bluuebunny/crossref_metadata_2025 huggingface dataset:
A Lightning Optical Automatic Detection Method Based on a Deep Neural Network
Authors: Wang Jialei , Song Lin , Zhang Qilin , Li Jie , Ge Quanbo , Yan Shengye , Wu Gaofeng , Yang Jing , Zhong Yuqing , Li Qingda
Published: March 2024

To achieve automatic recognition of lightning images, which cannot easily be handled using the existing methods and still requires significant human resources, we propose a lightning image dataset and a preprocessing method. The lightning image data over five months were collected using a camera based on two optical observation stations, and then a series of batch labeling methods were applied, which greatly reduced the workload of subsequent manual labeling, and a dataset containing more than 30,000 labeled samples was established. Considering that lightning varies rapidly over time, we propose a time sequence composite (TSC) preprocessing method that inputs lightning’s time-varying characteristics into a model for better recognition of lightning images. The TSC method was evaluated through an experiment on four backbones, and it was found that this preprocessing method enhances the classification performance by 40%. The final trained model could successfully distinguish between the “lightning” and “non-lightning” samples, and a recall rate of 86.5% and a false detection rate of 0.2% were achieved.
Multispectral Band-Aware Generation of Satellite Images across Domains Using Generative Adversarial Networks and Contrastive Learning
Authors: Mahara Arpan , Rishe Naphtali
Published: March 2024

Generative models have recently gained popularity in remote sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and, especially, variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces an adaptive approach that harnesses the capabilities of generative adversarial networks (GANs), augmented with contrastive learning, to generate target domain images that account for multispectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multispectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multispectral diversity.
Nature-Based Solutions vs. Human-Induced Approaches for Alpine Grassland Ecosystem: “Climate-Help” Overwhelms “Human Act” to Promote Ecological Restoration in the Three-River-Source Region of Qinghai–Tibet Plateau
Authors: Li Zhouyuan , Shen Qiyu , Fan Wendi , Dong Shikui , Wang Ziying , Xu Yudan , Ma Tianxiao , Cao Yue
Published: March 2024

How climate change and human activities drive the evolution of the regional environment and where the quality of ecosystems improve or decline over time have become widespread concerns. In this study, we took the Three-River-Source (TRS) region of the Qinghai–Tibet Plateau as a case, aiming to identify and quantify the contribution of the natural and anthropogenic factors to the ecosystem changes over the past years from 1980 to 2018 using the methods of remote sensing and spatial statistical analysis. Based on the land cover map interpreted by reference to satellite remote sensing imagery data, we defined the Ecological Restoration Area Proportion (ERAP) as the bare land patch decrement to indicate the ecologically restored quantity in space. Assembling the restoration project information, we digitalized and vectorized the ecological Restoration Intensity (RI) including the spatial range and temporal duration. Combining the ERAP and the net primary productivity (NPP), which indicates the quantity and quality of ecosystems, respectively, the ecological asset Index (EAI) was developed and calculated. Having integrated the datasets of the vegetation monitoring, climatic factors, geographical factors, and human activities, we performed multi-variable analysis of the attribution of how the change in the EAI, the NPP, and the EAI have been affected by these factors together. The NPP of the middle and eastern parts of the TRS region has improved the most, as the average growth rate of NPP reached approximately 2.5 kg C/m2/10a. Due to such dynamic pattern, we found that human-induced re-vegetation has made limited contributions in our multi-regression model as the variance explained by the RI merely amounts to 4.4% to 8.8%, while the changes were mostly dependent on the regional temperature and the precipitation which contributed over 45% to the ecological restoration on average. It was summarized that “climate-help” overwhelms “human act” in such alpine grassland ecosystem. The regression results for the different aspects of the ERAP and NPP demonstrated that the ecological restoration project helped most in regard to ecosystem quality improvement rather than the restored ecosystem quantity. Our study has developed a comprehensive assessment methodology that can be reused to account for more ecological asset. The case is an example of an alpine ecosystem in which the success of ecological restoration needs favorable climatic conditions as supporting evidence for the nature-based solution.
3-D Changes of Tropospheric O3 in Central and Eastern China Induced by Tropical Cyclones over the Northwest Pacific: Recent-Year Characterization with Multi-Source Observations
Authors: Jiang Yongcheng , Zhao Tianliang , Meng Kai , Cheng Xugeng , Lv Qiaoyi
Published: March 2024

In this study, the multi-year data of meteorology and O3 from remote sensing and ground observations are applied to characterize the 3-D changes of O3 in the troposphere over central and eastern China (CEC) induced by the tropical cyclones (TCs) in the tropical and subtropical ocean regions over Northwest Pacific. The CEC-regional average of near-surface O3 levels is significantly elevated with 6.0 ppb in the large coverage by the TCs in the subtropical ocean, while the TCs in the tropical ocean alter near-surface O3 weakly, indicating the latitudinal-located TCs in the subtropical offshore ocean could largely influence the O3 variations over CEC. The sub-seasonal change with the positive and negative anomalies of near-surface O3 is induced by the tropical TCs from June to July and from August to October. The peripheral circulation of TCs in the subtropical offshore ocean persistently enhances the O3 concentrations over CEC during the season of East Asian summer monsoons. The positive O3 anomalies maintain from the entire troposphere to the lower stratosphere over CEC in the peripheries of subtropical TCs, while the tropical TCs cause the positive O3 anomalies merely in the lower troposphere. The O3 transport and accumulation, photochemical production and stratospheric intrusion are climatologically confirmed as the major meteorological mechanisms of TCs affecting the O3 variations. This study reveals that the downward transport of stratospheric O3 of TCs in the subtropical ocean exerts a large impact on the atmospheric environment over CEC, while the regional O3 transport and photochemical productions dominate the lower troposphere over CEC with less impact of stratospheric intrusion from the TCs in the tropical ocean region. These results present the climatology of tropospheric O3 anomalies in China induced by the TCs over the Northwest Pacific with enhancing our comprehension of the meteorological impact on O3 variations over the East Asian monsoon region.
Ocean Satellite Data Fusion for High-Resolution Surface Current Maps
Authors: Kugusheva Alisa , Bull Hannah , Moschos Evangelos , Ioannou Artemis , Le Vu Briac , Stegner Alexandre
Published: March 2024

Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.
Radar-Jamming Decision-Making Based on Improved Q-Learning and FPGA Hardware Implementation
Authors: Zheng Shujian , Zhang Chudi , Hu Jun , Xu Shiyou
Published: March 2024

In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation.
Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks
Authors: Castro-Ospina Andrés Eduardo , Solarte-Sanchez Miguel Angel , Vega-Escobar Laura Stella , Isaza Claudia , Martínez-Vargas Juan David
Published: March 2024

Sound classification plays a crucial role in enhancing the interpretation, analysis, and use of acoustic data, leading to a wide range of practical applications, of which environmental sound analysis is one of the most important. In this paper, we explore the representation of audio data as graphs in the context of sound classification. We propose a methodology that leverages pre-trained audio models to extract deep features from audio files, which are then employed as node information to build graphs. Subsequently, we train various graph neural networks (GNNs), specifically graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs), to solve multi-class audio classification problems. Our findings underscore the effectiveness of employing graphs to represent audio data. Moreover, they highlight the competitive performance of GNNs in sound classification endeavors, with the GAT model emerging as the top performer, achieving a mean accuracy of 83% in classifying environmental sounds and 91% in identifying the land cover of a site based on its audio recording. In conclusion, this study provides novel insights into the potential of graph representation learning techniques for analyzing audio data.
Non-Contact Heart Rate Monitoring Method Based on Wi-Fi CSI Signal
Authors: Sun Juncong , Bian Xin , Li Mingqi
Published: March 2024

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies.
SPR and Double Resonance LPG Biosensors for Helicobacter pylori BabA Antigen Detection
Authors: Dyankov Georgi , Eftimov Tinko , Hikova Evdokiya , Najdenski Hristo , Kussovski Vesselin , Genova-Kalou Petia , Mankov Vihar , Kisov Hristo , Veselinov Petar , Ghaffari Sanaz Shoar , Kovacheva-Slavova Mila , Vladimirov Borislav , Malinowski Nikola
Published: March 2024

Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance in the study of H. pylori outer membrane proteins and their identified receptors. In the present study we assess the way in which outer membrane protein–cell receptor reactions are applicable in establishing a reliable diagnosis. Herein, as well as in other previous studies of ours, we explore the reliability of the binding reaction between the best characterized H. pylori adhesin BabA and its receptor, the blood antigen Leb. For the purpose we developed surface plasmon resonance (SPR) and double resonance long period grating (DR LPG) biosensors based on the BabA–Leb binding reaction for diagnosing H. pylori infection. In SPR detection, the sensitivity was estimated at 3000 CFU/mL—a much higher sensitivity than that of the RUT test. The DR LPG biosensor proved to be superior in terms of accuracy and sensitivity—concentrations as low as 102 CFU/mL were detected.
A Wireless Data Acquisition System Based on MEMS Accelerometers for Operational Modal Analysis of Bridges
Authors: Hasani Hamed , Freddi Francesco , Piazza Riccardo , Ceruffi Fabio
Published: March 2024

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system’s characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system’s performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter.
LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems
Authors: Papaioannou Alexios , Dimara Asimina , Kouzinopoulos Charalampos S. , Krinidis Stelios , Anagnostopoulos Christos-Nikolaos , Ioannidis Dimosthenis , Tzovaras Dimitrios
Published: March 2024

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.
Open-Ended Coaxial Probe for Effective Reconstruction of Biopsy-Excised Tissues’ Dielectric Properties
Authors: Canicattì Eliana , Fontana Nunzia , Barmada Sami , Monorchio Agostino
Published: March 2024

Dielectric characterization is extremely promising in medical contexts because it offers insights into the electromagnetic properties of biological tissues for the diagnosis of tumor diseases. This study introduces a promising approach to improve accuracy in the dielectric characterization of millimeter-sized biopsies based on the use of a customized electromagnetic characterization system by adopting a coated open-ended coaxial probe. Our approach aims to accelerate biopsy analysis without sample manipulation. Through comprehensive numerical simulations and experiments, we evaluated the effectiveness of a metal-coating system in comparison to a dielectric coating with the aim for replicating a real scenario: the use of a needle biopsy core with the tissue inside. The numerical analyses highlighted a substantial improvement in the reconstruction of the dielectric properties, particularly in managing the electric field distribution and mitigating fringing field effects. Experimental validation using bovine liver samples revealed highly accurate measurements, particularly in the real part of the permittivity, showing errors lower than 1% compared to the existing literature data. These results represent a significant advancement for the dielectric characterization of biopsy specimens in a rapid, precise, and non-invasive manner. This study underscores the robustness and reliability of our innovative approach, demonstrating the convergence of numerical analyses and empirical validation.
Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach
Authors: Calles-Esteban Francisco , Olmedo Alvaro Antonio , Hellín Carlos J. , Valledor Adrián , Gómez Josefa , Tayebi Abdelhamid
Published: March 2024

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.
Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms
Authors: Rodene Eric , Fernando Gayara Demini , Piyush Ved , Ge Yufeng , Schnable James C. , Ghosh Souparno , Yang Jinliang
Published: March 2024

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.
Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks
Authors: Choi Hyunseok , Lee Yoonhyeong , Kim Gayeong , Lee Euisin , Nam Youngju
Published: March 2024

Vehicular clouds represent an appealing approach, leveraging vehicles’ resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance.
Application of MOX Sensors to Determine the Emission of Volatile Compounds in Corn Groats as a Function of Vertical Pressure in the Silo and Moisture Content of the Bed
Authors: Rusinek Robert , Żytek Aleksandra , Stasiak Mateusz , Wiącek Joanna , Gancarz Marek
Published: March 2024

This study was focused on the analysis of the emission of volatile compounds as an indicator of changes in the quality degradation of corn groats with 14% and 17% moisture content (wet basis) using an electronic nose (Agrinose) at changing vertical pressure values. The corn groats were used in this study in an unconsolidated state of 0 kPa (the upper free layer of bulk material in the silo) and under a consolidation pressure of 40 kPa (approximately 3 m from the upper layer towards the bottom of the silo) and 80 kPa (approximately 6 m from the upper layer towards the bottom of the silo). The consolidation pressures corresponded to the vertical pressures acting on the layers of the bulk material bed in medium-slender and low silos. Chromatographic determinations of volatile organic compounds were performed as reference tests. The investigations confirmed the correlation of the electronic nose response with the quality degradation of the groats as a function of storage time. An important conclusion supported by the research results is that, based on the determined levels of intensity of volatile compound emission, the electronic nose is able to distinguish the individual layers of the bulk material bed undergoing different degrees of quality degradation.
Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach
Authors: Jamshidi Fatemeh , Ghiasi Mohammad , Mehrandezh Mehran , Wang Zhanle , Paranjape Raman
Published: March 2024

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.
Impact of Sexual Abuse on Post-Traumatic Stress Disorder in Children and Adolescents: A Systematic Review
Authors: Alves Ana Carolina , Leitão Maria , Sani Ana Isabel , Moreira Diana
Published: March 2024

Child sexual abuse (CSA), the most common type of maltreatment, is any action of a sexual nature by one or more adults towards a minor without the minor’s consent. This abuse represents one of the most damaging forms of trauma, has a severe impact on mental health and psychopathology, and can lead to several disorders, including post-traumatic stress disorder (PTSD). PTSD is characterized as a disorder that encompasses physical symptoms resulting from traumatic experiences that are experienced or witnessed by the victim. This systematic review aims to understand the impact of sexual abuse on post-traumatic stress disorder in children and adolescents. Studies focusing on the relationship between these two variables were obtained through multiple databases. Of the 940 documents collected, 24 were retained for further analysis and the objectives, methodologies, results, and main conclusions were registered. One of the main conclusions was that the earlier the abuse starts and the more severe and long-lasting it is, the symptomatology of PTSD will be aggravated and remain in the long term.
A Quantitative Analysis of the Need for High Conversion Efficiency PV Technologies in Carbon Mitigation Strategies
Authors: Hughes Kenneth M. , Phillips Chris C.
Published: March 2024

We consider the restrictions on photovoltaic (PV) capacity that are caused by limitations on where panels can be sited and find quantitative evidence for the need for high efficiencies. We define 15% of the UK’s energy consumption as a “significant” contribution and, with London as an exemplar, we perform an idealised calculation that makes the most optimistic possible assumptions about the capabilities of future PV technologies and use published surveys on energy usage, dwelling type and insolation. We find that covering every UK domestic roof with the highest power conversion efficiency (PCE) solar panels currently commercially available could produce up to 9% of the UK’s energy. A 15% contribution would require PV technologies with >37% PCE, more than the theoretical Shockley–Queisser limit. Replacing the idealising assumptions with more realistic estimates increases this by 2–3 times. Alternatively, a solar farm using the currently available PCEs would require a politically challenging ~1200 km2 of new land, roughly the area of Greater London, for this 15% contribution. We conclude that PCEs must be driven higher than even the Shockley–Queisser limit for PV to play a significant part in carbon mitigation.
Rain-Based Train Washing: A Sustainable Approach to Reduce PM Concentrations in Underground Environments
Authors: Cartenì Armando , Cascetta Furio , Falanga Antonella , Picone Mariarosaria
Published: March 2024

Fine particle concentrations measured in many underground rail systems around the world consistently exceed those observed at ground level, potentially posing significant implications for human health. While numerous authors have observed these high particle concentrations and analyzed both their atomic compositions and health impacts, few have investigated devices and technologies capable of reducing these high levels in underground environments. In light of these considerations and recognizing the multifaceted challenges associated with maintaining air quality in underground metro systems, the aim of this paper was to evaluate the usefulness and effectiveness of utilizing rainwater for washing trains to abate particulate matter (PM) concentrations in underground rail systems. To achieve this aim, an ad hoc case study was considered: the Naples Metro Line 1 (Italy), which is characterized by 4.5 km in the ground level and 13.5 km underground. A measurement campaign was carried out during storms of strong intensity through PM measuring instruments placed on station platforms along the metro line. Precisely, the trains were washed by the rain in the initial ground level section, and then continued wet within the underground one. The results of this measurement campaign were compared with those of a comparable survey carried out during average clear weather conditions, and the results showed that the train washing produces a significant PM10 concentration reduction of up to about 60% in the underground environment. If confirmed in other experimental settings, these results could lay the groundwork for the introduction of structured washing system devices (e.g., periodically washing trains and/or tunnels) for the reduction of PM concentration in underground metro systems. The present study sought to contribute valuable insights towards sustainable and environmentally conscious approaches to addressing air quality concerns, particularly by harnessing the natural resource of rainwater during specific meteorological events.
Corporate Social Responsibility Trends in the Airline Industry: A Bibliometric Analysis
Authors: Sorsa Kaisa , Bona-Sánchez Carolina
Published: March 2024

The aim of this study is to perform a bibliometric analysis of corporate social responsibility (CSR) research in the airline industry, underscoring current developments and future trends. Utilizing open-source R software (version 4.2.3), including the Bibliometrix R library (version 4.1.4) and VOSviewer (version 1.6.20), this study notes a significant rise in CSR research. It highlights influential studies, leading scholars, and key journals in the field. The co-word analysis shows CSR’s impact on efficiency, value, employee perceptions, and customer loyalty. Post-COVID trends indicate an expanded focus on health, safety, and environmental, social, and governance (ESG) factors. The research suggests a shift towards integrated CSR strategies in the airline industry, emphasizing sustainability, stakeholder inclusion, and transparent reporting. This shift marks a movement towards more comprehensive and effective CSR approaches in stakeholder communication.
Advances in Sustainability Research from the University of Oradea
Authors: Bungău Constantin , Badulescu Alina , Ilieș Dorina Camelia , Vesa Cosmin Mihai , Tit Delia Mirela
Published: March 2024

Interest in the topic of sustainability has been constantly increasing across all domains, among scholars, policy makers, and practitioners all over the world [...]
Is Alexa Happy or Angry? Perceptions and Attributions of Emotional Displays of Smart Technologies in Residential Homes
Authors: Barber Hayden , Reimer Torsten , Zhang Damin , Rayz Julia
Published: March 2024

Digital assistants such as Alexa can provide feedback to residents that affect energy consumption. One important characteristic of feedback refers to the emotionality of the provided feedback. Research on social cognition and attribution theory suggests that effects of emotional messages on behavior are contingent on the inferred cause of the emotion (e.g., why a message was said in a happy or neutral voice). As a prerequisite, to have the intended effects on energy saving behaviors, Alexa’s emotional messages have to trigger three basic social cognitions: (1) the emotional display has to be identified by residents; (2) residents have to correctly identify their behavior as a target of the emotional display; and (3) residents have to attribute the emotional display to that behavior. In two studies (N = 194 and N = 353), several conditions were identified that triggered these three basic social cognitions in a simulated environment.
Can Internet Use Narrow the Gap between Farmers’ Willingness and Behavior in Waste Classification? Empirical Evidence from Rural Areas in Jiangsu Province, China
Authors: Cui Wenchao , Chen Yanjun , Zeng Hengyuan
Published: March 2024

Household waste classification and treatment are important for environmental protection and sustainable development. The Logit model is used to analyze differences in farmers’ willingness and behavior regarding waste classification based on data from the China Land Economic Survey. Key findings include the following: (1) There is an evident discrepancy between waste classification willingness and action among rural residents. Despite nearly 90% of the sampled farmers expressing a willingness, nearly 40% do not practice waste classification. (2) Internet usage significantly reduces the discrepancy between farmers’ willingness and actual waste classification practices. This observation is valid even after robustness checks and endogeneity discussions. (3) There is a partial mediating effect between ecological awareness and knowledge perception on farmers’ willingness and behavior differences, which accounts for 12.9% and 52.6%, respectively, of the total impact. Notably, institutional limitations amplify the negative influence of Internet use on this discrepancy. (4) According to heterogeneity analysis, Internet use has a greater negative impact on farmers’ willingness and behavior in waste classification in suburban villages and villages with environmental governance projects. This study proposes policy suggestions such as strengthening the construction of digital infrastructure in rural areas, enhancing the promotion and training of waste classification among farmers, and improving incentives and restraint mechanisms for rural household waste classification.
Microplastic Occurrence Characteristics and Ecological Risk Assessment of Urban River in Cold Regions during Ice-Covered Periods
Authors: Chen Lei , Liao Yingfei , Zeng Kangjun , Wu Yining , Li Yongliang , Wang He
Published: March 2024

This paper focuses on the Harbin section of the Songhua River in the cold region of northern China. The occurrence characteristics and pollution level of microplastics (MPs) are explored in both the ice and water of urban inland rivers and estuaries during the ice-covered periods. The abundance of MPs in Majiagou ice averaged 324.4 ± 261.5 particles/L, and the abundance of MPs in Songhua ice averaged 65.0 ± 68.2 particles/L. In the water with different depths of Songhua, the MP abundance ranged from 1.0 ± 0.7 particles/L to 12.9 ± 9.4 particles/L, with an average of 5.6 ± 7.6 particles/L. The amount of MPs in ice is about 11 times that in water, where ice formation is mainly responsible for the decline in the MP abundance in aquatic environments. The abundance of MPs in urban inland rivers gradually increased from south to north, while that in the mainstream of the Songhua River showed an increasing trend from east to west. Detected MPs were mainly fiber and white in shape and color, respectively, with a particle size < 0.5 mm. The extent of microplastics in ice is greater than that in water, and melting in the following spring will exacerbate the environmental impact. The results identified the discharge of domestic sewage as the main source of MPs in urban inland rivers. Polyethylene, polypropylene, and polyacrylonitrile were the main types of polymers. The results of the ecological risk assessment showed that the MP pollution in the Harbin section of the Songhua River reached moderate and severe pollution levels during the ice-covered periods. Its potential risk should receive more attention, and control should be strengthened.
Exploring the Influences of Safety and Energy Expenditure Parameters on Cycling
Authors: Cappelli Giuseppe , D’Apuzzo Mauro , Nardoianni Sofia , Nicolosi Vittorio
Published: March 2024

Several determinants affect the reason to cycle or not, and some of them are described in a detailed way in the current technical literature review. The recent spread of new modes of active mobility brings up questions for urban transport planners on how to foresee future demand and assess safety conditions; from this comes the need to explore the relationships among several determinants. In this paper, after the collection of the main data required, three Regression Models are proposed, which demonstrate evidence for the role of safety and energy expenditure issues as important predictors. The method is applied to a dataset of 90 Italian cities selected according to their class of dimensionality and geographical position. The three models for each class of dimensionality (50,000–100,000 no. of inhabitants, 10,000–50,000 no. of inhabitants, and 0–10,000 no. of inhabitants) show a good accuracy (in terms of adj-R2 values of 0.6991, 0.7111, and 0.6619, respectively). The results show that energy expenditure, which is related to the terrain characteristics of an urban area and individual aerobic abilities, and safety perception, which is related to cycle network extensions, appear to be significant determinants in predicting bicycle modal share. The aim is to provide a useful and simplified tool, when only aggregated-type data are available, to help urban road designers and city planners in identifying and forecasting bike-sharing.
Multi-Dimensional Impacts of Climate Change on China’s Food Security during 2002–2021
Authors: Zhou Huanhuan , Cao Ning , Yang Lihua , Xu Jianjun
Published: March 2024

Climate change poses a significant threat to food security, necessitating a thorough examination across multiple dimensions. Establishing appropriate food security evaluation indicators that align with the evolving concept of food security is imperative. This study enhances food security evaluation by designing a multi-dimensional framework and analyzing the impact of climate variations across various regions from 2002 to 2021. By constructing a food security evaluation system that encompasses the production quantity and quality, sustainability, affordability, and resources, and utilizing the entropy method for accurate weighting, the impacts of climate variations on food security are accessed using a climate–economic model. The food security structure in China largely mirrors the regional division of grain, with the production quantity being the primary contributor. Overall, China’s food security has generally demonstrated improvements across various dimensions, with the exception of production quality. Regarding climate change, which encompasses variations in mean states and climate extremes, the panel regression analysis uncovers a negative linear relationship between food security and temperature. Conversely, the impact of precipitation on food security is non-linear, manifesting as inverse U-shaped patterns. In regions with balanced production and consumption, both accumulated temperatures and extreme high temperatures have a negative linear effect on food security. On the other hand, both accumulated and extreme precipitation exhibit inverse U-shaped non-linear impacts on food security in the main production and main consumption regions. These findings highlight the intricate interplay between climate change, regional disparities, and food security in China, emphasizing the need to consider multi-dimensional factors and regional variations in addressing food security challenges. These insights are invaluable for policymaking and planning aimed at enhancing food security in China.
Digital Twins within the Circular Economy: Literature Review and Concept Presentation
Authors: Mügge Janine , Seegrün Anne , Hoyer Tessa-Katharina , Riedelsheimer Theresa , Lindow Kai
Published: March 2024

Digital twins offer a promising approach to sustainable value creation by providing specific life cycle data and enabling the monitoring and implementation of circular economy strategies throughout the product’s life cycle. By analyzing product, component, and material data, as well as process data, it is possible to create transparency throughout a product’s life cycle, build a data-driven product ecosystem, and establish new business and value creation models, from SMEs to large enterprises. This paper identifies application scenarios, their technological readiness level, and the challenges of digital twins for the circular economy in the manufacturing industry based on a systematic literature review. Gaps such as ensuring a continuous flow of information and taking into account the different levels of digitalization of companies are identified. As a main result, a holistic concept for the scoping of a digital twin for the circular economy is presented. One specific use case for end-of-life decision-making is elaborated upon. It is shown that the circular economy can be supported by digital twin data, especially for the optimal decision on end-of-life vehicles.
Pyrolysis as a Method for Processing of Waste from Production of Cultivated Tobacco (Nicotiana tabacum L.)
Authors: Saletnik Bogdan , Fiedur Marcin , Kwarciany Radosław , Zaguła Grzegorz , Bajcar Marcin
Published: March 2024

Because of the current energy crisis, researchers are looking into new potential substrates for production of biofuels and for possible ways to enhance their parameters. In line with such efforts, the current study focuses on the feasibility of processing waste from the production of cultivated tobacco. The aim of this study was to assess the potential of tobacco waste as a raw material for the production of solid biofuels, such as biochar produced through pyrolysis, and to determine its basic physicochemical properties, compared to other materials used for the production of green fuels. The analyses showed calorific values of 16.16 MJ kg−1 for the raw biomass and those in the range of 24.16–27.32 MJ kg−1 for the products of pyrolysis conducted at temperatures of 400–500 °C and with a heating time in the range of 5 to 15 min. To address the safety-related issues, the study also measured the explosion index (Kst max), which, in the raw biomass, amounted to 72.62 bar s−1 and in the biochar was in the range between 82.42 and 88.11 bar s−1. The registered maximum explosion pressure was 7.37 bar in the case of raw biomass, whereas in the biochars, the value ranged from 8.09 to 8.94 bar. The findings show that tobacco waste has parameters comparable to those identified in the case of other solid biofuels, whereas the process of pyrolysis enhances the energy-related parameters without increasing the explosion class of the product.
Socio-Economic Dynamics Inhibiting Inclusive Urban Economic Development: Implications for Sustainable Urban Development in South African Cities
Authors: Kajiita Robert Mutemi , Kang’ethe Simon Murote
Published: March 2024

Globally, the need for sustainable urban development has never been more urgent than it is now. This is because estimates indicate that more than half of the global population will be living in urban areas by 2050, with South Africa estimated at 80%. This paper focuses on socio-economic nuances that impede sustainable urban development, as well as the strategies that can accelerate sustainable and inclusive urban development in South Africa. Through a literature-review methodology, the analysis identified the following issues as impediments to sustainable urban development in South Africa: informality, lack of consultations with urban citizens, unresolved apartheid legacies, crime and insecurity, and migration intricacies in South Africa. The paper also establishes prospective strategies to accelerate sustainable urban development, such as working with informality and not against it, promoting inclusivity and access to resources, accelerating policy reforms, investing in rural towns, and investing in superior technology and innovation. These findings imply that there is a need to seek alternative development models and approaches, to address poverty, unemployment, and poor housing, aspects that negatively impact urban development planning and sustainable development. The paper recommends inclusive urban housing as a remedy to address the challenges of access, affordability, and the quality of housing facilities in urban areas.