bluuebunny/crossref_metadata_2025 huggingface dataset:
Body Composition and Physical Performance by Playing Position in Amateur Female Soccer Players
Authors: Hernandez-Martinez Jordan , Perez-Carcamo Joaquin , Canales-Canales Sebastian , Coñapi-Union Bayron , Cid-Calfucura Izham , Herrera-Valenzuela Tomás , Branco Braulio Henrique Magnani , Valdés-Badilla Pablo
Published: July 2024

This study analyzed differences in body composition, jump performance, running speed, and ball-kicking speed according to playing position in amateur female soccer players. This cross-sectional study involved 160 females distributed into groups of goalkeepers (n = 20), defenders (n = 38), midfielders (n = 52), and forwards (n = 50), with a mean age of 27.1 ± 3.23 years. They were assessed for body fat percentage (BFP), fat-free mass (FFM), squat jump, countermovement jump, drop jump (DJ), and running sprint speed for 10 m, 20 m, and 30 m, and ball-kicking speed (BKS) with both feet. Significant differences were found between groups in FFM (F(3,96) = 17.4; p = 0.000) and BFP (F(3,96) = 7.00; p = 0.000), with a higher FFM in midfielders with respect to goalkeepers (p = 0.00; ES = 1.77; ∆ = 27%), defenders (p = 0.00; ES = 2.14; ∆ = 26.5%), and forwards (p = 0.00; ES = 1.13; ∆ = 15.8%), and a lower BFP in midfielders with respect to goalkeepers (p = 0.00; ES = 1.41; ∆ = 26.7%) and forwards (p = 0.00; ES = 1.05; ∆ = 27%). In addition, significant differences were found between groups in DJ (F(3,96) = 20.8; p = 0.000), with midfielders achieving greater height compared to goalkeepers (p = 0.00; ES = 1.94; ∆ = 25.1%), defenders (p = 0.00; ES = 1.59; ∆ = 19%), and forwards (p = 0.00; ES = 1.73; ∆ = 16.3%). Significant differences were found between groups in BKS for dominant (F(3,96) = 5.84; p = 0.001) and non-dominant (F(3,96) = 3.29; p = 0.02) feet, and these were lower in goalkeepers than defenders (p = 0.00; ES = 0.99; ∆ = 8.83%) and midfielders (p = 0.00; ES = 1.21; ∆ = 11.8%). In conclusion, midfielders presented significantly better body composition and physical performance than other playing positions.
Personalized News Recommendation Method with Double-Layer Residual Connections and Double Multi-Head Self-Attention Mechanisms
Authors: Zhang Dehai , Zhu Zhaoyang , Wang Zhengwu , Wang Jianxin , Xiao Liang , Chen Yin , Zhao Di
Published: July 2024

In today’s society, there is an urgent need to help users better access information that they are interested in, as there is an increasing amount of news and messages available with the development of the Internet. Many existing methods involve directly inputting text into a pre-trained model, which limits the effectiveness of text feature extraction. The personalized news recommendation model discussed in this article is a model that can enhance feature extraction from news articles. It consists of a candidate news module, a historically accessed news module, and an access prediction module. Using news titles that accurately summarize news content, a model with double multi-head attention mechanisms and double residual structures (DDM) is utilized to better capture the features of news articles historically accessed by users, thereby achieving an improved recommendation functionality. The candidate news module aims to help the model learn representations of news that users are likely to select from the news titles. The user historical click news module primarily serves to enable the model to learn personalized representations of users from news they have previously browsed. The model has been tested on MIND-small. The AUC reached 0.6665, the MRR reached 0.3205, the nDCG@5 reached 0.3532, and the nDCG@10 reached 0.4158. The results indicate this model has achieved good results in the downstream tasks of preprocessing news-title texts.
MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
Authors: Li Qiang , Zhang Fengjiao , Han Liguo
Published: July 2024

With the continuous depletion of conventional oil and gas reservoir resources, the beginning of exploration and development of unconventional oil and gas reservoir resources has led to the rapid development of microseismic monitoring technology. Addressing the challenges of low signal-to-noise ratio and inaccurate localization in microseismic data, we propose a new neural network MA_W-Net based on the U-Net network with the following improvements: (1) The foundational U-Net model was refined by evolving the single-channel decoder into a two-channel decoder, aimed at enhancing microseismic event localization and noise suppression capabilities. (2) The integration of attention mechanisms such as the convolutional block attention module (CBAM), coordinate attention (CA), and squeeze-and-excitation (SE) into the encoder to bolster feature extraction. We use synthetic data for evaluating the proposed method. Comparing with the normal U-net network, our accuracy in seismic recordings with a signal-to-noise ratio of −15 is improved from 78 percent to 93.5 percent, and the average error is improved from 2.60 m to 0.76 m. The results show that our method can accurately localize microseismic events and denoising processes from microseismic records with a low signal-to-noise ratio.
E-Waste Management in Serbia, Focusing on the Possibility of Applying Automated Separation Using Robots
Authors: Nišić Dragana , Lukić Branko , Gordić Zaviša , Pantelić Uroš , Vukićević Arso
Published: July 2024

To encourage proper waste management for electrical and electronic devices (e-waste), it is necessary to invest heavily in the development of recycling technologies. One way to improve the process is to automate separating the shredded parts of e-waste using a robot. This paper’s literature review, utilizing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, showcases potential robotic technologies for e-waste separation. However, the intricate design of these devices can pose significant challenges in their implementation. Various legal, organizational, and sociological obstacles have left Serbia’s e-waste management practice underdeveloped, resulting in an unsatisfactory recycling rate. In this paper, we examined the possibility of using robots in the precise example of recycling refrigerators in a recycling center in Eastern Serbia, concluding that such a solution would have multiple positive effects, both on the employees and the working environment, on the operations of the recycling center itself, and on increasing the e-waste recycling rate in the country.
pH-Dependent Morphology of Copper (II) Oxide in Hydrothermal Process and Their Photoelectrochemical Application for Non-Enzymatic Glucose Biosensor
Authors: Tran Trung Tin , Huynh Vo Anh Hao , Nguyen Thien Trang , Nguyen Anh Duong , Huynh Tran My Hoa , Tran Viet Cuong , Tran Trung Nghia
Published: July 2024

In this study, we investigated the influence of pH on the hydrothermal synthesis of copper (II) oxide CuO nanostructures with the aim of tuning their morphology. By varying the pH of the reaction medium, we successfully produced CuO nanostructures with three distinct morphologies including nanoparticles, nanorods, and nanosheets according to the pH levels of 4, 7, and 12, respectively. The observed variations in surface morphology are attributed to fluctuations in growth rates across different crystal facets, which are influenced by the presence of intermediate species within the reaction. This report also compared the structural and optical properties of the synthesized CuO nanostructures and explored their potential for photoelectrochemical glucose sensing. Notably, CuO nanoparticles and nanorods displayed exceptional performance with calculated limits of detection of 0.69 nM and 0.61 nM, respectively. Both of these morphologies exhibited a linear response to glucose within their corresponding concentration ranges (3–20 nM and 20–150 nM). As a result, CuO nanorods appear to be a more favorable photoelectrochemical sensing method because of the large surface area as well as the lowest solution resistance in electroimpedance analysis compared to CuO nanoparticles and nanosheets forms. These findings strongly suggest the promising application of hydrothermal-synthesized CuO nanostructures for ultrasensitive photoelectrochemical glucose biosensors.
A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
Authors: Zhang Yifan , Tian Ye , Xia Qi
Published: July 2024

Curved grid stiffeners, compared to straight stiffeners, offer greater flexibility in adjusting the force transmission paths and give better structural performance. In this paper, a level-set-based density method is employed to generate layouts of curved grid stiffeners so that the critical buckling load factor (BLF) of the stiffened structures is improved. During the optimization process, volume constraint is incorporated to control material utilization, and gradient constraints are employed to maintain uniformity in the width of the stiffeners. Finally, the proposed method is demonstrated through several numerical examples.
A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
Authors: Qarah Faisal , Alsanoosy Tawfeeq
Published: July 2024

Pretrained language models have achieved great success in various natural language understanding (NLU) tasks due to their capacity to capture deep contextualized information in text using pretraining on large-scale corpora. Tokenization plays a significant role in the process of lexical analysis. Tokens become the input for other natural language processing (NLP) tasks, like semantic parsing and language modeling. However, there is a lack of research on the evaluation of the impact of tokenization on the Arabic language model. Therefore, this study aims to address this gap in the literature by evaluating the performance of various tokenizers on Arabic large language models (LLMs). In this paper, we analyze the differences between WordPiece, SentencePiece, and BBPE tokenizers by pretraining three BERT models using each tokenizer while measuring the performance of each model on seven different NLP tasks using 29 different datasets. Overall, the model pretrained with text tokenized using the SentencePiece tokenizer significantly outperforms the other two models that utilize WordPiece and BBPE tokenizers. The results of this paper will assist researchers in developing better models, making better decisions in selecting the best tokenizers, improving feature engineering, and making models more efficient, thus ultimately leading to advancements in various NLP applications.
Wind Turbine Performance Evaluation Method Based on Dual Optimization of Power Curves and Health Regions
Authors: Guan Qixue , Han Jiarui , Geng Keying , Jiang Yueqiu
Published: July 2024

The wind power curve serves as a critical metric for assessing wind turbine performance. Developing a model based on this curve and evaluating turbine efficiency within a defined health region, derived from the statically optimized power curve, holds significant value for wind farm operations. This paper proposes an optimized wind power curve segmentation modeling method based on an improved PCF algorithm to address the inconsistency between the function curve and the wind power curve, as well as the issues of prolonged curve modeling training time and susceptibility to local optima. A health region optimization method based on data increment inflection points is developed, which enables the delineation of the health performance evaluation region for wind turbines. Through the aforementioned optimization, the performance evaluation method for wind turbines is significantly improved. The effectiveness of the performance evaluation method is validated through experimental case studies, combining the wind power curve with the rotational speed stability, power characteristic consistency coefficient, and power generation efficiency indicators. The proposed modeling technique achieves a precision level of 0.998, confirming its applicability and effectiveness in practical engineering scenarios.
Possibilities of Quality Management of Chicken Meat Produced in Polish Industrial Conditions Using an Own-Construction Device for Poultry Electric Stunning in a Water Bath
Authors: Banach Joanna Katarzyna , Żywica Ryszard , Grzywińska-Rąpca Małgorzata
Published: July 2024

The aim of the research was to determine the possibilities for managing chicken meat (fillet) quality by applying the own-construction (OC) device for electrical stunning. We determined the effects based on selected technological and visual features of fillets, providing a measurable basis for producers to take preventive actions to improve meat quality management practices. The experimental material consisted of fillets from broiler chickens. The process of electrically stunning chickens in a water bath was carried out in Polish industrial conditions using two devices: own-construction (OC) and commercial from a Polish company (PLC). We determined the quality of fresh and stored meat (14 days/4 °C) based on technological characteristics (pH, color, tenderness) and visual assessment (number of small and large hemorrhages, defects). As a result, the use of the own-construction (OC) device compared to the commercial (PLC) one has a beneficial effect on: (1) reducing the number of hemorrhages, (2) increasing the share of high-quality fillet production by approx. 50%, (3) brightening and uniforming the color of fillets, and (4) improving the tenderness of fresh meat and maintaining it during 14 days of storage. The effects of using the OC device are beneficial for meat producers on the market.
Effect of Hyperparameter Tuning on the Performance of YOLOv8 for Multi Crop Classification on UAV Images
Authors: Ajayi Oluibukun Gbenga , Ibrahim Pius Onoja , Adegboyega Oluwadamilare Samuel
Published: July 2024

This study investigates the performance of YOLOv8, a Convolutional Neural Network (CNN) architecture, for multi-crop classification in a mixed farm with Unmanned Aerial Vehicle (UAV) imageries. Emphasizing hyperparameter optimization, specifically batch size, the study’s primary objective is to refine the model’s batch size for improved accuracy and efficiency in crop detection and classification. Using the Google Colaboratory platform, the YOLOv8 model was trained over various batch sizes (10, 20, 30, 40, 50, 60, 70, 80, and 90) to automatically identify the five different classes (sugarcane, banana trees, spinach, pepper, and weeds) present on the UAV images. The performance of the model was assessed using classification accuracy, precision, and recall with the aim of identifying the optimal batch size. The results indicate a substantial improvement in classifier performance from batch sizes of 10 up to 60, while significant dips and peaks were recorded at batch sizes 70 to 90. Based on the analysis of the obtained results, Batch size 60 emerged with the best overall performance for automatic crop detection and classification. Although the F1 score was moderate, the combination of high accuracy, precision, and recall makes it the most balanced option. However, Batch Size 80 also shows very high precision (98%) and balanced recall (84%), which is suitable if the primary focus is on achieving high precision. The findings demonstrate the robustness of YOLOv8 for automatic crop identification and classification in a mixed crop farm while highlighting the significant impact of tuning to the appropriate batch size on the model’s overall performance.
Data-Driven Quality Improvement for Sustainability in Automotive Packaging
Authors: McKnight Tyler , Ward Tyler , Jenab Kouroush
Published: July 2024

This study addresses sustainability challenges in automotive packaging by introducing a novel data-driven approach to audit and improve packaging quality. Traditional manual auditing processes are both time-consuming and prone to errors. To counter these inefficiencies, we developed an automated system utilizing Microsoft Power Apps and SQL databases to streamline data collection and processing. The automated tools facilitate real-time data capture and visualization through Microsoft Power BI, enabling precise tracking and management of returnable and expendable packaging materials. Our results show that our approach offers a significant enhancement in the accuracy and speed of data analysis, leading to more informed decision-making. Our methodology supports sustainability by reducing waste and reliance on expendable packaging and improving the economic and operational efficiency of automotive packaging systems.
Development of a Novel Structured Mesh-Type Pd/γ-Al2O3/Al Catalyst on Nitrobenzene Liquid-Phase Catalytic Hydrogenation Reactions
Authors: Tian Haoran , Shu Qingli , Xie Zukun , Lu Hongye , Zhang Qi
Published: July 2024

Nitrobenzene liquid-phase catalytic hydrogenation is commonly regarded as one of the most effective technologies for aniline production. The traditional granular catalysts have the disadvantages that the reactor bed pressure drop is large and the mass transfer efficiency between gas and liquid phases is low. In this study, a novel structured mesh-type Pd/γ-Al2O3/Al catalyst was prepared by anodic oxidation and pore structures of γ-Al2O3/Al supports were constructed by acid pore-widening treatments. The results showed that acid pore-widening treatments can improve the pore size of γ-Al2O3/Al supports; the support with HNO3 pore-widening treatment exhibited the largest pore size, being enlarged from 3.7 nm to 4.6 nm. The Pd/γ-Al2O3/Al catalysts prepared with different acid pore-widening treatment supports contribute to the increased active metal Pd loading, more Pd0 content, and better dispersion of the Pd particles. The catalyst prepared with HNO3 pore-widening treatment support exhibited the largest active metal Pd loading, enlarging from 1.82% to 1.95%, the largest Pd0 content being enlarged from 52.1% to 58.5% and the smallest Pd particle size being reduced from 103 nm to 41 nm, resulting in the highest nitrobenzene conversion, increasing from 67.2% to 74.3%. Eventually, we calculated that the pressure drop of structured catalysts was 1/72 of that of granular catalysts, resulting in a better diffusion of the H2 through nitrobenzene solution to active sites on the catalyst surface and a significant increase in the catalytic activity.
Machine Learning for Predicting Neutron Effective Dose
Authors: Alghamdi Ali A. A.
Published: July 2024

The calculation of effective doses is crucial in many medical and radiation fields in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) from different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and it is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission on Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R2 metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry.
Experimental Study on the Factors Affecting Pedestrian Exit Selection on the Basis of the Mixed Reality Evacuation LVC Simulation System
Authors: Tao Zhenxiang , Li Ying , Huang Xubo , Wang Yisen , Chen Minze , Yang Rui
Published: July 2024

Evacuation rules for pedestrians in emergencies are of great significance for the design of building exits, exit management, and evacuation facilities. Based on the mixed reality evacuation LVC simulation system we developed, in this paper, three kinds of pedestrian exit selection experiments were carried out, in which the influence of distance and exit selection on pedestrian exit selection was studied. In line with common sense, during the single-factor control tests, the participants preferred the exit with fewer people and at a closer distance. The two-factor combined effect of the above two factors was also studied. It can be found that the participants preferred to choose the least crowded exit with a closer distance for evacuation. Among these two factors, the participants would give priority to the number of people at the exit. In addition, participants show different trajectories for the difference in the number of people at the two exits. Their walking trajectory was close to an arc line in the case of an equal number of people at two exits. The results of this paper provide a theoretical basis for research on designing evacuation facilities and guiding evacuees, and allows us to explore a new approach for mixed reality evacuation research by conducting virtual crowd experiments in a real environment.
Sport-Specific Abdominal Wall Muscle Differences: A Comparative Study of Soccer and Basketball Players Using Ultrasonography
Authors: Romero-Morales Carlos , Villafañe Jorge Hugo , Torres Unai , Miñambres-Martín Diego , Pareja-Galeano Helios , Rodríguez-Costa Isabel , Jiménez-Sáiz Sergio L.
Published: July 2024

Aim: This study aims to compare the thickness of abdominal wall muscles—the external oblique (EO), internal oblique (IO), transversus abdominis (TrAb), rectus abdominis (RA), and inter-recti distance (IRD)—between amateur soccer and basketball players using ultrasonography. Methods: This cross-sectional study was conducted with 35 male amateur athletes, including 17 soccer players and 18 basketball players. Ultrasonographic measurements of the EO, IO, TrAb, RA muscles, and IRD were taken while the muscles were in a relaxed state for all the participants in both sides. Results: Significant differences were found in the RA muscle thickness, with basketball players showing a greater mean thickness compared to soccer players. No significant differences were observed in the TrAb, IO, and EO muscles between the two groups. The IRD showed a trend towards larger separation in basketball players, though this was not statistically significant. Conclusions: This study highlights sport-specific adaptations in the RA muscle, likely due to the distinct physical demands of basketball and soccer. The findings underscore the importance of tailored training and rehabilitation programs that consider these morphological differences to enhance performance and reduce injury risks.
Factors Influencing the Effects of Triticale on Laying Hens’ Performance: A Meta-Analysis
Authors: Poaty Ditengou Junior Isaac Celestin , Ahn Sung-Il , Cho Sangbuem , Chae Byungho , Hirwa Fabrice , Cheon Inhyeok , Choi Nag-Jin
Published: July 2024

Multiple studies have yielded conflicting findings regarding the impact of incorporating triticale as a feed ingredient on laying hens’ production parameters. This article used a meta-analysis to assess the factors influencing its effects on layers’ performance. According to the PRISMA guidelines, articles examining the influence of triticale on layers’ egg production (EP), egg weight (EW), egg yolk color (EYC), feed intake (FI), and feed conversion ratio (FCR) were identified across Google Scholar, PubMed, and Science Direct. As a result, six articles were selected and categorized into 16 experiments for inclusion in our meta-analysis. Overall, the trim-and-fill method indicated that triticale had comparable effects to conventional cereals on the performance of laying hens. However, the meta-ANOVA emphasized that the Hy-Line Brown hen strain and Joesong and Juanilo triticale strains induced the best laying hen performance. Moreover, the meta-regression emphasized a positive correlation between the triticale inclusion percentage and the EW in Juanilo triticale diets and a negative correlation between the triticale inclusion percentages and the EYC in the triticale and laying hens strains studied. Therefore, this meta-analysis makes a valuable contribution to comprehending the factors that may influence the effects of triticale on the performance of layers.
Verse1-Chorus-Verse2 Structure: A Stacked Ensemble Approach for Enhanced Music Emotion Recognition
Authors: Raboy Love Jhoye Moreno , Taparugssanagorn Attaphongse
Published: July 2024

In this study, we present a novel approach for music emotion recognition that utilizes a stacked ensemble of models integrating audio and lyric features within a structured song framework. Our methodology employs a sequence of six specialized base models, each designed to capture critical features from distinct song segments: verse1, chorus, and verse2. These models are integrated into a meta-learner, resulting in superior predictive performance, achieving an accuracy of 96.25%. A basic stacked ensemble model was also used in this study to independently run the audio and lyric features for each song segment. The six-input stacked ensemble model surpasses the capabilities of models analyzing song parts in isolation. The pronounced enhancement underscores the importance of a bimodal approach in capturing the full spectrum of musical emotions. Furthermore, our research not only opens new avenues for studying musical emotions but also provides a foundational framework for future investigations into the complex emotional aspects of music.
Biological Assessment of Green Waste and Dredged Sediment Co-Composting for Nursery Plant Cultivation
Authors: Vannucchi Francesca , Peruzzi Eleonora , Doni Serena , Manzi Davide , Azzini Lapo , Pathan Shamina Imran , Pietramellara Giacomo , Arfaioli Paola , Nicese Francesco Paolo , Masciandaro Grazia , Macci Cristina
Published: July 2024

Co-composting efficiently reclaims dredged sediments (S) and green waste (GW), creating stable products for agricultural applications. However, the use of S-GW co-composts can be limited by legislative thresholds, especially for co-composts with a high S percentage. The evaluation of S-GW co-compost stability by biological assessment can allow for a better understanding of S and GW recycling, as well as the S-GW co-compost application. For this purpose, the microbial biomass, composition, respiration, and eco-enzyme stoichiometry (EST) were assessed, coupled with chemical analysis, in the co-composting of S and GW in different ratios. The Photinia x fraseri and Viburnum tinus L. growth was monitored in a plant trial, comparing the studied co-composts with a control substrate. The EST approach was applied as an indicator of the co-composting stability during the process and after the plant cultivation. The chemical and biological parameters confirmed the suitability of co-composting in the GW and S recovery and the EST approach highlighted a better stability for the 3S:1GW co-compost at the end of the process and after plant cultivation. Viburnum tinus showed a similar growth to the control, while Photinia x fraseri resulted in being more sensitive to the co-compost. The biological assessments were good indicators of the S-GW compost stability for their application in crop cultivation.
On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
Authors: Mieloch Dawid , Klóska Dominika , Stankiewicz Olgierd
Published: July 2024

In order to achieve high realism an acceptable level of user experience in immersive videos, it is crucial to provide both the best possible quality of depth maps and minimize computational time. In this paper, we propose a new approach to the decoder-side depth estimation (DSDE) problem, which uses the hierarchical alpha-expansion algorithm with additional improvements for the estimation designed to be more robust to compressed video artifacts and limited computational resources. As shown by the experimental results, the proposal simultaneously results in reduction of computation time of the estimation process (by almost 40%) and an improvement of quality of estimated depth maps. The increased quality is demonstrated by more than 6% Bjøntegaard delta gain compared to the Moving Picture Experts Group (MPEG) immersive video state-of-the-art DSDE method.
Barriers to Industry Adoption of AI Video Generation Tools: A Study Based on the Perspectives of Video Production Professionals in China
Authors: Yu Tao , Yang Wei , Xu Junping , Pan Younghwan
Published: July 2024

In recent years, the rapid advancement of artificial intelligence (AI) in the video sector has captured widespread attention. Despite the proliferation of AI video generation tools targeted at general consumers, there remains a gap in addressing the specialized requirements and advanced functionalities sought by industry professionals. This study aims to identify and overcome the barriers hindering the adoption of AI video tools in the Chinese video industry, thereby facilitating their seamless integration. The research unfolded in two phases employing a comprehensive methodology. Initially, we delineated the industry’s video usage standards, drawing upon both established norms and insights gleaned from seasoned industry experts through focus group interviews. These insights informed the formulation of semi-structured interview questions. Subsequently, in-depth semi-structured interviews were conducted with ten Chinese industry experts, leading to the identification of eight primary adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspectives and localization. In the second phase, a detailed questionnaire survey involving 401 Chinese industry practitioners validated these factors. A data analysis underscored the significant impact of these eight factors on industry adoption, particularly emphasizing technological maturity. Furthermore, exhaustive examinations and discussions were undertaken for each identified barrier. The findings of this study theoretically bridge the gaps in understanding the impediments to the application of AI video generation tools in the video industry. They offer valuable insights into the current application landscape and furnish pertinent suggestions for advancing technology promotion and development in the future. Ultimately, this research aspires to augment the integration and utilization of AI technology within the Chinese video production industry, thereby propelling its progress and development forward.
Effects of Different Pretreatments on the GABA Content of Germinated Brown Rice
Authors: Tsou Shao-Fu , Hsu Hsin-Yun , Chen Su-Der
Published: July 2024

Brown rice germination increases γ-aminobutyric acid (GABA) levels and enhances its antioxidant activity. In this study, Kaohsiung 145 brown rice was used as the raw material, and soaked in various solutions for 6 h before being processed with either high-pressure processing (HPP) or ultrasonic treatment to increase the GABA content. The GABA and antioxidant components of brown rice were analyzed after 42 h of germination and subsequent air-drying to obtain a moisture content of 14%. The results showed that non-germinated brown rice had GABA at 7.10 mg/100 g and treatment with various soaking solutions (0.1% CaCl2, 0.1% Glu, 0.2% CaCl2, and 0.2% Glu) increased GABA contents. Specifically, 0.1% CaCl2 and 0.1% Glu exhibited higher GABA content, at 42.51 and 44.64 mg/100 g. Furthermore, the GABA content increased synergistically when combined with HPP (100 MPa, 10 min) and ultrasonic (20 min) treatments after soaking. The results showed that the GABA contents in germinated rice were the greatest after ultrasonic treatment, followed by HPP treatment, and the least with only soaking treatment. The treatments with 0.1% CaCl2 and 0.2% Glu combined with ultrasonic processing for 20 min resulted in the highest GABA content at 102.38 and 110.88 mg/100 g, respectively. Finally, 0.1% CaCl2 with ultrasonic treatment for 20 min was chosen, as it demonstrated a considerable improvement in total polyphenols content and DPPH scavenging abilities, as seen by improved scores in subsequent taste evaluations.
Green Wave Control Model Simultaneously Considering Passenger Cars and Buses in Closed Road Networks
Authors: Jing Binbin , Huang Zhengjie
Published: July 2024

Existing green wave control methods for passenger cars and buses mainly focus on maximizing bandwidths at the arterial level. There is little research on green wave control for both at the closed road network level, which makes it difficult to improve the efficiency of the entire area. To address this, a green wave control model that considers both passenger cars and buses in closed road networks is presented in this paper. The objective function of the model is to maximize the sum of the weighted bandwidths of passenger cars and buses on each segment of the road network. The relationships between car green bands, bus green bands, offsets, phase sequences, red time, green time, etc. are analyzed on the level of arterials and road networks, respectively, using time–space diagrams. Based on these analyses, the key constraints of the model are constructed accordingly. In addition, 0/1 variables and a sufficiently large positive number M are introduced to relax some of the constraints to ensure that the presented model has feasible solutions. The results of the numerical example demonstrate that compared with the fixed phase sequence schemes 1, 2, and 3, the total weighted bandwidth generated by the presented model increased by 9.5%, 16.4%, and 17%, respectively. Compared with the model without constraint relaxation, the presented model can still find a global, optimal solution when the common cycle time is fixed, while the model without constraint relaxation has no feasible solution.
Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
Authors: Urbina Fredes Sebastián , Dehghan Firoozabadi Ali , Adasme Pablo , Zabala-Blanco David , Palacios Játiva Pablo , Azurdia-Meza Cesar
Published: July 2024

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment.
Long- and Medium-Term Financial Strategies on Equities Using Dynamic Bayesian Networks
Authors: Lewis Karl , Caruana Mark Anthony , Suda David Paul
Published: July 2024

Devising a financial trading strategy that allows for long-term gains is a very common problem in finance. This paper aims to formulate a mathematically rigorous framework for the problem and compare and contrast the results obtained. The main approach considered is based on Dynamic Bayesian Networks (DBNs). Within the DBN setting, a long-term as well as a short-term trading strategy are considered and applied on twelve equities obtained from developed and developing markets. It is concluded that both the long-term and the medium-term strategies proposed in this paper outperform the benchmark buy-and-hold (B&H) trading strategy. Despite the clear advantages of the former trading strategies, the limitations of this model are discussed along with possible improvements.
Evaluation of Dunaliella salina Growth in Different Salinities for Potential Application in Saline Water Treatment and Biomass Production
Authors: Tanoeiro João Rui , Fehrenbach Gustavo W. , Murray Patrick , Pedrosa Rui , Chen Yuanyuan
Published: July 2024

This study investigated the adaptability of Dunaliella salina to different salinity levels, with an emphasis on growth, pigment concentration, and desalination potential. It was found that among the 21 salinity levels, Salinity 75 produced consistently favorable results in cell count (13.08 × 103 ± 1.41 × 103 cells/mL), dry biomass (2.46 ± 0.06 g/L), pigment content (chlorophyll a = 97,500,000 ± 100,000 pg/L, chlorophyll b = 123,600,000 ± 300,000 pg/L), and desalination (9.32 ± 0.47 reduction). Therefore, Salinity 75 was selected for the final trial (scale-up), which revealed unanticipatedly high cell counts (58.96 × 103 ± 535.22 cells/mL), with the dry biomass weight being statistically different (higher) than expected (4.21 ± 0.02 g/L) (p < 0.0001), most likely due to the high cell count and energy reserve storage for high-salinity adaption in the form of bio-compounds. Pigment growth continued (chlorophyll a = 95,400,000 ± 2,200,000 pg/L, chlorophyll b = 128,100,000 ± 5,100,000 pg/L), indicating pigment production under salt stress. Notably, desalination did not occur in this stage, possibly due to the necessity for a bigger initial inoculate, prolonged exposure or bioaccumulation becoming the prevailing mechanism over desalination. Nevertheless, the trial highlights D. salina’s strong adaptation to various salinity levels. This suggests a promising future in halophyte research, particularly in understanding the mechanisms that prevent salt accumulation in cells and how to overcome this barrier. Additionally, these results suggest that microalgae could be a viable resource in saline-rich environments unsuitable for conventional agriculture, promoting industrial adaptation to adverse conditions.
Crude Polysaccharide Extract from the Microalga Porphyridium cruentum Improved Nonspecific Immune Responses and Resistance in Penaeus vannamei Exposed to Vibrio alginolyticus
Authors: Ozório Renata Ávila , Lopes Rafael Garcia , Vieira Felipe do Nascimento , Bolívar-Ramírez Norha Constanza , Oliveira Carlos Yure Barbosa de , Barracco Margherita Anna Antonia Maria , Owatari Marco Shizuo , Fracalossi Debora Machado , Derner Roberto Bianchini
Published: July 2024

Sulfated polysaccharides (SPs), especially those with immunostimulant effects on shrimp and fish, are bioactive compounds with potential use in aquaculture. In this study, Pacific white shrimp (Penaeus vannamei) were fed diets containing 0%, 0.5%, 1.0%, 1.5%, or 2.0% crude extracts from the microalga Porphyridium cruentum for 30 days. After dietary supplementation, the shrimp were exposed to Vibrio alginolyticus, allowing the assessment of phenoloxidase activity (PO) and total hemocyte count (THC) pre- and post-challenge, as well as shrimp survival rates. Shrimp that received the 1.0% treatment had greater survival (90%) at 48 h post-challenge, while the 0% and 2.0% treatments had survival rates close to 63% in the same period. Furthermore, at 48 h post-challenge, THC was significantly greater (p < 0.05) in the shrimp that received crude extract supplementation compared to that in shrimp of the control group (0%). PO showed a proportional increase according to crude extract inclusion levels. Such dose-dependency in response to PO increases became significantly greater (p < 0.05) in the 2.0% treatment group compared to that in the 0% treatment group in the pre-challenge period and that in the 0% and 0.5% treatment groups in the post-challenge period. These results showed that crude extracts from P. cruentum could be considered immunostimulants by increasing the resistance of P. vannamei exposed to V. alginolyticus, potentially representing a major breakthrough for marine shrimp farming.
Development of a Programmable System Used for the Preparation of a Mixture of Flammable/Explosive Gases
Authors: Simon Marinica Adrian Bogdan , Gaman George Artur , Pupazan Daniel , Ghicioi Emilian , Manea Florin , Suvar Marius Cornel , Prodan Maria , Suvar Niculina Sonia , Florea Gheorghe Daniel , Laszlo Robert
Published: July 2024

In the present paper, the use of programmable microprocessors to develop a computerized stand for the preparation of a mixture of flammable/toxic/explosive gases in order to obtain mixtures at concentrations in explosive range is presented. The operating principle of the stand is based on the mixing of two volumetric flows, controlled with the help of microprocessors, where gases are stored and circulated at atmospheric pressure through cylindrical injectors, driven by stepper motors so that the gas circuit does not require valves. The exit of the stand is a homogenization chamber, with agitator and sensor to confirm the desired concentration of the mixture. This automated stand eliminates mechanical, electric or pneumatic valves from the gas circuits, avoiding elements with high mechanical resistance suitable for high pressures/depressions, removing the possibility of the return of the gas flow, without sensitivity to sudden pressures variations.
Identification of Potential New Genes Related to the SREBP Pathway in Xanthophyllomyces dendrorhous
Authors: Venegas Maximiliano , Durán Alejandro , Campusano Sebastián , Barahona Salvador , Sepúlveda Dionisia , Baeza Marcelo , Cifuentes Víctor , Alcaíno Jennifer
Published: July 2024

The sterol regulatory element-binding protein (SREBP) pathway is an integral cellular mechanism that regulates lipid homeostasis, in which transcriptional activator SREBPs regulate the expression of various genes. In the carotenogenic yeast Xanthophyllomyces dendrorhous, Sre1 (the yeast SREBP homolog) regulates lipid biosynthesis and carotenogenesis, among other processes. Despite the characterization of several components of the SREBP pathway across various eukaryotes, the specific elements of this pathway in X. dendrorhous remain largely unknown. This study aimed to explore the potential regulatory mechanisms of the SREBP pathway in X. dendrorhous using the strain CBS.cyp61- as a model, which is known to have Sre1 in its active state under standard culture conditions, resulting in a carotenoid-overproducing phenotype. This strain was subjected to random mutagenesis with N-methyl-N’-nitro-N-nitrosoguanidine (NTG), followed by a screening methodology that focused on identifying mutants with altered Sre1 activation phenotypes. Single-nucleotide polymorphism (SNP) analysis of 20 selected mutants detected 5439 single-nucleotide variants (SNVs), narrowing them down to 1327 SNPs of interest after a series of filters. Classification based on SNP impact identified 116 candidate genes, including 49 genes with high impact and 68 genes with deleterious moderate-impact mutations. BLAST, InterProScan, and gene ontology enrichment analyses highlighted 25 genes as potential participants in regulating Sre1 in X. dendrorhous. The key findings of this study include the identification of genes potentially encoding proteins involved in protein import/export to the nucleus, sterol biosynthesis, the ubiquitin–proteasome system, protein regulatory activities such as deacetylases, a subset of kinases and proteases, as well as transcription factors that could be influential in SREBP regulation. These findings are expected to significantly contribute to the current understanding of the intricate regulation of the transcription factor Sre1 in X. dendrorhous, providing valuable groundwork for future research and potential biotechnological applications.
Green Synthesis of Silver Nanoparticles with Extracts from Kalanchoe fedtschenkoi: Characterization and Bioactivities
Authors: Mejía-Méndez Jorge L. , Sánchez-Ante Gildardo , Cerro-López Mónica , Minutti-Calva Yulianna , Navarro-López Diego E. , Lozada-Ramírez J. Daniel , Bach Horacio , López-Mena Edgar R. , Sánchez-Arreola Eugenio
Published: July 2024

In this work, the hexane, chloroform, and methanol extracts from Kalanchoe fedtschenkoi were utilized to green-synthesize silver nanoparticles (Kf1-, Kf2-, and Kf3-AgNPs). The Kf1-, Kf2-, and Kf3-AgNPs were characterized by spectroscopy and microscopy techniques. The antibacterial activity of AgNPs was studied against bacteria strains, utilizing the microdilution assay. The DPPH and H2O2 assays were considered to assess the antioxidant activity of AgNPs. The results revealed that Kf1-, Kf2-, and Kf3-AgNPs exhibit an average diameter of 39.9, 111, and 42 nm, respectively. The calculated ζ-potential of Kf1-, Kf2-, and Kf3-AgNPs were −20.5, −10.6, and −7.9 mV, respectively. The UV-vis analysis of the three samples demonstrated characteristic absorption bands within the range of 350–450 nm, which confirmed the formation of AgNPs. The FTIR analysis of AgNPs exhibited a series of bands from 3500 to 750 cm−1, related to the presence of extracts on their surfaces. SEM observations unveiled that Kf1- and Kf2-AgNPs adopted structural arrangements related to nano-popcorns and nanoflowers, whereas Kf3-AgNPs were spherical in shape. It was determined that treatment with Kf1-, Kf2-, and Kf3-AgNPs was demonstrated to inhibit the growth of E. coli, S. aureus, and P. aeruginosa in a dose-dependent manner (50–300 μg/mL). Within the same range, treatment with Kf1-, Kf2-, and Kf3-AgNPs decreased the generation of DPPH (IC50 57.02–2.09 μg/mL) and H2O2 (IC50 3.15–3.45 μg/mL) radicals. This study highlights the importance of using inorganic nanomaterials to improve the biological performance of plant extracts as an efficient nanotechnological approach.