bluuebunny/crossref_metadata_2025 huggingface dataset:
Biomarkers of Spinal Cord Injury in Patients Undergoing Complex Endovascular Aortic Repair Procedures—A Narrative Review of Current Literature
Authors: Sotir Anna , Klopf Johannes , Brostjan Christine , Neumayer Christoph , Eilenberg Wolf
Published: April 2023

Complex endovascular aortic repair (coEVAR) of thoracoabdominal aortic aneurysms (TAAA) has greatly evolved in the past decades. Despite substantial improvements of postoperative care, spinal cord injury (SCI) remains the most devastating complication of coEVAR being associated with impaired patient outcome and having an impact on long-term survival. The rising number of challenges of coEVAR, essentially associated with an extensive coverage of critical blood vessels supplying the spinal cord, resulted in the implementation of dedicated SCI prevention protocols. In addition to maintenance of adequate spinal cord perfusion pressure (SCPP), early detection of SCI plays an integral role in intra- and postoperative patient care. However, this is challenging due to difficulties with clinical neurological examinations during patient sedation in the postoperative setting. There is a rising amount of evidence, suggesting that subclinical forms of SCI might be accompanied by an elevation of biochemical markers, specific to neuronal tissue damage. Addressing this hypothesis, several studies have attempted to assess the potential of selected biomarkers with regard to early SCI diagnosis. In this review, we discuss biomarkers measured in patients undergoing coEVAR. Once validated in future prospective clinical studies, biomarkers of neuronal tissue damage may potentially add to the armamentarium of modalities for early SCI diagnosis and risk stratification.
Synthesis of Graphene Quantum Dots Coupled to Au Nanoparticles: A Facile and Versatile Route Using Different Carbon Sources
Authors: Ibarra David , Kharissova Oxana , Gomez Idalia
Published: April 2023

A top-down synthesis of graphene quantum dots (GQD) was carried out by hydrothermal method from different carbon sources (graphene, multi-walled carbon nanotubes, and black carbon) and H2O2 as an oxidizing agent, with an N source added in the reaction to modify the chemical surface of the GQD, giving rise to the nanomaterial N-GQD. The modified chemical surface of N-GQD partially allowed the nucleation and coupling of gold nanoparticles from a HAuCl4 solution. The X-ray diffraction spectrogram confirms the amorphization of the precursor materials, while the functionalized surface of N-GQD was characterized through UV-Vis, Fourier transform infrared, and photoluminescense spectrometry; TEM and FE-SEM show particle sizes between 8 and 15 nm. N-GQD@AuNP presence can be confirmed by UV-Vis spectroscopy and TEM analysis, showing partial coupling and nanoparticle nucleation of Au in the structure with particle sizes between 20 and 40 nm.
Recovery of Pd(II) Ions from Aqueous Solutions Using Activated Carbon Obtained in a Single-Stage Synthesis from Cherry Seeds
Authors: Michałek Tomasz , Wojtaszek Konrad , Małecki Stanisław , Kornaus Kamil , Wandor Szymon , Druciarek Julia , Fitzner Krzysztof , Wojnicki Marek
Published: April 2023

This paper describes a single-stage synthesis process for activated carbon using cherry seeds. The influences of the carbonization temperature and the time were investigated. Using the BET method, the surface area of the obtained activated carbons was determined, as well as the pore distribution, while SEM images provided further insight into the structure of the surface. Next, the adsorption isotherm was derived. For the test, Pd(II) chloride complex ions were used. It was found that the obtained activated carbon were suitable for palladium(II) recovery from diluted aqueous solutions. Out of the tested parameters of carbon synthesis, the most optimal one was found to be 500 °C for 3 h. Additionally, it was confirmed that the increase in the adsorption temperature affects the increase in palladium load from 1.6 mg/g at 20 °C to 15.6 mg/g at 50 °C (for the best-performing sample). This fact may suggest that the process of adsorption is associated with chemical reactions.
Microemulsion–Assisted Synthesis of Ag2CrO4@MIL–125(Ti)–NH2 Z–Scheme Heterojunction for Visible–Light Photocatalytic Inactivation of Bacteria
Authors: Yuan Haoyu , Zhang Chao , Chen Wenjing , Xia Yuzhou , Chen Lu , Huang Renkun , Si Ruiru , Liang Ruowen
Published: April 2023

Metal–organic frameworks (MOFs) are new porous materials composed of metal centers and organic ligand bridges, which received great attention in the field of photocatalysis. In this work, Ag2CrO4@MIL–125(Ti)–NH2 (denoted as AgCr@M125) Z–scheme heterojunctions were synthesized via a simple microemulsion method, by which highly dispersed nano–sized Ag2CrO4 can be anchored uniformly on the surfaces of porous MIL–125(Ti)–NH2 (denoted as M125). Compared with pure M125 and Ag2CrO4, the as–prepared AgCr@M125 hybrids show significant photocatalytic efficiency against inactivated Staphylococcus aureus (S. aureus), reaching over 97% inactivation of the bacteria after 15 min of visible light irradiation. Notably, the photocatalytic activity of the obtained 20%AgCr@M125 is about 1.75 times higher than that of AgCr–M125, which was prepared via a traditional precipitation method. The enhanced photocatalytic antibacterial activity of the AgCr@M125 photocatalytic system is strongly ascribed to a direct Z–scheme mechanism, which can be carefully discussed based on energy band positions and time–dependent electron spin response (ESR) experiments. Our work highlights a simple way to enhance the antibacterial effect by coupling with Ag2CrO4 and M125 via a microemulsion–assisted strategy and affords an ideal example for developing MOFs–based Z–scheme photocatalysts with excellent photoactivity.
Unraveling the Oncogenic Potential of VAV1 in Human Cancer: Lessons from Mouse Models
Authors: Shalom Batel , Salaymeh Yaser , Risling Matan , Katzav Shulamit
Published: April 2023

VAV1 is a hematopoietic signal transducer that possesses a GDP/GTP nucleotide exchange factor (GEF) that is tightly regulated by tyrosine phosphorylation, along with adapter protein domains, such as SH2 and SH3. Research on VAV1 has advanced over the years since its discovery as an in vitro activated oncogene in an NIH3T3 screen for oncogenes. Although the oncogenic form of VAV1 first identified in the screen has not been detected in human clinical tumors, its wild-type and mutant forms have been implicated in mammalian malignancies of various tissue origins, as well as those of the hematopoietic system. This review article addresses the activity of human VAV1 as an overexpressed or mutated gene and also describes the differences in the distribution of VAV1 mutations in the hematopoietic system and in other tissues. The knowledge accumulated thus far from GEMMs expressing VAV1 is described, with the conclusion that GEMMs of both wild-type VAV1 and mutant VAV1 do not form tumors, yet these will be generated when additional molecular insults, such as loss of p53 or KRAS mutation, occur.
Application of Molecularly Imprinted Microelectrode as a Promising Point-of-Care Biosensor for Alanine Aminotransferase Enzyme
Authors: Samy Mostafa Ahmed , Abdel-Tawab Muhammed Abdel-Hamied , Abdel-Ghani Nour. T. , El Nashar Rasha M.
Published: April 2023

Alanine amino transaminase (ALT) is an enzyme that can be used as a biomarker for liver injury and other diseases. In this work, we report the development of the first microelectrode based on a molecularly imprinted pyruvate oxidase enzyme to be applied as an electrochemical biosensor for ALT detection. The biosensor is based on pyruvate oxidase enzyme (POx), imprinted using 4-aminophenol (functional monomer-on-platinum microelectrode modified (PME)) with platinum nanoparticles and 4-aminoantypirine (4-AAP)/sodium pyruvate as an electrochemical indicator. The operational conditions of the biosensor were optimized and characterized morphologically using scanning electron microscopy (SEM) and electrochemically using electrochemical impedance spectroscopy (EIS). The biosensor was found to have a fast response towards ALT within a linear range of 25–700 U/L and a limit of detection of 2.97 U/L. The biosensor did not exhibit cross-reactivity towards other tested enzymes, including nicotinamide adenine dinucleotide (Beta-NAD), catalase (CAT), glutathione peroxidase (GPx), and L-glutathione reduced (GSH) enzymes. The biosensor was efficiently applied for the assay of ALT in plasma samples; with recovery values ranging from 99.80–103.82% and RSD of values 0.27–2.01% and these results were found to be comparable to those of the reference diagnostic kits, without any need for complicated procedures or protein extraction. In addition to being highly sensitive, low cost, and portable, the use of microelectrodes allows the application of the proposed sensor for point-of-care diagnostics of liver function and online monitoring of ALT levels in hospitalized patients without the need for withdrawing samples, which indicates the promising applicability of the presented ALT sensor for point-of-care diagnostics.
Children with FASD—Evolving Patterns of Developmental Problems and Intervention Costs in Ages 0 through 16 in Finland
Authors: Jolma Laura Mirjami , Koivu-Jolma Mikko , Sarajuuri Anne , Torkki Paulus , Autti-Rämö Ilona , Sätilä Heli
Published: April 2023

This is a retrospective chart review of 55 persons (mean age 11 years, range 2–28 years) diagnosed with fetal alcohol spectrum disorder (FASD) in one Finnish central hospital. The aim was to determine typical problems and interventions and estimate their costs during different periods of childhood between ages 0 and 16. During the first year, 29/38 (76.3%) were treated in the neonatal intensive care unit, 29/43 (67.4%) received physiotherapy, 15/43 (34.9%) were diagnosed with congenital malformation, 8/43 (18.6%) had heart defects. Between 1 and 6 years, 39/45 (86.7%) received occupational therapy, 25/45 (55.6%) speech therapy, and 12/45 (26.7%) were diagnosed with strabismus. Between 7 and 12 years, 25/37 (67.6%) were diagnosed with ADHD and special education was recommended for 30/37 (81.1%). Learning disorders and the need for psychiatric care increased with age. Between 13 and 16 years, 12/15 (80%) were treated in the psychiatric unit, and by this age, 8/15 (53.3%) were diagnosed with intellectual disability. Before 16 years, 44/55 (80%) were placed out of home, which caused 78.5% of the estimated cumulative mean extra costs of EUR 1,077,000 in 2022 currency. Except for psychiatric costs, health care costs were highest during early years. Charting typical patterns of problems may help in identifying children with FASD and planning follow-ups, content of assessments, and interventions.
A Proof-of-Concept Evaluation of the 1616 Story-Based Positive Youth Development Program
Authors: Côté Jean , Coletti Jennifer , McGuire Cailie S. , Erickson Karl , Saizew Kelsey , Maw Alex , Primeau Chris , Wolff Meredith , Ladd Brandy , Martin Luc J.
Published: April 2023

The 1616 Program is a newly developed and evidence-informed story-based positive youth development (PYD) program for young ice hockey players (10–12 years of age) in North America. The program uses elite ice hockey players as role models—through story-telling—to serve as inspirational figures to engage youth athletes and important social agents (i.e., parents, coaches) with evidence-informed PYD concepts. The objective of this study was to use a Proof-of-Concept evaluation to assess whether the 1616 Program ‘worked’ in enhancing PYD outcomes and to determine if the concepts were engaging and enjoyable for youth, their parents, and coaches. The 5 week Proof-of-Concept evaluation was conducted with 11 ice hockey teams (n = 160 youths, 93 parents, and 11 coaches), encompassing both qualitative (e.g., focus groups) and quantitative (e.g., retrospective pretest-posttest questionnaires) processes and outcome assessments. Results showed that the program was well received by participants and positively impacted the intended outcomes. Overall, the data presented in this Proof-of-Concept evaluation was deemed to support the development and implementation of the full-scale 1616 Program for a more comprehensive evaluation.
Approaches and Advantages of Increased Crop Genetic Diversity in the Fields
Authors: Joshi Bal Krishna , Ghimire Krishna Hari , Neupane Shree Prasad , Gauchan Devendra , Mengistu Dejene K.
Published: April 2023

Crop genetic diversity is the most important factor for a long-term sustainable production system. Breeding and production strategies for developing and growing uniform and homogenous varieties have created many problems. Such populations are static and very sensitive to unpredictable stresses. In Nepal, more than 80% of the seed system is informal, which has contributed greatly to creating and maintaining genetic diversity within the field. This paper aims to assess and present the approaches and advantages of increased crop genetic diversity in the fields, based on the experiences of implementing on-farm conservation activities carried out in Nepal for last two decades. Some of the evidence has been derived from an ongoing evolutionary plant breeding (EPB) project being implemented in Nepal. The information is supplemented with field assessments, focus group discussion, and a literature review. The major approaches to increase crop genetic diversity are evolutionary plant breeding, cultivar mixture, landrace enhancement, informal seed systems, the bulk method, diversifying the seed sources, participatory plant breeding, open pollination, etc. EPB and cultivar mixture are very simple and effective approaches to increase crop genetic diversity at field level. The involvement of farmers in these approaches helps to accelerate the population improvement, maintaining the higher degree of genetic diversity. The major advantages of increased crop genetic diversity are seed maintenance by farmers themselves, minimal risk of crop failure, resilience to unpredictable stresses, increased amount of diversified nutrition, production increment, ease of producing organically, etc. However, there are some issues and problems associated with mixtures and intra-varietal diversity; for example, not being able to harvest by machine, maturing at a different date, difficulty in maintaining seeds and registration, etc. Crop genetic diversity should be considered as a sustainable approach for a climate-resilient and self-dependent production system. The higher the genetic diversity in farming land, the more chance of receiving multiple benefits in the agriculture system.
Remote Sensing Data Preparation for Recognition and Classification of Building Roofs
Authors: Hristov Emil , Petrova-Antonova Dessislava , Petrov Aleksandar , Borukova Milena , Shirinyan Evgeny
Published: April 2023

Buildings are among the most significant urban infrastructure that directly affects citizens’ livelihood. Knowledge about their rooftops is essential not only for implementing different Levels of Detail (LoD) in 3D city models but also for performing urban analyses related to usage potential (solar, green, social), construction assessment, maintenance, etc. At the same time, the more detailed information we have about the urban environment, the more adequate urban digital twins we can create. This paper proposes an approach for dataset preparation using an orthophoto with a resolution of 10 cm. The goal is to obtain roof images into separate GeoTIFFs categorised by type (flat, pitched, complex) in a way suitable for feeding rooftop classification models. Although the dataset is initially elaborated for rooftop classification, it can be applied to developing other deep-learning models related to roof recognition, segmentation, and usage potential estimation. The dataset consists of 3617 roofs covering the Lozenets district of Sofia, Bulgaria. During its preparation, the local-specific context is considered.
STFTrack: Spatio-Temporal-Focused Siamese Network for Infrared UAV Tracking
Authors: Xie Xueli , Xi Jianxiang , Yang Xiaogang , Lu Ruitao , Xia Wenxin
Published: April 2023

The rapid popularity of UAVs has encouraged the development of Anti-UAV technology. Infrared-detector-based visual tracking for UAVs provides an encouraging solution for Anti-UAVs. However, it still faces the problem of tracking instability caused by environmental thermal crossover and similar distractors. To address these issues, we propose a spatio-temporal-focused Siamese network for infrared UAV tracking, called STFTrack. This method employs a two-level target focusing strategy from global to local. First, a feature pyramid-based Siamese backbone is constructed to enhance the feature expression of infrared UAVs through cross-scale feature fusion. By combining template and motion features, we guide prior anchor boxes towards the suspicious region to enable adaptive search region selection, thus effectively suppressing background interference and generating high-quality candidates. Furthermore, we propose an instance-discriminative RCNN based on metric learning to focus on the target UAV among candidates. By measuring calculating the feature distance between the candidates and the template, it assists in discriminating the optimal target from the candidates, thus improving the discrimination of the proposed method to infrared UAV. Extensive experiments on the Anti-UAV dataset demonstrate that the proposed method achieves outstanding performance for infrared tracking, with 91.2% precision, 66.6% success rate, and 67.7% average overlap accuracy, and it exceeded the baseline algorithm by 2.3%, 2.7%, and 3.5%, respectively. The attribute-based evaluation demonstrates that the proposed method achieves robust tracking effects on challenging scenes such as fast motion, thermal crossover, and similar distractors. Evaluation on the LSOTB-TIR dataset shows that the proposed method reaches a precision of 77.2% and a success rate of 63.4%, outperforming other advanced trackers.
Information Rates for Channels with Fading, Side Information and Adaptive Codewords
Authors: Kramer Gerhard
Published: April 2023

Generalized mutual information (GMI) is used to compute achievable rates for fading channels with various types of channel state information at the transmitter (CSIT) and receiver (CSIR). The GMI is based on variations of auxiliary channel models with additive white Gaussian noise (AWGN) and circularly-symmetric complex Gaussian inputs. One variation uses reverse channel models with minimum mean square error (MMSE) estimates that give the largest rates but are challenging to optimize. A second variation uses forward channel models with linear MMSE estimates that are easier to optimize. Both model classes are applied to channels where the receiver is unaware of the CSIT and for which adaptive codewords achieve capacity. The forward model inputs are chosen as linear functions of the adaptive codeword’s entries to simplify the analysis. For scalar channels, the maximum GMI is then achieved by a conventional codebook, where the amplitude and phase of each channel symbol are modified based on the CSIT. The GMI increases by partitioning the channel output alphabet and using a different auxiliary model for each partition subset. The partitioning also helps to determine the capacity scaling at high and low signal-to-noise ratios. A class of power control policies is described for partial CSIR, including a MMSE policy for full CSIT. Several examples of fading channels with AWGN illustrate the theory, focusing on on-off fading and Rayleigh fading. The capacity results generalize to block fading channels with in-block feedback, including capacity expressions in terms of mutual and directed information.
Energy Stability Property of the CPR Method Based on Subcell Second-Order CNNW Limiting in Solving Conservation Laws
Authors: Liu Ran , Yan Zhen-Guo , Zhu Huajun , Jia Feiran , Feng Xinlong
Published: April 2023

This paper studies the energy stability property of the correction procedure via reconstruction (CPR) method with staggered flux points based on second-order subcell limiting. The CPR method with staggered flux points uses the Gauss point as the solution point, dividing flux points based on Gauss weights, with the flux points being one more point than the solution points. For subcell limiting, a shock indicator is used to detect troubled cells where discontinuities may exist. Troubled cells are calculated by the second-order subcell compact nonuniform nonlinear weighted (CNNW2) scheme, which has the same solution points as the CPR method. The smooth cells are calculated by the CPR method. The linear energy stability of the linear CNNW2 scheme is proven theoretically. Through various numerical experiments, we demonstrate that the CNNW2 scheme and CPR method based on subcell linear CNNW2 limiting are energy-stable and that the CPR method based on subcell nonlinear CNNW2 limiting is nonlinearly stable.
Development of a High Min-Entropy Quantum Random Number Generator Based on Amplified Spontaneous Emission
Authors: Duda Charlotte K. , Meier Kristina A. , Newell Raymond T.
Published: April 2023

We present the theory, architecture, and performance characteristics of a quantum random number generator (QRNG) which operates in a PCI express form factor-compatible plug-and-play design. The QRNG relies on a thermal light source (in this case, amplified spontaneous emission), which exhibits photon bunching according to the Bose–Einstein (BE) statistics. We demonstrate that 98.7% of the unprocessed random bit stream min-entropy is traceable to the BE (quantum) signal. The classical component is then removed using a non-reuse shift-XOR protocol, and the final random numbers are generated at a 200 Mbps rate and shown to pass the statistical randomness test suites FIPS 140-2, Alphabit, SmallCrush, DIEHARD, and Rabbit of the TestU01 library.
Asymptotic Distribution of Certain Types of Entropy under the Multinomial Law
Authors: Rey Andrea A. , Frery Alejandro C. , Lucini Magdalena , Gambini Juliana , Chagas Eduarda T. C. , Ramos Heitor S.
Published: April 2023

We obtain expressions for the asymptotic distributions of the Rényi and Tsallis of order q entropies and Fisher information when computed on the maximum likelihood estimator of probabilities from multinomial random samples. We verify that these asymptotic models, two of which (Tsallis and Fisher) are normal, describe well a variety of simulated data. In addition, we obtain test statistics for comparing (possibly different types of) entropies from two samples without requiring the same number of categories. Finally, we apply these tests to social survey data and verify that the results are consistent but more general than those obtained with a χ2 test.
Digital Evaluation of Undergraduates’ Knowledge about Scientific Research in Databases during the COVID-19 Pandemic
Authors: da Silva Yngrid Monteiro , Miranda Yasmim Marçal Soares , de Medeiros Rebeca Antunes , Carneiro Paula Mendes Acatauassú , de Menezes Silvio Augusto Fernandes , Oliveira-Filho Aldemir Branco , Frade Paula Cristina Rodrigues , Laurentino Rogério Valois , de Souza Fonseca Ricardo Roberto , Machado Luiz Fernando Almeida
Published: April 2023

Background: COVID-19 pandemic times forced health education to go online, and, due to this necessity, long-term difficulties in education such as bibliographic search in databases like PubMed might have worsened even when platforms such as PubMed provide helping mechanisms to the user. These difficulties or even complete lack of knowledge are, unfortunately, not well documented in the literature. Therefore, this study aimed to describe doubts, lack of knowledge and questions of researchers regarding bibliographic research in PubMed as well as to solve all of those doubts by developing a didactic e-book in relation to bibliographic research in PubMed. Methods: This cross-sectional and populational-based study was conducted between January and April 2021. In northern Brazil, a total of 105 dentistry undergraduate students (DUS) received an anonymous digital form (Google® Forms Platform) using a non-probabilistic “snowball” sampling technique. The digital form was composed of four blocks of dichotomous and multiple-choice questions. After signing the informed consent term, the DUS were divided into three groups according to their period/semester in the dentistry program during the study time (G1: 1st period/semester; G2: 5th period/semester and G3: 10th period/semester). A total of 25 questions referring to demographic, educational and knowledge data about how to do scientific research and how to use bibliographic search in PubMed were asked, and all data were presented as descriptive percentages and then analyzed using the Chi square and G tests. Results: From 105 (100%), G1 had 29/105 (27.6%); G2 had 37/105 (35.2%); G3 had 39/105 (37.2%), the average age was 22.34 years and most participants were female 85/105 (81%). Among our sample, 56/105 (53.4%) had not used any type of search strategy, and 96/105 (91.4%) used database research methods. The main database for literature search used was Scielo 92/105 (87.6%), and 63/105 (60%) had general questions or doubts about bibliographic research. All these data had statistical significance p < 0.0001. Conclusions: The results demonstrate a lack of knowledge and doubts in DUS from three different periods/semesters, and this collected information can help in the formation of didactic material to solve such doubts.
Teachers’ Reactions to Educational Television Programs during the Pandemic and Their Implied Images of Mathematics Teaching
Authors: Tatsis Konstantinos , Maj-Tatsis Bożena
Published: April 2023

In this paper, we discuss the images of mathematics and its teaching, as expressed by mathematics teachers, in reaction to educational programs broadcasted by Polish public television channels during the first lockdown in 2020. For our analysis, we deployed a theoretical framework mostly drawn from the work of Paul Ernest. Our data came from a variety of sources such as educational fora, social networks, websites and email exchanges. Our results, based on an interpretative analysis, revealed four overarching images of mathematics and its teaching which, although related to the specificities of television, resembled images found by other relevant studies. The first image of mathematics views it as an unambiguous discipline in which there is no room for more than one point of view. The second image refers to the teacher as a presenter who is expected to possess particular verbal and non-verbal skills, especially given the fact that the television programs did not involve any live audiences. The third image refers to the unappealing nature of the broadcasted lessons, while the fourth image refers to the underlying approach of crude memorization associated with ‘traditional’ mathematics teaching. Our study’s contribution is twofold: in the improvement of televised educational programs (and possibly online courses) and in the improvement of the public’s image of mathematics.
From Active Learning to Innovative Thinking: The Influence of Learning the Design Thinking Process among Students
Authors: Baričević Martina , Luić Ljerka
Published: April 2023

Active learning is a set of activities that allow the student to actively participate in the learning process. Active learning can be implemented using various methods and strategies. design thinking is a creative human-centric approach used for innovation. In this article, the authors consider learning the design thinking process as a strategy for active learning. After performing a thorough literature review, the authors display the results of pilot research conducted with business economics graduate students enrolled in the Disruptive Innovations and Design Thinking elective course. The research consisted of a survey and an experiment, and it was executed as part of the project “a/AR-Learning: Active learning by applying augmented reality”. The goal was to study the effects of learning the design thinking process and whether it could be used as an active learning method in order to influence the development of innovative thinking among students. The research question was, “How does learning the design thinking process affect the development of students’ innovative way of thinking?” The results of the pilot study confirmed all three hypotheses (H1 = After learning the design thinking process, students recognize the main characteristics of innovation; H2 = Active learning of the design thinking process affects critical assessment; H3 = The innovative way of thinking developed through active learning of the design thinking process differs among students), as well as showing effectiveness and a change in critical assessment, but also the need for an even more active approach. The authors suggest that broader research be undertaken involving a bigger sample and a larger scale.
Joint Optimization of Trajectory and Discrete Reflection Coefficients for UAV-Aided Backscatter Communication System with NOMA
Authors: Du Chenyang , Guo Jing , Yu Hanxiao , Cui Li , Fei Zesong
Published: April 2023

Backscatter communication is a promising technology for the Internet of Things (IoT) systems with low-energy consumption, in which the data transmission of the backscatter devices relies on reflecting the incident signal. However, limited by the low power characteristic of the reflected signal from backscatter devices, achieving efficient data collection for the widely distributed backscatter devices is a thorny problem. Considering that unmanned aerial vehicles (UAVs) have flexible deployment capability, employing UAVs in a backscatter communication network can achieve feasible data collection for backscatter devices. In this paper, we consider a UAV-aided backscatter system and introduce Non-orthogonal multiple access (NOMA) to enable the UAV to collect signals from multiple backscatter devices simultaneously. We formulate an optimization problem to maximize the communication throughput of the considered system by jointly designing the backscatter device matching, the trajectory of the UAV, and the reflection coefficients of the backscatter devices, which is a non-convex optimization problem and challenging to solve. Hence, we decouple the original problem into three sub-problems and propose an efficient iterative algorithm based on Block Coordinate Descent (BCD) to solve them. In detail, a game-based matching algorithm is designed to ensure the transmission needs of remote backscatter devices. The UAV trajectory and reflection coefficients of backscatter devices are optimized through the Successive Convex Approximation (SCA) algorithm and relaxation algorithm. By iterative optimization of the sub-problems, the original problem is solved. The simulation results show that the proposed scheme can obtain a significant throughput gain compared to benchmark schemes.
Energy Efficiency and Throughput Optimization in 5G Heterogeneous Networks
Authors: Arshad Rabia , Farooq-i-Azam Muhammad , Muzzammel Raheel , Ghani Arfan , See Chan Hwang
Published: April 2023

Device-to-device communication offers a promising technology for the 5G network that aims to enhance the data rate, reduce latency and cost, improve energy efficiency, and provide other desired features. The 5G heterogeneous network (5GHN) with a decoupled association strategy of downlink (DL) and uplink (UL) is a promising solution for the challenges faced in the 4G heterogeneous network (4GHN). The research presented in this paper evaluates the performance of the 4GHN as well as a DL-and-UL-coupled (DU-CP) access scheme in comparison with the 5GHN with a DL-and-UL-decoupled (DU-DCP) access scheme in terms of the energy efficiency and network throughput in four-tier heterogeneous networks. The energy and throughput are optimized for both scenarios, i.e., DU-CP and DU-DCP, and the results are compared. Detailed performance analyses of the DU-CP and DU-DCP access schemes were conducted with the help of comparisons of the results achieved by implementing a genetic algorithm (GA) and particle swarm optimization (PSO). Both of these algorithms are suited for the non-linear problem under investigation in which the search space is large. The simulation results have shown that the DU-DCP access scheme gives a better performance than the DU-CP scheme in a four-tier heterogeneous network in terms of network throughput and energy efficiency. The PSO achieves an energy efficiency of 12 Mbits/joule for the DU-CP and 42 Mbits/joule for the DU-DCP, whereas the GA yields an energy efficiency of 28 Mbits/joule for the DU-CP and 55 Mbits/joule for the DU-DCP. The performance of the proposed method is compared with those of three other schemes. The results show that the DU-DCP scheme using the GA outperforms the compared methods.
Design of 6 GHz Variable-Gain Low-Noise Amplifier Using Adaptive Bias Circuit for Radar Receiver Front End
Authors: Nam Hyungseok , Nguyen Dang-An , Kim Yanghyun , Seo Chulhun
Published: April 2023

This paper presents a variable-gain low-noise amplifier (VGLNA) based on an adaptive bias (ADB) circuit for the radar receiver front end. The ADB circuit processes the signal separated by a coupler at the LNA output port. First, the ADB circuit rectifies the coupled signal into positive DC voltage through a rectifier, which is then inverted to control a junction-gate field-effect transistor (JFET). The voltage-controlled current of JFET flows through a voltage-divider network and finally produces the DC biasing voltage for the BJT base termination, which decreases with the increase in the input RF power. The proposed VGLNA operates automatically in high gain at low input power and low gain at high input power, providing a wider dynamic range as compared to the constant-bias counterpart. For validation, a prototype is fabricated and measured at 6 GHz. As observed, the base biasing voltage generated by the ADB circuit is changed from 858 mV to 798 mV as the input power increases from −50 dBm to 0 dBm. As a result, the dynamic range represented by the input P1dB point (IP1dB) has an increase of 6.5 dB, while LNA still maintains a high gain of 15.15 dB at low input power.
Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot
Authors: Shen Yi , Gao Yunqiang , Yuan Mingxin , Sun Hongwei , Guo Zhenjie
Published: April 2023

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.
Memory-Based Sand Cat Swarm Optimization for Feature Selection in Medical Diagnosis
Authors: Qtaish Amjad , Albashish Dheeb , Braik Malik , Alshammari Mohammad T. , Alreshidi Abdulrahman , Alreshidi Eissa Jaber
Published: April 2023

The rapid expansion of medical data poses numerous challenges for Machine Learning (ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant features. As a result, it is critical to pick the most pertinent features for the classification task, which is referred to as Feature Selection (FS). Among the FS approaches, wrapper methods are designed to select the most appropriate subset of features. In this study, two intelligent wrapper FS approaches are implemented using a new meta-heuristic algorithm called Sand Cat Swarm Optimizer (SCSO). First, the binary version of SCSO, known as BSCSO, is constructed by utilizing the S-shaped transform function to effectively manage the binary nature in the FS domain. However, the BSCSO suffers from a poor search strategy because it has no internal memory to maintain the best location. Thus, it will converge very quickly to the local optimum. Therefore, the second proposed FS method is devoted to formulating an enhanced BSCSO called Binary Memory-based SCSO (BMSCSO). It has integrated a memory-based strategy into the position updating process of the SCSO to exploit and further preserve the best solutions. Twenty one benchmark disease datasets were used to implement and evaluate the two improved FS methods, BSCSO and BMSCSO. As per the results, BMSCSO acted better than BSCSO in terms of fitness values, accuracy, and number of selected features. Based on the obtained results, BMSCSO as a FS method can efficiently explore the feature domain for the optimal feature set.
A Disruptive Technology Identification Method for New Power Systems Based on Patent Evolution Analysis
Authors: Pan Dong , Ren Xijun , Zhang Li , Song Zhumeng , Nie Yuanhong , Zhang Long , Ma Meiling , Han Dong
Published: April 2023

Disruptive technologies have been employed in various fields with a strategic priority in several countries as the core driving force of the fourth industrial revolution, significantly impacting the development of new power systems. It is a kernel to effectively identify the future potential of disruptive technologies. To overcome the subjectivity and limitations of existing disruptive technology identification methods, we propose a disruptive technology identification method based on patent evolution analysis. Firstly, the evolution matrix of the patent data is calculated. Afterward, we dig into the characteristics of disruptive technologies to build a more targeted identification index system. Finally, the fields of electric power communication and energy generation are selected as typical cases to build the patent data sets. The future development of the identified technologies, including the identified quantum technologies and controlled fusion, is analyzed. The results demonstrate that the proposed model can identify the key technologies of new power systems accurately and contribute to completing the industrial upgrading and transformation more rapidly.
A User Purchase Behavior Prediction Method Based on XGBoost
Authors: Wang Wenle , Xiong Wentao , Wang Jing , Tao Lei , Li Shan , Yi Yugen , Zou Xiang , Li Cui
Published: April 2023

With the increasing use of electronic commerce, online purchasing users have been rapidly rising. Predicting user behavior has therefore become a vital issue based on the collected data. However, traditional machine learning algorithms for prediction require significant computing time and often produce unsatisfactory results. In this paper, a prediction model based on XGBoost is proposed to predict user purchase behavior. Firstly, a user value model (LDTD) utilizing multi-feature fusion is proposed to differentiate between user types based on the available user account data. The multi-feature behavior fusion is carried out to generate the user tag feature according to user behavior patterns. Next, the XGBoost feature importance model is employed to analyze multi-dimensional features and identify the model with the most significant weight value as the key feature for constructing the model. This feature, together with other user features, is then used for prediction via the XGBoost model. Compared to existing machine learning models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), the eXtreme Gradient Boosting (XGBoost) model outperforms with an accuracy of 0.9761, an F1 score of 0.9763, and a ROC value of 0.9768. Thus, the XGBoost model demonstrates superior stability and algorithm efficiency, making it an ideal choice for predicting user purchase behavior with high levels of accuracy.
Approach to the Impact of Digital Technologies on Sustainability Reporting through Structural Equation Modeling and Artificial Neural Networks
Authors: Mehedintu Anca , Soava Georgeta
Published: April 2023

The motivation for study derives from the requirements imposed by the European Union Corporate Sustainability Reporting Directive, which increases the sustainability reporting scope and the need for companies to use emerging digital technologies. The research aim is to evaluate the digital transformation impact of the European Union companies on sustainability reporting expressed through three sustainable performance indicators (economic, social, and ecological) based on a conceptual model. The data were collected from Eurostat for 2011–2021. The study proposes a framework for sustainable performance analysis through linear regression models and structural equations. Additionally, a hierarchy of digitization indicators is created by modeling structural equations, depending on their impact on sustainability performance indicators, which is validated using neural networks. The results indicate that the company’s digital transformation indicators positively influence economic and social performance and lead to an improved environmental protection (a decrease in pollution), proving the established hypotheses’ validity. The proposed model can be the basis for companies to create their dashboards for analyzing and monitoring sustainable performance. This research can be the basis of other studies, having a significant role in establishing economic and environmental strategies to stimulate an increase of companies that carry out sustainability reporting.
Effect of Plasto-Oil Blended with Diesel Fuel on the Performance and Emission Characteristics of Partly Premixed Charge Compression Ignition Engines with and without Exhaust Gas Recirculation (EGR)
Authors: Rajendran Krishna Moorthy , Kumar Deepak , Lamba Bhawna Yadav , Ghodke Praveen Kumar , Sharma Amit Kumar , Matsakas Leonidas , Patel Alok
Published: April 2023

Municipal mixed plastic waste (MMPW) recycling is an innovative way to turn environmental waste into energy fuels. In the present study, a thermochemical process was applied to depolymerize MMPW to produce hydrocarbon fuels known as plasto-oil. The obtained plasto-oil was blended with conventional diesel to test the performance of the PCCI-mode single-cylinder, four-stroke, direct-injection diesel engine. The PCCI combustion mixture was tested with 15% and 30% fuel vapor to ensure homogeneity with and without exhaust gas recirculation. The modified engine findings were compared to a standard conventional engine. At higher loads, PCCI combustion showed reduced emission of carbon monoxide and nitrogen oxides. While the thermal braking efficiency was marginally reduced at all engine loads while using the blends. The results showed that with and without 10% exhaust gas recirculation, an increase in air mix reduced NOx emissions; however, in the case of smoke emissions, an opposite trend was observed. A blend of plasto-oils also decreased CO and unburned hydrocarbon (HC) emissions at higher loads. In conclusion, it was shown that plasto-oils combined with conventional diesel fuel outperformed diesel fuel alone.
Energetic Analysis of Low Global Warming Potential Refrigerants as Substitutes for R410A and R134a in Ground-Source Heat Pumps
Authors: Fedele Laura , Bobbo Sergio , Menegazzo Davide , De Carli Michele , Carnieletto Laura , Poletto Fabio , Tarabotti Andrea , Mendrinos Dimitris , Mezzasalma Giulia , Bernardi Adriana
Published: April 2023

The European building sector is responsible for approximately 40% of total energy consumption and for 36% of greenhouse gas emissions. Identifying technological solutions capable of reducing energy consumption and greenhouse gas emissions is one of the main objectives of the European Commission. Ground source heat pumps (GSHPs) are of particular interest for this purpose, promising a considerable reduction in greenhouse gas emissions of HVAC systems. This paper reports the results of the energetic analysis carried out within the EU research project GEO4CIVHIC about the performance of geothermal heat pumps working with low-GWP refrigerants as alternatives for R134a and R410A. The work has been carried out through computer simulations based on base and regenerative reverse cycles. Several heat sink and heat source temperature conditions have been considered in order to evaluate the GSHPs’ performance in the whole range of real conditions that can be found in Europe. Particular attention has been paid to the evaluation of compression isentropic efficiency and its influence on the overall cycle performance when dealing with steady-state heat pump simulations. To do so, five different scenarios of isentropic efficiency calculation have been studied and discussed.
Numerical Simulation of Nonlinear Processes in the “Thruster—Downhole Motor—Bit” System While Extended Reach Well Drilling
Authors: Kunshin Andrey A. , Buslaev George V. , Reich Matthias , Ulyanov Dmitriy S. , Sidorkin Dmitriy I.
Published: April 2023

The relevance of the application of hydraulic thruster technology is determined by the technological limitations of drilling both vertical and horizontal wells. The existing experimental studies confirm the effectiveness of the technology, but its widespread implementation is hindered by the lack of scientific foundations for its operation in combination with a downhole motor and bit. Our research methodology includes methods for analyzing scientific and technical information as well as methods of numerical modeling using programming languages and ready-made software packages for CFD calculations. Verification of the simulation results was carried out on the basis of the experimental field studies previously conducted with the participation of the authors of the article. This article presents the results of the analysis of the current state of the problem and computer physical and mathematical modeling of the work of the thruster together with the bit and downhole motor when drilling a deviated section of a well. Based on the simulation results, the expediency of using hydraulic thrusters in the practice of drilling wells with the possibility of predicting and operatively regulating the operation parameters of the “Hydraulic Thrusting Device—Downhole Motor—Bit” system is theoretically substantiated and scientifically confirmed.