bluuebunny/crossref_metadata_2025 huggingface dataset:
Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI
Authors: Rehman Ikram Ur , Sobnath Drishty , Nasralla Moustafa M. , Winnett Maria , Anwar Aamir , Asif Waqar , Sherazi Hafiz Husnain Raza
Published: October 2021

The new ‘normal’ defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses.
Diagnostic Accuracy of Non-Invasive Imaging for Detection of Colonic Inflammation in Patients with Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis
Authors: Alshammari Meshari T. , Stevenson Rebecca , Abdul-Aema Buraq , Zou Guangyong , Jairath Vipul , Radford Shellie , Marciani Luca , Moran Gordon W.
Published: October 2021

Endoscopy is the gold standard for objective assessment of colonic disease activity in inflammatory bowel disease (IBD). Non-invasive colonic imaging using bowel ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) may have a role in quantifying colonic disease activity. We reviewed the diagnostic accuracy of these modalities for assessment of endoscopically or histopathologically defined colonic disease activity in IBD. We searched Embase, MEDLINE, and the Web of Science from inception to 20 September 2021. QUADAS-2 was used to evaluate the studies’ quality. A meta-analysis was performed using a bivariate model approach separately for MRI and US studies only, and summary receiver operating characteristic (ROC) curves were obtained. CT studies were excluded due to the absence of diagnostic test data. Thirty-seven studies were included. The mean sensitivity and specificity for MRI studies was 0.75 and 0.91, respectively, while for US studies it was 0.82 and 0.90, respectively. The area under the ROC curves (AUC) was 0.88 (95% CI, 0.82 to 0.93) for MRI, and 0.90 (95% CI, 0.75 to 1.00) for US. Both MRI and US show high diagnostic accuracy in the assessment of colonic disease activity in IBD patients.
Viral Ecology and Natural Infection Dynamics of Kaeng Khoi Virus in Cave-Dwelling Wrinkle-Lipped Free-Tailed Bats (Chaerephon plicatus) in Thailand
Authors: Neill William A. , Kading Rebekah C.
Published: October 2021

Kaeng Khoi virus (KKV; Order: Bunyavirales), is an endemic viral infection of the wrinkle-lipped free-tailed bat (Chaerephon plicatus aka Tadarida plicata plicata). Little is known about the ecology and maintenance of KKV within the bat population, nor the infection dynamics and transmission among bats or between bats and other vertebrates. Therefore, KKV was studied in Kaeng Khoi cave, Saraburi province, Thailand, during 1973–1974 with the objectives to (1) characterize the seasonal infection rates of KKV in the context of the bat population ecology, and (2) describe the infection dynamics and viral shedding by naturally- and experimentally-infected bats. To this end, the free-tailed bat population was estimated by a series of timed photographs taken during the evening exodus. The case population of 900,000 adult bats doubled at the time of weaning of the young and returned to its previous level soon thereafter. The newborn bats had neutralizing antibodies to KKV that were likely to be maternal in origin. The KKV antibody prevalence in adult bats was high (69–91%) in March–May and low (29–40%) in August and September. Kaeng Khoi virus was isolated from 75% of dead and 50% of moribund bats, but was not found in nearly 400 apparently healthy bats. Virus was present in saliva, urine and blood of most of the naturally-moribund bats tested. Consistent with observations from naturally-infected bats, experimental infection of bats with KKV revealed significant liver pathology, also suggestive that this is not a benign infection. Kaeng Khoi virus is an endemic, year-round infection maintained by the annual recruitment of a large number of immunologically-naïve juvenile bats. Moreover, it produces an acute infection in the bat, either leading to death by hepatitis, or immunity.
Moody’s Ratings Statistical Forecasting for Industrial and Retail Firms
Authors: Caridad y López del Río Lorena , García-Moreno García María de los Baños , Caro-Barrera José Rafael , Pérez-Priego Manuel Adolfo , Caridad y López del Río Daniel
Published: October 2021

Long-term ratings of companies are obtained from public data plus some additional nondisclosed information. A model based on data from firms’ public accounts is proposed to directly obtain these ratings, showing fairly close similitude with published results from Credit Rating Agencies. The rating models used to assess the creditworthiness of a firm may involve some possible conflicts of interest, as companies pay for most of the rating process and are, thus, clients of the rating firms. Such loss of faith among investors and criticism toward the rating agencies were especially severe during the financial crisis in 2008. To overcome this issue, several alternatives are addressed; in particular, the focus is on elaborating a rating model for Moody’s long-term companies’ ratings for industrial and retailing firms that could be useful as an external check of published rates. Statistical and artificial intelligence methods are used to obtain direct prediction of awarded rates in these sectors, without aggregating adjacent classes, which is usual in previous literature. This approach achieves an easy-to-replicate methodology for real rating forecasts based only on public available data, without incurring the costs associated with the rating process, while achieving a higher accuracy. With additional sampling information, these models can be extended to other sectors.
Low Inter-Rater Reliability of a High Stakes Performance Assessment of Teacher Candidates
Authors: Lyness Scott A. , Peterson Kent , Yates Kenneth
Published: October 2021

The Performance Assessment for California Teachers (PACT) is a high stakes summative assessment that was designed to measure pre-service teacher readiness. We examined the inter-rater reliability (IRR) of trained PACT evaluators who rated 19 candidates. As measured by Cohen’s weighted kappa, the overall IRR estimate was 0.17 (poor strength of agreement). IRR estimates ranged from −0.29 (worse than expected by chance) to 0.54 (moderate strength of agreement); all were below the standard of 0.70 for consensus agreement. Follow-up interviews of 10 evaluators revealed possible reasons we observed low IRR, such as departures from established PACT scoring protocol, and lack of, or inconsistent, use of a scoring aid document. Evaluators reported difficulties scoring the materials that candidates submitted, particularly the use of Academic Language. Cognitive Task Analysis (CTA) is suggested as a method to improve IRR in the PACT and other teacher performance assessments such as the edTPA.
The Double Whammy of Pandemic and War: A Systematic Review of India’s Education Diplomacy to Address Educational Inequities in Afghanistan
Authors: Singh Bawa , Kaur Jaspal , Sen Rajinder Kumar , Singh Balinder , Chattu Vijay Kumar
Published: October 2021

Higher education is considered an important tool for the overall development of any country, and it holds true in the context of Afghanistan as well. At the same time, a good eco-environment in terms of political will, leaders’ farsighted vision, a fair budget, good infrastructure, and a good teaching community are some of the basic requirements for higher education to move in the direction of new and higher horizons. However, due to the ongoing war during the last couple of decades, the country’s education system has become out of reach for a substantial part of the population due to poverty, lack of infrastructure, refugees and internally displaced, digital division, etc., critically affecting the education equity. This systematic review examines India’s education diplomacy in addressing the inequities in Afghanistan’s education system and making them more equitable. Education was further dilapidated with the outbreak of the COVID-19 pandemic. Afghanistan is caught between a war and a pandemic and suffers from a double whammy in losses. Subsequently, given their chilling effects, higher education becomes devoid of multiples equities, including education. However, because of their historical and geo-civilizational ties, India has focused on development diplomacy in general and education diplomacy (E.D) in particular to improve educational infrastructures.
Individual-Centred Approaches to Accessibility in STEM Education
Authors: Davey Theresa , Salazar Luces José Victorio , Davenport Rebecca
Published: October 2021

Equitable access to high-quality higher education is in line with the United Nations Sustainable Development Goals 4, 5, and 10, which indicate that it is crucial for a future sustainable society. Globalisation and reductions in systemic barriers to university admission are creating increasingly diverse higher education classrooms, but traditional education methods may unfairly disadvantage some groups of students. Creating equity in access to high-quality education requires teaching approaches that are considerate of each student’s individual sociocultural context as it affects their educational attainment. Building on discipline-based education research (DBER) principles in science, technology, engineering, and mathematics (STEM) education, a modified holistic approach is proposed that primarily centres on students and tailors the teaching methods to the needs of the individuals and the dynamic of the whole class. This work demonstrates that educational attainment and student confidence was improved by applying an individual-centred teaching approach in a highly diverse undergraduate engineering classroom. Trials of this approach in a pilot classroom showed clear and consistent improvement over standard active learning approaches. Best practice guidelines for individual-centred teaching in STEM classrooms are provided. Further work is needed to examine the efficacy of this approach in a generalised setting, but the positive outcomes for student attainment are in line with existing research in the literature. The best practice guidelines presented herein may serve as a starting point for other educators to become more aware of the sociocultural needs of their individual students and classrooms, which may result in a move towards equity in STEM higher education.
An Explainable DL-Based Condition Monitoring Framework for Water-Emulsified Diesel CR Systems
Authors: Akpudo Ugochukwu Ejike , Hur Jang-Wook
Published: October 2021

Despite global patronage, diesel engines still contribute significantly to urban air pollution, and with the ongoing campaign for green automobiles, there is an increasing demand for controlling/monitoring the pollution severity of diesel engines especially in heavy-duty industries. Emulsified diesel fuels provide a readily available solution to engine pollution; however, the inherent reduction in engine power, component corrosion, and/or damage poses a major concern for global adoption. Notwithstanding, on-going investigations suggest the need for reliable condition monitoring frameworks to accurately monitor/control the water-diesel emulsion compositions for inevitable cases. This study proposes the use of common rail (CR) pressure differentials and a deep one-dimensional convolutional neural network (1D-CNN) with the local interpretable model-agnostic explanations (LIME) for empirical diagnostic evaluations (and validations) using a KIA Sorento 2004 four-cylinder line engine as a case study. CR pressure signals were digitally extracted at various water-in-diesel emulsion compositions at various engine RPMs, pre-processed, and used for necessary transient and spectral analysis, and empirical validations. Results reveal high model trustworthiness with an average validation accuracy of 95.9%.
Low-Frequency 1/f Noise Characteristics of Ultra-Thin AlOx-Based Resistive Switching Memory Devices with Magneto-Resistive Responses
Authors: Hong Jhen-Yong , Chen Chun-Yen , Ling Dah-Chin , Martínez Isidoro , González-Ruano César , Aliev Farkhad G.
Published: October 2021

Low-frequency 1/f voltage noise has been employed to probe stochastic charge dynamics in AlOx-based non-volatile resistive memory devices exhibiting both resistive switching (RS) and magneto-resistive (MR) effects. A 1/fγ noise power spectral density is observed in a wide range of applied voltage biases. By analyzing the experimental data within the framework of Hooge’s empirical relation, we found that the Hooge’s parameter α and the exponent γ exhibit a distinct variation upon the resistance transition from the low resistance state (LRS) to the high resistance state (HRS), providing strong evidence that the electron trapping/de-trapping process, along with the electric field-driven oxygen vacancy migration in the AlOx barrier, plays an essential role in the charge transport dynamics of AlOx-based RS memory devices.
Use of Different Metal Oxide Coatings in Stainless Steel Based ECDs for Smart Textiles
Authors: Rozman Martin , Cetin Nikolina , Bren Urban , Lukšič Miha
Published: October 2021

Electrochromism is the ability of a material to selectively change its coloration under the influence of an external electric current/potential and maintain it even after the power source has been disconnected. Devices that use such a mechanism are known as electrochromic devices (ECDs). Over the years, significant effort has been invested into the development of flexible ECDs. Such electrochromic tapes or fibers can be used as smart textiles. Recently, we utilized a novel geometrical approach in assembling electrochromic tapes which does not require the use of optically transparent electrodes. The so-called inverted sandwich ECD configuration can employ various color-changing mechanisms, e.g., intercalation, redox reactions of electrolytes or reactions on electrode surfaces. One of the most frequently used electrochromic metal oxides is WO3. However, other metal oxides with different coloration responses also exist. In this paper, we explore the use of V2O5 and TiO2 in metal-tape-based ECDs in the inverted sandwich configuration and compare their performance with WO3-based devices. Morphological features of metal oxide thin layers were investigated with scanning electron microscopy (SEM), and the performance of the tapes was investigated electrochemically and spectroscopically. We demonstrate that well-established preparation techniques (e.g., sol–gel synthesis) along with coating approaches (e.g., dipping) are adequate to prepare optically nontransparent fiber electrodes. Depending on the metal oxide, flexible electrochromic fiber devices exhibiting different coloration patterns can be assembled. Devices with TiO2 showed little coloration response, while much better performance was achieved in the case of V2O5 and WO3 ECDs.
Analog Gaussian Function Circuit: Architectures, Operating Principles and Applications
Authors: Alimisis Vassilis , Gourdouparis Marios , Gennis Georgios , Dimas Christos , Sotiriadis Paul P.
Published: October 2021

This review paper explores existing architectures, operating principles, performance metrics and applications of analog Gaussian function circuits. Architectures based on the translinear principle, the bulk-controlled approach, the floating gate approach, the use of multiple differential pairs, compositions of different fundamental blocks and others are considered. Applications involving analog implementations of Machine Learning algorithms, neuromorphic circuits, smart sensor systems and fuzzy/neuro-fuzzy systems are discussed, focusing on the role of the Gaussian function circuit. Finally, a general discussion and concluding remarks are provided.
Low-Latency Hardware Implementation of High-Precision Hyperbolic Functions Sinhx and Coshx Based on Improved CORDIC Algorithm
Authors: Fu Wenjia , Xia Jincheng , Lin Xu , Liu Ming , Wang Mingjiang
Published: October 2021

CORDIC algorithm is used for low-cost hardware implementation to calculate transcendental functions. This paper proposes a low-latency high-precision architecture for the computation of hyperbolic functions sinhx and coshx based on an improved CORDIC algorithm, that is, the QH-CORDIC. The principle, structure, and range of convergence of the QH-CORDIC are discussed, and the hardware circuit architecture of functions sinhx and coshx using the QH-CORDIC is plotted in this paper. The proposed architecture is implemented using an FPGA device, showing that it has 75% and 50% latency overhead over the two latest prior works. In the synthesis using TSMC 65 nm standard cell library, ASIC implementation results show that the proposed architecture is also superior to the two latest prior works in terms of total time (latency × period), ATP (area × total time), total energy (power × total time), energy efficiency (total energy/efficient bits), and area efficiency (efficient bits/area/total time). Comparison of related works indicates that it is much more favorable for the proposed architecture to perform high-precision floating-point computations on functions sinhx and coshx than the LUT method, stochastic computing, and other CORDIC algorithms.
Conception and Simulation of a 2-Then-1-Bit/Cycle Noise-Shaping SAR ADC
Authors: Kim Kihyun , Oh Sein , Chae Hyungil
Published: October 2021

A 2-then-1-bit/cycle noise-shaping successive-approximation register (SAR) analog-to-digital converter (ADC) for high sampling rate and high resolution is presented. The conversion consists of two phases of a coarse 2-bit/cycle SAR conversion for high speed and a fine 1-bit/cycle noise-shaping SAR conversion for high accuracy. The coarse conversion is performed by both voltage and time comparison for low power consumption. A redundancy after the coarse conversion corrects the error caused by a jitter noise during the time comparison. Additionally, a mismatch error between signal and reference paths is eliminated with the help of a tail-current-sharing comparator. The proposed ADC was designed in a 28 nm CMOS process, and the simulation result shows a 68.2 dB signal-to-noise distortion (SNDR) for a sampling rate of 480 MS/s and a bandwidth of 60 MHz with good energy efficiency.
Basic Principles, Most Common Computational Tools, and Capabilities for Building Energy and Urban Microclimate Simulations
Authors: Stavrakakis George M. , Katsaprakakis Dimitris Al. , Damasiotis Markos
Published: October 2021

This paper presents basic principles of built-environment physics’ modelling, and it reviews common computational tools and capabilities in a scope of practical design approaches for retrofitting purposes. Well-established simulation models and methods, with applications found mainly in the international scientific literature, are described by means of strengths and weaknesses as regards related tools’ availability, easiness to use, and reliability towards the determination of the optimal blends of retrofit measures for building energy upgrading and Urban Heat Island (UHI) mitigation. The various characteristics of computational approaches are listed and collated by means of comparison among the principal modelling methods as well as among the respective computational tools that may be used for simulation and decision-making purposes. Insights of coupling between building energy and urban microclimate models are also presented. The main goal was to provide a comprehensive overview of available simulation methods that can be used at the early design stages for planning retrofitting strategies and guiding engineers and technical professionals through the simulation tools’ options oriented to the considered case study.
Electrospun Composite Proton-Exchange and Anion-Exchange Membranes for Fuel Cells
Authors: Shang Zhihao , Wycisk Ryszard , Pintauro Peter
Published: October 2021

A fuel cell is an electrochemical device that converts the chemical energy of a fuel and oxidant into electricity. Cation-exchange and anion-exchange membranes play an important role in hydrogen fed proton-exchange membrane (PEM) and anion-exchange membrane (AEM) fuel cells, respectively. Over the past 10 years, there has been growing interest in using nanofiber electrospinning to fabricate fuel cell PEMs and AEMs with improved properties, e.g., a high ion conductivity with low in-plane water swelling and good mechanical strength under wet and dry conditions. Electrospinning is used to create either reinforcing scaffolds that can be pore-filled with an ionomer or precursor mats of interwoven ionomer and reinforcing polymers, which after suitable processing (densification) form a functional membrane. In this review paper, methods of nanofiber composite PEMs and AEMs fabrication are reviewed and the properties of these membranes are discussed and contrasted with the properties of fuel cell membranes prepared using conventional methods. The information and discussions contained herein are intended to provide inspiration for the design of high-performance next-generation fuel cell ion-exchange membranes.
Relationship between Organic Geochemistry and Reservoir Characteristics of the Wufeng-Longmaxi Formation Shale in Southeastern Chongqing, SW China
Authors: Wang Shengxiu , Wang Jia , Zhang Yuelei , Li Dahua , Jiao Weiwei , Wang Jinxi , Lei Zhian , Yu Zhongqiang , Zha Xiaojun , Tan Xianfeng
Published: October 2021

Shale gas accumulates in reservoirs that have favorable characteristics and associated organic geochemistry. The Wufeng-Longmaxi formation of Well Yucan-6 in Southeast Chongqing, SW China was used as a representative example to analyze the organic geochemical and reservoir characteristics of various shale intervals. Total organic carbon (TOC), vitrinite reflectance (Ro), rock pyrolysis, scanning electron microscopy (SEM), and nitrogen adsorption analyses were conducted, and a vertical coupling variation law was established. Results showed the following: the Wufeng-Longmaxi formation shale contains kerogen types I and II2; the average TOC value at the bottom of the formation is 3.04% (and the average value overall is 0.78%); the average Ro value is 1.94%; the organic matter is in a post mature thermal evolutionary stage; the shale minerals are mainly quartz and clay; and the pores are mainly intergranular, intragranular dissolved pores, organic matter pores and micro fractures. In addition, the average specific surface area (BET) of the shale is 5.171 m2/g; micropores account for 4.46% of the total volume; the specific surface area reaches 14.6%; and mesopores and macropores are the main pore spaces. There is a positive correlation between TOC and the quartz content of Wufeng-Longmaxi shale, and porosity is positively correlated with the clay mineral content. It is known that organic pores and the specific area develop more favorably when the clay mineral content is higher because the adsorption capacity is enhanced. In addition, as shale with a high clay mineral content and high TOC content promotes the formation of a large number of nanopores, it has a strong adsorption capacity. Therefore, the most favorable interval for shale gas exploration and development in this well is the shale that has a high TOC content, high clay mineral content, and a suitable quartz content. The findings of this study can help to better identify shale reservoirs and predict the sweet point in shale gas exploration and development.
Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy
Authors: Le Vincent , Ramirez Joshua , Alamaniotis Miltiadis
Published: October 2021

This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms; thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models.
In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System
Authors: Voss Mike F. , Haveman Steven P. , Bonnema Gerrit Maarten
Published: October 2021

Current electric vehicle (EV) charging systems have limited smart functionality, and most research focuses on load-balancing the national or regional grid. In this article, we focus on supporting the early design of a smart charging system that can effectively and efficiently charge a company’s EV fleet, maximizing the use of self-generated Photo-Voltaic energy. The support takes place in the form of the Vehicle Charging Simulation (VeCS) model. System performance is determined by operational costs, CO2 emissions and employee satisfaction. Two impactful smart charging functions concern adaptive charging speeds and charging point management. Simulation algorithms for these functions are developed. The VeCS model is developed to simulate implementation of a smart charging system incorporating both charging infrastructure and local Photo-Voltaics input, using a company’s travel and energy data, prior to having the EVs in place. The model takes into account travel behaviour, energy input and energy consumption on a daily basis. The model shows the number of charged vehicles, whether incomplete charges occur, and energy flow during the day. The model also facilitates simulation of an entire year to determine overall cost and emission benefits. It also estimates charging costs and CO2 emissions that can be compared to the non-EV situation. With the VeCS model, the impact of various system design and implementation choices can be explored before EVs are used. Two system designs are proposed for the case company; a short-term version with current technology and a future version with various smart functionalities. Overall, the model can contribute to substantiated advice for a company regarding implementation of charging infrastructure.
Case Study of Multi-Period MILP HENS with Heat Pump and Storage Options for the Application in Energy Intensive Industries
Authors: Prendl Leopold , Hofmann René
Published: October 2021

The environmental goals of initiatives such as the European Green Deal, which aims to achieve climate neutrality for the EU by 2050, increase the importance of improving and optimizing industrial processes. Mathematical optimization methods like heat exchange network synthesis (HENS) are crucial tools in enabling industry to identify potential energy savings and cost reductions. The lack of publicly available industry data suitable for comprehensive testing of novel optimization procedures is often a major obstacle in development and research. To tackle this problem for extended HENS with potential heat pump and storage integration and show the potential of energy integration in energy-intensive industries (EII), the authors introduce a set of four use-cases based on representative industrial processes from the EII. The application of a previously presented a HENS approach for the integration of heat pumps and storage on these cases resulted in a potential reduction of total annual costs up to 55.43% and total external energy demand up to 87.1%. The presented cases, their solutions, and the open-access mathematical formulation of the optimization procedure make a valuable contribution to the literature and future research in the field of HENS.
Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews
Authors: Pinciroli Luca , Baraldi Piero , Ballabio Guido , Compare Michele , Zio Enrico
Published: October 2021

The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.
Evaluation of Ground Motion Amplification Effects in Slope Topography Induced by the Arbitrary Directions of Seismic Waves
Authors: Yin Chao , Li Wei-Hua , Wang Wei
Published: October 2021

The incident directions of seismic waves can change the ground motions of slope topography. To elaborate on the influences of the directions of seismic waves, a dynamic analysis of the slope topography was performed. Seismic waves were input using an equivalent nodal force method combined with a viscous-spring artificial boundary. The amplification of ground motions in double-faced slope topographies was discussed by varying the angles of incidence. Meanwhile, the components of seismic waves (P waves and SV waves), slope materials and slope geometries were all investigated with various incident earthquake waves. The results indicated that the pattern of the amplification of SV waves was stronger than that of P waves in the slope topography, especially in the greater incident angels of the incident waves. Soft materials intensely aggravate the acceleration amplification, and more scattered waves are produced under oblique incident earthquake waves. The variations in the acceleration amplification ratios on the slope crest were much more complicated at oblique incident waves, and the ground motions were underestimated by considering only the vertical incident waves. Therefore, in the evaluation of ground motion amplification of the slope topography, it is extremely important to consider the direction of incident waves.
Unconventional Well Test Analysis for Assessing Individual Fracture Stages through Post-Treatment Pressure Falloffs: Case Study
Authors: Ellafi Abdulaziz , Jabbari Hadi
Published: October 2021

Researchers and operators have recently become interested in the individual stage optimization of unconventional reservoir hydraulic fracture. These professionals aim to maximize well performance during an unconventional well’s early-stage and potential Enhanced Oil Recovery (EOR) lifespan. Although there have been advances in hydraulic fracturing technology that allow for the creation of large stimulated reservoir volumes (SRVs), it may not be optimal to use the same treatment design for all stages of a well or many wells in an area. We present a comprehensive review of the main approaches used to discuss applicability, pros and cons, and a detailed comparison between different methodologies. Our research outlines a combination of the Diagnostic Fracture Injection Test (DFIT) and falloff pressure analysis, which can help to design intelligent production and improve well performance. Our field study presents an unconventional well to explain the objective optimization workflow. The analysis indicates that most of the fracturing fluid was leaked off through natural fracture surface area and resulted in the estimation of larger values compared to the hydraulic fracture calculated area. These phenomena might represent a secondary fracture set with a high fracture closure stress activated in neighbor stages that was not well-developed in other sections. The falloff pressure analysis provides significant and vital information, assisting operators in fully understanding models for fracture network characterization.
Design of Nonlinear Backstepping Double-Integral Sliding Mode Controllers to Stabilize the DC-Bus Voltage for DC–DC Converters Feeding CPLs
Authors: Ghosh Subarto Kumar , Roy Tushar Kanti , Pramanik Md. Abu Hanif , Mahmud Md. Apel
Published: October 2021

This paper proposes a composite nonlinear controller combining backstepping and double-integral sliding mode controllers for DC–DC boost converter (DDBC) feeding by constant power loads (CPLs) to improve the DC-bus voltage stability under large disturbances in DC distribution systems. In this regard, an exact feedback linearization approach is first used to transform the nonlinear dynamical model into a simplified linear system with canonical form so that it becomes suitable for designing the proposed controller. Another important feature of applying the exact feedback linearization approach in this work is to utilize its capability to cancel nonlinearities appearing due to the incremental negative-impedance of CPLs and the non-minimum phase problem related to the DDBC. Second, the proposed backstepping double integral-sliding mode controller (BDI-SMC) is employed on the feedback linearized system to determine the control law. Afterwards, the Lyapunov stability theory is used to analyze the closed-loop stability of the overall system. Finally, a simulation study is conducted under various operating conditions of the system to validate the theoretical analysis of the proposed controller. The simulation results are also compared with existing sliding mode controller (ESMC) and proportional-integral (PI) control schemes to demonstrate the superiority of the proposed BDI-SMC.
Modeling Transient Pipe Flow in Plastic Pipes with Modified Discrete Bubble Cavitation Model
Authors: Urbanowicz Kamil , Bergant Anton , Kodura Apoloniusz , Kubrak Michał , Malesińska Agnieszka , Bury Paweł , Stosiak Michał
Published: October 2021

Most of today’s water supply systems are based on plastic pipes. They are characterized by the retarded strain (RS) that takes place in the walls of these pipes. The occurrence of RS increases energy losses and leads to a different form of the basic equations describing the transient pipe flow. In this paper, the RS is calculated with the use of convolution integral of the local derivative of pressure and creep function that describes the viscoelastic behavior of the pipe-wall material. The main equations of a discrete bubble cavity model (DBCM) are based on a momentum equation of two-phase vaporous cavitating flow and continuity equations written initially separately for the gas and liquid phase. In transient flows, another important source of pressure damping is skin friction. Accordingly, the wall shear stress model also required necessary modifications. The final partial derivative set of equations was solved with the use of the method of characteristics (MOC), which transforms the original set of partial differential equations (PDE) into a set of ordinary differential equations (ODE). The developed numerical solutions along with the appropriate boundary conditions formed a basis to write a computer program that was used in comparison analysis. The comparisons between computed and measured results showed that the novel modified DBCM predicts pressure and velocity waveforms including cavitation and retarded strain effects with an acceptable accuracy. It was noticed that the influence of unsteady friction on damping of pressure waves was much smaller than the influence of retarded strain.
PVcheck—A Software to Check Your Photovoltaic System
Authors: Rinio Markus
Published: October 2021

Having a photovoltaic (PV) system raises the question of whether it runs as expected. Measuring its energy yield takes a long time and the result still contains uncertainties from varying weather conditions and possible shading of the modules. Here, a free software PVcheck to measure the peak power of the system is announced, using the power data of a single sunny day. The software loads a data file of the generated power as a function of time from this day. This data file is provided by typical inverters. The software then simulates this power curve using known parameters like angle and location of the PV system. The assumed peak power of the simulation can then be adjusted so that the simulated curve matches the measured one. The software runs under Microsoft Windows™ and makes use of the free library pvlib python. The simulation can be refined by importing weather data like temperature, wind speed, and insolation. Furthermore, curves describing the nominal module efficiency as a function of the illumination intensity as well as the power-dependent inverter efficiency can be included in the simulation. First results reveal a good agreement of the simulation with experimental data. The software can be used to detect strong problems in PV systems after installation and to monitor their long-time operation.
Numerical Study on Influences of Drag Reducing Additive in Supercritical Flow of Kerosene in a Millichannel
Authors: Li Biao , Li Wenxi , Zheng Xin , Wang Yue , Tang Mingming , Cai Weihua
Published: October 2021

To improve the performance of a high-pressure refueling liquid oxy-kerosene engine, the influence of drag-reducing additive on the heat transfer characteristics in the supercritical flow of kerosene in a microchannel for regenerative cooling is explored. The finite-volume CFD numerical simulation method is applied using the RNG k-ε turbulence model and enhanced wall function. The current work faithfully represents the effect of the drag-reducing additive in kerosene through numerical calculations by combining a 10-component model for the physical properties of the kerosene and the Carreau non-Newtonian fluid constitutive model from rheological measurements. Results suggest that the 10-component kerosene surrogate can describe the supercritical physical properties of kerosene. The inlet temperature, inlet velocity, and the heat flux on the channel wall are driving factors for the supercritical kerosene flow and heat transfer characteristics. The pressure influence on the heat transfer is negligible. With polymer additives, the loss in pressure drop and heat transfer performance of supercritical kerosene flow decrease 46.8% and 37.5% respectively. The enhancement of engine thrust caused by reduction in pressure drop is an attractive improvement of concern.
Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning
Authors: Mohana Mohamed , Saidi Abdelaziz Salah , Alelyani Salem , Alshayeb Mohammed J. , Basha Suhail , Anqi Ali Eisa
Published: October 2021

Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.
Changes in Thornthwaite Moisture Index and Reactive Soil Movements under Current and Future Climate Scenarios—A Case Study
Authors: Karim Md Rajibul , Rahman Md Mizanur , Nguyen Khoi , Cameron Donald , Iqbal Asif , Ahenkorah Isaac
Published: October 2021

Expansive soils go through significant volume changes due to seasonal moisture variations resulting in ground movements. The ground movement related problems are likely to worsen in the future due to climate change. It is important to understand and incorporate likely future changes in design to ensure the resilience of structures built on such soils. However, there has been a limited amount of work quantifying the effect of climate change on expansive soils movement and related behaviour of structures. The Thornthwaite Moisture Index (TMI) is one of the commonly used climate classifiers in quantifying the effect of atmospheric boundary on soil behaviour. Using the long-term weather data and predicted future changes under different emission scenarios, a series of TMI maps are developed for South Australia. Potential changes in ground movement are then estimated for a selected area using a simplified methodology where the effect of future climate is captured through changes in TMI. Results indicate that South Australia is likely to face a significant reduction in TMI under all emission scenarios considered in this study. The changes in TMI will lead to a considerable increase in potential ground movement which will influence the behaviour of structures built on them and in some areas may lead to premature failure if not considered in the design.
Determinants of the Energy Development Based on Renewable Energy Sources in Poland
Authors: Bednarczyk Jan L. , Brzozowska-Rup Katarzyna , Luściński Sławomir
Published: October 2021

In this article, we aim to identify the determinants that profoundly impact renewable energy sources development in Poland. To this end, the authors have conducted qualitative and quantitative analyses of Poland’s renewable energy sector. In this paper, we demonstrate an original approach considering the different development levels of the given sector across provinces (voivodeships). It uses panel data from the Local Data Bank of the Statistics Poland on electricity production from renewable energy sources in individual voivodeships in Poland from 2005 through 2019. The study confronts the results of previous studies and sheds light on the situation in Poland—specifically, upon the changes that have happened over the ten years (2010–2019). The qualitative analysis shows a negative correlation between energy consumption and the share of renewable energy sources in total energy production. Evidence shows that favorable changes are underway in the energy production structure: RES share is growing and by degrees satisfying energy demand, and there is growing potential of energy entities in Poland. Furthermore, the analysis shows that R&D and total expenditures on environmental protection and water management investments do not significantly affect the development of RES. A dynamic panel data model has been used to analyze the group and time effects on the dependent variable. The findings confirm the existence of the persistency effect and indicate positive effects of total installed electric capacity (IEC) and household electricity consumption (HEC). Nonhousehold electricity consumption (NHEC) has a negative effect on the endogenous variable, i.e., the renewable energy sources share in the gross final energy consumption (RESS). The research results may be applicable as recommendations for energy efficiency policy development based on renewable energy sources depending on the RES development level in the regions of Poland.