bluuebunny/crossref_metadata_2025_split huggingface dataset:
Degradation of sulfuryl fluoride by dielectric barrier discharge synergistically with reactive gas
Authors: Zhang Ying , Wang Mingwei , Zhou Chang , Li Yalong , Yang Zhaodi , Zhang Xiaoxing 
Published: January 2024

SO2F2 is widely used as a fumigant pesticide owing to its strong diffusion permeability, low residue, and high insecticidal speed. However, the strong greenhouse effect limits its application, and harmless treatment is also one of the problems. Dielectric barrier discharge (DBD) is an effective way to deal with harmful gas, and the treatment efficiency can be significantly improved by adding active gases such as H2O, H2, etc. In this paper, the effect of H2O and H2 on SO2F2 degradation by DBD is investigated. It shows the synergistic effect of reactive gas and DBD plasma on SO2F2 degradation that reactive particles generated from the discharge participate in the degradation of SO2F2 and hinder its recovery. When the input power is 80 W, the 2% SO2F2/1% H2O/97% Ar system achieves a degradation removal efficiency (DRE) of 86.26% and an energy yield (EY) of 13.55 g/kWh, while the 2% SO2F2/1% H2/97% Ar system shows a DRE and an EY of 80.29% and 12.61 g/kWh, respectively. However, in the 2% SO2F2/Ar system, the DRE and the EY are only 64.13% and 10.11 g/kWh, respectively. Moreover, the decomposition path of SO2F2 is analyzed, and it is found that SO2 and SOF2 are the main products in both H2O and H2 systems while the H2 addition generates solid S via the reduction of SO2F2. The results show that the addition of reactive gas can effectively promote the degradation and regulatory product of SO2F2, which provides support for the efficient and harmless degradation of SO2F2 in industry.
Thermal effects associated with the crack of high-temperature superconducting magnetic levitation system
Authors: Li Hui , Huang Yi 
Published: January 2024

Based on the magnetic flux pinning characteristics of the non-ideal type II superconductor YBa2Cu3O7−x, the high-temperature superconducting magnetic levitation system has the advantages of self-stability in levitation and low energy consumption. Thermal stress, electromagnetic force, and other mechanical stress may cause the micro-cracks to expand and eventually lead to fractures in the application of superconducting materials, significantly affecting the superconductor’s ability to transmit current. The superconducting magnetic levitation system with low damping is prone to nonlinear vibration of large amplitude under external interference, which affects the system’s regular operation. Due to the limitations of experimental conditions, it is difficult to analyze complex physical phenomena with cracks and obtain the distribution characteristics of electromagnetic, heat, and force inside the superconductor in the nonlinear vibration process, as theoretical modeling can compensate for this deficiency. In this paper, we study the fracture behavior of the YBa2Cu3O7−x bulk superconductor under nonlinear vibration based on the flux creep and flow models. The temperature of the superconductor in the nonlinear vibration of the superconducting levitation system is calculated with center cracks. The flux flow phenomenon in the bulk superconductor for various cracks under the bifurcation vibration is presented. The results show that the temperature of the superconductor will dramatically rise in nonlinear vibration under thermal insulation conditions, and the distribution position is affected by the cracks. For the 15 and 12 mm center cracks, a large amount of heat is generated around the crack and causes the temperature to rise above the critical temperature.
Applying narratology to nursing practice
Authors: Maagaard Cindie Aaen , Lærkner Eva Ann 
Published: January 2024

Abstract
This article presents an exercise in applied narratology within the context of intensive care nursing, specifically the writing of diaries by nurses for patients to fill in memory gaps and alleviate trauma. The article discusses narrative from three perspectives: (1) as nursing practice, resulting in patient diaries with narrative characteristics and purposes; (2) as analysis of this practice, in a study coupling narrative and nursing theory and practice; and (3) as the application of narratological concepts into practice through dialogues with nurses, with the aim of transferring insights gained from the analysis and enabling nurses’ reflection on their positions, practices and power as narrators for patients. The article argues that narratology can be operationalized for nursing work, and there are compelling reasons for why it should, which necessitate methodological reflexivity and mutual curiosity, trust, and learning, and considerations about how far the remit of the narratologist, as a non-expert, extends.
Further step toward a definition of the core professional development needs of teachers of Languages for Specific
Purposes in the European Higher Education Area
Authors: Jurkovič Violeta , Mertelj Darja , Podgoršek Saša 
Published: January 2024

Abstract
In the attempt to make a further step toward a research-based definition of the core professional development
needs of teachers of languages for specific purposes (LSP), this paper aims to upgrade the framework of professional development
needs of LSP teachers developed by Bocanegra-Valle and Basturkmen (2019). With this
objective in mind, the paper will examine the differences in the professional development needs of LSP teachers in the European
Higher Education Area (EHEA) based on their experience with LSP teaching in higher education, qualification level, and LSP taught.
The results suggest that the core LSP teacher professional development needs are distributed across the categories of course
development, knowledge of the target discipline/profession/industry, knowledge of language use in the target
discipline/profession/industry, peer collaboration, and professional development opportunities. Efficient LSP teacher professional
development could be based on a three-stage model: a general LGP teaching methodology course, followed by a general LSP teaching
methodology course, and finally discipline-specific acculturation.
Deep-ultraviolet LEDs with an Al-graded p-AlGaN layer exhibiting high wall-plug efficiency and high modulation bandwidth simultaneously
Authors: Cui Bingyue , Yang Jie , Gao Xingfa , He Jiaheng , Liu Zhe , Cheng Zhe , Zhang Yun 
Published: January 2024

Abstract
This work demonstrated a deep-ultraviolet (DUV) LED with an Al-graded p-AlGaN contact layer above the electron blocking layer to alleviate p-type contact resistance, the asymmetry of carriers transport, and the polarization effect. The fitting results from the ABC + f(n) model revealed that the LED has a higher radiative recombination coefficient than the conventional structures ever reported, which contributes to a lower carrier lifetime. The light output power of the LED at 350 mA is 44.71 mW, the peak external quantum efficiency (EQE) at 22.5 mA is 5.12%, the wall-plug efficiency at 9 mA is 4.40%. The 3 dB electrical-to-optical modulation bandwidth of the graded p-AlGaN contact layer LED is 390 MHz after impedance matching. In short, this study provides an in-depth analysis of the physical mechanism of the enhanced EQE and decreased carrier lifetime of DUV LEDs with Al-graded AlGaN as a p-type contact layer.
Nano-electro-mechanical conduct of boron nitride nanotube as piezoelectric nanogenerators and nanoswitches
Authors: Ertekin Nilüfer 
Published: January 2024

Abstract
This study investigates various aspects related to the Internet of Things (IoT) and piezoelectric nanoswitches applications, including the frequency band and set-up of piezoelectric nanogenerators, the electrical-mechanical interactions of nanoswitch arrays and their switching times. To address these issues, the molecular dynamics simulations conducted to investigate the performance of a boron nitride nanotube (BNNT) in piezoelectric nanogenerator and nanoswitch applications. For the piezoelectric nanogenerator, BNNT with a diameter-to-length ratio of 0.09 and subjected to 1% compressing exhibited a bistable configuration with a snap-through activation energy of 0.8 meV and a resonance frequency of 48 GHz. These resonance conditions can be achieved by millimeter-wave frequencies under the U-band (40–60 GHz), resulting in axial polarization of 4 mC.m−2 and axial voltage of 13.4 volts. These results demonstrate the potential of BNNT as a broadband and non-linear piezoelectric nanogenerator. For piezoelectric nanoswitches applications, the BNNT zigzag type with a diameter-to-length ratio of 0.32 and subjected to 2.5% compressing displayed 0.017 C.m−2 axial polarization, 22 V axial voltage, and a rapid switching time of approximately 2.0 ns.
Q-homotopy analysis method for time-fractional Newell–Whitehead equation and time-fractional generalized Hirota–Satsuma coupled KdV system
Authors: Liu Di , Gu Qiongya , Wang Lizhen 
Published: January 2024

Abstract
In this paper, two types of fractional nonlinear equations in Caputo sense, time-fractional Newell–Whitehead equation (FNWE) and time-fractional generalized Hirota–Satsuma coupled KdV system (HS-cKdVS), are investigated by means of the q-homotopy analysis method (q-HAM). The approximate solutions of the proposed equations are constructed in the form of a convergent series and are compared with the corresponding exact solutions. Due to the presence of the auxiliary parameter h in this method, just a few terms of the series solution are required in order to obtain better approximation. For the sake of visualization, the numerical results obtained in this paper are graphically displayed with the help of Maple.
An improved effective liquid drop model for cluster radioactivity
Authors: Cui 崔 JianPo 建坡 , Xing 邢 FengZhu 凤竹 , Gao 高 YongHao 永浩 , Qi 齐 LiQian 立倩 , Wang 王 YanZhao 艳召 , Gu 顾 JianZhong 建中 
Published: January 2024

Abstract
The effective liquid drop model (ELDM) is improved by introducing an accurate nuclear charge radius formula and an analytic expression for assaulting frequency. Within the improved effective liquid drop model (IMELDM), the experimental cluster radioactivity half-lives of the trans-lead region are calculated. It is shown that the accuracy of the IMELDM is improved compared with that of the ELDM. At last, the cluster radioactivity half-lives that are experimentally unavailable for the trans-lead nuclei are predicted by the IMELDM. These predictions may be useful for searching for new candidates for cluster radioactivity in future experiments.
Leveraging patent analysis to measure relatedness between technology domains: an application on offshore wind energy
Authors: Wang Yiwen , Baker Erin , Goldstein Anna 
Published: January 2024

Abstract
As the global energy sector transitions towards a cleaner and more sustainable future, observational evidence suggests that many new energy technologies share a close relationship with well-established technologies. Yet, the topic of how closely technologies are related has not been addressed rigorously, rather it has been the purview of practitioner know-how and informal expert opinion. In this study, we propose a quantitative method to supplement practitioners’ subjective understanding of the relatedness between technology domains. The method uses patents to represent the position of a technology in knowledge space and calculates the Hausdorff distance between patent domains to proxy the relatedness between technologies. We apply this method to investigate the relatedness of offshore wind energy technology to two more mature domains: onshore wind energy technology and offshore oil and gas technology. We examine the technological relatedness of individual offshore wind components to these two technologies, and represent the changes in relatedness through time. The results confirm that offshore wind components such as foundations, installation, and maintenance are more related to the offshore oil and gas industry; while other components, such as rotors and nacelles, are more related to onshore wind energy. The results also suggest that many offshore wind energy components are becoming less related through time to both of these domains, possibly indicating increasing innovation. This method can provide quantitative parameters to improve the modeling of technological change and guide practitioners in strategic decision-making regarding the positioning of industries and firms within those industries.
Control Strategies of DFIG Technology-based Variable-Speed Wind Turbines-A Review
Authors: Giri Jayprakash , Mishra Neraj Kumar , Patra Ashish , Shukla M. K 
Published: January 2024

Abstract
This review paper examines the advancements and limitations of wind energy technology, while concentrating on the utilization of Doubly Fed Induction Generators (DFIG) to capture maximum power in variable speed winds. The paper evaluates the efficacy of several control strategies for DFIG relying on WT (wind turbines), on the basis of their simulation results, key features, and control objectives. The paper highlights the potential areas for investigation to improvise the performance as well as efficiency of wind energy generation.
Predicting Gross Primary Productivity of the Forest Ecosystems using Machine Learning Techniques: A Review of Existing Approaches
Authors: Agarwal G , Burman P K D , Kosamkar P K , Kulkarni V Y 
Published: January 2024

Abstract
Photosynthesis is a biotic process in which the plants assimilate the atmospheric CO2 into the sugar molecules in the presence of solar energy. The carbon uptake by plants in this process is defined as gross primary productivity (GPP). A part of this assimilated carbon is used by the plants to support their physiological activities which are defined as the respiration. The sequestration of carbon by the terrestrial ecosystems holds significance as a vital element of Earth’s carbon cycle and constitutes a major sink for the climate change mitigation. The crop yield of any agricultural field is directly linked with its GPP which is important in the aspect of food security and economy. Hence, quantifying the GPP of terrestrial ecosystems is an active branch of study and several methods have been used to address this. In recent times, the machine learning (ML) methods connecting the benefits of artificial intelligence (AI) have gained increased interest and different such methods are being used to address different scientific and technological problems. In addition to the traditional methods, several ML techniques have also been explored by several researchers for the GPP estimation. Studies have shown that ML models can produce GPP predictions with more accuracy. A comprehensive review of these methods will be helpful for the researchers due to a rapid development in this field. This paper offers a comprehensive analysis of various existing ML techniques to estimate the GPP, providing a comparative review of their effectiveness.
Optimizing Carbon Footprint and Operational Productivity of Horizontal Boring Machines: An EFBA-Based Approach
Authors: Ghatorha Kashmir Singh , Sharma Parveen , Kang Amardeep Singh 
Published: January 2024

Abstract
In modern-day industries, the presence of bottleneck processes, where the throughput capacity falls short of the workload, can result in reduced efficiency, increased energy consumption, and higher production costs. Such inefficiencies are deemed unacceptable in today’s highly competitive market, coupled with stringent environmental regulations. To address these challenges, many industries have adopted lean and green manufacturing concepts to enhance workflow and environmental performance. This research paper integrates bottleneck analysis with carbon footprinting, specifically employing the EFBA (Energy Focussed Bottleneck Analysis) approach, to identify and eliminate bottlenecks and high energy consumption within the machining section of a press manufacturing industry. The study focuses on improving the setup time, machining time, and carbon footprint associated with the boring operation of the journal head. By implementing the proposed approach, significant improvements were achieved, with setup time reduced by 108 min, machining time reduced by 64 min, and carbon dioxide emissions reduced by 32.2 kg. These outcomes highlight the effectiveness of the integrated EFBA-based methodology in enhancing operational efficiency and reducing the environmental impact of the manufacturing process. The findings of this study offer valuable insights and practical implications for other industries seeking to optimize their production systems, reduce energy consumption, and minimize carbon emissions. By identifying and addressing bottlenecks through a combined bottleneck analysis and carbon footprinting approach, companies can enhance their operational productivity while promoting sustainability in line with environmental regulations and market demands.
Traffic Surveillance Using Computer Vision and Deep Learning Methods
Authors: Bedi Harpreet Singh , Malik Praveen Kumar , Singh Rajesh , Singh Ramendra , Bisht Nitasha 
Published: January 2024

Abstract
From the early 21st century to the present date, Traffic surveillance has been one of the most challenging problems and over the years many solutions were proposed for tackling this problem. However, flaws are can still be noticed in the currently existing systems for performing this task and some of them will be further addressed in this work. In our project Traffic Surveillance using Computer Vision and Deep Learning, we propose a smart vision-based system which accurately provides live traffic statistics like the speed of the traffic and the count of Vehicles passed on a road in between a time interval. The system takes a video feed as an input, which could be live footage provided by a CCTV camera at a traffic junction, and on querying starting and ending timestamps, it outputs the count of the vehicles and speed of the traffic in between the queried timestamps. We divide this problem into two major sub-problems, firstly detecting a vehicle, followed by tracking the detected vehicle. Although there exist many algorithms for detecting objects as well as for tracking objects, one of the major problems is integrating both. As ambiguities like detecting an already detected vehicle, many occur while tracking the vehicles. In our work, we emphasize handling such ambiguities using Deep Learning and we also provide a comparative performance analysis of our system with the already existing ones.
Machine Learning based Precision Agriculture Model for Farm Irrigation to Optimize Water Usage
Authors: Patil S B , Kulkarni R B , Patil S S , Kharade P A 
Published: January 2024

Abstract
The food demand is ever increasing each year and to meet this demand precision agricultural approach using machine learning tools play an important role. Precision irrigation systems integrate cutting-edge technologies, such as sensors, controllers, data analytics and internet, to achieve sustainability in agriculture and maximize water use so as to improve crop production while minimizing water wastage and climate impact. The main purpose of this article is to find out the precise water requirements for a particular area of the land by using soil moisture sensors. These sensors provide real-time data that is transmitted to a central control unit, which utilizes data driven algorithms to analyze moisture levels in the soil and controls the water supply. Furthermore, the model developed offers remote monitoring and control capabilities, enabling farmers to access and manage the system from anywhere using mobile or web application. This feature allows farmers to remotely adjust irrigation schedules, receive real-time alerts and notifications, and track water consumption, promoting convenient and efficient management of water resources. Thus by using effective water management techniques such as precision irrigation, controlling the water quality, will accomplish optimizing water usage and intern optimizes the yield.
Battery/Supercapacitor Energy Management system for Electric Vehicle
Authors: Rade Minal , More Pritam , Kanse Anuja 
Published: January 2024

Abstract
Electric vehicles (EVs) require appropriate design and sizing of their energy sources and proper management. The solution will be an energy efficient with cost-effective, increased lifespan, and an increase in the vehicle’s range. As per theory of evolution the model and rating calculations are discussed. Also this paper addresses the distribution of power within two energy sources, adopting a converter. Only one energy element unable to meet each preferred parameters. In addition to assisting electric vehicles in acceleration, the Hybrid Energy Storage System captures regenerative braking energy. By controlling the current of battery as consistent as viable at the time of picks, power management is primarily concerned with reducing battery stress and merging supercapacitors to supply instantaneous current at the time of pick and to recover power during deceleration. According to the results of HESS and experimental setup, current of battery is controlled as consistent as at the time of acceleration when the power management algorithm is implemented. In other words, only normal current is provided by the battery at the time of picks, while transitory current is provided via the ultracapacitor during acceleration.
Defect Induced high-color-rendering white light emissions from Ag-doped ZnO
Authors: Jayaprakash R , Bakshi S , Rani K , Rani S 
Published: January 2024

Abstract
It is crucial to find high-quality white-light phosphors that are simple to synthesize in order to create efficient lighting. In this pursuit, the focus has been on achieving the emission of white light in ZnO and Ag-doped ZnO emerging as a promising phosphor candidate. The optimized ZnO nano phosphors demonstrated visible-light emission with ideal Commission international de I E “Eclairage frequently abbreviated as coordinates (x = 0.33, y = 0.33 ) and a correlated color temperature of 6200 K, and a color rendering index of 100 for equivalent to day white light. Furthermore, under certain ideal processing conditions, neutral white-light emission (x = 0.33, y = 0.33 at λex = 280 and 370 nm) with a Color correlated temperature (CCT) of 5500 K(approx.) was achieved. The Color Rendering Index (CRI) of the ZnO and Ag:ZnO nanoparticles exceeded 90, with high values approaching 96%, which is necessary for accurately portraying object colors in comparison to natural sunshine. The research findings revealed a decrease in the FS emission intensity with all the UV excitation when Ag doped in ZnO, which aligns well with the principles of Stern-Volmer quenching. A reduction in intensity was observed which may be due to the Ag dopant interacting with the luminescent nanoparticles and causing a reduction in their emitted light. The study successfully develops and optimizes ZnO-based nano phosphors with tailored white-light emission characteristics, presenting a promising solution for achieving energy-efficient and high-quality white-light sources for various lighting applications.