bluuebunny/crossref_metadata_2025 huggingface dataset:
Delayed Effect of Dendritic Cells Vaccination on Survival in Glioblastoma: A Systematic Review and Meta-Analysis
Authors: Cozzi Salvatore , Najafi Masoumeh , Gomar Marzieh , Ciammella Patrizia , Iotti Cinzia , Iaccarino Corrado , Dominici Massimo , Pavesi Giacomo , Chiavelli Chiara , Kazemian Ali , Jahanbakhshi Amin
Published: February 2022

Background: Dendritic cell vaccination (DCV) strategies, thanks to a complex immune response, may flare tumor regression and improve patients’ long-term survival. This meta-analysis aims to assess the efficacy of DCV for newly diagnosed glioblastoma patients in clinical trials. Methods: The study databases, including PubMed, Web of Knowledge, Google Scholar, Scopus, and Cochrane, were searched by two blinded investigators considering eligible studies based on the following keywords: “glioblastoma multiforme”, “dendritic cell”, “vaccination”, “immunotherapy”, “immune system”, “immune response”, “chemotherapy”, “recurrence”, and “temozolomide”. Among the 157 screened, only 15 articles were eligible for the final analysis. Results: Regimens including DCV showed no effect on 6-month progression-free survival (PFS, HR = 1.385, 95% CI: 0.822–2.335, p = 0.673) or on 6-month overall survival (OS, HR = 1.408, 95% CI: 0.882–2.248, p = 0.754). In contrast, DCV led to significantly longer 1-year OS (HR = 1.936, 95% CI: 1.396–2.85, p = 0.001) and longer 2-year OS (HR = 3.670, 95% CI: 2.291–5.879, p = 0.001) versus control groups. Hence, introducing DCV could lead to increased 1 and 2-year survival of patients by 1.9 and 3.6 times, respectively. Conclusion: Antitumor regimens including DCV can effectively improve mid-term survival in patients suffering glioblastoma multiforme (GBM), but its impact emerges only after one year from vaccination. These data indicate the need for more time to achieve an anti-GBM immune response and suggest additional therapeutics, such as checkpoint inhibitors, to empower an earlier DCV action in patients affected by a very poor prognosis.
Morphological and Molecular Evidence for Two New Species within Russula Subgenus Brevipes from China
Authors: Song Jie , Li Haijiao , Wu Shijun , Chen Qianqian , Yang Guang , Zhang Jinyun , Liang Junfeng , Chen Bin
Published: February 2022

Two new Russula species, R. subbrevipes and R. callainomarginis, from China are described based on morphological and molecular characteristics. Russula subbrevipes has thus far only been found in southwestern China at altitudes of higher than 3400 m and is characterized by a yellow ochre pileal surface, glabrous or tomentose stipe, fruity odor, subglobose to ellipsoid basidiospores, isolated or partially connected warts and pleurocystidia with a cap appendage. Russula callainomarginis is characterized by a cream to white pileus, light turquoise lamellae margin, spongy stipe, light turquoise zone on the top of the stipe, pungent odor, globose to ellipsoid basidiospores and dominant isolated warts. The phylogenetic tree of Russula was constructed with multi-gene sequences, including the internal transcribed spacer regions (ITS), the ribosomal large subunit (nrLSU), the small subunit of the mitochondrial rRNA gene (mtSSU) and the second largest subunit of RNA polymerase II (RPB2). The results show that both R. subbrevipes and R. callainomarginis represent new lineages in Russula subg. Brevipes. Description and illustration of the two new species are presented.
Seasonal and Interannual Dynamics of Pelagic Rotifers in a Tropical, Saline, Deep Lake
Authors: Fernández Rocío , Alcocer Javier , Lugo Alfonso , Oseguera Luis A. , Guadarrama-Hernández Sandra
Published: February 2022

This is the first long-term study (monthly samples at two 4-year intervals: 1998 to 2001 and 2013 to 2016) on rotifers in a saline, deep lake. The pelagic rotifer assemblage of Lake Alchichica is simple and comprised by two species, both new and most likely endemic: Brachionus sp. Mexico (related to B. plicatilis) and Hexarthra sp. (related to H. jenkinae). Similar low species richness and composition are found in other saline lakes associated with salinity. Rotifers in Lake Alchichica were an irregular component of the zooplankton community. Rotifers’ overall abundance (471 ± 1211 ind m−2) and biomass (24 ± 63 mg DW m−2) were low; Brachionus sp. Mexico and Hexarthra sp. contributed similarly to the annual mean abundance (54% and 46%, respectively) and biomass (53% and 47%, respectively). Abundance and biomass were tightly coupled, but there was no regular pattern in their seasonal dynamics. When co-existing, Brachionus sp. Mexico showed a higher abundance than Hexarthra sp. The dominant (≈80%) phytoplankton biomass in Lake Alchichica, the large (35–63 µm) diatom Cyclotella alchichicana, is inedible for rotifers, thus rotifers most probably relied only on nanophytoplankton (≤20 µm). Seasonal and interannual differences in rotifers seem related to food availability (oligotrophy) and probably to biotic interactions (e.g., competition). Rotifer abundance and biomass values in 1998–2001 went down to 12.5% in 2013–2016. Climate change and stochastics events leading to pulses of the rotifers’ food, and biotic interactions seem to be the most plausible explanation.
Genetic Diversity and Mating System of Two Mangrove Species (Rhizophora apiculata and Avicennia marina) in a Heavily Disturbed Area of China
Authors: Lu Wenxun , Zou Zhen , Hu Xueying , Yang Shengchang
Published: February 2022

Mangrove forests are distributed in the intertidal zones of tropical and subtropical regions, and have been severely damaged by anthropogenic activities, climate change, and stochastic events. Although much progress has been made in the conservation and restoration of mangroves in China, studies of the genetic diversity of mangroves are lacking, especially for isolated populations, yet such studies are essential for guiding conservation and restoration efforts. Here, we evaluated the genetic diversity, spatial genetic structure, and mating system of two mangrove species, Rhizophora apiculata and Avicennia marina, in a heavily disturbed area in Tielu Harbor, Sanya City, Hainan Island, China, using 18 nuclear microsatellite markers. We found that the genetic diversity of R. apiculata, which is classified as ‘Vulnerable’ in the China Red List categories, was high and similar compared with the genetic diversity estimates of other populations reported in previous studies. In contrast, the genetic diversity of A. marina, which is classified as a species of ‘Least Concern’, was low compared with the genetic diversity estimates of other populations. We then evaluated the presence of genetic bottlenecks, spatial genetic structure, and the mating system to determine the effects that habitat destruction has had on these two species. Our findings indicate that distinct conservation and restoration approaches are needed for these two species. Generally, our results provide valuable information that will aid the development of conservation and restoration strategies for the mangroves of Tielu Harbor.
Overcoming Variability in Printed RF: A Statistical Method to Designing for Unpredictable Dimensionality
Authors: Berry Katherine , Brown Eric M. , Pothier Bradley , Fedorka Samuel , Akyurtlu Alkim , Armiento Craig , Walsh Gary F. , Shemelya Corey
Published: February 2022

As additively manufactured radio frequency (RF) design expands towards higher frequencies, performance becomes ever more sensitive to print-induced dimensional variations. These slight deviations from design dimensions typically skew RF performance, resulting in low yields or poor device performance. In order to overcome this limitation, RF design paradigms must be developed for non-uniform process and material-specific variations. Therefore, a new generalized approach is developed to explore variation-tolerant designs for printed RF structures. This method evaluates the feature fidelity and S11 performance of micro-dispensed, X-band (8–12 GHz) patch antennas by evaluating the standard deviation in as-printed features, surface roughness, and thickness. It was found that the traditional designs based on optimal impedance matching values did not result in the most robust performance over multiple printing sessions. Rather, performance bounds determined by print deviation could be utilized to improve large-batch S11 results by up to 7 dB. This work demonstrates that establishing the average standard deviation of printed dimensions in any RF printing system and following the formulated design procedure could greatly improve performance over large datasets. As such, the method defined here can be applied to improve large-scale, printed RF yields and enable predictive performance metrics for any given printing method.
Diagnostic Modalities of Non-Alcoholic Fatty Liver Disease: From Biochemical Biomarkers to Multi-Omics Non-Invasive Approaches
Authors: Martinou Eirini , Pericleous Marinos , Stefanova Irena , Kaur Vasha , Angelidi Angeliki M.
Published: February 2022

Non-Alcoholic Fatty Liver Disease (NAFLD) is currently the most common cause of chronic liver disease worldwide, and its prevalence is increasing globally. NAFLD is a multifaceted disorder, and its spectrum includes steatosis to steatohepatitis, which may evolve to advanced fibrosis and cirrhosis. In addition, the presence of NAFLD is independently associated with a higher cardiometabolic risk and increased mortality rates. Considering that the vast majority of individuals with NAFLD are mainly asymptomatic, early diagnosis of non-alcoholic steatohepatitis (NASH) and accurate staging of fibrosis risk is crucial for better stratification, monitoring and targeted management of patients at risk. To date, liver biopsy remains the gold standard procedure for the diagnosis of NASH and staging of NAFLD. However, due to its invasive nature, research on non-invasive tests is rapidly increasing with significant advances having been achieved during the last decades in the diagnostic field. New promising non-invasive biomarkers and techniques have been developed, evaluated and assessed, including biochemical markers, imaging modalities and the most recent multi-omics approaches. Our article provides a comprehensive review of the currently available and emerging non-invasive diagnostic tools used in assessing NAFLD, also highlighting the importance of accurate and validated diagnostic tools.
Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection
Authors: Khan Mamun Mohammad Mahbubur Rahman , Alouani Ali T.
Published: February 2022

The cuffless blood pressure (BP) measurement allows for frequent measurement without discomfort to the patient compared to the cuff inflation measurement. With the availability of a large dataset containing physiological waveforms, now it is possible to use them through different learning algorithms to produce a relationship with changes in BP. In this paper, a novel cuffless noninvasive blood pressure measurement technique has been proposed using optimized features from electrocardiogram and photoplethysmography based on multivariate symmetric uncertainty (MSU). The technique is an improvement over other contemporary methods due to the inclusion of feature optimization depending on both linear and nonlinear relationships with the change of blood pressure. MSU has been used as a selection criterion with algorithms such as the fast correlation and ReliefF algorithms followed by the penalty-based regression technique to make sure the features have maximum relevance as well as minimum redundancy. The result from the technique was compared with the performance of similar techniques using the MIMIC-II dataset. After training and testing, the root mean square error (RMSE) comes as 5.28 mmHg for systolic BP and 5.98 mmHg for diastolic BP. In addition, in terms of mean absolute error, the result improved to 4.27 mmHg for SBP and 5.01 for DBP compared to recent cuffless BP measurement techniques which have used substantially large datasets and feature optimization. According to the British Hypertension Society Standard (BHS), our proposed technique achieved at least grade B in all cumulative criteria for cuffless BP measurement.
Performance of WHO-Endorsed Rapid Tests for Detection of Susceptibility to First-Line Drugs in Patients with Pulmonary Tuberculosis in Bangladesh
Authors: Rahman S. M. Mazidur , Ather Md. Fahim , Nasrin Rumana , Hoque Mohammad Ariful , Khatun Razia , Rahman Tanjina , Uddin Mohammad Khaja Mafij , Ahmed Shahriar , Banu Sayera
Published: February 2022

The fast and accurate detection of susceptibility in drugs is a major challenge for a successful tuberculosis (TB) control programme. This study evaluated the performance of WHO-endorsed rapid diagnostic tools, such as BACTEC MGIT 960 SIRE (MGIT SIRE), GenoType MTBDRplus (MTBDRplus) and Xpert MTB/RIF (Xpert), for detecting susceptibility to first-line anti-TB drugs among pulmonary TB patients in Bangladesh. A total of 825 sputum samples with results from drug susceptibility testing (DST) against first-line anti-TB drugs in the MGIT SIRE, MTBDRplus and Xpert assays were evaluated and compared with the gold standard proportion susceptibility method of the Lowenstein–Jensen (LJ) medium. The overall sensitivities of MGIT SIRE were 97.6%, 90.0%, 61.3% and 44.9%, while specificities were 89.9%, 94.5%, 91.3% and 92.2% for detection of susceptibility to isoniazid (INH), rifampicin (RIF), streptomycin (STR) and ethambutol (EMB), respectively. For MTBDRplus, the sensitivities were 88.0% and 88.7%, and the specificities were 97.4% and 97.8% for the detection of susceptibility to INH and RIF, respectively. Xpert demonstrated a sensitivity and specificity of 94.8% and 99.5%, respectively, for the detection of RIF susceptibility. All tests performed significantly better in retreated TB patients compared with primary TB cases. For detection of RIF and INH susceptibility, all three assays showed almost perfect agreement with the LJ method, although MGIT SIRE exhibited low agreement for STR and EMB. Considering the high performance, shorter turnaround time and ease of use, molecular-based approaches Xpert and MTBDRplus can be widely implemented throughout the country for the rapid detection of drug-resistant TB.
Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance
Authors: Wang Hsin-Yao , Liu Yu-Hsin , Tseng Yi-Ju , Chung Chia-Ru , Lin Ting-Wei , Yu Jia-Ruei , Huang Yhu-Chering , Lu Jang-Jih
Published: February 2022

The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.
Drones in B5G/6G Networks as Flying Base Stations
Authors: Amponis Georgios , Lagkas Thomas , Zevgara Maria , Katsikas Georgios , Xirofotos Thanos , Moscholios Ioannis , Sarigiannidis Panagiotis
Published: February 2022

Advances in the fields of networking, broadband communications and demand for high-fidelity low-latency last-mile communications have rendered as-efficient-as-possible relaying methods more necessary than ever. This paper investigates the possibility of the utilization of cellular-enabled drones as aerial base stations in next-generation cellular networks. Flying ad hoc networks (FANETs) acting as clusters of deployable relays for the on-demand extension of broadband connectivity constitute a promising scenario in the domain of next-generation high-availability communications. Matters of mobility, handover efficiency, energy availability, optimal positioning and node localization as well as respective multi-objective optimizations are discussed in detail, with their core ideas defining the structure of the work at hand. This paper examines improvements to the existing cellular network core to support novel use-cases and lower the operation costs of diverse ad hoc deployments.
An Efficient Parallel Reverse Conversion of Residue Code to Mixed-Radix Representation Based on the Chinese Remainder Theorem
Authors: Selianinau Mikhail , Povstenko Yuriy
Published: February 2022

In this paper, we deal with the critical problems in residue arithmetic. The reverse conversion from a Residue Number System (RNS) to positional notation is a main non-modular operation, and it constitutes a basis of other non-modular procedures used to implement various computational algorithms. We present a novel approach to the parallel reverse conversion from the residue code into a weighted number representation in the Mixed-Radix System (MRS). In our proposed method, the calculation of mixed-radix digits reduces to a parallel summation of the small word-length residues in the independent modular channels corresponding to the primary RNS moduli. The computational complexity of the developed method concerning both required modular addition operations and one-input lookup tables is estimated as Ok2/2, where k equals the number of used moduli. The time complexity is Olog2k modular clock cycles. In pipeline mode, the throughput rate of the proposed algorithm is one reverse conversion in one modular clock cycle.
A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology
Authors: Zhang Junwei , Li Deyu
Published: February 2022

Web services have the advantage of being able to generate new value-added services based on existing services. To effectively compose Web services, the composition process necessitates that the services that will participate in a given composite service are more trustworthy than those that provide similar functionality. The trust mechanism appears to be a promising way for determining service selection and composition. Existing trust evaluation approaches do not take into account customer expectations. Based on fuzzy set theory and probability theory, this work proposes a unique Web service trust evaluation approach that is notable for its ability to provide personalized service selection based on customer expectations and preferences. The proposed approach defines trust as a fuzzy notion that is related to prior experiences and ratings, and expresses trust in two different forms. This work mainly solves two key issues in Web service trust architectures, bootstrapping trust for the newcomer services and deriving trust for composite services. The proposed approach combines the solutions to numerous issues in a natural way. The case study and approaches comparison demonstrate that the proposed approach is feasible.
Thermodynamic Analysis and Crystallographic Properties of MFe2O4, MCr2O4 and MAl2O4 (M = Fe, Ni, Zn) Formed on Structural Materials in Pressurized Water Reactor Primary Circuit under Zinc and Zinc-aluminum Water Chemistry
Authors: Jiao Yang , Zhang Shenghan , Tan Yu
Published: February 2022

Zinc injection technology (zinc water chemistry, ZWC) was widely applied in pressurized water reactor (PWR) primary circuits to reduce radiation buildup and improve corrosion resistance of structural materials. The simultaneous injection of zinc-aluminium (ZAWC) is a novel implement created to replace part of Zn2+ by Al3+. It was reported ZAWC can improve further corrosion resistance of carbon steels and stainless steels. However, ZAWC sometimes showed even negative effects on Nickel-alloys. In this study, mechanism of formation of oxide film on metals was investigated. The reactions of Fe2+ Ni2+ in oxide films replaced by Zn2+, or Fe3+ replaced by Al3+ in ZAWC were analysed. The thermodynamic data and solubility of mixed oxides (ZnFe2O4, ZnCr2O4, and ZnAl2O4), the products of replace reactions, were calculated. According to the Gibbs free energy difference between products and reactants, ΔGθ(T) values of the formation reaction of ZnFe2O4, ZnCr2O4, and ZnAl2O4 are extremely negative. Solubility of ZnAl2O4 is the lowest among mixed oxide products, which implies the oxide film composites of ZnAl2O4 may show a lower corrosion rate. In addition, the preferential formation of NiAl2O4 on Ni-based-alloy, under ZAWC, was discussed based on crystallographic properties of spinel, which was considered as the cause of negative effects of ZAWC on corrosion resistance of Nickel-alloys. This research provides an analytical basis for the study of thermodynamic stability of oxide films under different chemical chemistry and a theoretical basis for improving corrosion resistance of different metals and optimizing the chemical conditions of PWR primary circuit.
Capitalization and Capital Return in Boreal Carbon Forestry
Authors: Kärenlampi Petri P.
Published: February 2022

In this paper, an attempt is made to determine an intangible capitalization premium based on an expected further value increment of forest stands. Such premium cannot be determined through exponential interpolation. Firstly, any discount rate depending on maturity proposes clearcuttings soon after thinning as a computational artifact. Secondly, exponential interpolation with a constant discount rate violates an internal consistency criterion as the rotation age increases. Omitting the intangible capitalization premium, the carbon stock of boreal forest can be increased in a variety of ways (albeit at the expense of a capital return rate deficiency). A small excess volume can be economically gained by increasing sapling density. Greater excess volume is best achieved by restricting thinnings. A large excess volume is best achieved by omitting thinnings. Regardless of the technique used, enhanced carbon storage requires financial compensation in terms of a carbon rent. With the present European emission prices, there is no financial difficulty in establishing such a carbon rent arrangement.
Interrelations between Income Inequality and Sustainable Economic Growth: Contradictions of Empirical Research and New Results
Authors: Dorofeev Mikhail Lvovitch
Published: February 2022

The idea that high levels of economic inequality negatively affect the rate and sustainability of economic growth is quite popular in the scientific literature. Therefore, it is usually proposed to take some regulative measures to reduce economic inequality in order to boost economic growth. Should the thesis be considered as a populist slogan or a scientifically proven fact? This article analyzes the results of 22 empirical studies on the relationship between economic inequality and economic growth conducted during the period of 1917–2018. We used meta-analysis to examine and systematize the results of previous empirical studies. The literature review and the analysis of the results from previous studies mainly indicate a negative relationship between economic inequality and economic growth (59% of the previous empirical research). The research gap is that on average these studies are rather controversial to each other and we cannot say that we understand these relationships correctly. To answer the questions concerning the interrelations between income inequality and sustainable economic growth, we made our own empirical research. To do this we used a qualitative pairwise correlation comparison method and analyzed panel data of 39 countries for the period of 1980–2019. The correlation between income inequality and sustainable economic growth was evaluated basing on the Gini index (GI) and Gross Domestic Product (GDP). Our calculations and analysis show that on average, in approximately 57.8% of moments in the period between 1980–2018, this correlation was positive in our sample of countries. The novelty of our study is that we show in detail how income inequality and economic growth are interconnected for each particular country and on average for the whole sample of countries. In addition, we used the inequality transparency index to adjust our calculations for data quality. Three hypotheses were tested in the study. Only one of them was confirmed by our research. The level of income inequality determines the direction of its impact on economic growth. An increase in income inequality in countries with low levels of inequality in most cases boosts economic growth and vice versa. Additionally, we received partial confirmation of our other hypothesis and found out that the correlation between economic growth and income inequality is definitely more negative for countries with low income and more positive in countries with high income per capita.
Alleviating the Challenges with Remote Learning during a Pandemic
Authors: Morgan Hani
Published: February 2022

The COVID-19 pandemic has caused many challenges that resulted from the suspension of face-to-face classes. Some of these challenges involved the problems associated with remote learning. One problem with this approach to learning is that it can be didactic. Remote learning can contribute to loneliness as well, and the lack of access to reliable technological resources that many students from low-income families experience can contribute to learning setbacks. Although the pandemic has created challenges related to providing remote learning opportunities and keeping students emotionally and physically healthy, some schools and colleges have used various methods to alleviate these concerns. This study focuses on the challenges associated with remote learning when it is implemented during the suspension of face-to-face classes. It also explores the practices that can be used to alleviate the obstacles that may cause students to experience setbacks.
Embodied Learning for Well-Being, Self-Awareness, and Stress Regulation: A Randomized Trial with Engineering Students Using a Mixed-Method Approach
Authors: Rodríguez-Jiménez Rosa-María , Carmona Manuel , García-Merino Sonia , Díaz-Ureña Germán , Lara Bercial Pedro J.
Published: February 2022

The continuous changes in our society require adapted training that encompasses both technical and competency knowledge. There is a high level of demand, especially in areas such as engineering, which can affect the mental health of students, producing high levels of stress and psychological distress, hindering self-efficacy and academic performance. Embodied learning working on self-awareness, stress reduction and self-knowledge can help to generate healthier environments. Creative therapies can be a tool to promote the prevention of health problems in this group of the population. In particular, Dance Movement Therapy has demonstrated its effectiveness in improving health in clinical and non-clinical settings. In this work, a body awareness program based on Dance Movement Therapy is developed and implemented in engineering students. Through a mixed pre-post methodology, its impact is evaluated and analyzed in an experimental group of engineering students. Psychometric tests, physiological variables and reflective diaries are used as data sources. The results show that the experimental group, compared with the control group, increased their levels of body awareness and connectedness, well-being and life satisfaction and reduced their stress levels. The results were supported by cortisol measures. Likewise, the students acknowledged having increased their levels of self-awareness and self-knowledge and considered the inclusion of these bodily approaches in higher education to be necessary.
Coverage Probability Analysis for Device-to-Device Communication Underlaying Cellular Networks
Authors: Xu Fangmin , Hu Yuqin , Hu Zhirui , Cao Haiyan
Published: February 2022

As one of the most promising techniques in wireless communication systems, device-to-device (D2D) has drawn much attention due to its superior performance. Meanwhile, the interference between cellular users and D2D users is still a challenging problem and needs to be mitigated effectively. A large number of simulation experiments for D2D communications have been studied to reduce the impact of the interference in the existing literature. However, theoretical research is still lacking. Thus, in this paper, we use stochastic geometry to evaluate the uplink performance of users by considering the impact of the previous moment on the next moment, which captures the effect of temporal and the spatial correlation of the interference in D2D communication underlaying cellular networks. Using a Poisson point process to model locations of D2D users, we derive an analytic expression for conditional probability and unconditional probability of link success, and prove that the probability of link success is temporally correlated. Moreover, we provide a theoretical framework for interference mitigation in D2D underlaying cellular networks.
Automatic Tracking of Muscle Fiber Direction in Ultrasound Images Based on Improved Kalman Filter
Authors: Liu Shangkun , Chai Qingwei , Zheng Weimin
Published: February 2022

Ultrasound myograph (SMG) is a real-time and dynamic acquisition of muscle structure parameter changes by recording ultrasound images of muscle contraction through an ultrasound instrument. Muscle parameters are essential for judging whether the muscle and the human body are healthy. In order to solve the problem of muscle fiber tracking in a sequence of ultrasound muscle images, we propose a method to track the direction of muscle fibers automatically based on the improved Kalman filter. Firstly, the measurement value of the muscle fiber direction is obtained by introducing a reference line into the ultrasound muscle image based on deep learning. Secondly, the framework of a Kalman filter is improved by introducing a set of neural units. Finally, the optimal estimated value of muscle fiber direction is obtained by combining the measured value with the improved Kalman filter. It is verified by conducting experiments where the result obtained by our proposed method is closer to the manually labeled value compared with the original measurement method, and the root mean square error is reduced by about 10%.
Autonomous Maneuver Decision Making of Dual-UAV Cooperative Air Combat Based on Deep Reinforcement Learning
Authors: Hu Jinwen , Wang Luhe , Hu Tianmi , Guo Chubing , Wang Yanxiong
Published: February 2022

Autonomous maneuver decision making is the core of intelligent warfare, which has become the main research direction to enable unmanned aerial vehicles (UAVs) to independently generate control commands and complete air combat tasks according to environmental situation information. In this paper, an autonomous maneuver decision making method is proposed for air combat by two cooperative UAVs, which is showcased by using the typical olive formation strategy as a practical example. First, a UAV situation assessment model based on the relative situation is proposed, which uses the real-time target and UAV location information to assess the current situation or threat. Second, the continuous air combat state space is discretized into a 13 dimensional space for dimension reduction and quantitative description, and 15 typical action commands instead of a continuous control space are designed to reduce the difficulty of UAV training. Third, a reward function is designed based on the situation assessment which includes the real-time gain due to maneuver and the final combat winning/losing gain. Fourth, an improved training data sampling strategy is proposed, which samples the data in the experience pool based on priority to accelerate the training convergence. Fifth, a hybrid autonomous maneuver decision strategy for dual-UAV olive formation air combat is proposed which realizes the UAV capability of obstacle avoidance, formation and confrontation. Finally, the air combat task of dual-UAV olive formation is simulated and the results show that the proposed method can help the UAVs defeat the enemy effectively and outperforms the deep Q network (DQN) method without priority sampling in terms of the convergence speed.
Experimental Study of AM and PM Noise in Cascaded Amplifiers
Authors: Badillo Inari , Portilla Joaquín
Published: February 2022

An experimental study of amplitude modulation (AM) and phase modulation (PM) noise spectra in cascaded amplifiers was carried out as a function of the number of amplification stages and the input power. Flicker and white noise contributions were determined, as well as effective noise figure (NF) from AM and PM noise spectra from small-signal to large-signal regimes. Simultaneous measurements of AM and PM noise were performed, and associated correlation was measured as a function of the offset frequency from the carrier. Measurements exhibited, in general, quite low AM–PM correlation levels both in the flicker and white noise parts of the spectrum. In some particular amplifier configurations, however, measurements showed some peaks in the correlation at some specific input power levels in the transition zone, from a quasi-linear to strong compression. The results show that the effective noise figure decreases with the number of stages for a given carrier output power level.
Features and Always-On Wake-Up Detectors for Sparse Acoustic Event Detection
Authors: Gazivoda Marko , Oletić Dinko , Bilas Vedran
Published: February 2022

The need to understand and manage our surroundings has led to increased interest in sensor networks for the continuous monitoring of events and processes of interest. To reduce the power consumption required for continuous monitoring, dedicated always-on wake-up detectors have been designed, with an emphasis on their low power consumption, simple and robust design, and reliable and accurate detection. An especially interesting application of these wake-up detectors is in detecting acoustic signals. In this paper, we present a study on the features and detectors applicable for the detection of sporadic acoustic events. We perform a state-of-the-art acoustic detector analysis, grouping the detectors based on the features they utilize and their implementations. This analysis shows that acoustic wake-up detectors predominantly utilize spectro-temporal (56%) and temporal features (36%). Following the state-of-the-art analysis, we select two detector architecture candidates for a case study on passing motor vehicle detection. We utilize our previously developed spectro-temporal decomposition detector and develop a novel level-crossing rate detector. The results of the case study shows that the proposed level-crossing rate detector has lower component count (44 compared to 70) and power consumption (9.1 µW compared to 34.6 µW) and is an optimal solution for SNRs over 0 dB.
Content-Addressable Memory System Using a Nanoelectromechanical Memory Switch
Authors: Kim Hyunju , Cho Mannhee , Lee Sanghyun , Kwon Hyug Su , Choi Woo Young , Kim Youngmin
Published: February 2022

Content-addressable memory (CAM) performs a parallel search operation by comparing the search data with all content stored in memory during a single cycle, instead of finding the data using an address. Conventional CAM designs use a dynamic CMOS architecture for high matching speed and high density; however, such implementations require the use of system clocks, and thus, suffer from timing violations and design limitations, such as charge sharing. In this paper, we propose a static-based architecture for a low-power, high-speed binary CAM (BCAM) and ternary CAM (TCAM), using a nanoelectromechanical (NEM) memory switch for nonvolatile data storage. We designed the proposed CAM architectures on a 65 nm process node with a 1.2 V operating voltage. The results of the layout simulation show that the proposed design has up to 23% less propagation delay, three times less matching power, and 9.4 times less area than a conventional design.
Fatigue Strength Analysis of a Prototype Francis Turbine in a Multilevel Lifetime Assessment Procedure Part II: Method Application and Numerical Investigation
Authors: Doujak Eduard , Unterluggauer Julian , Fillinger Gerald , Nocker Armin , Haller Franz , Maier Michael , Stadler Simon
Published: February 2022

Part I of the publication series addressed the fundamentals of lifetime assessment of prototype Francis turbines. This paper (Part II) focuses on the numerical part of the procedure. The essential steps and requirements shall be presented (background). The starting points for the numerical considerations are the pressure fields of the transient CFD simulations, which are exported per time step and applied to the existing structure via a fluid–structure interaction. That enables a transient mechanical stress calculation to be conducted, resulting in the fatigue analysis of the component to estimate the remaining lifetime. The individual model requirements should be represented accordingly and applied to the prototype facility (method). The results obtained from this application should be discussed and evaluated. It has to be mentioned that the validation of the numerical results will be performed at Part IV of this publication series (results). The present paper will end up discussing the results and conclusions about further data processing (Conclusion).
Green Economy and Sustainable Development: The Outlook
Authors: Zhironkin Sergey , Cehlár Michal
Published: February 2022

Modern theories that make up the paradigm of sustainable development, and the best practices derived from them, are based on the consistency of individual and public needs, factors of economic growth and ecosystem conservation [...]
Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints
Authors: Dong Xiaogang , Gan Jinqiang , Wu Hao , Deng Changchang , Liu Sisheng , Song Chaolong
Published: February 2022

In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of observer and reducing the control complexity. In addition, the Zeno behavior is excluded through detailed analysis. Furthermore, the stability of the algorithm is verified by theoretical derivation of Lyapunov. Finally, the effectiveness of the algorithm is proved by simulation.
A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy
Authors: Jalalzad Mahvizani Seyed , Yektamoghadam Hossein , Haghighi Rouzbeh , Dehghani Majid , Nikoofard Amirhossein , Khosravy Mahdi , Senjyu Tomonobu
Published: February 2022

In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.
2-Stroke RCCI Engines for Passenger Cars
Authors: Mattarelli Enrico , Rinaldini Carlo Alberto , Marmorini Luca , Caprioli Stefano , Legrottaglie Francesco , Scrignoli Francesco
Published: February 2022

Reactivity Controlled Compression Ignition (RCCI) is one of the most promising solutions among the low temperature combustion concepts, in terms of thermal efficiency and pollutant emissions. However, for values of brake mean effective pressure higher than 10 bar, in-cylinder peak pressure rise rates tend to be too high, limiting the specific power of any 4-Stroke (4S) engine. Such a limitation can be canceled by moving to the 2-Stroke (2S) cycle. Among many alternatives, the “Uniflow” scavenging system with exhaust poppet valves on the cylinder head allows the designer to reproduce the same identical combustion patterns of a 4-stroke RCCI engine, while increasing the indicated power output. The goal of the paper is to explore the potential of a 2-stroke RCCI engine, on the basis of a comprehensive experimental campaign carried out on a modified automotive 2.0 L, 4-stroke, four-cylinder, four-valve diesel engine. The developed prototype can run with one cylinder operating in 4-stroke RCCI mode (gasoline–diesel), while the others work in the standard diesel mode. A One Dimensional-Computational Fluid Dynamics (1D-CFD) model has been built to predict the performance of the same prototype, when operating all four cylinders in RCCI mode. In parallel, an equivalent 2-stroke RCCI virtual engine has been developed, by means of 1D-CFD simulations and empirical assumptions. A numerical comparison between the 4S and the 2S engines is finally presented, in terms of performance and emissions at full load. The study demonstrates that a 2S RCCI engine can maintain all of the advantages of the RCCI combustion, strongly reducing the penalization in terms of performance, in comparison to a standard 4S diesel engine.
Integrating System and Operator Perspectives for the Evaluation of Power-to-Gas Plants in the Future German Energy System
Authors: Schaffert Johannes , Gils Hans Christian , Fette Max , Gardian Hedda , Brandstätt Christine , Pregger Thomas , Brücken Nils , Tali Eren , Fiebrandt Marc , Albus Rolf , Burmeister Frank
Published: February 2022

In which way, and in which sectors, will renewable energy be integrated in the German Energy System by 2030, 2040, and 2050? How can the resulting energy system be characterised following a −95% greenhouse gas emission reduction scenario? Which role will hydrogen play? To address these research questions, techno-economic energy system modelling was performed. Evaluation of the resulting operation of energy technologies was carried out from a system and a business point of view. Special consideration of gas technologies, such as hydrogen production, transport, and storage, was taken as a large-scale and long-term energy storage option and key enabler for the decarbonisation of the non-electric sectors. The broad set of results gives insight into the entangled interactions of the future energy technology portfolio and its operation within a coupled energy system. Amongst other energy demands, CO2 emissions, hydrogen production, and future power plant capacities are presented. One main conclusion is that integrating the first elements of a large-scale hydrogen infrastructure into the German energy system, already, by 2030 is necessary for ensuring the supply of upscaling demands across all sectors. Within the regulatory regime of 2020, it seems that this decision may come too late, which jeopardises the achievement of transition targets within the horizon 2050.
A Review on Optimization of Active Power Filter Placement and Sizing Methods
Authors: Buła Dawid , Grabowski Dariusz , Maciążek Marcin
Published: February 2022

Distortions of current and voltage waveforms from a sinusoidal shape are, not only a source of technical problems, but also have serious economic effects. Their occurrence is related to the common use of loads with nonlinear current-voltage characteristics. These are both high-power loads (most often power electronic switching devices supplying high-power drives), but also widely used low-power loads (power supplies, chargers, energy-saving light sources). The best way to eliminate these distortions is to use active power filters. The cost of these devices is relatively high. Therefore, scientists all over the world are conducting research aimed at developing techniques for the proper placement of these devices, in order to minimize their investment costs. The best solution to this problem is to use optimization techniques. This paper compares the methods and criteria used by the authors of publications dealing with this topic. The summary also indicates a possible direction for further work.
Cell-to-Module Simulation Analysis for Optimizing the Efficiency and Power of the Photovoltaic Module
Authors: Yousuf Hasnain , Zahid Muhammad Aleem , Khokhar Muhammad Quddamah , Park Jinjoo , Ju Minkyu , Lim Donggun , Kim Youngkuk , Cho Eun-Chel , Yi Junsin
Published: February 2022

A 60-cell photovoltaic (PV) module was analyzed by optimizing the interconnection parameters of the solar cells to enhance the efficiency and increase the power of the PV module setup. The cell-to-module (CTM) losses and gains varied substantially during the various simulation iterations. Optimization was performed to inspect and augment the gain and loss parameters for the 60-cell PV module. The power and efficiency of the module were improved by refining several parameters, such as number of busbars, size of the contact pads, interconnected ribbon width, thickness of the core, and distance between the solar cells and strings, to obtain the maximum efficiency of 21.09%; the CTM efficiency achieved was 94.19% for the proposed strategy related to the common interconnection setup of the ribbon-based system. The CTM efficiency was improved by optimizing the geometrical, optical, and electrical parameters precisely, the power enhancement was up to 325.3 W, and a CTM power of 99.1% was achieved from a standard PV module with rectangular ribbon interconnections.