bluuebunny/crossref_metadata_2025 huggingface dataset:
DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection
Authors: Latif Ghazanfar
Published: November 2022

The proper segmentation of the brain tumor from the image is important for both patients and medical personnel due to the sensitivity of the human brain. Operation intervention would require doctors to be extremely cautious and precise to target the brain’s required portion. Furthermore, the segmentation process is also important for multi-class tumor classification. This work primarily concentrated on making a contribution in three main areas of brain MR Image processing for classification and segmentation which are: Brain MR image classification, tumor region segmentation and tumor classification. A framework named DeepTumor is presented for the multistage-multiclass Glioma Tumor classification into four classes; Edema, Necrosis, Enhancing and Non-enhancing. For the brain MR image binary classification (Tumorous and Non-tumorous), two deep Convolutional Neural Network) CNN models were proposed for brain MR image classification; 9-layer model with a total of 217,954 trainable parameters and an improved 10-layer model with a total of 80,243 trainable parameters. In the second stage, an enhanced Fuzzy C-means (FCM) based technique is proposed for the tumor segmentation in brain MR images. In the final stage, an enhanced CNN model 3 with 11 hidden layers and a total of 241,624 trainable parameters was proposed for the classification of the segmented tumor region into four Glioma Tumor classes. The experiments are performed using the BraTS MRI dataset. The experimental results of the proposed CNN models for binary classification and multiclass tumor classification are compared with the existing CNN models such as LeNet, AlexNet and GoogleNet as well as with the latest literature.
Comparison of Conventional Imaging and 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in the Diagnostic Accuracy of Staging in Patients with Intrahepatic Cholangiocarcinoma
Authors: Nishioka Eiko , Tsurusaki Masakatsu , Kozuki Ryohei , Im Sung-Woon , Kono Atsushi , Kitajima Kazuhiro , Murakami Takamichi , Ishii Kazunari
Published: November 2022

We aimed to examine the accuracy of tumor staging of intrahepatic cholangiocarcinoma (ICC) by using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT). From January 2001 to December 2021, 202 patients underwent PET-CT, CT, and MRI for the initial staging of ICC in two institutions. Among them, 102 patients had undergone surgical treatment. Ninety patients who had a histopathological diagnosis of ICC were retrospectively reviewed. The sensitivity and specificity of 18F-FDG PET-CT, CT, and magnetic resonance imaging (MRI) in detecting tumors, satellite focus, vascular invasion, and lymph node metastases were analyzed. Ninety patients with histologically diagnosed ICC were included. PET-CT demonstrated no statistically significant advantage over CT and MR in the diagnosis of multiple tumors and macrovascular invasion, and bile duct invasion. The overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET-CT in lymph node metastases were 84%, 86%, 91%, 84%, and 86%, respectively. PET-CT revealed a significantly higher accuracy compared to CT or MRI (86%, 67%, and 76%, p < 0.01, respectively) in the diagnosis of regional lymph node metastases. The accuracy of tumor staging by PET-CT was higher than that by CT/MRI (PET-CT vs. CT vs. MRI: 68/90 vs. 47/90 vs. 51/90, p < 0.05). 18F-FDG PET-CT had sensitivity and specificity values for diagnosing satellite focus and vascular and bile duct invasion similar to those of CT or MRI; however, PET-CT showed higher accuracy in diagnosing regional lymph node metastases. 18F-FDG PET-CT exhibited higher tumor staging accuracy than that of CT/MRI. Thus, 18FDG PET-CT may support tumor staging in ICC.
A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models
Authors: Sarra Raniya R. , Dinar Ahmed M. , Mohammed Mazin Abed , Ghani Mohd Khanapi Abd , Albahar Marwan Ali
Published: November 2022

Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.
Pap Smear Images Classification Using Machine Learning: A Literature Matrix
Authors: Alias Nur Ain , Mustafa Wan Azani , Jamlos Mohd Aminudin , Alquran Hiam , Hanafi Hafizul Fahri , Ismail Shahrina , Rahman Khairul Shakir Ab
Published: November 2022

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.
Thermodynamics of the Ising Model Encoded in Restricted Boltzmann Machines
Authors: Gu Jing , Zhang Kai
Published: November 2022

The restricted Boltzmann machine (RBM) is a two-layer energy-based model that uses its hidden–visible connections to learn the underlying distribution of visible units, whose interactions are often complicated by high-order correlations. Previous studies on the Ising model of small system sizes have shown that RBMs are able to accurately learn the Boltzmann distribution and reconstruct thermal quantities at temperatures away from the critical point Tc. How the RBM encodes the Boltzmann distribution and captures the phase transition are, however, not well explained. In this work, we perform RBM learning of the 2d and 3d Ising model and carefully examine how the RBM extracts useful probabilistic and physical information from Ising configurations. We find several indicators derived from the weight matrix that could characterize the Ising phase transition. We verify that the hidden encoding of a visible state tends to have an equal number of positive and negative units, whose sequence is randomly assigned during training and can be inferred by analyzing the weight matrix. We also explore the physical meaning of the visible energy and loss function (pseudo-likelihood) of the RBM and show that they could be harnessed to predict the critical point or estimate physical quantities such as entropy.
Hyperchaos, Intermittency, Noise and Disorder in Modified Semiconductor Superlattices
Authors: Bonilla Luis L. , Carretero Manuel , Mompó Emanuel
Published: November 2022

Weakly coupled semiconductor superlattices under DC voltage bias are nonlinear systems with many degrees of freedom whose nonlinearity is due to sequential tunneling of electrons. They may exhibit spontaneous chaos at room temperature and act as fast physical random number generator devices. Here we present a general sequential transport model with different voltage drops at quantum wells and barriers that includes noise and fluctuations due to the superlattice epitaxial growth. Excitability and oscillations of the current in superlattices with identical periods are due to nucleation and motion of charge dipole waves that form at the emitter contact when the current drops below a critical value. Insertion of wider wells increases superlattice excitability by allowing wave nucleation at the modified wells and more complex dynamics. Then hyperchaos and different types of intermittent chaos are possible on extended DC voltage ranges. Intrinsic shot and thermal noises and external noises produce minor effects on chaotic attractors. However, random disorder due to growth fluctuations may suppress any regular or chaotic current oscillations. Numerical simulations show that more than 70% of samples remain chaotic when the standard deviation of their fluctuations due to epitaxial growth is below 0.024 nm (10% of a single monolayer) whereas for 0.015 nm disorder suppresses chaos.
Understanding Hierarchical Processes
Authors: Buntine Wray
Published: November 2022

Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks. They allow, for instance, networks of probability vectors to be used in general statistical modelling, intrinsically supporting information sharing through the network. This paper presents a general theory of hierarchical stochastic processes and illustrates its use on the gamma process and the generalised gamma process. In general, most of the convenient properties of hierarchical Dirichlet processes extend to the broader family. The main construction for this corresponds to estimating the moments of an infinitely divisible distribution based on its cumulants. Various equivalences and relationships can then be applied to networks of hierarchical processes. Examples given demonstrate the duplication in non-parametric research, and presents plots of the Pitman–Yor distribution.
Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators
Authors: Vigo-Pereira Caio , Laurini Márcio
Published: November 2022

We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are efficiently exploiting all the information available in the market. Estimators from the GEL family present some optimal statistical properties, such as robustness to misspecifications and better properties in finite samples. Unlike generalized method of moments (GMM) estimators, the bias for GEL estimators does not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. Due to these better properties in finite samples, our main hypothesis is that portfolio efficiency tests using GEL estimators may have better properties in terms of size, power, and robustness. Using Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. Extensive empirical analyses show the properties of the estimators for different sample sizes and portfolio types for two asset pricing models.
External Locus-of-Hope and Collectivist Coping in Students from Three Asian Cities
Authors: Bernardo Allan B. I. , Yabut Homer J. , Wang Tulips Yiwen , Yeung Susanna S.
Published: November 2022

The positive education movement has called attention to the importance of student well-being and the role of positive constructs, such as hope, in the educational process. The external locus-of-hope dimensions, or positive cognitions about the role of external actors in the pursuit of important goals, positively predict student well-being, learning approaches, and achievement. However, external locus-of-hope dimensions were found to be associated with maladaptive coping styles among Asian students. In this study, we revisit this relationship between external locus-of-hope dimensions and coping among students, by focusing on collectivist coping strategies that are assumed to be more relevant to Asian students. A total of 780 university students from three Asian cities (Hong Kong, n = 295; Macau, n = 225; Manila, n = 260) were asked to complete a questionnaire on collectivist coping styles, internal and external locus-of-hope dimensions. Separate multiple regression analyses indicated that the coping style of acceptance/reframing/striving was mainly predicted by internal locus-of-hope in the three groups, but the coping styles of family support and religious coping were consistently predicted by external locus-of-hope dimensions in all three groups of students. The two other coping styles of avoidance/detachment and personal emotional outlets were also predicted by specific external locus-of-hope dimensions, but only in particular groups. The results are discussed in terms of how external locus-of-hope dimensions might evoke both adaptive and maladaptive coping among Asian students, which may be associated with primary and secondary control dimensions of the collectivist coping styles.
Application of Artificial Intelligent Techniques for Power Quality Improvement in Hybrid Microgrid System
Authors: Das Soumya Ranjan , Mishra Alok Kumar , Ray Prakash Kumar , Salkuti Surender Reddy , Kim Seong-Cheol
Published: November 2022

The hybrid AC-DC microgrid (MG) has gained popularity recently as it offers the benefits of AC and DC systems. Interconnecting AC-DC converters are necessary since the MG has both DC and AC sub-grids. Adding an extra harmonic adjustment mechanism to the interlinking converters is promising because non-linear AC loads can worsen the quality of the voltage on the AC bus. The interlinking converters’ primary function is to interchange real and reactive power between DC and AC sub-grids, so the typical harmonic controlling approach implemented for active power filters (APFs) might not be appropriate for them. When the MG’s capacity is high, it is desirable that the switching frequency be lesser than the APFs. The performance of harmonic correction or even system stability may suffer at low switching frequencies. In this study, a harmonic compensating technique appropriate for hybrid AC-DC interlinking converters with lower switching frequencies is planned. The suggested strategy, modeling techniques, stability analysis, and a thorough virtual impedance design are discussed in this work.
Classification of Task Types in Software Development Projects
Authors: Wysocki Włodzimierz , Miciuła Ireneusz , Mastalerz Marcin
Published: November 2022

Managing software development processes is still a serious challenge and offers the possibility of introducing improvements that will reduce the resources needed to successfully complete projects. The article presents the original concept of classification of types of project tasks, which will allow for more beneficial use of the collected data in management support systems in the IT industry. The currently used agile management methods—described in the article—and the fact that changes during the course of projects are inevitable, were the inspiration for creating sets of tasks that occur in software development. Thanks to statistics for generating tasks and aggregating results in an iterative and incremental way, the analysis is more accurate and allows planning the further course of work in the project, selecting the optimal number of employees in task teams, and identifying bottlenecks that may decide on faster completion of the project with success. The use of data from actual software projects in the IT industry made it possible to classify the types of tasks and the necessary values for further work planning, depending on the nature of the planned software development project.
3D Printed Electronic Circuits from Fusible Alloys
Authors: Podsiadły Bartłomiej , Bezgan Liubomir , Słoma Marcin
Published: November 2022

This work aims to evaluate the possibility of fabricating conductive paths for printed circuit boards from low-temperature melting metal alloys on low-temperature 3D printed substrates and mounting through-hole electronic components using the fused deposition modeling for metals (FDMm) for structural electronics applications. The conductive materials are flux-cored solder wires Sn60Pb40 and Sn99Ag0.3Cu0.7. The deposition was achieved with a specially adapted nozzle. A comparison of solder wires with and without flux cores is discussed to determine whether the solder alloys exhibit adequate wettability and adhesion to the polymer substrate. The symmetrical astable multivibrator circuit based on bipolar junction transistors (BJT) was fabricated to demonstrate the possibility of simultaneous production of conductive tracks and through-hole mountings with this additive technique. Additional perspectives for applying this technique to 3D-printed structural electronic circuits are also discussed.
A Hierarchical Searchable Encryption Scheme Using Blockchain-Based Indexing
Authors: Li Yuxi , Zhou Fucai , Ji Dong , Xu Zifeng
Published: November 2022

Focusing on the fine-grained access control challenge of multi-user searchable encryption, we propose a hierarchical searchable encryption scheme using blockchain-based indexing (HSE-BI). First, we propose a hierarchical search index structure based on a DAG-type access policy and a stepwise hierarchical key derivation mechanism; which we outsourced to the blockchain network to achieve reliable hierarchical search. We design a dynamic append-only update protocol for the blockchain-based index to deal with adding and deleting files. Secondly, we propose a hierarchical authorization mechanism based on broadcast encryption to achieve fine-grained search permission granting and revoking, which can prevent a malicious server from colluding with corrupted users. The security and complexity analysis shows that HSE-BI achieves optimal search time while satisfying adaptive secure and revocation secure. Our experimental results are encouraging, e.g., compared with the traditional multi-user searchable encryption schemes, HSE-BI’s hierarchical search policy does not impact the search performance visually. The growth rate of the search latency decreases with the increasing number of hierarchical users, which can act as an efficient crypto tool to open up venues for other applications. We demonstrate that HSE-BI is more suitable for actual applications with fine-grained access requirements and can act as an efficient crypto tool to open up venues for other applications.
Building Security Awareness of Interdependent Services, Business Processes, and Systems in Cyberspace
Authors: Amanowicz Marek , Kamola Mariusz
Published: November 2022

Protection against a growing number of increasingly sophisticated and complex cyberattacks requires the real-time acquisition of up-to-date information on identified threats and their potential impact on an enterprise’s operation. However, the complexity and variety of IT/OT infrastructure interdependencies and the business processes and services it supports significantly complicate this task. Hence, we propose a novel solution here that provides security awareness of critical infrastructure entities. Appropriate measures and methods for comprehensively managing cyberspace security and resilience in an enterprise are provided, and these take into account the aspects of confidentiality, availability, and integrity of the essential services offered across the underlying business processes and IT infrastructure. The abstraction of these entities as business objects is proposed to uniformly address them and their interdependencies. In this paper, the concept of modeling the cyberspace of interdependent services, business processes, and systems and the procedures for assessing and predicting their attributes and dynamic states are depicted. The enterprise can build a model of its operation with the proposed formalism, which takes it to the first level of security awareness. Through dedicated simulation procedures, the enterprise can anticipate the evolution of actual or hypothetical threats and related risks, which is the second level of awareness. Finally, simulation-driven analyses can serve in guiding operations toward improvement with respect to resilience and threat protection, bringing the enterprise to the third level of awareness. The solution is also applied in the case study of an essential service provider.
Parity-Check-CRC Concatenated Polar Codes SSCFlip Decoder
Authors: Jan Qasim , Hussain Shahid , Furqan Muhammad , Pan Zhiwen , Liu Nan , You Xiaohu
Published: November 2022

Successive cancellation flip decoding requires a large number of extra successive cancellation decoding attempts at low signal-to-noise ratios (SNRs), resulting in high decoding complexity. In addition, it has a long decoding latency. Although modifications have been proposed in successive cancellation flip decoding, these still have high computational complexity at low SNRs due to a huge number of additional successive cancellation decoding attempts. It is desirable to detect the unsuccessful successive cancellation decoding process at an early stage in the additional successive cancellation flip attempts and stop it that can reduce the decoding complexity. This paper combines the parity-check-CRC concatenated polar codes with the low-latency simplified successive cancellation decoding and proposes a parity-check-CRC concatenated polar codes simplified successive cancellation flip (PC-CRC-SSCFlip) decoder. It further employs the parity-check vector to identify the unsuccessful simplified successive cancellation flip decoding at an early stage and terminates so that it can minimize the decoding complexity on average. Additionally, this work proposes an error-prone flipping list by incorporating the empirically observed indices based on channel-induced error distribution along with the first bit of each Rate-1 node. The proposed technique can identify more than one error-prone bit through a flipping list and correct them. In addition, the parity-check vector further narrows down the search space for the identification of erroneous decisions. Simulation results show that 60% of unsuccessful additional successive cancellation decoding attempts terminate early rather than decode the whole codeword. The proposed PC-CRC-SSCFlip decoder has approximately 0.7 dB and 0.3 dB gains over successive cancellation and successive cancellation flip decoders, respectively, at a fixed block error rate (BLER) = 10−3. Additionally, it reduces the average computational complexity and decoding latency of the successive cancellation flip decoder at low-to-medium SNRs while approaching successive cancellation decoding complexity at medium-to-high SNRs.
Adaptive Internal Model Control Based on Parameter Adaptation
Authors: Chen Peng , Zhang Yunfeng , Wang Jianhong , Azar Ahmad Taher , Hameed Ibrahim A. , Ibraheem Ibraheem Kasim , Kamal Nashwa Ahmad , Abdulmajeed Farah Ayad
Published: November 2022

In actual industrial control, many objects have the characteristics of large lag and time-varying parameters, which makes it difficult for traditional PID control to obtain satisfactory control effects. Therefore, engineers need a control algorithm with a better control effect and a simple structure, and thus the internal model control is introduced into industrial process control. Internal model control has received a lot of attention in the control field because of its excellent control effect, and in ideal conditions, it can perfectly suppress external disturbances, and the output strictly tracks the input. On the basis of the internal model control, a two-degrees-of-freedom internal model control was developed. Compared with the conventional feedback control, the internal model control structure mainly embeds an internal model consistent with the control object in the control object, so the deviation between the internal model and the control object determines the quality of the control effect. In this paper, the internal model is changed to an adjustable parameter model, and the model parameters are adjusted in real time using the parameter adaptive algorithm, so that the model output error is as small as possible, or even zero. In order to solve the influence of interference on the system, compensation based on MRAC theory is used. In order to verify the feasibility of the algorithm, it was applied to the landing process control of fixed-wing Unmanned aerial vehicle) UAV and achieved satisfactory results.
Parametric Analysis of a Solar Water Heater Integrated with PCM for Load Shifting
Authors: Mellouli Sofiene , Alqahtani Talal , Algarni Salem
Published: November 2022

Integrating a solar water heater (SWH) with a phase change material (PCM)-based latent heat storage is an attractive method for transferring load from peak to off-peak hours. This transferring load varies as the physical parameters of the PCM change. Thus, the aim of this study is to perform a parametric analysis of the SWH on the basis of the PCM’s thermophysical properties. A mathematical model was established, and a computation code was developed to describe the physical phenomenon of heat storage/release in/from the SWH system. The thermal energy stored and the energy efficiency are used as key performance indicators of the new SWH–PCM system. The obtained numerical results demonstrate that the used key performance indicators were significantly impacted by the PCM thermo-physical properties (melting temperature, density, and latent heat). Using this model, various numerical simulations are performed, and the results indicate that, SWH with PCM, 20.2% of thermal energy on-peak periods load is shifted to the off-peak period. In addition, by increasing the PCM’s density and enthalpy, higher load shifting is observed. In addition, the PCM, which has a lower melting point, can help the SWH retain water temperature for a longer period of time. There are optimal PCM thermo-physical properties that give the best specific energy recovery and thermal efficiency of the SWH–PCM system. For the proposed SWH–PCM system, the optimal PCM thermo-physical properties, i.e., the melting temperature is 313 K, the density is 3200 kg/m3, and the latent heat is 520 kg/kg.
View Factors of Flat Collectors, Including Photovoltaics, Visible to Partial Sky
Authors: Appelbaum Joseph , Aronescu Avi
Published: November 2022

The sky view factor of collectors in photovoltaic (PV) fields is a parameter that determines the amount of incident diffuse radiation. The diffuse radiation may contribute significantly to the generated electric energy. PV systems are deployed in multiple rows and separated from each other and from nearby structures. Rows in front and nearby structures may block part of the visible sky to the collectors, thus decreasing the sky view factor. The distance between collectors and objects is an important parameter for the design of photovoltaic systems. The present study develops the expression for sky view factors for cases encountered in deployments of PV fields and presents numerical values for the sky view factors for distances between collectors and from obscuring structures. Sky view factors for flat collectors in the presence of adjacent collectors or structures have not been treated in the literature, besides for simple or common cases. Based on the values of the sky view factor, the PV system designer may assess the diffuse radiation losses, which are a dominant component of the global radiation losses. For example, for a collector installed at a distance 2.75 m from a building, the sky view factor is reduced from 0.97 to 0.85, i.e., a reduction of 12.4% in the incident diffuse radiation on the collector.
Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect
Authors: Chen Chaoyang , Liu Hualing , Xiao Yong , Zhu Fagen , Ding Li , Yang Fuwen
Published: November 2022

In the past two decades, clean energy such as hydro, wind, and solar power has achieved significant development under the “green recovery” global goal, and it may become the key method for countries to realize a low-carbon energy system. Here, the development of renewable energy power generation, the typical hydro-wind-photovoltaic complementary practical project, is summarized, and some key problems in complementary systems such as the description and prediction of the power generation law in large-scale stations, risk management, and coordinated operation are analyzed. In terms of these problems, this paper systematically summarizes the research methods and characteristics of a hydro-wind-solar hybrid system and expounds upon the technical realization process from the prediction and description of wind and solar power station cluster output, the risks brought about by large-scale renewable energy grid-connected operation, and the long-term and short-term coordination modeling and resolution thoughts on the hydro-wind-solar hybrid system in cluster mode. Finally, based on the aforementioned analysis, the existing research gaps are discussed from the standpoints of generation forecast, risk management, and cluster scheduling, and the future work outlook is presented accordingly. A hybrid system that combines hydro, wind, and solar energy is emerging as a way to make up for each other’s shortcomings and will be a fruitful area of study in the future.
Analysis of the Influence of the Gas Infrared Heater and Equipment Element Relative Positions on Industrial Premises Thermal Conditions
Authors: Borisov Boris Vladimirovich , Vyatkin Alexander Vitalievich , Kuznetsov Geniy Vladimirovich , Maksimov Vyacheslav Ivanovich , Nagornova Tatiana Aleksandrovna
Published: November 2022

The creation of local heated areas in large-sized premises using systems based on gas infrared heaters has recently become the most rational alternative in terms of energy efficiency. However, the lack of information about the thermal characteristics in such areas limits the effective application of these systems. To determine the main thermal parameters characterizing the scheduled thermal conditions in heated local working areas of industrial premises, experimental and mathematical modeling of heat transfer processes in a closed area with the presence of equipment in it was carried out. The experimental area was equipped with a gas infrared heater and a model of the equipment (a horizontally oriented panel). The system of equations of thermal conductivity, radiant heat transfer, as well as energy and Navier–Stokes was solved by the finite element method. A significant influence of the equipment position on the temperature field and the air movement hydrodynamics in the local working area has been established. The equipment presence in the room intensifies the air movement due to thermal convection and, as a result, a more uniform temperature distribution over the local working area was obtained. Analysis of the obtained results shows the possibility to control the temperature fields’ formation in local working areas during the gas infrared heater operation by varying the position and configuration of the equipment in the room.
A Comprehensive Review on Advances in TiO2 Nanotube (TNT)-Based Photocatalytic CO2 Reduction to Value-Added Products
Authors: Hossen Md. Arif , Solayman H. M. , Leong Kah Hon , Sim Lan Ching , Yaacof Nurashikin , Abd Aziz Azrina , Lihua Wu , Monir Minhaj Uddin
Published: November 2022

The photocatalytic reduction of CO2 into solar fuels by using semiconductor photocatalysts is one of the most promising approaches in terms of pollution control as well as renewable energy sources. One of the crucial challenges for the 21st century is the development of potential photocatalysts and techniques to improve CO2 photoreduction efficiency. TiO2 nanotubes (TNTs) have recently attracted a great deal of research attention for their potential to convert CO2 into useful compounds. Researchers are concentrating more on CO2 reduction due to the rising trend in CO2 emissions and are striving to improve the rate of CO2 photoreduction by modifying TNTs with the appropriate configuration. In order to portray the potential applications of TNTs, it is imperative to critically evaluate recent developments in synthesis and modification methodologies and their capability to transform CO2 into value-added chemicals. The current review provides an insightful understanding of TNT production methods, surface modification strategies used to enhance CO2 photoreduction, and major findings from previous research, thereby revealing research gaps and upcoming challenges. Stability, reusability, and the improved performance of TNT photocatalysts under visible light as well as the selection of optimized modification methods are the identified barriers for CO2 photoreduction into valuable products. Higher rates of efficacy and product yield can be attained by synthesizing suitable photocatalysts with addressing the limitations of TNTs and designing an optimized photoreactor in terms of the proper utilization of photocatalysts, incident lights, and the partial pressure of reactants.
Pressure Losses Downstream of a Compact Valve in the Inlet Chamber of an Intermediate-Pressure Steam Turbine
Authors: Slama Vaclav , Simurda David , Lenhard Richard
Published: November 2022

Deep knowledge about pressure and energy losses in each part of a steam turbine is crucial for assuring the required efficiency and operational reliability. This paper presents the experimental as well as the numerical study of pressure losses in the inlet chamber of an intermediate-pressure steam turbine. Measurements were carried out on a complex model, where not only was there an inlet turbine chamber, but also a compact valve assembly situated upstream and nozzles situated downstream. The compact valve as well as the turbine inlet chamber were relatively small. Therefore, greater pressure losses were expected. The aerodynamic laboratory of the Institute of Thermomechanics of the Czech Academy of Sciences was responsible for acquiring the measurements, which were carried out in a modular in-draft wind tunnel. In order to learn further details, numerical simulations were carried out. Doosan Skoda Power was responsible for this. A package of ANSYS software tools was used. Measured data were described and compared with numerical ones. Pressure losses were generalized in the form of the total pressure loss coefficient. As a result, pressure losses in similar turbine compact inlet chambers can be predicted with the required accuracy.
Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea
Authors: Lee Yeji , Choi Doosung , Jung Yongho , Ko Myeongjin
Published: November 2022

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.
Inter-Fuel Substitution, Technical Change, and Carbon Mitigation Potential in Pakistan: Perspectives of Environmental Analysis
Authors: Raza Muhammad Yousaf , Tang Songlin
Published: November 2022

Currently, Pakistan is in a stage of urbanization and industrialization, raising its energy demand and supply and carbon dioxide emissions (CO2Es) due to the excessive use of fossil fuels. In meeting future demand and supply predictions, much emphasis should be given to both energy consumption and the level of inter-factor and inter-fuel substitution possibilities. Specifically, future outcomes for energy demand are more valid when production models contemplate substitution elasticity occurring during the period. To analyze the potential for little reliance on fossil fuels and diminish CO2Es, the present research has examined the potential for the substitution of energy and non-energy factors (i.e., natural gas, electricity, petroleum, labor, and capital) by using translog productions function over the period between 1986–2019. The ridge regression method is applied to evade the multicollinearity issue in the data. The model analyzes the output elasticity, substitution elasticity, technical progress, and carbon emission scenarios. The results show that the output elasticities are growing, presenting that the contribution of all factors adds to economic growth. The inputs between capital-petroleum, capital-electricity, labor-electricity, capital-natural gas, and natural gas-electricity are extreme substitutes. These substitutes are increasing capital growth and production sizes. The relative difference in technical progress shows a small positive change between 3–7% with convergence evident. Lastly, the investment scenarios under 5% and 10% investment in petroleum reduction are evidence that the CO2Es would reduce by 7.5 Mt and 10.43 Mt under scenario 1 and 7.0 Mt and 10.9 Mt under scenario 2. The results have broader suggestions for energy-conserving policies, particularly under the China–Pakistan Economic Corridor.
Effect of Synthesis Conditions on Capacitive Properties of Porous Carbon Derived from Hemp Bast Fiber
Authors: Bembenek Michał , Kotsyubynsky Volodymyr , Boychuk Volodymyra , Rachiy Bogdan , Budzulyak Ivan , Kowalski Łukasz , Ropyak Liubomyr
Published: November 2022

A systematic study of the influence of synthesis conditions on the structural, morphological, and electrical properties, as well as the electrochemical performance of hemp fiber-derived carbon materials was performed. An analysis of the capacitive response of carbons obtained under various activation conditions with additional treatment with HNO3 and annealing was completed. The contribution of the formation of an electrical double layer at the outer electrode–electrolyte interface, as well as on surfaces inside micropores, has been studied and analyzed in terms of the effect of the turbostratic carbon properties (average lateral size of graphite crystallites, pore size distribution, BET surface area).
Environmental and Economic Impact Assessments of a Photovoltaic Rooftop System in the United Arab Emirates
Authors: Abuzaid Haneen , Samara Fatin
Published: November 2022

The shift toward renewable energy resources, and photovoltaic systems specifically, has gained a huge focus in the past two decades. This study aimed to assess several environmental and economic impacts of a photovoltaic system that installed on the rooftop of an industrial facility in Dubai, United Arab Emirates (UAE). The life cycle assessment method was employed to study all the flows and evaluate the environmental impacts, while several economic indicators were calculated to assess the feasibility and profitability of this photovoltaic system. The results showed that the production processes contributed the most to the environmental impacts, where the total primary energy demand was 1152 MWh for the whole photovoltaic system, the total global warming potential was 6.83 × 10–2 kg CO2-eq, the energy payback time was 2.15 years, the carbon dioxide payback time was 1.87 years, the acidification potential was 2.87 × 10–4 kg SO2-eq, eutrophication potential was 2.45 × 10–5 kg PO43-eq, the ozone layer depletion potential was 4.685 × 10–9 kgCFC-11-eq, the photochemical ozone creation potential was 3.81 × 10–5 kg C2H4-eq, and the human toxicity potential was 2.38 × 10–2 kg1,4-DB-eq for the defined function unit of the photovoltaic system, while the economic impact indicators for the whole system resulted in a 3.5 year payback period, the benefit to cost ratio of 11.8, and 0.142 AED/kWh levelized cost of electricity. This was the first study to comprehensively consider all of these impact indicators together. These findings are beneficial inputs for policy- and decision-makers, photovoltaic panel manufacturers, and photovoltaic contractors to enhance the sustainability of their processes and improve the environment.
Consequence of Blowby Flow and Idling Time on Oil Consumption and Particulate Emissions in Gasoline Engine
Authors: Berthome Vincent , Chalet David , Hetet Jean-François
Published: November 2022

Pollutant emission standards and, in particular, those concerning particles from an internal combustion engine (ICE) are becoming increasingly restrictive. Thus, it is important to determine the main factors related to the production of particulate matter. In this article, the phenomenon of oil sweeping by the blowby gases between the rings/piston/cylinder is investigated. First, a blowby gas simulation model based on experimental results from a Turbocharged Gasoline Direct Injection (TGDI) is developed. From this model, it is possible to characterise the amount of oil swept by the blowby gases. This depends on the endgap position of both the compression and sealing rings. It also depends on the intensity of the blowby flow rate, which is highest at low rpm and high load. At 1500 rpm and full load, this flowrate exceeds 25 mg.cycle−1. From this result, it is possible to quantify the amount of oil swept by these gases as a function of the endgap position. For θrings=180°,  the quantity of oil swept rises to 20 µg.cycle−1 while for θrings=30°, this decreases to 6 µg.cycle−1. The oil concentration of the blowby gas has a direct impact on the particulate emissions because the oil concentration of the backflow gas is inversely proportional to the blowby gas flowrate. As the backflow gases return to the cylinder, the oil oxidises and produces particles. Therefore, it is essential to control the oil concentration of the backflow gases. In addition, the simulation model shows the blowby flowrate becomes negative and decreases to −3.4 mg. cycle−1 in idle conditions. The amount of oil swept by the blowby is no longer directed towards the oil pan, but towards the piston crown. This phenomenon of oil storage of the piston crown in idle condition is proportional to the duration of the idle time. In order to confirm these results, experimental tests are carried out on a TGDI engine. It appears that when the idling time changes from 0 s to 7 s between two strictly identical accelerations, the level of particulate emissions is multiplied by 1.3. When the idling time changes from 0 s to 22 s between two strictly identical accelerations, the level of particulate emissions is multiplied by 3. These results confirm the mechanism of oil storage at idle highlighted by the simulation model.
Experimental Analysis for Determining Potential of Wastewater Sludge Mixed with Degraded Biomass as Substrates for Biogas Production
Authors: Cioabla Adrian Eugen , Popescu Francisc , Bacos Timotei Bogdan
Published: November 2022

In the context of increased energy demands and finding solutions for energetic autonomy, the use of waste base materials has increased in recent years in all areas of research. In this context, waste waters, sludge or slurry, biodegradable materials, second generation effluents, and their byproducts are some of the key substrates of interest to obtain biogas through AD (anaerobe digestion). The goal is to utilize these materials as a base for the generation of renewable energy and thereby make it the main source of energy for households or industries. This paper aims to characterize, from a physical and chemical point of view, wastewater from Timis county as a base substrate material in the co-fermentation process with cereal residual biomass. We aimed to determine the energetic potential of the wastewater and perform preliminary testing at a small and pilot-sized experimental installation for biogas production with Timis wastewater as the main energy carrier. The novelty of the paper stems from the use of a patented pilot installation as our experimental approach, as well as the use of a different comparative scale co-fermentation process for biogas production. The conclusions will be traced relative to the obtained results, and preliminary solutions will be proposed for further avenues on this topic.
A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems
Authors: Devarakonda Ashwin , Karuppiah Natarajan , Selvaraj Tamilselvi , Balachandran Praveen , Shanmugasundaram Ravivarman , Senjyu Tomonobu
Published: November 2022

The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel’s available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques.