bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Complementarity of Wind and Solar Energy in Kentucky
Authors: Syed Abbas Shah , Patrick Aron , Lauf Adrian , Elmaghraby Adel
Published: July 2024

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio.
Power Quality Transient Disturbance Diagnosis Based on Dynamic Large Convolution Kernel and Multi-Level Feature Fusion Network
Authors: Zheng Chen , Li Qionglin , Liu Shuming , Dai Shuangyin , Zhang Bo , Liu Yajuan
Published: July 2024

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability.
Experimental Study and Process Simulation on Pyrolysis Characteristics of Decommissioned Wind Turbine Blades
Authors: Zhang Dongwang , Huang Zhong , Shi Xiaobei , Sun Xiaofei , Zhou Tuo , Yang Hairui , Bie Rushan , Zhang Man
Published: July 2024

The development of wind power has brought about increasing challenges in decommissioning, among which DWTBs (decommissioned wind turbine blades) are the most difficult component to deal with. To enable the cost-effective, energy-efficient, and environmentally friendly large-scale utilization of DWTBs, an experimental study on thermogravimetric and pyrolysis characteristics of DWTBs was carried out. A new process involving recycling glass fiber with pyrolysis gas re-combustion and flue gas recirculation as the pyrolysis medium was innovatively proposed, and the simulation calculation was carried out. Thermogravimetric experiments indicated that glass fiber reinforced composite (GFRC) was the main heat-generating part in the heat utilization process of blades, and the blade material could basically complete pyrolysis at 600 °C. As the heating rate increased, the formation temperature, peak concentration, and proportion of combustible gas in the pyrolysis gas also increased. The highest peak concentration of CO gas was observed, with CO2 and C3H6 reaching their peaks at 700 °C. The solid product obtained from pyrolysis at 600 °C could be oxidized at 550 °C for 40 min to obtain clean glass fiber. And the pyrolysis temperature increased with the increase in the proportion of recirculation flue gas. When the proportion of recirculation flue gas was 66%, the pyrolysis temperature could reach 600 °C, meeting the necessary pyrolysis temperature for wind turbine blade materials. The above research provided fundamental data support for further exploration on high-value-added recycling of DWTBs.
The Role of Community Energy Schemes in Reducing Energy Poverty and Promoting Social Inclusion: A Systematic Literature Review
Authors: Boostani Pariman , Pellegrini-Masini Giuseppe , Klein Jørgen
Published: July 2024

Sustainable energy projects can help societies reduce climate change’s negative impacts. In the present paper, published studies regarding European community energy schemes (CESs) were analysed to investigate CESs’ barriers and drivers towards including community members and, more specifically, vulnerable groups. This review followed the PRISMA guidelines to ensure transparency. Exclusion and inclusion criteria were defined to select the proper articles and books that were aligned with the scope of this review. A total of 143 publications from 2019 to February 2024 were considered and selected. Content analysis was used to outline the various definitions of energy poverty, vulnerable groups, and CESs as well as the different strategies and approaches followed by European countries to tackle energy poverty and increase public engagement in CESs. This research suggests that environmental citizenship is a concept that can bring individuals and society together to promote changes in energy-related behaviours. The outcome reveals factors that enhance community energy acceptance and social cohesion such as providing energy education or education for sustainability to community members, financial support, increased prosocial culture, and the participation of vulnerable groups in CESs. This can be achieved through various interventions to help community members, including vulnerable groups, understand sustainable energy behaviour and thus reduce energy poverty.
Determination of Sodium Ion Diffusion Coefficient in Tin Sulfide@Carbon Anode Material Using GITT and EIS Techniques
Authors: Nowak Andrzej P. , Rutecki Paweł , Szkoda Mariusz , Trzciński Konrad
Published: July 2024

The electroanalytical behavior of SnSx (x = 1, 2) encapsulated into a carbon phase was studied using the galvanostatic intermittent titration technique (GITT) and electrochemical impedance spectroscopy (EIS). These techniques are widely utilized in battery systems to investigate the diffusion of alkali metal cations in anode and cathode materials depending on the concentration of ions in the host material. Here, we report different calculation methods showing how the applied model affects the derived diffusion coefficient. The calculated value of the apparent chemical diffusion coefficient of sodium ions (DNa+) is in the range of 1 × 10−10 to 1 × 10−15 cm2/s depending on the technique, mathematical protocol, geometry of the electrode material, and applied potential.
Promoting Decarbonization in China: Revealing the Impact of Various Energy Policies on the Power Sector Based on a Coupled Model
Authors: Liu Minwei , Tang Lang , Zeng Jincan , Huang Guori , Liu Xi , Yao Shangheng , He Gengsheng , Shang Nan , Tao Hai , Ren Songyan , Wang Peng
Published: July 2024

The carbon emissions of the power industry account for over 50% of China’s total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China’s energy transition, economic development, and the power sector’s energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China’s energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector’s generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended.
Business Models on the Energy Market in the Era of a Low-Emission Economy
Authors: Sułek Arkadiusz , Borowski Piotr F.
Published: July 2024

In the energy market, we observe a dynamic development of innovative business models that take into account various aspects related to the direction of zero-emission economic growth. Companies are intensifying their efforts in utilizing renewable energy sources, implementing significant photovoltaic projects, and advancing technologies related to wind and hydrodynamic energy. Within this trend, microgrids become a crucial element, enabling efficient management of local energy sources. Contemporary energy companies also focus on innovative digital technologies, harnessing the potential of the Internet of Things (IoT) and artificial intelligence (AI). These tools allow for precise monitoring and optimization of energy consumption, resulting in increased operational efficiency. The expansion of subscription-based energy services encompasses not only traditional energy deliveries but also new aspects, such as intelligent management of home energy installations or the provision of advisory services on energy conservation. This approach emphasizes the customer as a partner in sustainable energy usage. Hybrid energy models, integrating diverse energy sources, constitute a key element in the transformation of the sector. The combination of photovoltaic, wind, and traditional power plants allows for flexible adaptation to changing conditions and maintains stability in the energy supply. In the face of a changing energy landscape, companies consistently strive for sustainable practices, implementing strategies that not only reduce their carbon footprint but also contribute to improving efficiency, ecology, and the decentralization of the energy system. Adapting to these dynamic changes becomes not only a challenge but also an opportunity to create a more sustainable energy future. The objective of this research is to analyze key business models in the energy market and identify their impact on operational efficiency and market competitiveness. The main results indicate significant improvements in energy management and sustainability through the adoption of these models.
Does Climate Change and Energy Consumption Affect the Food Security of European Union Countries? Empirical Evidence from a Panel Study
Authors: Łącka Irena , Suproń Błażej , Szczepaniak Iwona
Published: July 2024

This study aims to provide empirical evidence on whether using renewable and non-renewable energy, CO2 emissions, and the resulting changes in temperature and precipitation can alter cereal yields in European Union countries. To this end, a panel data set covering 1992–2021 has been used to analyse the impact of climate change and economic factors on cereal production in European Union countries. The study employed various analytical techniques to better understand the impact of climate change and economic factors on cereal crop yields. These included using annual average temperature and rainfall to measure climate change, energy consumption, and economic growth to analyse the economic aspects. To achieve the objective, feasible generalised least squares (FGLS) models with robust standard errors and bootstrap and a quantile regression (QR) model with marginal analysis were employed. The Westerlund cointegration test confirmed the presence of cointegration between cereal production and climate variables, economic growth, and energy consumption. The results indicate that increases in temperature and precipitation may contribute to increased cereal production in European Union countries. In contrast, energy consumption, including renewable energy and CO2 emissions, may have a nonlinear effect on cereal production. Quantile regression analysis suggests climate change may impact crop yields most in countries with low and moderate cereal production per hectare. Increased CO2 emissions may increase yields in the initial period, but excessive CO2 may negatively impact cereal production in the long term. It would be advisable for countries with low and moderate crop productivity to consider implementing technological advances and combating global warming by modernising cultivation methods and making greater use of renewable energy sources.
Navigating Environmental Concerns: Assessing the Ecological Footprint of Photovoltaic-Produced Energy
Authors: Falfushynska Halina
Published: July 2024

The European Union’s Green Deal concept prioritizes the installation of photovoltaic and wind turbine systems, with the aim of significantly reducing greenhouse gas emissions and expanding the use of renewable energy. The inclusion of metals/metaloids such as Cd, Pb, Ni, and As to PV panels may be a matter of concern because they may provoke numerous negative environmental effects, especially after decommissioning. Although the release of Pb and Cd from solar panels is generally low, these releases may increase, posing long-term harm. Cd and Pb, if only released from solar panels, can enter the environment, including soil and water, posing a significant risk to human health and ecosystems. Cd, in particular, can have profound and lasting negative impacts on animals and humans, affecting cellular responses, enzyme operations, and immune system functionality. Pb exposure, in turn, can induce oxidative stress and neurotoxicity, disrupt ion regulatory pathways, and impair immune function. Despite efforts to reduce the release of toxic metals from PV panels, controlling their disposal and avoiding environmental contamination remains challenging. Discovering substitute materials for PV panel manufacture, implementing enhanced recycling procedures, performing bioremediation, and enforcing stronger restrictions are among the strategies to mitigate environmental concerns.
Physiological Response to Low-Temperature Stress and Cold Resistance Evaluation of Ziziphus jujuba var. spinosa Clones from Different Provenances
Authors: Li Biao , Zhang Yuncheng , Kang Ying , Wang Yijin , Liu Ruilin , Liu Qingbai , Dong Shengjun
Published: July 2024

To investigate the low-temperature adaptability of different provenances of Ziziphus jujuba var. spinosa, we used 21 clones from seven provenances as experimental materials and observed the changes in physiological and biochemical indicators and the characteristics of anatomical structures under low-temperature stress. A comprehensive evaluation of their cold resistance was conducted using the membership function method. As the temperature decreased, the relative electrical conductivity (REC) of clone 89 became stable and had the lowest LT50 value (−44.04 °C). The cold-resistant Z. jujuba var. spinosa had a higher bound water/free water (BW/FW) ratio and antioxidant enzyme activity and accumulated large quantities of osmotic regulatory substances. Higher xylem, phloem, and xylem–cortex ratios and greater conduit density enhanced the cold resistance of Z. jujuba var. spinosa. The membership function values of clones 89, 90, 91, 604, and 612 were greater than 0.6, indicating that they could be evaluated as resources with the potential for low-temperature resistance. The cold resistance rankings for the different provenances were as follows: Kazuo, Liaoning > Jiaxian, Shaanxi > Fuxing, Heibei > Changqing, Shandong > Neiqiu, Heibei > Yanchuan, Shaanxi > Xiaxian, Shanxi. These results provide a scientific basis for the rapid and accurate identification of cold resistance in Z. jujuba var. spinosa resources and the breeding and cultivation of new cold-resistant varieties of this subspecies.
SIMCB-Yolo: An Efficient Multi-Scale Network for Detecting Forest Fire Smoke
Authors: Yang Wanhong , Yang Zhenlin , Wu Meiyun , Zhang Gui , Zhu Yinfang , Sun Yurong
Published: July 2024

Forest fire monitoring plays a crucial role in preventing and mitigating forest disasters. Early detection of forest fire smoke is essential for a timely response to forest fire emergencies. The key to effective forest fire monitoring lies in accounting for the various levels of forest fire smoke targets in the monitoring images, enhancing the model’s anti-interference capabilities against mountain clouds and fog, and reducing false positives and missed detections. In this paper, we propose an improved multi-level forest fire smoke detection model based on You Only Look Once v5s (Yolov5s) called SIMCB-Yolo. This model aims to achieve high-precision detection of forest fire smoke at various levels. First, to address the issue of low precision in detecting small target smoke, a Swin transformer small target monitoring head is added to the neck of Yolov5s, enhancing the precision of small target smoke detection. Then, to address the issue of missed detections due to the decline in conventional target smoke detection accuracy after improving small target smoke detection accuracy, we introduced a cross stage partial network bottleneck with three convolutional layers (C3) and a channel block sequence (CBS) into the trunk. These additions help extract more surface features and enhance the detection accuracy of conventional target smoke. Finally, the SimAM attention mechanism is introduced to address the issue of complex background interference in forest fire smoke detection, further reducing false positives and missed detections. Experimental results demonstrate that, compared to the Yolov5s model, the SIMCB-Yolo model achieves an average recognition accuracy (mAP50) of 85.6%, an increase of 4.5%. Additionally, the mAP50-95 is 63.6%, an improvement of 6.9%, indicating good detection accuracy. The performance of the SIMCB-Yolo model on the self-built forest fire smoke dataset is also significantly better than that of current mainstream models, demonstrating high practical value.
Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
Authors: Pieska Marcus , Kassler Andreas , Brunstrom Anna , Rakocevic Veselin , Amend Markus
Published: July 2024

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm.
Experimental Study on the Inerting Effect of Premixed Inert Gas of CO2 and N2 in Goaf
Authors: Shi Bobo , Zhao Jiaxing
Published: July 2024

As the conventional inert gas, it is used for the prevention and control of coal’s spontaneous combustion, mainly N2 and CO2. However, there is limited research focusing on the inerting effect of composite inert gas. This paper studied the impact of using a premixed inert gas (N2 accounted for 50%, 60%, 70%, and 80%) instead of CO2 on the inerting effect of nearly horizontal and gently inclined goaf by building a physical similarity simulation experiment platform. The experimental results showed that the inerting effect of premixed inert gas was better than that of CO2. For instance, in a nearly horizontal goaf, the inerting effect of the premixed inert gas was optimal when the N2 accounted for 70%. The average O2 concentration in the monitored area decreased from 9.7% with CO2 to 6.4%. In addition, in the gently inclined goaf, the premixed inert gas exhibited an accumulation state similar to CO2, primarily occurring in the lower part region adjacent to the working face. Furthermore, the accumulation state of premixed inert gas was inversely proportional to its inerting effect. This study has important reference significance for applying inert gas fire prevention and extinguishing technology in mines.
Effect of Pretreatments on the Chemical, Bioactive and Physicochemical Properties of Cinnamomum camphora Seed Kernel Extracts
Authors: Wang Pengbo , Wang Zhixin , Zhang Manqi , Yan Xianghui , Xia Jiaheng , Zhao Junxin , Yang Yujing , Gao Xiansi , Wu Qifang , Gong Deming , Yu Ping , Zeng Zheling
Published: July 2024

Cinnamomum camphora seed kernels (CCSKs) are rich in phytochemicals, especially plant extracts. Phytochemicals play a vital role in therapy due to their strong antioxidant and anti-inflammatory activities. Extracts from CCSK can be obtained through multiple steps, including pretreatment, extraction and purification, and the purpose of pretreatment is to separate the oil from other substances in CCSKs. However, C. camphora seed kernel extracts (CKEs) were usually considered as by-products and discarded, and their potential bioactive values were underestimated. Additionally, little has been known about the effect of pretreatment on CKE. This study aimed to investigate the effects of pretreatment methods (including the solvent extraction method, cold pressing method, aqueous extraction method and sub-critical fluid extraction method) on the extraction yields, phytochemical profiles, volatile compounds and antioxidant capacities of different CKE samples. The results showed that the CKE samples were rich in phenolic compounds (15.28–20.29%) and alkaloids (24.44–27.41%). The extraction yield, bioactive substances content and in vitro antioxidant capacity of CKE pretreated by the sub-critical fluid extraction method (CKE-SCFE) were better than CKEs obtained by other methods. CKE pretreated by the solvent extraction method (CKE-SE) showed the best lipid emulsion protective capacity. Moreover, the volatile substances composition of the CKE samples was greatly influenced by the pretreatment method. The results provided a fundamental basis for evaluating the quality and nutritional value of CKE and increasing the economic value of by-products derived from CCSK.
Behavior of Spoilage Bacterial Communities in Different Cuts of Enshi Black Pork under Refrigerated Storage (4 °C)
Authors: Zhang Ying , Yang Jiang , Peng Lijuan , Liao E , Wang Haibin
Published: July 2024

The Enshi black pig is a Chinese native breed known for its rich nutrition content and high quality, which has notable traction in the consumer market. In this study, the potential impact of the main commercial cuts from Enshi black pork carcasses (ham, loin, and belly) on the bacteria community of spoiled meat under refrigerated storage (4 °C) was assessed by using a high-throughput sequencing method. Moreover, the spoilage potential of isolated strains from spoiled pork was investigated. The results demonstrated significant differences (p < 0.05) in bacterial community diversity and composition among spoiled ham, loin, and belly samples. Linear discriminant analysis effect size (LEfSe) analysis revealed a total of 20 significantly different potential bacterial biomarkers, with the dominant genera of Pseudomonas, Psychrobacter, Shewanella and Carnobacterium. Additionally, C. divergens THT1-5, isolated from spoiled ham samples, displayed cold adaptation and higher spoilage potential in Enshi black pork. These findings are helpful for identifying key factors contributing to spoilage in Enshi black pork and developing strategies to inhibit bacterial growth during preservation.
Actor-Critic Neural-Network-Based Fractional-Order Sliding Mode Control for Attitude Tracking of Spacecraft with Uncertainties and Actuator Faults
Authors: Jing Chenghu , Ma Xiaole , Zhang Kun , Wang Yanfeng , Yan Bingsheng , Hui Yanbo
Published: July 2024

This paper investigates the attitude control of rigid spacecraft in the presence of uncertainties, disturbances, and actuator faults. In order to effectively address these challenges and improve the performance of the system, a novel actor-critic neural-network-based fractional-order sliding mode control (ACNNFOSMC) has been developed for spacecraft. The integration of actor-critic neural network, fractional-order theory, and sliding mode control enables dual functionality: the actor-critic neural network serves to approximate the aggregate of uncertain parameters, disturbances, and actuator faults, thereby facilitating their compensation, while the fractional-order sliding mode control mechanism significantly improves the system’s tracking precision and overall robustness against uncertainties. Theoretical analyses are presented to analyze the stability of the proposed control framework. Thorough examination via simulation experiments affirms the effectiveness and control precision of attitude of our proposed control strategy, even in complex operational scenarios.
Modeling and Control Research of Fractional-Order Cascaded H-Bridge Multilevel STATCOM
Authors: Xu Junhua , Tang Songqin , He Guopeng , Gong Zheng , Lin Guangqing , Liu Jiayu
Published: July 2024

This paper introduces fractional-order capacitors and fractional-order inductors into the conventional integer-order cascaded H-bridge multilevel static compensator (ICHM-STATCOM), thereby constructing the main circuit of the fractional-order cascaded H-bridge multilevel static compensator (FCHM-STATCOM). Mechanism-based modeling is employed to establish switching function models and low-frequency dynamic models for the FCHM-STATCOM in the three-phase stationary coordinate system (a-b-c). Subsequently, fractional-order rotating coordinate transformation is introduced to establish the mathematical model of the FCHM-STATCOM in the synchronous rotating coordinate system (d-q). Additionally, a fractional-order proportional-integral (FOPI)-based fractional-order dual closed-loop current decoupling control strategy is proposed. Finally, this paper validates the correctness of the established mathematical models through digital simulation. Moreover, the simulation results demonstrate that by appropriately selecting the order of fractional-order capacitors and fractional-order inductors, the FCHM-STATCOM exhibits superior dynamic and static characteristics compared to the conventional ICHM-STATCOM, and the FCHM-STATCOM provides a more flexible reactive power compensation solution for power systems.
What Chinese Women Seek in Mental Health Apps: Insights from Analyzing Xiaohongshu User Posts during the COVID-19 Pandemic
Authors: Qin Zhenzhen , Ng Sandy , Wu Wenqing , Zhang Suxin
Published: July 2024

Gender disparity poses a prominent obstacle to achieving effective mental health outcomes in digital healthcare. Despite women being more inclined to use mental health apps and seeking designs tailored to their specific needs, there is limited research on the factors influencing female users’ engagement with these apps. The COVID-19 pandemic has further exacerbated its disproportionate impact on women’s mental health. This study investigates female users’ posts (n = 5538) about mental health apps during the pandemic, using data collected via a Python web crawler from Xiaohongshu, a popular female-centric social media platform in China. A mixed-methods approach used qualitative thematic analysis and quantitative descriptive statistics. Among these posts, therapeutic functionality emerged as the highest priority, followed by credibility and user experience, with specific design elements highlighted as particularly significant. These findings provide valuable insights for mental health researchers and developers, including you, aiming to create gender-tailored mobile solutions to address the mental health challenges faced by women, especially during future pandemics.
Improving the Selection of PV Modules and Batteries for Off-Grid PV Installations Using a Decision Support System
Authors: Serrano-Gomez Luis , Gil-García Isabel C. , García-Cascales M. Socorro , Fernández-Guillamón Ana
Published: July 2024

In the context of isolated photovoltaic (PV) installations, selecting the optimal combination of modules and batteries is crucial for ensuring efficient and reliable energy supply. This paper presents a Decision Support System (DSS) designed to aid in the selection process of the development of new PV isolated installations. Two different multi-criteria decision-making (MCDM) approaches are employed and compared: AHP (Analytic Hierarchy Process) combined with TOPSIS (technique for order of preference by similarity to ideal solution) and Entropy combined with TOPSIS. AHP and Entropy are used to weight the technical and economic criteria considered, and TOPSIS ranks the alternatives. A comparative analysis of the AHP + TOPSIS and Entropy + TOPSIS methods was conducted to determine their effectiveness and applicability in real-world scenarios. The results show that AHP and Entropy produce contrasting criteria weights, yet TOPSIS converges on similar top-ranked alternatives using either set of weights, with the combination of lithium-ion batteries with the copper indium gallium selenide PV module as optimal. AHP allows for the incorporation of expert subjectivity, prioritising costs and an energy yield intuitive to PV projects. Entropy’s objectivity elevates criteria with limited data variability, potentially misrepresenting their true significance. Despite these discrepancies, this study highlights the practical implications of using structured decision support methodologies in optimising renewable energy systems. Even though the proposed methodology is applied to a PV isolated system, it can effectively support decision making for optimising other stand-alone or grid-connected installations, contributing to the advancement of sustainable energy solutions.
MKDAT: Multi-Level Knowledge Distillation with Adaptive Temperature for Distantly Supervised Relation Extraction
Authors: Long Jun , Yin Zhuoying , Han Yan , Huang Wenti
Published: July 2024

Distantly supervised relation extraction (DSRE), first used to address the limitations of manually annotated data via automatically annotating the data with triplet facts, is prone to issues such as mislabeled annotations due to the interference of noisy annotations. To address the interference of noisy annotations, we leveraged a novel knowledge distillation (KD) method which was different from the conventional models on DSRE. More specifically, we proposed a model-agnostic KD method, Multi-Level Knowledge Distillation with Adaptive Temperature (MKDAT), which mainly involves two modules: Adaptive Temperature Regulation (ATR) and Multi-Level Knowledge Distilling (MKD). ATR allocates adaptive entropy-based distillation temperatures to different training instances for providing a moderate softening supervision to the student, in which label hardening is possible for instances with great entropy. MKD combines the bag-level and instance-level knowledge of the teacher as supervisions of the student, and trains the teacher and student at the bag and instance levels, respectively, which aims at mitigating the effects of noisy annotation and improving the sentence-level prediction performance. In addition, we implemented three MKDAT models based on the CNN, PCNN, and ATT-BiLSTM neural networks, respectively, and the experimental results show that our distillation models outperform the baseline models on bag-level and instance-level evaluations.
Technological Acceptance of E-Commerce by Generation Z in Portugal
Authors: Duarte Carolina , Messias Inês , Oliveira Abílio
Published: July 2024

E-commerce allows consumers to make online purchases easier, faster and at any time, making it different from traditional commerce. This technology has grown recently and has become popular among younger generations. This is the case of Generation Z, a technological generation that will become the leading group of consumers in the coming years. Despite the positive results regarding this practice, the expected results for the Western world, a world of which Portugal is a part, have yet to be achieved. The main objective of this investigation was to understand the acceptance of e-commerce by Generation Z in Portugal. The nature of this study is quantitative and used the questionnaire for data collection based on the proposed conceptual model built through the literature review. The dimensions obtained were analysed using the SmartPLS 4 and IBM SPSS Statistics 26 tools. The results allowed responding to the proposed objectives to support or reject the defined hypotheses associated with the variables trust, perceived risk, perceived ease of use, attitude, perceived usefulness, intention to use, privacy and security. It was also possible to define the digital consumer profile of Generation Z in Portugal.
Damage Detection in External Tendons of Post-Tensioned Bridges
Authors: Tabiatnejad Dariya , Tabiatnejad Bardia , Khedmatgozar Dolati Seyed Saman , Mehrabi Armin
Published: July 2024

This study investigates damage detection in the tendons of post-tensioned segmental box girder bridges, focusing on the vibration-based technique and its application in conjunction with the Precursor Transformation Matrix (PTM). Due to the critical role of bridge tendons in structural integrity, efficient and timely detection methods are essential. The methodology combines theoretical modeling with Finite Element Method (FEM) simulations and empirical data collection to evaluate the PTM’s effectiveness in identifying tendon damage. Key results indicate that the PTM, when paired with vibration analysis, enhances the identification and localization of damage, proving to be a robust method in structural health monitoring. This approach not only speeds up damage detection but also potentially lowers maintenance costs by pinpointing specific damage sites, thereby preventing widespread structural failures. The findings offer a promising tool for ensuring the longevity and safety of post-tensioned bridges.
Cost Optimization of Graphene Oxide-Modified Ultra-High-Performance Concrete Based on Machine Learning Methods
Authors: Lv Hui , Du Mingfeng , Li Zijian , Xiao Li , Zhou Shuai
Published: July 2024

The use of carbon nanomaterials in ultra-high-performance concrete (UHPC) to improve its mechanical properties and durability is growing. Graphene oxide (GO) has emerged as one of the most promising nanomaterials in recent years for enhancing the properties of UHPC. The majority of research so far has been on the properties of UHPC enhanced with GO, but its high cost has limited its application in engineering. This work suggests a machine learning (ML)-based approach to optimize the mix ratio in order to lower the cost of graphene oxide-modified UHPC. To do this, an artificial neural network (ANN) is used to create the prediction model for the 28-day compressive strength and slump flow of UHPC. The performance of this model is then compared using nine different ML techniques. Subsequently, considering the restrictions of the UHPC component content, component proportion, and absolute volume, a genetic algorithm (GA) is adopted to lower the UHPC cost. The sensitivity analysis is carried out in the end. This study’s findings indicate that there is a decent degree of prediction accuracy since the difference between the ANN model’s predictions and the experimental outcomes is just 10%. The cost of UHPC optimized by GA is reduced to 776 $/m3, significantly lower than the average cost of UHPC.
Synthesis of TiO2/SBA-15 Nanocomposites by Hydrolysis of Organometallic Ti Precursors for Photocatalytic NO Abatement
Authors: El Atti Ons , Hot Julie , Fajerwerg Katia , Lorber Christian , Lebeau Bénédicte , Ryzhikov Andrey , Kahn Myrtil , Collière Vincent , Coppel Yannick , Ratel-Ramond Nicolas , Ménini Philippe , Fau Pierre
Published: July 2024

The development of advanced photocatalysts for air pollution removal is essential to improve indoor air quality. TiO2/mesoporous silica SBA-15 nanocomposites were synthesized using an organometallic decoration method, which leverages the high reactivity of Ti precursors to be hydrolyzed on the surface water groups of silica supports. Both lab-made Ti(III) amidinate and commercial Ti(IV) amino precursors were utilized to react with water-rich SBA-15, obtained through a hydration process. The hydrated SBA-15 and the TiO2/SBA-15 nanocomposites were characterized using TGA, FTIR, 1H and 29Si NMR, TEM, SEM, N2 physisorption, XRD, and WAXS. This one-step TiO2 decoration method achieved a loading of up to 51.5 wt.% of approximately 9 nm anatase particles on the SBA-15 surface. This structuring provided excellent accessibility of TiO2 particles for photocatalytic applications under pollutant gas and UV-A light exposure. The combination with the high specific surface area of SBA-15 resulted in the efficient degradation of 400 ppb of NO pollutant gas. Due to synergistic effects, the best nanocomposite in this study demonstrated a NO abatement performance of 4.0% per used mg of TiO2, which is 40% more efficient than the reference photocatalytic material TiO2 P-25.
An Effective Fluorescent Marker for Tracking the Dispersal of Small Insects with Field Evidence of Mark–Release–Recapture of Trissolcus japonicus
Authors: Paul Ryan L. , Hagler James R. , Janasov Eric G. , McDonald Nicholas S. , Voyvot Saliha , Lee Jana C.
Published: July 2024

Understanding insect dispersal helps us predict the spread of insect pests and their natural enemies. Dispersal can be studied by marking, releasing, and recapturing insects, known as mark–release–recapture (MRR). MRR techniques should be convenient, economical, and persistent. Currently, there are limited options for marking small parasitoids that do not impact their fitness and dispersal ability. We evaluated commercially available fluorescent markers used in forensics. These fluorophores can easily be detected by ultraviolet (UV) light, requiring minimal costs and labor to process the marked specimens. This fluorophore marking technique was evaluated with the pest Drosophila suzukii and three parasitoids: Trissolcus japonicus, Pachycrepoideus vindemiae, Ganaspis brasiliensis (=G. kimorum). We evaluated the persistence of the marks on all the insects over time and examined the parasitoids for impacts on longevity, parasitism, locomotor activity, and flight take-off. The green fluorophore marker persisted for over 20 days on all four species. Marking generally did not consistently reduce the survival, parasitism rate, locomotor activity, or take-off of the parasitoids tested. Marked T. japonicus were recaptured in the field up to 100 m away from the release point and three weeks after release, indicating that this technique is a viable method for studying parasitoid dispersal.
Combining the Optimized Maximum Entropy Model to Detect Key Factors in the Occurrence of Oedaleus decorus asiaticus in the Typical Grasslands of Central and Eastern Inner Mongolia
Authors: Ding Xiaolong , Du Bobo , Lu Longhui , Lin Kejian , Sa Rina , Gao Yang , Guo Jing , Wang Ning , Huang Wenjiang
Published: July 2024

Grasshoppers pose a significant threat to both natural grassland vegetation and crops. Therefore, comprehending the relationship between environmental factors and grasshopper occurrence is of paramount importance. This study integrated machine learning models (Maxent) using the kuenm package to screen MaxEnt models for grasshopper species selection, while simultaneously fitting remote sensing data of major grasshopper breeding areas in Inner Mongolia, China. It investigated the spatial distribution and key factors influencing the occurrence of typical grasshopper species in grassland ecosystems. The modelling results indicate that a typical steppe has a larger suitable area. The soil type, above biomass, altitude, and temperature, predominantly determine the grasshopper occurrence in typical steppes. This study explicitly delineates the disparate impacts of key environmental factors (meteorology, vegetation, soil, and topography) on grasshopper occurrence in typical steppes. Furthermore, it provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be instrumental in formulating future management measures to guarantee grass ecological environment security and the sustainable development of grassland.
Prognostic Value of Serum Galectin-3 for Survival in Patients with Cardiac Light-Chain Amyloidosis
Authors: Yang Xinglin , Huang Jin , Zhang Jinghong , Li Jian , Tian Zhuang
Published: July 2024

Background: Amyloid light-chain (AL) amyloidosis is a multisystem disorder, with cardiac amyloid infiltration being a prevalent manifestation. This study aimed to explore the prognostic value of galectin-3 (Gal-3), a soluble marker associated with fibrosis, inflammation, heart failure, and kidney injury, in patients with cardiac AL amyloidosis. Methods: A total of 60 patients who were diagnosed with cardiac AL amyloidosis from January 2015 to May 2018 were enrolled. The prognostic value of Gal-3 was assessed. Receiver operating characteristic (ROC) curves were used to evaluate the predictive accuracy of Gal-3. A Gal-3 cut-off value was identified to predict survival rates. Results: The ROC curves demonstrated a moderate predictive accuracy of Gal-3 for 0.5- and 5-year survival, with area under the curve (AUC) values of 0.722 and 0.788, respectively. A Gal-3 cut-off value of 15.154 ng/mL was found to predict survival. Kaplan–Meier survival analysis revealed a significant difference in mean overall survival between patients with Gal-3 levels below and above the established cut-off (69.2 months versus 42.1 months, respectively; p = 0.036). Multivariate analysis confirmed that Gal-3 > 15.154 ng/mL remained an independent predictor of survival (HR 2.451, 95% CI 1.017–5.910, p = 0.046). Conclusions: This study suggests that Gal-3 holds independent prognostic value for survival in patients with cardiac AL amyloidosis. Gal-3 could potentially enhance the prognostic capabilities of the current soluble markers, thereby improving the management of cardiac AL amyloidosis. However, further validation in larger prospective studies is warranted.
Impact of Obstructive Sleep Apnea and Sympathetic Nervous System on Cardiac Health: A Comprehensive Review
Authors: Maniaci Antonino , Lavalle Salvatore , Parisi Federica Maria , Barbanti Marco , Cocuzza Salvatore , Iannella Giannicola , Magliulo Giuseppe , Pace Annalisa , Lentini Mario , Masiello Edoardo , La Via Luigi
Published: July 2024

A prevalent condition linked to an elevated risk of cardiovascular disease is sleep apnea. This review examines the connections between cardiac risk, the sympathetic nervous system, and sleep apnea. The increased risk of hypertension, arrhythmias, myocardial infarction, and heart failure was highlighted in the pathophysiology of sleep apnea and its effect on sympathetic activation. It is also important to consider potential processes such as oxidative stress, inflammation, endothelial dysfunction, and autonomic imbalance that may relate sleep apnea-induced sympathetic activation to cardiac risk. With implications for creating innovative diagnostic and treatment approaches to lessen the cardiovascular effects of sleep apnea, the goal of this investigation is to improve the understanding of the intricate link between sympathetic activity, cardiac risk, and sleep apnea. This study aimed to clarify the complex relationship between cardiovascular health and sleep apnea by synthesizing the available research and highlighting the crucial role played by the sympathetic nervous system in moderating this relationship. Our thorough investigation may have important therapeutic ramifications that will direct the creation of focused therapies to enhance cardiovascular outcomes in sleep apnea sufferers.
Association of Abnormal Cardiac Biomarkers and Cardiovascular Complications, with Mortality in Patients with SARS-CoV-2 Infection in Latin America
Authors: Gómez-Mesa Juan Esteban , Escalante Manuela , Muñoz-Ordoñez Juan Andrés , Azcárate-Rodriguez Valeria , Peláez-Martínez Juan David , Arteaga-Tobar Andrea Alejandra , León-Giraldo Hoover , Valencia-Orozco Andrea , Perna Eduardo Roque , Romero Alexander , Mendoza Iván , Wyss Fernando , Barisani José Luis , Speranza Mario , Alarco Walter , Herrera Cesar , Lugo-Peña Julián , Cárdenas-Aldaz Liliana Patricia , Rossel Victor , Sierra Daniel
Published: July 2024

Background: The COVID-19 pandemic has highlighted a correlation between cardiac complications and elevated cardiac biomarkers, which are linked to poorer clinical outcomes. Objective: This study aims to determine the clinical impact of cardiac biomarkers in COVID-19 patients in Latin America. Subjects and methods: The CARDIO COVID 19-20 Registry is a multicenter observational study across 44 hospitals in Latin America and the Caribbean. It included hospitalized COVID-19 patients (n = 476) who underwent troponin, natriuretic peptide, and D-dimer tests. Patients were grouped based on the number of positive biomarkers. Results: Among the 476 patients tested, 139 had one positive biomarker (Group C), 190 had two (Group B), 118 had three (Group A), and 29 had none (Group D). A directly proportional relationship was observed between the number of positive biomarkers and the incidence of decompensated heart failure. Similarly, there was a proportional relationship between the number of positive biomarkers and increased mortality. In Group B, patients with elevated troponin and natriuretic peptide and those with elevated troponin and D-dimer had 1.4 and 1.5 times higher mortality, respectively, than those with elevated natriuretic peptide and D-dimer. Conclusions: In Latin American COVID-19 patients, a higher number of positive cardiac biomarkers is associated with increased cardiovascular complications and mortality. These findings suggest that cardiac biomarkers should be utilized to guide acute-phase treatment strategies.