bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid Nanocomposites of Plasmonic Metal Nanostructures and 2D Nanomaterials for Improved Colorimetric Detection
Authors: Serafinelli Caterina , Fantoni Alessandro , Alegria Elisabete C. B. A. , Vieira Manuela
Published: June 2022

Plasmonic phenomena and materials have been extensively investigated for a long time and gained popularity in the last few years, finding in the design of the biosensors platforms promising applications offering devices with excellent performances. Hybrid systems composed of graphene, or other 2D materials, and plasmonic metal nanostructures present extraordinary optical properties originated from the synergic connection between plasmonic optical effects and the unusual physicochemical properties of 2D materials, thus improving their application in a broad range of fields. In this work, firstly, an overview of the structures and properties of 2D nanomaterials will be provided along with the physics of surface plasmon resonance and localized surface plasmon resonance. In the second part of the work, some examples of colorimetric biosensors exploiting the outstanding properties of hybrids nanocomposites will be presented. Finally, concluding perspectives on the actual status, challenges, and future directions in plasmonic sensing biosensing will be provided. Special emphasis will be given to how this technology can be used to support digitalization and virtualization in pandemic handling.
Personal and Social Resources Are Linked to Cognition and Health-Related Quality of Life in Childhood Cancer Survivors
Authors: Siegwart Valerie , Schürch Kirstin , Benzing Valentin , Roessler Jochen , Everts Regula
Published: June 2022

Personal and social resources may buffer the adverse effects of childhood cancer and its impact on cognition and quality of life. While childhood cancer survivors show domain-specific cognitive difficulties, little is known about their personal and social resources. We therefore investigated personal and social resources and their association with cognitive and quality-of-life outcomes in childhood cancer survivors. Seventy-eight survivors of childhood cancer of different etiologies (aged 7–16 years; ≥one year since treatment) and fifty-six healthy controls were included. Cognitive outcome was assessed by neuropsychological tests; personal and social resources, as well as health-related quality of life, were assessed by standardized questionnaires. In the social resource domain, peer integration was worse in survivors than in controls (puncorr < 0.04, d = 0.33). Personal resources and all other subscales of social resources did not significantly differ between survivors and controls. In survivors, the global resource score was significantly correlated with processing speed (r = 0.39, pcorr < 0.001) and quality of life (parent: r = 0.44; self-report: r = 0.46; pscorr < 0.001). In controls, no association occurred between resources and cognitive outcome, and the correlation between the global resource score and quality of life did not withstand correction for multiple comparison (parent: r = 0.28; self-report: r = 0.40, psuncorr < 0.001). After an adverse event such as childhood cancer, resources might play a particularly buffering role on cognitive performance and quality of life (when compared to the everyday life of healthy controls). This highlights the importance of interventions that strengthen the resources of children and their families, even years after cancer. Such resource-focused intervention could help to counteract long-term sequelae in cognitive outcomes and health-related quality of life.
Proactive and Reactive Recruitment of Black and Latino Adolescents in a Vaping Prevention Randomized Controlled Trial
Authors: Cartujano-Barrera Francisco , Hernández-Torres Ruthmarie , Orfin Rafael H. , Chávez-Iñiguez Arlette , Alvarez Lopez Olga , Azogini Chiamaka , Bermudez Diana , Arana-Chicas Evelyn , Cai Xueya , McIntosh Scott , Ossip Deborah J. , Cupertino Ana Paula
Published: June 2022

The purpose of this study was to assesses the effectiveness of proactive and reactive methods in the recruitment of Black and Latino adolescents into a vaping-prevention randomized controlled trial (RCT). This study also assessed the characteristics of study participants by recruitment method. Proactive recruitment strategies included study presentations at community-based events (e.g., festivals, health fairs), school-based events (e.g., back-to-school events, after-school programs), and recreational centers (e.g., fitness centers, malls). Reactive recruitment strategies included study advertisements via social media (e.g., Facebook posts shared by local community-based organizations), word of mouth, and an academic-based research hub. Using proactive and reactive methods, in a 4-month period, 362 Black and Latino adolescents were successfully enrolled into the RCT. Compared to the proactive method, adolescents screened reactively were equally likely to be eligible but significantly more likely to enroll in the study. However, both proactive and reactive strategies made notable contributions to the overall recruitment effort. Moreover, proactive and reactive methods attracted adolescents with different characteristics (e.g., age, gender, sexual orientation, etc.). These findings suggest that both proactive and reactive recruitment strategies should be implemented for studies interested in recruiting a diverse sample of Black and Latino adolescents.
Strength and VO2max Changes by Exercise Training According to Maturation State in Children
Authors: Enríquez-del-Castillo Liliana Aracely , Ornelas-López Andrea , De León Lidia G. , Cervantes-Hernández Natanael , Quintana-Mendias Estefanía , Flores Luis Alberto
Published: June 2022

The health benefits of physical activity (PA) are widely recognized; however, biological maturation contributions are a subject that has been little studied, which is why the aim of this study was to analyze the effect of a six-week training program at moderate-intensity on the muscular strength and aerobic capacity in children between nine and 13 years (13 ± 1.0 years) according to their maturation state. Twenty-six schoolchildren (15 girls) participated in a six-week physical exercise program based on aerobic/anaerobic capacity and coordination skills. Maximal oxygen uptake (VO2max), trunk-lift, push-ups, curl-ups, and handgrip strength (both hands) were measured as response variables. Body mass index (BMI), skeletal maturity indicator (SMI), peak height velocity (PHV), age on peak height velocity (APHV) and sex were considered as covariates. The results of VO2max, push-ups, curl-ups, and handgrip strength were higher after the exercise program in the whole group (p < 0.05). The VO2max showed a greater increase in the normal-weight than in the overweight-obesity children (p = 0.001). Higher results in dominant handgrip strength were observed in girls (p = 0.003). The PHV before intervention presented a positive correlation with the dominant handgrip strength in all kids (r = 0.70, p = 0.001). As a conclusion, the six-week training program improved the physical fitness of children independent of the maturation state. Somatic maturation increases the physical abilities in schoolchildren.
Meta Deep Learn Leaf Disease Identification Model for Cotton Crop
Authors: Memon Muhammad Suleman , Kumar Pardeep , Iqbal Rizwan
Published: June 2022

Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%.
Bioclimatic Envelopes for Two Bat Species from a Tropical Island: Insights on Current and Future Distribution from Ecological Niche Modeling
Authors: Bandara A. P. Malsha J. , Madurapperuma Buddhika D. , Edirisinghe Gayan , Gabadage Dinesh , Botejue Madhava , Surasinghe Thilina D.
Published: June 2022

Bats perform critical ecosystem functions, including the pollination, seed dispersal, and regulation of invertebrate populations. Yet, bat populations are declining worldwide primarily due to habitat loss and other anthropogenic stressors. Thus, studies on bat ecology, particularly on environmental determinants of bat occupancy, are paramount to their conservation. High mobility, nocturnal behavior, and roosting site selection of bats make conventional surveys challenging. Moreover, little is known about geographic distribution, habitat suitability, and responses to climate change among tropical bat species. To bridge these research gaps, we applied ecological niche modeling to two Ceylonese bat species, Kerivoula malpasi and Kerivoula picta, to map their geographic distribution. Seasonal variations in temperature and precipitation were critical environmental predictors of bat distribution in general. Southwestern lowland forests contained the most optimal habitats for the relatively wide-ranging Kerivoula picta, while the central highlands provided the most suitable habitats for the narrow-ranging Kerivoula malpasi. No tangible changes in the highly suitable habitats were evident in response to projected climate change for either species. Yet, the optimal ranges of K. malpasi can become fragmented in the future, whereas the most optimal habitats for K. picta are likely to become spatially contiguous in the future. Habitat availability or fundamental niche alone is insufficient to reliably forecast species persistence, thus we caution against considering these two bat species as resilient to climate change. Our findings will enable the conservation authorities to initiate preemptive conservation strategies, such as the establishment of landscape-scale habitat connectivity and management of buffer zones around conservation lands. We also encourage conservation authorities to employ ecological niche models to map potential species distributions and to forecast range shifts due to climate change.
Genes of the Glutamatergic System and Tardive Dyskinesia in Patients with Schizophrenia
Authors: Fedorenko Olga Yu. , Paderina Diana Z. , Kornetova Elena G. , Poltavskaya Evgeniya G. , Pozhidaev Ivan V. , Goncharova Anastasiia A. , Freidin Maxim B. , Bocharova Anna V. , Bokhan Nikolay A. , Loonen Anton J. M. , Ivanova Svetlana A.
Published: June 2022

Background: Tardive dyskinesia (TD) is an extrapyramidal side effect of the long-term use of antipsychotics. In the present study, the role of glutamatergic system genes in the pathogenesis of total TD, as well as two phenotypic forms, orofacial TD and limb-truncal TD, was studied. Methods: A set of 46 SNPs of the glutamatergic system genes (GRIN2A, GRIN2B, GRIK4, GRM3, GRM7, GRM8, SLC1A2, SLC1A3, SLC17A7) was studied in a population of 704 Caucasian patients with schizophrenia. Genotyping was performed using the MassARRAY Analyzer 4 (Agena Bioscience™). Logistic regression analysis was performed to test for the association of TD with the SNPs while adjusting for confounders. Results: No statistically significant associations between the SNPs and TD were found after adjusting for multiple testing. Since three SNPs of the SLC1A2 gene demonstrated nominally significant associations, we carried out a haplotype analysis for these SNPs. This analysis identified a risk haplotype for TD comprising CAT alleles of the SLC1A2 gene SNPs rs1042113, rs10768121, and rs12361171. Nominally significant associations were identified for SLC1A3 rs2229894 and orofacial TD, as well as for GRIN2A rs7192557 and limb-truncal TD. Conclusions: Genes encoding for mGlu3, EAAT2, and EAAT1 may be involved in the development of TD in schizophrenia patients.
Highly Specific and Rapid Detection of Hepatitis C Virus Using RT-LAMP-Coupled CRISPR–Cas12 Assay
Authors: Kham-Kjing Nang , Ngo-Giang-Huong Nicole , Tragoolpua Khajornsak , Khamduang Woottichai , Hongjaisee Sayamon
Published: June 2022

Hepatitis C virus (HCV) infection can be cured with pan-genotypic direct-acting antiviral agents. However, identifying individuals with current hepatitis C remains a major challenge, especially in resource-limited settings where access to or availability of molecular tests is still limited. The goal of this study was to develop and validate a molecular assay for the rapid detection of HCV RNA in resource-limited settings. It is based on a combination of reverse transcription loop-mediated isothermal amplification (RT-LAMP) with the clustered regularly interspaced short palindromic repeats–CRISPR-associated protein 12a (CRISPR–Cas12a) cleavage assay that allows the recognition of specific HCV nucleic acid sequences. Amplified products after the cleavage reactions can be visualized on lateral flow strips or measured with a fluorescence detector. When tested on clinical samples from individuals infected with HCV, HIV, or HBV, or from healthy donors, the RT-LAMP-coupled CRISPR–Cas12 assay yielded 96% sensitivity, 100% specificity, and 97% agreement as compared to the reference method (Roche COBAS AmpliPrep/COBAS TaqMan HCV Test). This assay could detect HCV RNA concentrations as low as 10 ng/µL (an estimated 2.38 Log10 IU/mL). Therefore, this sensitive and specific assay may represent an affordable and reliable point-of-care test for the identification of individuals with active hepatitis C in low-resource settings.
Causal Inference in Time Series in Terms of Rényi Transfer Entropy
Authors: Jizba Petr , Lavička Hynek , Tabachová Zlata
Published: June 2022

Uncovering causal interdependencies from observational data is one of the great challenges of a nonlinear time series analysis. In this paper, we discuss this topic with the help of an information-theoretic concept known as Rényi’s information measure. In particular, we tackle the directional information flow between bivariate time series in terms of Rényi’s transfer entropy. We show that by choosing Rényi’s parameter α, we can appropriately control information that is transferred only between selected parts of the underlying distributions. This, in turn, is a particularly potent tool for quantifying causal interdependencies in time series, where the knowledge of “black swan” events, such as spikes or sudden jumps, are of key importance. In this connection, we first prove that for Gaussian variables, Granger causality and Rényi transfer entropy are entirely equivalent. Moreover, we also partially extend these results to heavy-tailed α-Gaussian variables. These results allow establishing a connection between autoregressive and Rényi entropy-based information-theoretic approaches to data-driven causal inference. To aid our intuition, we employed the Leonenko et al. entropy estimator and analyzed Rényi’s information flow between bivariate time series generated from two unidirectionally coupled Rössler systems. Notably, we find that Rényi’s transfer entropy not only allows us to detect a threshold of synchronization but it also provides non-trivial insight into the structure of a transient regime that exists between the region of chaotic correlations and synchronization threshold. In addition, from Rényi’s transfer entropy, we could reliably infer the direction of coupling and, hence, causality, only for coupling strengths smaller than the onset value of the transient regime, i.e., when two Rössler systems are coupled but have not yet entered synchronization.
Estimating Sentence-like Structure in Synthetic Languages Using Information Topology
Authors: Back Andrew D. , Wiles Janet
Published: June 2022

Estimating sentence-like units and sentence boundaries in human language is an important task in the context of natural language understanding. While this topic has been considered using a range of techniques, including rule-based approaches and supervised and unsupervised algorithms, a common aspect of these methods is that they inherently rely on a priori knowledge of human language in one form or another. Recently we have been exploring synthetic languages based on the concept of modeling behaviors using emergent languages. These synthetic languages are characterized by a small alphabet and limited vocabulary and grammatical structure. A particular challenge for synthetic languages is that there is generally no a priori language model available, which limits the use of many natural language processing methods. In this paper, we are interested in exploring how it may be possible to discover natural ‘chunks’ in synthetic language sequences in terms of sentence-like units. The problem is how to do this with no linguistic or semantic language model. Our approach is to consider the problem from the perspective of information theory. We extend the basis of information geometry and propose a new concept, which we term information topology, to model the incremental flow of information in natural sequences. We introduce an information topology view of the incremental information and incremental tangent angle of the Wasserstein-1 distance of the probabilistic symbolic language input. It is not suggested as a fully viable alternative for sentence boundary detection per se but provides a new conceptual method for estimating the structure and natural limits of information flow in language sequences but without any semantic knowledge. We consider relevant existing performance metrics such as the F-measure and indicate limitations, leading to the introduction of a new information-theoretic global performance based on modeled distributions. Although the methodology is not proposed for human language sentence detection, we provide some examples using human language corpora where potentially useful results are shown. The proposed model shows potential advantages for overcoming difficulties due to the disambiguation of complex language and potential improvements for human language methods.
The Influence of an Export Manager on Export Marketing Policies: Evidence from Exporting Olive Oil Companies in Crete
Authors: Lamprinidou Fani , Semos Anastasios , Tsakiridou Efthimia , Sergaki Panagiota
Published: June 2022

Cretan olive oil is known for its premium quality worldwide. In the post pandemic international environment, the rising demand for olive oil due to its health benefits has generated greater competition amongst traditionally non-producer and producer countries. Olive oil exports affect both the financial growth and stability of companies that trade it and the economic prosperity of the Crete prefecture. Various factors can influence a company’s export performance, and of those it is widely agreed that the presence of an export manager yields a positive impact. The aim of this paper is to investigate the influence of an export manager on export marketing policies. The application of the Chi-Square Test (χ2) was judged as the most suitable criterion for the elaboration of the research. The χ2 test showed that there are statistically significant correlations between variables. The findings of the research indicate an exports manager’s influence on promotions abroad and in conducting market research before exporting to a new foreign market. The employment of an export manager and the level of his/her certified knowledge appear to have a positive impact on the export marketing policies of companies that export Cretan olive oil.
Implementation of Service-Learning as a Strategy to Foster Intercultural Coexistence in the Local Community: A Case Study
Authors: Ferrer-Aracil Javier , Giménez-Bertomeu Víctor M. , Cortés-Florín Elena M.
Published: June 2022

Service-learning (SL) is a participatory teaching–learning methodology through which students learn certain content while also meeting a number of real social needs in their environment. The implementation of SL in different areas and educational stages has been extensively described. However, its potential as a community and intercultural development strategy at the local level has not been widely studied. Through a documentary analysis, the present work sought to understand the characteristics of socio-educational interventions, which, based on the service-learning methodology, aim at improving coexistence in local communities with a high degree of cultural diversity. A total of 18 projects were included in a community programme implemented in the municipality of Elche (Spain) between 2010 and 2016. They all focused or included the promotion of intercultural coexistence among their objectives. The study design was quantitative, with a descriptive and explanatory, univariate and bivariate analysis using the IBM Statistical Package for Social Sciences 26. The results showed that service-learning can contribute to the improvement of intercultural coexistence. Moreover, a number of SL basic, pedagogical and organisational components are enhanced when integrated into broader community development processes.
Monitoring Time-Non-Stable Surfaces Using Mobile NIR DLP Spectroscopy
Authors: Gąsiorowski Marek , Szymak Piotr , Patryn Aleksy , Naus Krzysztof
Published: June 2022

In recent years, Near Infrared (NIR) spectroscopy has increased in popularity and usage for different purposes, including the detection of particular substances, evaluation of food quality, etc. Usually, mobile handheld NIR spectroscopy devices are used on the surfaces of different materials, very often organic ones. The features of these materials change as they age, leading to changes in their spectra. The ageing process often occurs only slowly, i.e., corresponding reflection spectra can be analyzed each hour or at an even longer interval. This paper undertakes the problem of analyzing surfaces of non-stable, rapidly changing materials such as waxes or adhesive materials. To obtain their characteristic spectra, NIR spectroscopy using a Digital Light Projection (DLP) spectrometer was used. Based on earlier experiences and the current state of the art, Artificial Neural Networks (ANNs) were used to process spectral sequences to proceed with an enormous value of spectra gathered during measurements.
Learning Facial Motion Representation with a Lightweight Encoder for Identity Verification
Authors: Sun Zheng , Sumsion Andrew W. , Torrie Shad A. , Lee Dah-Jye
Published: June 2022

Deep learning became an important image classification and object detection technique more than a decade ago. It has since achieved human-like performance for many computer vision tasks. Some of them involve the analysis of human face for applications like facial recognition, expression recognition, and facial landmark detection. In recent years, researchers have generated and made publicly available many valuable datasets that allow for the development of more accurate and robust models for these important tasks. Exploiting the information contained inside these pretrained deep structures could open the door to many new applications and provide a quick path to their success. This research focuses on a unique application that analyzes short facial motion video for identity verification. Our proposed solution leverages the rich information in those deep structures to provide accurate face representation for facial motion analysis. We have developed two strategies to employ the information contained in the existing models for image-based face analysis to learn the facial motion representations for our application. Combining with those pretrained spatial feature extractors for face-related analyses, our customized sequence encoder is capable of generating accurate facial motion embedding for identity verification application. The experimental results show that the facial geometry information from those feature extractors is valuable and helps our model achieve an impressive average precision of 98.8% for identity verification using facial motion.
Scene Classification, Data Cleaning, and Comment Summarization for Large-Scale Location Databases
Authors: Cheng Hsu-Yung , Yu Chih-Chang
Published: June 2022

This paper presents a framework that can automatically analyze the images and comments in user-uploaded location databases. The proposed framework integrates image processing and natural language processing techniques to perform scene classification, data cleaning, and comment summarization so that the cluttered information in user-uploaded databases can be presented in an organized way to users. For scene classification, RGB image features, segmentation features, and the features of discriminative objects are fused with an attention module to improve classification accuracy. For data cleaning, incorrect images are detected using a multilevel feature extractor and a multiresolution distance calculation scheme. Finally, a comment summarization scheme is proposed to overcome the problems of unstructured sentences and the improper usage of punctuation marks, which are commonly found in customer reviews. To validate the proposed framework, a system that can classify and organize scenes and comments for hotels is implemented and evaluated. Comparisons with existing related studies are also performed. The experimental results validate the effectiveness and superiority of the proposed framework.
A Stochastic Confocal Elliptic-Cylinder Channel Model for 3D MIMO in Millimeter-Wave High-Speed Train Communication System
Authors: Assiimwe Eva , Marye Yihenew Wondie
Published: June 2022

Massive MIMO technology is among the most promising solutions for achieving higher gain in 5G millimeter-wave (mmWave) channel models for high-speed train (HST) communication systems. Based on stochastic geometry methods, it is fundamental to accurately develop the associated MIMO channel model to access system performance. These MIMO channel models could be extended to massive MIMO with antenna arrays in more than one plane. In this paper, the proposed MIMO 3D geometry-based stochastic model (GBSM) is composed of the line of sight component (LOS), one sphere, and multiple confocal elliptic cylinders. By considering the proposed GBSM, the local channel statistical properties are derived and investigated. The impacts of the distance between the confocal points of the elliptic cylinder, mmWave frequencies of 28 GHz and 60 GHz, and non-stationarity on channel statistics are studied. Results show that the proposed 3D simulation model closely approximates the measured results in terms of stationary time. Consequently, findings show that the proposed 3D non-wide-sense stationary (WSS) model is better for describing mmWave HST channels in an open space environment.
A Low-Power ADPLL with Calibration-Free RO-Based Injection-Locking TDC for BLE Applications
Authors: Chen Qinan , Shan Qiang , Wei Zihui , Wang Xiaosong , Huang Shuilong , Liu Yu
Published: June 2022

This paper proposes a low-power all-digital phase-locked loop (ADPLL) with calibration-free ring oscillator (RO)-based injection-locking time to digital converter (TDC) for BLE applications. The RO is reused as the delay cell of TDC, and the quantization step of TDC is always tracked with the RO period; hence no calibration is needed in this architecture. We adopt RO tuning to lower the injection-locking bandwidth so as to decrease the power consumption of the injection current. Moreover, the fractional part of phase error detection is turned down in the coarse tuning of ADPLL to save power. An LC-based digital-controlled oscillator (LCDCO) with a 6.4 nH inductor and a resistive bias is used to have a low power and better phase noise performance. The ADPLL is fabricated in 40 nm CMOS with a 1 V supply and consumes 1.4 mW when it is locked. The measured phase noise is −114 dBc/Hz at 1 MHz offset. The test results show significant power saving. Thus, it can be a promising candidate for BLE applications.
Power Compression and Phase Analysis of GaN HEMT for Microwave Receiver Protection
Authors: Song Wenhan , Guo Haowen , Gu Yitian , Zhou Junmin , Sui Jin , Chen Baile , Huang Wei , Zou Xinbo
Published: June 2022

This paper reports a high-performance microwave receiver protector (RP) based on a single gallium nitride (GaN) high electron mobility transistor (HEMT) at an operation frequency of 30 to 3000 MHz. The HEMT-based RP exhibits multi features: high power compression, constant output power, tunable threshold power level, and insensitivity to frequency variation. With a low drain voltage (Vds) of 3 V, constant output power of 9.9 dBm was acquired for input power over its threshold power of 3.2 dBm. Power compression of 13.3 dB was achieved at the input power of Pin = 20 dBm. In addition, adjustable threshold power level Pth could be obtained by merely tuning drain voltage. Transducer gain measurement results were employed to explain the occurrence of output power saturation. Relatively higher Pth was linked to wider gate voltage swing which extended the linear region of the Pout-Pin characteristic. In addition, the GaN HEMT’s power compression capability shows great immunity to frequency variation, which is promising for protecting sensitive receiver components at both low and high frequencies. Finally, the phase shift of the GaN HEMT RP at high input power was measured and analyzed by the nonlinear behaviors of input capacitance Cgs.
An Efficient Non-Inverting Buck-Boost Converter with Improved Step Up/Down Ability
Authors: Abdel-Rahim Omar , Chub Andrii , Blinov Andrei , Vinnikov Dmitri , Peftitsis Dimosthenis
Published: June 2022

In this article, a new non-inverting buck-boost converter with superior characteristics in both bucking and boosting is presented. The proposed converter has some distinct features, such as high step-up/-down ability and low voltage/current stress on its switching devices. The voltage gain of the proposed converter is double the reported value for the traditional buck-boost converter. Although it has three switches, the three switches operate simultaneously, hence no dead-time is required. Two out of the three switches are under voltage stress equal to half of the output voltage. The overall efficiency of the system is promising because of the ability to select devices with low voltage drops. Converter analysis and steady-state performance in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are presented in detail. A 1 kW hardware prototype of the converter was implemented in the laboratory; with a step-up ratio of 3.5 and 1 kW power, the measured efficiency is above 95.4%, and with step-up ratio 8, it is around 91.5%.
Sustainability Framework for Assessment of Mergers and Acquisitions in Energy Sector
Authors: Andriuškevičius Karolis , Štreimikienė Dalia
Published: June 2022

According to Thomson Reuters DataStream database, 22,458 merger and acquisition (M&A) transactions with a deal value exceeding 7016 billion Euros have occurred in the energy worldwide sector during 1995–2020. International M&A enable promotion of sustainable competitive advantages, accelerates industry developments and helps to promote sustainable social and economic development. Our research aims to systemize empirical studies, which would enable assessment of the relationship between M&A transactions and the principles of sustainable development in the energy sector. To do so, SALSA (search, appraisal, synthesis and analysis) methodology with additional snowballing technique (chain-referral sampling) is conducted. The current research contributes to existing knowledge by providing an extensive systematic literature review of the academic publications on sustainable development and M&A deals. This paper may be insightful for practitioners and scholars because it highlights the most relevant lines of research on the topic and provides a synthesis of the interdisciplinary literature. Practical contributions of this paper come from its synthesis of interconnections between sustainable development and M&A deals in the energy sector, because sustainability matters may be reflected during different M&A processes: target selection, deal due diligence, deal valuation and post-acquisition integration.
Graphic Method to Evaluate Power Requirements of a Hydraulic System Using Load-Holding Valves
Authors: Berne Luis Javier , Raush Gustavo , Roquet Pedro , Gamez-Montero Pedro-Javier , Codina Esteban
Published: June 2022

It is very well known that the use of a load-holding valve (LHV) in a hydraulic system introduces additional energy consumption. This article presented a simplified graphical method for analyzing the power requirements of hydraulic systems equipped with load-holding valves for overrunning load control. The method helps to understand the performance of load-holding valves during actuator movement. In addition, it allows visualization of the influence on the overall system consumption of the main parameters (pilot ratio, set pressure) and others such as flow rate, back pressure, and load force. The method is attractive because, with only the pressures at the three ports and the valve relief function curve, it is sufficient to evaluate the energy consumption and to define the power ratio as an index indicating the percentage of energy that is to be used to open the LHV valve. The method was applied to real cases, in particular to two types of lifting mobile machines. It was validated following several outdoor tests on two mobile machines where experimental data were obtained. During tests, both machines were equipped with a set of seven different performance LHV valves. The described method could be beneficial for hydraulic machine manufacturers engaged in designing lifting devices when selecting a suitable valve for energy efficiency applications, especially now that the trend towards electrification is a reality.
Autonomous DC-Bus Voltage Regulation in DC Microgrid Using Distributed Energy Storage Systems
Authors: Choi Jung-Sik , Oh Seung-Yeol , Cha Dae-Seak , Ko Byoung-Sun , Kim Mina
Published: June 2022

A DC microgrid has many advantageous features, such as low power losses, zero reactive power, and a simple interface with renewable energy sources (RESs). A bipolar DC microgrid is also highlighted due to its high-power quality, improved reliability, and enhanced system efficiency. However, the bipolar DC microgrid has high DC bus voltage fluctuation due to the load power unbalance between the poles. Therefore, this paper analyzes the DC bus voltage fluctuation that can occur in the bipolar DC microgrid. An autonomous grid voltage regulation method is introduced to regulate the DC bus voltage of a bipolar DC microgrid using distributed energy storage systems (ESSs). The proposed grid voltage regulation scheme using the distributed ESSs could regulate DC bus voltage in real time, regardless of the structure of the DC microgrid without external communication. Lastly, experimental results using a lab-scale bipolar DC microgrid prototype verified the proposed method.
Efficient Heat Exchange Configuration for Sub-Cooling Cycle of Hydrogen Liquefaction Process
Authors: Park Sihwan , Noh Wonjun , Park Jaedeuk , Park Jinwoo , Lee Inkyu
Published: June 2022

The hydrogen liquefaction process is highly energy-intensive owing to its cryogenic characteristics, and a large proportion of the total energy is consumed in the subcooling cycle. This study aimed to develop an efficient configuration for the subcooling cycle in the hydrogen liquefaction process. The He-Ne Brayton cycle is one of the most energy-efficient cycles of the various proposed hydrogen liquefaction processes, and it was selected as the base case configuration. To improve its efficiency and economic potential, two different process configurations were proposed: (configuration 1) a dual-pressure cycle that simplified the process configuration, and (configuration 2) a split triple-pressure cycle that decreased the flow rate of the medium- and high-pressure compressors. The ortho–para conversion heat of hydrogen is considered by using heat capacity data of equilibrium hydrogen. Genetic algorithm-based optimization was also conducted to minimize the energy consumption of each configuration, and the optimization results showed that the performance of configuration 1 was worse than that of the base case configuration. In this respect, although less equipment was used, the compression load on each compressor was very intensive, which increased the energy requirements and costs. Configuration 2 provided the best results with a specific energy consumption of 5.69 kWh/kg (3.2% lower than the base case configuration). The total expense of configuration 2 shows the lowest value which is USD 720 million. The process performance improvements were analyzed based on the association between the refrigerant composition and the heat exchange efficiency. The analysis demonstrated that energy efficiency and costs were both improved by dividing the pressure levels and splitting the refrigerant flow rate in configuration 2.
Simulation Analysis of Power Consumption and Mixing Time of Pseudoplastic Non-Newtonian Fluids with a Propeller Agitator
Authors: Wang Shiji , Wang Peng , Yuan Jianping , Liu Jinfeng , Si Qiaorui , Li Dun
Published: June 2022

In order to study the effect of a high twist rate propeller on the flow field characteristics of pseudoplastic non-Newtonian fluids, the numerical simulation method was used to analyze the mixing flow field of pseudoplastic non-Newtonian fluids at different concentrations in this paper. By changing the rotational speed and the blade installation height, the vorticity, turbulent energy, mixing power consumption, mixing time and mixing energy of the flow field were analyzed. By analyzing and comparing the research results, it was found that increasing the mixing propeller speed can effectively improve the mixing effect. Single-layer arrangement of mixing propeller is not suitable to be placed close to the bottom of the tank, and the mixing of the upper flow field is weaker. Under the same conditions, when the viscosity of pseudoplastic non-Newtonian fluid is increased, the high vorticity and high turbulence energy area is reduced to the mixing propeller area, and the time required for mixing 1.25% CMC solution is 246 times longer than that for mixing 0.62% CMC solution and the required mixing energy also increases sharply. The accuracy of the numerical simulation was verified by experiments. Considering the mixing effect and the mixing power consumption, the single-layer arrangement propeller is more suitable for mixing pseudoplastic non-Newtonian fluids with mass fraction of 0.62% CMC or below. This study can provide a reference for the practical application of propeller mixers to mix pseudoplastic non-Newtonian fluids.
Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine
Authors: El Maghraoui Adila , Ledmaoui Younes , Laayati Oussama , El Hadraoui Hicham , Chebak Ahmed
Published: June 2022

The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many of which have direct implications for humanity’s survival. The forecast of mine site energy use is one of the low-cost approaches for energy conservation. Accurate predictions do indeed assist us in better understanding the source of high energy consumption and aid in making early decisions by setting expectations. Machine Learning (ML) methods are known to be the best approach for achieving desired results in prediction tasks in this area. As a result, machine learning has been used in several research involving energy predictions in operational and residential buildings. Only few research, however, has investigated the feasibility of machine learning algorithms for predicting energy use in open-pit mines. To close this gap, this work provides an application of machine learning algorithms in the RapidMiner tool for predicting energy consumption time series using real-time data obtained from a smart grid placed in an experimental open-pit mine. This study compares the performance of four machine learning (ML) algorithms for predicting daily energy consumption: Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The models were trained, tested, and then evaluated. In order to assess the models’ performance four metrics were used in this study, namely correlation (R), mean absolute error (MAE), root mean squared error (RMSE), and root relative squared error (RRSE). The performance of the models reveals RF to be the most effective predictive model for energy forecasting in similar cases.
Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm
Authors: Chhabra Amit , Sahana Sudip Kumar , Sani Nor Samsiah , Mohammadzadeh Ali , Omar Hasmila Amirah
Published: June 2022

Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.
Performance Comparison of Native and Hybrid Android Mobile Applications Based on Sensor Data-Driven Applications Based on Bluetooth Low Energy (BLE) and Wi-Fi Communication Architecture
Authors: Kaczmarczyk Aleksander , Zając Piotr , Zabierowski Wojciech
Published: June 2022

In the age of smartphones and tablets, the popularity of mobile applications is rising extremely quickly. There are countless purposes for using them, such as communication, social media, news, sending emails, shopping, paying, watching videos and streams, and playing games. Google’s Android is currently the most frequently used mobile system in the world. The main aim of this article is to compare native and hybrid mobile applications created for this system, taking into account the needs and challenges of designing systems using BLE and Wi-Fi. The comparison was based on mobile applications implemented in the Java 8 language and the Ionic framework. The aim was to compare the efficiency of data processing in both technologies, in order to indicate dependencies that can help in the selection of technologies in the projects under development based on BLE, Wi-Fi and sensor networks.
Activated Corrosion Products Evaluations for Occupational Dose Mitigation in Nuclear Fusion Facilities
Authors: Terranova Nicholas , Breidokaité Simona , Contessa Gian Marco , Di Pace Luigi , Gasparrini Claudia , Kaliatka Tadas , Mariano Giovanni
Published: June 2022

Activated corrosion products generation in primary heat transfer systems of nuclear fusion facilities is a relevant radiological source term for occupation dose assessments. The formation of the Chalk River Undefined Deposit, already well known in nuclear fission power plants, represents a significant safety issue in fusion applications due to the intense high energy neutron fluences (about 14 MeV in Deuterium-Tritium operation). The activated corrosion products formation is a multi-physical problem. The combined synergy of activation, corrosion, dissolution, erosion, ejection, precipitation, and transport phenomena induces the contamination of coolant loop regions located outside the bio-shield, where scheduled worker operation might take place. The following manuscript shows how activated corrosion products are evaluated for the nuclear fusion power plant design under investigation by the Safety and Environment Work Package (WPSAE) of the Eurofusion Consortium (i.e., the European Demonstration power plant, EU-DEMO). The major issues in activated corrosion products estimations are here exposed and the main results for mass and activity inventories are briefly shown for some main Primary Heat Transfer Systems of EU-DEMO.
The Impact of Multiple Pond Conditions on the Performance of Dike-Pond Extraction
Authors: Zhou Jinhao , Zhou Wu , Zhou Qiqi , Zhu Yuanhui , Xie Fei , Liang Shen , Hu Yueming
Published: June 2022

Dike-ponds in fisheries often present multiple pond conditions such as pure, suspended sediment, water bloom, semidry conditions, etc. However, the impact of these conditions on the performance of extracting dike-pond from remote sensing images has not been studied. To solve this problem, we explore the existence of such impacts by comparing the performance of four rule-based methods in two groups of test regions. The first group has few multiple pond conditions, while the second has more. The results show that various measure values deteriorate as the proportion of multiple pond conditions in the regions increases. All four methods performed worse in the second group than the first, where the overall accuracy decreased by 8.80%, misclassification error increased by 3.69%, omission error raised by 10.53%, and correct quantity rate dropped by 8.23%, respectively. The extraction method that ingested multiple pond conditions performed indistinguishably from the other methods in the first group. However, it outperformed the other methods in the second group, with a 4.22% improvement in overall accuracy, a 10.25% decrease in misclassification error, and a 19.03% increase in the correct quantity rate. These findings suggest that multiple pond conditions can negatively impact the extraction performance and should be considered in dike-pond applications that require a precise pond size, number, and shape.