on Opt-TBIs successfully predicted soil Cu concentrations. The Opt-TBIs-RF model provided higher accuracy in estimating soil Cu among the four models. Using only four Opt-TBIs as input variables, the model maintained a stable performance in estimating Cu concentrations in different mining areas (R2Val = 0.72, RPDVal = 1.90). In conclusion, Opt-TBIs as input variables demonstrate good predictive capabilities for soil Cu concentrations in the study area, providing a basis for the formulation of sustainable strategies for soil reclamation and environmental protection in Inner Mongolia.
Formation of Organic Monolayers on KF-Etched Si Surfaces
Authors: Li Tiexin , Datson Zane , Darwish Nadim
Published: May 2024

Silicon is the most commonly used material in the microelectronics industry, due to its inherent advantages of high natural abundance, low cost, and high purity, coupled with the chemical and electrical stability at the interface with its oxide. For molecular electronics applications, oxide-free Si surfaces are widely used because of the relative ease of removing the oxide (SiOx) by chemical means, yielding a surface which forms strong covalent bonds with a wide range of chemical functional groups; another advantage is that these surfaces remain oxide-free in the absence of oxidising agents. Standard procedures require the use of either HF, NH4F, or a mixture of both as the etching solution; however, these two chemicals are highly corrosive and toxic, posing a significant risk to the experimentalist. Here, we report that for silicon wafers etched by using potassium fluoride, a less toxic chemical, the resulting surface is free of oxides and can be functionalized by self-assembled monolayers of 1,8-nonadiyne. To demonstrate this, Si/SiOx wafers were etched by using either KF or NH4F, followed by hydrosilylation with 1,8-nonadiyne and a click reaction of the terminal alkyne with azidomethylferrocene. The surface coverages and electron transfer kinetics of the ferrocene-terminated KF-etched surfaces are comparable to those formed by acidic fluoride etching procedures. This is the first study comparing the differences between surfaces functionalized by self-assembled monolayers of 1,8-nonadiyne which were etched by KF and NH4F. KF could be used as a replacement chemical for etching silicon wafers when a less corrosive and toxic chemical is required.
On Neutrosophic Fuzzy Metric Space and Its Topological Properties
Authors: Ghosh Samriddhi , Sonam  , Bhardwaj Ramakant , Narayan Satyendra
Published: May 2024

The present research introduces a novel concept termed “neutrosophic fuzzy metric space”, which extends the traditional metric space framework by incorporating the notion of neutrosophic fuzzy sets. A thorough investigation of various structural and topological properties within this newly proposed generalization of metric space has been conducted. Additionally, counterparts of well-known theorems such as the Uniform Convergence Theorem and the Baire Category Theorem have been established for this generalized metric space. Through rigorous analysis, a detailed understanding of its fundamental characteristics has been attained, illuminating its potential applications and theoretical significance.
Publisher’s Note: Therapeutics as a Spin-Off Journal of Medicines
Authors: Lin Shu-Kun
Published: May 2024

We are publishing volume 11 of Medicines in 2024 [...]
Assessment of Sustainable Mobility Initiatives Developed in Montevideo, Uruguay
Authors: Nesmachnow Sergio , Hipogrosso Silvina
Published: May 2024

This article presents an assessment of sustainable mobility initiatives developed in Montevideo, Uruguay, in the period from 2020 to 2023. The significance of sustainable mobility is underscored due to its far-reaching implications for the environment, energy efficiency, and the overall quality of life of citizens. This study focuses on crucial aspects of four initiatives deployed in Montevideo in 2020–2023: electric mobility solutions using scooters, the development of infrastructure and services for urban cycling, the development of electric public transportation, and private electric transportation. Important results are obtained and commented on for each of the studied initiatives, regarding efficiency, environmental impact, accessibility, the quality of the service, and other relevant indicators. Based on the analysis, valuable knowledge is acquired to guide the future development of efficient and sustainable transportation modes in Montevideo, Uruguay.
mRNA Splicing of UL44 and Secretion of Alphaherpesvirinae Glycoprotein C (gC) Is Conserved among the Mardiviruses
Authors: Xu Huai , Vega-Rodriguez Widaliz , Campos Valeria , Jarosinski Keith W.
Published: May 2024

Marek’s disease (MD), caused by gallid alphaherpesvirus 2 (GaAHV2) or Marek’s disease herpesvirus (MDV), is a devastating disease in chickens characterized by the development of lymphomas throughout the body. Vaccine strains used against MD include gallid alphaherpesvirus 3 (GaAHV3), a non-oncogenic chicken alphaherpesvirus homologous to MDV, and homologous meleagrid alphaherpesvirus 1 (MeAHV1) or turkey herpesvirus (HVT). Previous work has shown most of the MDV gC produced during in vitro passage is secreted into the media of infected cells although the predicted protein contains a transmembrane domain. We formerly identified two alternatively spliced gC mRNAs that are secreted during MDV replication in vitro, termed gC104 and gC145 based on the size of the intron removed for each UL44 (gC) transcript. Since gC is conserved within the Alphaherpesvirinae subfamily, we hypothesized GaAHV3 (strain 301B/1) and HVT also secrete gC due to mRNA splicing. To address this, we collected media from 301B/1- and HVT-infected cell cultures and used Western blot analyses and determined that both 301B/1 and HVT produced secreted gC. Next, we extracted RNAs from 301B/1- and HVT-infected cell cultures and chicken feather follicle epithelial (FFE) skin cells. RT-PCR analyses confirmed one splicing variant for 301B/1 gC (gC104) and two variants for HVT gC (gC104 and gC145). Interestingly, the splicing between all three viruses was remarkably conserved. Further analysis of predicted and validated mRNA splicing donor, branch point (BP), and acceptor sites suggested single nucleotide polymorphisms (SNPs) within the 301B/1 UL44 transcript sequence resulted in no gC145 being produced. However, modification of the 301B/1 gC145 donor, BP, and acceptor sites to the MDV UL44 sequences did not result in gC145 mRNA splice variant, suggesting mRNA splicing is more complex than originally hypothesized. In all, our results show that mRNA splicing of avian herpesviruses is conserved and this information may be important in developing the next generation of MD vaccines or therapies to block transmission.
HPV and RNA Binding Proteins: What We Know and What Remains to Be Discovered
Authors: Graham Sheila V.
Published: May 2024

Papillomavirus gene regulation is largely post-transcriptional due to overlapping open reading frames and the use of alternative polyadenylation and alternative splicing to produce the full suite of viral mRNAs. These processes are controlled by a wide range of cellular RNA binding proteins (RPBs), including constitutive splicing factors and cleavage and polyadenylation machinery, but also factors that regulate these processes, for example, SR and hnRNP proteins. Like cellular RNAs, papillomavirus RNAs have been shown to bind many such proteins. The life cycle of papillomaviruses is intimately linked to differentiation of the epithelial tissues the virus infects. For example, viral late mRNAs and proteins are expressed only in the most differentiated epithelial layers to avoid recognition by the host immune response. Papillomavirus genome replication is linked to the DNA damage response and viral chromatin conformation, processes which also link to RNA processing. Challenges with respect to elucidating how RBPs regulate the viral life cycle include consideration of the orchestrated spatial aspect of viral gene expression in an infected epithelium and the epigenetic nature of the viral episomal genome. This review discusses RBPs that control viral gene expression, and how the connectivity of various nuclear processes might contribute to viral mRNA production.
Advances in the Management of Solid Waste and Wastewater Treatment
Authors: Costa Carlos
Published: May 2024

The management of solid waste and wastewater treatment is an ever-pressing concern for countries around the world [...]
KAJIAN AUDIT REPORT LAG DARI PERSPEKTIF TATA KELOLA KORPORASI: EKSPLORASI PERAN KUALITAS AUDIT SEBAGAI VARIABEL MODERASI
Authors: Saputra Dede Yudha , Zaitul Zaitul , Mahendri Meihendri , Yulistia Muslim Resti , Ilona Desi
Published: May 2024

Audit report lag is an important aspect of accounting quality. However, there is a lack of previous studies investigating why companies have auditors sign financial reports late; others need less time to do so. This study determines the factors affecting the audit report lag from a corporate governance perspective during COVID-19. In addition, the role of audit quality as a moderating variable between corporate governance and audit report lag is also rare. Ninety-seven publicly listed companies participated in this study. The secondary data from 2019 to 2021 were involved. Agency theory is applied to explain the phenomenon. Moderated regression analysis is applied to gain the result. The result shows that audit tenure significantly affects the audit report lag. In addition, audit quality plays a moderating variable between board size and audit report lag. Hence, audit quality also moderates the relationship between audit tenure and audit report lag. The research has practical implications for reducing audit tenure by reducing auditing report lag. In addition to practical implications, this research also implies the theory where the results of this research can be explained by the agency theory in which to improve the quality of accounting information through the timely submission of financial reports and to shorten the time of signing an audit opinion through the governance mechanisms.

Keywords: Size Of The Board Of Commissioners; Board Commissioner Meeting Frequency; Audit Tenure; Audit Opinion; And Audit Quality; Audit Report Lag
PENGARUH CARBON EMISSION DISCLOSURE (CED), CORPORATE SOCIAL RESPONSIBILITY (CSR), DAN GREEN ACCOUNTING TERHADAP NILAI PERUSAHAAN DENGAN PROFITABILITAS SEBAGAI VARIABEL INTERVENING
Authors: Hariadi Sugeng , Nurwanda Rifa Mufticha
Published: May 2024

This study aims to determine the effect of Carbon Emission Disclosure (CED), Corporate Social Responsibility (CSR) and Green Accounting on Firm Value with Profitability as an intervening variable. This research is a type of causality research with a quantitative approach. This study uses data on annual reports, sustainability, and energy sector finances on the ASEAN Stock Exchange website with a total of 22 companies. The sampling technique used purposive sampling and obtained 66 energy company data. The analysis method uses SEMPLS with SmartPLS 3.0 software. The results of this study indicate that 1) Carbon Emission Disclosure has no significant effect on firm value, 2) Corporate Social Responsibility has a significant effect on firm value, 3) Green Accounting has no effect on firm value, 4) Carbon Emission Disclosure has no significant effect on profitability, 5) Corporate Social Responsibility has a significant effect on profitability 6) Green Accounting has no effect on profitability, 7) Profitability has a significant effect on firm value, 8) Carbon Emission Disclosure through profitability has no significant effect on firm value. 9) Corporate Social Responsibility through profitability has no significant effect on firm value, 10) Green Accounting through profitability has no significant effect on firm value.

Keywords: Carbon Emission Disclosure, Corporate Social Responsibility, Green Accounting, Profitability, Firm Value