bluuebunny/crossref_metadata_2025 huggingface dataset:
Accommodation and Compromise in the Contact Zone: Christianity and Chinese Culture in Modern Hong Kong Literature
Authors: Yang Yi
Published: May 2024

Situated in the unique historical context of Hong Kong—a contact zone between East and West—this study explores how Christianity’s introduction through British colonialism and missionary efforts has intertwined with and influenced Chinese cultural traditions. By examining selected works of Xu Dishan and Chen Zanyi, this study reveals the dynamic negotiations of identity and values between these two cultural and religious traditions. These literary works not only depict the complexities of cultural hybridity but also provide insights into the evolving nature of cultural identity in Hong Kong, illustrating how global religions and local traditions can merge and transform each other. This study contributes to understanding the intricate dance of religious exchange, conflict, and compromise in Hong Kong’s cross-culture setting, suggesting that such literary explorations can bridge Christianity with the socio-economic, cultural, and historical fabric of Chinese society.
Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass
Authors: Fan Wen , Tian Jiaojiao , Knoke Thomas , Yang Bisheng , Liang Fuxun , Dong Zhen
Published: May 2024

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.
Improvement of Qualitative Analyses of Aliphatic Alcohols Using Direct Catalytic Fuel Cell and Chemometric Analysis Format
Authors: Tomassetti Mauro , Marini Federico , Pezzilli Riccardo , Castrucci Mauro , Di Natale Corrado , Campanella Luigi
Published: May 2024

Direct catalytic methanol fuel cells (DCMFCs) have been studied for several years for energy conversion. Less extensive is the investigation of their analytical properties. In this paper, we demonstrate that the behavior of both the discharge and charger curves of DCMFCs depends on the chemical composition of the solution injected in the fuel cell. Their discharge and charge curves, analyzed using a chemometric data fusion method named ComDim, enable the identification of various types of aliphatic alcohols diluted in water. The results also show that the identification of alcohols can be obtained from the first portion of the discharge and charge curves. To this end, the curves have been described by a set of features related to the slope and intercept of the initial portion of the curves. The ComDim analysis of this set of features shows that the identification of alcohols can be obtained in a time that is about thirty times shorter than the time taken to achieve steady-state voltage.
Three-Dimensional ERT Advanced Detection Method with Source-Position Electrode Excitation for Tunnel-Boring Machines
Authors: Zhao Shuanfeng , Liu Bo , Ren Bowen , Wang Li , Luo Zhijian , Yao Jian , Bai Yunrui
Published: May 2024

Tunnel-boring machines (TBMs) are widely used in urban underground tunnel construction due to their fast and efficient features. However, shield-tunnel construction faces increasingly complex geological environments and may encounter geological hazards such as faults, fracture zones, water surges, and collapses, which can cause significant property damage and casualties. Existing geophysical methods are subject to many limitations in the shield-tunnel environment, where the detection space is extremely small, and a variety of advanced detection methods are unable to meet the required detection requirements. Therefore, it is crucial to accurately detect the geological conditions in front of the tunnel face in real time during the tunnel boring process of TBM tunnels. In this paper, a 3D-ERT advanced detection method using source-position electrode excitation is proposed. First, a source-position electrode array integrated into the TBM cutterhead is designed for the shield-tunnel construction environment, which provides data security for the inverse imaging of the anomalous bodies. Secondly, a 3D finite element tunnel model containing high- and low-resistance anomalous bodies is established, and the GREIT reconstruction algorithm is utilized to reconstruct 3D images of the anomalous body in front of the tunnel face. Finally, a physical simulation experiment platform is built, and the effectiveness of the method is verified by laboratory physical modeling experiments with two different anomalous bodies. The results show that the position and shape of the anomalous body in front of the tunnel face can be well reconstructed, and the method provides a new idea for the continuous detection of shield construction tunnels with boring.
Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges
Authors: O’Leary Aisling , Lahey Timothy , Lovato Juniper , Loftness Bryn , Douglas Antranig , Skelton Joseph , Cohen Jenna G. , Copeland William E. , McGinnis Ryan S. , McGinnis Ellen W.
Published: May 2024

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.
Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders
Authors: Bampoula Xanthi , Nikolakis Nikolaos , Alexopoulos Kosmas
Published: May 2024

The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model.
Development of an NO2 Gas Sensor Based on Laser-Induced Graphene Operating at Room Temperature
Authors: Soydan Gizem , Ergenc Ali Fuat , Alpas Ahmet T. , Solak Nuri
Published: May 2024

A novel, in situ, low-cost and facile method has been developed to fabricate flexible NO2 sensors capable of operating at ambient temperature, addressing the urgent need for monitoring this toxic gas. This technique involves the synthesis of highly porous structures, as well as the specific development of laser-induced graphene (LIG) and its heterostructures with SnO2, all through laser scribing. The morphology, phases, and compositions of the sensors were analyzed using scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and Raman spectroscopy. The effects of SnO2 addition on structural and sensor properties were investigated. Gas-sensing measurements were conducted at room temperature with NO2 concentrations ranging from 50 to 10 ppm. LIG and LIG/SnO2 sensors exhibited distinct trends in response to NO2, and the gas-sensing mechanism was elucidated. Overall, this study demonstrates the feasibility of utilizing LIG and LIG/SnO2 heterostructures in gas-sensing applications at ambient temperatures, underscoring their broad potential across diverse fields.
IDAC: Federated Learning-Based Intrusion Detection Using Autonomously Extracted Anomalies in IoT
Authors: Ohtani Takahiro , Yamamoto Ryo , Ohzahata Satoshi
Published: May 2024

The recent rapid growth in Internet of Things (IoT) technologies is enriching our daily lives but significant information security risks in IoT fields have become apparent. In fact, there have been large-scale botnet attacks that exploit undiscovered vulnerabilities, known as zero-day attacks. Several intrusion detection methods based on network traffic monitoring have been proposed to address this issue. These methods employ federated learning to share learned attack information among multiple IoT networks, aiming to improve collective detection capabilities against attacks including zero-day attacks. Although their ability to detect zero-day attacks with high precision has been confirmed, challenges such as autonomous labeling of attacks from traffic information and attack information sharing between different device types still remain. To resolve the issues, this paper proposes IDAC, a novel intrusion detection method with autonomous attack candidate labeling and federated learning-based attack candidate sharing. The labeling of attack candidates in IDAC is executed using information autonomously extracted from traffic information, and the labeling can also be applied to zero-day attacks. The federated learning-based attack candidate sharing enables candidate aggregation from multiple networks, and it executes attack determination based on the aggregated similar candidates. Performance evaluations demonstrated that IDS with IDAC within networks based on attack candidates is feasible and achieved comparable detection performance against multiple attacks including zero-day attacks compared to the existing methods while suppressing false positives in the extraction of attack candidates. In addition, the sharing of autonomously extracted attack candidates from multiple networks improves both detection performance and the required time for attack detection.
Designing a Novel Hybrid Technique Based on Enhanced Performance Wideband Millimeter-Wave Antenna for Short-Range Communication
Authors: Islam Tanvir , Hussain Dildar , Alsunaydih Fahad N. , Alsaleem Fahd , Alhassoon Khaled
Published: May 2024

This paper presents the design of a performance-improved 4-port multiple-input–multiple-output (MIMO) antenna proposed for millimeter-wave applications, especially for short-range communication systems. The antenna exhibits compact size, simplified geometry, and low profile along with wide bandwidth, high gain, low coupling, and a low Envelope Correlation Coefficient (ECC). Initially, a single-element antenna was designed by the integration of rectangular and circular patch antennas with slots. The antenna is superimposed on a Roger RT/Duroid 6002 with total dimensions of 17 × 12 × 1.52 mm3. Afterward, a MIMO configuration is formed along with a novel decoupling structure comprising a parasitic patch and a Defected Ground Structure (DGS). The parasitic patch is made up of strip lines with a rectangular box in the center, which is filled with circular rings. On the other side, the DGS is made by a combination of etched slots, resulting in separate ground areas behind each MIMO element. The proposed structure not only reduces coupling from −17.25 to −44 dB but also improves gain from 9.25 to 11.9 dBi while improving the bandwidth from 26.5–30.5 GHz to 25.5–30.5 GHz. Moreover, the MIMO antenna offers good performance while offering strong MIMO performance parameters, including ECC, diversity gain (DG), channel capacity loss (CCL), and mean effective gain (MEG). Furthermore, a state-of-the-art comparison is provided that results in the overperforming results of the proposed antenna system as compared to already published work. The antenna prototype is also fabricated and tested to verify software-generated results obtained from the electromagnetic (EM) tool HFSS.
Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review
Authors: Bolpagni Marco , Pardini Susanna , Dianti Marco , Gabrielli Silvia
Published: May 2024

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.
Sensor Fault Reconstruction Using Robustly Adaptive Unknown-Input Observers
Authors: Huang Qiang , Gao Zhi-Wei , Liu Yuanhong
Published: May 2024

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.
Refinement of Different Frequency Bands of Geomagnetic Vertical Intensity Polarization Anomalies before M > 5.5 Earthquakes
Authors: Faheem Haris , Li Xia , Zhu Weiling , Ji Yingfeng , Feng Lili , Zhu Ye
Published: May 2024

Geomagnetic vertical intensity polarization is a method with a clear mechanism, mature processing methods, and a strong ability to extract anomalous information in the quantitative analysis of seismogenic geomagnetic disturbances. The existing analyses of geomagnetic vertical intensity polarization are all based on the 5~100 s frequency band without refinement of the partitioning process. Although many successful results have been obtained, there are still two problems in the process of extracting anomalies: the geomagnetic anomalies that satisfy the determination criteria are still high in occurrence frequency; and the anomalies are distributed over too large an area in space, which leads to difficulties in determining the location of the epicenter. In this study, based on observations from western China, where fluxgate observation points are positioned in areas with frequent, densely distributed medium-strength earthquakes, we refined the frequency bands of geomagnetic vertical intensity polarization, recalculated the spatial and temporal evolution characteristics of geomagnetic disturbances before earthquakes, and improved the crossover frequency anomaly prediction index while promoting the application of the method in earthquake forecasting.
A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks
Authors: Lorincz Josip , Kukuruzović Amar , Blažević Zoran
Published: May 2024

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks’ energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks’ energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices’ energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.
Examining the Effects of Altitude on Workload Demands in Professional Basketball Players during the Preseason Phase
Authors: Ibáñez Sergio J. , Gómez-Carmona Carlos D. , González-Espinosa Sergio , Mancha-Triguero David
Published: May 2024

Basketball involves frequent high-intensity movements requiring optimal aerobic power. Altitude training can enhance physiological adaptations, but research examining its effects in basketball is limited. This study aimed to characterize the internal/external workload of professional basketball players during preseason and evaluate the effects of altitude and playing position. Twelve top-tier professional male basketball players (Liga Endesa, ACB; guards: n = 3, forwards: n = 5, and centers: n = 4) participated in a crossover study design composed of two training camps with nine sessions over 6 days under two different conditions: high altitude (2320 m) and sea level (10 m). Internal loads (heart rate, %HRMAX) and external loads (total distances covered across speed thresholds, accelerations/decelerations, impacts, and jumps) were quantified via wearable tracking and heart rate telemetry. Repeated-measures MANOVA tested the altitude x playing position effects. Altitude increased the total distance (+10%), lower-speed running distances (+10–39%), accelerations/decelerations (+25–30%), average heart rate (+6%), time in higher-intensity HR zones (+23–63%), and jumps (+13%) across all positions (p < 0.05). Positional differences existed, with guards accruing more high-speed running and centers exhibiting greater cardiovascular demands (p < 0.05). In conclusion, a 6-day altitude block effectively overloads training, providing a stimulus to enhance fitness capacities when structured appropriately. Monitoring workloads and individualizing training by playing position are important when implementing altitude training, given the varied responses.
Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm
Authors: Shahin Mohammad , Chen F. Frank , Maghanaki Mazdak , Hosseinzadeh Ali , Zand Neda , Khodadadi Koodiani Hamid
Published: May 2024

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.
A Detection Transformer-Based Intelligent Identification Method for Multiple Types of Road Traffic Safety Facilities
Authors: Lu Lingxin , Wang Hui , Wan Yan , Xu Feifei
Published: May 2024

Road traffic safety facilities (TSFs) are of significant importance in the management and maintenance of traffic safety. The complexity and variety of TSFs make it challenging to detect them manually, which renders the work unsustainable. To achieve the objective of automatic TSF detection, a target detection dataset, designated TSF-CQU (TSF data collected by Chongqing University), was constructed based on images collected by a car recorder. This dataset comprises six types of TSFs and 8410 instance samples. A detection transformer with an improved denoising anchor box (DINO) was selected to construct a model that would be suitable for this scenario. For comparison purposes, Faster R-CNN (Region Convolutional Neural Network) and Yolov7 (You Only Look Once version 7) were employed. The DINO model demonstrated the highest performance on the TSF-CQU dataset, with a mean average precision (mAP) of 82.2%. All of the average precision (AP) values exceeded 0.8, except for streetlights (AP = 0.77) and rods (AP = 0.648). The DINO model exhibits minimal instances of erroneous recognition, which substantiates the efficacy of the contrastive denoising training approach. The DINO model rarely makes misjudgments, but a few missed detection.
Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle
Authors: Chaysri Piyabhum , Spatharis Christos , Vlachos Kostas , Blekas Konstantinos
Published: May 2024

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.
Reliability and Validity of the Strain Gauge “GSTRENGTH” for Measuring Peak Force in the Isometric Belt Squat at Different Joint Angles
Authors: Varela-Olalla Daniel , Balsalobre-Fernández Carlos , Romero-Moraleda Blanca , Jiménez-Sáiz Sergio L.
Published: May 2024

Since isometric training is gaining popularity, some devices are being developed to test isometric force as an alternative to the more expensive force plates (FPs); thus, the aim of this study was to test the reliability and validity of “GSTRENGTH” for measuring PF in the isometric belt squat exercise. Five subjects performed 24 contractions at three different knee angles (90°, 105° and 120°) on two occasions (120 total cases). Peak force data were measured using FPs and a strain gauge (SG) and analyzed by Pearson’s product–moment correlation coefficient, ICCs, Cronbach’s alpha, a paired sample t-test and Bland–Altman plots. Perfect or almost perfect relationships (r: 0.999–1) were found with an almost perfect or perfect level of agreement (ICCs: 0.992–1; α: 0.998–1). The t-test showed significant differences for the raw data but not for the predictions by the equations obtained with the SG values. The Bland–Altman plots, when significant, showed trivial to moderate values for systematic bias in general. In conclusion, “GSTRENGTH” was shown to be a valid alternative to FPs for measuring PF.
Age and Burnout: The Mediating Role of Emotion-Regulation Strategies
Authors: Mendes Bianca , Miguel Isabel
Published: May 2024

In the context of an aging workforce, this study explores the interaction between age, burnout, and emotion-regulation strategies (ERS). Despite recognized challenges in managing age diversity and employee well-being, the direct impact of age on burnout and the mediating role of ERS remain unexplored. Analyzing data from 604 Portuguese workers (aged 18–65), this study utilizes a mediation model to investigate if age is directly related to the main problems that affect the workforce presently, focusing specifically on burnout and the role that emotion-regulation abilities (cognitive reappraisal and expressive suppression) have in controlling the burnout effects (measured by emotional exhaustion and disengagement). The findings indicate that age does not have a straightforward linear relationship with burnout or ERS choice. Although age alone does not significantly influence burnout outcomes, ERS markedly impacts these outcomes, suggesting that factors beyond age predominantly drive ERS selection and effectiveness in managing burnout. This study emphasizes the critical role of ERS in influencing burnout, suggesting the importance of equipping workers with effective emotion-regulation skills to mitigate burnout risks. Further research is warranted to disentangle the complex interrelations among age, burnout, and ERS in organizational contexts.
The Impact of the Digital Economy on Population Dividends in China: Based on the Dual Perspective of Quantity and Quality
Authors: Qin Jingyi , Xu Qingyu
Published: May 2024

The demographic dividend plays an important role in promoting sustainable development in China. Here, we ask the question of how to use the digital economy to coordinate the “one body and two sides” of the demographic dividend. This study empirically examines the impact of digital economic development on the demographic dividend in a multidimensional way based on the panel data from 30 provincial-level administrative regions in China from 2011 to 2020. The study results show that (1) the digital economy significantly promotes the demographic quality dividend but exhibits a suppressive effect on the demographic quantity dividend; (2) the digital economy can indirectly influence the demographic quality and quantity dividends through urbanization; (3) when examining the threshold effects, the study uncovers noteworthy dynamics, whereby the urbanization levels serve as significant thresholds, showcasing “diminishing marginal effects” in the digital economy’s influence on both population quantity and quality dividends; (4) digital economic development has a positive spillover effect on the demographic quantity dividend in adjacent areas. By clarifying these dynamics, the research results provide valuable insights into China’s sustainable use of the digital economy to create a demographic dividend.
Advances in the Sustainable Production of Fertilizers from Spent Zinc-Based Batteries
Authors: Barragán-Mantilla Silvia Patricia , Ortiz Raquel , Almendros Patricia , Sánchez-Martín Laura , Gascó Gabriel , Méndez Ana
Published: May 2024

Wastes from spent batteries are a secondary source of raw materials. To ensure this, it is mandatory to design sustainable and low-cost processes. In the case of alkaline and zinc–carbon-based batteries, the high content of Zn and Mn makes them of interest in the development of fertilizers. The main objective of this research is to study the fertilizers production from spent zinc-based batteries, using sulfuric acid, citric acid (CIT) and glycine (GLY) solutions as leaching agents. Leaching with glycine at alkaline pHs shows a high selectivity of Zn over Mn, whereas the use of citric and sulfuric solutions leads to recoveries of Zn and Mn. Solutions with the highest Zn recoveries were tested in sand columns. Commercial ZnSO4 heptahydrate was used as a control. For sulfuric acid, two solutions (H2SO4 2M and 0.25M) were used. The elution of leached Zn and Mn in sand columns depended on the solution added. The Zn-Mn-CIT treatment showed a slight but steady increase in the leachates, reaching 70% and 75% of the total leached Zn and Mn, respectively, in the medium term. The Zn-Mn-H2SO4 2M and ZnSO4 treatments showed a similar behavior in Zn release. Both Zn-Mn-GLY and Zn-Mn-H2SO4 0.25M treatments showed similar amounts of leached Mn in the medium term (77% of total leached Mn), differing in the leached Zn. Solutions from the leaching of spent black mass batteries, especially Zn-Mn-CIT or Zn-Mn-GLY, showed promising behavior as fertilizer from the point of view of Zn and Mn availability as nutrients.
Seasonal Prevalence of Skin Lesions on Dolphins across a Natural Salinity Gradient
Authors: Guinn Makayla A. , Toms Christina N. , Sinclair Carrie , Orbach Dara N.
Published: May 2024

Bottlenose dolphins (Tursiops truncatus) inhabit waters across a broad natural salinity gradient and exhibit changes in skin condition based on the quality of their environment. Prolonged exposure to low salinities (≤10–20 ppt) degenerates the epidermal barrier and causes cutaneous lesions in dolphins, while the role of high salinity exposure (>35 ppt) in lesion development remains unknown. We assessed seasonal lesion prevalence in three free-ranging dolphin stocks inhabiting coastal Gulf of Mexico (GoM) waters of different salinities (0–30 ppt, 22–35 ppt, and 36+ ppt) using images of dolphin bodies. Lesions were documented on 44% of the dolphins photographed (n = 432), and lesion occurrence was significantly related to cold seasons and water temperatures but not salinity. Cold water temperatures may heighten dolphin susceptibility to infectious pathogens and disease and compound the effects of anthropogenic pollutants in the GoM. As dolphins are a bioindicator species of marine habitat welfare, natural studies assessing dolphin skin may reveal environmental degradation with potential impacts on marine ecosystems and human health.
Battery Electric Vehicles: Travel Characteristics of Early Adopters
Authors: Feng Yunwen , Saphores Jean-Daniel , Nixon Hilary , Ramirez Ibarra Monica
Published: May 2024

Do U.S. households with battery electric vehicles (BEVs) drive less or more than U.S. households with internal combustion engine vehicles (ICEVs)? Answering this question is important to policymakers and transportation planners concerned with reducing vehicle miles traveled and the emissions of greenhouse gases from transportation. So far, this question has not been answered satisfactorily, possibly because of the relatively low number of EVs in the U.S. until recently, but also because of methodological issues. In this paper, we aim to fill this gap by analyzing data from the 2017 National Household Travel Survey (NHTS). We apply propensity score matching (PSM), a quasi-experimental method, to examine the differences in self-reported annual mileage and calculated daily mileage for various trip purposes among households with only BEVs (BEV-only), households with both BEVs and ICEVs (BEV+), and households without BEVs (non-BEV households). Our findings indicate that households with BEVs drive fewer annual miles than non-BEV households, but typically travel no less than they do for daily activities. This apparent discrepancy is likely due to taking fewer longer trips because the public charging infrastructure was still in its infancy in 2017, and its reliability was questionable. As technological progress is helping to overcome current battery limitations, policymakers may consider measures for fostering fast charging technologies while pondering new measures to fund both the charging infrastructure and the road network.
The Operational Risk Disclosure Threshold Effect in the Earnings Management–Sustainability Firm Performance Nexus in Saudi Arabia: A Dynamic Panel Threshold Regression Model
Authors: Alsulami Faizah
Published: May 2024

Although the relationship between earnings management and firm performance has been well explored in the literature, sustainable performance has not yet been examined. Furthermore, the literature has not addressed the issue of nonlinearity between earnings management and firm performance. Therefore, this paper aims to examine the potential nonlinear relationship between earnings management and sustainable firm performance in Saudi Arabia using a sample of 70 listed firms over the 2015–2022 period. Specifically, it investigates the operational risk disclosure threshold effect in the earning management–sustainable firm performance nexus. To do so, the dynamic panel threshold regression model (DPTR) is performed. The result proves that there is a threshold effect of operational risk disclosure in the relationship between earning management and sustainable firm performance. Specifically, the threshold values of operational risk disclosure for the three models are estimated at 6 between the low- and the high-operational-risk-disclosure regimes. In the lower regime, firm performance decreases when earning management increases; however, in the higher regime, firm performance increases when earning management increases. These outcomes support the predictions of agency and positive accounting theories.
Re-Inventories at Two Study Sites in the Oberpfalz (Bavaria, Germany): Increase in RWA Nests, Herb Biodiversity and Dead Wood in Sustainably Managed Forests
Authors: Berberich Gabriele M. , Berberich Martin B.
Published: May 2024

A large-scale re-inventory of red wood ant (RWA; Formica rufa-group) nests and a comparative analysis of their presence/absence data was carried out for the first time in 2023 at two study sites in the Oberpfalz, NE Bavaria, Germany, to investigate the suspected decline of nests, their relationship with forestry aspects, and possible changes in spatial distribution due to tectonic activity (GeoBio-Interactions). We inventoried, in two sustainably managed forests, outstanding nest occurrences (5393 nests including 5276 active nests) and an increase of ≈8% over 4–7 years. Our results do not support claims that the number of nests in the Oberpfalz is in sharp decline. RWA preferred mature and medium–mature pine-dominated forests. Standing and downed dead wood did not negatively affect nests. The number of nests with woodpecker cavities and the diversity of the herb layer increased. Nests clusters in NW–SE direction suggest a strong interaction between nest distribution and active tectonics. Our (re-)inventory approach combined with the extensive photo database is a valuable tool for (a) monitoring the entire forest habitat including natural restocking, herb biodiversity, woodpecker cavities, and dead wood in, at, and around nests, and (b) identifying GeoBio-Interactions. Such an approach will lead to more realistic counts of RWA nests and provide scientific evidence of the current situation of nests occurrence. We suggest seven to eight years as an appropriate time interval for re-inventories.
Changes in Land Use and Cover and Their Environmental Impacts in the Cerrado of Mato Grosso Do Sul, Brazil
Authors: Fushimi Melina , de Lima Gabriela Narcizo , Capoane Viviane
Published: May 2024

In Brazilian regional landscapes, the Cerrado has one of the richest flora among the savannas in the world, with a high level of endemism; however, many plant species are threatened with extinction as a consequence of spatio-temporal changes in land use and cover. This study aimed to analyze changes in land use and cover in the upper course of the Ceroula stream basin, located in the Cerrado of Mato Grosso do Sul, Brazil, based on maps of land use and cover in 1985 and 2022, the normalized difference vegetation index (NDVI), precipitation data, and fieldwork. The results indicated that in 1985, forest vegetation was replaced by pasture, and in 2022, in addition to pasture, there was the introduction of soybean monoculture with corn in the off-season, influenced by the international commodities market. These land use and cover alterations, without adequate management and in the absence of conservation practices, led to environmental impacts, such as accelerated linear erosive processes (rill, ravine, and gully). The results may help provide important insights into the dynamics of land use and cover, the consequences of the lack of conservation practices, and the environmental impacts in the Cerrado of Mato Grosso do Sul, contributing to better understanding of the environmental challenges faced in the region and the need to provide subsidies for the development of sustainable management strategies.
The Relationship between Perceptions of High-Performance Work Systems and Corporate Social Responsibility (CSR) Considering the Moderating Effect of a Positive Diversity Climate
Authors: Abuzaid Ahmad Nasser , Al-Faouri Elham Hmoud , Alateeq Manal Mohammad , Madadha Saif-aldeen Marwan , Ghadi Mohammed Yasin , Al-Qatawenh Aymn Sulieman , Abdelkarim Almajali Dmaithan , Almajali Haya Abdul Kareem
Published: May 2024

Companies have shown interest in advanced human resource management as a means to secure distinctive competitive advantages for organizational survival and growth through sustainable management systems. Hence, in the current context, where sustainability in business is a growing concern, the objective of this study was to investigate the relationship between high-performance work systems and a company’s social performance, taking into account the organizational climate as a situational variable. Specifically, this study aimed to analyze the impact of perceptions of a high-performance work system on the perception of a company’s social responsibility (CSR) among employees of Jordanian companies with an organizational size of 300 or more. This study used 175 valid questionnaires, and SPSS and AMOS 24 were used to test the hypotheses. The results showed that there was a positive relationship between the perception of a high-performance work system and the perception of a company’s CSR. Furthermore, this study found that a positive diversity climate could enhance the positive relationship between the two. These findings suggest that high-performance work systems can contribute to the development of sustainable human resource management systems and that a positive diversity climate is essential in shaping these systems. Based on these results, it is recommended that companies implement HPWSs and encourage workforce diversity to maximize the value of CSR activities and ensure their sustainability.
Implementation of the Sustainability Compass: A Bottom-Up Social Learning Approach in Initial Pilot Studies
Authors: Sajeva Maurizio , Kotta Jonne , Valonen Matti , Korhonen Olli , Kinnunen Pekka , Aalto Laura , Noro Kirsi , Horne Paula
Published: May 2024

This paper presents the Sustainability Compass as an emerging innovative bottom-up framework that promotes social learning about overall sustainability—i.e., human wellbeing and prosperity within environmental boundaries—by mean of its practical implementation in the PlanWise4Blue geoportal. The Sustainability Compass aims to put the theoretical idea of sustainability into practice by a systemic approach that continuously generates, refines and updates appropriate themes and metrics, through bottom-up enlarged participatory learning involving all researchers, entrepreneurs, environmental managers, and anyone else with an interest. Such inclusiveness promotes a common understanding of sustainability, cultivates a collective vision and facilitates the assessment of sustainability levels in different contexts, including national and regional planning efforts. The Sustainability Compass operationalises the generic, top-down and differently interpretable UN SDGs into more practical and bottom-up practice. This article outlines the finalised structure of the Sustainability Compass and presents its first application through case studies of cross-border aquaculture in Finland and Estonia, and a collaborative initiative in Finnish wind energy. Through detailed analysis of interviews and workshops, the article explains the empirical findings and offers a comprehensive guide to the use of the Sustainability Compass. This is exemplified by its integration with the PlanWise4Blue geoportal, demonstrating its practicality and effectiveness in real-world applications.
Knowledge Element Relationship and Value Co-Creation in the Innovation Ecosystem
Authors: Yang Gening
Published: May 2024

In the era of Innovation 3.0, more and more enterprises are working together to build an innovation ecosystem to achieve value creation. The various participants in the innovation ecosystem promote resource aggregation and integration through cross boundary collaboration, jointly creating the value of the ecosystem. Value co-creation has become the core goal pursued by the participants in the innovation ecosystem. As an innovative participant in the innovation ecosystem, it is particularly necessary for enterprises to explore the formation mechanism of value co-creation from the perspective of enterprise knowledge characteristics. This article analyzes the value co-creation mechanism of innovation ecosystems from the perspective of including internal knowledge bases and external relationships. Based on the dimension of knowledge element relationships, a theoretical model is constructed to investigate the impact of enterprise knowledge element relationships on value co-creation in innovation ecosystems and explore the mediating role of knowledge synergy and the moderating role of innovation ecosystem normativity. The relationships are modeled based on survey data collected from 427 Chinese companies, and multiple regression analysis and bootstrap methods are used to empirically test the hypotheses. The research results show that different dimensions of knowledge element relationships have different impacts on value co-creation. Knowledge element substitution negatively affects value co-creation, while knowledge combination diversity positively affects value co-creation. However, there is an inverted U-shaped relationship between knowledge element complementarity and value co-creation. Knowledge synergy partially mediates the relationship between knowledge element relationships and value co-creation, and innovation ecosystem normativity has a two-stage moderating effect on the path from knowledge element relationships to knowledge synergy to value co-creation. This article enriches the research content of knowledge management in innovation ecosystems, guides enterprises in the innovation ecosystem to reasonably construct their own knowledge systems, and promotes the formation of innovation ecosystem norms, thus promoting the development of value co-creation activities.