bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Research Progress in Carbon Monitoring and Carbon Metering Methods: Comparison at Home and Abroad
Authors: Wang Dongxu , Sha Wenhui , Hu Yingwen , Li Yitao , Wei Shuzhou , Gu Yongzheng , Wang Pingping , Xiong Zhuo
Published: November 2024

Carbon monitoring and carbon measurement are not only important foundations for realizing the marketization of carbon trading, but also a key link in realizing China’s strategic “dual carbon” goal. The aim of this research is to comprehensively summarize and compare carbon monitoring and carbon metering technologies, as well as to analyze their current status and challenges. This study adopts literature research, comparative analysis, case analysis, policy interpretation, and other methods to comprehensively and deeply explore the relevant content of carbon monitoring and carbon metering technology. An in-depth exploration of relevant methods, standards, and applications provides a reference for promoting the sustainable development of global carbon monitoring and carbon metering technologies. By summarizing the difficulties of carbon monitoring and the characteristics of existing technologies, as well as comparing carbon measurement methods and the relevant measurement standards, this paper focuses on the difficulty of carbon monitoring, which lies in the credibility and accuracy of the data, where remote sensing technology possesses higher applicability. The principles of carbon measurement methods mainly include direct underlying data measurements, indirect measurements through statistical modelling, and market mechanism measurements. The relevance and precision of carbon measurement methods have been gradually strengthened as the measurement standards have been developed and implemented. Finally, future development directions and relevant suggestions will be described in detail and put forward in combination with the application of carbon monitoring and carbon measurement. Among them, blockchain technology is considered to be one potential area for future development, and data standardization will play an important role in the development of carbon monitoring and measurement technology. We recommended establishing and perfecting data-sharing mechanisms in future policies to improve the accuracy and credibility of data.
Shared Memory and History: The Abrahamic Legacy in Medieval Judaeo-Arabic Poetry from the Cairo Genizah
Authors: Sheir Ahmed Mohamed
Published: November 2024

The Cairo Genizah collections provide scholars with a profound insight into Jewish culture, history, and the deeply intertwined relationships between Jews, Muslims, and Christians. Among these treasures are often overlooked Arabic poetic fragments from the eleventh to fifteenth centuries, which illuminate the shared Abrahamic legacy. This paper explores mainly two unpublished poetic fragments written in Judaeo-Arabic (Arabic in Hebrew script), analyzing how they reflect a shared Jewish–Muslim cultural memory and history, particularly through the reverence for Abraham, Isaac, Jacob, and other key figures central to both traditions across the medieval Mediterranean and Middle East. By situating these poetic voices within broader historical and cultural contexts, this study underscores the role of poetry in reflecting sociocultural and historical dimensions while fostering cross-cultural and religious coexistence. It demonstrates how poetry acts as a bridge between religion, history, and culture by revealing the shared Abrahamic heritage of Jews and Muslims within two Arabic poetic fragments from the Cairo Genizah.
Pratītyasamutpāda, the Doctrine of Dependent Origination in Old Uyghur Buddhism: A Study of Printed Texts
Authors: Yakup Abdurishid
Published: November 2024

Pratītyasamutpāda, the doctrine of dependent origination, has a long history in Old Uyghur Buddhism. It was first articulated in the Early Old Uyghur Buddhist texts and is evident in the terminology of Maitrisimit and the Daśakarmapathāvadāna-mālā. The dependent origination is systematically illustrated in at least three Pratītyasamutpāda texts, one text with Brāhmī elements, and the other two in Dunhuang and Turfan prints. The latter two are discussed in detail in this paper. The Dunhuang print provides the most comprehensive demonstration of the Old Uyghur understanding of dependent origination. The structure of the text is largely consistent with the corresponding passages in the Abhidharmamahāvibhāṣaśāstra and other Abhidharma texts. The text offers a more comprehensive account than the Chinese text. The Turfan prints, which consist of four fragments, are derived from two distinct prints. Print U 4170 is an Abhidharma text, and it has parallels in the Abhidharmakośabhāṣya. It seems plausible to suggest that the print bearing the abbreviated titles Pratyitasamutpad in Old Uyghur and Buladi 布剌帝 in Chinese may have been translated from a Chinese text sharing the same or a similar Chinese name. However, as with the Dunhuang print, the Turfan prints may have been produced by the Old Uyghurs from some Abhidharma texts. The Dunhuang print and the Tufan prints are unique within the corpus of known Old Uyghur prints. These texts represent the first known printed examples of the Abhidharma tradition. Moreover, the illustration employed in the Dunhuang print is not known in other printed texts discovered in Dunhuang and Turfan, representing the first instance of such an illustration in printed form.
Spirituality in Practice: Paul’s Call Toward a Pastoral Theology of Relationships
Authors: Gentry Dawn
Published: November 2024

This article examines Paul’s letter to the Romans (with supporting texts) to assess the intersection between beliefs about God, behaviors for God, and being in community. Additionally, it offers suggestions to deepen Christian spiritual formation among congregants in churches and small groups with a view toward missiology and communal witness.
Arterial Embolization with n-Butyl-Cyanoacrylate for a Large Gluteal Intramuscular Hemangioma: A Case Report
Authors: Papalexis Nicolas , Carta Michela , Peta Giuliano , Quarchioni Simone , Di Carlo Maddalena , Miceli Marco , Facchini Giancarlo
Published: November 2024

Background and Clinical Significance: We wished to review the use of arterial embolization with n-butyl-cyanoacrylate (NBCA) to treat large high-flow vascular malformations due to its rapid polymerization and ability to permanently occlude large and small vessels. Case Presentation: A 52-year-old male presented with a two-year history of progressively worsening pain and swelling in the right gluteal area. Imaging techniques (color Doppler ultrasonography, CT, DSA, and MRI) were utilized for the diagnosis of a large high-flow intramuscular hemangioma. The mass displaced the surrounding tissues but showed no signs of lymphadenopathy or distant metastasis. The treatment involved targeting different arterial feeders over several sessions. Each procedure used NBCA–Lipiodol under fluoroscopic guidance, progressively reducing the malformation’s size and alleviating his symptoms. After the final embolization, the patient showed significant pain relief and a reduction in the size of the malformation, confirmed by follow-up imaging, demonstrating NBCA embolization’s effectiveness. The protocol’s safety and efficacy in this context are discussed. Conclusions: Arterial embolization with NBCA is a promising treatment for large high-flow vascular malformations, providing symptom relief and reductions in lesion size. While this case report highlights the procedure’s efficacy, further research is needed for a broader understanding of its long-term outcomes and potential complications.
Debt Sustainability in the Context of Population Ageing: A Risk Management Approach
Authors: Ajovalasit Samantha , Consiglio Andrea , Provenzano Davide
Published: November 2024

The ageing of the population has negative effects on the gross domestic product (GDP), influencing various economic and social aspects. These effects, in turn, contribute to an increase in the debt-to-GDP ratio, raising concerns about the long-term sustainability of public debt. The objective of our study is to evaluate the possible dynamics of debt sustainability with a certain level of probability. The analysis employs the stochastic modelling of risk factors influencing the debt-to-GDP ratio, particularly emphasising the economic consequences of population ageing. Using advanced risk management techniques, we aim to provide a robust assessment of how future demographic outlooks impact debt sustainability.
The Spatiotemporal Evolution of Vegetation in the Henan Section of the Yellow River Basin and Mining Areas Based on the Normalized Difference Vegetation Index
Authors: Chen Zhichao , Liu Xueqing , Feng Honghao , Wang Hongtao , Hao Chengyuan
Published: November 2024

The Yellow River Basin is rich in coal resources, but the ecological environment is fragile, and the ecological degradation of vegetation is exacerbated by the disruption caused by high-intensity mining activities. Analyzing the dynamic evolution of vegetation in the Henan section of the Yellow River Basin and its mining areas over the long term run reveals the regional ecological environment and offers a scientific foundation for the region’s sustainable development. In this study, we obtained a long time series of Landsat imageries from 1987 to 2023 on the Google Earth Engine (GEE) platform and utilized geographically weighted regression models, Sen (Theil–Sen median) trend analysis, M-K (Mann–Kendall) test, coefficient of variation (CV), and the Hurst index to investigate the evolution of vegetation cover based on the kNDVI (the normalized difference vegetation index). This index is used to explore the spatial and temporal characteristics of vegetation cover and its future development trend. Our results showed that (1) The kNDVI value in the Henan section of the Yellow River Basin exhibited a trend of fluctuating upward at a rate of 0.0509/10a from 1987 to 2023. The kNDVI trend in the mining areas of the region aligned closely with the overall trend of the Henan section; however, the annual kNDVI in each mining area consistently remained lower than that of the Henan section and displayed a degree of fluctuation, predominantly characterized by medium–high variability, with areas of moderate and high fluctuations accounting for 73.5% of the total. (2) The kNDVI in the study area showed a significant improvement in vegetation cover and its future development trends. We detected a significant improvement in the kNDVI index in the area; yet, significant improvement in this index in the future might cause vegetation degradation in 87% of the study area, which may be closely related to multiple factors such as the intensity of mining at the mine site, anthropogenic disturbances, and climate change. (3) The vegetation status of the Henan section of the Yellow River Basin shows a significant positive correlation with distance from mining areas, accounting for 90.9% of the total, indicating that mining has a strong impact on vegetation cover. This study provides a scientific basis for vegetation restoration, green development of mineral resources, and sustainable development in the Henan section of the Yellow River Basin.
An Improved DETR Based on Angle Denoising and Oriented Boxes Refinement for Remote Sensing Object Detection
Authors: Wang Hongmei , Li Chenkai , Wu Qiaorong , Wang Jingyu
Published: November 2024

Remote sensing image object detection presents significant challenges, due to the difficulty in accurately predicting the rotational angles of ground-oriented objects, coupled with issues such as the false and missed detection caused by insufficient object information. Moreover, traditional convolutional neural networks are inherently limited in their capacity to capture global contextual information. To address these challenges, a DETR-based remote sensing image object detection model is designed for oriented objects. Except for the backbone, transformer encoders and decoders, scenario query guiding modules, oriented boxes refinement modules, auxiliary multiple detectors, and oriented boxes denoising modules are also designed and included in our network. The scenario query guiding module is proposed that implicitly guides the decoder to focus more on object classification information specific to that scene when inferring. The multiple deformable attention mechanism is improved to the oriented one and utilized into the oriented boxes refinement module which repeatedly corrects the oriented boxes, enhancing the network’s ability to predict the oriented boxes precisely. At the same time, the improved auxiliary multiple detectors and oriented boxes denoising module are applied only for the training process to enhance the learning ability of the encoder and decoder for oriented objects. The ablation experiments proved the effectiveness of the designed modules. The detection accuracy of our network on DOTAv1.0 (76.77%) and HRCS2016 (97.01%) is improved compared with the state-of-the-art methods, which are especially significantly higher than DETR detection algorithms.
Satellite-Based Assessment of Rocket Launch and Coastal Change Impacts on Cape Canaveral Barrier Island, Florida, USA
Authors: Cho Hyun Jung , Burow Daniel , San Antonio Kelly M. , McCarthy Matthew J. , Herrero Hannah V. , Zhou Yao , Medeiros Stephen C. , Colbert Calvin D. , Jones Craig M.
Published: November 2024

The Cape Canaveral Barrier Island, home to the National Aeronautics and Space Administration (NASA)’s Kennedy Space Center and the United States (U.S.) Space Force’s Cape Canaveral Space Force Station, is situated in a unique ecological transition zone that supports diverse wildlife. This study evaluates the recent changes in vegetation cover (2016–2023) and dune elevation (2007–2017) within the Cape Canaveral Barrier Island using high-resolution optical satellite and light detection and ranging (LiDAR) data. The study period was chosen to depict the time period of a recent increase in rocket launches. The study objectives include assessing changes in vegetation communities, identifying detectable impacts of liquid propellant launches on nearby vegetation, and evaluating dune elevation and tide level shifts near launchpads. The results indicate vegetation cover changes, including mangrove expansion in wetland areas and the conversion of coastal strands to denser scrubs and hardwood forests, which were likely influenced by mild winters and fire management. While detectable impacts of rocket launches on nearby vegetation were observed, they were less severe than those caused by solid rocket motors. Compounding challenges, such as rising tide levels, beach erosion, and wetland loss, potentially threaten the resilience of launch operations and the surrounding habitats. The volume and scale of launches continue to increase, and a balance between space exploration and ecological conservation is required in this biodiverse region. This study focuses on the assessment of barrier islands’ shorelines.
Comparative Analysis of Two Methods for Valuing Local Cooling Effect of Forests in Inner Mongolia Plateau
Authors: Bo Wenjing , Xiao Yi , Sun Jiazhe , Cao Yun , Chen Le
Published: November 2024

Studies have extensively examined the cooling effects of forests. Various methods exist for evaluating climate regulation at regional and global levels. Local-scale cooling effects and their valuing methods, however, remain poorly understood. In this study, the temperature difference and energy balance methods were compared to assess the value of cooling services of three forest types at a local scale. Using the window searching strategy, land surface temperature and sensible heat flux differences between forest and open land were compared. The average cooling temperature of broad-leaved forests was found to be 0.229 °C, significantly higher than that of coniferous forests, at 0.205 °C, while mixed coniferous–broad-leaved forests were not significantly different to the other two types. The average sensible heat flux differences in broad-leaved, coniferous, and coniferous–broad-leaved forests were found to be 0.23, 0.079, and 0.11 MJ/m2/day, respectively. According to the correlation analysis, the sensible heat flux was significantly correlated with the cooling degree (R = 0.33, p = 0.05), suggesting consistency between the two approaches. However, the total cooling value calculated with the energy balance method was CNY 0.51 billion, significantly higher than the temperature difference method at CNY 0.11 billion. The main reason for the differences between the two approaches is the uncertainty in cooling volume and cooling time for the temperature difference method and energy balance method, respectively. The impact of vegetation on the microclimate depends on the vegetation type, topography, local climate, and other factors. It is also important to note that cooling services are not required at all times of the day, and energy differences can hardly be calculated based on the hour. However, surface radiation and evapotranspiration generally occur during the daytime, which is also when the surface temperature is high. Therefore, there is a certain coincidence with the time when cooling is needed. The energy balance method presented herein provides a novel alternative approach to assessing the cooling services of local-scale forests, offering advantages over the commonly used temperature difference approach, which is associated with large uncertainty.
Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
Authors: Yan Rui , Huang Jianping , Lin Jian , Wang Qiao , Zhang Zhenxia , Yang Yanyan , Chu Wei , Liu Dapeng , Xu Song , Lu Hengxin , Pu Weixing , Wang Lu , Zhou Na , Li Wenjing , Tan Qiao , Zhima Zeren
Published: November 2024

The China Seismo-Electromagnetic Satellite (CSES-01) is the first satellite of the space-based observational platform for the earthquake (EQ) monitoring system in China. It aims to monitor the ionospheric disturbances related to EQ activities by acquiring global electromagnetic fields, ionospheric plasma, energy particles, etc., opening a new path for innovative explorations of EQ prediction. This study analyzed 47 shallow strong EQ cases (Ms ≥ 7 and depth ≤ 100 km) recorded by CSES-01 from its launch in February 2018 to February 2023. The results show that: (1) For the majority (90%) of shallow strong EQs, at least one payload onboard CSES-01 recorded discernible abnormal signals before the mainshocks, and for over 65% of EQs, two or three payloads simultaneously recorded ionospheric disturbances; (2) the majority of anomalies recorded by different payloads onboard CSES-01 predominantly manifest within one week before or on the mainshock day, or occasionally about 11–15 days or 20–25 days before the mainshock; (3) typically, the abnormal signal detected by CSES-01 does not directly appear overhead the epicenter, but rather hundreds of kilometers away from the epicenter, and more preferably toward the equatorward direction; (4) the anomaly recognition rate of each payload differs, with the highest rate reaching more than 70% for the Electric Field Detector (EFD), Search-Coil Magnetometer (SCM), and Langmuir Probe (LAP); (5) for the different parameters analyzed in this study, the plasma density from LAP, and electromagnetic field in the ULF band recorded by EFD and SCM, and energetic electrons from the High-Energy Particle Package (HEPP) show a relatively high occurrence of abnormal phenomena during the EQ time. Although CSES-01 has recorded prominent ionospheric anomalies for a significant portion of EQ cases, it is still challenging to accurately extract and confirm the real seismic precursor signals by relying solely on a single satellite. The combination of seismology, electromagnetism, geodesy, geochemistry, and other multidisciplinary means is needed in the future’s exploration to get infinitely closer to addressing the global challenge of EQ prediction.
An Identification Method for Road Hypnosis Based on the Fusion of Human Life Parameters
Authors: Wang Bin , Wang Jingheng , Wang Xiaoyuan , Chen Longfei , Jiao Chenyang , Zhang Han , Liu Yi
Published: November 2024

A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers’ life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study.
Movement Sensing Opportunities for Monitoring Dynamic Cognitive States
Authors: Brunyé Tad T. , McIntyre James , Hughes Gregory I. , Miller Eric L.
Published: November 2024

In occupational domains such as sports, healthcare, driving, and military, both individuals and small groups are expected to perform challenging tasks under adverse conditions that induce transient cognitive states such as stress, workload, and uncertainty. Wearable and standoff 6DOF sensing technologies are advancing rapidly, including increasingly miniaturized yet robust inertial measurement units (IMUs) and portable marker-less infrared optical motion tracking. These sensing technologies may offer opportunities to track overt physical behavior and classify cognitive states relevant to human performance in diverse human–machine domains. We describe progress in research attempting to distinguish cognitive states by tracking movement behavior in both individuals and small groups, examining potential applications in sports, healthcare, driving, and the military. In the context of military training and operations, there are no generally accepted methods for classifying transient mental states such as uncertainty from movement-related data, despite its importance for shaping decision-making and behavior. To fill this gap, an example data set is presented including optical motion capture of rifle trajectories during a dynamic marksmanship task that elicits variable uncertainty; using machine learning, we demonstrate that features of weapon trajectories capturing the complexity of motion are valuable for classifying low versus high uncertainty states. We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human–machine interfaces to support a wide range of cognitive functions.
Excitation-Dependent pKa Extends the Sensing Range of Fluorescence Lifetime pH Sensors
Authors: Haynes Emily P. , Canzano Mary , Tantama Mathew
Published: November 2024

Biological activity is strongly dependent on pH, which fluctuates within a variety of neutral, alkaline, and acidic local environments. The heterogeneity of tissue and subcellular pH has driven the development of sensors with different pKa values, and a huge assortment of fluorescent sensors have been created to measure and visualize pH in living cells and tissues. In particular, sensors that report based on fluorescence lifetime are advantageous for quantitation. Here, we apply a theoretical framework to derive how the apparent pKa of lifetime-based pH sensors depends on fluorescence excitation wavelength. We demonstrate that theory predicts the behavior of two different fluorescent protein-based pH sensors in solution as proofs-of-concept. Furthermore, we show that this behavior has great practical value in living cells because it extends the sensing range of a single sensor by simply choosing appropriate detection parameters to match the physiological pH range of interest. More broadly, our results show that the versatility of a single lifetime-based sensor has been significantly underappreciated, and our approach provides a means to use a single sensor across a range of pH environments.
Technologies and Sensors for Artificial Muscles in Rehabilitation
Authors: Basu Vina , Cheng Li , Zheng Bin
Published: November 2024

Muscles are very important parts of the human body. When there is an injury to a muscle that causes long-term dysfunctionality, sensors and artificial muscles can be used to help alleviate problems. Muscles have complex structures; thus, ultrasound and other types of scans may be needed to determine their parameters and model their shapes. Additionally, the measurement of chemicals in muscles plays a significant role in analyzing their performance and potential diseases in humans. All the above-mentioned components are needed for understanding the structure and function of muscles. The areas studied in this review include artificial muscles and exoskeletons, determining muscle parameters and modelling, assessing musculoskeletal functions, chemicals in muscles, and various applications, including those of wearable sensors. In future studies, we would like to understand the link between the brain and muscles and develop technologies that can assist in augmenting the motor skills of individuals affected by various debilitating conditions.
Detecting Unusual Repetitive Patterns of Behavior Indicative of a Loop-Based Attack in IoT
Authors: Munshi Asmaa
Published: November 2024

Given the high risk of Internet of Things (IoT) device compromise, it is crucial to discuss the attack detection aspect. However, due to the physical limitations of IoT, such as battery life and sensing and processing power, the widely used detection techniques, such as signature-based or anomaly-based detection, are quite ineffective. This research extracted loop-based cases from the transmission session dataset of “CTU-IoT-Malware-Capture-7-1” (“Linux, Mirai”) and implemented a loop-based detection machine learning approach. The research employed nine machine learning models to illustrate how the loop patterns of the datasets can facilitate detection. The results of this study indicate that the XGBoost model achieves the best performance in terms of “Accuracy: 8.85%”, “Precision: 96.57% (Class)”, “Recall: 96.72% (Class 1)”, and “F1-Score: 6.24%”. The XGBoost model demonstrated exceptional performance across all metrics, indicating its capability in handling large IoT datasets effectively. It provides not only high accuracy but also strong generalization, which is crucial for detecting intricate and diverse patterns of malicious behavior in IoT networks. Its precision and recall performance further highlight its robustness in identifying both attack and normal activity, reducing the chances of false positives and negatives, making it a superior choice for real-time IoT threat detection.
A Ku-Band Compact Offset Cylindrical Reflector Antenna with High Gain for Low-Earth Orbit Sensing Applications
Authors: Esmail Bashar A. F. , Isleifson Dustin , Shafai Lotfollah
Published: November 2024

The rise of CubeSats has unlocked opportunities for cutting-edge space missions with reduced costs and accelerated development timelines. CubeSats necessitate a high-gain antenna that can fit within a tightly confined space. This paper is primarily concerned with designing a compact Ku-band offset cylindrical reflector antenna for a CubeSat-based Earth Observation mission, with the goal of monitoring Arctic snow and sea ice. The development of a Ku-band offset cylindrical reflector, with a compact aperture of 110 × 149 mm2 (6.3λ × 8.5λ), is described alongside a patch array feed consisting of 2 × 8 elements. The patch array feed is designed using a lightweight Rogers substrate and is utilized to test the reflector. Adopting an offset configuration helped prevent gain loss due to feed blockage. Analyzing the reflector antenna, including the feed, thorough simulations and measurements indicates that achieving a gain of 25 dBi and an aperture efficiency of 52% at 17.2 GHz is attainable. The reflector’s cylindrical shape and compact size facilitate the design of a simple mechanism for reflector deployment, enabling the antenna to be stored within 1U. The array feed and reflector antenna have been fabricated and tested, demonstrating good consistency between the simulation and measurement outcomes.
Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction
Authors: Zhang Zikai
Published: November 2024

This paper analyzes the rounD dataset to advance motion forecasting algorithms for autonomous vehicles navigating complex roundabout environments. We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-based modules to effectively model vehicle interactions. Our primary objective is to evaluate the generalizability of the proposed model across diverse training and testing datasets. Through extensive experiments, we investigate how varying data distributions—such as different road configurations and recording times—impact the model’s prediction accuracy and robustness. This study provides key insights into the challenges of domain generalization in autonomous vehicle trajectory prediction.
Exploring the Psychological and Physiological Effects of Operating a Telenoid: The Preliminary Assessment of a Minimal Humanoid Robot for Mediated Communication
Authors: Nakae Aya , Bu-Omer Hani M. , Chang Wei-Chuan , Kishimoto Chie , Sumioka Hidenobu
Published: November 2024

Background: As the Internet of Things (IoT) expands, it enables new forms of communication, including interactions mediated by teleoperated robots like avatars. While extensive research exists on the effects of these devices on communication partners, there is limited research on the impact on the operators themselves. This study aimed to objectively assess the psychological and physiological effects of operating a teleoperated robot, specifically Telenoid, on its human operator. Methods: Twelve healthy participants (2 women and 10 men, aged 18–23 years) were recruited from Osaka University. Participants engaged in two communication sessions with a first-time partner: face-to-face and Telenoid-mediated. Telenoid is a minimalist humanoid robot teleoperated by a participant. Blood samples were collected before and after each session to measure hormonal and oxidative markers, including cortisol, diacron reactive oxygen metabolites (d-ROMs), and the biological antioxidat activity of plasma (BAP). Psychological stress was assessed using validated questionnaires (POMS-2, HADS, and SRS-18). Results: A trend of a decrease in cortisol levels was observed during Telenoid-mediated communication, whereas face-to-face interactions showed no significant changes. Oxidative stress, measured by d-ROMs, significantly increased after face-to-face interactions but not in Telenoid-mediated sessions. Significant correlations were found between oxytocin and d-ROMs and psychological stress scores, particularly in terms of helplessness and total stress measures. However, no significant changes were observed in other biomarkers or between the two conditions for most psychological measures. Conclusions: These findings suggest that cortisol and d-ROMs may serve as objective biomarkers for assessing psychophysiological stress during robot-mediated communication. Telenoid’s minimalist design may help reduce social pressures and mitigate stress compared to face-to-face interactions. Further research with larger, more diverse samples and longitudinal designs is needed to validate these findings and explore the broader impacts of teleoperated robots.
Modified Fly Ash as an Adsorbent for the Removal of Pharmaceutical Residues from Water
Authors: Vukčević Marija , Trajković Dušan , Maletić Marina , Mirković Miljana , Perić Grujić Aleksandra , Živojinović Dragana
Published: November 2024

In this work, different methods for fly ash modification were applied to obtain an adsorbent for the efficient removal of selected pharmaceuticals from a multiclass aqueous solution. Morphological and surface properties of the modified fly ash samples were analyzed by scanning electron microscopy, X-ray fluorescence, X-ray diffraction, Fourier transform infrared spectroscopy, and point of zero charge, and the influence of the applied modifications was determined by comparison with the results obtained for unmodified fly ash. Experimental parameters of the adsorption of the pharmaceutical onto the modified fly ash were optimized, and special attention was paid to the influence of different parameters on the adsorption capacities. Multivariate methods of analysis, such as artificial neural networks, applied to the obtained results showed that the contact time, the initial concentration of the pharmaceutical solution, and the pH value had the strongest influence on the adsorption process. Fly ash modified with chitosan and magnetic iron oxide showed the best adsorption properties (removal efficiency above 80% for the majority of the selected pharmaceuticals), and artificial neural networks confirmed its susceptibility to the modeling process.
Violence Against Men and Its Effects Within the Workplace: A Literature Review
Authors: Garro Maria , Bonfanti Rubinia Celeste , Argento Adriana , Ruggieri Stefano
Published: November 2024

Over the years, there has been increasing interest in violence against women and its significant effects on victims. However, there has been comparatively little interest regarding violence against men and its effects within the workplace. This is despite the fact that scholarly research has highlighted the frequency of this phenomenon, further exacerbated by the poor functioning of support services, which intensifies distress among male victims. The present review focused on examining publications indexed in bibliographic databases (Scopus, PubMed, and Web of Science) from the years 2012–2023 to comprehend the contemporary landscape of academic literature concerning violence against men, along with its implications within the workplace. A total of 20 studies met inclusion criteria, and all selected papers focused on male victims of violence were included in this review. The results highlight the significant presence of male victims within the context of gender-based violence and harassment, domestic violence, and intimate partner violence. This literature review additionally highlights that social representation and gender stereotypes seem to contribute to the low demand for intervention by victims and the imbalance in their effects on the public and practitioners. Moreover, the results underscore the intricate and profound repercussions of violence against men within the workplace. This review, offering an overview of existing research in this field, emphasizes the necessity for future research to enhance understanding of quantitative methodologies and to facilitate specialized training for practitioners. Hence, it becomes imperative to actively identify interventions tailored to safeguarding the well-being of male victims of violence.
Short-Term Effects of Cover Crops and Tillage Management on Soil Physical Properties on Silt Loam Soil
Authors: Patton David Scott , Green Vern Steven , Morris Dennis Keith , Shumway Calvin R.
Published: November 2024

Silt loam soils in the mid-southern United States are prone to soil erosion, crusting, and general soil degradation. A field experiment was established at three field sites in northeast Arkansas to evaluate the effect of cover crop and tillage management on cash crop yield and the physical properties of soil health, specifically infiltration rate and aggregate stability. Cover crop management included cereal rye, wheat and crimson clover, and a winter fallow. Tillage management included tillage and no-tillage. During the two-year study, yield was not significantly influenced by different tillage treatments. The cover crop treatment had greater yield than the no-cover crop treatment (5091 vs. 4264 kg ha−1) at one site in one of the years. Water infiltration was significantly improved with cover crops compared to with no-cover crops, with a 52% and 64% increase at Walcott and Magnolia, respectively. Soil aggregate stability was significantly improved with no-tillage as compared with tillage in both years at Walcott, with a 16% and 58% increase in 2015 and 2016, respectively. Both cover crop and tillage management can have significant impacts on soil physical properties in a short period of time.
Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda
Authors: Mendoza Nadia B. , Lin Chii-Dean , Kiene Susan M. , Menzies Nicolas A. , Wanyenze Rhoda K. , Schmarje Katherine A. , Naigino Rose , Ediau Michael , Kalichman Seth C. , Bailey Barbara A.
Published: November 2024

Standard statistical analyses often exclude incomplete observations, which can be particularly problematic when predicting rare outcomes, such as HIV positivity. In the linkage to the HIV care dataset, there were initially 553 complete HIV positive cases, with an additional 554 cases added through imputation. Imputation methods amelia, hmisc, mice and missForest were evaluated. Simulations were conducted across various scenarios using the complete data to guide imputation for the full dataset. A random forest model was used to predict HIV status, assessing imputation precision, overall prediction accuracy, and sensitivity. While missForest produced imputed values closer to the observed ones, this did not translate into better predictive models. Hmisc and mice imputations led to higher prediction accuracy and sensitivity, with median accuracy increasing from 64% to 76% and median sensitivity rising from 0.4 to 0.75. Hmisc and amelia were the fastest imputation methods. Additionally, oversampling the minority class combined with undersampling the majority class did not improve predictions of new HIV positive cases using only the complete observations. However, increasing the minority class information through imputation enhanced sensitivity for predicting cases in this class.
Article 700 Identification in Judicial Judgments: Comparing Transformers and Machine Learning Models
Authors: Mahmoudi Sid Ali , Condevaux Charles , Zambrano Guillaume , Mussard Stéphane
Published: November 2024

Predictive justice, which involves forecasting trial outcomes, presents significant challenges due to the complex structure of legal judgments. To address this, it is essential to first identify all claims across different categories before attempting to predict any result. This paper focuses on a classification task based on the detection of Article 700 in judgments, which is a rule indicating whether the plaintiff or defendant is entitled to reimbursement of their legal costs. Our experiments show that conventional machine learning models trained on word and document frequencies can be competitive. However, using transformer models specialized in legal language, such as Judicial CamemBERT, also achieves high accuracies.
The Impact of High-Tech Policies on Corporate ESG Performance: Evidence from China
Authors: Qin Fei , Zhang Mao
Published: November 2024

In light of resource and environmental constraints, the economic growth model is undergoing significant transformations. New quality productivity is the latest high-tech policy in China’s current development. Comprehensively improving the new quality productivity level of enterprises is the core driving force behind promoting productivity progress and sustainable development. The objective of this study was to employ machine learning techniques to comprehensively assess the level of new quality productivity exhibited by enterprises and to identify the spatiotemporal evolution characteristics thereof. Additionally, the study sought to investigate the impact and the specific mechanism of new quality productivity on the ESG performance of enterprises from a micro perspective. The study revealed that new quality productivity has a considerable impact on the ESG performance of enterprises, particularly in cases where the enterprise has a long history and is engaged in significant pollution. The underlying mechanism is that new quality productivity can enhance an enterprise’s ESG performance by alleviating financing constraints and improving the efficiency of employee innovation. Additionally, further research indicated that new quality productivity primarily influences the environmental and social ratings of enterprises.
Digital Transformation in Family Businesses: An Analysis of Drivers with fsQCA
Authors: Porfírio José António Ferreira , Santos Pedro , Rodrigues Ricardo M.
Published: November 2024

The digital transformation (DT) encompasses profound technological changes. Businesses face the challenge of adapting their activities to the pace of new technologies to increase the efficiency of business processes, improve the customer experience, and create innovative value propositions while recognizing the importance of sustainability. This research focuses on how family businesses, which are a relevant part of global economic activity and job creation, address the challenges associated with the DT. Based on the case of the automotive retail sector in a small European country characterized by the high acceptance of technologies, we use fuzzy-set qualitative comparative analysis (fsQCA) to understand how critical success factors (causal conditions) combine towards adopting technologies representing different perspectives of the DT. The results confirm the existence of multiple configurations based on the importance of organizational culture, change management, knowledge, human capital, IT systems, and DT strategy towards the adoption of customer relationship management, digital marketing, and social networks technologies, thus helping family businesses to understand the adoption of these technologies leading to organizational success in the context of the DT.
Prediction Study of Pollutants in Artificial Wetlands Enhanced by Electromagnetic Fields
Authors: Yin Fajin , Ma Rong , Liu Yungen , Xiong Liechao , Luo Hu
Published: November 2024

Predictive modelling is very important for water pollution management. In this study, based on an electromagnetic field-enhanced vertical flow artificial wetland and using the actual measured data as inputs to the model, the ammonia nitrogen (NH4+-N) effluent concentration of this wetland system was analyzed by Pearson’s correlation analysis to be related to six key factors, which were the NH+-N raw water concentration, the chemical oxygen demand (COD) raw water concentration, the treatment time, the magnetic field strength, the aeration time, and the electric field strength. Then, different artificial neural network models were constructed for comparison and the constructed models were evaluated based on statistical parameters. The results show that the PSO algorithm can improve the prediction effect of the BP neural network, but the prediction accuracy of the CNN model is better compared to the others. The prediction accuracy of the RF model is the highest compared to the others, and the evaluation parameters of R2, RMSE, and MAE of the test set are (0.9446, 2.4328, and 3.0943), respectively. The prediction error of this model is the smallest, and the model can predict the concentration of electric and magnetic fields in a wetland system with high accuracy compared to other models. This model can more accurately predict the NH4+-N effluent concentration of the magnetic field-enhanced wetland system, which can provide a certain basis for the study of the management of water pollution.
Rethinking the Design of Bikes and Bike Networks for Seniors: Sustainability, Climate Change, Alzheimer’s Disease, and Caregivers
Authors: Lusk Anne , Mazie Linda , Gale Seth A. , Savage Heidi
Published: November 2024

Bikes and bike networks are for younger fit bicyclists, and the U.S. continues to not serve older individuals, with and without dementia, and caregivers. Biking is a sustainable form of transportation, and expanding the biking population would address climate change while improving health. To our knowledge, research has not been conducted in which seniors indicate their preferences for bike styles and networks, health concerns, and desires to bike. Conducted in four senior-living communities in New England, the Visual and Verbal Preference Survey involved 178 participants (female—50%/male—29.8%; age 20–85 52.8%; age > 85 41.6% with 19.6% missing gender and 5.6% missing age). Bike test riding in two senior communities involved 50 participants (female—50%/male—40%; age 66–75 14%; 76–85 60%; age 86–95 16% with 10% missing gender and age). Seniors preferred the adult tricycle, followed by the three- and four-wheeled two-seated bikes, and to bicycle for 30 min 2 days a week, have a bathroom break every hour, and have a bicycle loop. Balance and fear of falling were major concerns, but they wanted to bicycle with family, children, and grandchildren. In two of the communities, seniors test rode three senior-friendly Van Raam bikes. The results mirrored the pre-test survey responses, where the adult tricycle (Easy Rider) was most preferred, followed by the three-wheeled two-seater bike (Fun2Go) and the low-step regular bike (Balance Bike). One community purchased the Fun2Go.
Influencing Path of Consumer Digital Hoarding Behavior on E-Commerce Platforms
Authors: Yue Zhikun , Zheng Xungang , Zhang Shasha , Zhong Linling , Zhang Wang
Published: November 2024

Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies.