bluuebunny/crossref_metadata_2025 huggingface dataset:
Population Dynamics of Wild Mongolian Gerbils: Quadratic Temperature Effects on Survival and Density-Dependent Effects on Recruitment
Authors: Liu Wei , Deng Ke
Published: July 2022

It has been hypothesized that animal populations respond nonlinearly to the environment, and such responses are important to understand the effects of climate change population dynamics of small mammals in arid environments at northern latitudes. The aim of this study was to test the following hypotheses: (1) that small rodent populations increase as their semiarid habitat conditions improve from low to intermediate levels of temperature or precipitation, and decline beyond the optimum climate because of decreased survival, and (2) that increased population density would result in stronger negative effects on recruitment than on survival. A wild population of Mongolian gerbils (Meriones unguiculatus), a granivorous rodent distributed in Inner Mongolia, China, was live-trapped half-monthly between April and October from 2014 to 2017 and the effects of climate and density on their apparent survival probabilities and recruitment rates were estimated using mark-recapture methods. Increased temperatures initially had a positive effect on population growth rates, and then had negative effects on population growth rates primarily, which was mediated by quadratic effects on survival probabilities, further supporting the optimum habitat hypothesis. Moreover, the increases in temperature had a positive effect on the recruitment of gerbils, whereas population density had a more markedly negative effect on recruitment than on survival. The results of this study suggested that the density-dependent feedback to recruitment may be a primary regulatory mechanism of small mammal populations, and the complex responses of populations to temperature, which is a limiting ecological factor, may raise concerns for the fate of populations of small mammals at northern latitudes, in view of the predicted global climate change scenarios.
Arbuscular Mycorrhizal Fungi Associated with Roots Reveal High Diversity Levels at Different Elevations in Tropical Montane Rainforests
Authors: Zhang Mengge , Shi Zhaoyong , Xu Xiaofeng , Wang Xugang
Published: July 2022

(1) Background: Understanding the diversity of communities of arbuscular mycorrhizal fungi (AMF) is the basis for understanding the ecological functions of AMF. (2) Methods: The community diversity and distribution of AMF at different elevations in tropical montane rainforests of Mt. Jianfenfling and Mt. Diaoluo were explored using high-throughput sequencing technology. (3) Results: A total of 283 AMF operational taxonomic units (OTUs) were identified from roots and the number of unique OTUs was 173, accounting for 61.13% of the total number discovered in these tropical montane rainforests. At different altitudes, high turnovers of AMF were observed, with the maximum proportion of unique OTUs between two altitudes being 45.16%, recorded between a.s.l. 250 m and 900 m on Mt. Diaoluo. The highest Sobs, Shannon and Pielou diversity indices appeared at 650 m on Mt. Diaoluo. For the two mountains, the soil properties of C, N and C/N have significant impacts on the genera Scutellospora, Paraglomus and unclassified in Archaeosporaceae, while the genera Glomus, Diversispora and Acaulospora are significantly affected by soil P and pH. It can be considered that altitude probably determines the presence of AMF communities by affecting edaphic properties. (4) Conclusions: There are abundant AMF associated with roots in the tropical montane rainforests of China. Furthermore, a high turnover of OTUs was found to exist between the mountains and at different altitudes, revealing diverse AMF community structures in tropical montane rainforests.
Morphological Parameters of the Hip Joint and Its Relation to Gender, Joint Side and Age—A CT-Based Study
Authors: Hawellek Thelonius , Meier Marc-Pascal , Seitz Mark-Tilman , Uhlig Johannes , Hosseini Ali Seif Amir , Beil Frank Timo , Lehmann Wolfgang , Hubert Jan
Published: July 2022

Background: Physiological reference values for morphological parameters of the hip (MPH) are of clinical importance for the treatment of painful, degenerated or fractured hip joints, as well as to detect morphological deformities, which could result in early osteoarthritis of the hip. Currently, sufficient data for MPH are lacking. Therefore, it remains unclear if age-dependent alterations in adult hip morphology are physiological and if there are side- and gender-dependent differences. The aim of the study was to analyze MPH according to gender, side and age in a large-scaled cohort by CT scans. Methods: A total of 1576 hip joints from 788 patients (female: 257, male: 531; mean age: 58.3 years (±18.9; 18–92 years)) were analyzed by CT. For all hips, acetabular anteversion (AcetAV); lateral centrum edge angle (LCE); acetabular index (AI); femoral neck version (FNV); centrum-collum-diaphyseal angle (CCD); and anterior alpha angle (AαA) were measured. Results: The mean values in this cohort were: AcetAV 20.5° (±6.9); LCE 40.8° (±8.8); AI 0.3° (±5.3); FNV 11.0° (±9.8); CCD 129.9° (±7.4); and AαA 41.2° (±7.7). There was a detectable side-specific difference for AcetAV (p = 0.001); LCE (p < 0.001); CCD (p < 0.001); and AαA (p < 0.001). All the analyzed parameters showed a significant gender-specific difference, except for AI (p = 0.37). There was a significant correlation between age and AcetAV (r = 0.17; p < 0.001); LCE (r = 0.39; p < 0.001); AI (r = −0.25; p < 0.001); CCD (r = −0.15; p < 0.001); and AαA (r = 0.09; p < 0.001), except FNV (p = 0.79). Conclusions: There are side-, gender- and age-specific alterations in hip morphology, which have to be considered in treating hip joint pathologies.
Lung Ultrasound to Diagnose Pneumonia in Neonates with Fungal Infection
Authors: Liu Jing , Ma Hai-Ran , Fu Wei
Published: July 2022

With the improvement in survival rates of low-birthweight and very premature infants, neonatal fungal infection, especially fungal pneumonia, is becoming more and more common, but the diagnosis is always challenging. Recently, lung ultrasound (LUS) has been used to diagnose pneumonia in newborn infants, but not fungal pneumonia. This paper summarizes the ultrasonographic features of seven cases of neonatal fungal pneumonia, such as lung consolidation with air bronchograms, shred signs, lung pulse, pleural line abnormalities, and different kinds of B-lines. It was confirmed that LUS plays an important role in the diagnosis of fungal pneumonia in newborn infants.
Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course
Authors: Santarossa Monty , Tatli Ayse , von der Burchard Claus , Andresen Julia , Roider Johann , Handels Heinz , Koch Reinhard
Published: July 2022

Optical coherence tomography (OCT) and fundus autofluorescence (FAF) are important imaging modalities for the assessment and prognosis of central serous chorioretinopathy (CSCR). However, setting the findings from both into spatial and temporal contexts as desirable for disease analysis remains a challenge due to both modalities being captured in different perspectives: sparse three-dimensional (3D) cross sections for OCT and two-dimensional (2D) en face images for FAF. To bridge this gap, we propose a visualisation pipeline capable of projecting OCT labels to en face image modalities such as FAF. By mapping OCT B-scans onto the accompanying en face infrared (IR) image and then registering the IR image onto the FAF image by a neural network, we can directly compare OCT labels to other labels in the en face plane. We also present a U-Net inspired segmentation model to predict segmentations in unlabeled OCTs. Evaluations show that both our networks achieve high precision (0.853 Dice score and 0.913 Area under Curve). Furthermore, medical analysis performed on exemplary, chronologically arranged CSCR progressions of 12 patients visualized with our pipeline indicates that, on CSCR, two patterns emerge: subretinal fluid (SRF) in OCT preceding hyperfluorescence (HF) in FAF and vice versa.
Strong Correlations between the Binding Antibodies against Wild-Type and Neutralizing Antibodies against Omicron BA.1 and BA.2 Variants of SARS-CoV-2 in Individuals Following Booster (Third-Dose) Vaccination
Authors: Suntronwong Nungruthai , Assawakosri Suvichada , Kanokudom Sitthichai , Yorsaeng Ritthideach , Auphimai Chompoonut , Thongmee Thanunrat , Vichaiwattana Preeyaporn , Duangchinda Thaneeya , Chantima Warangkana , Pakchotanon Pattarakul , Chansaenroj Jira , Nilyanimit Pornjarim , Srimuan Donchida , Thatsanatorn Thaksaporn , Sudhinaraset Natthinee , Wanlapakorn Nasamon , Mongkolsapaya Juthathip , Poovorawan Yong
Published: July 2022

This study examined the neutralizing activity and receptor-binding domain (RBD) antibody levels against wild-type and omicron BA.1 and BA.2 variants in individuals who received three doses of COVID-19 vaccination. The relationship between the anti-RBD IgG against wild-type and live virus neutralizing antibody titers against omicron BA.1 and BA.2 variants was examined. In total, 310 sera samples from individuals after booster vaccination (third-dose) were tested for specific IgG wild-type SARS-CoV-2 RBD and the omicron BA.1 surrogate virus neutralization test (sVNT). The live virus neutralization assay against omicron BA.1 and BA.2 was performed using the foci-reduction neutralization test (FRNT50). The anti-RBD IgG strongly correlated with FRNT50 titers against BA.1 and BA.2. Non-linear regression showed that anti-RBD IgG at the cut-off value ≥148 BAU/mL and ≥138 BAU/mL were related to the threshold for FRNT50 titers ≥20 against BA.1 and BA.2, respectively. A moderate correlation was observed between the sVNT and FRNT50 titers. At FRNT50 titers ≥20, the predicted sVNT for BA.1 and BA.2 was ≥10.57% and ≥11.52%, respectively. The study identified anti-RBD IgG and sVNT levels that predict detectable neutralizing antibodies against omicron variants. Assessment and monitoring of protective immunity support vaccine policies and will help identify optimal timing for booster vaccination.
Feasibility of Support by Family Practitioners in Reducing Colorectal Cancer-Related Death among Outpatients Who Have Not Undergone Colorectal Cancer Screening
Authors: Sugaya Ryo , Kanno Takeshi , Yasaka Hirohisa , Masu Misuzu , Otomo Masataka , Koike Tomoyuki
Published: July 2022

We aimed to clarify the effectiveness of interventions in outpatients who did not undergo colorectal cancer (CRC) screening. From September 2012 to August 2013, we conducted a project, which showed that the immunological fecal occult blood test (FOBT) was actively recommended for outpatients who were ≥40 years of age, attended the Marumori Hospital regularly, and were not screened for CRC in the previous two years. We evaluated the detection rate of CRC and the disease specific survival ratio in February 2021 among patients with positive FOBT results during the retrospective cohort study. Overall, 388 (91%) out of the 425 outpatients submitted their stool samples. Among 388 outpatients, 66 tested positive for FOBT. While both the positive rate of FOBT and the detection rate of CRC (17% and 0.77%, respectively) were significantly higher than those in the nationwide administrative examination (5.7% and 0.13%, respectively) (p < 0.05), there was no statistically significant difference in the detection rate, compared with the group aged 65 years and older in the nationwide administrative examination. The 7-year CRC-specific survival ratio was 98.5%. Active promotion of FOBT at primary care institutions for outpatients who did not undergo CRC screening may contribute to reducing the frequency of CRC-related deaths.
The Effects of Use of Long-Term Second-Generation Antipsychotics on Liver and Kidney Function: A Prospective Study
Authors: Papatriantafyllou Evangelia , Efthymiou Dimitris , Markopoulou Maria , Sakellariou Efthymia-Maria , Vassilopoulou Emilia
Published: July 2022

(1) Background: The second-generation antipsychotics (SGAPs) induce metabolic and inflammatory side effects, but documentation of their effects on the liver and kidneys is scarce. Aim: To study the three-year fluctuation of selected markers of renal and hepatic function in forensic psychiatric patients receiving SGAPs for more than five years. (2) Methods: Thirty-five forensic psychiatric patients (N = 35) were classified into two groups according to the type of SGAPs used for their treatment and the relevant risk of weight gain and metabolic complications. The three-year medication history, anthropometric data and biochemical data relevant to renal and hepatic function were retrieved from the individual medical files, specifically: serum levels of urea, uric acid, creatinine, alkaline phosphatase and amylase; the liver function enzymes, serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT) and gamma-glutamyl transpeptidase(γ-GT), and also the inflammatory index C-reactive protein (CRP). (3) Results: The patients receiving the SGAPs with a low risk for weight gain showed no significant fluctuation in the biochemical markers over the three-year period. The patients receiving the SGAPs with a high risk for weight gain showed significant differences between at least two measurements of uric acid (p = 0.015), SGOT (p = 0.018) and SGPT (p = 0.051). They showed significantly higher levels of creatinine in the third year compared to the second year (p = 0.029), and SGOT in the second year compared to the first (p = 0.038), and lower levels of SGPT in the third year compared to the second (p = 0.024). (4) Conclusion:In addition to consideration of possible metabolic and inflammatory complications, the choice of an antipsychotic drug for long-term treatment should also take into account the risk of hepatotoxicity and kidney damage.
An Approach to Environmental Knowledge of Undergraduates in Engineering (Spain): Effect of Environmental Subjects
Authors: Laca Amanda , Patiño Yolanda
Published: July 2022

An approach to the effect of environmental subjects on the environmental knowledge of undergraduates in Engineering was examined in this work. Two subjects, common to five different Engineering degrees (Industrial, Electric and Electronic, Mechanic, Industrial Chemistry and Automatic), taught at University of Oviedo, were selected for this study: “Environmental Engineering” (6 ECTS credits), a compulsory subject corresponding with the third year, and “Ecodesign” (6 ECTS credits), an optional subject corresponding with the fourth year. Twenty-three students between 20 and 23 years old and twenty-five students between 21 and 24 years old who took “Environmental Engineering” and “Ecodesign”, respectively, participated in the study. The students’ knowledge was evaluated prior and once the subjects had been taught by the same questionnaire using a 5-point Likert scale. Results showed that, considering the initial knowledge, the students were aware of the current environmental problems and, furthermore, their knowledge improved after taking the subjects. Additionally, women showed a better initial perception of environmental issues than men, whereas students living in rural areas exhibited a better knowledge about environmental problems than those living in urban areas. Even though this study is limited, it may still offer important insights regarding the environmental perception of Engineering undergraduates. It is an issue of great interest, since many of them will become the professionals that would have to face environmental challenges in the future. Certainly, this work stresses the importance of additional research on this complex issue.
Improving the Scientific Literacy of Food Engineering Students in Electrohydrodynamic Processing by Means of Zein Solutions
Authors: Torres-Giner Sergio , Yuste Alberto , González-Martínez Chelo
Published: July 2022

Electrohydrodynamic processing (EHDP) has revolutionized nanotechnology since it is a simple method for developing microstructures and nanostructures from a wide range of polymer solutions using the application of high voltages. However, EHDP has scarcely been introduced into food engineering courses at any educational level, whereas it is fundamental for professionals in the discipline. The present educational article aims to introduce, for the first time, the basis of the EHDP technology and its management to students in postgraduate courses for food emulsion and related disciplines. To this end, the document reports the step-by-step preparation of zein solutions in aqueous ethanol with varying weight protein contents and the characterization of such solutions in terms of their density, viscosity, surface tension, and conductivity. Then, the methodology also describes the processability of the resultant zein solutions using EHDP. Process parameters, including applied voltage, flow-rate, and tip-to-collector distance, were studied to optimize proper deposition of zein materials in the form of a wide range of morphologies, from nanobeads to microfibers. The attained results were related to the zein solution properties and processing conditions, which can help to understand the effect of these parameters and exemplify the potential of this technology to develop novel ultrathin food hydrocolloid structures. Finally, the application of this methodology was assessed through online surveys taken during food engineering courses and findings indicate that postgraduate students appreciate the exposure provided by the experimental activities, particularly to achieve familiarity with food hydrocolloid solutions and scientific literacy in the EHDP technology.
ECG Heartbeat Classification Using CONVXGB Model
Authors: Rawi Atiaf A. , Elbashir Murtada K. , Ahmed Awadallah M.
Published: July 2022

ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification using a convolutional neural network (CNN) with eXtreme Gradient Boosting (XBoost), ConvXGB. This model was established by stacking two convolutional layers for automatic feature extraction from ECG signals, followed by XGBoost as the last layer, which is used for classification. This technique simplified ECG classification in comparison to other methods by minimizing the number of required parameters and eliminating the need for weight readjustment throughout the backpropagation phase. Furthermore, experiments on two famous ECG datasets–the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets–demonstrated that this technique handled the ECG signal classification issue better than either CNN or XGBoost alone. In addition, a comparison showed that this model outperformed state-of-the-art models, with scores of 0.9938, 0.9839, 0.9836, 0.9837, and 0.9911 for accuracy, precision, recall, F1-score, and specificity, respectively.
Fast and Accurate Approximation Methods for Trigonometric and Arctangent Calculations for Low-Performance Computers
Authors: Kusaka Takashi , Tanaka Takayuki
Published: July 2022

In modern computers, complicated signal processing is highly optimized with the use of compilers and high-speed processing using floating-point units (FPUs); therefore, programmers have little opportunity to care about each process. However, a highly accurate approximation can be processed in a small number of computation cycles, which may be useful when embedded in a field-programmable gate array (FPGA) or micro controller unit (MCU), or when performing many large-scale operations on a graphics processing unit (GPU). It is necessary to devise algorithms to obtain the desired calculated values without an accelerator or compiler assistance. The residual correction method (RCM) developed here can produce simple and accurate approximations of certain nonlinear functions with minimal multiply–add operations. In this study, we designed an algorithm for the approximate computation of trigonometric and inverse trigonometric functions, which are nonlinear elementary functions, to achieve their fast and accurate computation. A fast first approximation and a more accurate second approximation of each function were created using RCM with a less than 0.001 error using multiply–add operations only. This achievement is particularly useful for MCUs, which have a low power consumption but limited computational power, and the proposed approximations are candidate algorithms that can be used to stabilize the attitude control of robots and drones, which require real-time processing.
Recent Progress of Using Knowledge Graph for Cybersecurity
Authors: Liu Kai , Wang Fei , Ding Zhaoyun , Liang Sheng , Yu Zhengfei , Zhou Yun
Published: July 2022

In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.
IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning
Authors: Nancy A Angel , Ravindran Dakshanamoorthy , Raj Vincent P M Durai , Srinivasan Kathiravan , Gutierrez Reina Daniel
Published: July 2022

The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of diseases is crucial in ensuring preventive care alongside early intervention for people at risk. With the widespread adoption of electronic clinical records, creating prediction models with enhanced accuracy is key to harnessing recurrent neural network variants of deep learning possessing the ability to manage sequential time-series data. The proposed system acquires data from IoT devices, and the electronic clinical data stored on the cloud pertaining to patient history are subjected to predictive analytics. The smart healthcare system for monitoring and accurately predicting heart disease risk built around Bi-LSTM (bidirectional long short-term memory) showcases an accuracy of 98.86%, a precision of 98.9%, a sensitivity of 98.8%, a specificity of 98.89%, and an F-measure of 98.86%, which are much better than the existing smart heart disease prediction systems.
Experimental Investigation on the Heat Transfer Characteristics of Multi-Point Heating Microchannels for Simulating Solar Cell Cooling
Authors: Yang Qi , Huang Yanpei , Niu Zitian , Guo Yuandong , Wu Qi , Miao Jianyin
Published: July 2022

Concentrating photovoltaic power generation technology is a highly efficient way of utilizing solar energy resources with the efficiency limited by cell cooling conditions. For the heat dissipation problem from multi-point solar cell cooling, a microchannel heat sink is used to resolve the issue. Ammonia is chosen as the working fluid and two diamond microchannel heat sinks in series for the 16 simulated solar cells cooling with typical size. The heat sink consists of 31 triangular microchannels, each with a hydraulic diameter of 237 μm and a flow path length of 40 mm. It is experimentally verified that the diamond microchannel heat sink has excellent multi-point heat source heat dissipation capability. The surface temperature of the heat source can be controlled below 65.9 °C under the heat flux of 351.5 W/cm2, and the maximum temperature difference between the multi-point heat sources is only 1.4 °C. The effects of heat flux, mass flux and inlet state on the flow boiling heat transfer capacity within the series heat sinks were investigated and the ranges of the operating conditions are as follows: heat flux 90.8–351.5 W/cm2, mass flux 108–611 kg/(m2s), saturation temperature 15–23 °C and inlet temperature 15–21 °C. The results show that within the range of experimental conditions, the flow boiling heat transfer capacity of the series heat sink increases with the increase of heat flux and is less influenced by the mass flux, showing the typical two-phase heat transfer characteristics dominated by the nucleation boiling mechanism. Between the upstream and downstream heat sinks, the thermal resistance of the upstream heat sink is larger and the temperature uniformity of the downstream heat sink is poor because of the difference of the inlet state.
The Effect of Replacing Si-MOSFETs with WBG Transistors on the Control Loop of Voltage Source Inverters
Authors: Bernacki Krzysztof , Rymarski Zbigniew
Published: July 2022

The operation of a voltage source inverter (VSI) depends on its output LC filter and the PWM modulator delay. The VSI model includes serial equivalent resistance based on the resistances of the active inverter bridge transistors, the filter coil winding, and additional PCB elements, such as traces and connectors, in addition to the large equivalent resistances that result from power losses in the coil core and switches. These dynamic power losses depend on the switching frequency and the inverter load. This paper investigates the change in equivalent serial resistance that occurs if the standard Si-MOSFET switches are replaced with wide bandgap (WBG) transistors with correspondingly lower equivalent serial resistance. The paper further investigates how such a change influences the design of the controller, given that replacement of the switches shifts the roots of the closed-loop characteristic equation. Theoretical analyses of the influence of equivalent serial resistance for a multi-input single-output passivity-based controller and a single-input single-output coefficient diagram method are also presented. These analyses are applied to both types of switches. Two methods are used to measure the serial equivalent resistance for a given VSI using Si and WBG switches. The possibility of replacing switches with WBG technology in existing inverters was assessed, and the corresponding controller adjustment that would be required. The theoretical analysis is verified via the use of an experimental VSI.
Conditions Driving Eco-Innovation in a Catching-Up Country—ICT vs. Industry in Poland
Authors: Świadek Arkadiusz , Gorączkowska Jadwiga , Godzisz Karolina
Published: July 2022

There is a necessity to combine the development of the European Union with a model of a sustainable economy, which is important to stimulate eco-innovation. The government of each member state is obliged to create support mechanisms that will encourage entrepreneurs to implement pro-environmental solutions. This requires the identification of determinants for eco-innovation. This paper identifies and compares conditions of eco-innovation in two sectors in Poland: ICT (information and communication technologies) and industry. Putting together many different types of conditions into one model (including government, science, industry, and ICT) allows us to explain what is more important when making a decision about implementing new eco-solutions. The study covered over 3000 enterprises. Stepwise logit regression was used to examine all relationships of interest. We discovered two separate (independent) paths of approach to eco-innovation for ICT and industry. This means that another condition should be fulfilled to reach any eco-innovation in each of the sectors. There are just a few factors supporting new environmental solutions in ICT with strong impact—cooperation with suppliers, journals as a source of new knowledge, or financial support by credit institutions with high materials and energy efficiency at the end of the process. Industry could be described more like a horizontal approach—a wide number of conditions with low impact on eco-innovation. Therefore, innovation policies in the catching-up country should be more sophisticated and take care of more relevant tools for both sectors.
Coupled FEM and CFD Modeling of Structure Deformation and Performance of PEMFC Considering the Effects of Membrane Water Content
Authors: Dong Zizhe , Liu Yuwen , Qin Yanzhou
Published: July 2022

Based on a coupled finite element method (FEM) and computational fluid dynamics (CFD) model, the structural deformation and performance of a proton exchange membrane fuel cell (PEMFC) under different membrane water contents are studied. The water absorption behavior of the membrane is investigated experimentally to obtain its expansion coefficient with water content, and the Young’s modulus of the membrane and catalyst (CL) are obtained through a tensile experiment. The simulation results show that the deformation of the membrane increases with water content, and membrane swelling under the channel is larger than that under the rib, forming a surface bump under the channel. The structural changes caused by the membrane water content have little effect on the performance of PEMFC in the low-current density range; while its influence is significant in the medium- and high-current density range. A medium membrane water content value of 12 achieves the best fuel cell performance due to the balance of membrane resistance and mass transport.
Development of Correlations of the Charging and Discharging Times of Carboxyl-Functionalized Multi-Walled Carbon Nanotubes (MWCNT-COOH) and Water with and without Polyethylene Glycol in Spherical Encapsulation
Authors: Lago Taynara , Ismail Kamal , Lino Fátima , Arruda Victor , Junior Vivaldo
Published: July 2022

This investigation shows the results of a coupled numerical and experimental study on the solidification and melting of spherical capsules and the development of correlations for solidification time and melting time with parameters that impact the complete phase change time of nanofluids and water with and without polyethylene glycol inside plastic spherical capsules. Experiments included the investigation of different configurations of plastic spherical capsule diameters, external temperature, the initial temperature of phase change material (PCM), and PCMs. The PCMs used were water, water with a concentration of polyethylene glycol from 10% to 50%, and multi-wall carbon nanotubes functionalized with carboxylic acid group (COOH-MWCNT) with MWCNT at 0.125, 0.25, 0.5, and 1.0%. The simplified model was validated with available experimental results from the present work and from the literature, showing maximum deviations in the range of 0.25 to 12%. The simulation results showed that the use of nanoparticles in the base fluid increased the velocity of the solidification and melting processes and shortened the time for complete solidification and melting. The correlations for the complete solidification time and complete melting time followed the experimental results, with a maximum deviation of about 6%, which proves an excellent concordance of the correlations with the experiments.
Research Progress on Magnetic Catalysts and Its Application in Hydrogen Production Area
Authors: Wang Feng , Guan Delun , Li Yatian , Zhong Jingxuan
Published: July 2022

The noncontact heating technology of IH targets heat directly where it is needed through the electromagnetic energy adsorption and conversion of magnetic materials. Unlike conventional heating methods, the heat generated by electromagnetic induction of magnetic materials can be applied directly into the reactor without heating the entire device; this new heating method is not only more energy efficient but also safer, cleaner and more sustainable if renewable electricity is adopted; moreover, magnetic catalysts can be recovered and reused by separating chemical reactants and products from the catalyst by the application of a magnetic field, and it can provide the required heat source for the reaction without altering its catalytic properties. Magnetic catalysts with an electric field have been applied to some industrial areas, such as the preparation of new materials, catalytic oxidation reactions, and high-temperature heat absorption reactions. It is a trend that is used in the hydrogen production process, especially the endothermic steam reforming process. Therefore, in this paper, the heat release mechanism, properties, preparation methods and the application of magnetic catalysts were presented. Highlights of the application and performance of magnetic catalysts in the hydrogen production area were also discussed.
Qualitative Analysis of Enterprise Risk Management Systems in the Largest European Electric Power Companies
Authors: Pecina Ena , Miloš Sprčić Danijela , Dvorski Lacković Ivana
Published: July 2022

Enterprise risk management (ERM) is an important element of an efficient and comprehensive corporate governance system. It represents a combination of activities that minimise the negative impacts of the risk exposures on the company’s value and long-term corporate sustainability. Recently, there has been a growing awareness on the role and importance of the risk management function. Such trends are partly driven by the consequences of the last economic and financial crisis on the one hand, and on the other by legal and regulatory requirements. The economic downturn caused by the COVID-19 pandemic, volatility in the energy markets and increased uncertainty expected in the upcoming period reiterate the importance of timely risk management practices, because organisations with developed risk management systems are more resilient in case of crisis. This paper analyses the organisation and level of development of ERM systems in the ten largest European electric power companies. The companies’ data on risk management practices are collected from annual reports and analysed by applying Content Analysis (CA), searching for 29 characteristics of a developed ERM system. Research results reveal that ERM in the largest EU electric power companies can be considered as advanced as it applies the five dimensions of the COSO 2017 framework. The analysis confirms the existence of 27 out of 29 characteristics of a developed ERM system, confirming that these characteristics are rooted not only in the relevant ERM theory, but also in the practice of large and successful electric power companies.
Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost
Authors: Zhang Mei , Chen Wanli , Yin Jun , Feng Tao
Published: July 2022

Aiming to accurately identify the state of health (SOH) and the remaining useful life (RUL) of lithium-ion batteries, in this paper, we propose an algorithm for the health factor extraction and SOH prediction of the batteries based on discrete wavelet transform and the Cauchy–Gaussian variation tent sparrow search algorithm (DWT-CGTSSA). Firstly, concerning the inconsistent data length, discrete wavelet transform (DWT) was adopted to decompose the battery’s signals and extract features. Then, the Cauchy–Gaussian variation tent sparrow search algorithm (CGTSSA) was utilized to extract features and obtain the optimal feature subset after encoding. Finally, the optimal feature subset was used to establish a prediction model based on CatBoost for predicting the SOH of lithium-ion batteries. Experiments were conducted for verification. The experimental results showed that the model established in this research is capable of realizing the prediction between different battery packs. The B0005 battery from dataset A was taken as the training set to predict the complete SOH of B0006 and B0007 batteries. For the prediction model of CGTSSA-CatBoost, the goodness of fit (R2) exceeded 0.99, and the value of mean square error (MSE) was less than 1‰. A comparison with other state-of-the-art prediction models verified the superior performance of the CGTSSA-CatBoost model. Under different working conditions, the R2 of all models in dataset B exceeded 0.98.
Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation
Authors: Nan Jinrui , Deng Bo , Cao Wanke , Hu Jianjun , Chang Yuhua , Cai Yili , Zhong Zhiwei
Published: July 2022

Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning.
Differences in Macromolecular Structure Evolution during the Pyrolysis of Vitrinite and Inertinite Based on In Situ FTIR and XRD Measurements
Authors: Zhao Meng , Wang Anmin , Cao Daiyong , Wei Yingchun , Ding Liqi
Published: July 2022

An accurate understanding of molecular structure evolution during pyrolysis is essential for the clean utilization of coal. In this study, middle-rank coal was taken as the research subject, and vitrinite and inertinite samples were stripped from coal using a hand picking and sink–float separation process. In situ Fourier transform infrared (FTIR) spectroscopy and in situ X-ray diffraction (XRD) were performed to compare the macromolecular structure changes between vitrinite and inertinite during pyrolysis. The results show that the aromaticity (I), the polycondensation degree of aromatic rings (DOC), the average lateral sizes (La) of basic structure unit (BSU), and the stacking heights (Lc) of BSU in both vitrinite and inertinite during pyrolysis increase continuously with increasing temperature. The values of these parameters for inertinite are higher than those of vitrinite, suggesting that the aromatization degree of inertinite has always been higher than that of vitrinite. In situ FTIR spectroscopy shows that the macromolecular structure evolution of vitrinite and inertinite could be divided into three stages based on temperature: 30–200 °C, 200–300 °C, and 300–500 °C. The content of C–O–C, aromatic C=O, O–H groups, and aromatic ring C=C gradually decreases, while that of the CH2 in aliphatic hydrocarbons increases between 30 °C and 200 °C. The 200–300 °C stage is mainly filled by the synergistic effects of aliphatic and aromatic groups. The content of aliphatic groups, C–O–C groups, aromatic C=O, and aromatic ring C=C of both vitrinite and inertinite decreases greatly. The 300–500 °C stage is dominated by the aromatization and condensation of macromolecules. The substituents of the aromatic system gradually detach, leading to an increase in I and DOC. From 30 °C to 1000 °C, in situ XRD results reveal a difference in macromolecular structural evolution between vitrinite and inertinite. The arrangement of aromatic layers in vitrinite tends to be ordered during pyrolysis, whereas there are no significant changes in the inertinite. However, the aromatic layers of inertinite are always more compact than that of vitrinite. In addition, the aliphatic side chains of inertinite are more stable than that of vitrinite during the pyrolysis process.
Application of Generation Adequacy Analysis for Reliability Evaluation of a Floating Production Storage and Offloading Platform Power System
Authors: Castro Patricio F. , Rodriguez Yuri Percy M. , Carvalho Fabricio B. S.
Published: July 2022

This paper proposes the application of generation adequacy analysis for reliability evaluation of an insulated power generation system that supplies a FPSO (Floating Production Storage and Offloading) oil and gas production platform. The frequency and duration method was adopted for generating system reliability evaluation. The historical reliability data of the floating production storage and offloading platform power system and the continuous Markov process are used to determine the generator’s reliability model. The load model was also based on the platform daily peak load variation curve. The system risk indexes were obtained using Monte Carlo simulation. Two system scenarios were simulated using different failure data for one of the generators and the software PowerFactory© has been used as a tool for this simulation. For a complete generation system modeling, frequency and duration methods were developed to calculate the probabilities, frequencies and duration of the system states. Numerical results are presented and discussed to demonstrate the applicability of the proposed method.
In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures
Authors: Copeland Claire , Turner Britta , Powells Gareth , Wilson Kevin
Published: July 2022

In this study, we considered a bridging strategy between qualitative and quantitative research with the aim of achieving complementarity. A pilot case study using the Sheffield Elicitation Framework “SHELF” to estimate appropriate inputs for a quantitative energy systems model (based on a qualitative energy future scenario) was used to gain insights. Of novelty are the ethnographic insights of an example translation procedure as well as the methodological approach of the translation procedure itself. This paper reports the findings from this exercise concerning the practicalities of applying such a technique and the observations from the expert elicitation process itself. Based on this pilot, we make two recommendations. The first is the importance of devising a strategy in projects, and research programmes, where bridging between qualitative and quantitative research activities would be most effective. The second is that observations of discussions during the expert elicitation process provide value in the provenance of the estimates for quantitative modelling purposes and provide considerations for further development of qualitative future scenarios.
Initial Findings from US Department of Energy’s Better Plants Virtual in-Plant Training on 50001 Ready
Authors: Guo Wei , Wenning Thomas , Travis Jennifer , Stowe Michael , Armstrong Kristina , Nimbalkar Sachin , Levine Eli
Published: July 2022

Manufacturing facilities use about 35% of the domestic energy in the United States every year. Implementing an effective energy management system (EnMS) is one of the most important approaches to improve energy efficiency. However, the implementation of EnMS is low for many countries (including the US) and even for energy-intensive sectors. The reasons for the low implementation rate of energy management systems had been investigated by multiple researchers, but very few studies have focused on the barriers and challenges of implementing ISO 50001-based energy management systems. To contribute to this understudied area, this paper discusses the implementation and outcomes of the first Better Plants 50001 Ready Virtual In-plant Training. This paper first provides an overview of 50001 Ready and the 50001 Ready Navigator Tool. Then, it provides details on this training event and its outcomes. Finally, it discusses findings from the responses to 40 live polling questions about the status of the 25 tasks of the 50001 Ready Navigator for participating companies, key components of the participating manufacturing companies’ energy management systems, and challenges and barriers that these companies are facing. The findings suggest that although many companies understood the importance of an effective energy management system, about half of them do not understand the required resources for building energy management systems, and most of them have only just begun establishing these systems and need more assistance and resources in multiple areas. More specifically, more assistance is necessary for the following: (1) improving corporate management’s understanding of the time and resources needed to build an EnMS as well as the benefits; (2) creating linear regression models for more accurate energy performance tracking; (3) understanding energy use, collecting and analyzing energy performance data; (4) optimizing equipment operational controls, and creating action plans.
Society, Work and Precarity
Authors: Ebert Norbert
Published: July 2022

One of sociology’s core tasks is to explain how societies work and change. Work plays a crucial and fundamental role in the formation of societies and is also a major driver of social change. It is therefore of key sociological interest to understand how work creates and changes the social conditions we call societies. However, work also creates different levels of freedom and equality; which manifest as different types and degrees of precarity in what I call ‘work societies’.