bluuebunny/crossref_metadata_2025 huggingface dataset:
The Design and Construction of a 12-Channel Electrocardiogram Device Developed on an ADS1293 Chip Platform
Authors: Nguyen Thanh-Nghia , Duong Thanh-Tai , Omer Hiba , Sulieman Abdelmoneim , Bradley David A.
Published: May 2023

An accurate and compact electrocardiogram (ECG) device will greatly assist doctors in diagnosing heart diseases. It will also help to address the increasing number of deaths caused by heart disease. Accordingly, the goal of the project is to design and construct an easy-to-use compact 12-lead electrocardiogram device that communicates with a computer to create a system that can continuously monitor heart rate and which can be connected to allied medical systems. The design is based on an ECG receiver circuit utilizing an IC ADS1293 and an Arduino Nano. The ADS1293 has built-in input Electromagnetic Interference (EMI) filters, quantizers, and digital filters, which help in reducing the size of the device. The software has been created using the C# programming language, with Windows Presentation Foundation (WPF), aiding the collection of the ECG signals from the receiving circuit via the computer port. An ECG Multiparameter Simulator has been used to calibrate the ECG device. Finally, a plan has been developed to connect the arrangement to health systems according to HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) through Representational State Transfer Application Programming Interface (Rest API). The ECG device, completed at the cost of U$169 excluding labor, allows for the signal of 12 leads of ECG signal to be obtained from 10 electrodes mounted on the body. The processed ECG data was written to a JSON file with a maximum recording time of up to three days, managed by a Structured Query Language Server (SQL) Server database. The software retrieves patient data from electrical medical records in accordance with HL7 FHIR standards. A compact and easy-to-use ECG device was successfully designed to record ECG signals. An in-house developed software was also completed to display and store the ECG signals.
Monocular Depth Estimation for 3D Map Construction at Underground Parking Structures
Authors: Li Jingwen , Song Xuedong , Gao Ruipeng , Tao Dan
Published: May 2023

Converting the actual scenes into three-dimensional models has inevitably become one of the fundamental requirements in autonomous driving. At present, the main obstacle to large-scale deployment is the high-cost lidar for environment sensing. Monocular depth estimation aims to predict the scene depth and construct a 3D map via merely a monocular camera. In this paper, we add geometric consistency constraints to address the non-Lambertian surface problems in depth estimation. We also utilize the imaging principles and conversion rules to produce a 3D scene model from multiple images. We built a prototype and conduct extensive experiments in a corridor and an underground parking structure, and the results show the effectiveness for indoor location-based services.
An Extended Approach to the Evaluation of Energy Storage Systems: A Case Study of Li-Ion Batteries
Authors: Cellura Salvatore , Mazza Andrea , Bompard Ettore , Corgnati Stefano
Published: May 2023

Energy storage technologies can act as flexibility sources for supporting the energy transition, enabling the decarbonisation of the grid service provision and the active engagement of the customers (both prosumers and consumers), opening for them new business opportunities. Within storage technologies, Lithium-ion (Li-ion) batteries represent an interesting solution for dealing with the majority of these services. In this context, this study addresses an evaluation of economic, environmental and geopolitical risks with reference to the critical raw materials used in the manufacturing of Lithium Iron Phosphate (LFP) Li-ion batteries. The assessment entailes grid and prosumer services that these batteries can provide. The exploited economic indicator is the Levelised Cost of Storage, whereas six environmental indicators are used for environmental impact estimation. Cycle stages accounted for in the analysis are the manufacturing and use phases. Finally, the evaluation of the impact of critical raw materials is performed by deploying a Supply Risk indicator, which is instead assessed considering every single material and the overall risk for the battery. High-risk materials are represented by Graphite and Phosphorous. Results denote that, for each service, the number of cycles and the discharge duration are pivotal to make the investment economically and environmentally sustainable. The reduction in the Net Import Reliance, as well as the increase in the Recycling Rate, could sensibly reduce the risk associated with battery raw materials.
Doppler Positioning of Dynamic Targets with Unknown LEO Satellite Signals
Authors: Yang Chaoqun , Zang Bo , Gu Bowen , Zhang Liang , Dai Chuanjin , Long Lulan , Zhang Zongchang , Ding Linlin , Ji Hongbing
Published: May 2023

A new concept of global navigation based on Doppler measurements from a large low Earth orbit (LEO) constellation is investigated that has potential to serve as a complement or backup to global navigation satellite systems (GNSSs) to provide navigation and positioning services in GNSS denial environments. In this work, we investigate the potential of LEO communication satellite opportunity signals in dynamic navigation, establish LEO satellite Doppler positioning equations, derive the main error sources affecting the Doppler positioning results of a dynamic target, and analyze the impact of each error source on positioning accuracy through a simulation. The results show that the orbit error and clock drift had a large impact on positioning accuracy. This navigation scheme would be more competitive if it could provide high-precision satellite orbits and accurate Doppler measurements. The obtained results show that the LEO satellite signals used as navigation opportunity signals are an attractive alternative in GNSS rejection environments for high dynamic targets.
TEEDAG: A High-Throughput Distributed Ledger Based on TEE and Directed Acyclic Graph
Authors: Lu Xiaofeng , Jiang Cheng
Published: May 2023

With the arrival of the 5G era, the Internet of Things (IoT) has entered a new stage, and the amount of IoT data is growing rapidly. The traditional blockchain cannot handle massive amounts of data, which presents scalability challenges for blockchain technology. Existing blockchain improvement technologies such as off-chain payments, protocol improvements, and sharding techniques have performance bottlenecks and limitations in the data, which is rapidly growing. The blockchain is fundamentally a decentralized distributed ledger, and the traditional chain structure is inadequate for addressing concerns such as forks, double-spending attacks, and other factors in the current IoT landscape. In this paper, we propose a high-throughput distributed ledger based on Directed Acyclic Graph (DAG) named TEEDAG. We design a consensus algorithm based on self-referencing parallel chains combined with Trusted Execution Environment (TEE) to ensure the security of the consensus process. The experiment proves that TEEDAG demonstrates a significantly higher throughput compared to traditional blockchain solutions and offers improved security and efficiency compared to existing DAG-based distributed ledger solutions.
Automatic Sleep Staging Using BiRNN with Data Augmentation and Label Redirection
Authors: Gong Yulin , Wang Fatong , Lv Yudan , Liu Chang , Li Tianxing
Published: May 2023

Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation factors. In this paper, we propose an automatic sleep stage classification model based on the Bidirectional Recurrent Neural Network (BiRNN) with data bundling augmentation and label redirection for accurate sleep staging. Through extensive analysis, we discovered that the incorrect classification labels are primarily concentrated in the transition and nonrapid eye movement stage I (N1). Therefore, our model utilizes a sliding window input to enhance data bundling and an attention mechanism to improve feature enhancement after label redirection. This approach focuses on mining latent features during the N1 and transition periods, which can further improve the network model’s classification performance. We evaluated on multiple public datasets and achieved an overall accuracy rate of 87.3%, with the highest accuracy rate reaching 93.5%. Additionally, the network model’s macro F1 score reached 82.5%. Finally, we used the optimal network model to study the impact of different EEG channels on the accuracy of each sleep stage.
T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting
Authors: Liang Mengkun , Guo Renjing , Li Hongyu , Wu Jiaqi , Sun Xiangdong
Published: May 2023

Electricity is an essential resource that plays a vital role in modern society, and its demand has increased rapidly alongside industrialization. The accurate forecasting of a country’s electricity demand is crucial for economic development. A high-precision electricity forecasting framework can assist electricity system managers in predicting future demand and production more accurately, thereby effectively planning and scheduling electricity resources and improving the operational efficiency and reliability of the electricity system. To address this issue, this study proposed a hybrid forecasting framework called T-LGBKS, which incorporates TPE-LightGBM, k-nearest neighbor (KNN), and the Shapley additive explanation (SHAP) methods. The T-LGBKS framework was tested using Chinese provincial panel data from 2005 to 2021 and compared with seven other mainstream machine learning models. Our testing demonstrated that the proposed framework outperforms other models, with the highest accuracy (R2=0.9732). This study also analyzed the interpretability of this framework by introducing the SHAP method to reveal the relationship between municipal electricity consumption and socioeconomic characteristics (such as how changes in economic strength, traffic levels, and energy structure affect urban electricity demand). The findings of this study provide guidance for policymakers and assist decision makers in designing and implementing electricity management systems in China.
Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model
Authors: Junior Osni Silva , Coninck Jose Carlos Pereira , Magrin Fabiano Gustavo Silveira , Ganacim Francisco Itamarati Secolo , Pombeiro Anselmo , Fernandes Leonardo Göbel , Romaneli Eduardo Félix Ribeiro
Published: May 2023

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule’s law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure.
Molecular Characterization of Hydrocarbons in Petroleum by Ultrahigh-Resolution Mass Spectrometry
Authors: Liu Jikun , Wang Litao , Chen Fei , Hu Wenya , Dong Chenglong , Wang Yinghao , Han Yehua
Published: May 2023

The progress in ultrahigh-resolution mass spectrometry (UHRMS) and related technologies has significantly promoted the development of petroleomics. The study of petroleomics has greatly broadened our knowledge of the composition of petroleum at the molecular level, especially heavy fractions that cannot be analyzed by gas chromatography (GC)-based methods. To date, heteroatom-containing (O, N, S, and metal atoms) compounds in petroleum have been studied extensively by UHRMS. Nevertheless, the main components of petroleum, hydrocarbon compounds, have remained out of reach of petroleomics characterization for a long time because they are too difficult to be softly and efficiently ionized for UHRMS analysis. It is undoubtedly the case that petroleum hydrocarbons should not be the missing part of petroleomics characterization since they play important roles not only as feeding pools for various high value-added petroleum products but also as critical biomarkers for geochemistry studies. Here, the most recent breakthroughs in petroleomics characterization of hydrocarbon compounds using UHRMS rather than conventional methods have been reviewed. Innovations of soft ionization methods can transform non-polar hydrocarbon molecules into molecular ions or quasi-molecular ions that are reachable for UHRMS without fragmentation. The development of data processing methods has assisted the decoding of the complicated UHRMS data to visualize the molecular composition and structure of hydrocarbon compounds. These advances make it possible to see the whole picture of petroleum compositions, from light distillation fractions to heavy distillation fractions, and from small volatile molecules to large non-volatile ones. The applications of UHRMS-based methods for petroleomics characterization of hydrocarbon compounds in crude oils and various petroleum samples including fuel oil, slurry, and even asphaltene have made a great contribution to petrochemistry and geochemistry studies, especially in the fields of molecular refining and biomarker discovery.
Output Power Capacity Improvement Based on Three-Phase Current Balance Control for the Doubly Salient Electromagnetic Generator System
Authors: Min Guilu , Xu Yanwu
Published: May 2023

The doubly salient electromagnetic generator (DSEG) has the advantages of flexible control, high robustness, and low cost. Since multiple stator poles share a common set of field windings, the magnetic circuit of each phase is asymmetrical, which limits the output performance of the DSEG. The three-phase current balance control for the DSEG system was proposed. By investigating the current waveform under the angular position control (APC), a three-phase current prediction method was proposed. The conduction angle of the power switches in the active rectifier was predicted to make the three-phase current balanced. Based on the analysis and experiment, the current waveform was further analyzed, and the effectiveness of the three-phase current balance control was verified. Compared with the APC method, the output power capacity was improved using the three-phase current balance control under a limited winding current density. The maximum potential of the DSEG was explored, and the electromagnetic load capacity of the DSEG was fully released.
Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition
Authors: Lei Shuang , He Yu , Zhang Jing , Deng Kun
Published: May 2023

The capacity configuration of the energy storage system plays a crucial role in enhancing the reliability of the power supply, power quality, and renewable energy utilization in microgrids. Based on variational mode decomposition (VMD), a capacity optimization configuration model for a hybrid energy storage system (HESS) consisting of batteries and supercapacitors is established to achieve the optimal configuration of energy storage capacity in wind–solar complementary islanded microgrids. Firstly, based on the energy mapping relationship between the frequency domain and time domain, the decomposition mode number K of VMD is determined based on the principle of minimum total mode aliasing energy. Then, considering the smoothing fluctuation characteristics of different energy storage components, the dividing point N of high frequency and low frequency in the unbalanced power between the source and load in the microgrid is selected to allocate charging and discharging power instructions for the battery and supercapacitor. Finally, taking the annual comprehensive cost of the HESS as the objective function, a hybrid energy storage capacity optimization configuration model is established, and the dividing point N is used as the optimization variable to solve the model in order to obtain the optimal configuration results. The case study results show that the proposed method is more economical and feasible than the existing energy storage configuration methods.
An Investigation of Tourism, Economic Growth, CO2 Emissions, Trade Openness and Energy Intensity Index Nexus: Evidence for the European Union
Authors: Meșter Ioana , Simuț Ramona , Meșter Liana , Bâc Dorin
Published: May 2023

Tourism has become one of the most important sectors in many countries, significantly contributing to their economic growth and development. However, the expansion of tourism has also brought about various environmental and social challenges. The relationship between tourism, economic growth, trade openness, and the environment is diverse and complex. The objective of this paper is to investigate the relationship between the international tourism development index, GDP per capita, CO2 emissions, trade openness index as well as the energy intensity index in EU 27, over the 1995–2019 period. A composite index for international tourism was developed using the Principal Component Analysis (PCA). Panel Autoregressive distributed lag (ARDL) approach is used to reveal the long- and short-run impact of GDP per capita, CO2 emissions, trade openness index as well as the energy intensity index on the tourism development index. Panel ARDL estimates confirm some of our research hypotheses: at the level of EU countries, there is a short-run relationship between tourism and GDP per capita, but only in a few EU countries, trade openness influences tourism development index. Dumitrescu-Hurlin causality test confirms long-run feedback relationship between tourism development index and trade openness, between tourism development index and CO2 emissions, and between tourism development index and GDP and unilateral causality running from tourism development index towards energy efficiency.
Treatment of Wastewater from Soil Washing with Soluble Humic Substances Using Biochars and Activated Carbon
Authors: Gusiatin Mariusz Z. , Pasieczna-Patkowska Sylwia , Bálintová Magdaléna , Kuśmierz Marcin
Published: May 2023

Energy can be obtained by pyrolysis of organic wastes, and the solid residue of pyrolysis (biochar) can be used as an adsorbent for the treatment of various types of wastewater. Although soil washing can effectively remediate metal-contaminated soils, it can generate significant amounts of soil washing wastewater (SWW). This study investigated the effectiveness of using activated carbon and various biochars to treat SWW from the remediation of soil heavily contaminated with cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) with soluble humic substances (SHS) from municipal sewage sludge. Willow biochar (BW), plant biomass biochar (BPB), coconut shell biochar (BCH), and Norit SX2 activated carbon (ACN) were tested at different dosages (12.5–100 g/L) and adsorption times (30–1440 min) for the treatment of SWW. At 100 g/L dosage, biochar removed Cd, Cu, Ni, Pb, and Zn with 56–83%, 32–41%, 18–42%, 75–83%, and 44–83% efficiency, respectively, while ACN removed them with 87–95% efficiency. Only BW and ACN removed soluble organics with efficiencies of 49% and 94%, respectively, at the highest dosage. Adsorption of metals and soluble organics was mainly controlled by physisorption and chemisorption. Diffusion of metals and soluble organics into the different pore sizes was not the most important rate-limiting step. ACN and BW had better structural properties and treated SWW most effectively. BPB and BCH removed metals but not soluble organics, which could be beneficial for SHS recycling.
Technologies and Strategies to Support Energy Transition in Urban Building and Transportation Sectors
Authors: De Rosa Mattia , Bianco Vincenzo , Barth Henrik , Pereira da Silva Patricia , Vargas Salgado Carlos , Pallonetto Fabiano
Published: May 2023

More than half of the world population live in urban settlements which are responsible for a large share of energy consumption and, consequently, carbon emissions. The transition towards a more sustainable urban environment requires a change in paradigm in terms of how we design and manage our cities. Urban areas require innovative technologies and strategies to reduce energy consumption and carbon emissions, and to be included in comprehensive plans encompassing all technical, social and economic dimensions which characterise cities. This involves the transformation of urban contexts, with a focus on local and urban-level mitigation measures, such as the construction of positive energy buildings, deployment of renewable energy, promotion of a sustainable mobility, creation of resilient urban infrastructure, implementation of circular economy and recycling practices, etc. The present article provides a perspective on the sustainable energy transition in cities, focusing on the building and transportation sectors. Furthermore, insights on supporting mechanisms and innovative management strategies are presented.
A Review of Hybrid Renewable Energy Systems: Architectures, Battery Systems, and Optimization Techniques
Authors: León Gómez Juan Carlos , De León Aldaco Susana Estefany , Aguayo Alquicira Jesus
Published: May 2023

This paper aims to perform a literature review and statistical analysis based on data extracted from 38 articles published between 2018 and 2023 that address hybrid renewable energy systems. The main objective of this review has been to create a bibliographic database that organizes the content of the articles in different categories, such as system architecture, energy storage systems, auxiliary generation components used, and software employed, in addition to showing the algorithms and economic and reliability criteria for the optimization of these systems. In total, 38 articles have been analyzed, compared, and classified to provide an overview of the current status of simulation and optimization projects for hybrid renewable energy systems, highlighting clearly and appropriately the relevant trends and conclusions. A list of review articles has also been provided, which cover the aspects required for understanding HRESs.
Stand-Level Biomass and Leaf Trait Models for Young Naturally Regenerated Forests of European Hornbeam
Authors: Konôpka Bohdan , Murgaš Vlastimil , Šebeň Vladimír , Pajtík Jozef , Merganičová Katarína
Published: May 2023

European hornbeam (Carpinus betulus L.) is a tree species widely distributed in Europe and the Asian part of the Near East. However, since European hornbeam is not very attractive for commercial purposes, scientific interest in this species has been rather sparse. Our study focused on dense young (up to 10 years old) European hornbeam stands originating from natural regeneration from seeds in Slovakia because in future the importance of this species may increase due to the climate change. We combined previously constructed tree-level biomass models, data on basic leaf traits, i.e., weight and area, and measurements from thirty plots located at ten different sites across Slovakia to construct stand-level allometric relations of the biomass stock in tree components, i.e., leaves, branches, bark, stem under bark and roots, to mean stand diameter at stem base, i.e., at the ground level. Moreover, we calculated and modelled leaf characteristics, namely the specific leaf area (SLA), leaf area ratio (LAR) and leaf area index (LAI), at a stand level. The total tree biomass stock including all tree components ranged between 0.75 and 13.63 kg per m2, out of which the biomass of stem with bark was from 0.31 to 8.46 kg per m2. The biomass models showed that the contribution of roots (omitting those with a diameter under 2 mm) decreased with the increasing mean stand diameter at stem base, whereas the opposite pattern was observed for branches and stem biomass. Further, we found that the mean stand diameter at stem base was a good predictor of both LAR and LAI. The results indicated the high photosynthetic efficiency of European hornbeam leaves per one-sided surface leaf area. Moreover, the growth efficiency (GE), expressed as the biomass increment of woody parts per leaf area unit, of young European hornbeam trees was high. The models proved a close positive linear correlation between LAI and stand biomass stock that may be used for estimating the biomass in young stands from LAI that can be measured using non-destructive terrestrial or aerial methods. The results further indicated that young stands may sequester a non-negligible quantity of carbon; therefore, they should not be omitted from local or country-wide estimates of carbon stocks in forest vegetation.
Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects
Authors: Tian Lei , Wu Xiaocan , Tao Yu , Li Mingyang , Qian Chunhua , Liao Longtao , Fu Wenxue
Published: May 2023

Quantifying forest aboveground biomass (AGB) is essential for elucidating the global carbon cycle and the response of forest ecosystems to climate change. Over the past five decades, remote-sensing techniques have played a vital role in forest AGB estimation at different scales. Here, we present an overview of the progress in remote sensing-based forest AGB estimation. More in detail, we first describe the principles of remote sensing techniques in forest AGB estimation: that is, the construction and use of parameters associated with AGB (rather than the direct measurement of AGB values). Second, we review forest AGB remotely sensed data sources (including passive optical, microwave, and LiDAR) and methods (e.g., empirical, physical, mechanistic, and comprehensive models) alongside their limitations and advantages. Third, we discuss possible sources of uncertainty in resultant forest AGB estimates, including those associated with remote sensing imagery, sample plot survey data, stand structure, and statistical models. Finally, we offer forward-looking perspectives and insights on prospective research directions for remote sensing-based forest AGB estimation. Remote sensing is anticipated to play an increasingly important role in future forest AGB estimation and carbon cycle studies. Overall, this comprehensive review may (1) benefit the research communities focused on carbon cycle, remote sensing, and climate change elucidation, (2) provide a theoretical basis for the study of the carbon cycle and global climate change, (3) inform forest ecosystems and carbon management, and (4) aid in the elucidation of forest feedbacks to climate change.
Golden Camellia as a Driver of Forest Regeneration and Conservation: A Case Study of Value-Chain Forestry with Camellia quephongensis in Que Phong, Nghe An, North-Central Vietnam
Authors: Takahashi Kazuya , Nishikawa Hiroaki , Tanabe Reiko , Tran Dong Quang
Published: May 2023

Golden camellia is a highly valued commercial plant owing to its flowers’ medicinal substances. One species of golden camellia, Camellia quephongensis Hakoda et Ninh, is used as a non-timber forest product for value-chain forestry (VCF) in Que Phong District, Nghe An Province, North-Central Vietnam; its production and sale is an important business activity which contributes substantially to local livelihood improvement. According to previous studies and in situ observation, golden camellias, including C. quephongensis, inhabit regenerated forests, especially along streams and rivers. This encourages VCF with C. quephongensis to function as a driver of forest regeneration and conservation. However, the ecological aspect of this species in regenerated forests is poorly understood. Thus, this study aimed to determine this species’ growth patterns and population expansion during forest regeneration. We surveyed the morphological characteristics of this species at different ages in several populations, and revealed that the plants reached a tree height of 150 cm and started blooming eight years after germination. Comparing C. quephongensis-populations inhabiting different succession-staged forests, we observed that the population in long-term regenerated forests was significantly larger and had a pyramid-shaped age distribution pattern to possibly expand its population size. Based on this, we conclude that C. quephongensis is a good indicator of forest regeneration and contributes to local livelihoods and forest regeneration and conservation.
Single-Chamber Electrofermentation of Rumen Fluid Increases Microbial Biomass and Volatile Fatty Acid Production without Major Changes in Diversity
Authors: López-Hernández Rocío , Cercado-Quezada Bibiana , Gómez-Velázquez Haiku D. J. , Robles-Rodríguez Carolina , González-Dávalos Laura , Varela-Echavarría Alfredo , Shimada Armando , Mora Ofelia
Published: May 2023

Rumen fermentation (RF) is a complex system of oxidation–reduction reactions governed by the rumen microbiota, and its end-products are the volatile fatty acids (VFAs) which constitute the main energy source for the host animal. We speculated that the process could be manipulated through electrofermentation (EF) by utilizing solid-state electrodes as electron sources. This study aimed to evaluate the effects of single-chamber EF applied (0.4 vs. 0.9 V) for 6, 12, or 24 h to rumen fluid on the resulting microbial biomass, dry matter digestibility (DMD), VFA production, NADH/NAD ratio, and composition of the rumen fluid’s bacterial communities. The application of a 0.9 V electric potential for 24 h produced the most significant changes in RF compared to the control treatment (0V), increasing microbial biomass (12%), DMD (58%), and VFA concentrations (~84%), and reducing NAD/NADH ratio (~50%) without a significant impact on bacterial diversity. These results suggest that the application of a 0.9 V electric potential allows the microbiota to better hydrolyze and digest the feed’s components and could offer an electron sink for the production of VFAs. Hence, EF can provide innovative solutions to a range of challenges that limit the RF process. Nonetheless, more studies are needed to understand how the solid electrodes and external electric energy function.
From NFT 1.0 to NFT 2.0: A Review of the Evolution of Non-Fungible Tokens
Authors: Guidi Barbara , Michienzi Andrea
Published: May 2023

Non-fungible tokens (NFT) represent one of the most important technologies in the space of Web3. Thanks to NFTs, digital or physical assets can be tokenised to represent their ownership through the usage of smart contracts and blockchains. The first generation of this technology, called NFT 1.0, considers static tokens described by a set of metadata that cannot be changed after token creation. The static nature prevents their wide spread as they do not support any meaningful user interaction. For this reason, its evolution, called NFT 2.0, has been proposed to make tokens interactive and dynamic and enhance user experience, opening the possibility to use NFTs in more ways and scenarios. The purpose of this article is to review the transition from NFT 1.0 to NFT 2.0, focusing on the newly introduced properties and features and the rising challenges. In particular, we discuss the technical aspects of blockchain technology and its impact on NFTs. We provide a detailed description of NFT properties and standards on various blockchains and discuss the support of the most important blockchains for NFTs. Then, we discuss the properties and features introduced by NFT 2.0 and detail the technical challenges related to metadata and dynamism. Lastly, we conclude by highlighting the new application scenarios opened by NFT 2.0. This review paper serves as a solid base for future research on the topic as it highlights the current technological challenges that must be addressed to help a wide adoption of NFTs 2.0.
Elastic Stack and GRAPHYP Knowledge Graph of Web Usage: A Win–Win Workflow for Semantic Interoperability in Decision Making
Authors: Azeroual Otmane , Fabre Renaud , Störl Uta , Qi Ruidong
Published: May 2023

The use of Elastic Stack (ELK) solutions and Knowledge Graphs (KGs) has attracted a lot of attention lately, with promises of vastly improving business performance based on new business insights and better decisions. This allows organizations not only to reap the ultimate benefits of data governance but also to consider the widest possible range of relevant information when deciding their next steps. In this paper, we examine how data management and data visualization are used in organizations that use ELK solutions to collect integrated data from different sources in one place and visualize and analyze them in near-real time. We also present some interpretable Knowledge Graphs, GRAPHYP, which are innovative by processing an analytical information geometry and can be used together with an ELK to improve data quality and visualize the data to make informed decisions in organizations. Good decisions are the backbone of successful organizations. Ultimately, this research is about integrating a combined solution between ELK and SKG GRAPHYP and showing users the advantages in this area.
In-Depth Co-Design of Mental Health Monitoring Technologies by People with Lived Experience
Authors: Patrickson Bronwin , Musker Mike , Thorpe Dan , van Kasteren Yasmin , Bidargaddi Niranjan ,   The Consumer and Carer Advisory Group (CCAG)
Published: May 2023

Advancements in digital monitoring solutions collaborate closely with electronic medical records. These fine-grained monitoring capacities can generate and process extensive electronic record data. Such capacities promise to enhance mental health care but also risk contributing to further stigmatization, prejudicial decision-making, and fears of disempowerment. This article discusses the problems and solutions identified by nine people with lived experience of being mental health care consumers or informal carers. Over the course of ten facilitated focus group format sessions (two hours) between October 2019 and April 2021, the participants shared their lived experience of mental health challenges, care, and recovery within the Australian context. To support the development, design, and implementation of monitoring technologies, problems, and solutions were outlined in the following areas—access, agency, interactions with medical practitioners, medication management, and self-monitoring. Emergent design insights include recommendations for strengthened consent procedures, flexible service access options, and humanized consumer interactions. While consumers and carers saw value in digital monitoring technologies that could enable them to take on a more proactive involvement in their personal wellness, they had questions about their level of access to such services and expressed concerns about the changes to interactions with health professionals that might emerge from these digitally enabled processes.
Multimode Graded Index Fiber with Random Array of Bragg Gratings and Its Raman Lasing Properties
Authors: Kuznetsov Alexey G. , Wolf Alexey A. , Munkueva Zhibzema E. , Dostovalov Alexander V. , Babin Sergey A.
Published: May 2023

Light propagation in multimode fibers is known to experience various nonlinear effects, which are being actively studied. One of the interesting effects is the brightness enhancement at the Raman conversion of the multimode beam in graded index (GRIN) fiber due to beam cleanup at Raman amplification and mode selective feedback in the Raman laser cavity based on fiber Bragg gratings (FBGs) with special transverse structure. It is also possible to explore random distributed feedback based on Rayleigh backscattering on natural refractive index fluctuations in GRIN fibers, but it is rather weak, requiring very high power multimode pumping for random lasing. Here, we report on the first realization of femtosecond pulse-inscribed arrays of weak randomly spaced FBGs in GRIN fibers and study Raman lasing at its direct pumping by highly multimode (M2~34) 940-nm laser diodes. The fabricated 1D–3D FBG arrays are used as a complex output mirror, together with the highly reflective input FBG in 1-km fiber. Above threshold pump power (~100 W), random lasing of the Stokes beam at 976 nm is obtained with output power exceeding 28 W at 174 W pumping. The beam quality parameter varies for different arrays, reaching M2~2 at the linewidth narrowing to 0.1–0.2 nm due to the interference effects, with the best characteristics for the 2D array.
Unlocking the Potential of Blockchain Technology in the Textile and Fashion Industry
Authors: Cuc Sunhilde
Published: May 2023

The textile and fashion industry is on the brink of a major disruption, and blockchain technology (BT) presents a promising solution that could transform the industry by facilitating supply chain transparency, traceability, and sustainability. This article explores the potential of BT in the textile and fashion industry, with a focus on its current applications and potential impact. Using case studies and analyzing all announced blockchain projects from January 2017 to January 2023, we examine the diversity of blockchain applications across different aspects of the textile and fashion industry, including smart contracts and payment processing, supply chain tracking, sustainability applications, and customer engagement. The findings suggest an increasing number of companies are adopting BT, and that BT has the potential to revolutionize the T and F industry by creating a more transparent and efficient supply chain, reducing fraud and counterfeiting, and increasing customer confidence in products. We also identified the challenges and difficulties that may arise during the implementation of BT. This article contributes to the literature on BT in the textile and fashion industry, providing critical insights into its potential impact.
The Effect of Cu2+ Exposure on the Nrf2 Signaling Pathway of Tilapia Hepatocyte, Base on Experiments In Vitro
Authors: Li Linming , Wang Ruoxuan , Zhang Ziping
Published: May 2023

Copper is a common component of industrial heavy metal waste and a major component of some fish medicines, which can cause oxidative stress and damage the health of farmed fish. The Nrf2 signaling pathway is an important pathway related to the oxidative stress on vertebrates. Exploring the effect of copper on the Nrf2 signaling pathway in fish hepatocytes would help improve the understanding of the molecular mechanism of antioxidants in fish hepatocytes and provide theoretical data for relevant toxicological research. Adult tilapia were cultured under properly controlled conditions for two weeks to adapt to laboratory culture conditions. Primary tilapia hepatocytes were obtained by cell culture. MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay was used to detect the effect of copper ions on the viability of tilapia hepatocytes. The lipid peroxidation level (MDA) and antioxidant ability of tilapia hepatocytes (SOD and CAT activity) were detected. Quantitative PCR (qPCR) was used to detect the differential expression of each gene (Nrf2, Keap1a, Keap1b, CuZnSOD, MnSOD, HO-1, and GSTA) in the Nrf2 signaling pathway. The results suggested that after treatment with 100 μM copper ions for 4 h, 8 h, and 24 h, the viability of hepatocytes significantly decreased (p < 0.05). LDH and MDA after 8 h and 24 h treatment were significantly higher than those in the control group (p < 0.05). CAT activity significantly decreased after 4 h (p < 0.05), and SOD activity significantly decreased after 8 h and 24 h (p < 0.05). The results of qPCR showed that the expression of MnSOD significantly increased after a treatment with copper ions for 4 h, and the expression of Nrf2, Keap1a, CuZnSOD, HO-1 as well as GSTA significantly increased after a treatment with copper ions for 8 h, compared with the control group (p < 0.05). After being treated with copper ions for 24 h, the expression of Nrf2 and CuZnSOD significantly increased compared with the control group (p < 0.05). There was no significant difference in the expression of Keap1b or CAT at each time point. In conclusion, with copper ions exposure, the viability of tilapia hepatocytes was reduced, causing lipid peroxidation, a reduction in the antioxidant capacity of cells, the activation of the Nrf2 signaling pathway, and the increase in the expression of most genes in this pathway, which are defensive responses of hepatocytes to oxidative stress caused by copper ions. This study can provide theoretical data for related toxicological research.
Vortex Model of Plane Couette Flow
Authors: Mironov Victor L. , Mironov Sergey V.
Published: May 2023

We present the theoretical description of plane Couette flow based on the previously proposed equations of vortex fluid, which take into account both the longitudinal flow and the vortex tubes rotation. It is shown that the considered equations have several stationary solutions describing different types of laminar flow. We also discuss the simple model of turbulent flow consisting of vortex tubes, which are moving chaotically and simultaneously rotating with different phases. Using the Boussinesq approximation, we obtain an analytical expression for the stationary profile of mean velocity in turbulent Couette flow, which is in good agreement with experimental data and results of direct numerical simulations. Our model demonstrates that near-wall turbulence can be described by a coordinates-independent coefficient of eddy viscosity. In contrast to the viscosity of the fluid itself, this parameter characterizes the turbulent flow and depends on Reynolds number and roughness of the channel walls. Potentially, the proposed model can be considered as a theoretical basis for the experimental measurement of the eddy viscosity coefficient.
Identification of Eight High Yielding Strains via Morpho-Molecular Characterization of Thirty-Three Wild Strains of Calocybe indica
Authors: Nath Manoj , Barh Anupam , Sharma Annu , Verma Parul , Bairwa Rakesh Kumar , Kamal Shwet , Sharma Ved Prakash , Annepu Sudheer Kumar , Sharma Kanika , Bhatt Deepesh , Bhatt Pankaj , Gupta Dharmesh , Singh Akoijam Ratankumar
Published: May 2023

Calocybe indica, generally referred as milky mushroom, is one of the edible mushroom species suitable for cultivation in the tropical and sub-tropical regions of the world. However, lack of potential high yielding strains has limited its wider adaptability. To overcome this limitation, in this study, the germplasms of C. indica from different geographical regions of India were characterized based on their morphological, molecular and agronomical attributes. Internal transcribed spacers (ITS1 and ITS4)-based PCR amplification, sequencing and nucleotide analysis confirmed the identity of all the studied strains as C. indica. Further, evaluation of these strains for morphological and yield parameters led to the identification of eight high yielding strains in comparison to the control (DMRO-302). Moreover, genetic diversity analysis of these thirty-three strains was performed using ten sequence-related amplified polymorphism (SRAP) markers/combinations. The Unweighted Pair-group Method with Arithmetic Averages (UPGMA)-based phylogenetic analysis categorized the thirty-three strains along with the control into three clusters. Cluster I possesses the maximum number of strains. Among the high yielding strains, high antioxidant activity and phenol content was recorded in DMRO-54, while maximum protein content was observed in DMRO-202 and DMRO-299 as compared with the control strain. The outcome of this study will help the mushroom breeders and growers in commercializing C. indica.
The Impact of Culinary Processing, including Sous-Vide, on Polyphenols, Vitamin C Content and Antioxidant Status in Selected Vegetables—Methods and Results: A Critical Review
Authors: Kosewski Grzegorz , Kowalówka Magdalena , Drzymała-Czyż Sławomira , Przysławski Juliusz
Published: May 2023

This study presents various research methods and results analysis of the total antioxidant status (TAS), polyphenols content (PC) and vitamin C content in selected plant materials (vegetables) subjected to various technological processes, including sous-vide. The analysis included 22 vegetables (cauliflower white rose, romanesco type cauliflower, broccoli, grelo, col cabdell cv. pastoret, col lllombarda cv. pastoret, brussels sprouts, kale cv. crispa–leaves, kale cv. crispa–stem, toscana black cabbage, artichokes, green beans, asparagus, pumpkin, green peas, carrot, root parsley, brown teff, white teff, white cardoon stalks, red cardoon stalks and spinach) from 18 research papers published in 2017 to 2022. The results after processing by various methods such as conventional, steaming and sous-vide cooking were compared to the raw vegetable results. The antioxidant status was mainly determined by the radical DPPH, ABTS and FRAP methods, the polyphenol content by the Folin–Ciocalteu reagent and the vitamin C content using dichlorophenolindophenol and liquid chromatography methods. The study results were very diverse, but in most studies, the cooking techniques contributed to reducing TAS, PC and vitamin C content, with the sous-vide process being most beneficial. However, future studies should focus on vegetables for which discrepancies in the results were noted depending on the author, as well as lack of clarity regarding the analytical methods used, e.g., cauliflower white rose or broccoli.
Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China
Authors: Qian Jianping , Li Jiali , Geng Bojian , Chen Cunkun , Wu Jianjin , Li Haiyan
Published: May 2023

The effectiveness evaluation of the traceability system (TS) is a tool for enterprises to achieve the required traceability level. It plays an important role not only for planning system implementation before development but also for analyzing system performance once the system is in use. In the present work, we evaluate traceability granularity using a comprehensive and quantifiable model and try to find its influencing factors via an empirical analysis with 80 vegetable companies in Tianjin, China. We collect granularity indicators mostly through the TS platform to ensure the objectivity of the data and use the TS granularity model to evaluate the granularity score. The results show that there is an obvious imbalance in the distribution of companies as a function of score. The number of companies (21) scoring in the range (50,60) exceeded the number in the other score ranges. Furthermore, the influencing factors on traceability granularity were analyzed using a rough set method based on nine factors pre-selected using a published method. The results show that the factor “number of TS operation staff” is deleted because it is unimportant. The remaining factors rank according to importance as follows: Expected revenue > Supply chain (SC) integration degree > Cognition of TS > Certification system > Company sales > Informationization management level > System maintenance investment > Manager education level. Based on these results, the corresponding implications are given with the goal of (i) establishing the market mechanism of high price with high quality, (ii) increasing government investment for constructing the TS, and (iii) enhancing the organization of SC companies.