bluuebunny/crossref_metadata_2025 huggingface dataset:
Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction
Authors: Liu Chunhong , Jiao Jie , Li Weili , Wang Jingxiong , Zhang Junna
Published: December 2022

Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud platforms. There are massive amounts of short-workload sequences in the cloud platform, and the small amount of data and the presence of outliers make accurate workload sequence prediction a challenge. For the above issues, this paper proposes an ensemble learning method based on sample weight transfer and long short-term memory (LSTM), termed as Tr-Predictor. Specifically, a selection method of similar sequences combining time warp edit distance (TWED) and transfer entropy (TE) is proposed to select a source domain dataset with higher similarity for the target workload sequence. Then, we upgrade the basic learner of the ensemble model two-stage TrAdaBoost.R2 to LSTM in the deep model and enhance the ability of the ensemble model to extract sequence features. To optimize the weight adjustment strategy, we adopt a two-stage weight adjustment strategy and select the best weight for the learner according to the sample error and model error. Finally, the above process determines the parameters of the target model and uses the target model to predict the short-task sequences. In the experimental validation, we arbitrarily select nine sets of short-workload data from the Google dataset and three sets of short-workload data from the Alibaba cluster to verify the prediction effectiveness of the proposed algorithm. The experimental results show that compared with the commonly used cloud workload prediction methods Tr-Predictor has higher prediction accuracy on the small-sample workload. The prediction indicators of the ablation experiments show the performance gain of each part in the proposed method.
On the Correspondence between Subshifts of Finite Type and Statistical Mechanics Models
Authors: Corona Luis Armando , Salgado García Raúl , Ugalde Edgardo
Published: December 2022

Several classical problems in symbolic dynamics concern the characterization of the simplex of measures of maximal entropy. For subshifts of finite type in higher dimensions, methods of statistical mechanics are ideal for dealing with these problems. R. Burton and J. Steif developed a strategy to construct examples of strongly irreducible subshifts of finite type admitting several measures of maximal entropy. This strategy exploits a correspondence between equilibrium statistical mechanics and symbolic dynamics—a correspondence which was later formalized by O. Häggström. In this paper, we revisit and discuss this correspondence with the aim of presenting a simplified version of it and present some applications of rigorous results concerning the Potts model and the six-vertex model to symbolic dynamics, illustrating in this way the possibilities of this correspondence.
The Contribution of Ornamental Plants to Urban Ecosystem Services
Authors: Francini Alessandra , Romano Daniela , Toscano Stefania , Ferrante Antonio
Published: December 2022

Urban areas can be differently anthropized; often, high-density populations lead to higher amounts of pollution. Nowadays, ornamental plants can represent important living components of urban areas, and if appropriate species are used, they can provide important ecosystem services. The relationships between green infrastructures and ecosystem services have been recognized for a long time, but the role of ornamental plant species has not been studied as much. In this frame, the different ecosystem services of ornamental plants, i.e., provisioning (e.g., food, air, and water cleaning), regulating (e.g., rain water, climate, nutrient recycling, pollination, and the formation of fertile soils), and cultural (e.g., recreation opportunities or the inspiration we draw from nature) will be critically analyzed to select the most suitable ornamental plant species able to assure the better performance. The action mechanisms will also be analyzed and discussed to individuate the best ideotypes of plant species able to better assure water purification, air quality, space for recreation, climate mitigation and adaptation, human wellbeing, and health. This information is suitable to ensure that the protection, restoration, creation, and enhancement of green infrastructure become integral parts of urban spatial planning and territorial development.
Young Employees’ Perceptions about Employability Skills for E-Commerce
Authors: Kovács Ildikó , Keresztes Éva Réka
Published: December 2022

With the digital transformation of businesses, digital marketing has been a prominent feature of organizations in the 21st century. Changing consumer behavior has also created a need for versatile hard and soft skills for marketing professionals. Inspired by the ongoing disruption to businesses across the world, this research focuses on employability skills in the field of e-commerce. A systematic review of the managerial research literature on digital marketing and e-commerce competencies has revealed a research gap regarding the investigation of the perceived importance of employability skills. The aim of this paper is to find out how important these skills are for future and current employees. The novelty of this study is the comparison between perceptions related to nonworking versus working young employees based on five key skill categories across 30 soft and hard skills. The study is quantitative in nature: a self-administered computer-assisted personal interviewing (CAPI) method is used for data collection. The findings reveal that the examined skills are perceived as important from the employee point of view. In addition to exploring the perceived relevance of the employability skills categories, the study identifies differences among the skills that young workers consider important in terms of their work experience. The research results reveal significant differences by work segments in relation to soft skills, such as teamwork, stress resilience, planning, organization and time management, precision, and attention to detail.
The HOMESIDE Music Intervention: A Training Protocol for Family Carers of People Living with Dementia
Authors: Odell-Miller Helen , Blauth Laura , Bloska Jodie , Bukowska Anna A. , Clark Imogen N. , Crabtree Sarah , Engen Runa B. , Knardal Solgunn , Kvamme Tone K. , McMahon Kate , Petrowitz Carina , Smrokowska-Reichmann Agnieszka , Stensæth Karette , Tamplin Jeanette , Wosch Thomas , Wollersberger Nina , Baker Felicity A.
Published: December 2022

Background: The number of people living with dementia (PwD) worldwide is expected to double every 20 years. Many continue living at home, receiving support from family caregivers who may experience significant stress, simultaneously to that of the PwD. Meaningful and effective home-based interventions to support PwD and their caregivers are needed. The development of a theory- and practice-driven online home-based music intervention (MI) is delivered by credentialed music therapists, nested within the HOMESIDE RCT trial. Methods: Dyads including the PwD and their family carer are randomised to MI, reading (RI) or standard care (SC). MI aims to support health wellbeing and quality of life by training caregivers to intentionally use music (singing, instrument playing, movement/dancing, and music listening) with their family member (PwD) in daily routines. MI is underpinned by cognitive, relational, social, and psychological theories of mechanisms of change. Results: Preliminary sub-cohort results analyses show MI can be delivered and is accepted well by participants and music-therapist interventionists across five countries. Conclusions: The specialist skills of a music therapist through MI enable carers to access music when music therapists are not present, to meet carer and PwD needs. Music therapists embrace this changing professional role, observing therapeutic change for members of the dyads.
Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions
Authors: Salcedo Dixon , Guerrero Cesar , Saeed Khalid , Mardini Johan , Calderon-Benavides Liliana , Henriquez Carlos , Mendoza Andres
Published: December 2022

Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants.
Anomaly Detection in Fog Computing Architectures Using Custom Tab Transformer for Internet of Things
Authors: Alzahrani Abdullah I. A. , Al-Rasheed Amal , Ksibi Amel , Ayadi Manel , Asiri Mashael M. , Zakariah Mohammed
Published: December 2022

Devices which are part of the Internet of Things (IoT) have strong connections; they generate and consume data, which necessitates data transfer among various devices. Smart gadgets collect sensitive information, perform critical tasks, make decisions based on indicator information, and connect and interact with one another quickly. Securing this sensitive data is one of the most vital challenges. A Network Intrusion Detection System (IDS) is often used to identify and eliminate malicious packets before they can enter a network. This operation must be done at the fog node because the Internet of Things devices are naturally low-power and do not require significant computational resources. In this same context, we offer a novel intrusion detection model capable of deployment at the fog nodes to detect the undesired traffic towards the IoT devices by leveraging features from the UNSW-NB15 dataset. Before continuing with the training of the models, correlation-based feature extraction is done to weed out the extra information contained within the data. This helps in the development of a model that has a low overall computational load. The Tab transformer model is proposed to perform well on the existing dataset and outperforms the traditional Machine Learning ML models developed as well as the previous efforts made on the same dataset. The Tab transformer model was designed only to be capable of handling continuous data. As a result, the proposed model obtained a performance of 98.35% when it came to classifying normal traffic data from abnormal traffic data. However, the model’s performance for predicting attacks involving multiple classes achieved an accuracy of 97.22%. The problem with imbalanced data appears to cause issues with the performance of the underrepresented classes. However, the evaluation results that were given indicated that the proposed model opened new avenues of research on detecting anomalies in fog nodes.
Research on Logistics Service Assessment for Smart City: A Users’ Review Sentiment Analysis Approach
Authors: Zhang Shaozhong , Zhong Haidong , Wei Chao , Zhang Dingkai
Published: December 2022

The innovative development of logistics has become a powerful starting point and strong support for the construction of smart cities. An accurate evaluation of logistics service quality can promote intelligent transformation, and upgrading logistics enterprises can improve the urban public service infrastructure. In this study, we propose a logistics service quality evaluation model based on a combination of sentiment analysis technology and a traditional index evaluation system. With the help of sentiment analysis technology, the model focuses on extracting the sentiment characteristics of logistics service quality from user reviews and analyzing user attitudes from different aspects. We designed a new logistics service quality evaluation index system by improving the SERVQUAL model. The system uses sentiment analysis technology to explore evaluation content through feature extraction and builds relations between the evaluation content and indices. Additionally, we use sentiment orientation analysis with different indices to comprehensively evaluate service quality. The experimental analysis shows that the proposed model and algorithm have high accuracy.
Research on Deep Compression Method of Expressway Video Based on Content Value
Authors: Shen Yang , Lu Jinqin , Zhu Li , Deng Fangming
Published: December 2022

Aiming at the problem that the storage space and network bandwidth of expressway surveillance video are occupied largely due to data redundancy and sparse information, this paper proposes a deep compression method of expressway video depth based on content value. Firstly, the YOLOv4 algorithm is used to analyze the content value of the original video, extract video frames with vehicle information, and eliminate unintentional frames. An improved CNN is then designed by adding Feature Pyramids and the Inception module to accelerate the extraction and fusion of features at all levels and improve the performance of image classification and prediction. Finally, the whole model is integrated into HEVC encoder for compressing the preprocessed video. The experimental results show that at the expense of only a 5.96% increase of BD-BR, and only a 0.19 dB loss of BD-PSNR, the proposed method achieves a 64% compression ratio and can save 62.82% coding time compared with other classic data compression methods based on deep learning.
True Random Number Generation Capability of a Ring Oscillator PUF for Reconfigurable Devices
Authors: Rojas-Muñoz Luis F. , Sánchez-Solano Santiago , Martínez-Rodríguez Macarena C. , Brox Piedad
Published: December 2022

This paper presents the validation of a novel approach for a true-random number generator (TRNG) based on a ring oscillator–physical unclonable function (RO-PUF) for FPGA devices. The proposal takes advantage of the different noise sources that affect the electronic implementation of the RO-PUF to extract the entropy required to guarantee its function as a TRNG, without anything more than minimal changes to the original design. The new RO-PUF/TRNG architecture has been incorporated within a hybrid HW/SW embedded system designed for devices from the Xilinx Zynq-7000 family. The degree of randomness of the generated bit streams was assessed using the NIST 800-22 statistical test suite, while the validation of the RO-PUF proposal as an entropy source was carried out by fulfilling the NIST 800-90b recommendation. The features of the hybrid system were exploited to carry out the evaluation and validation processes proposed by the NIST publications, online and on the same platform. To establish the optimal configuration to generate bit streams with the appropriate entropy level, a statistical study of the degree of randomness was performed for multiple TRNG approaches derived from the different implementation modes and configuration options available on the original RO-PUF design. The results show that the RO-PUF/TRNG design is suitable for secure cryptographic applications, doubling its functionality without compromising the resource–efficiency trade-off already achieved in the design.
Area Dependence of Effective Electromechanical Coupling Coefficient Induced by On-Chip Inductance in LiNbO3-Based BAW Resonators
Authors: Lv Lu , Shuai Yao , Huang Shitian , Zhu Dailei , Wang Yuedong , Luo Wenbo , Pan Xinqiang , Wu Chuangui , Zhang Wanli
Published: December 2022

To solve the problem of filter bandwidth in 5G communication, it is urgent to develop an acoustic resonator with a large effective electromechanical coupling coefficient (Keff2). In this paper, the dependence between the resonance area and the performance of the bulk acoustic wave (BAW) resonator is studied. The solidly mounted resonators (SMRs) based on 43° Y cut lithium niobate (LN) were fabricated by the wafer transfer technique. The on-chip inductor was integrated with the BAW resonator through a pad electrode. Resonators with different resonant areas were fabricated and tested. Finite element modeling (FEM) simulation of acoustic resonators and electromagnetic (EM) simulation of layout were carried out, respectively. The Modified Butterworth Van Dyke (MBVD) model was used to analyze the results, and simulation of the Mason model was adopted. The results show that the dependency relationship between the resonant area and the effective electromechanical coupling coefficient can be induced by on-chip inductance. In the resonant area range of 20 × 20 μm2~160 × 160 μm2, the Keff2 increases from 11.97% to 43.28%.
A Secure Storage and Deletion Verification Scheme of Microgrid Data Based on Integrating Blockchain into Edge Computing
Authors: Zhang Lihua , Liu Chunhui , Li Boping , Fang Haodong , Gu Jinguang
Published: December 2022

A microgrid generates a large amount of power data during daily operation, which needs to be safely transferred, stored, and deleted. In this paper, we propose a secure storage and deletion verification scheme that combines blockchain and edge computing for the problems of limited storage capacity of blockchain and unverifiable data deletion. Firstly, edge computing is used to preprocess power data to reduce the amount of data and to improve the quality of data. Secondly, a hybrid encryption method that combines the improved ElGamal algorithm and the AES-256 algorithm is used to encrypt outsourcing data, and a secure storage chain is built based on the K-Raft consensus protocol to ensure the security of data in the transmission process. Finally, after initiating a data deletion request and successfully deleting the data, a deletion proof is generated and stored in the chain built, based on the Streamlet consensus protocol. The experimental results illustrate that the basic computing cost, block generation time, and communication delay of this scheme are the most efficient; the efficiency of the improved ElGamal algorithm is three times that of the traditional algorithm; the transaction throughput of the the double-layer blockchain can reach 13,000 tps at most. This scheme can realize the safe storage of microgrid data, and can also realize the efficient deletion and verification of outsourcing data.
Cyclic Federated Learning Method Based on Distribution Information Sharing and Knowledge Distillation for Medical Data
Authors: Yu Liang , Huang Jianjun
Published: December 2022

Federated learning has been attracting increasing amounts of attention for its potential applications in disease diagnosis within the medical field due to privacy preservation and its ability to solve data silo problems. However, the inconsistent distributions of client-side data significantly degrade the performance of traditional federated learning. To eliminate the adverse effects of non-IID problems on federated learning performance on multiple medical institution datasets, this paper proposes a cyclic federated learning method based on distribution information sharing and knowledge distillation for medical data (CFL_DS_KD). The method is divided into two main phases. The first stage is an offline preparation process in which all clients train a generator model on local datasets and pass the generator to neighbouring clients to generate virtual shared data. The second stage is an online process that can also be mainly divided into two steps. The first step is a knowledge distillation learning process in which all clients first initialise the task model on the local datasets and share it with neighbouring clients. The clients then use the shared task model to guide the updating of their local task models on the virtual shared data. The second step simply re-updates the task model on the local datasets again and shares it with neighbouring clients. Our experiments on non-IID datasets demonstrated the superior performance of our proposed method compared to existing federated learning algorithms.
Reaction Kinetics-Based Modeling and Parameter Sensitivity Analysis of Direct Ethanol Fuel Cells
Authors: de Oliveira Deborah S. B. L. , Colmati Flavio , de Sousa Ruy
Published: December 2022

Ethanol is considered an alternative fuel to power fuel cells, especially due to its ease of transport and storage and renewable production on a large scale. However, its use in direct ethanol fuel cells (DEFC) is still limited by incomplete electro-oxidation and slow reaction kinetics. Modeling approaches have focused on investigating different reaction mechanisms, but so far, no formal analysis of model parameter sensitivity has been conducted. This work modeled and identified sensitive parameters for different types of Pt–Sn catalysts previously prepared by our research group that displayed good performance in the 5–15 mW/cm2 range (relative to a performance of 12 mW/cm2 achieved by a commercial ETEK catalyst). Analyses to study the effect of these parameters on coverage fraction distribution, reaction rates and possible correlations were also performed. The model was developed based on Butler–Volmer kinetics and on a reaction mechanism previously reported in the literature. Statistical developments were considered to compute parameter uncertainties for a non-linear system with non-linear restrictions. The model achieved very good fits to experimental data, with low RMSE values between 0.22 × 10−4 and 4.2 × 10−4 A/cm2, while also showing surface coverage fraction distributions in agreement with other experiment-based works from the literature. All catalysts taken into account, the most sensitive parameters were the reaction rate constants associated with the formation of adsorbed CH3CO, and the direct and reverse water dissociative adsorption reactions, respectively. Additional analyses suggested that there is not much correlation between the parameters. The results from this work could contribute to the state-of-the-art DEFC models by providing insights into which variables may be assumed constant or which ones have the greatest impact on the model output, thus helping to reduce the model size and computational time for future broader DEFC models.
Detection and Evaluation Technologies for Using Existing Salt Caverns to Build Energy Storage
Authors: Liu Yuanxi , Li Yinping , Ma Hongling , Shi Xilin , Zheng Zhuyan , Dong Zhikai , Zhao Kai
Published: December 2022

Underground salt caverns are widely used in large-scale energy storage, such as natural gas, compressed air, oil, and hydrogen. In order to quickly build large-scale natural gas reserves, an unusual building method was established. The method involves using the existing salt caverns left over from solution mining of salt to build energy storages. In 2007, it was first applied to Jintan Natural Gas Storage of China. Based on this successful project, several existing salt caverns were screened to build energy storages in China. Engineering experience indicates that the key to successful reusing is how to select the most suitable of the numerous available caverns and confirm it. This paper summarizes and reviews relevant theories and testing methods, including: (1) the primary selection principle for using existing salt caverns to build energy storage, (2) the testing method and evaluation theory of tightness of the existing salt cavern, and (3) the typical project case of using the existing salt caverns to build energy storage in China. From the practical application results, the selection principle proposed in this paper can quickly screen available existing salt caverns with energy storage potential, and the brine injection method can effectively evaluate their tightness. It provides a technical roadmap for the subsequent implementation of existing salt cavern utilization projects on a large scale.
Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources
Authors: Krechowicz Adam , Krechowicz Maria , Poczeta Katarzyna
Published: December 2022

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper.
Rapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis
Authors: Liu Hongling , Bie Chuanyu , Luo Fan , Kang Jianqiang , Zhang Yuping
Published: December 2022

In order to solve the problems of long-time consumption and high energy consumption in existing capacity detection methods of retired Ni-MH batteries, a fast and reliable capacity prediction method for retired Ni-MH batteries by multi-parameter driven analysis was proposed in this paper. This method mainly obtains several parameters through short-time measurement and pulse rapid nondestructive testing. Then, Pearson correlation coefficient and KS-test were used to analyze the correlation between the two parameters and verify the same distribution. Finally, SVR was used to predict the battery discharge capacity. The results show that the volume expansion thickness difference Δd, AC internal resistance R, terminal voltage U of the battery, charge and discharge polarization internal resistance Rf1 and Rf2 and pulse charging power P2 of the battery are strongly negatively correlated with the discharge capacity, and these characteristic parameters can effectively and reliably reflect the internal structural characteristics of the battery. Additionally, the mean relative error of the established capacity model is 5.87%, and the lowest error is 1.32%. The prediction effect is good, which provides a certain reference value for the subsequent consistent sorting method.
Applications of Virtual Machine Using Multi-Objective Optimization Scheduling Algorithm for Improving CPU Utilization and Energy Efficiency in Cloud Computing
Authors: Choudhary Rajkumar , Perinpanayagam Suresh
Published: December 2022

Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evaluated with a proper validation in CloudSim. Based on the results, multi-objective PSO (particle swarm optimization) optimization can achieve better and more efficient effects for different parameters than multi-objective GA (genetic algorithm) optimization can.
Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review
Authors: Li Wei , Li Hang , He Zheng , Ji Weijie , Zeng Jing , Li Xue , Zhang Yiyong , Zhang Peng , Zhao Jinbao
Published: December 2022

Lithium-ion batteries (LIBs) have been widely used in mobile devices, energy storage power stations, medical equipment, and other fields, became an indispensable technological product in modern society. However, the capacity degradation of LIBs limits their long-term deployment, which is not conducive to saving resources. What is more, it will lead to safety problems when the capacity of the battery is degraded. Failure of the battery is a key issue in the research and application of LIBs. Faced with the problem of capacity degradation, various aspects of LIBs have been studied. This paper reviews the electrochemical degradation mechanism of LIBs’ life fade, detection technologies for battery failure, methods to regulate battery capacity degradation, and battery lifetime prognostics. Finally, the development trend and potential challenges of battery capacity degradation research are prospected. All the key insights from this review are expected to advance the research on capacity fading and lifetime prediction techniques for LIBs.
A Review on Power Electronic Topologies and Control for Wave Energy Converters
Authors: Darwish Ahmed , Aggidis George A.
Published: December 2022

Ocean energy systems (OESs) convert the kinetic, potential, and thermal energy from oceans and seas to electricity. These systems are broadly classified into tidal, wave, thermal, and current marine systems. If fully utilized, the OESs can supply the planet with the required electricity demand as they are capable of generating approximately 2 TW of energy. The wave energy converter (WEC) systems capture the kinetic and potential energy in the waves using suitable mechanical energy capturers such as turbines and paddles. The energy density in the ocean waves is in the range of tens of kilowatts per square meter, which makes them a very attractive energy source due to the high predictability and low variability when compared with other renewable sources. Because the final objective of any renewable energy source (RES), including the WECs, is to produce electricity, the energy capturer of the WEC systems is coupled with an electrical generator, which is controlled then by power electronic converters to generate the electrical power and inject the output current into the utility AC grid. The power electronic converters used in other RESs such as photovoltaics and wind systems have been progressing significantly in the last decade, which improved the energy harvesting process, which can benefit the WECs. In this context, this paper reviews the main power converter architectures used in the present WEC systems to aid in the development of these systems and provide a useful background for researchers in this area.
Changes in the Composition of Digestate Liquid Fraction after Ozone and Ultrasonic Post-Treatment
Authors: Chuda Aleksandra , Jastrząbek Konrad , Ziemiński Krzysztof
Published: December 2022

There is a growing concern about environmental pollution with digestate, which is produced in significant amounts in the anaerobic digestion process. The inappropriate application of digestate in agriculture may lead to ammonia emission to the atmosphere, nutrients infiltration to groundwater and surface waters eutrophication. There is a great interest in the development of efficient downstream technologies that will help better handle digestate. This study assessed the effect of three different disintegration methods (ozonation, ultrasonication, combination of ozonation and ultrasound) on solids content, nutrient concentration and biodegradability of the liquid fraction of sugar beet pulp digestate. The influence of initial pH (7, 8, 9), ozone dose (0.05–0.45 g O3/g TS), specific ultrasound energy (10,381–51,903 kJ/kg TS) and vibration amplitude (50, 100%) on the performance of digestate liquid fraction treatment was investigated. The highest removal efficiencies of organic substances, total and ammonia nitrogen averaging at 13.81, 20.04 and 28.70%, respectively, in separate ozonation and ultrasonication processes, was obtained at ozone dose of 0.25 g O3/g TS, specific energy of 41,522 kJ/kg TS and amplitude of 100%. The application of combined processes, first ultrasonication and then ozonation, resulted in an increase in the above-mentioned removal efficiencies to 58.16, 36.60 and 48.71%, respectively.
Efficiency and Slippage in Draw Gears with Flat Belts
Authors: Krawiec Piotr , Warguła Łukasz , Waluś Konrad Jan , Gawrońska Elżbieta , Ságová Zuzana , Matijošius Jonas
Published: December 2022

This paper presents the measured energy losses in a draw gear with NE22, XH500, LL2, and CFG flat belts. The ranges of torque capacity, slippage occurrence, and transmission efficiency of selected drive belts are also presented. Knowing the exact values of these parameters allows the selection of the most suitable belts for different applications. In addition, belt manufacturers do not provide these data accurately, leading to machine failures and downtime. The paper describes the dependence of belt efficiency and belt slippage as a function of transmission load. Running transmissions with high slippage values are associated with a significant loss of energy and efficiency, rapid wear of the belt and pulleys, and increased operating temperature. In addition, when flat belts are under excessive load, it is common for the belt to quickly fall off the pulleys, interrupting the operation of machinery and equipment. Experiments on a test bench can accurately determine the energy loss caused by transmission belt slippage. The maximum achievable torque of the belt selected for the study, which differed in construction and materials, was around 6 Nm for LL2, XH500, and CFG and 12 Nm for NE22. Slippage reached values of 0.005 to 0.1, while efficiency ranged between 0.60 and 0.97.
Review of Methods for Addressing Challenging Issues in the Operation of Protection Devices in Microgrids with Voltages of up to 1 kV That Integrates Distributed Energy Resources
Authors: Ilyushin Pavel , Volnyi Vladislav , Suslov Konstantin , Filippov Sergey
Published: December 2022

With the large-scale integration of distributed energy resources (DER) into passive distribution networks with voltages of up to 1 kV, these networks are being converted into microgrids. When the topology and operating conditions change, several challenging issues arise related to the functioning of the protection devices (PD) that are in operation. Most DERs, including renewable generators, are integrated into microgrids by means of inverters. In the event of short circuits (SC) in microgrids, these DERs provide a fault current contribution of no more than 1.2–2.0 Irated at the fault location. This makes it difficult to identify the fault location and to carry out the selective disconnection of the faulty element by means of conventional PDs. This article provides an overview of engineering solutions for improving conventional protection schemes that have been historically used in passive distribution networks, as well as for creating modern protection schemes based on innovative principles and new methods. The use of adaptive protections built on decentralized and centralized principles in most cases ensures the reliable protection of microgrids. Modern intelligent electronic devices (IEDs), where protection functions are implemented, rank higher with respect to their technical perfection in terms of reliability, sensitivity, selectivity, and speed performance. The use of multi-agent systems in the implementation of modern protection schemes requires the availability of broadband communication channels, which hinders their use because of the high cost. The combined use of fault current limiters (FCL) and energy storage systems (ESS) allows for the reliable operation of microgrid protections. The use of modern PDs ensures the reliable operation of DERs and power supply to consumers in microgrids, both in the case of grid-connected and islanded operation modes. Since there is no unified concept of designing protection schemes for microgrids with DERs, the choice of specific approaches to the design of protection schemes should be based on the results of a comparative technical and economic analysis of different options.
Cyclic Supercritical Multi-Thermal Fluid Stimulation Process: A Novel Improved-Oil-Recovery Technique for Offshore Heavy Oil Reservoir
Authors: Tian Jie , Yan Wende , Qi Zhilin , Huang Shiwen , Yuan Yingzhong , Dong Mingda
Published: December 2022

Cyclic supercritical multi-thermal fluid stimulation (CSMTFS) is a novel technology that can efficiently recover heavy oil, while the heating effect, production and heat loss characteristics of CSMTFS have not been discussed. In this study, a physical simulation experiment of CSMTFS is conducted with a three-dimensional experimental system. The results of the study indicate that the whole process of CSMTFS can be divided into four stages, namely, the preheating stage, production increase stage, production stable stage and production decline stage, of which the production stable stage is the main oil production stage, and the production decline stage is the secondary oil production stage. In the first two stages of the CSMTFS process, there is no supercritical multi-thermal fluid chamber, and only a relatively small supercritical multi-thermal fluid chamber is formed in the last stage of the CSMTFS process. Out of the supercritical multi-thermal fluid chamber, supercritical water in the thermal fluids condensates to hot water and flows downward to heat the subjacent oil layer. At the same time, the non-condensate gas in the thermal fluids accumulates to the upper part of the oil layer and reduces heat loss. The analysis of heat loss shows that the heat loss rate gradually increases at first and then tends to be stable. Compared with conventional thermal fluid, the CSMTFS can more effectively reduce heat loss. The enthalpy value of supercritical multi-thermal fluid is significantly increased compared with that of multi-thermal fluid, which effectively solves the problem of insufficient heat carrying capacity of multi-thermal fluid. Overall, cyclic supercritical multi-thermal fluid stimulation can effectively solve the problems of conventional heavy oil thermal recovery technology in offshore heavy oil recovery; it is indeed a new improved-oil-recovery technique for offshore heavy oil. The findings of this study can help in better understanding the cyclic supercritical multi-thermal fluid stimulation process. This study is significant and helpful for application of CSMTFS technology in heavy oil recovery.
Effect of Waste Heat Utilization on the Efficiency of Marine Main Boilers
Authors: Behrendt Cezary , Szczepanek Marcin
Published: December 2022

As a result of technological advancements in the 21st century, steam-driven ships, with an efficiency of between 36 and 38%, have been replaced by diesel-driven ships, with an efficiency of up to 55%. Accordingly, the manufacturers of the main boilers and steam turbines in ships have fought for their products to remain on the market by modernizing their construction in order to increase their competitiveness in relation to diesel drives. Based on a review of data in the literature, this article presents an analysis of the increased efficiency of ship boilers with the use of waste heat from boiler exhaust gas. This paper considers the utility of using this heat to heat up the water that supplies the boilers as well as the use of the heated air in the combustion process. The analysis takes into account the different types of heat exchangers used for these purposes and the different efficiencies of boilers made by the leading boiler manufacturers. The formulae and calculation methodologies used are applied in the following analysis of the impact of using waste heat to increase boiler efficiency.
Neuroendocrine Blockade of the Reproductive Axis in Female Athletes
Authors: Rossmanith Winfried G.
Published: December 2022

This review aims at defining the neuroendocrine mechanisms underlying the sport-induced restrictions of the reproductive axis in female athletes. Episodic gonadotropin release was found to be compromised, presumably a result of impaired hypothalamic pulsatile GnRH release. Any deviation from optimal gonadotropin release may result in a suboptimal function of the ovaries, leading to disorders of the menstrual cycle and ovulation. A whole spectrum of menstrual dysfunctions ranging from ovulatory eumenorrhea to luteal phase defects and amenorrhea has been reported in sportive women. As essential neuroendocrine factors underlying these observations, activation of the adrenal axis and altered central nervous neurotransmitter activity have been identified to transfer metabolic, nutritional, and stress signals into the hypothalamic GnRH release. The degree by which the neuroendocrine axis governing reproduction is impaired critically depends on the intensity and duration of exercise and the state of training. Other decisive factors may be energy expenditure and availability, nutritional components, and the maturity of the hypothalamic-pituitary-ovarian (HPO) axis when sport activity was initiated. In conclusion, the gradual cessation of reproductive function observed in female athletes may be interpreted as an adaptive mechanism in response to physical and psychological endurance during sport. This sport-induced restriction of reproductive capacity may serve as protection (endogenous contraception) to preserve a woman’s health.
Infrared Spectroscopy for the Quality Control of a Granular Tebuthiuron Formulation
Authors: Johnson Joel B. , Farquhar Hugh , Ismay Mansel , Naiker Mani
Published: December 2022

Tebuthiuron is a selective herbicide for woody species and is commonly manufactured and sold as a granular formulation. This project investigated the use of infrared spectroscopy for the quality analysis of tebuthiuron granules, specifically the prediction of moisture content and tebuthiuron content. A comparison of different methods showed that near-infrared spectroscopy showed better results than mid-infrared spectroscopy, while a handheld NIR instrument (MicroNIR) showed slightly improved results over a benchtop NIR instrument (Antaris II FT-NIR Analyzer). The best-performing models gave an R2CV of 0.92 and RMSECV of 0.83% w/w for moisture content, and R2CV of 0.50 and RMSECV of 7.5 mg/g for tebuthiuron content. This analytical technique could be used to optimise the manufacturing process and reduce the costs of post-manufacturing quality assurance.
Improving Biomass-Degradation Properties and Nano-Mechanics of Moso Bamboo via a Simple Nitrogen Heat Treatment
Authors: Xie Xuqin , Xi Jingyu , Dai Yueping , Yuan Tiancheng , Li Yanjun , Wang Xinzhou
Published: December 2022

Nitrogen is generally used as a protective gas to provide an oxygen-free environment for the heat treatment of biomaterials. In order to indicate the effect of nitrogen heat treatment of bamboo, the changes in terms of the chemical composition, chemical functional groups, cellulose crystallinity index, surface color, micro-mechanics and anti-mildew properties of bamboo, and the interaction relationship among the properties, were analyzed. The mass loss ratio of treated bamboo samples increased significantly during the process of thermal modification. In detail, the hemicellulose exhibited a decreasing tendency from 23.7% to 16.6%, while the lignin content presented an increasing tendency. The decreased hemicellulose and cellulose contents are a benefit to enhancing lignin content and crystallinity degree, thus increasing the modulus of elasticity and hardness of treated bamboo cell walls. The obtained bamboo sample treated at 190 °C/3 h displayed the best micro-mechanical properties. It presented a maximum modulus of elasticity of 22.1 GPa and a hardness of 0.97 GPa. Meanwhile, the lignin and cellulose content was proven to increase in the bamboo surface in chemical composition analysis, resulting in lower free-hydroxyl groups on the bamboo surface. Thus, the contact angle value of bamboo increased. Furthermore, nitrogen thermal modification positively contributed to the mildew resistance of bamboo specimens.
Correlations among Soil Properties, Growth Characteristics, and Ginsenoside Contents in Wild-Simulated Ginseng with Different Ages
Authors: Yun Yeong-Bae , Huh Jeong-Hoon , Um Yurry
Published: December 2022

Wild-simulated ginseng (WSG) is naturally cultivated in forest environment without any artificial facilities or chemicals. Soil property is one of the major factors affecting the growth and active compound synthesis of vegetation. Therefore, growth characteristics and ginsenoside contents of WSG can be affected by soil properties of the cultivation field. Therefore, the aim of this study was to investigate correlations among soil properties, growth characteristics, and ginsenoside contents of WSG with different ages using Spearman’s coefficient analysis method. It was found that most of growth characteristics of WSG except for rhizome length were significantly increased in 7-year-old WSG for both the above-ground part and root part. Soil pH, and exchangeable cations (Ca, Mg) of 13-year-old WSG cultivation site were significantly higher than those of 4-year-old WSG. However, available phosphate of 4-year-old WSG soil was higher than that of 13-year old WSG soil. Contents of ginsenosides of 4-year-old WSG were higher than those of 13-year-old WSG in the above-ground part. Otherwise, in the root part, contents of ginsenosides of 13-year-old WSG were higher than those of 4-year-old WSG. In correlation analysis, growth characteristics of 4-year-old WSG were correlated with more ginsenoside types. Correlations between soil properties and ginsenoside contents in leaves and roots of WSG with the same age also differed. These results suggest that soil properties play essential role in growth and ginsenoside synthesis of WSG. Based on results of this study, growth characteristics, soil properties, and ginsenoside contents of WSG in different WSG cultivation sites need to be further investigated to identify the most suitable cultivation site for WSG.