bluuebunny/crossref_metadata_2025 huggingface dataset:
Acute Toxicity and Quality of Life in a Post-Prostatectomy Ablative Radiation Therapy (POPART) Multicentric Trial
Authors: Lucchini Raffaella , Franzese Ciro , Vukcaj Suela , Purrello Giorgio , Panizza Denis , Faccenda Valeria , Andreoli Stefano , Poli Gian Luca , Baldaccini Davide , Lo Faro Lorenzo , Tomatis Stefano , Cazzaniga Luigi Franco , Scorsetti Marta , Arcangeli Stefano
Published: November 2022

Background: The aim of this study was to investigate the feasibility of ultrahypofractionated radiotherapy to the prostate bed in patients with biochemical and/or clinical relapse following radical prostatectomy who were enrolled in the prospective, observational, multicentric POPART trial (NCT04831970). Methods: Patients with post-radical prostatectomy PSA levels of ≥0.1–2.0 ng/mL and/or local relapse at PSMA PET/CT or multiparametric MRI were treated with Linac-based SBRT on the prostate bed up to a total dose of 32.5 Gy in five fractions every other day (EQD21.5 = 74.2 Gy). Maximum acute toxicity was assessed using the Common Terminology Criteria for Adverse Events version 5 scale. International Consultation on Incontinence Questionnaire—Short Form (ICIQ-SF) and Prostate Cancer Index Composite for Clinical Practice (EPIC-CP) scores were assessed at baseline and during the follow-up. Results: From April 2021 to June 2022, thirty men with a median age of 72 years (range 55–82) were enrolled in three centers. The median PSA level before RT was 0.30 ng/mL (range 0.18–1.89 ng/mL). At 3 months post-treatment, no GI or ≥2 GU side effects were reported; three patients (10%) experienced Grade 1 GU toxicity. No changes in ICIQ-SF or in the urinary domains of EPIC-CP were observed, while a transient worsening was registered in the bowel domain. At the same time point, all but two patients, who progressed distantly, were found to be biochemically controlled with a median post-treatment PSA level of 0.07 ng/mL (range 0–0.48 ng/mL). Conclusions: Our preliminary findings show that SBRT can be safely extended to the postoperative setting, without an increase in short-term toxicity or a significant decline in QoL. Long-term results are needed to confirm this strategy.
Richness of Arbuscular Mycorrhizal Fungi in a Brazilian Tropical Shallow Lake: Assessing an Unexpected Assembly in the Aquatic-Terrestrial Gradient
Authors: Gomes Stephania Ruth Basilio Silva , de Queiroz Mariana Bessa , Leroy Juliana Aparecida Souza , de Lima Juliana Luiza Rocha , Freire Fúlvio Aurélio de Morais , Jobim Khadija , de Souza Francisco Adriano , Goto Bruno Tomio
Published: November 2022

Aquatic ecosystems are historically overlooked regarding the occurrence of Arbuscular Mycorrhizal Fungi (AMF). Tropical lakes in the southern hemisphere are generally impacted by human actions, such as those in Brazil, although they still preserve a great diversity of macrophyte species that can support AMF communities. Thus, the study aimed to test (i) whether AMF community structure (composition, richness, diversity, dominance, and evenness) differs between aquatic and terrestrial conditions, and (ii) between seasons—rainy and dry. A total of 60 AMF species, distributed in 10 families and 17 genera, were found, with a difference in AMF composition between conditions (terrestrial and aquatic) and seasons (dry and rainy). The absolute species richness differed between conditions, seasons, and interactions. The aquatic/rainy season, which retrieved the most significant number of species, had the highest absolute richness and number of glomerospores and differed significantly from the terrestrial/rainy season. The results suggest that a shallow oligotrophic lake harbors a high AMF richness. In addition, this environment has a distinct AMF community from the adjacent coastal sand plain vegetation and is affected by seasonality.
Effects of Land Use Changes on the Plant Community Characteristics in the Wetlands of the Semi-Arid Regions
Authors: Wang Le , Wang Xiaodong , An Yu , Song Tiejun , Tong Shouzheng , Wang Xuan
Published: November 2022

Human disturbance is the main driving factor of wetland vegetation degradation, and plant community changes can directly characterize the process of wetland degradation. The wetlands in semi-arid region of Songnen Plain perform the important ecological functions, especially the habitat of waterbirds. Recently, the succession of wetland plant community has been accelerated by land use changes. In this study, we investigated the variations of plant community in wetlands undergoing land use changes (natural, mowing, light grazing + mowing, moderate grazing and heavy grazing wetlands) in the western Songnen Plain. The results showed that the plant communities were significantly affected by land use changes. The typical wetland plant Calamagrostis angustifolia was the dominant species in natural wetlands, and its dominance was gradually decreased in mowing or grazing wetlands in where Carex spp. or Artemisia selengensis acting as the dominant species. The height, density, and biomass in natural wetlands were significantly higher than those in other wetlands, whereas the species diversity and richness in natural wetlands were significantly lower. The similarity index of plant community in wetlands undergoing land use changes to natural wetlands ranged from 17.7–45.1%, being the highest in mowed wetlands and the lowest in heavily grazed wetlands. The linear regression further indicated that the plant diversity index was negatively correlated with the aboveground biomass of grasses and positively correlated with the aboveground biomass of forbs. Therefore, the land use changes in wetlands drove the replacement of dominant species of wetland vegetation and changed plant community characteristics and the species diversity, and the maintenance of species diversity is linked with the variability in plant functional strategies. The results of community variations and their relationships with functional changes can be used for assessing the effects of degradation and ecological function in response of land use changes in wetlands.
Discrete Phase Shifts of Intelligent Reflecting Surface Systems Considering Network Overhead
Authors: Kim Jaehong , Yu Heejung , Kang  Xin , Joung  Jingon
Published: November 2022

In this study, the performance of intelligent reflecting surfaces (IRSs) with a discrete phase shift strategy is examined in multiple-antenna systems. Considering the IRS network overhead, the achievable rate model is newly designed to evaluate the practical IRS system performance. Finding the optimal resolution of the IRS discrete phase shifts and a corresponding phase shift vector is an NP-hard combinatorial problem with an extremely large search complexity. Recognizing the performance trade-off between the IRS passive beamforming gain and IRS signaling overheads, the incremental search method is proposed to present the optimal resolution of the IRS discrete phase shift. Moreover, two low-complexity sub-algorithms are suggested to obtain the IRS discrete phase shift vector during the incremental search algorithms. The proposed incremental search-based discrete phase shift method can efficiently obtain the optimal resolution of the IRS discrete phase shift that maximizes the overhead-aware achievable rate. Simulation results show that the discrete phase shift with the incremental search method outperforms the conventional analog phase shift by choosing the optimal resolution of the IRS discrete phase shift. Furthermore, the cumulative distribution function comparison shows the superiority of the proposed method over the entire coverage area. Specifically, it is shown that more than 20% of coverage extension can be accomplished by deploying IRS with the proposed method.
Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion
Authors: Tang Tiequn , Li Feng , Jiang Minshan , Xia Xunpeng , Zhang Rongfu , Lin Kailin
Published: November 2022

Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity and facilitation among lung nodule location information, global coarse area, and edge information. Specifically, we first consider the importance of global features of nodules in segmentation and propose a cross-scale weighted high-level feature decoder module. Then, we develop a low-level feature decoder module for edge feature refinement. Finally, we construct a complementary module to make information complement and promote each other. Furthermore, we weight pixels located at the nodule edge on the loss function and add an edge supervision to the deep supervision, both of which emphasize the importance of edges in segmentation. The experimental results demonstrate that our model achieves robust pulmonary nodule segmentation and more accurate edge segmentation.
Critical Sensemaking: A Framework for Interrogation, Reflection, and Coalition Building toward More Inclusive College Environments
Authors: Taylor Leonard D. , Williams Krystal L.
Published: November 2022

Given the oppressive nature of higher education environments, educators must support students in making sense of their experiences—especially minoritized students. Although this kind of sensemaking often happens informally, college educators are primed to support students in understanding: (1) their experiences within and their interactions with higher education environments; (2) how those experiences and interactions reflect and are shaped by systemic issues; and (3) the implications of these experiences and interactions during college and beyond. Accordingly, we introduce critical sensemaking (CSM) as a pedagogical tool that educators can expose students to and create a more equitable college environment. CSM is a framework that encourages students to interrogate their collegiate environments and experiences, focusing on power and systems of oppression and how they interpret and negotiate their environments. Only by gaining this understanding can students press for institutional transformation in a manner that reflects various aspects of their experiences; hence, creating an institutional accountability structure. Moreover, educators supporting students’ acquisitions of CSM skills provides opportunities to build trust and meaningful coalitions toward creating more inclusive college environments.
Resolving Security Issues in the IoT Using Blockchain
Authors: Malik Hafiz Abid Mahmood , Shah Asghar Ali , Muhammad Abdul Hafeez , Kananah Ahmad , Aslam Ayesha
Published: November 2022

The Internet of Things is a system of interconnected smart devices that can communicate with each other and with other devices over the Internet, with or without human-to-human or human-to-computer interaction. Although IoT devices, which have IPs, make living easier, they are also a threat to the security and privacy of people. This research work presents a solution to the problem of security in the network of IoT, based on the idea of implementing the blockchain in IoT. Blockchain is a decentralized technology that adds blocks at the end of the chain. It saves the hash value for every block, and corresponds to the previous block. The decentralized behavior of blockchain is best for IoT as an extensive network, because IoT must not have a single point of failure, and one entity must not decide what to do. All the capable storage devices will save the same data entered from any device, removing the risk of receiving altered data.
Stereoscopic Projection Policy Optimization Method Based on Deep Reinforcement Learning
Authors: An Jing , Si Guang-Ya , Zhang Lei , Liu Wei , Zhang Xue-Chao
Published: November 2022

Based on the good performance of deep reinforcement learning (DRL) in policy optimization, a stereoscopic projection policy optimization method is proposed, which combines the simulation experiment method with the DRL method. On the basis of policy optimization research, a deep learning framework is selected according to the research problems, and a DRL stereoscopic project policy model based on the asynchronous advantage actor–critic (A3C) algorithm, which uses two groups of neural networks, is constructed. The optimized stereoscopic projection policy is obtained by the interactive learning between the DRL model and the simulation. The effectiveness of the cooperative optimization policy between the DRL and the simulation experiment is verified.
A 180 nm CMOS Integrated Optoelectronic Sensing System for Biomedical Applications
Authors: Di Patrizio Stanchieri Guido , De Marcellis Andrea , Faccio Marco , Palange Elia , Battisti Graziano , Guler Ulkuhan
Published: November 2022

This paper reports on a CMOS fully integrated optoelectronic sensing system composed of a Si photodiode and a transimpedance amplifier acting as the electronic analog front-end for the conditioning of the photocurrent generated by the photodiode. The proposed device has been specifically designed and fabricated for wearable/portable/implantable biomedical applications. The massive employment of sensor systems in different industrial and medical fields requires the development of small sensing devices that, together with suitable electronic analog front ends, must be designed to be integrated into proper standard CMOS technologies. Concerning biomedical applications, these devices must be as small as possible, making them non-invasive, comfortable tools for patients and operating with a reduced supply voltage and power consumption. In this sense, optoelectronic solutions composed of a semiconductor light source and a photodiode fulfill these requirements while also ensuring high compatibility with biological tissues. The reported optoelectronic sensing system is implemented and fabricated in TSMC 180 nm integrated CMOS technology and combines a Si photodiode based on a PNP junction with a Si area of 0.01 mm2 and a transimpedance amplifier designed at a transistor level requiring a Si area of 0.002 mm2 capable to manage up to nanoampere input currents generated by the photodiode. The transimpedance amplifier is powered at a 1.8 V single supply showing a maximum power consumption of about 54 μW, providing a high transimpedance gain that is tunable up to 123 dBΩ with an associated bandwidth of about 500 kHz. The paper reports on both the working principle of the developed ASIC and the experimental measurements for its full electrical and optoelectronic characterizations. Moreover, as case-examples of biomedical applications, the proposed integrated sensing system has also been validated through the optical detection of emulated standard electrocardiography and photoplethysmography signal patterns.
The Role of ML, AI and 5G Technology in Smart Energy and Smart Building Management
Authors: Mazhar Tehseen , Malik Muhammad Amir , Haq Inayatul , Rozeela Iram , Ullah Inam , Khan Muhammad Abbas , Adhikari Deepak , Ben Othman Mohamed Tahar , Hamam Habib
Published: November 2022

With the help of machine learning, many tasks can be automated. The use of computers and mobile devices in “intelligent” buildings may make tasks such as controlling the indoor climate, monitoring security, and performing routine maintenance much easier. Intelligent buildings employ the Internet of Things to establish connections among the many components that make up the structure. As the notion of the Internet of Things (IoT) gains attraction, smart grids are being integrated into larger networks. The IoT is an integral part of smart grids since it enables beneficial services that improve the experience for everyone inside and individuals are protected because of tried-and-true life support systems. The reason for installing Internet of Things gadgets in smart structures is the primary focus of this investigation. In this context, the infrastructure behind IoT devices and their component units is of the highest concern.
Preliminary Study of a G-Band Extended Interaction Oscillator Operating in the TM31-3π Mode Driven by Pseudospark-Sourced Multiple Electron Beams
Authors: Peng Ruibin , Wang Bin , Yin Yong , Li Hailong , Yuan Xuesong , Xu Xiaotao , Bi Liangjie , Qin Yu , Meng Lin
Published: November 2022

This paper presents the first design that combines pseudospark-sourced (PS) electron beams with a multiple-beam extended interaction oscillator (EIO). The PS electron beam is an excellent choice for driving EIOs because it has high current density and does not require a focusing magnetic field. The EIO with coaxial structure adopts the method of multiple electron beams, which plays a crucial role in improving the average output power. At the same frequency, the EIO operating in the high-order TM31-3π mode has a larger cavity size than the EIO operating in the traditional TM01-2π mode. The high-order TM31-3π mode solves the problem of the EIO’s manufacture at high frequency. In order to verify the above points, a G-band PS multiple-beam EIO operating in TM31-3π mode has been designed. The beam–wave interaction particle-in-cell simulation results show that the EIO’s peak output power is 39.2 kW at 217 GHz, and that its efficiency is around 6.1%. The EIO with six pencil beams operates at a voltage of 43 kV. The total current of the six electron beams is 15 A (equally distributed among the six beams), and the corresponding current density is about 5000 A/cm2. Considering the ohmic loss and the effect of skin depth, the conductivity used in these simulations is 2 × 107 S/m. The design is an excellent way to improve the output power of EIO operating at high frequency.
An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids
Authors: Sarmas Elissaios , Strompolas Sofoklis , Marinakis Vangelis , Santori Francesca , Bucarelli Marco Antonio , Doukas Haris
Published: November 2022

Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning.
Multi-Model Inference Accelerator for Binary Convolutional Neural Networks
Authors: de Sousa André L. , Véstias Mário P. , Neto Horácio C.
Published: November 2022

Binary convolutional neural networks (BCNN) have shown good accuracy for small to medium neural network models. Their extreme quantization of weights and activations reduces off-chip data transfer and greatly reduces the computational complexity of convolutions. Further reduction in the complexity of a BCNN model for fast execution can be achieved with model size reduction at the cost of network accuracy. In this paper, a multi-model inference technique is proposed to reduce the execution time of the binarized inference process without accuracy reduction. The technique considers a cascade of neural network models with different computation/accuracy ratios. A parameterizable binarized neural network with different trade-offs between complexity and accuracy is used to obtain multiple network models. We also propose a hardware accelerator to run multi-model inference throughput in embedded systems. The multi-model inference accelerator is demonstrated on low-density Zynq-7010 and Zynq-7020 FPGA devices, classifying images from the CIFAR-10 dataset. The proposed accelerator improves the frame rate per number of LUTs by 7.2× those of previous solutions on a ZYNQ7020 FPGA with similar accuracy. This shows the effectiveness of the multi-model inference technique and the efficiency of the proposed hardware accelerator.
Multi-Criteria Analysis of the Selection of Vehicles with Electric, Hybrid, and Conventional Drive for Car-Sharing Services from the Perspective of Polish Occasional System Users
Authors: Turoń Katarzyna
Published: November 2022

Car-sharing systems, i.e., short-term car rentals, are one of the solutions that can be an alternative to individual motorization in cities. However, these services must adapt well to customers’ needs to develop appropriately. Currently, there have been attempts in scientific research to make numerous optimizations of car-sharing systems, but little emphasis was placed on the type of car fleet used in the systems, which is particularly important at the stage of transformation of the automotive industry towards electric mobility. In response to this research gap, this article was dedicated to the selection of vehicles for car-sharing systems. The work focuses on users of Polish car-sharing systems who use the services of short-term vehicle rental occasionally, i.e., up to five times a year. The research made it possible to determine the types of vehicles (cars with electric, hybrid and internal combustion engines) that the users are interested in and to indicate the specific features that the cars should have. Interestingly, large family vehicles with hybrid engines turned out to be the leading ones. In turn, small city cars, so far dominant in car-sharing, turned out to be the worst choice from the point of view of occasional users. This showed that the current systems are not properly adapted to the needs of society. The results support car-sharing operators who want to upgrade their vehicle fleet accordingly to encourage the public to use car-sharing more often. It is also a guide to identifying a vehicle for fleet modernization towards hybrid and electric vehicles.
Doweled cross Laminated Timber (DCLT) Building Air Tightness and Energy Efficiency Measurements: Case Study in Poland
Authors: Świrska-Perkowska Jadwiga , Wicher Anna , Pochwała Sławomir , Anweiler Stanisław , Böhm Michał
Published: November 2022

A contemporary challenge for the construction industry is to develop a technology based on natural building materials which at the same time provides high energy efficiency. This paper presents the results of an airtightness test and a thermal imaging study of a detached house built with technology using cross laminated dowelled timber panels. The thermal conductivity coefficients of the wood wool used to insulate the walls and ceiling of the building have also been measured, the linear heat transfer coefficients of the structural nodes have been numerically determined, and calculations have been made regarding the energy efficiency of the building. On the basis of the research, it was found that the air exchange rate in the analyzed building n50 is at the level of 4.77 h−1. Air leaks were also observed in the places of connection of longitudinal walls with the roof and at the junction of window frames with external walls. The experimentally determined thermal conductivity coefficient of the wood wool was ~10% higher than that declared by the manufacturer. Calculations for the energy performance certificate showed that an increase of ~10% in the thermal conductivity coefficient of the wood wool used to insulate the building results in a heating demand increase of 2.1%. It was also found that changing the value of the parameter n50 from 1.0 h−1 to 4.77 h−1 leads to a 40.1% increase in heat demand for heating the building. At the same time, the indicators for final energy demand EK and non-renewable primary energy demand EP increase by 18.1%.
Experimental Assessment of the Performance and Fine Particulate Matter Emissions of a LPG-Diesel Dual-Fuel Compression Ignition Engine
Authors: Toledo Eliezer , Guerrero Fabián , Amador German , Toledo Mario
Published: November 2022

The present work is focused on the assessment of the performance and fine particulate matter emissions (PM2.5) of a turbocharged four-cylinder direct injection diesel engine operating under dual-fuel mode with Liquefied Petroleum Gas (LPG). For load levels of 30%, 60% and 100%, measurements were taken, keeping the engine speed constant at 2200, 2500 and 3200 rpm, while the engine knock detonation was detected through a non-invasive internal system. According to experimental measurements, the abnormal knock combustion occurred at full load operation with a maximum LPG energy fraction of ~60%. The brake fuel conversion efficiency increased by 2.6% with an LPG energy fraction of 10%, where a fuel saving of 11.9% was achieved with respect to the diesel-only operation. The reduction of diesel consumption was around 50% with respect to 100% diesel operation at full load operations, where the highest brake fuel conversion efficiency was achieved. The brake fuel conversion efficiency decreased as LPG addition increased for all the engine loads. Regarding emissions, PM2.5 decreased with the addition of LPG. However, HC and CO emissions increased as LPG injection was higher. NOx emissions and exhaust gas temperatures were reduced for operation with higher LPG fractions, except for full load levels at 2200 and 2500 rpm.
Enhanced Biodiesel Synthesis via a Homogenizer-Assisted Two-Stage Conversion Process Using Waste Edible Oil as Feedstock
Authors: Hsiao Ming-Chien , Liao Peir-Horng , Yang Kuo-Chou , Lan Nguyen Vu , Hou Shuhn-Shyurng
Published: November 2022

In this study, a homogenizer in conjunction with a two-stage process was utilized to facilitate biodiesel production from waste edible oil (WEO). This paper contributes to the improvement of the yield and the shortening of the reaction time for biodiesel synthesis. Sulfuric acid was used in the first stage which was the esterification of the free fatty acids (FFA) of the WEO; then the transesterification reaction of triglycerides took place in the second stage with an alkaline catalysis. The present investigation aimed to explore the parameters affecting the reactions, including homogenizer speed, alcohol/oil molar ratio, catalyst dosage, reaction temperature, and reaction time. Under the operating conditions of the first stage (the reaction temperature was 65 °C, the homogenizer speed was 8000 rpm, the methanol/oil molar ratio was 15:1, and the amount of sulfuric acid was 4 wt%), the acid value fell to below 2 mg KOH/g after 10 min. The best base-catalyzed conditions in the second stage were: homogenizer speed of 8000 rpm, NaOH catalyst concentration of 1 wt%, methanol/oil molar ratio of 9:1 (mol/mol), reaction temperature of 65 °C, and reaction time 10 min. Consequently, the conversion rate from WEO to biodiesel achieved 97% after only 20 min, in line with the EU EN14214 standard, which requires a biodiesel production rate of at least 96.5%.
Hydrogen Production System through Dimethyl Ether Autothermal Reforming, Based on Model Predictive Control
Authors: Zhang Tie-Qing , Jung Seunghun , Kim Young-Bae
Published: November 2022

In this study, a thermodynamic analysis of the low temperature autothermal reforming (ATR) of dimethyl ether (DME) for hydrogen production was conducted. The Pd/Zn/γ-Al2O3 catalyst coated on the honeycomb cordierite ceramic was applied to catalyze the reaction, and the optimum activity temperature of this catalyst was demonstrated experimentally and through simulations to be 400 °C. Furthermore, an optimal model predictive control (MPC) strategy was designed to control the hydrogen production rate and the catalyst temperature. Experimental and simulation results indicated that the controller was automated and continuously reliable in the hydrogen production system. By establishing the state-space equations of the autothermal reformer, it can precisely control the feed rates of DME, high-purity air and deionized water. Ultimately, the hydrogen production rate can be precisely controlled when the demand curve changed from 0.09 to 0.23 mol/min, while the catalyst temperature was maintained at 400 °C, with a temporary fluctuation of 4 °C during variations of the hydrogen production rate. Therefore, the tracking performance of the hydrogen production and the anti-disturbance were satisfactory.
Study of Bulk Properties Relation to SARA Composition Data of Various Vacuum Residues Employing Intercriteria Analysis
Authors: Stratiev Dicho , Shishkova Ivelina , Palichev Georgi Nikolov , Atanassov Krassimir , Ribagin Simeon , Nenov Svetoslav , Nedanovski Dimitar , Ivanov Vitaly
Published: November 2022

Twenty-two straight run vacuum residues extracted from extra light, light, medium, heavy, and extra heavy crude oils and nine different hydrocracked vacuum residues were characterized for their bulk properties and SARA composition using four and eight fractions (SAR-ADTM) methods. Intercriteria analysis was employed to determine the statistically meaningful relations between the SARA composition data and the bulk properties. The determined strong relations were modeled using the computer algebra system Maple and NLPSolve with the Modified Newton Iterative Method. It was found that the SAR-ADTM saturates, and the sum of the contents of saturates and ARO-1 can be predicted from vacuum residue density, while the SAR-ADTM asphaltene fraction content, and the sum of asphaltenes, and resins contents correlate with the softening point of the straight run vacuum residues. The softening point of hydrocracked vacuum residues was found to strongly negatively correlates with SAR-ADTM Aro-1 fraction, and strongly positively correlates with SAR-ADTM Aro-3 fraction. While in the straight run vacuum residues, the softening point is controlled by the content of SAR-ADTM asphaltene fraction in the H-Oil hydrocracked vacuum residues, the softening point is controlled by the content of SAR-ADTM Aro-3 fraction content. During high severity H-Oil operation, resulting in higher conversion, hydrocracked vacuum residue with higher SAR-ADTM Aro-3 fraction content is obtained, which makes it harder and more brittle.
Proposal of a System to Identify Failures and Evaluate the Efficiency of Internal Combustion Engines of Thermal Power Plants
Authors: Ramos Lilia Carneiro , Assuncao Frederico de Oliveira , Villa-Nova Helcio Francisco , Lambert-Torres Germano , Bonaldi Erik Leandro , Oliveira Levy Ely de Lacerda de , Sant’Ana Wilson Cesar , Junior Ronny Francis Ribeiro , Teixeira Carlos Eduardo , Medeiros Paulo Germano Pinto de
Published: November 2022

Thermoelectric plants are one of the main forms of energy generation in the world, being the second main source of generation in Brazil. However, with rising fuel costs and greater concern for the environment, controlling the efficiency levels of these plants has become critical. This work presents a system to identify failures and evaluate the efficiency of internal combustion engines used in thermal power plants. To assess efficiency, the developed system monitors subsystem losses (such as cooling, lubrication, turbocharger, etc.). In addition, sensors for cylinder pressure and instantaneous speed were installed and comprise an online monitoring system for the pressure condition of each cylinder of the engines. All this is combined into a supervisory system that presents the Sankey diagram of the engine as its main information online and remotely. To validate the system, experiments were carried out in a controlled configuration (where faults were purposely inserted) and in a Brazilian thermal power plant. The results show that by using in-cylinder pressure and the WOIS database, it was possible to detect the presence of a fault as well as pinpoint its location.
Analysis of an Evaporative Condensation System Coupled to a Microchannel-Separated Heat Pipe for Data Centers
Authors: Wang Ying , Huang Xiang , Chu Junjie , Du Yan , Tang Xing , Dai Cong , Ma Gang
Published: November 2022

In the age of the digital economy, the data center is the most crucial piece of infrastructure. The issue of the excessive power consumption of a data center’s cooling system needs to be addressed as the national objective of “peak carbon and carbon neutrality” is increasingly promoted. In this study, a microchannel-separated heat pipe-cooling system with evaporative condensation is introduced. The system may switch between three modes of operation in response to changes in outdoor air quality parameters, thereby maximizing the utilization of natural cooling sources while lowering data centers’ cooling costs. The purpose of this paper is to analyze the energy-saving potential of the hybrid system through experimental tests. The results show that 114.4% is the ideal liquid-loading rate for the heat pipe system. Under working conditions in Xi’an, the annual operating hours of the three modes accounted for 47.2%, 6.1%, and 46.7%. The hybrid cooling system may save 62.04% of the energy used annually compared to the standard cooling system and the cooling system in the server room thanks to its yearly average COP of 9.43.
Review of Selected Determinants Affecting Use of Municipal Waste for Energy Purposes
Authors: Rajca Przemysław , Skibiński Andrzej , Biniek-Poskart Anna , Zajemska Monika
Published: November 2022

The aim of the article is to analyse the impact of selected conditions on the use of municipal solid waste in energy production processes. The authors in this publication indicated that the factors determining the effective use of solid municipal waste may be, in particular, formal and legal conditions, methods used in the processes of thermal waste transformation, recovery logistics or emergency situations such as pandemics or armed conflicts. Their knowledge can be very useful in the processes of the effective use of municipal waste for the production of thermal energy, especially in the current period of the energy crisis faced by most EU countries. This paper also emphasizes the importance of RDF (refuse-derived fuel) for the thermal energy sector, which can be used both in large combustion plants and in less powerful facilities, including local heating and combined heat and power plants as an alternative to traditional fossil fuels. The article was prepared by means of the systematic literature review (SLR) method, utilising the Scopus database and secondary sources.
Near-Zero-Energy Building Management Based on Arduino Microcontroller—On-Site Lighting Management Application
Authors: Chekired Fathia , Taabli Oussama , Khellili Zakaria Mehdi , Tilmatine Amar , de Almeida Aníbal T. , Canale Laurent
Published: November 2022

Near-Zero-Energy Buildings are a challenge in terms of energy production, storage, consumption and management, but these technological solutions remain financially difficult to access in developing countries. To this end, a complete low-cost and reliable home energy-management prototype was first developed and implemented on a scale model. A PWM charge controller drove the flow of energy produced and consumed in order to ensure the optimization of both the consumption of energy and energy savings. Battery storage was also managed by the home automation module using a set of sensors. The prototype of the scale model incorporated complete energy management of all electrical devices with group priorities through a graphical interface in a real-time mode. After testing this system, the lighting management part was implemented in a large-scale smart solar home. A smart lighting system via a complete algorithm integrated on an Arduino Mega board was then realized and implemented in the life-size house. This first step focuses, above all, on the users’ comfort and, in particular, on the lighting management. The results show that this smart device thus makes it possible to achieve additional energy savings on an essential and yet already energy-efficient device: lighting.
Analysis of the Effects of Grid-Connected Charging/Discharging Stations on Relay Protection
Authors: Wang Qingjie , Ma Jing , Shang Lei , Chen Shuangyin
Published: November 2022

The grid-connected operation of charging/discharging stations changes the original load, power supply, and network structures of the distribution network. It also affects the power flow level and direction and leads to a reduction in the sensitivity and reliability of the relay protection system. The grid connections may even lead to improper operation or failure of the relay protection protocols. In order to solve the above problems, this paper proposes a method that constructs the protection criteria by using the characteristics of the positive sequence comprehensive impedance after internal and external faults. Specifically, based on the difference in amplitude of the differential positive sequence impedance in the feeder after the charging/discharging station is connected to the grid, positive sequence comprehensive impedance is proposed for the purpose of establishing longitudinal differential protection. The results show that the vertical protection principle based on the positive sequence comprehensive impedance has a certain versatility for the protection of distribution networks with charging/discharging stations. Additionally, the protection principle has high sensitivity and is generally unaffected by transition resistance. The applicability of the proposed positive sequence comprehensive impedance vertical protection principle is verified by simulation.
Influence of Shading on Solar Cell Parameters and Modelling Accuracy Improvement of PV Modules with Reverse Biased Solar Cells
Authors: Atia Abdulhamid , Anayi Fatih , Gao Min
Published: November 2022

This paper presents an experimental investigation on the influence of shading on mono-crystalline (mono-Si) solar cell parameters. The variations of equivalent circuit parameters with shading were determined and then used in modelling a mono-Si solar cell and a mono-Si photovoltaic (PV) module under partial shading. It was found that the simulation by considering the parameter variations with shading in the single cell model did not lead to a noticeable improvement in modelling accuracy. However, for the PV module, a significant improvement in modelling accuracy in the reverse bias region was achieved when considering all parameter variations in the model. A further investigation was performed to identify the key parameters that are responsible for the improvement. The results revealed that in addition to the photo-generated current, the shunt resistance also has a significant effect on the model accuracy. A modelling approach was thus proposed, which includes the variation of the shunt resistance with shading, in addition to the variation of the photo-generated current. This approach was experimentally validated using a mono-Si PV module. The results show that the proposed approach is more accurate, compared to the approach that considers only the variation of the photo-generated current, without the need to include an avalanche breakdown term.
Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks
Authors: Laouali Inoussa , Gomes Isaías , Ruano Maria da Graça , Bennani Saad Dosse , Fadili Hakim El , Ruano Antonio
Published: November 2022

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.
Aerodynamic Performance Analysis of Trailing Edge Serrations on a Wells Turbine
Authors: Alkhalifa Abdullah Saad , Uddin Mohammad Nasim , Atkinson Michael
Published: November 2022

The primary objective of this investigation was to explore the aerodynamic impact of adding trailing edge serrations to a Wells turbine. The baseline turbine consists of eight NACA 0015 blades. The blade chord length was 0.125 m and the span was 0.100 m. Two modified serrated blade configurations were studied: (1) full-span, and (2) partial-span covering 0.288c of the trailing edge. The numerical simulations were carried out by solving the three-dimensional, incompressible steady-state Reynolds Averaged Navier-Stokes (RANS) equations using the k-ω SST turbulence model in ANSYS™ (CFX). The aerodynamic performance of the modified Wells turbine was compared to the baseline by calculating non-dimensional parameters (i.e., torque coefficient, pressure drop coefficient, and turbine efficiency). A comparison of the streamlines was performed to analyze the flow topology around the turbine blades for a flow coefficient range of 0.075 ≤ ϕ ≤ 0.275, representing an angle of attack range of 4.29° ≤ α ≤ 15.3°. The trailing edge serrations generated a substantial change in surface pressure and effectively reduced the separated flow region, thus improving efficiency in most cases. As a result, there was a modest peak efficiency increase of 1.51% and 1.22%, for the partial- and full-span trailing edge serrations, respectively.
Research on a Denoising Method of Vibration Signals Based on IMRSVD and Effective Component Selection
Authors: Chen Xihui , Shi Xinhui , Liu Chang , Lou Wei
Published: November 2022

This paper proposes a denoising method of vibration signal based on improved multiresolution singular value decomposition (IMRSVD) and effective component selection. A new construction method of trajectory matrix is used, which can enhance the oscillating component of the original signal. Next, based on the improved trajectory matrix, singular value decomposition (SVD), which plays the role of pre-decomposition, is used to obtain multiple one-dimensional components, and the further decomposition of that is achieved by multiresolution singular value decomposition (MRSVD). Finally, the effective components selection of a series of decomposed signal components is achieved based on the proposed feature evaluation index (FEI). The denoising experiments are carried out using the simulation signal and the vibration signal of planetary gear, respectively. The experimental results show that the proposed method performs better than the traditional SVD denoising method, and the weak fault feature in the vibration signal can be extracted successfully. In addition, the comparison between periodic modulation intensity (PMI) and FEI displays that the proposed method has better robustness and accuracy than the interference components with similar frequency. Thus, the proposed method is an effective weak fault feature extraction and denoising tool of vibration signals for fault diagnosis.
Detection of Malicious Websites Using Symbolic Classifier
Authors: Anđelić Nikola , Baressi Šegota Sandi , Lorencin Ivan , Glučina Matko
Published: November 2022

Malicious websites are web locations that attempt to install malware, which is the general term for anything that will cause problems in computer operation, gather confidential information, or gain total control over the computer. In this paper, a novel approach is proposed which consists of the implementation of the genetic programming symbolic classifier (GPSC) algorithm on a publicly available dataset to obtain a simple symbolic expression (mathematical equation) which could detect malicious websites with high classification accuracy. Due to a large imbalance of classes in the initial dataset, several data sampling methods (random undersampling/oversampling, ADASYN, SMOTE, BorderlineSMOTE, and KmeansSMOTE) were used to balance the dataset classes. For this investigation, the hyperparameter search method was developed to find the combination of GPSC hyperparameters with which high classification accuracy could be achieved. The first investigation was conducted using GPSC with a random hyperparameter search method and each dataset variation was divided on a train and test dataset in a ratio of 70:30. To evaluate each symbolic expression, the performance of each symbolic expression was measured on the train and test dataset and the mean and standard deviation values of accuracy (ACC), AUC, precision, recall and f1-score were obtained. The second investigation was also conducted using GPSC with the random hyperparameter search method; however, 70%, i.e., the train dataset, was used to perform 5-fold cross-validation. If the mean accuracy, AUC, precision, recall, and f1-score values were above 0.97 then final training and testing (train/test 70:30) were performed with GPSC with the same randomly chosen hyperparameters used in a 5-fold cross-validation process and the final mean and standard deviation values of the aforementioned evaluation methods were obtained. In both investigations, the best symbolic expression was obtained in the case where the dataset balanced with the KMeansSMOTE method was used for training and testing. The best symbolic expression obtained using GPSC with the random hyperparameter search method and classic train–test procedure (70:30) on a dataset balanced with the KMeansSMOTE method achieved values of ACC¯, AUC¯, Precsion¯, Recall¯ and F1-score¯ (with standard deviation) 0.9992±2.249×10−5, 0.9995±9.945×10−6, 0.9995±1.09×10−5, 0.999±5.17×10−5, 0.9992±5.17×10−6, respectively. The best symbolic expression obtained using GPSC with a random hyperparameter search method and 5-fold cross-validation on a dataset balanced with the KMeansSMOTE method achieved values of ACC¯, AUC¯, Precsion¯, Recall¯ and F1-score¯ (with standard deviation) 0.9994±1.13×10−5, 0.9994±1.2×10−5, 1.0±0, 0.9988±2.4×10−5, and 0.9994±1.2×10−5, respectively.