bluuebunny/crossref_metadata_2025_split huggingface dataset:
POST-FILTERING OF LOSSY COMPRESSED NOISY IMAGES AND ITS EFFICIENCY PREDICTION
Authors: Rebrov Volodymyr , Vozel Benoit , Lukin Vladimir 
Published: September 2024

The object of the study is the process of lossy compression of noisy images and their post-filtering. The subject of the study is the approach to efficient two-stage processing (compression and post-filtering) for better portable graphics (BPG) coder and prediction of its efficiency. The goal of the study is to analyze performance characteristics of the considered two-stage approach and to propose an approach to their prediction. Methods used: numerical simulation, regression, statistical analysis. Results obtained: 1) the considered approach advantage is that it is able to provide improvement of quality of compressed noisy image under condition that an image is compressed with compression ratio smaller than that one corresponding to optimal operation point; 2) the approach efficiency depends on several factors including noise intensity, image complexity, and filter type and parameters; 3) the main characteristics of the two-step procedure can be quite accurately predicted in advance and this allows offering useful information for decision undertaking on what value of the coder parameter to apply; 4) this leads to either improving the compressed and processed image quality compared to its original version or, at least, to avoiding quality degradation. Conclusions: based on the results of the study, it is worth 1) predicting performance characteristics for the two-stage processing; 2) adapting the processing to image complexity and noise intensity.
Career Development Analysis of Employees at PT Perusahaan Gas Negara, Tbk
Authors: Ardila Sekar Kinanti  , Christian Wiradendi Wolor  , Maulana Amirul Adha  
Published: September 2024

Abstract
This research was conducted at PT Perusahaan Gas Negara, Tbk with the aim of knowing the factors that affect career development. Researchers need to explore employees' perceptions of the development opportunities that companies have provided, the extent to which their expectations are met, and whether training programs are appropriate for individual needs. Data for this study were obtained through several methods, namely literature study, observation, interviews, and documentation. This research analysis was prepared using descriptive qualitative analysis methods to provide a comprehensive picture of the career development of employees of PT Perusahaan Gas Negara, Tbk. This descriptive method is used to describe systematically, factually, and accurately the existing conditions regarding the facts investigated. Therefore, this research is expected to be used as a reference in developing theories related to employee career development factors in a company and as a learning vehicle, especially in improving employee career development in a company.
Design and Optimisation of Body in White of a four-wheel vehicle
Authors: Mohiuddin Mohammed Sadiq , Jalleda Anjaneyulu 
Published: September 2024

Abstract

The Body in White (BIW) is a critical component in automobile design, providing essential structural integrity to withstand various loads and stresses during vehicle operation. This paper presents the simulation and analysis of a BIW chassis for a Bolero vehicle using finite element (FE) analysis to evaluate its performance under different loading and boundary conditions. The study focuses on structural and crash analysis to ensure high-quality vehicles that offer adequate protection to passengers during accidents. A geometrical model of the BIW chassis was designed using CATIA V5 software, and simulations were conducted using Ansys Workbench. Modal analysis was performed to observe natural frequencies and mode shapes, while the deformation and stress induced during side and frontal impacts were analyzed. The materials considered for the BIW structure included CP Steel, Duralumin, and a combination of both. A parametric study on weight optimization was conducted, determining the thickness of structural members based on allowable stress and deformation to ensure structural integrity and performance. The results showed an 18.6% reduction in vehicle weight using a combination of Duralumin (dominating in reinforcement parts) and CP Steel (in other areas). The combined material structure demonstrated less deformation in static structure, side crash, and frontal impact scenarios compared to using either material individually at 1.2mm thickness. Additionally, stress levels were observed to be comparatively lower.
An AI-Based “Do-It-Yourself” Module for Interstitial Glucose Forecasting for People with Type 1 Diabetes
Authors: Almeida Antonio José Rodríguez , Marrero Guillermo Valentín Socorro , Acosta Carmelo Betancort , Zamorano Garlene Zamora , García Alejandro Déniz , Malé María Luisa Álvarez , Årsand Eirik , Ruiz Cristina Soguero , Wägner Ana Maria , Granja Conceiçao , Callico Gustavo , Fabelo Himar 
Published: September 2024

Abstract

Diabetes mellitus (DM) is a chronic condition defined by increased blood glucose, which is suffered by more than 500 million adults. Type 1 diabetes (T1D), predominantly onset in childhood, needs to be treated with insulin. Keeping glucose within the desired range is a challenge. Despite the advances in the mHealth field, the successful appearance of the so-called do-it-yourself (DIY) tools, and the progress in glucose level prediction based on deep learning (DL), these tools fail to engage the users in the long-term. This limits the potential positive impact that they could have on the daily self-management of the condition, specifically by providing an accurate prediction of their short-term glucose level. Hence, this work proposed a DL-based DIY framework for interstitial glucose prediction using continuous glucose monitoring (CGM) data to generate one fully personalized DL model per user (i.e., without using additional data from other people). The DIY module has been designed to read the CGM raw data (as it would be uploaded by the potential users of this tool), and automatically prepare them to train and validate a DL model for his or her glucose prediction up to one hour ahead. For the DL models training and validation, one year of CGM data from 29 subjects (independently processed) with T1D collected at Complejo Hospitalario Insular-Materno Infantil de Las Palmas de Gran Canaria were used. Results show that this fully personalized approach presents state-of-the-art prediction performance, using only CGM data. Besides, to the best of our knowledge, this work is the first one in providing a DIY approach, since the code of this framework is open source and prepared for the DIY use standalone or in a broader application. Finally, this module has been tested in two different operating systems, demonstrating its easy integration as a standalone tool to help people to effectively aid with T1D self-management.
Variations in Extreme Precipitation and Associated Impacts on Natural Disasters in Southeastern Brazil: a 1979-2023 Analysis
Authors: Mendes Monica Mendes , Muniz Linconl Alves 
Published: September 2024

Abstract

The knowledge of the distribution of precipitation and its extremes, both spatially and temporally, is important for planning various human activities such as agriculture, civil defence, energy production, and urban planning. Heavy precipitation events associated with climate change had become more frequent and pronounced in many regions of Brazil, culminating in significant environmental, social, and economic damage across the country. In this context, the main objective of this work was to identify the trends and behavior of intense precipitation events (above the 95th percentile) across all states in southeastern Brazil. Daily precipitation data, at grid points, were obtained from the Climate Prediction Center (CPC) for the period from 1979 to 2023. The results showed that the number of days above the 95th percentile exhibited a significant increase during the summer and spring months, particularly in the south and west of São Paulo and the north of Minas Gerais. In general, negative trends above the 95th percentile were observed over the east of São Paulo and the metropolitan region of Rio de Janeiro, indicating that extreme rainfall was not constant over time. These results seemed contradictory when compared with the number of days above the 95th percentile, which varied between 150 and 300 days during the summer, autumn, and spring months, and around 400 days in the winter months. Therefore, it was concluded that intense precipitation was concentrated in a few days, separated by longer dry spells. By relating the patterns of rainfall trends above the 95th percentile and the number of days with records of landslides and floods, it was found that these disasters were located in areas of extreme rainfall or positive trends, especially during the summer and spring months. In general, persistent events were observed with more than two (three) consecutive days with rainfall above P95 over the states of MG and RJ (MG and SP). Finally, the periods with the highest number of days above the 95th percentile can could be related to the predominance of the South Atlantic Convergence Zone and more frontal system activity.
Quantum Delocalization of a Levitated Nanoparticle
Authors: Novotny Lukas , Rossi Massimiliano , Militaru Andrei , Carlon-Zambon Nicolas , Riera-Campeny Andreau , Romero-Isart Oriol , Frimmer Martin 
Published: September 2024

Abstract

Every massive particle behaves like a wave, according to quantum physics. Yet, this characteristic wave nature has only been observed in double-slit experiments with microscopic systems, such as atoms and molecules [1, 2]. The key aspect is that the wavefunction describing the motion of these systems extends coherently over a distance comparable to the slit separation, much larger than the size of the system itself [3]. Preparing these states of more massive and complex objects remains an outstanding challenge. While the motion of solid-state oscillators can now be controlled at the level of single quanta, their coherence length remains comparable to the zero-point motion, limited to subatomic distances [4–7]. Here, we prepare a delocalized state of a levitating solid-state nanosphere with coherence length exceeding the zero-point motion. We first cool its motion to the ground state. Then, by modulating the stiffness of the confinement potential, we achieve more than a threefold increment of the initial coherence length with minimal added noise. Optical levitation gives us the necessary control over the confinement that other mechanical platforms lack. Our work is a stepping stone towards the generation of delocalization scales comparable to the object size, a crucial regime for macroscopic quantum experiments [8], and towards quantum-enhanced force sensing with levitated particles [9].
Statistical sedimentary aspects of collapsed volcanic edifices along subduction zones.
Authors: bernard karine 
Published: September 2024

Abstract

Using Google Earth imagery and published data on volcanoes worldwide, several examples of volcanic debris avalanche deposits have been compared to characterize the dynamics of mass flow and matrix transformation during climate change. Sedimentological studies of these deposits help to correlate the spatial and temporal distribution of collapsed edifices with the stratigraphy and textural variations of the matrix. Parameters such as percentage of matrix, mean grain size, ratio of matrix sand to gravel and other statistical sedimentary parameters have been calculated to characterize the matrix breccias in relation to the spatio-temporal variability of the mass flow deposits. Factors such as quaternary erosion, nival surface conditions and high intensity rainfall contribute to the transformation of the reworked avalanche deposits into debris flows. The sedimentary characteristics of volcanic debris avalanche deposits are influenced by the profile shape, volcanic area and slope gradient of the collapsed edifices, which are related to the critical stability and quaternary cyclic evolution of the stratovolcanoes. Empirical equations have been developed to propose a co-genetic evolution of avalanche deposits in terms of their spatio-temporal distribution, morphological variations of the collapsed edifices, and climatic changes. Similar correlations between quaternary occurrence, matrix percentage, matrix/gravel ratio, and median grain size show a positive association with the obliquity of the Earth's axis and the precession of the equinoxes. These results highlight the importance of semi-quantitative sedimentological analyses of deposits from collapsed volcanic edifices in refining our understanding of subsequent hazardous flows associated with climate change.
Research on Automotive Radar Mutual Interference Mitigation Method based on V2X Communication Assistance
Authors: Gao yangshui , kui Liping , Yang Qinbiao , Xiong Lei , Zhang Rong , Chen Zhenting 
Published: September 2024

Abstract

The integration of automotive radar communication is the future development trend and will be installed on more and more vehicles, which will cause serious interference to radar detection and communication, seriously affecting the detection accuracy of radar and communication reliability. This article analyzes the interference models between radar and radar, radar and communication, and communication and communication. To improve the anti-interference ability of the network, the interference is transformed into a signal-to-noise ratio optimization model constrained by resources and power. Use reinforcement learning algorithms to find the optimal solution to minimize system interference. We use the vehicle position, as well as the signal-to-noise ratio of radar and communication, as the state and action space, and the total throughput increment serves as the reward function for Q-learning, and obtain the optimal joint optimization strategy through training. The evaluation results indicate that compared with existing schemes, the proposed algorithm can more effectively reduce radar and communication interference, thereby improving radar detection accuracy and communication throughput.
Explainable AI for Energy Prediction and Anomaly Detection in Solar Energy Systems
Authors: Sriniva Krupa , Shinde Sakshi Chandrakant , Acharya Prachi , Roy Sritama 
Published: September 2024

Abstract

The integration of solar energy systems into power grids is gaining momentum globally, promising sustainable and renewable energy solutions. However, the intermittent and dynamic nature of solar energy poses challenges for its efficient utilization and management. In response, this research investigates the application of Explainable Artificial Intelligence (XAI) techniques for enhancing the prediction accuracy and anomaly detection in solar energy systems. By leveraging advanced machine learning algorithms and interpretable models, this study aims to provide insights into the underlying factors influencing solar energy generation, thus enabling better decision-making and optimization strategies. In order to make machine learning models more understandable, approaches such as Explainable Artificial Intelligence (XAI) have been developed. The understanding and confidence that are ingrained in the output of machine learning algorithms is what makes XAI significant. In order to forecast Key Performance Indicators (KPIs), the current work constructs a thorough XAI model and evaluates the effectiveness of several learning algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R^2) coefficient of determination, the number of iterations, and execution time. The proposed framework holds significant potential to improve the reliability, efficiency, and performance of solar energy systems, contributing to the transition towards a more sustainable and resilient energy infrastructure.
Evaluation of ecological restoration effectiveness of Zoige grassland wetland in the upper reaches of the Yellow River
Authors: Xianhua YANG , hui WEN , lv ZHANG , yu WANG , rubing HAN , mengyao ZHANG 
Published: September 2024

Abstract

In order to scientifically demonstrate the progress and effectiveness of ecological restoration in the Zoige grassland wetland, and to guide the standardized implementation, supervision, and management of ecological restoration projects in local or similar areas, this study selected multi-source remote sensing data from 2021 before ecological restoration and governance, and from 2023 during ecological restoration and governance. This enabled the accurate acquisition of the status quo and change information of ecological restoration, and the scientific construction of a monitoring index system and effectiveness evaluation model. The study focused on monitoring and evaluating the stage restoration effect, yielding the following results: (1) Utilizing multi-source remote sensing techniques including high-resolution satellite imagery, multi-spectral remote sensing, and 3D modeling, a scientifically constructed monitoring index system and evaluation model comprehensively, accurately, and chronologically obtained progress and effectiveness information of ecological restoration projects. (2) The area with a significant and good ecological restoration and management effect accounted for 9%, mainly located in the Heihe River basin, Hongyuan County, Baihe River basin, and Dadu River basin of Aba County. These areas showed significant improvements in water conservation, vegetation coverage, and a reduction in ecological and environmental problems such as land desertification and soil erosion. (3) The Zoige grassland wetland ecological restoration project has not been fully completed. The next steps include continuing to optimize the monitoring index system and evaluation model, and conducting further monitoring and evaluation research on the effectiveness of ecological restoration.
Estimation of Electromagnetically Controlled Ionizing Radiation Therapy Effectiveness based on Probabilistic Modelling
Authors: Lal Radhey , Singh Rajiv Kumar , Nishad D. K. , Khalid Saifullah 
Published: September 2024

Abstract

Background
Various radiobiological models aim to estimate crucial tumor cell-killing effects for radiotherapy and radiation risk assessment, each with unique applications. This paper presents a specific probabilistic model for predicting tumor control probability (TCP) and introduces a user-friendly standalone simulation app tailored for this purpose.
Methods
A pragmatic probabilistic model is suggested for estimating tumor control probability (TCP) by incorporating a fractionated treatment approach. Within this model, ionizing radiation induces the formation of killed cells (KC), sublethally damaged cells (SLDC), and undamaged cells (UDC), the impact of which is contingent upon the radiosensitivity of cells. This triad of cell types can be influenced by radiation during subsequent fractions, providing a nuanced understanding of the treatment dynamics.
Results
A MATLAB app has been developed for a tumor control probability simulator. This simulator employs probabilistic modeling to describe radiation biological effects in a tumor subjected to homogeneous irradiation with a specified dose per fraction in a fractionated treatment. Key input parameters for the simulation include a cell radiosensitivity of 1.2, radiosensitivity of cell sub-lethal damage at 3, tumor cell volume of 1 cubic cm, tumor cell density of \(\:0.1\times\:{10}^{7}\), 30 virtual simulations, and 40 fractional radiation doses. Post-simulation, the resulting tumor control probability is determined to be 86.7%.
Conclusion
The study's simulator is a crucial tool for modeling radiation-induced biological effects in fractionated irradiation of tumors. Its use of probabilistic foundations generates hypotheses and assesses the efficacy of fractionated radiation therapy, holding promise for enhancing the safety and effectiveness of cancer treatment.
A Transparent Blockchain Based Mechanism for contributors of Archaeological Artifacts
Authors: Aziz Omer , Farooq Muhammad Shoaib , Khelifi Adel 
Published: September 2024

Abstract

In cultural heritage, archaeological artifact preservation is paramount, underscoring the importance of safeguarding history. The sub-domain of volunteer incentivization in open-source software for archaeological photogrammetry software (OSSAP) is vital for fostering effective volunteer engagement and rewards. Yet, OSSAP's lack of a structured reward system and financial limitations in archaeology impede proper volunteer compensation. Our introduction of the Trust-Based Blockchain Model for Contribution (TBBMC) addresses these gaps by providing ethical and efficient incentives. TBBMC utilizes cryptocurrency wallets and an economic model for better auditability and safety, while Initial Coin Offerings (ICOs) and Contribution Rewards (CR) facilitate financial transactions and fundraising. Smart contracts enhance operational efficiency and transparency, improving communication, coordination, and trust in archaeological volunteering. Finally, TBBMC's innovative reward system not only motivates volunteers but also bolsters the ongoing preservation and discovery of archaeological artifacts.
WITHDRAWN: A Multi-Task Allocation Framework for Mobile Crowdsensing: Leveraging Mobility Prediction for Enhanced Task Distribution
Authors: Xu Jiantao , Xu Jianbo 
Published: September 2024

Abstract

This paper explores Mobile Crowd Sensing (MCS) within smart cities and the Internet of Things (IoT), focusing on enhancing data collection efficiency and quality in smart cities, intelligent transportation, and environmental monitoring. Addressing the challenges of uneven task distribution, low participant engagement, and subpar data quality in MCS, we propose a novel multi-task allocation scheme that leverages mobility prediction to optimize task allocation. By forecasting participants' movements, our optimization model aims to balance task completion rate, coverage, execution time, and worker distribution, thereby improving task allocation rationality and participant engagement. Our contribution lies in a novel task allocation mechanism that not only tackles current issues but also integrates mobility prediction, enhancing MCS data collection efficiency and quality. This work offers new insights into MCS system design and practical solutions for smart city and environmental monitoring applications, advancing MCS technology application.
The experiences of childhood leukemia survivors in returning to school: An in-depth qualitative study
Authors: Ou Yunxia , Yang Yan , Zhang Chunmei 
Published: September 2024

Abstract

Purpose
To explore and describe the experiences of childhood leukemia survivors in the transition from home to school following cancer treatment.
Methods
Study participants include 16 survivors who visited a hospital for follow-up care following treatment for leukemia at a university hospital in Wenzhou. We conducted an in-depth, semi-structured interviews with 16 participants. The interview data were analyzed according to the thematic analysis method.
Results
We extracted 5 themes from the interviews: (a) Being on the right track to school, (b) Perceived personal happiness, (c) Discovering the gap with others, (d) Feelings of being excessive care, and (e) Concerns about returning to school. Five main themes suggest that the return to school hallmarked the beginning of a new life and the experience of a new environment. Participants reported having different feelings after returning to school. On the one hand, personal happiness is enhanced, and on the other hand, individuals need to face up to the practical problems encountered in school.
Conclusions
Childhood leukemia survivors can seek self-worth and identity after returning to school, but also face friendship, learning and physical difficulties. Medical staff should pay more attention to this group and give them enough support to better adapt to campus life.