bluuebunny/crossref_metadata_2025 huggingface dataset:
Epidemiological situation of bovine and bubaline tuberculosis in the state of Pará, Amazon region of Brazil
Authors: Oliveira Bruno Cesar Ribeiro da Silva , Oliveira Jefferson Pinto , Pinho Ana Paula Vilhena Beckman , Dias Ricardo Augusto , Grisi Filho José Henrique Hidebrand , Gonçalves Vitor Salvador Picão , Heinemann Marcos Bryan , Amaku Marcos , Telles Evelise Oliveira , Ferreira Bruno Fontana Soares , Ferreira Fernando , Ferreira Neto José Soares
Published: November 2024

Introduction

Bovine tuberculosis is one of the primary infectious diseases affecting cattle. Although several countries have managed to eradicate this zoonosis it remains endemic and uncontrolled across many countries in Africa, Asia, Latin America, and the Middle East. Brazil launched its national control and eradication program in 2001, and since then, epidemiological studies have been carried out to define optimal control strategies and to enable the management of the process in each region.

Methods

This study covered the state of Pará, which was divided into three regions, in each of which a pre-established number of properties were randomly selected, and within each property, a minimum number of animals were drawn to be tested by the tuberculin test to classify the farm as infected or free of the disease. A questionnaire was administered to the selected properties to identify the risk factors for the disease.

Results

A total of 976 properties comprising 17,151 animals were tested. The prevalence of infected properties in the regions ranged from 3.1% [1.3; 4.9] to 18.6% [14.3; 22.9], while tuberculin-positive animals ranged from 0.24% [0.09; 0.40] to 4.8% [2.4; 7.3]. The introduction of cattle untested for bTB and the renting of pastures have been identified as risk factors for the disease in this state.

Discussion

Pará has one of the highest prevalences of bovine tuberculosis in Brazil, and the implications of these results for public health were discussed. We therefore proposed that the state’s Official Veterinary Service, together with the private dairy and meat sectors, pursue the objective of controlling or eradicating the disease, along with mechanisms to verify its effectiveness. The differences between the two objectives were discussed, but in both cases, an education program is necessary to inform cattle and buffalo breeders that they need to test animals for tuberculosis before introducing them to their farms, and also to avoid renting pastures for cattle to graze and rest while moving on foot, as these practices have contributed the most to the spread of bTB in the state.
Acceptance of Organs from Deceased Donors With Resolved or Active SARS-CoV-2 Infection: A Survey From the Council of Europe
Authors: Peghin Maddalena , Graziano Elena , De Martino Maria , Balsamo Maria Luisa , Isola Miriam , López-Fraga Marta , Cardillo Massimo , Feltrin Giuseppe , Domínguez-Gil González Beatriz , Grossi Paolo Antonio ,   The COVIDonors COE Study Group
Published: November 2024

SARS-CoV-2 infection represents a new challenge for solid organ transplantation (SOT) with evolving recommendations. A cross-sectional survey was performed (February–June 2024) to describe practices among Member States of the Council of Europe (COE) on the use of organs from deceased donors with resolved or active SARS-CoV-2 infection. Overall, 32 out of 47 Member States with a transplant program participated in the study. Four (12.5%) countries did not use organs from deceased donors either with resolved or with active SARS-CoV-2 infection and 8 (25%) countries accepted organs only from deceased donors with resolved SARS-CoV-2 infection. Donor evaluation for SARS-CoV-2 included universal screening with standard PCR testing on respiratory specimens generally (61.4%) performed within 24 h prior to organ recovery. Further microbiological, immunological and radiological investigations varied. Most waitlisted patients receiving organs from a deceased donor with active (94.5%) or resolved (61.5%) SARS-CoV-2 infection were preferred to have natural, vaccine-induced or hybrid SARS-CoV-2 immunity. Most countries did not require recipients to undergo specific anti-SARS-CoV-2 treatment as pre-exposure (0%), post-exposure prophylaxis (15.4%) or modification of immunosuppression regimen (24%). This study highlights similarities and heterogeneities in the management of SARS-CoV-2 positive donors between COE countries, and a potential to safely expand donors’ pool.
Vehicle Attitude Control of Magnetorheological Semi-Active Suspension Based on Multi-Objective Intelligent Optimization Algorithm
Authors: Han Kailiang , Hu Yiming , Zeng Dequan , Yu Yinquan , Xiao Lei , Yang Jinwen , Liu Weidong , Gao Letian
Published: November 2024

A multi-objective intelligent optimization algorithm-based attitude control strategy for magnetorheological semi-active suspension is proposed to address the vehicle attitude imbalance generated during steering and braking. Firstly, the mechanical properties of the magnetorheological damper (MRD) are tested, and the parameters in the hyperbolic tangent model of the magnetorheological damper are identified through experiments. Secondly, a simulation model of the whole vehicle multi-degree-of-freedom vehicle dynamics including magnetorheological damper is established, and the whole-vehicle Linear Quadratic Regulator (LQR) controller is designed. Then, the optimization design model of the joint vehicle controller and vehicle dynamics is established to design the optimization fitness function oriented to the body attitude control performance, and the attitude optimal controller is calculated with the help of multi-objective intelligent optimization algorithm. Simulation results show that the proposed control method is able to improve the body roll angle, body pitch angle, and suspension dynamic deflection well on the basis of ensuring no deterioration in other performance indexes, ensuring good attitude control capability of the vehicle and verifying the feasibility of the control strategy.
Followership in Business Transformation: Hyundai Motor Group Case
Authors: Sunwoo Kyounghee , Choi Jinhee , Blazquez Victor
Published: November 2024

This study explored the employees’ perception of followership and its evolution in business transformation, given the case of the Hyundai Motor Group (HMG). Using a case study approach for explorative research, we conducted informal conversations and in-depth interviews with twenty participants to solicit employees’ and stakeholders’ perceptions of followership and its cultural formation through interpersonal relationships. Drawing on Kelley’s followership typology, we found three characteristics in participants’ followership perceptions: Ill-sa-bul-lan (extraordinarily well-organized), speed, and devotion. To explore the cultural formation of followership between leaders and followers, which is under-explored in Kelley’s theory, we adopted alternative theories (i.e., responsible leadership and implicit followership) to analyze the interpersonal relationship during the business transformation. Consequently, we found that the HMG followership, shaped by the relationships between leaders and followers within the Korean cultural context, evolved to facilitate successful business transformation. Initiated by top management, new and diverse elements from outside spurred the drive for business transformation and instigated changes in organizational culture, including perceptions of followership. This evolution of followership, in turn, reinforced the business transformation, creating a virtuous cycle. This study offers valuable insights into the dynamics of followership and its impact on organizational performance, providing a foundation for further empirical research in this underexplored area of followership.
Current Situation of Honey-Producing Apiaries—Quantitative Study of Honey Production Characteristics Based on a Questionnaire Survey in Hungary
Authors: Feketéné Ferenczi Aliz , Kovácsné Soltész Angéla , Szűcs István , Bauerné Gáthy Andrea
Published: November 2024

This study examined the conditions and factors affecting honey production in Hungary from the perspective of Hungarian beekeepers. Due to the scarcity of research on Hungary, this study can be considered a missing piece. The survey was based on a questionnaire survey (N = 632). Basic statistical tools (frequencies, means) were used to describe the data. To assess differences between some categorical variables, cross-stability was assessed using chi-square tests, and non-parametric tests were used. Based on the study results, the study population has an average of 101 colonies per beekeeper. Beekeepers with a low number of colonies are mainly hobby beekeepers for whom non-economic goals are a priority. The average honey yield is 40 kg per bee colony, with a wide variation between counties. The primary function of beekeeping is production and selling, followed by the importance of pollinating crops and other plants, maintaining natural landscapes and habitats, and conserving plant biodiversity. Around 80% of beekeepers keep bees primarily for income, which is consistent with production and selling as their primary activity. The main problems identified are bee mite infestation (Varroa destructor Anderson and Trueman), bee poisoning from pesticides, Nosema (Nosema apis Zander; Nosema ceranae Huang) infection, problems with sales and profitability, and lack of bee pastures.
Agronomic Performance and Technological Attributes of Sugarcane Cultivars Under Split-Irrigation Management
Authors: Oliveira Henrique Fonseca Elias de , Arriel Fernando Henrique , Soares Frederico Antônio Loureiro , Silva Edson Cabral da , Mesquita Marcio , Silva Thiago Dias , Silva Jhon Lennon Bezerra da , Sousa Cleiton Mateus , Silva Marcos Vinícius da , Carvalho Ailton Alves de , Silva Thieres George Freire da
Published: November 2024

In addition to being an important instrument in the search for increasingly greater productivity, agricultural production with adequate use of irrigation systems significantly minimizes the impact on water resources. To meet high productivity and yield, as well as industrial quality, a series of studies on sugarcane cultivation are necessary. Despite being able to adapt to drought, sugarcane is still a crop highly dependent on irrigation to guarantee the best quality standards. Our study aimed to analyze the agronomic performance and technological attributes of two sugarcane cultivars, evaluating the vegetative and productive pattern, as well as the industrial quality of the cultivars RB92579 and SP80–1816, which were cultivated under split-irrigation management in the Sugarcane Research Unit of IF Goiano—Campus Ceres, located in the state of Goiás in the Central-West region of Brazil. A self-propelled sprinkler irrigation system (IrrigaBrasil) was used, duly equipped with Twin 120 Komet sprinklers (Fremon, USA). The cultivars were propagated vegetatively and planted in 0.25 m deep furrows with 1.5 m between rows. The experiment was conducted in a completely randomized design (CRD), with a bifactorial split-plot scheme (5 × 2), with four replications, where the experimental plots were subjected to one of the following five split-irrigation management systems: 00 mm + 00 mm; 20 mm + 40 mm; 30 mm + 30 mm; 40 mm + 20 mm; or 60 mm + 00 mm. At 60 and 150 days after planting (DAP), the following respective irrigation management systems were applied: 00 mm + 00 mm and 20 mm + 40 mm. Biometric and technological attributes, such as plant height (PH) and stem diameter (SD), were evaluated in this case at 30-day intervals, starting at 180 DAP and ending at 420 DAP. Measurements of soluble solids content (°Brix), apparent sucrose content (POL), fiber content (Fiber), juice purity (PZA), broth POL (BP), reducing sugars (RS), and total recoverable sugars (TRS) were made by sampling stems at harvest at 420 DAP. RB92579 showed total recoverable sugar contents 11.89% and 8.86% higher than those recorded for SP80–1816 under split-irrigation with 40 mm + 20 mm and 60 mm + 00 mm, respectively. Shoot productivity of RB92579 reached 187.15 t ha−1 under split-irrigation with 60 mm + 00 mm, which was 42.16% higher than the shoot productivity observed for SP80–1816. Both cultivars showed higher qualitative and quantitative indices in treatments that applied higher volumes of water in the initial phase of the culture, coinciding with the dry season. Sugarcane cultivar RB92579 showed a better adaptation to the prevailing conditions in the study than the SP80–1816 cultivar.
Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches
Authors: Arumugam Gopalakrishnan Meena , Chellappan Gopalakrishnan , Patil Santhosh Ganapati , Rathod Santosha , Ayyanar Kamalakannan , Ramasamy Jagadeeswaran , Nagaranai Karuppasamy Sathyamoorthy , Swaminathan Manonmani
Published: November 2024

Magnaporthe oryzae, the source of the rice blast, is a serious threat to the world’s rice supply, particularly in areas like Tamil Nadu, India. In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, Artificial Neural Networks (ANNs), and Support Vector Regression (SVR), to forecast the incidence of rice blast disease. Between 2015 and 2023, information on rice blast occurrence was gathered weekly from three locations (Thanjavur, Tirunelveli, and Coimbatore), together with relevant meteorological data like temperature, humidity, rainfall, sunshine, evaporation, and sun radiation. The associations between the occurrence of rice blast and environmental factors were investigated using stepwise regression analysis, descriptive statistics, and correlation. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model’s prediction ability. The best prediction accuracy was given by the ANN, which outperformed SVR and INGARCHX in every location, according to the results. The complicated and non-linear relationships between meteorological variables and disease incidence were well-represented by the ANN model. The Diebold–Mariano test further demonstrated that ANNs are more predictive than other models. This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. The application of these models can help farmers make timely decisions to minimize crop losses. The findings suggest that machine learning models offer promising potential for accurate disease forecasting and improved rice management.
Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil
Authors: Ratke Rafael Felippe , Viana Paulo Roberto Nunes , Teodoro Larissa Pereira Ribeiro , Baio Fábio Henrique Rojo , Teodoro Paulo Eduardo , Santana Dthenifer Cordeiro , Santos Carlos Eduardo da Silva , Zuffo Alan Mario , Aguilera Jorge González
Published: November 2024

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the associations between spectral variables and soil physicochemical attributes, as well as to predict these attributes using spectral variables as inputs in machine learning models. One thousand soil samples were selected from agricultural areas 0–20 cm deep and collected from Northeast Mato Grosso do Sul state of Brazil. A total of 20 g of the dried and homogenized soil sample was added to the Petri dish to perform spectral measurements. Reflectance spectra were obtained by CROP CIRCLE ACS-470 using three spectral bands: green (532–550 nm), red (670–700 nm), and red-edge (730–760 nm). The models were developed with the aid of the Weka environment to predict the soil chemical attributes via the obtained dataset. The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. According to our findings, sulfur exhibited a correlation greater than 0.6 and a reduced mean absolute error, with better performance for the M5P and RF algorithms. On the other hand, the macronutrients S, Ca, Mg, and K presented modest r values (approximately 0.3), indicating a moderate correlation with actual observations, which are not recommended for use in soil analysis. This soil analysis technique requires more refined correlation models for accurate prediction.
Development, Prevention, and Detection of Pineapple Translucency: A Review
Authors: Li Chuanling , Li Mingwei , Zhang Miaolin , Chen Linpan , Wu Qingsong , He Junjun , Xue Zhong , Zhang Xiumei , Yao Yanli
Published: November 2024

Pineapple is one of the most important crops in tropical and subtropical areas. However, its production has been seriously impacted by the issue of fruit translucency in the past several decades. Fruit translucency is a physiological disorder of pineapple flesh with water-soaked core which results in a decline in pineapple quality. It has become a significant challenge for the sustainability of pineapple industry. Currently, the cause and pathophysiological development of pineapple translucency still have not been fully understood. The preventative and remedial measurements on the disease have yet to be effectively implemented in the production process. This review provides comprehensive information and the latest research progress on the possible pathogenesis, initiating factors, preventive and control practices, and detection techniques for pineapple translucency. Furthermore, the progress of research on apple and pear fruit translucency in recent years is reviewed and compared with pineapple translucency. The review offers theoretical guidance and insightful knowledge for the investigation of pineapple translucency disease.
Fostering Sustainable Potato Production: A Collaborative European Approach
Authors: Morugán-Coronado Alicia , Gómez-López María Dolores , Meno Laura , Fernández-Calviño David , Wustenberghs Hilde , Schrader Stefan , Bind David-Alexander , Põder Anne , Shanskiy Merrit , Pouta Eija , Tienhaara Annika , Calatrava Javier
Published: November 2024

Potato production faces increasingly severe agronomic problems, including intensive production and pedoclimatic changes. Increasing pest/disease incidence is contributing to inadequate application of pesticides and external fertilizers. This study aims to identify critical agri-environmental challenges currently faced by potato growers in Europe, assessing the needs and priorities of end-users to determine the feasibility of integrating more sustainable farming practices into potato cultivation. Additionally, we identified sustainable strategies to reduce reliance on external inputs. A total of 203 potato stakeholders from six European pedoclimatic areas completed a survey in 2020 to identify agronomic and environmental problems, priorities for action, and best-suited sustainable farming practices. Statistical and multicriteria decision analysis was then performed. Subsequently, focus group meetings with stakeholders were organized to present and discuss results and validate and complement them. Stakeholders perceived that more sustainable potato production involved reducing tillage intensity, using organic nutrient sources, increasing soil organic matter and, especially, diversifying crop rotations. Barriers to adopting new sustainable practices included farmers’ lack of knowledge regarding novel farming practices and the need for expert technical advice. Some practices are complex, but also economic impediments. Therefore, thorough research, clear demonstrations, and tailored advice are crucial to farmers to lead agriculture toward profitable, sustainable systems.
Effective Control of Neofusicoccum parvum in Grapevines: Combining Trichoderma spp. with Chemical Fungicides
Authors: Gomez-Garay Aranzazu , Astudillo Calderón Sergio , Tello Mariscal Mª Luisa , López Beatriz Pintos
Published: November 2024

Vitis vinifera is highly susceptible to grapevine trunk diseases, with Neofusicoccum parvum recognized as a highly destructive pathogen. This study investigates the biocontrol potential of five Trichoderma species (T. harzianum, T. viride, T. asperellum, and T. virens) against N. parvum, evaluating multiple biocontrol mechanisms (mycoparasitism, competition for nutrients, production of volatile organic compounds (VOCs), and antibiosis) as well as their compatibility with the fungicides copper oxychloride and sulfur. Results demonstrated that (1) Trichoderma harzianum effectively suppressed N. parvum through VOC production, mycoparasitism, and nutrient competition, significantly reducing pathogen growth in planta while showing compatibility with both fungicides, highlighting its suitability for integrated disease management; (2) Trichoderma viride showed high inhibition of N. parvum in vitro, but its phytotoxicity in planta limits its field application. These findings support T. harzianum as a promising agent within integrated disease management strategies, offering a sustainable alternative to reduce chemical fungicide reliance in controlling grapevine trunk pathogens.
A Fully Integrated High Linearity CMOS Dual-Band Power Amplifier for WLAN Applications in 55-Nm CMOS
Authors: Shen Haoyu , Wu Bin
Published: November 2024

This paper presents a dual-band fully integrated high linearity CMOS power amplifier (PA). The PA employs a reconfigurable transformer in the input matching network to achieve low reflection coefficient across both bands, demonstrating significant flexibility in the design of dual-band power amplifiers with high output powers. Additionally, a detailed design methodology for the dual-band matching network is introduced. By utilizing this methodology, the PA has been designed using 55 nm CMOS technology. For continuous-wave operation, the PA achieves a saturated power (Psat) of 28.03 dBm and 27.5–28.2 dBm, with power-added efficiency (PAE) of 33.2% and 24.6–31.1%, in the 2.4 GHz and 5 GHz WLAN bands, respectively. Concurrently, the PA power cells, which employ multi-gate transistor (MGTR) technology, achieve an intermodulation distortion (IMD3) of below 30 dBc at an output power of 15 dBm in both the 2.4 GHz and 5 GHz WLAN bands. The proposed PA outperforms other dual-band or multi-band PAs in terms of output power and exhibits great potential for WLAN applications.
Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
Authors: Ding Yujian , Ma Xiaoxu , Yang Bingxue
Published: November 2024

As dataset environments evolve, the adaptability of deep models has weakened due to biases in training data collection. Consequently, a critical challenge has emerged: enabling models to effectively learn invariant features across diverse environments while ignoring spurious features introduced by environmental changes. This article proposes an image feature extraction algorithm based on invariant learning, which trains a ResNet18 model that can fully learn invariant features. On the basis of this model, GRAD-CAM algorithm is used to extract environmental features of images. Based on this feature dataset, images are classified according to different environments through K-means clustering, achieving environmental partitioning of mixed datasets. The results show that on the test set, the IRM-ResNet18 network’s prediction accuracy is 88.6%, and its accuracy and stability are significantly better than those of ResNet18. It can fully learn and extract invariant features from images. By segmenting the image based on the extracted environmental features, The findings indicate that the IRM-ResNet18 network’s total environmental segmentation accuracy is 88.2%, which confirms the efficacy of the image environmental segmentation algorithm proposed in this paper.
Preamble-Based Noncoherent Synchronization in Molecular Communication: A Machine Learning Approach
Authors: Moon Seok-Hwan , Singh Pankaj , Jung Sung-Yoon
Published: November 2024

In the field of wireless communication, there is growing interest in molecular communication (MC), which integrates nano-, bio-, and communication technologies. Inspired by nature, MC uses molecules to transmit data, especially in environments where EM waves struggle to penetrate. In MC, signals can be distinguished based on molecular concentration, known as concentrated-encoded molecular communication (CEMC). These molecules diffuse through an MC channel and are received via ligand–receptor binding mechanisms. Synchronization in CEMC is critical for minimizing errors and enhancing communication performance. This study introduces a novel preamble-based noncoherent synchronization method, specifically designed for resource-constrained environments like nanonetworks. The method’s simple, low-complexity structure makes it suitable for nanomachines, while machine learning (ML) techniques are used to improve synchronization accuracy by adapting to the nonlinear characteristics of the channel. The proposed approach leverages ML to achieve robust performance. Simulation results demonstrate a synchronization probability of 0.8 for a transmitter-receiver distance of 1 cm, given a molecular collection time duration four times the pulse duration. These results confirm the significant benefits of integrating ML, showcasing improved synchronization probability and reduced mean square error. The findings contribute to the advancement of efficient and practical MC systems, offering insights into synchronization and error reduction in complex environments.
TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
Authors: Zhang Shouhua , Zhou Jiehan , Ma Xue , Pirttikangas Susanna , Yang Chunsheng
Published: November 2024

Efficient and accurate fault diagnosis of rotating machinery is extremely important. Fault diagnosis methods using vibration signals based on convolutional neural networks (CNNs) have become increasingly mature. They often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application of Transformer-based Vision Transformer (ViT) methods to fault diagnosis is gaining attraction. Nonetheless, these methods typically require extensive preprocessing, which increases computational complexity, potentially reducing the efficiency of the diagnosis process. Addressing this gap, this paper presents the Time Series Vision Transformer (TSViT), tailored for effective fault diagnosis. The TSViT incorporates a convolutional layer to extract local features from vibration signals alongside a transformer encoder to discern long-term temporal patterns. A thorough experimental comparison of three diverse datasets demonstrates the TSViT’s effectiveness and adaptability. Moreover, the paper delves into the influence of hyperparameter tuning on the model’s performance, computational demand, and parameter count. Remarkably, the TSViT achieves an unprecedented 100% average accuracy on two of the test sets and 99.99% on the other, showcasing its exceptional fault diagnosis capabilities for rotating machinery. The implementation of this model will bring significant economic benefits.
An Interpretable Machine Learning-Based Hurdle Model for Zero-Inflated Road Crash Frequency Data Analysis: Real-World Assessment and Validation
Authors: Ben Khedher Moataz Bellah , Yun Dukgeun
Published: November 2024

Road traffic crashes pose significant economic and public health burdens, necessitating an in-depth understanding of crash causation and its links to underlying factors. This study introduces a machine learning-based hurdle model framework tailored for analyzing zero-inflated crash frequency data, addressing the limitations of traditional statistical models like the Poisson and negative binomial models, which struggle with zero-inflation and overdispersion. The research employs a two-stage modeling process using CatBoost. The first stage uses binary classification to identify road segments with potential crash occurrences, applying a customized loss function to tackle data imbalance. The second stage predicts crash frequency, also utilizing a customized loss function for count data. SHapley Additive exPlanations (SHAP) analysis interprets the model outcomes, providing insights into factors affecting crash likelihood and frequency. This study validates the model’s performance with real-world crash data from 2011 to 2015 in South Korea, demonstrating superior accuracy in both the classification and regression stages compared to other machine learning algorithms and traditional models. These findings have significant implications for traffic safety research and policymaking, offering stakeholders a more accurate and interpretable tool for crash data analysis to develop targeted safety interventions.
An Investigation of Energy Consumption Characteristics of the Pump-Control System for Electric Excavator Arms
Authors: He Aihuan , Wei Liejiang , Lu Quanfeng , He Pengfei
Published: November 2024

The conventional hydraulic system of excavators suffers from significant valve throttling losses and inadequate matching between the hydraulic power source and the load, which substantially impact the system’s overall energy consumption and severely impede the trend toward electrification and energy efficiency in construction machinery. To address this issue, a pump-controlled hydraulic cylinder system has been implemented to replace the original valve-controlled hydraulic system that utilizes a single pump with multiple actuators. The influence of energy conversion efficiency and the speed between the motor and the hydraulic pump under varying load-power conditions has been determined through experimental investigations. Based on these findings, a compound-control strategy is proposed that adjusts the displacement of the hydraulic pump to achieve precise control over the position of the hydraulic cylinder and facilitates both the speed and displacement coordination while ensuring optimal motor speed matching with the load power. This strategy is implemented in the boom pump’s hydraulic cylinder control system. The research findings indicate that this combined-control approach enhances efficiency by approximately 18.9% compared with traditional variable-speed pump-controlled hydraulic cylinder systems. Furthermore, energy consumption is reduced by about 39% compared with the conventional valve-controlled hydraulic system.
Optimizing Renewable Energy Systems Placement Through Advanced Deep Learning and Evolutionary Algorithms
Authors: Stergiou Konstantinos , Karakasidis Theodoros
Published: November 2024

As the world shifts towards a low-carbon economy, the strategic deployment of renewable energy sources (RESs) is critical for maximizing energy output and ensuring sustainability. This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). A key innovation of GREENIA is its ability to provide natural language explanations (NLEs), enabling transparent and interpretable insights for both technical and non-technical stakeholders. Applied in Greece, the framework addresses the challenges posed by the interplay of meteorological factors from 10 different meteorological stations across the country. Validation against real-world data demonstrates improved prediction accuracy using metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHAP analysis enhances transparency by identifying key meteorological influences, such as temperature and humidity, while NLE translates these insights into actionable recommendations in natural language, improving accessibility for energy planners and policymakers. The resulting strategic plan offers precise, intelligent, and interpretable recommendations for deploying RES technologies, ensuring maximum efficiency and sustainability. This approach not only advances renewable energy optimization but also equips stakeholders with practical tools for guiding the strategic deployment of RES across diverse regions, contributing to sustainable energy management.
Building Traceability Between Functional Requirements and Component Architecture Elements in Embedded Software Using Structured Features
Authors: Yoo Insun , Park Hyoseung , Lee Seok-Won , Ryu Ki-Yeol
Published: November 2024

In embedded software for critical domains such as medical devices and defense, requirement traceability is essential for ensuring quality attributes. Standards and regulations mandate traceability between requirements and artifacts such as design elements and code. However, existing methods often overlook the hardware-dependent nature of embedded systems or conduct traceability retroactively, which may affect consistency. This study introduces a structured feature-based approach to component architecture design, bridging the gap between requirements and design to ensure traceability. The structured feature model supports traceability between functional requirements, software components, and hardware elements in embedded systems. A case study demonstrates that structured features can effectively map the requirements to design artifacts, helping to visualize relationships through a traceability matrix. Although the process is manual, structured features improve efficiency in the early stages of design and create traceable links between requirements and architectural elements.
Modeling Data Sovereignty in Public Cloud—A Comparison of Existing Solutions
Authors: Galij Stanisław , Pawlak Grzegorz , Grzyb Sławomir
Published: November 2024

Data sovereignty has emerged as a critical concern for enterprises, cloud service providers (hyperscalers), end-users, and policymakers at both national and international levels. In response, cloud-based distributed computing models have been proposed as frameworks to enforce data sovereignty requirements. This study aims to evaluate and enhance data sovereignty practices within public cloud environments. Through a comprehensive literature review, we analyze existing reference architectures and solutions that address data sovereignty, identifying the technological and economic constraints they impose, such as increased computational costs associated with specific frameworks and cryptographic measures. To address these challenges, we propose an abstract data sovereignty model designed to aid system designers and architects in developing compliant cloud-based systems. Additionally, we conduct computational experiments assessing the performance of the IDS connector, a key data sovereignty tool, deployed on the Google Cloud Platform and Microsoft Azure. Results reveal that while the geographic location of the software significantly impacts performance, the choice of cloud platform minimally influences the IDS connector’s efficiency. These findings offer insights into optimizing data sovereignty strategies for cloud solutions, with implications for future system design and policy development.
Citrus: From Symbolism to Sensuality—Exploring Luxury and Extravagance in Western Muslim Bustān and European Renaissance Gardens
Authors: Rivera Diego , Navarro Julio , Camarero Inmaculada , Valera Javier , Rivera-Obón Diego-José , Obón Concepción
Published: November 2024

This study delves into the multifaceted realm of citrus fruits, exploring their significance and socioeconomic implications from their early introduction to Western Muslim and Renaissance gardens, tracing their journey throughout history. Employing a multidisciplinary approach, drawing from biological, archaeobotanical, iconographic, and textual sources, our study offers a comprehensive exploration of citrus symbolism and cultural significance, integrating historical, artistic, horticultural, and socioeconomic viewpoints. The genus Citrus (Rutaceae) comprises around thirty species and its natural habitat spans from the southern slopes of the Himalayas to China, Southeast Asia, nearby islands, and Queensland. Originating from only four of these species, humans have cultivated hundreds of hybrids and thousands of varieties, harnessing their culinary, medicinal, and ornamental potential worldwide. We delve into the symbolic value of citrus fruits, which have served as indicators of economic status and power. From their early presence in Mediterranean religious rituals to their depiction in opulent Roman art and mythical narratives like the Garden of the Hesperides, citrus fruits have epitomized luxury and desire. Christian lore intertwines them with the forbidden fruit of Eden, while Islamic and Sicilian gardens and Renaissance villas signify their prestige. We analyze diverse perspectives, from moralists to hedonists, and examine their role in shaping global agriculture, exemplified by rare varieties like aurantii foetiferi.
Spatiotemporal Variation Characteristics of Extreme Precipitation in Henan Province Based on RClimDex Model
Authors: Gu Zhijia , Li Yuemei , Qin Mengchen , Ji Keke , Yi Qiang , Li Panying , Feng Detai
Published: November 2024

Global warming has led to an increasing frequency and intensity of extreme precipitation events worldwide. The extreme precipitation of Henan Province in central China usually occurs in summer, with the climate transition from the northern subtropical to the warm temperate climate. Compared with the study of extreme precipitation events in other regions, the study of Henan Province pays less attention. In order to systematically understand the spatial and temporal characteristics of extreme precipitation in Henan Province, this study applied RClimDex model to obtain nine extreme precipitation indices based on daily precipitation data from 90 meteorological stations from 1981 to 2020. Linear propensity estimation, M-K mutation test, Morlet wavelet analysis, and geostatistical analysis were used to investigate the spatial and temporal variation characteristics of the extreme precipitation indices in the region. The results indicated that continuous dry days (CDD), number of heavy rain days (R20mm), maximum daily precipitation (Rx1day), maximum precipitation for 5 consecutive days (Rx5day), and precipitation intensity (SDII) showed an overall increasing trend, but none passed the significance test (p > 0.01). Extremely strong precipitation (R99p) and Rx5day changed abruptly in 1994, and Rx1day and SDII changed abruptly in 2004. The seven extreme precipitation indices, except CDD and continuous wet days (CWD), had a 30-year cyclical pattern. The multi-year average of extreme precipitation indices showed that the CDD gradually decreased from north to south, CWD and R20mm gradually increased from north to south. Rx1day and Rx5day gradually increased from northwest to southeast, and SDII increased from west to east. The results can contribute valuable insights to extreme precipitation trends and future climate predictions in Henan Province and provide scientific support for coping with extreme precipitation changes and disaster prevention.
Impact of Large-Scale Circulations on Ground-Level Ozone Variability over Eastern China
Authors: Li Jinlan , Li Ying
Published: November 2024

The seasonal and interannual variations in ground-level ozone across eastern China from 2014 to 2022 were strongly influenced by meteorological conditions and large-scale atmospheric circulations. We applied empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses to explore these relationships. The EOF analysis identified three primary patterns of ozone variability: a dominant seasonal cycle over most of mainland China, an anti-correlation between northern and southern China during transitional seasons, and elevated springtime ozone concentrations in coastal regions. The SVD results further demonstrated that seasonal ozone variability was primarily driven by the annual radiation cycle across much of China. In contrast, the East Asian summer monsoon (EASM) was linked to the relatively low summer ozone levels observed in southern China. The anti-correlation between northern and southern China was associated with western Pacific subtropical high (WPSH) movement, which promoted sunny weather conditions and was conducive to ozone formation. Additionally, high springtime ozone levels in northern coastal regions were influenced by pollutant transport from continental cold high (CCH) events, while the cloud-free conditions and intense solar radiation in southern China contributed to elevated ozone concentrations.
Innovative Approaches to Industrial Odour Monitoring: From Chemical Analysis to Predictive Models
Authors: Franchina Claudia , Cefalì Amedeo Manuel , Gianotti Martina , Frugis Alessandro , Corradi Corrado , De Prosperis Giulio , Ronzio Dario , Ferrero Luca , Bolzacchini Ezio , Cipriano Domenico
Published: November 2024

This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical species: two values for the concentration of VOCs recorded via the PID sensor (VPID) and the EC sensor (VEC), and concentrations of sulfuric acid (VH2S) and benzene (VC6H6). Using Random Forest and least squares regression analysis, the study identifies VH2S and VC6H6 as key contributors to odour concentration (CcOD). Three Random Forest models (RF0, RF1, RF2), with different characteristics for splitting between the test set and the training set, were tested, with RF1 showing superior predictive performance due to its training approach. All models highlighted VH2S and VC6H6 as significant predictors, while VPID and VEC had less influence. A significant correlation between odour concentration and specific chemical sensor readings was found, particularly for VH2S and VC6H6. However, predicting odour concentrations below 1000 ouE/m3 proved challenging. Linear regression further confirmed the importance of VH2S and VC6H6, with a moderate R-squared value of 0.70, explaining 70% of the variability in odour concentration. The study demonstrated the effectiveness of combining Random Forest and least squares regression for robust and interpretable results. Future research should focus on expanding the dataset and incorporating additional variables to enhance model accuracy. The findings underscore the necessity of specific sensor training and standardised procedures for accurate odour monitoring and characterisation.
Extremal k-Connected Graphs with Maximum Closeness
Authors: Hayat Fazal , Otera Daniele Ettore
Published: November 2024

Closeness is a measure that quantifies how quickly information can spread from a given node to all other nodes in the network, reflecting the efficiency of communication within the network by indicating how close a node is to all other nodes. For a graph G, the subset S of vertices of V(G) is called vertex cut of G if the graph G−S becomes disconnected. The minimum cardinality of S for which G−S is either disconnected or contains precisely one vertex is called connectivity of G. A graph is called k-connected if it stays connected even when any set of fewer than k vertices is removed. In communication networks, a k-connected graph improves network reliability; even if up to k−1 nodes fail, the network remains operational, maintaining connectivity between devices. This paper aims to study the concept of closeness within n-vertex graphs with fixed connectivity. First, we identify the graphs that maximize the closeness among all graphs of order n with fixed connectivity k. Then, we determine the graphs that achieve the maximum closeness within all k-connected graphs of order n, given specific fixed parameters such as diameter, independence number, and minimum degree.
Power Truncated Positive Normal Distribution: A Quantile Regression Approach Applied to Health Databases
Authors: Santoro Karol I. , Gómez Héctor J. , Cortés Isaac E. , Magalhães Tiago M. , Gallardo Diego I.
Published: November 2024

In this paper we present a new extension of the truncated positive normal (TPN) model, called power truncated positive normal. This extension incorporates a shape parameter that provides more flexibility to the model. In addition, this new extension was reparameterized based on the p-th quantile of the distribution in order to perform quantile regression. The initial values were calculated from a modification of the moment estimators, which allowed the maximum likelihood estimators to be obtained. A simulation study was carried out which suggests good behavior of the maximum likelihood estimators in finite samples. Finally, two applications using health databases are presented.
Factorization of Hausdorff Operators
Authors: Roopaei Hadi
Published: November 2024

Throughout this study, we will gain a deeper understanding of Hausdorff operators that are commonly used in operator theory. The Hausdorff matrices Gamma, Cesàro, and Hölder are factorized here to derive novel inequalities. Specifically, a factorization based on the Gamma operator has also been introduced for these three operators. In addition, the author introduces a factorization for the Hölder operator based on the Cesàro operator, which was previously discovered by famous mathematicians but this time is handled with an entirely different approach.
Aspect-Based Sentiment Analysis of Patient Feedback Using Large Language Models
Authors: Alkhnbashi Omer S. , Mohammad Rasheed , Hammoudeh Mohammad
Published: November 2024

Online medical forums have emerged as vital platforms for patients to share their experiences and seek advice, providing a valuable, cost-effective source of feedback for medical service management. This feedback not only measures patient satisfaction and improves health service quality but also offers crucial insights into the effectiveness of medical treatments, pain management strategies, and alternative therapies. This study systematically identifies and categorizes key aspects of patient experiences, emphasizing both positive and negative sentiments expressed in their narratives. We collected a dataset of approximately 15,000 entries from various sections of the widely used medical forum, patient.info. Our innovative approach integrates content analysis with aspect-based sentiment analysis, deep learning techniques, and a large language model (LLM) to analyze these data. Our methodology is designed to uncover a wide range of aspect types reflected in patient feedback. The analysis revealed seven distinct aspect types prevalent in the feedback, demonstrating that deep learning models can effectively predict these aspect types and their corresponding sentiment values. Notably, the LLM with few-shot learning outperformed other models. Our findings enhance the understanding of patient experiences in online forums and underscore the utility of advanced analytical techniques in extracting meaningful insights from unstructured patient feedback, offering valuable implications for healthcare providers and medical service management.