bluuebunny/crossref_metadata_2025 huggingface dataset:
Proposal of a New Technique to Obtain Some Fuel Cell Internal Parameters Using Polarization Curve Tests and EIS Results
Authors: Gómez Guillermo , Argumosa Pilar , Correro Adrian , Maellas Jesús
Published: November 2021

Nowadays, fuel cells are becoming a real alternative to power several applications, from portable electronic devices to cars, buses, or stationary facilities. Usually, a basic analysis of a fuel cell includes polarization curve test, as this method is excellent to characterize the behavior of a fuel cell as a whole, because it integrates all the different physical process that happens inside in current and voltage signals. On the other hand, it does not provide accurate information of these physical processes as individual. In this research, we relate the results of polarization curve test and EIS (Electrochemical Impedance Spectroscopy) test through two mathematical expressions. Then, using equivalent electrical circuit elements to model EIS curves, and applying the developed expressions, we correlate the EIS and polarization curve results, allowing us to interpret the physical meaning of these circuit elements and obtain a deeper vision of the internal processes that happen.
Integrity Analysis of the Sheath Considering Temperature Effect under Deep and Large-Scale Multi-Section Hydraulic Fracturing
Authors: Zhang Xiaoyu , Wang Lei , Yang Chunhe , Chang Xin , Guo Yintong , Bi Zhenhui , Yang Hanzhi
Published: November 2021

Different operations make the borehole temperature change and cause periodic stresses, which often cause variations in the stress state of the sheath or damage. In this paper, the effect of temperature on sheath integrity is investigated. First, the mechanical model of sheath is established and analyzed by shakedown theory. Then, compression experiments of well cement at different temperatures are carried out, and the law of mechanical properties with temperature is obtained. Finally, combining the theoretical analysis and mechanical experiments, the results show that (1) when only the temperature inside the sheath cyclically varies, the negative influence of temperature caused by the practical operations can be negligible. (2) When the internal pressure and temperature act together, the effect of temperature on the sheath is reflected in temperature stress and the change of the cement properties. (3) With the increase of temperature difference (∆T), the cohesion of cement decreases while the internal friction angle increases, and the plasticity characteristics of the cement are enhanced, and the negative effect on the Pmax ascends slowly. (4) The temperature stress is in a positive relationship with the ∆T, and its weakening on the Pmax is about 6% to 7%. (5) Combining the temperature stress and the change of the cement properties, total negative effect of temperature on the sheath accounts for 10% to 12%, when ∆T ranges from 60 to 110 °C.
Recent Achievements in Microalgal Photobiological Hydrogen Production
Authors: Touloupakis Eleftherios , Faraloni Cecilia , Silva Benavides Ana Margarita , Torzillo Giuseppe
Published: November 2021

It is well known that over the last 60 years the trend of long-lived greenhouse gas emissions have shown a strong acceleration. There is an increasing concern and a mounting opposition by public opinion to continue with the use of fossil energy. Western countries are presently involved in a so-called energy transition with the objective of abandoning fossil energy for renewable sources. In this connection, hydrogen can play a central role. One of the sustainable ways to produce hydrogen is the use of microalgae which possess two important natural catalysts: photosystem II and hydrogenase, used to split water and to combine protons and electrons to generate gaseous hydrogen, respectively. For about 20 years of study on photobiological hydrogen production, our scientific hopes were based on the application of the sulfur protocol, which indisputably represented a very important advancement in the field of hydrogen production biotechnology. However, as reported in this review, there is increasing evidence that this strategy is not economically viable. Therefore, a change of paradigm for the photobiological production of hydrogen based on microalgae seems mandatory. This review points out that an increasing number of microalgal strains other than Chlamydomonas reinhardtii are being tested and are able to produce sustainable amount of hydrogen without nutrient starvation and to fulfill this goal including the application of co-cultures.
Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region
Authors: Noussan Michel , Jarre Matteo
Published: November 2021

Effective solutions are needed to decrease the greenhouse gases emissions of the transport sector, not only in terms of supply-side measures, but also including demand-side solutions. This paper focuses on the passenger demand related to daily commuting, either for work or study purposes. A bottom-up analysis is presented, which draws from detailed data for Lombardy, the most populous region in Northern Italy, to build an estimate of the annual energy consumption and emissions related to commuting. The potential of different measures to decrease emissions is evaluated, including the renovation of the vehicle stock, higher levels of remote working, and the deployment of carpooling schemes. The results show that the largest part of the current emissions from commuting is caused by car use, both due to its higher modal share and to the higher specific emissions, which are in turn also contributed by the low occupancy rates. The renewal of the current vehicle stock can lead to significant emission savings, thanks to both improved efficiency and higher shares of electrification. Remote working could also play a significant part, especially when it is applied to workers that face the longest commuting distances. Conversely, carpooling seems to be providing lower benefits, not so much because of lower effectiveness but more so because of constraints and barriers to its implementation.
Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization
Authors: Qasim Faraz , Lee Doug Hyung , Won Jongkuk , Ha Jin-Kuk , Park Sang Jin
Published: November 2021

As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation.
Unbending the Winding Path of a Low-Income Country’s Energy Sector amid the COVID-19 Pandemic: Perspectives from Malawi
Authors: Zalengera Collen , Chitawo Maxon L. , Chitedze Isaac , To Long Seng , Mwale Vincent , Gondwe Kondwani T. , Maroyi Timeyo
Published: November 2021

This paper discusses the impact of COVID-19’s vulnerability context on Malawi’s Energy Sector and outlines mechanisms for enhancing Malawi’s energy-sector resilience based on experiences from a range of stakeholders. The investigation was conducted online by inviting purposively selected stakeholders to create presentations responding to thematic questions. The final sample had 19 stakeholders with representation from policy-makers, regulatory bodies, national grid supply players, off-grid players, development agencies, bankers, professional bodies, civil society, and women’s rights bodies. The presentations from the stakeholders highlighted how COVID-19 affects the operation costs of energy systems and implementation of energy systems projects in areas that require stimulus packages to contain energy system delivery costs and prevent disruption of essential services amid the COVID-19 pandemic. These services include stakeholder responses to COVID-19 in the energy sector, the role of digital payments particularly when purchasing electricity units, and the state of third-party service providers such as banks and mobile network operators to enhance preparedness and continuity of operations for the energy sector. Based on the findings in these thematic areas and an application of systems thinking in the analysis, the paper finally makes recommendations on how Malawi and similar low-income countries can strategise to enhance energy systems resilience.
Design and Modelling of Eco-Friendly CH3NH3SnI3-Based Perovskite Solar Cells with Suitable Transport Layers
Authors: Mottakin M. , Sobayel K. , Sarkar Dilip , Alkhammash Hend , Alharthi Sami , Techato Kuaanan , Shahiduzzaman Md. , Amin Nowshad , Sopian Kamaruzzaman , Akhtaruzzaman Md.
Published: November 2021

An ideal n-i-p perovskite solar cell employing a Pb free CH3NH3SnI3 absorber layer was suggested and modelled. A comparative study for different electron transport materials has been performed for three devices keeping CuO hole transport material (HTL) constant. SCAPS-1D numerical simulator is used to quantify the effects of amphoteric defect based on CH3NH3SnI3 absorber layer and the interface characteristics of both the electron transport layer (ETL) and hole transport layer (HTL). The study demonstrates that amphoteric defects in the absorber layer impact device performance significantly more than interface defects (IDL). The cell performed best at room temperature. Due to a reduction in Voc, PCE decreases with temperature. Defect tolerance limit for IL1 is 1013 cm−3, 1016 cm−3 and 1012 cm−3 for structures 1, 2 and 3 respectively. The defect tolerance limit for IL2 is 1014 cm−3. With the proposed device structure FTO/PCBM/CH3NH3SnI3/CuO shows the maximum efficiency of 25.45% (Voc = 0.97 V, Jsc = 35.19 mA/cm2, FF = 74.38%), for the structure FTO/TiO2/CH3NH3SnI3/CuO the best PCE is obtained 26.92% (Voc = 0.99 V, Jsc = 36.81 mA/cm2, FF = 73.80%) and device structure of FTO/WO3/CH3NH3SnI3/CuO gives the maximum efficiency 24.57% (Voc = 0.90 V, Jsc = 36.73 mA/cm2, FF = 74.93%) under optimum conditions. Compared to others, the FTO/TiO2/CH3NH3SnI3/CuO system provides better performance and better defect tolerance capacity.
Travel Dynamics Analysis and Intelligent Path Rectification Planning of a Roadheader on a Roadway
Authors: Ji Xiaodong , Zhang Minjun , Qu Yuanyuan , Jiang Hai , Wu Miao
Published: November 2021

The tunneling work belongs to the group operation of semi-closed space, and the work is difficult with a high risk coefficient. It is an urgent requirement of coal mining to achieve unmanned and intelligent tunneling work. The path rectification planning of roadheaders is a necessary step before roadway cutting. In the traditional dynamic modeling analysis of roadhead tracks, problems such as compaction resistance, bulldozing resistance, steering resistance, tunnel dip angle, ditching, and obstacle-crossing capacity are not considered. In order to approximate the kinematic and dynamic parameters of a roadheader’s deviation correction under actual working conditions, this paper establishes kinematic and dynamic models of a roadheader’s path rectification at low speeds and under complex working conditions, and calculates the obstacle-crossing ability of roadheaders in the course of path rectification by modes based on roadway conditions, crawler resistance, and driving performance of the roadheader. Field experiments were carried out to verify the effectiveness of the dynamic model. The dynamic roadheader model was used in combination with actual working conditions of roadways in order to establish a roadway grid model. The grid model was simplified using rectifying influence degree and distance cost. The roadheader dynamic model and grid model were then used to propose a path rectification planning and tracking algorithm based on particle swarm optimization of the actual roadway conditions and roadheader driving performance. Finally, the effectiveness and superiority of the algorithm were verified using MATLAB simulation. The algorithm can provide strong technical guarantee for the intelligence of roadheader and unmanned mining. The results presented here can provide theoretical and technical support for the structural optimization and intelligent travel control of roadheaders.
Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
Authors: Huang Jianfei , Cheng Xinchun , Shen Yuying , Kong Dewen , Wang Jixin
Published: November 2021

Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel loaders and the environment. In this paper, a deep-learning-based prediction model is developed to predict the throttle value and state for wheel loaders by learning from driving data. Multiple long–short-term memory (LSTM) networks are used to extract the temporal features of different stages during the operation of the wheel loader. Two backward-propagation neural networks (BPNNs), which use the temporal feature extracted by LSTM as the input, are designed to output the final prediction results of throttle value and state, respectively. The proposed prediction model is trained and tested using the data from two different conditions. The end-to-end LSTM prediction model and BPNNs are used as benchmark models. The results indicate that the proposed prediction model has good prediction accuracy and adaptability. Furthermore, the relationship between the prediction performance and signal sampling frequency is also studied. The proposed prediction method that combines driving data and deep learning can make the throttle action conform to the decisions of an experienced operator, providing technical support for the autonomous operation of construction machinery.
Design of a Highly Efficient N-Stage Harmonic Terminated Voltage Multiplier for Wireless Power Transfer
Authors: Kim Juwan , Park Inho , Ku Hyunchul
Published: November 2021

This paper proposes a 5.8 GHz highly efficient rectifier design using harmonic termination for wireless power transfer. The diode used to convert the received RF power to DC is a non-linear device, and a harmonic component is generated, which causes performance degradation. Therefore, in this paper, we designed a band stop filter for harmonic termination and proposed the N-stage harmonic terminated voltage multiplier (N-stage HTVM). The number of stages N can be designed differently to operate with high efficiency at various input powers for the proposed rectifier. In the proposed rectifier circuit, mathematical analysis of output DC voltage, power loss of the diode, and the power conversion efficiency (PCE) were evaluated through voltage/current waveform analysis of the diode. The design method of the filter for terminating harmonics is presented. Furthermore, the change of PCE according to the increase in the number of stages was analyzed using the equivalent model of the proposed circuit and verified through measurement. The maximum PCE of one-stage HTVM was 68% when 18 dBm of input power was applied. The DC output voltage was measured to 11.6 V. When the RF input power was 25 dBm and the load was 1500 Ω, the maximum PCE of the two-stage HTVM was 71% and the maximum DC output voltage was measured as 15.8 V. The measured performance of three-stage HTVM had a PCE of 67% and DC output voltage of 19.8 V when the input power was 30 dBm.
Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
Authors: Jo Sungwoo , Jung Sunkyu , Roh Taemoon
Published: November 2021

Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.
Description of Acid Battery Operating Parameters
Authors: Pszczółkowski Józef
Published: November 2021

In this paper, the operating principles of the acid battery and its features are discussed. The results of voltage tests containing the measurements conducted at the terminals of a loaded battery under constant load conditions, and dependent on time, are presented. The article depicts the principles of the development of electric models of acid batteries and their various descriptions. The principles for processing the results for the purpose of the determination and description of the battery model are characterized. The characteristics under stationary and non-stationary conditions are specified using glued functions and linear combinations of exponential functions, and the electrical parameters of the battery are determined as the components of the circuit, i.e., its electromotive force, resistance, and capacity. The dynamic characteristic of the battery in the form of transmittance was determined, using the Laplace transform. Possible uses of the crankshaft driving signals as diagnostic signals of the battery, electric starter, and internal combustion engine are also indicated.
Model Development and Transient Analysis of the HCPB BB BOP DEMO Configuration Using the Apros System Code
Authors: Szogradi Marton , Norrman Sixten
Published: November 2021

Extensive modeling and analytical work has been carried out considering the Helium-Cooled Pebble Bed Breeding Blanket (HCPB BB) Balance Of Plant (BOP) configuration of the Demonstration Power Plant (DEMO) using the Apros system code, developed by VTT Technical Research Centre of Finland Ltd. and Fortum. The integral plant model of the HCPB BB plant has been improved with respect to the blanket and steam generator models. Based on HCPB-BL2017 v1 data, reported in 2019, the blanket has been remodeled by separate Apros process components, dedicated to average inboard and outboard segments, where the power deposition scheme of the breeding units took into account the output of high-fidelity neutronic analyses. A new helical coil steam generator model has been developed for primary–secondary system coupling using CAD data provided by EUROfusion partner University of Palermo. Transient analyses have been performed with Apros on the plant configuration that utilizes a molten salt technology-based small Energy Storage System (ESS).
Biopolymers in Aerobic Granular Sludge—Their Role in Wastewater Treatment and Possibilities of Re-Use in Line with Circular Economy
Authors: Cydzik-Kwiatkowska Agnieszka
Published: November 2021

Aerobic granular sludge (AGS) technology for wastewater treatment ensures better quality effluent and higher process sustainability than wastewater treatment systems based on activated sludge. One of the reasons for the better operational results of AGS systems is the high content of extracellular polymers (EPS) in the granule structures. EPS produced during granulation have a very complex composition with a predo minance of polysaccharides and proteins, and one of the main components, alginate, may have a wide variety of practical applications. This review summarizes up-to-date information on the composition of EPS in AGS, the manner in which their production and composition are affected by the operational parameters of wastewater treatment, and the effects of EPS in biomass on wastewater treatment and sludge management. Additionally, the possibility of polymer recovery from AGS is presented together with information regarding potential applications based on the newest findings. Re-use of AGS-derived polymers will increase the sustainability of wastewater treatment processes by making them more economical and reducing the amount of sludge that requires management.
Morphology-Governed Performance of Multi-Dimensional Photocatalysts for Hydrogen Generation
Authors: Wei Zhishun , Mogan Tharishinny Raja , Wang Kunlei , Janczarek Marcin , Kowalska Ewa
Published: November 2021

In the past few decades, extensive studies have been performed to utilize the solar energy for photocatalytic water splitting; however, up to the present, the overall efficiencies reported in the literature are still unsatisfactory for commercialization. The crucial element of this challenging concept is the proper selection and design of photocatalytic material to enable significant extension of practical application perspectives. One of the important features in describing photocatalysts, although underestimated, is particle morphology. Accordingly, this review presents the advances achieved in the design of photocatalysts that are dedicated to hydrogen generation, with an emphasis on the particle morphology and its potential correlation with the overall reaction performance. The novel concept of this work—with the content presented in a clear and logical way—is based on the division into five parts according to dimensional arrangement groups of 0D, 1D, 2D, 3D, and combined systems. In this regard, it has been shown that the consideration of the discussed aspects, focusing on different types of particle morphology and their correlation with the system’s efficiency, could be a promising route for accelerating the development of photocatalytic materials oriented for solar-driven hydrogen generation. Finally, concluding remarks (additionally including the problems connected with experiments) and potential future directions of particle morphology-based design of photocatalysts for hydrogen production systems have been presented.
Gamifying Computer Science Education for Z Generation
Authors: Jawad Hadeel Mohammed , Tout Samir
Published: November 2021

Generation Z members use their smart devices as part of their everyday routine. Teaching methods may need to be updated to make learning materials more interesting for this generation. This paper suggests gamifying computer science subjects to enhance the learning experience for this generation. Additionally, many students face difficulty in understanding computer science materials and algorithms. Gamifying computer science education is one of the suggested teaching methods to simplify topics and increase students’ engagement. Moreover, the field of computer science is dominated by males. The use of gamification could increase women’s interest in this field. This paper demonstrates different techniques that were developed by the researchers to employ gamification in teaching computer science topics. The data was collected at the end of the two different courses. Results show that students enjoyed the suggested teaching method and found it useful. This paper also demonstrates two tools and their gamification elements. These tools were developed by the researchers to help people learn computer programming and information security.
Flotation Depression of Arsenopyrite Using Sodium Nitrobenzoate under Alkaline Conditions
Authors: Sun Xiaohao , Wu Bozeng , Hu Mingzhen , Qiu Hongxin , Deng Jiushuai , Cai Jiaozong , Jin Xiaoli
Published: November 2021

Arsenopyrite is a common arsenic-containing mineral that is often closely associated with sulfide minerals, such as pyrite, chalcopyrite, pyrrhotite, galena, and sphalerite, and with precious metals, such as gold and silver. The selective inhibition of arsenopyrite is an important method used to reduce the arsenic content of processed products, the cost of arsenic removal in metallurgical processes, and its impact on the environment. In this study, we discovered a chemical sodium, m-nitrobenzoate (m-NBO), that can effectively inhibit the flotation behaviors of arsenopyrite via sodium butyl xanthate (NaBX), and these effects were studied by flotation experiments. The results showed that, using NaBX as a collector, arsenopyrite had good floatability under acidic conditions, but the floatability decreased under alkaline conditions. Furthermore, the organic inhibitor m-NBO had a significant inhibitory effect on arsenopyrite under alkaline conditions. In addition, the adsorption between m-NBO and NaBX was competitive, and a hydrophilic layer formed on the surface of arsenopyrite. The passivation film prevents dixanthogen from being adsorbed on the surface of the mineral. Due to the effect of m-NBO on arsenopyrite, the redox potential and oxide content of the arsenopyrite surface increased, the hydrophobicity of the arsenopyrite surface was reduced, and the flotation of arsenopyrite was inhibited. These results provide options for separating multimetal sulfide minerals and arsenic-containing minerals.
Disconnection and the Healing Practice of Imagination for Mormon Environmental Ethics
Authors: Blair Kristen
Published: November 2021

The Church of Jesus Christ of Latter-day Saints possesses a subversive and fecund interpretation of the Christian creation narrative. This interpretation, denying creation ex nihilo, bespeaks a particular attention to and care for the living earth. However, Latter-day Saint praxis is wounded by a searing disconnect between the theopoetics of its conceptual creation and its lived practice. I argue that the Church must understand this disconnect as a wound and attend to it as such. I turn to theopoetics, arguing that it is in the lived practices of Latter-day Saints engaging somatically with the Earth that can restore our imaginative potential and move toward healing. I begin by exploring the Christian conception of creation ex nihilo and juxtapose this with the Latter-day Saint understanding of formareex materia. I then explore the implications of such a cosmology for environmental ethics and probe the disconnections between theory and practice in Mormonism broadly construed. I propose that the healing salve for disconnection is imagination, a salve found in the first heartbeat of the Latter-day Saint story. I speak with Latter-day Saint theopoetics and indigenous voices, proposing ultimately that is with them that the healing of theology and praxis must begin.
The Value of Adapting Counseling to Client’s Spirituality and Religion: Evidence-Based Relationship Factors
Authors: Evans Amelia L. , Nelson Jennifer Koenig
Published: November 2021

There is a strong tradition of attention to relationship factors in the field of counseling. The research on the importance of the relationship and adapting to client factors continues to grow, supporting the importance of professional multicultural competence. The field of counseling, specifically within the United States context, has focused on Multicultural Counseling Competencies with more recent emphasis on social justice through the Multicultural and Social Justice Counseling Competencies. Within these competencies, spirituality and religion are mentioned as multicultural components to consider as potentially salient to clients. Yet, there has been less emphasis on ways to adapt counseling to a client’s spirituality and religion compared to other multicultural components of one’s identity, such as race, gender, and culture. Historically, a lack of training, fear of causing offense, or concerns about influencing clients, resulted in clients’ spirituality and religion being overlooked far too often in counseling. Despite this tendency, recent clinical evidence on relational responsiveness identifies the adaptation of counseling to a client’s spirituality and religion as highly effective. In this article, the authors discuss how adapting counseling to a client’s spirituality and religion, in relation to all multicultural factors salient to the client, enhances relational responsiveness and treatment effectiveness. The authors also discuss the implications for training, supervision, and practice.
Whispering Gallery Mode Resonator Temperature Compensation and Refractive Index Sensing in Glucose Droplets
Authors: Brice Inga , Grundsteins Karlis , Draguns Kristians , Atvars Aigars , Alnis Janis
Published: November 2021

Among the different types of photonic sensor devices, optical whispering gallery mode resonators (WGMRs) have attracted interest due to their high level of sensitivity, small size, and ability to perform real-time temperature measurements. Here we demonstrate the applicability of temperature measurements using WGMR in both air and liquid environments. We also show that WGMR allowed measurements of the refractive index variations in an evaporating glucose–water solution droplet. The thermal tuning of WGMR can be reduced by coating WGMRs with a thin layer of polymethyl methacrylate (PMMA). Dip-coating the silica microsphere multiple times significantly reduced the resonance shift, partially compensating for the positive thermo-optical coefficient of silica. The shift direction changed the sign eventually.
Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
Authors: Khan Samee Ullah , Haq Ijaz Ul , Khan Zulfiqar Ahmad , Khan Noman , Lee Mi Young , Baik Sung Wook
Published: November 2021

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.
The More, the Better? Improving VR Firefighting Training System with Realistic Firefighter Tools as Controllers
Authors: Jeon Seunggon , Paik Seungwon , Yang Ungyeon , Shih Patrick C. , Han Kyungsik
Published: November 2021

A virtual reality (VR) controller plays a key role in supporting interactions between users and the virtual environment. This paper investigates the relationship between the user experience and VR control device modality. We developed a VR firefighting training system integrated with four control devices adapted from real firefighting tools. We iteratively improved the controllers and VR system through a pilot study with six participants and conducted a user study with 30 participants to assess two salient human factor constructs—perceived presence and cognitive load—with three device modality conditions (two standard VR controllers, four real tools, and a hybrid of one real tool and one standard VR controller). We found that having more realistic devices that simulate real tools does not necessarily guarantee a higher level of user experience, highlighting a strategic approach to the development and utilization of VR control devices. Our study gives empirical insights on establishing appropriate combinations of VR control device modality in the context of field-based VR simulation and training.
Development of an Algorithm for an Automatic Determination of the Soil Field Capacity Using of a Portable Weighing Lysimeter
Authors: Soler-Méndez Manuel , Parras-Burgos Dolores , Cisterne-López Adrián , Mas-Espinosa Estefanía , Intrigliolo Diego S. , Molina-Martínez José Miguel
Published: November 2021

The challenge today is to optimize agriculture water consumption and minimize leaching of pollutants in agro-ecosystems in order to ensure a sustainable agriculture. The use of different technologies and the adoption of different irrigation strategies can facilitate efficient fertigation management. In this respect, the determination of soil field capacity point is of utmost importance. The use of a portable weighing lysimeter allows an accurate quantification of crop water consumption and water leaching, as well as the detection of soil field capacity point. In this work, a novel algorithm is developed to obtain the soil field capacity point, in order to give autonomy and objectivity to efficient irrigation management using a portable weighing lysimeter. The development was tested in field grown horticultural crops and proved to be useful for optimizing irrigation management.
A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection
Authors: Zhang Xueting , Fang Xiaohai , Pan Mian , Yuan Luhua , Zhang Yaxin , Yuan Mengyi , Lv Shuaishuai , Yu Haibin
Published: November 2021

Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework’s ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms.
Optimal Performance and Modeling of Wireless Technology Enabling Smart Electric Metering Systems Including Microgrids
Authors: Suárez Carlos , Inga Esteban
Published: November 2021

This work is focused on the performance analysis and optimal routing of wireless technology for intelligent energy metering, considering the inclusion of micro grids. For the study, a geo-referenced scenario has been taken into account, which will form the structure of a graph to be solved using heuristic-based algorithms. In the first instance, the candidate site of the world geography to perform the case study is established, followed by deploying infrastructure devices and determining variables and parameters. Then, the model configuration is programmed, taking into account that a set of nodes and vertices is established for proper routing, resulting in a preliminary wireless network topology. Finally, from a set of restrictions, a determination of users connected to the concentrator and optimal routing is performed. This procedure is treated as a coverage set problem. Consequently, to establish the network parameters, two restrictions are specifically considered, capacity and range; thus, can be determined the best technology to adapt to the location. Finally, a verification of the resulting network topologies and the performance of the infrastructure is done by simulating the wireless network. With the model created, scenarios are tested, and it is verified that the optimization model demonstrates its effectiveness.
Comparison between Different Channel Coding Techniques for IEEE 802.11be within Factory Automation Scenarios
Authors: Fanari Lorenzo , Iradier Eneko , Bilbao Iñigo , Cabrera Rufino , Montalban Jon , Angueira Pablo
Published: November 2021

This paper presents improvements in the physical layer reliability of the IEEE 802.11be standard. Most wireless system proposals do not fulfill the stringent requirements of Factory Automation use cases. The harsh propagation features of industrial environments usually require time retransmission techniques to guarantee link reliability. At the same time, retransmissions compromise latency. IEEE 802.11be, the upcoming WLAN standard, is being considered for Factory Automation (FA) communications. 802.11be addresses specifically latency and reliability difficulties, typical in the previous 802.11 standards. This paper evaluates different channel coding techniques potentially applicable in IEEE 802.11be. The methods suggested here are the following: WLAN LDPC, WLAN Convolutional Codes (CC), New Radio (NR) Polar, and Long Term Evolution (LTE)-based Turbo Codes. The tests consider an IEEE 802.11be prototype under the Additive White Gaussian Noise (AWGN) channel and industrial channel models. The results suggest that the best performing codes in factory automation cases are the WLAN LDPCs and New Radio Polar Codes.
Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks
Authors: Bereta Michał
Published: November 2021

Convolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of vectors of features, the order of which does not matter, the use of convolutional neural networks is not justified. This paper presents a new method of representing non-sequential data as images that can be analyzed by a convolutional network. The well-known Kohonen network was used for this purpose. After training on non-sequential data, each example is represented by so-called U-image that can be used as input to a convolutional layer. A hybrid approach was also presented, where the neural network uses two types of input signals, both U-image representation and the original features. The results of the proposed method on traditional machine learning databases as well as on a difficult classification problem originating from the analysis of measurement data from experiments in particle physics are presented.
Monitoring and Assessment of Indoor Environmental Conditions after the Implementation of COVID-19-Based Ventilation Strategies in an Educational Building in Southern Spain
Authors: Aguilar Antonio J. , de la Hoz-Torres María L. , Martínez-Aires Mª Dolores , Ruiz Diego P.
Published: November 2021

Since students and teachers spend much of their time in educational buildings, it is critical to provide good levels of indoor environmental quality (IEQ). The current COVID-19 pandemic has shown that maintaining a good indoor air quality level is an effective measure to control the transmission of the SARS-CoV-2 virus. This study used sensors to monitor key IEQ factors and assess several natural ventilation scenarios in a classroom of the University of Granada. Subsequently, the IEQ factors (temperature, relative humidity, CO2 concentration, acoustic environment, and air velocity) were evaluated for the selected ventilation scenarios in the occupied classroom, and the field monitoring was carried out in two different assessment periods, winter and summer. The obtained results show that the CO2 concentration levels were well below the recommended limits. However, the maintenance of the recommended thermal and acoustic IEQ factors was significantly affected by the natural ventilation strategies (temperature and relative humidity values were very close to the outside values, and the background sound pressure level was over 35 dBA during the entire assessment). The proper measurements and careful selection of the appropriate ventilation scenarios become of utmost importance to ensure that the ventilation rates required by the health authorities are achieved.
Line Position-Dependent Effect in Line-by-Line Inscribed Fiber Bragg Gratings
Authors: Li Hongye , Zhao Xiaofan , Rao Binyu , Wang Meng , Wu Baiyi , Wang Zefeng
Published: November 2021

Line-by-line direct writing by femtosecond laser has been proved to be a simple and effective method for the fabrication of low-loss fiber Bragg gratings (FBGs), and is more flexible compared with the traditional ultraviolet exposure method. In this paper, the line-position-dependent characteristics of cladding modes coupling in line-by-line FBGs have been studied, to the best of our knowledge, for the first time. Both theoretical and experimental results show that off-center inscribing could compress the bandwidth of the Bragg resonance and excite more abundant cladding mode coupling, in which the core-guided fundamental mode would couple to the cladding-guided LP0n and LP1n simultaneously. By aligning the line positions across the core region, the first apodized line-by-line FBG was achieved. This work enriches the theories of line-by-line FBGs and provides an inscription guidance to meet different application requirements.
Constant-Beamwidth Beamforming with Concentric Ring Arrays
Authors: Kleiman Avital , Cohen Israel , Berdugo Baruch
Published: November 2021

Designing beampatterns with constant beamwidth over a wide range of frequencies is useful in many applications in speech, radar, sonar and communication. In this paper, we design constant-beamwidth beamformers for concentric ring arrays. The proposed beamformers utilize the circular geometry to provide improved beamwidth consistency compared to beamformers which are designed for linear sensor arrays of the same order. In the proposed configuration, all sensors on each ring share the same weight value. This constraint significantly simplifies the beamformers and reduces the hardware and computational resources required in a physical setup. Furthermore, a theoretical justification of the beamforming method is provided. We demonstrate the advantages of the proposed beamformers compared to the one-dimensional configuration in terms of directivity index, white noise gain and sidelobe attenuation.
Reliable Route Selection for Wireless Sensor Networks with Connection Failure Uncertainties
Authors: Lyu Jianhua , Ren Yiran , Abbas Zeeshan , Zhang Baili
Published: November 2021

For wireless sensor networks (WSN) with connection failure uncertainties, traditional minimum spanning trees are no longer a feasible option for selecting routes. Reliability should come first before cost since no one wants a network that cannot work most of the time. First, reliable route selection for WSNs with connection failure uncertainties is formulated by considering the top-k most reliable spanning trees (RST) from graphs with structural uncertainties. The reliable spanning trees are defined as a set of spanning trees with top reliabilities and limited tree weights based on the possible world model. Second, two tree-filtering algorithms are proposed: the k minimum spanning tree (KMST) based tree-filtering algorithm and the depth-first search (DFS) based tree-filtering algorithm. Tree-filtering strategy filters the candidate RSTs generated by tree enumeration with explicit weight thresholds and implicit reliability thresholds. Third, an innovative edge-filtering method is presented in which edge combinations that act as upper bounds for RST reliabilities are utilized to filter the RST candidates and to prune search spaces. Optimization strategies are also proposed for improving pruning capabilities further and for enhancing computations. Extensive experiments are conducted to show the effectiveness and efficiency of the proposed algorithms.
Validity and Reliability of an Inertial Sensor Device for Specific Running Patterns in Soccer
Authors: Pillitteri Guglielmo , Thomas Ewan , Battaglia Giuseppe , Navarra Giovanni Angelo , Scardina Antonino , Gammino Viviana , Ricchiari Dario , Bellafiore Marianna
Published: November 2021

Electronic performance tracking devices are largely employed in team sports to monitor performance and improve training. To date, global positioning system (GPS) based devices are those mainly used in soccer training. The aim of this study was to analyse the validity and reliability of the inertial sensor device (ISD) in monitoring distance and speed in a soccer-specific circuit and how their performance compare to a GPS system. 44 young male soccer players (age: 14.9 ± 1.1, range 9–16, years, height: 1.65 ± 0.10 m, body mass: 56.3 ± 8.9 kg) playing in a non-professional soccer team in Italy, participated in the study. We assessed the players trough a soccer running sport-specific circuit. An ISD and a GPS were used to assess distance and speed. Data was compared to a video reference system, and the difference were quantified by means of the root mean square error (RMSE). Significant differences were found for both GPS and ISD devices for distance and speed. However, lower error for distance (dRMSE 2.23 ± 1.01 m and 5.75 ± 1.50 m, respectively) and speed (sRMSE 0.588 ± 0.152 m·s–1 and 1.30 ± 0.422 m·s–1, respectively) were attained by the ISD compared to the GPS. Overall, our results revealed a statistically significant difference between systems in data monitoring for either distance and speed. However, results of this study showed that a smaller error was obtained with the ISD than the GPS device. Despite caution is warranted within the interpretation of these results, we observed a better practical applicability of the ISD due to its small size, lower cost and the possibility to use the device indoor.