bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models
Authors: Hussain Shahzad , Siddiqui Hafeez , Saleem Adil , Raza Muhammad , Alemany-Iturriaga Josep , Velarde-Sotres Álvaro , Díez Isabel , Dudley Sandra
Published: September 2024

Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. Each landmark was represented by four features, which were used for classification. The core challenge addressed in this research involves ensuring accurate and real-time exercise classification across diverse body morphologies and exercise types. Several tree-based classifiers, including Random Forest, Extra Tree Classifier, XGBoost, LightGBM, and Hist Gradient Boosting, were employed. Furthermore, two novel ensemble models called RandomLightHist Fusion and StackedXLightRF are proposed to enhance classification accuracy. The RandomLightHist Fusion model achieved superior accuracy of 99.6%, demonstrating the system’s robustness and effectiveness. This innovation offers a practical solution for providing real-time feedback in telephysiotherapy, with potential to improve patient outcomes through accurate monitoring and assessment of exercise performance.
Degradation Type-Aware Image Restoration for Effective Object Detection in Adverse Weather
Authors: Huang Xiaochen , Wang Xiaofeng , Teng Qizhi , He Xiaohai , Chen Honggang
Published: September 2024

Despite significant advancements in CNN-based object detection technology, adverse weather conditions can disrupt imaging sensors’ ability to capture clear images, thereby adversely impacting detection accuracy. Mainstream algorithms for adverse weather object detection enhance detection performance through image restoration methods. Nevertheless, the majority of these approaches are designed for a specific degradation scenario, making it difficult to adapt to diverse weather conditions. To cope with this issue, we put forward a degradation type-aware restoration-assisted object detection network, dubbed DTRDNet. It contains an object detection network with a shared feature encoder (SFE) and object detection decoder, a degradation discrimination image restoration decoder (DDIR), and a degradation category predictor (DCP). In the training phase, we jointly optimize the whole framework on a mixed weather dataset, including degraded images and clean images. Specifically, the degradation type information is incorporated in our DDIR to avoid the interaction between clean images and the restoration module. Furthermore, the DCP makes the SFE possess degradation category awareness ability, enhancing the detector’s adaptability to diverse weather conditions and enabling it to furnish requisite environmental information as required. Both the DCP and the DDIR can be removed according to requirement in the inference stage to retain the real-time performance of the detection algorithm. Extensive experiments on clear, hazy, rainy, and snowy images demonstrate that our DTRDNet outperforms advanced object detection algorithms, achieving an average mAP of 79.38% across the four weather test sets.
Sowing Depth Monitoring System for High-Speed Precision Planters Based on Multi-Sensor Data Fusion
Authors: Wang Song , Yi Shujuan , Zhao Bin , Li Yifei , Li Shuaifei , Tao Guixiang , Mao Xin , Sun Wensheng
Published: September 2024

High-speed precision planters are subject to high-speed (12~16 km/h) operation due to terrain undulation caused by mechanical vibration and sensor measurement errors caused by the sowing depth monitoring system’s accuracy reduction problems. Thus, this study investigates multi-sensor data fusion technology based on the sowing depth monitoring systems of high-speed precision planters. Firstly, a sowing depth monitoring model comprising laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit is established. Secondly, these three single sensors are filtered using the Kalman filter. Finally, a multi-sensor data fusion algorithm for optimising four key parameters in the extended Kalman filter (EKF) using an improved sparrow search algorithm (ISSA) is proposed. Subsequently, the filtered data from the three single sensors are integrated to address the issues of mechanical vibration interference and sensor measurement errors. In order to ascertain the superiority of the ISSA-EKF, the ISSA-EKF and SSA-EKF are simulated, and their values are compared with the original monitoring value of the sensor and the filtered sowing depth value. The simulation test demonstrates that the ISSA-EKF-based sowing depth monitoring algorithm for high-speed precision planters, with a mean absolute error (MAE) of 0.083 cm, root mean square error (RMSE) of 0.103 cm, and correlation coefficient (R) of 0.979 achieves high-precision monitoring. This is evidenced by a significant improvement in accuracy when compared with the original monitoring value of the sensor, the filtered value, and the SSA-EKF. The results of a field test demonstrate that the ISSA-EKF-based sowing depth monitoring system for high-speed precision planters enhances the precision and reliability of the monitoring system when compared with the three single-sensor monitoring values. The average MAE and RMSE are reduced by 0.071 cm and 0.075 cm, respectively, while the average R is improved by 0.036. This study offers a theoretical foundation for the advancement of sowing depth monitoring systems for high-speed precision planters.
Covert Information Mapped Generalized Spatial and Direction Modulation toward Secure Wireless Transmission
Authors: Zhong Yuan , Ji Zhengyu , Li Xianglu , Fei Peng , Hou Dong , Wang Zhigang , Tian Jie
Published: September 2024

In this paper, for the sake of enhancing the security of wireless transmission, we proposed a novel system based on spatial and direction modulation (SDM) combined with generalized spatial modulation (GSM) which is aided by covert information mapping (CIM), termed as the CIM-GSDM system. In such a system, the legitimated user is equipped with distributed receivers so as to demodulate the conveyed signal by exploiting its indices while disturbing eavesdroppers for information security. More specifically, part of the information is modulated into the indices of the legitimated distributed receiver subsets with the aid of the mapped covert information and the interference matrix, while another part of the message is arranged by conventional amplitude-phase modulation. The proposed system can reap the benefits from both GSM and CIM to make eavesdropper suffer great mixture. Furthermore, the detection scheme and theoretical analysis of error performance are discussed as well. The simulation results exhibit that the bit error rate (BER) performance of legitimate user is much better than that of the eavesdropper while the proposed scheme improves the security compared to the original CIM-SDM system at the same spectral efficiency.
Statistical Analysis of the Influence of Various Types of Graphite Precursors and Oxidation Methods on the Gas Sensor Properties of Reduced Graphene Oxide
Authors: Drewniak Łukasz , Drewniak Sabina , Sajdak Marcin , Muzyka Roksana
Published: September 2024

The fabrication process of reduced graphene oxide depends on many factors (e.g., graphite precursor, methods of oxidation, reduction, and exfoliation) which have a significant influence on the properties of this material. Therefore, their selection is not easy due to the large number of possible combinations of these factors. To overcome this problem, we proposed to use a multivariate analysis of variance method of finding associations between the qualitative type of independent variables and the quantitative type of dependent variable. Using ANOVA, we showed that the combination (interaction) of these variables is more important than the individual influence of the variables on the fabricated rGO. Knowing how the particular variables and their combinations affect the properties of rGO, it is easier to plan the fabrication process of this material. In this paper, we analyzed the number of oxide layers and designated the most promising oxides in terms of sensor gas application. Independently, we fabricated chemiresistor sensors and studied their response to NO2 in the analyzed atmosphere. We were able to combine the experimental results with statistical analysis indicating which oxidation methods and which graphite precursors will provide the best sensitivity.
Lure Monitoring for Mediterranean Fruit Fly Traps Using Air Quality Sensors
Authors: Hernández Rosas Miguel , Espinosa Flores-Verdad Guillermo , Peregrina Barreto Hayde , Liedo Pablo , Altamirano Robles Leopoldo
Published: September 2024

Effective pest population monitoring is crucial in precision agriculture, which integrates various technologies and data analysis techniques for enhanced decision-making. This study introduces a novel approach for monitoring lures in traps targeting the Mediterranean fruit fly, utilizing air quality sensors to detect total volatile organic compounds (TVOC) and equivalent carbon dioxide (eCO2). Our results indicate that air quality sensors, specifically the SGP30 and ENS160 models, can reliably detect the presence of lures, reducing the need for frequent physical trap inspections and associated maintenance costs. The ENS160 sensor demonstrated superior performance, with stable detection capabilities at a predefined distance from the lure, suggesting its potential for integration into smart trap designs. This is the first study to apply TVOC and eCO2 sensors in this context, paving the way for more efficient and cost-effective pest monitoring solutions in smart agriculture environments.
Mechanism-Based Fault Diagnosis Deep Learning Method for Permanent Magnet Synchronous Motor
Authors: Li Li , Liao Shenghui , Zou Beiji , Liu Jiantao
Published: September 2024

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time–frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time–frequency format. Subsequently, a CNN model is developed to extract and classify these time–frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities.
Analyzing Arabic Handwriting Style through Hand Kinematics
Authors: Babushkin Vahan , Alsuradi Haneen , Al-Khalil Muhamed Osman , Eid Mohamad
Published: September 2024

Handwriting style is an important aspect affecting the quality of handwriting. Adhering to one style is crucial for languages that follow cursive orthography and possess multiple handwriting styles, such as Arabic. The majority of available studies analyze Arabic handwriting style from static documents, focusing only on pure styles. In this study, we analyze handwriting samples with mixed styles, pure styles (Ruq’ah and Naskh), and samples without a specific style from dynamic features of the stylus and hand kinematics. We propose a model for classifying handwritten samples into four classes based on adherence to style. The stylus and hand kinematics data were collected from 50 participants who were writing an Arabic text containing all 28 letters and covering most Arabic orthography. The parameter search was conducted to find the best hyperparameters for the model, the optimal sliding window length, and the overlap. The proposed model for style classification achieves an accuracy of 88%. The explainability analysis with Shapley values revealed that hand speed, pressure, and pen slant are among the top 12 important features, with other features contributing nearly equally to style classification. Finally, we explore which features are important for Arabic handwriting style detection.
Muscle Tone Assessment by Machine Learning Using Surface Electromyography
Authors: Rezende Andressa Rastrelo , Alves Camille Marques , Marques Isabela Alves , de Souza Luciane Aparecida Pascucci Sande , Naves Eduardo Lázaro Martins
Published: September 2024

Muscle tone is defined as the resistance to passive stretch, but this definition is often criticized for its ambiguity since some suggest it is related to a state of preparation for movement. Muscle tone is primarily regulated by the central nervous system, and individuals with neurological disorders may lose the ability to control normal tone and can exhibit abnormalities. Currently, these abnormalities are mostly evaluated using subjective scales, highlighting a lack of objective assessment methods in the literature. This study aimed to use surface electromyography (sEMG) and machine learning (ML) for the objective classification and characterization of the full spectrum of muscle tone in the upper limb. Data were collected from thirty-nine individuals, including spastic, healthy, hypotonic and rigid subjects. All of the classifiers applied achieved high accuracy, with the best reaching 96.12%, in differentiating muscle tone. These results underscore the potential of the proposed methodology as a more reliable and quantitative method for evaluating muscle tone abnormalities, aiming to address the limitations of traditional subjective assessments. Additionally, the main features impacting the classifiers’ performance were identified, which can be utilized in future research and in the development of devices that can be used in clinical practice.
The Removal of Organic Contaminants from Condensed Wastewater Using Electrolysis Combined with Ozonation: A Pilot-Scale Study
Authors: Ding Yalei , Wang Jie , Tan Bin
Published: September 2024

A pilot-scale investigation of ozonation combined with electrolysis (E-O3) was performed to treat concentrated wastewater from a reverse osmosis system from the printing and dyeing industry. It was found that E-O3 only exhibits an efficiency advantage after the removal of carbonate ions. The synergy of ozone and electrolysis lies not only in the generation of hydroxyl radicals, but also in the degradation of organic compounds. Moreover, the combination of electrolysis and ozonation has an inhibitory effect on the decrease in pH, which plays an important role in the synergistic generation of hydroxyl radicals. This pilot-scale study holds reference significance for the engineering applications of the E-O3 technology.
Development and Evaluation of Peace-Oriented Education Activity for Preschool Children within the Scope of Sustainable Development
Authors: Amca Toklu Dervişe , Acar Ayber , Akcil Umut , Dagli Gokmen
Published: September 2024

The aim of this research is to develop contemporary peace education that can be implemented in the pre-school period to ensure global citizenship. In this study, action research design, one of the qualitative research methods, was used. The study group of this research consisted of five-year-old children who attend a public kindergarten and their parents, classroom teacher, and researcher. The data were collected by means of the peace value attitude questionnaire prepared for parents and classroom teachers, the technology-supported child interview form, the teacher diary form, and the researcher diary form. Research was carried out in three stages: needs analysis, implementation, and evaluation. Educational activities covering peace education were developed, and they were administered to the children for 12 weeks. Within the framework of the results obtained, it was determined that the five-year-old children who made up the study group consciously gained the value of peace, were sensitive to the multicultural structure, and strengthened their communication with their peers. In light of these results, we should develop integrated programs in preschool education, place the value of peace at the center, and provide peace education widely in the preschool period by all stakeholders for global citizenship.
The Impact of Technological Innovations on Agricultural Productivity and Environmental Sustainability in China
Authors: Huang Weilun , Wang Xucheng
Published: September 2024

Agricultural productivity in China is a fundamental driver of food security and economic growth. Yet, the sector faces profound challenges due to environmental degradation and climate change, which threaten sustainable agricultural practices. This research examines the effects of technological innovations on agricultural Total Factor Productivity and environmental sustainability in China from 2012 to 2022. The study seeks to understand how technological advancements, when considered alongside socioeconomic variables, impact agricultural output while balancing ecological integrity. Employing a comprehensive methodological framework, this research integrates fixed-effects, random-effects, and multilevel mixed-effects models to analyze crucial factors including rural education, technological capability, and environmental conservation initiatives. The study further utilizes structural equation modeling to evaluate both the direct and indirect effects of these determinants on productivity. The results demonstrate that technological innovations substantially enhance agricultural productivity, particularly in provinces with higher socioeconomic development. Additionally, sustainable farming practices and tailored policy interventions are identified as vital in addressing regional productivity imbalances. The research concludes by underscoring the necessity for the continued integration of environmental considerations and emerging technologies to ensure the sustainability of agricultural growth in the long term.
The Impact of ESG Risks on the Economic Growth in the Western Balkan Countries
Authors: Delova-Jolevska Evica , Ilievski Andrej , Jolevski Ljube , Csiszárik-Kocsir Ágnes , Varga János
Published: September 2024

The economy is significantly impacted by environmental, social, and governance (ESG) risks. The growth of the economy can be sped up by the effective management of ESG risks through sustainable business practices. To promote sustainable development and to secure the long-term welfare of employees, customers, and all other stakeholders in the economy, companies must adapt and reposition their business strategies and organizational cultures. The goal of this paper is to determine how a set of common ESG elements, chosen from the viewpoints of sustainability and well-being, influence economic growth in the Western Balkan countries. For each ESG component, we used different variables. The information pertains to the five Western Balkan countries of North Macedonia, Albania, Montenegro, Bosnia and Herzegovina, and Serbia. Because of a lack of data, Kosovo is excluded from the study. Then, we compared results from the analysis of the Western Balkan countries with a set of countries in Southeast Europe, which are members of the European Union and essentially coincide with the Western Europe countries. We performed multiple regression analysis with applied fixed effects to the data model. According to the study’s findings, each of the independent variables had no significant impact on the GDP’s annual growth of the Western Balkan countries, but two of the variables, life expectancy at birth and labor force participation, have certain impact on the GDP growth of Southeast Europe countries, which are members of the European Union. The green transition has gained significant importance in the Western Balkan countries as a crucial pathway toward sustainable economic growth, though it introduces a range of new social and economic challenges. Economically, these nations are confronted with considerable funding requirements for development. To build sustainable societies, it would be beneficial for these countries to explore more creative financing strategies. It is advised to establish financing frameworks that not only increase the transparency in policymaking but also ensure greater accountability in their execution.
Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System
Authors: Yan Qingyuan , Wang Zhaoyi , Xing Ling , Zhu Chenchen
Published: September 2024

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.
A Study on Carbon Emission Reduction in the Entire Process of Retrofitting High-Rise Office Buildings Based on the Extraction of Typical Models
Authors: Chen Yixuan , Wang Zhenyu , Peng Zhen
Published: September 2024

The building sector is one of the largest contributors to carbon emissions globally, with high-rise office buildings being a major source due to their energy-intensive operations. This study aims to address the critical issue of carbon emission reductions through the retrofitting of existing high-rise office buildings, focusing on the entire life cycle of these buildings, including the embodied, operational, and demolition phases. Existing research has primarily concentrated on energy consumption and carbon emissions during the operational phase, neglecting the carbon impact of the retrofitting process itself. This research seeks to fill that gap by quantifying the carbon reduction benefits of retrofitting across all life-cycle stages. Using data from 100 high-rise office buildings in Hangzhou’s Gongshu District, five typical models were extracted based on their construction eras and architectural features. Retrofitting strategies tailored to these models were developed, and the carbon reduction benefits were calculated using the carbon emission factor method. The primary findings indicated that the shape and orientation of buildings are crucial factors influencing the carbon reduction benefits of retrofitting. Buildings oriented east–west tend to exhibit greater carbon reductions after retrofitting. During the embodied and demolition phases, retrofitting emissions remain similar for models constructed in the same era due to consistent material inputs. However, emissions vary for models from different eras, primarily due to differences in envelope materials and subsequent material consumption. High-rise office buildings constructed between 2007 and 2021 demonstrate higher overall retrofit carbon reduction rates compared to those built before 2007, despite the latter achieving greater reductions during the operational phase. The shorter remaining lifespans of pre-2007 buildings diminish their life-cycle carbon reduction advantages. Notably, complex-shaped buildings from the same era do not necessarily exhibit lower overall retrofit carbon reduction rates compared with rectangular or L-shaped buildings, with comparable reductions per unit area. This suggests that complex-shaped buildings should not be disregarded for retrofitting based solely on shape considerations. Furthermore, the remaining lifespan of a building significantly impacts its post-retrofitting carbon reduction benefits; longer lifespans result in greater benefits, and vice versa. In practical engineering applications, structural reinforcement measures can be implemented prior to retrofitting to extend a building’s structural lifespan, ultimately enhancing its carbon reduction benefits.
Tourism in the Era of Social Responsibility and Sustainability: Understanding International Tourists’ Destination Choices
Authors: Baena Verónica , Cerviño Julio
Published: September 2024

Over the past three decades, global tourism has significantly contributed to the world economy, driven by factors such as globalization, technological advancements, and rising disposable incomes. However, alongside these economic benefits, tourism’s environmental impact remains a pressing concern, involving resource depletion, pollution, and substantial carbon emissions. Despite extensive research on these issues, there remains a gap in the literature regarding how state social responsibility and sustainability can be effectively integrated into tourism policies, particularly in prominent tourist destinations like Spain. This study addresses this gap by employing a combined qualitative (content analysis) and quantitative (survey) approach to explore the dual role of tourism in economic growth and environmental sustainability. Focusing on Spain as a case study, the research highlights both the challenges and opportunities associated with sustainable tourism practices. It examines the influence of factors such as the host country’s image, quality of life, the home country’s purchasing power parity (PPP), and the geographical distance between home and host countries on tourists’ destination choices within the framework of Stakeholder Theory. The novelty of this research lies in its comprehensive analysis of these factors, offering critical insights for researchers and policymakers striving to balance tourism growth with environmental sustainability globally.
Correction: Kandemir et al. Women in Parliaments and Environmentally Friendly Fiscal Policies: A Global Analysis. Sustainability 2024, 16, 7669
Authors: Kandemir Aysen Simsek , Lone Ramshah Rashid , Simsek Rasim
Published: September 2024

In the original publication [...]
How Traditional Fishing Villages Move towards Sustainable Management: A Case Study of Industrial Transformation and Multi-Party Governance Models
Authors: He Ming-Ming , Wang Jing , Lee Su-Hsin , Tsai Shu-Chen
Published: September 2024

The purpose of this study is to explore the key roles and functions of institutions or organizations in the process of the spatial activation and industrial transformation of leisure fishing villages and to summarize this operating model. This study takes Xunpu Village, Quanzhou City, Fujian Province as the research object, which is well-known for its recreational fisheries. It mainly uses case study methods and uses on-site surveys and interviews to understand the formation time, spatial distribution, and business type information of the Zanhuawei shops in Xunpu Village. Zanhuawei, which refers to the traditional hairstyle of Xunpu women, was later applied for national intangible cultural heritage status in China. After the outbreak, the spread of Zanhuawei on the Internet inspired a large number of tourists to visit Xunpu Village to experience Zanhuawei. The study found that (1) Zanhuawei industry replaced traditional fishery and became the leading industry type in Xunpu Village. (2) The operating model led by the government and integrating public and private funds has had a stimulating effect on the revitalization of local commercial and residential space, and a large number of idle spaces have been reactivated. (3) The cooperation model between a large number of investors and local fisherwomen has completely changed the economic model of the village. Zanhuawei industry mobilized all official and media resources to promote spatial activation at various scales, including regions, settlements and commercial spaces.
The Incentive Effect of Digital Finance on Innovation of Small- and Medium-Sized Enterprises Considering Heterogeneity: An Empirical Study Based on Chinese-Listed Firms
Authors: Zheng Wanteng , Ye Zixuan
Published: September 2024

The development of digital finance provides new opportunities for solving the dilemma of innovation financing for small- and medium-sized enterprises (SMEs). This study empirically examined the heterogeneous characteristics and mediating mechanisms of digital finance and its incentive effects on SME innovation using panel data of Chinese and GEM board-listed companies from 2010 to 2021. It was found that digital finance can significantly incentivize SME innovation; however, there are differences in efficacy among digital finance sub-dimensions, with breadth of coverage having the strongest effect, followed by depth of use, and digitization degree having a non-significant effect. Meanwhile, there is heterogeneity in the incentive effect of digital finance on SME innovation, which is manifested as private SMEs and SMEs in regions with stronger financial regulations and a higher degree of marketization being more likely to be incentivized by digital finance to innovate. In addition, digital finance can indirectly incentivize SMEs to innovate through three paths: alleviating financing constraints, improving risk tolerance, and solving information asymmetry.
Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula
Authors: Wang Xinghua , Li Zilv , Fu Chenyang , Liu Xixian , Yang Weikang , Huang Xiangyuan , Yang Longfa , Wu Jianhui , Zhao Zhuoli
Published: September 2024

With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components’ results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support.
A Sustainable Production Planning Scheme for New Energy Vehicles in China
Authors: Xiao Lu , Yang Feiyue , Yang Yong , Chen Che , Ha Wuer
Published: September 2024

The carbon emissions of new energy vehicles (NEVs)have transited from the use stage to the production stage, indicating that the environmental impact of NEVs in the manufacturing stage cannot be ignored. To reduce carbon emissions and maintain profits, this study proposes a fuzzy multi-objective optimization model to achieve a sustainable production planning scheme for NEVs. The proposed model not only considers the maximum profits of automobile enterprises but also the minimum target of carbon emissions in the production process, to coordinate the optimal production quantity. The results show that the output of NEVs in different price ranges has different proportions. The market share of blade electric vehicles is the highest, accounting for 39% of the NEV market, and the proportion of plug-in hybrid and blade electric vehicles is increasing. The sensitivity analysis further reflects the impact of government subsidy “recession” and body lightweight on the output, carbon emissions, and annual profits of NEVs in China. Accordingly, this paper provides policy implications for achieving a sustainable production planning scheme for NEVs in China.
Current Profiling Control for Torque Ripple Reduction in the Generating Mode of Operation of a Switched Reluctance Motor Drive
Authors: Agrawal Aniruddha , Bilgin Berker , Haridas Amrutha K.
Published: September 2024

The benefits of utilizing Switched Reluctance Motor (SRM) drives in traction applications can be realized fully by improving the electromagnetic performance of the machine in the generating mode of operation. This is because the generating capability of an SRM drive could be utilized for regenerative braking and also for the machine to generate power for the vehicle while the engine is in operation. In this paper, a current profiling-based control strategy is proposed to reduce the torque ripple in an SRM drive in the generating mode. The reference current profile is determined using a multi-step computation method to minimize torque ripple and maximize the average torque. The reference current profile is derived based on the reference torque command by utilizing the torque–current–angle look up table. The flux linkage characteristics of the SRM are considered when deriving the phase reference current profile. Then, the performance of the proposed profiling method, analytical linear and cubic torque sharing functions (TSFs), and the average torque optimization scheme are compared using simulation results. Finally, an experimental correlation is performed to validate the efficacy of the proposed control scheme.
Fractional-Order Correlation between Special Functions Inspired by Bone Fractal Operators
Authors: Jian Zhimo , Luo Chaoqian , Zhou Tianyi , Peng Gang , Yin Yajun
Published: September 2024

In recent years, our research on biomechanical and biophysical problems has involved a series of symmetry issues. We found that the fundamental laws of the aforementioned problems can all be characterized by fractal operators, and each type of operator possesses rich invariant properties. Based on the invariant properties of fractal operators, we discovered that the symmetry evolution laws of functional fractal trees in the physical fractal space can reveal the intrinsic correlations between special functions. This article explores the fractional-order correlation between special functions inspired by bone fractal operators. Specifically, the following contents are included: (1) showing the intrinsic expression in the convolutional kernel function of bone fractal operators and its correlation with special functions; (2) proving the following proposition: the convolutional kernel function of bone fractal operators is still related to the special functions under different input signals (external load, external stimulus); (3) using the bone fractal operators as the background and error function as the core, deriving the fractional-order correlation between different special functions.
On a Symmetry-Based Structural Deterministic Fractal Fractional Order Mathematical Model to Investigate Conjunctivitis Adenovirus Disease
Authors: Jeelani Mdi Begum , Alharthi Nadiyah Hussain
Published: September 2024

In the last few years, the conjunctivitis adenovirus disease has been investigated by using the concept of mathematical models. Hence, researchers have presented some mathematical models of the mentioned disease by using classical and fractional order derivatives. A complementary method involves analyzing the system of fractal fractional order equations by considering the set of symmetries of its solutions. By characterizing structures that relate to the fundamental dynamics of biological systems, symmetries offer a potent notion for the creation of mechanistic models. This study investigates a novel mathematical model for conjunctivitis adenovirus disease. Conjunctivitis is an infection in the eye that is caused by adenovirus, also known as pink eye disease. Adenovirus is a common virus that affects the eye’s mucosa. Infectious conjunctivitis is most common eye disease on the planet, impacting individuals across all age groups and demographics. We have formulated a model to investigate the transmission of the aforesaid disease and the impact of vaccination on its dynamics. Also, using mathematical analysis, the percentage of a population which needs vaccination to prevent the spreading of the mentioned disease can be investigated. Fractal fractional derivatives have been widely used in the last few years to study different infectious disease models. Hence, being inspired by the importance of fractal fractional theory to investigate the mentioned human eye-related disease, we derived some adequate results for the above model, including equilibrium points, reproductive number, and sensitivity analysis. Furthermore, by utilizing fixed point theory and numerical techniques, adequate requirements were established for the existence theory, Ulam–Hyers stability, and approximate solutions. We used nonlinear functional analysis and fixed point theory for the qualitative theory. We have graphically simulated the outcomes for several fractal fractional order levels using the numerical method.
Optimization Method for Allocating Peak-Period Parking Demand in Hub Parking Lot Clusters
Authors: Zhang Chu , Liu Weidong , Yan Chenyu , Ye Xiaofei , Chen Jun
Published: September 2024

With the expansion of urban scale and the popularization of multi-modal transportation, transportation hubs, as the link of multi-modal travel, are becoming increasingly important in urban development and residents’ lives. In situations of high parking demand, the increase in road traffic volume and parking search delays exacerbates the service pressure on hub parking lots and the traffic congestion on surrounding roads. Therefore, reasonable parking demand allocation is one of the key solutions to this problem. Based on the analysis of the vehicle parking search process, this paper constructs a model for estimating parking search delay on roads outside hub parking lots and proposes an optimization model for parking demand allocation aimed at minimizing the total parking search delay of vehicles. Finally, taking a major transportation hub in Nanjing as a case study, data were obtained through field investigations and simulation experiments to identify peak parking demand periods and calibrate the model parameters. The results show that the average vehicle delay was reduced by 4.5%, with a total reduction of 13,860 s in vehicle delay for parking demands at the hub within one hour. Therefore, by optimizing the allocation of parking demand, the average delay for vehicles searching for parking can be reduced to a certain extent.
IdentiFLY: The Development and Validation of a 15-Plex SNP Assay for Forensic Identification of UK Blowfly Species (Calliphoridae)
Authors: Godfrey Helen , Smith Judith A.
Published: September 2024

Members of the blowfly family (Calliphoridae) are usually the first insect species to arrive at a corpse, using the body as an oviposition site, and, as such, they are the most important group of insects used to estimate the post-mortem interval (PMI). PMI estimations are based on species-specific developmental timings; therefore, accurate species identification is crucial. Current identification methods are based on morphological characteristics, which are time-consuming and difficult to perform on damaged, immature specimens and closely related species. Advances have led to specimens being identified via a host of molecular techniques, mainly DNA sequencing. Although molecular identification is becoming increasingly more common, there is currently a lack of genetic data regarding UK Calliphoridae species. This study aimed to address this issue. We present the development and validation of an identification assay capable of differentiating six UK species (Calliphora vicina, Calliphora vomitoria, Lucilia sericata, Lucilia illustris, Lucilia caesar, and Protophormia terranovae). The sequencing of six genes, including both nuclear (28S rRNA and Elongation factor 1 alpha) and mitochondrial markers (Cytochrome oxidase I and II, Cytochrome b and 16S rRNA) identified 298 species-specific single nucleotide polymorphisms (SNPs). Fifteen SNPs from six genes were chosen for inclusion in a SNaPshot™ multiplex assay. The developed assay is capable of differentiating the species based on between 4 and 12 SNPs. Validation following guidelines by the International Society of Forensic Genetics (ISFG) demonstrated the assay to be accurate, reproducible, sensitive, and specific.
The Healthy City Reimagined: Walkability, Active Mobility, and the Challenges of Measurement and Evaluation
Authors: Paglione Lorenzo , Donato Maria Assunta , Cofone Luigi , Sabato Marise , Appolloni Letizia , D’Alessandro Daniela
Published: September 2024

Recently, there has been a growing interest in exploring the relationship between walkability and various aspects such as health, urban planning, and sustainability. This interest stems from the acknowledgement of the impact of walking on issues such as obesity, pollution, and other diseases, even if these are only weakly correlated. This scoping review was carried out to shed light on the link between walkability and health. The main objectives of this review are to describe the tools used to evaluate walkability, to highlight the variations in measurement methods, and to offer a critical evaluation of these tools. The final analysis includes a review of the most recent and comprehensive studies and online resources. Finally, an evaluation of the tools was carried out, with a focus on urban and architectural expertise relating to design, functions, and activities in public spaces, analysing the few available bibliographic references that correlate walkability with measured health outcomes using validated tools and health information flow. Neighbourhood walkability is influenced by the availability of functional amenities, activities, and safety. Failure to consider its importance on human health could lead to urban social inequalities and thus failure to create salutogenic cities, which could have major implications in terms of public health.
Data Discovery for Digital Building Logbook (DBL): Directly Implementing and Enabling a Smarter Urban Built Environment
Authors: Mêda Pedro , Calvetti Diego , Sousa Hipólito , Moreira Joaquim
Published: September 2024

Digital Building Logbooks (DBLs) are the EU repositories for all building-related data. Logbook implementation conveys challenges, but it must be recognised that relevant things already exist. This article bridges the gap at the data discovery level by assessing the existing data and comparing it with EU DBL studies. Action research is the methodology, employing Portugal as an example. A deductive approach and interpretivism are used, supporting the data discovery journey. When evaluating existing datasets with DBL EU guidelines data requirements, the findings demonstrate a match from 90.6% to 82.6%, depending on the level: cadastral parcel, building or building unit. Several additional observed datasets suit the DBL framework, constituting a path for future research. Insights into the dataset landscape from a specific perspective are offered. Given the deliverables’ characteristics, the study results can be generalised. The data discovery journey led to the understanding that duplicates and inconsistencies exist. A strategic approach for data sharing, governance and usage should be established to solve them, increasing digital maturity, integration and interoperability. Revising the legal framework is found to be paramount. Working from the existing elements and aligning them with data space assumptions can make DBL implementation more straightforward.
Taking AIM at Influenza: The Role of the AIM2 Inflammasome
Authors: Xu Dianne W. , Tate Michelle D.
Published: September 2024

Influenza A viruses (IAV) are dynamic and highly mutable respiratory pathogens that present persistent public health challenges. Inflammasomes, as components of the innate immune system, play a crucial role in the early detection and response to infections. They react to viral pathogens by triggering inflammation to promote immune defences and initiate repair mechanisms. While a strong response is necessary for early viral control, overactivation of inflammasomes can precipitate harmful hyperinflammatory responses, a defining characteristic observed during severe influenza infections. The Absent in Melanoma 2 (AIM2) inflammasome, traditionally recognised for its role as a DNA sensor, has recently been implicated in the response to RNA viruses, like IAV. Paradoxically, AIM2 deficiency has been linked to both enhanced and reduced vulnerability to IAV infection. This review synthesises the current understanding of AIM2 inflammasome activation during IAV and explores its clinical implications. Understanding the nuances of AIM2’s involvement could unveil novel therapeutic avenues for mitigating severe influenza outcomes.