bluuebunny/crossref_metadata_2025 huggingface dataset:
Examining the Capability of the VLF Technique for Nowcasting Solar Flares Based on Ground Measurements in Antarctica
Authors: Wang Shiwei , Zhou Ruoxian , Gu Xudong , Xu Wei , Hu Zejun , Ni Binbin , Cheng Wen , Feng Jingyuan , Ma Wenchen , Xu Haotian , Pan Yudi , Li Bin , He Fang , Chen Xiangcai , Hu Hongqiao
Published: June 2024

Measurements of Very-Low-Frequency (VLF) transmitter signals have been widely used to investigate the effects of various space weather events on the D-region ionosphere, including nowcasting solar flares. Previous studies have established a method to nowcast solar flares using VLF measurements, but only using measurements from dayside propagation paths, and there remains limited focus on day–night mixed paths, which are important for method applicability. Between March and May of 2022, the Sun erupted a total of 56 M-class and 6 X-class solar flares, all of which were well captured by our VLF receiver in Antarctica. Using these VLF measurements, we reexamine the capability of the VLF technique to nowcast solar flares by including day–night mixed propagation paths and expanding the path coverage in longitude compared to that in previous studies. The amplitude and phase maximum changes are generally positively correlated with X-ray fluxes, whereas the time delay is negatively correlated. The curve-fitting parameters that we obtain for the X-ray fluxes and VLF signal maximum changes are consistent with those in previous studies for dayside paths, even though different instruments are used, supporting the flare-nowcasting method. Moreover, the present results show that, for day–night mixed paths, the amplitude and phase maximum changes also scale linearly with the logarithm of the flare X-ray fluxes, but the level of change is notably different from that for dayside paths. The coefficients used in the flare-nowcasting method need to be updated for mixed propagation paths.
Experimental Co-Polarimetric GPR Survey on Artificial Vertical Concrete Cracks by the Improved Time-Varying Centroid Frequency Scheme
Authors: Zhang Xuebing , Pei Junxuan , Sha Xianda , Feng Xuan , Hu Xin , Chen Changle , Song Zhengchun
Published: June 2024

The experimental setup is devised to simulate the presence of vertical cracks with varying widths within concrete structures. Co-polarimetric ground-penetrating radar (GPR) surveys are carried out to acquire the “VV” and “HH” polarization data. The time-varying centroid frequency attribute is employed to describe the vertical variation in the center frequency of the radar wave, unveiling a gradual vertical decay in the centroid frequency at the locations of vertical cracks. An improved time-varying centroid frequency attribute based on the adaptive sparse S-transform (ASST) is proposed and tested by a finite-difference time-domain model and co-polarimetric GPR data, which can offer better resolution compared to that of the conventional S-transform. By analyzing the waveform and centroid frequency properties of the two polarizations, we conclude that the “VV” polarization is relatively sensitive to centimeter scale cracks, while the “HH” polarization is more sensitive to millimeter scale cracks.
The Generation of High-Resolution Mapping Products for the Lunar South Pole Using Photogrammetry and Photoclinometry
Authors: Liu Pengying , Geng Xun , Li Tao , Zhang Jiujiang , Wang Yuying , Peng Zhen , Wang Yinhui , Ma Xin , Wang Qiudong
Published: June 2024

High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km2. The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m.
Low-Sidelobe Imaging Method Utilizing Improved Spatially Variant Apodization for Forward-Looking Sonar
Authors: Yan Lu , Yang Juan , Xu Feng , Piao Shengchun
Published: June 2024

For two-dimensional forward-looking sonar imaging, high sidelobes significantly degrade the quality of sonar images. The cosine window function weighting method is often applied to suppress the sidelobe levels in the angular and range dimensions, at the expense of the main lobe resolutions. Therefore, an improved spatially variant apodization imaging method for forward-looking sonar is proposed, to reduce sidelobes without degrading the main lobe resolution in angular-range dimensions. The proposed method is a nonlinear postprocessing operation in which the raw complex-valued sonar image produced by a conventional beamformer and matched filter is weighted by a spatially variant coefficient. To enhance the robustness of the spatially variant apodization approach, the array magnitude and phase errors are calibrated to prevent the occurrence of beam sidelobe increase prior to beamforming operations. The analyzed results of numerical simulations and a lake experiment demonstrate that the proposed method can greatly reduce the sidelobes to approximately −40 dB, while the main lobe width remains unchanged. Moreover, this method has an extremely simple computational process.
Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process
Authors: Choudhary Asha , Vuković Matej , Mutlu Belgin , Haslgrübler Michael , Kern Roman
Published: June 2024

In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoder, UnSupervised Anomaly Detection on Multivariate Time Series (USAD), and Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (TranAD), showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.
Integration of Hollow Microneedle Arrays with Jellyfish-Shaped Electrochemical Sensor for the Detection of Biomarkers in Interstitial Fluid
Authors: Luo Fangfang , Li Zhanhong , Shi Yiping , Sun Wen , Wang Yuwei , Sun Jianchao , Fan Zheyuan , Chang Yanyi , Wang Zifeng , Han Yutong , Zhu Zhigang , Marty Jean-Louis
Published: June 2024

This study integrates hollow microneedle arrays (HMNA) with a novel jellyfish-shaped electrochemical sensor for the detection of key biomarkers, including uric acid (UA), glucose, and pH, in artificial interstitial fluid. The jellyfish-shaped sensor displayed linear responses in detecting UA and glucose via differential pulse voltammetry (DPV) and chronoamperometry, respectively. Notably, the open circuit potential (OCP) of the system showed a linear variation with pH changes, validating its pH-sensing capability. The sensor system demonstrates exceptional electrochemical responsiveness within the physiological concentration ranges of these biomarkers in simulated epidermis sensing applications. The detection linear ranges of UA, glucose, and pH were 0~0.8 mM, 0~7 mM, and 4.0~8.0, respectively. These findings highlight the potential of the HMNA-integrated jellyfish-shaped sensors in real-world epidermal applications for comprehensive disease diagnosis and health monitoring.
Study of the Bias of the Initial Phase Estimation of a Sinewave of Known Frequency in the Presence of Phase Noise
Authors: Alegria Francisco A. C. , Xie Lian , Pasadas Dário
Published: June 2024

The estimation of the parameters of a sinusoidal signal is of paramount importance in various applications in the fields of sensors, signal processing, parameter estimation, and device characterization, among others. The presence, in the measurement system, of non-ideal phenomena such as additive noise in the signals, phase noise in the stimulus generation, jitter in the sampling system, frequency error in the experimental setup, among others, leads to increased uncertainty and bias in the estimated quantities obtained by least squares methods and those derived from them. Therefore, from a metrological point of view, it is important to be able to theoretically predict and quantify those uncertainties in order to properly design the measurement system and its parameters, such as the number of samples to acquire or the stimulus signal amplitude to use to minimize the uncertainty in the estimated values. Previous works have shown that the presence of these non-ideal phenomena leads to increased uncertainty and bias in the estimation of the sinewave amplitude. The present work complements this knowledge by focusing specifically on the effect of phase noise and sampling jitter in the bias of the initial phase estimation of a sinusoidal signal of known frequency (three‑parameter sine fitting procedure). A theoretical derivation of the bias of initial phase estimation that takes into consideration the presence of phase noise in the sinewave is presented. Since a Taylor series approximation was used where only the first term was retained, it was necessary to validate the analytical derivations with numerical simulations using a Monte Carlo type of procedure. This process was applied to different conditions regarding the phase noise standard deviation, initial phase value, and number of samples. It is concluded that, in most scenarios, initial phase estimation using sine fitting is unbiased in the presence of phase noise or jitter. It is shown, however, that in cases of extremely high phase noise standard deviation and a very low number of samples, a bias occurs.
Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination
Authors: Chen Yeye , Tian Jia-Hong , Tian Han-Wen , Ma Rong , Wang Ze-Han , Pan Yu-Chen , Hu Xin-Yue , Guo Dong-Sheng
Published: June 2024

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.
On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads
Authors: Hong Sara , Kim Yeeun , Nam Jaehyun , Kim Seongmin
Published: June 2024

A recent development in cloud computing has introduced serverless technology, enabling the convenient and flexible management of cloud-native applications. Typically, the Function-as-a-Service (FaaS) solutions rely on serverless backend solutions, such as Kubernetes (K8s) and Knative, to leverage the advantages of resource management for underlying containerized contexts, including auto-scaling and pod scheduling. To take the advantages, recent cloud service providers also deploy self-hosted serverless services by facilitating their on-premise hosted FaaS platforms rather than relying on commercial public cloud offerings. However, the lack of standardized guidelines on K8s abstraction to fairly schedule and allocate resources on auto-scaling configuration options for such on-premise hosting environment in serverless computing poses challenges in meeting the service level objectives (SLOs) of diverse workloads. This study fills this gap by exploring the relationship between auto-scaling behavior and the performance of FaaS workloads depending on scaling-related configurations in K8s. Based on comprehensive measurement studies, we derived the logic as to which workload should be applied and with what type of scaling configurations, such as base metric, threshold to maximize the difference in latency SLO, and number of responses. Additionally, we propose a methodology to assess the scaling efficiency of the related K8s configurations regarding the quality of service (QoS) of FaaS workloads.
MPI System with Bore Sizes of 75 mm and 100 mm Using Permanent Magnets and FMMD Technique
Authors: Jeong Jae Chan , Kim Tae Yi , Cho Hyeon Sung , Seo Beom Su , Krause Hans Joachim , Hong Hyo Bong
Published: June 2024

We present two magnetic particle imaging (MPI) systems with bore sizes of 75 mm and 100 mm, respectively, using three-dimensionally arranged permanent magnets for excitation and frequency mixing magnetic detection (FMMD) coils for detection. A rotational and a translational stage were combined to move the field free line (FFL) and acquire the MPI signal, thereby enabling simultaneous overall translation and rotational movement. With this concept, the complex coil system used in many MPI systems, with its high energy consumption to generate the drive field, can be replaced. The characteristic signal of superparamagnetic iron oxide (SPIO) nanoparticles was generated via movement of the FFL and acquired using the FMMD coil. The positions of the stages and the occurrence of the f1 + 2f2 harmonics were mapped to reconstruct the spatial location of the SPIO. Image reconstruction was performed using Radon and inverse Radon transformations. As a result, the presented method based on mechanical movement of permanent magnets can be used to measure the MPI, even for samples as large as 100 mm. Our research could pave the way for further technological developments to make the equipment human size, which is one of the ultimate goals of MPI.
Teaching about Marginalized Groups Using a Digital Human Library: Lessons Learned
Authors: Chan Chitat
Published: June 2024

This paper presents lessons learned from a project inspired by digital storytelling and the human library to reduce prejudices against marginalized groups. By comparing the outcomes of similar participants in different settings over the same period, the study explored which types of activities might be pivotal when influencing the perspective-taking attitudes of participants. The study used a case study approach, with data from the digital human library project, and selected participants from three different engagement contexts: participants in group A were involved in reading story abstracts online, having short face-to-face meetings regarding human books, and engaging in editorial activities; participants in group B were involved in extended face-to-face sharing provided by human books, followed by question-and-answer interaction; and participants in group C were involved in the reading of stories online without interaction. Convenience sampling was used and included 250 registered participants who completed pre-test and post-test questionnaires. The study found that merely reading stories online (group C) did not significantly reduce prejudice, and face-to-face contact on its own (group B) was also not the most effective in changing attitudes. Group A participants who combined short face-to-face meetings and story-retelling activities showed the most significant changes in perspective-taking attitudes. These findings imply that dialogic cognitive processes in narrative activities, rather than the mode of contact, may be pivotal in enhancing perspective-taking attitudes. This paper calls for further research into the scalability of digital human library hybrids and more rigorous experimental research designs. It underscores the potential of these interventions to foster more inclusive societies, mitigate social biases, and support equity.
Conformational Analysis of Trifluoroacetyl Triflate, CF3C(O)OSO2CF3: Experimental Vibrational and DFT Investigation
Authors: Spaltro Agustín , Peluas Melina G. , Della Védova Carlos O. , Romano Rosana M.
Published: June 2024

The conformations of trifluoroacetyl triflate, CF3C(O)OSO2CF3, were investigated through experimental vibrational methods (gas-phase FTIR, liquid-phase Raman, and Ar matrix FTIR spectroscopy) and density functional theory (DFT) calculations. A potential energy surface was computed using the B3P86/6-31+g(d) approximation as a function of the dihedral angles τ1 = CC−OS and τ2 = CO−SC. The surface reveals three minima, which were further optimized using the B3LYP method with various basis sets (6-31++G(d), 6-311++G(d), tzvp, and cc-pvtz). The global minimum corresponds to a syn–anti conformer (the C=O double-bound syn with respect the O−S single bond and the C−O single bond anti with respect to S−C single bond). The other two minima represent enantiomeric syn–gauche forms. The Ar matrix FTIR spectrum exhibited clear evidence of the presence of two conformers. Furthermore, the randomization process observed following broadband UV–visible irradiation facilitated the identification of the IR absorption of each conformer. Based on the Ar matrix FTIR experiments, the vapour phase of trifluoroacetyl triflate at room temperature was composed of approximately 60–70% of the syn–anti conformer and 30–40% of the syn–gauche form.
Optimizing Components Selection in Blended Learning: Toward Sustainable Students Engagement and Success
Authors: Alammary Ali Saleh
Published: June 2024

Selecting the most appropriate components for a blended learning course is a multifaceted challenge influenced by various criteria. The impact of these influential criteria on the design process is not always obvious. The aim of this study is to assist academics in designing sustainable and engaging blended courses by investigating the impact of these criteria on the selection of blended learning components. By selecting the right mix of components, academics can foster a sustainable and meaningful involvement of students in their learning process over time, ensuring that students’ engagement is both enduring and beneficial in achieving academic success. A modified Delphi survey was utilized in this study, involving the participation of eighteen experts experienced in instructional design and online teaching. The analysis primarily relied on quantitative methods, utilizing the mean (to indicate central tendency) and standard deviation (to measure dispersion) for presenting the experts’ responses. Additionally, qualitative analysis of experts’ comments provided deeper insights into their quantitative ratings. Findings indicate that face-to-face collaborative activities should be the preferred method of delivery for academics aiming to enhance students’ engagement and foster their higher-order thinking skills, which students often find challenging. However, this approach is most effective when the group size is manageable. For larger student groups, online collaborative work can be a suitable alternative, provided there is ample online resource support. The results also indicate that online self-paced learning can be advantageous for lower-order thinking learning outcomes, particularly in situations where teaching staff is limited.
Comprehensive Multidisciplinary Electric Vehicle Modeling: Investigating the Effect of Vehicle Design on Energy Consumption and Efficiency
Authors: Aslan Eyyup , Yasa Yusuf , Meseci Yunus , Keskin Arabul Fatma , Arabul Ahmet Yigit
Published: June 2024

In this study, an electric vehicle (EV) dynamic model is devised that amalgamates mechanical design aspects—such as aerodynamic effects, tire friction, and vehicle frontal area—with crucial components of the electrical infrastructure, including the electric motor, power converters, and battery systems. Verification of the model is executed through a comprehensive multidisciplinary analysis utilizing CATIA, ANSYS Electromagnetics, ANSYS Fluent, and MATLAB–Simulink tools, which are applied to evaluate two alternative lightweight EV prototypes. The process involves initial computations of critical inputs for the dynamic model, including aerodynamic lift (C1), drag coefficients (Cd), and frontal area (Af). Subsequent stages entail the detailed design and analysis of a 2 kW brushless permanent magnet electric motor in ANSYS Electromagnetics to map efficiency contours across various speed–torque values. Integration of these parameters into a MATLAB–Simulink dynamic model, connected with motor drive inverter and battery models, allows for simulation-based energy consumption analysis under race track slope profiles. Remarkably, the findings underscore the considerable impact of neglected parameters on energy consumption, often exceeding fifty percent of the total. Consequently, an energy-efficient EV prototype is manufactured and rigorously tested under specified drive conditions, affirming the validation of the comprehensive multidisciplinary EV dynamic model.
Custom-Trained Large Language Models as Open Educational Resources: An Exploratory Research of a Business Management Educational Chatbot in Croatia and Bosnia and Herzegovina
Authors: Alfirević Nikša , Praničević Daniela Garbin , Mabić Mirela
Published: June 2024

This paper explores the contribution of custom-trained Large Language Models (LLMs) to developing Open Education Resources (OERs) in higher education. Our empirical analysis is based on the case of a custom LLM specialized for teaching business management in higher education. This custom LLM has been conceptualized as a virtual teaching companion, aimed to serve as an OER, and trained using the authors’ licensed educational materials. It has been designed without coding or specialized machine learning tools using the commercially available ChatGPT Plus tool and a third-party Artificial Intelligence (AI) chatbot delivery service. This new breed of AI tools has the potential for wide implementation, as they can be designed by faculty using only conventional LLM prompting techniques in plain English. This paper focuses on the opportunities for custom-trained LLMs to create Open Educational Resources (OERs) and democratize academic teaching and learning. Our approach to AI chatbot evaluation is based on a mixed-mode approach, combining a qualitative analysis of expert opinions with a subsequent (quantitative) student survey. We have collected and analyzed responses from four subject experts and 204 business students at the Faculty of Economics, Business and Tourism Split (Croatia) and Faculty of Economics Mostar (Bosnia and Herzegovina). We used thematic analysis in the qualitative segment of our research. In the quantitative segment of empirical research, we used statistical methods and the SPSS 25 software package to analyze student responses to the modified BUS-15 questionnaire. Research results show that students positively evaluate the business management learning chatbot and consider it useful and responsive. However, interviewed experts raised concerns about the adequacy of chatbot answers to complex queries. They suggested that the custom-trained LLM lags behind the generic LLMs (such as ChatGPT, Gemini, and others). These findings suggest that custom LLMs might be useful tools for developing OERs in higher education. However, their training data, conversational capabilities, technical execution, and response speed must be monitored and improved. Since this research presents a novelty in the extant literature on AI in education, it requires further research on custom GPTs in education, including their use in multiple academic disciplines and contexts.
Simplicity Matters: Unraveling the Impact of Minimalist Packaging on Green Trust in Daily Consumer Goods
Authors: Ding Yuechun , Meng Xing , Sun Cong
Published: June 2024

This study delves into the complex dynamics of consumer attitudes toward sustainable products, particularly within the fast-moving consumer goods category, highlighting the significant correlation between elegant and minimalist design aesthetics and the establishment of green trust in sustainable product offerings. Interestingly, while visually complex packaging may capture visual attention, it does not generate higher levels of green trust compared to simpler alternatives within the same product category. The findings underscore the importance of simple packaging design in mitigating consumer skepticism toward manipulative intentions and enhancing trust in sustainable products. Furthermore, this research reveals that individuals with higher levels of expertise possess deeper insights in perceiving complexity as a potential form of manipulation, thereby exacerbating skepticism toward sustainable products. In summary, the research outcomes guide sustainable product packaging design, emphasizing alternative solutions to convoluted designs that may lead to confusion. The effectiveness of simple design expression in sustainable products is emphasized, fostering the transition toward sustainable business efficacy and long-term development.
Spatial Analysis and Risk Assessment of Meteorological Disasters Affecting Cotton Cultivation in Xinjiang: A Comprehensive Model Approach
Authors: Zhang Ping , Chen Zhuo , Ding Gang , Fang Jiaqi , Fan Jinglong , Li Shengyu
Published: June 2024

A systematic understanding of the spatial distribution of meteorological disasters that affect cotton growth, such as rainstorms, gales, and hail, is important for reducing plant losses and promoting sustainable development. Our study aimed to evaluate the risk of meteorological disasters during cotton growth and analyze their spatial distribution and driving factors. A risk assessment model for major meteorological disasters during cotton cultivation in Xinjiang was established by integrating entropy weight methods and an analytic hierarchy process. A cotton meteorological disaster risk assessment index system, including the vulnerability of disaster-bearing bodies, hazards of disaster-causing factors, and exposure of disaster-bearing bodies, was constructed using Google Earth Engine. We determined the comprehensive risk levels of major meteorological disasters for cotton in various regions of Xinjiang. Research shows that the selection of indicators is very important, and crop risk assessment with a clear disaster-bearing body can make the results more accurate. It is necessary to consider the risk assessment of multiple disaster species for meteorological disaster risk assessment. The results revealed spatial differences in the meteorological disaster risk for cotton in 2020. The very high and high risks for cotton accounted for 42% of the cotton planting area, mainly distributed in Karamay, Tacheng, Kashgar, Changjizhou, Kezhou, and Ilizhou. Consequently, this study provides a scientific basis for cotton cultivation in Xinjiang, China.
Massese, Sarda and Lacaune Dairy Sheep Breeds: An Environmental Impact Comparison
Authors: Sodi Irene , Martini Mina , Sanjuàn Neus , Saia Sergio , Altomonte Iolanda , Andreucci Andrea , Fronte Baldassare , Pedonese Francesca , Giuliotti Lorella , Ciampolini Roberta , Salari Federica
Published: June 2024

The dairy sheep sector is an important sector in semiarid and arid areas. So far, the environmental impact of sheep milk production in these areas is scarcely known. This study aimed to assess the environmental impact of milk production on three farms that differ in the breed reared, namely Sarda (S), Lacaune (L) and Massese (M), in Tuscany (a Mediterranean region in central Italy). The Life Cycle Assessment methodology was applied to calculate the environmental performance of the farms, and the following impact categories were studied: climate change, freshwater, marine and terrestrial eutrophication, acidification, water use and land use. The L farm showed the lowest values for most impact categories and the M farm the highest. These results can be attributed to the greater productivity and efficiency of the L breed compared to the other two. Only for water use did the M farm cause a lower impact, underscoring the importance of applying characterization factors at the sub-watershed level.
The Usefulness of Cleaner Production Projects as an Element of the Initial Assessment of the Circularity of SMEs in the Context of Obtaining Funds Supporting the Implementation of the Circular Economy
Authors: Stasiuk-Piekarska Anna , Hordyńska Małgorzata , Michalska Monika
Published: June 2024

The authors of the article decided to verify the requirements for companies looking for sources of financing for projects that will enable them to transform the circular economy. Clarifying the requirements will allow companies to initially verify their ideas. For this purpose, an analysis was carried out of the requirements set for enterprises by committees assessing circular economy projects and allocating funds for their implementation. The analysis allowed us to identify several features that indicate whether a given project fits into the circular economy model. The article proposes a system model that can be used to prepare the company for changes towards the Green Deal (GD) by implementing the Cleaner Production (CP) strategy in the company. The implementation of a Cleaner Production strategy initiates further changes, gradually developing the company’s potential towards implementing the circular economy assumptions. This solution is much cheaper than the expensive services of consulting companies and much simpler than trying to prepare a competition application on your own, the authors asked themselves a research question: can the implementation of a Cleaner Production project be an initial action supporting SMEs in obtaining external funds (domestic and foreign) intended for the implementation of circular economy solutions?
Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education
Authors: Jeon In-Seong , Kim Shin-Yu , Kang Seong-Joo
Published: June 2024

As artificial intelligence (AI) and data science education gain importance in K-12 curricula, there is a growing need for well-designed sustainable educational datasets tailored to different school levels. Sustainable datasets should be reusable, adaptable, and accessible to support long-term AI and data science education goals. However, research on the systematic categorization of difficulty levels in educational datasets is limited. This study aims to address this gap by developing a framework for sustainable educational dataset standards based on learners’ developmental stages and data preprocessing requirements. The proposed framework consists of five levels: Level 1 (grades 1–4), where data preprocessing is unnecessary; Level 2 (grades 5–6), involving basic data cleaning; Level 3 (grades 7–9), requiring attribute manipulation; Level 4 (grades 10–12), involving feature merging and advanced preprocessing; and Level 5 (teachers/adults), requiring the entire data science process. An expert validity survey was conducted with 22 elementary and secondary school teachers holding advanced degrees in AI education. The results showed high validity for Levels 1–4 but relatively lower validity for Level 5, suggesting the need for separate training and resources for teachers. Based on the CVR results and expert feedback, the standards for Educational Datasets were revised, particularly for Stage 5, which targets teachers and adult learners. The findings highlight the importance of expert validation, step-by-step experiences, and an interdisciplinary approach in developing educational datasets. This study contributes to the theoretical understanding of educational datasets and provides practical implications for teachers, students, educational institutions, and policymakers in implementing effective and sustainable AI and data science education in K-12 settings, ultimately fostering a more sustainable future.
Urban Traffic Dominance: A Dynamic Assessment Using Multi-Source Data in Shanghai
Authors: Mei Yuyang , Wang Shenmin , Gong Mengjie , Chen Jiazheng
Published: June 2024

This study redefines the evaluation of urban traffic dominance by integrating complex network theory with multi-source spatiotemporal trajectory data, addressing the dynamic nature of various transportation modes, including public transit and shared mobility. Traditional traffic studies, which focus predominantly on static road traffic characteristics, overlook the fluid dynamics integral to urban transport systems. We introduce Relative Weighted Centrality (RWC) as a novel metric for quantifying dynamic traffic dominance, combining it with traditional static metrics to forge a comprehensive traffic dominance evaluation system. The results show the following: (1) Both static and dynamic traffic dominance display core-periphery structures centered around Huangpu District. (2) Dynamically, distinct variations in RWC emerge across different times and transport modes; during the early hours (0:00–6:00), shared bicycles show unique spatial distributions, the subway network experiences a notable decrease in RWC yet maintains its spatial pattern, and taxis exhibit intermediate characteristics. Conversely, the RWC for all modes generally increases during morning (6:00–12:00) and evening (18:00–24:00) peaks, with a pronounced decrease in subway RWC in the latter period. (3) The integration of dynamic evaluations significantly modifies conventional static results, emphasizing the impact of population movements on traffic dominance. This comprehensive analysis provides crucial insights into the strategic management and development of urban traffic infrastructure in Shanghai.
Analysis of Spatial and Temporal Distribution and Changes in Extreme Climate Events in Northwest China from 1960 to 2021: A Case Study of Xinjiang
Authors: Yang Yang , Chang Wei
Published: June 2024

Xinjiang, as a climate-sensitive region in Northwest China, holds significant importance in studying extreme climate events for agricultural production and socioeconomic development. Using data spanning from 1960 to 2021 from 52 meteorological stations across Xinjiang, encompassing 23 indices of extreme climate events, the 5-year moving average, linear trend fitting, and inverse distance weighting (IDW) are used to analyze the distribution patterns and temporal changes in extreme climatic phenomena within the region. The results indicate that, over the period from 1960 to 2021, the Amplitude Temperature Index, Heat Index, and Warm Spell Duration Index in Xinjiang exhibited a marked increasing trend, whereas the Cold Index and Cold Spell Duration Index displayed a significant decreasing trend. The range of changes in the extreme temperature indices from 1990 to 2021 is higher than that of 1960 to 1989. The areas with high values of amplitude temperature extreme indices are primarily concentrated in the southern part, while the areas with high values of cold indices are mainly distributed in the northern part. The upward/downward trends all account for over 80.00% of the entire region. The precipitation scale indices, precipitation day indices, intense precipitation index, and extreme precipitation index all showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indices from 1990 to 2021 was lower than that from 1960 to 1989. Furthermore, areas with high precipitation values and regions with high trend values of climate tendency are predominantly concentrated in the northern and western parts of Xinjiang, with over 71.00% of the entire region experiencing an upward trend. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.
Symmetry-Enabled Resource-Efficient Systolic Array Design for Montgomery Multiplication in Resource-Constrained MIoT Endpoints
Authors: Ibrahim Atef , Gebali Fayez
Published: June 2024

In today’s TEST interconnected world, the security of 5G Medical IoT networks is of paramount concern. The increasing number of connected devices and the transmission of vast amounts of data necessitate robust measures to protect information integrity and confidentiality. However, securing Medical IoT edge nodes poses unique challenges due to their limited resources, making the implementation of cryptographic protocols a complex task. Within these protocols, modular multiplication assumes a crucial role. Therefore, careful consideration must be given to its implementation. This study focuses on developing a resource-efficient hardware implementation of the Montgomery modular multiplication algorithm over GF(2l), which is a critical operation in cryptographic algorithms. The proposed solution introduces a bit-serial systolic array layout with a modular structure and local connectivity between processing elements. This design, inspired by the principles of symmetry, allows for efficient utilization of resources and optimization of area and delay management. This makes it well-suited for deployment in compact Medical IoT edge nodes with limited resources. The suggested bit-serial processor structure was evaluated through ASIC implementation, which demonstrated substantial improvements over competing designs. The results showcase an average area reduction of 24.5% and significant savings in the area–time product of 26.2%.
Analytical Investigation of Thermal Radiation Effects on Electroosmotic Propulsion of Electrically Conducting Ionic Nanofluid with Single-Walled Carbon Nanotube Interaction in Ciliated Channels
Authors: Mehboob Junaid , Ellahi Rahmat , Sait Sadiq Mohammad
Published: June 2024

This study examines the behavior of single-walled carbon nanotubes (SWCNTs) suspended in a water-based ionic solution, driven by the combined mechanisms of electroosmosis and peristalsis through ciliated media. The inclusion of nanoparticles in ionic fluid expands the range of potential applications and allows for the tailoring of properties to suit specific needs. This interaction between ionic fluids and nanomaterials results in advancements in various fields, including energy storage, electronics, biomedical engineering, and environmental remediation. The analysis investigates the influence of a transverse magnetic field, thermal radiation, and mixed convection acting on the channel walls. The novel physical outcomes include enhanced propulsion efficiency due to SWCNTs, understanding the influence of thermal radiation on fluid behavior and heat exchange, elucidation of the interactions between SWCNTs and the nanofluid, and recognizing implications for microfluidics and biomedical engineering. The Poisson–Boltzmann ionic distribution is linearized using the modified Debye–Hückel approximation. By employing real-world approximations, the governing equations are simplified using long-wavelength and low-Reynolds-number approximation. Conducting sensitivity analyses or exploring the impact of higher-order corrections on the model’s predictions in recent literature might alter the results significantly. This acknowledges the complexities of the modeling process and sets the groundwork for further enhancement and investigation. The resulting nonlinear system of equations is solved through regular perturbation techniques, and graphical representations showcase the variation in significant physical parameters. This study also discusses pumping and trapping phenomena in the context of relevant parameters.
Semi-Symmetric Metric Connections and Homology of CR-Warped Product Submanifolds in a Complex Space Form Admitting a Concurrent Vector Field
Authors: Khan Meraj Ali , Al-Dayel Ibrahim , Chaubey Sudhakar Kumar
Published: June 2024

In this paper, we conduct a thorough study of CR-warped product submanifolds in a Kaehler manifold, utilizing a semi-symmetric metric connection within the framework of warped product geometry. Our analysis yields fundamental and noteworthy results that illuminate the characteristics of these submanifolds. Additionally, we investigate the implications of our findings on the homology of these submanifolds, offering insights into their topological properties. Notably, we present a compelling proof demonstrating that, under a specific condition, stable currents cannot exist for these warped product submanifolds. Our research outcomes contribute significant knowledge concerning the stability and behavior of CR-warped product submanifolds equipped with a semi-symmetric metric connection. Furthermore, this work establishes a robust groundwork for future explorations and advancements in this particular field of study.
Update on Non-Interchangeability of Botulinum Neurotoxin Products
Authors: Brin Mitchell F. , Nelson Mariana , Ashourian Nazanin , Brideau-Andersen Amy , Maltman John
Published: June 2024

The growing use of botulinum neurotoxins (BoNTs) for medical and aesthetic purposes has led to the development and marketing of an increasing number of BoNT products. Given that BoNTs are biological medications, their characteristics are heavily influenced by their manufacturing methods, leading to unique products with distinct clinical characteristics. The manufacturing and formulation processes for each BoNT are proprietary, including the potency determination of reference standards and other features of the assays used to measure unit potency. As a result of these differences, units of BoNT products are not interchangeable or convertible using dose ratios. The intrinsic, product-level differences among BoNTs are compounded by differences in the injected tissues, which are innervated by different nerve fiber types (e.g., motor, sensory, and/or autonomic nerves) and require unique dosing and injection sites that are particularly evident when treating complex therapeutic and aesthetic conditions. It is also difficult to compare across studies due to inherent differences in patient populations and trial methods, necessitating attention to study details underlying each outcome reported. Ultimately, each BoNT possesses a unique clinical profile for which unit doses and injection paradigms must be determined individually for each indication. This practice will help minimize unexpected adverse events and maximize efficacy, duration, and patient satisfaction. With this approach, BoNT is poised to continue as a unique tool for achieving individual goals for an increasing number of medical and aesthetic indications.
Applying Flow Virometry to Study the HIV Envelope Glycoprotein and Differences Across HIV Model Systems
Authors: Burnie Jonathan , Fernandes Claire , Patel Ayushi , Persaud Arvin Tejnarine , Chaphekar Deepa , Wei Danlan , Lee Timothy Kit Hin , Tang Vera A. , Cicala Claudia , Arthos James , Guzzo Christina
Published: June 2024

The HIV envelope glycoprotein (Env) is a trimeric protein that facilitates viral binding and fusion with target cells. As the sole viral protein on the HIV surface, Env is important both for immune responses to HIV and in vaccine designs. Targeting Env in clinical applications is challenging due to its heavy glycosylation, high genetic variability, conformational camouflage, and its low abundance on virions. Thus, there is a critical need to better understand this protein. Flow virometry (FV) is a useful methodology for phenotyping the virion surface in a high-throughput, single virion manner. To demonstrate the utility of FV to characterize Env, we stained HIV virions with a panel of 85 monoclonal antibodies targeting different regions of Env. A broad range of antibodies yielded robust staining of Env, with V3 antibodies showing the highest quantitative staining. A subset of antibodies tested in parallel on viruses produced in CD4+ T cell lines, HEK293T cells, and primary cells showed that the cellular model of virus production can impact Env detection. Finally, in addition to being able to highlight Env heterogeneity on virions, we show FV can sensitively detect differences in Env conformation when soluble CD4 is added to virions before staining.
Hospital-Based Influenza and Pneumococcal Vaccination for Cancer Patients on Active Treatment and Their Family Members during the COVID-19 Pandemic in Italy: A Single-Center Experience
Authors: Dalu Davide , Ridolfo Anna Lisa , Ruggieri Lorenzo , Cona Maria Silvia , Riva Agostino , De Francesco Davide , Tricella Chiara , Fasola Cinzia , Ferrario Sabrina , Gambaro Anna , Lombardi Stocchetti Benedetta , Smiroldo Valeria , Rebecchi Gaia , Piva Sheila , Carrozzo Giorgia , Antinori Spinello , La Verde Nicla
Published: June 2024

In patients with cancer, tumor- and treatment-induced immunosuppression are responsible for a four-fold increase in morbidity and mortality caused by influenza and invasive Streptococcus pneumoniae infections compared to the general population. The main oncology societies strongly recommend vaccination in patients with cancer to prevent these infections. However, vaccine hesitancy is a main concern in this population. The aim of this study was to assess the feasibility of in-hospital vaccination for patients under anticancer treatment and their family members (FMs) against influenza and pneumococcal infections during the COVID-19 pandemic in order to increase vaccine coverage. This was a single-center, prospective, observational study conducted at the Department of Oncology of Luigi Sacco University Hospital (Milan, Italy) between October 2020 and April 2021. The main primary outcome was the incidence of influenza-like illness (ILI) and pneumococcal infections. The main secondary outcome was safety. A total of 341 subjects were enrolled, including 194 patients with cancer and 147 FMs. The incidence of ILI was higher among patients than among FMs (9% vs. 2.7%, OR 3.92, p = 0.02). Moreover, two subjects were diagnosed with pneumococcal pneumonia. The most frequent vaccine-related AEs were pain in the injection site (31%) and fatigue (8.7%). In conclusion, this hospital-based vaccination strategy was feasible during the COVID-19 pandemic, representing a potential model to maximize vaccine coverage during a public health emergency.