Miracle-dz/UniFAR-FTUs huggingface dataset:
Grouping Strategies of Discrete Elements for Efficient Power Pattern Tolerance Analysis of Antennas/Radomes Using Monte Carlo Method

This work presents a simple grouping method for efficient power pattern tolerance analysis of antennas/radomes using Monte Carlo method. The discrete elements, as antenna array elements or antenna/radome discretized meshes, are grouped into a limited number of clusters to reduce the number of independent random variables. Three grouping strategies based on Cartesian coordinates, polar coordinates and random case are presented to group the discrete elements into 8 types. A method combining two sets of results with pattern main-lobe-region and sidelobe-region optimal grouping numbers is proposed to improve the tolerance analysis precision. A 60×60 uniformly-excited rectangular array antenna with excitation errors, and its variants with amplitude taper or circular aperture, and a spherical radome with more than 1100 discrete meshes are considered. The results show that the proposed method can yield desirable electromagnetic (EM) performance variation ranges using 103~2×103 random samplings. Through grouping, the obtained EM performance interval percentages with respect to the interval analysis results are improved from 3%~6% to 47%~88% for the antenna cases, and from 1%~2% to 19%~30% for the radome cases, respectively.
Knowledge-Assisted DRL for Energy Harvesting Based Multi-Access Wireless Communications

This paper investigates the use of deep reinforcement learning (DRL) to maximize the long-term throughput of multi-access wireless communication systems. Specifically, we consider the scenario in which an access point (AP) without dedicated power supply harvests energy from the ambient environment and uses the harvested energy to deliver data packets to multiple users. We formulate the access control problem as a Markov Decision Process (MDP) with unknown system dynamics and apply the deep reinforcement learning (DRL) approach to solve this problem, i.e., the optimal access strategy is approximated by using a double deep Q-network (DDQN). As the considered system usually has large state and action spaces, some domain knowledge is embedded into the training process to improve the performance of DDQN. It is shown that with the help of domain knowledge, access control strategies which can achieve a higher system throughput can be found by the knowledge-embedded DDQN. Moreover, the performance of the access control policy obtained by using the knowledge-embedded DDQN is compared with that of several conventional transmission schemes. Experiment results show that the transmission policy obtained by using our proposed model can achieve better performance.
Analysis of comb formation process for sowing sugar beets on heavy soils

Improving the efficiency of sugar beet production is possible by developing alternative technologies for growing root crops and substantiation of rational parameters of advanced technological processes and tools for pre-sowing tillage. In particular, these areas of research are relevant and have scientific priority in sowing of sugar beet seeds and their cultivation on heavy soils. Based on the analysis of known technologies, a ridge method of sowing sugar beet seeds and a technical device (cultivator-comb-former) for the formation of pre-sowing ridges on heavy soils are proposed. The technological process of field surface profiling in autumn is considered, the analytical analysis of the geometric model of ridge formation by cultivator-comb-former is developed and given. The constructive-technological scheme of the cultivator, its comb sections is substantiated. At the analytical level, the design parameters of the working body for the formation of the profile of pre-sowing ridges from the condition of stabilizing the movement of the working body of the comb sections of the cultivator at a given depth are analysed. Experimental studies of the process of forming the profile of the ridge of the proposed cultivator are conducted in the soil channel modeling the soil environment in the form of heavy soils. The regression equation of the dependence of the change in the depth of the working body of the cultivator and the height of the formed ridges on the length of the leash and the stiffness of the spring of the parallelogram mechanism of the comb sections developed. It is established that the rational geometric parameters of the ridge surface when sowing sugar beet seeds on heavy soils with row spacing 0.45 m will be: substantiated: the length of the leash of the parallelogram suspension is 0.27 + 0.01 m; the height of the crest 0.12 ± 0.01 m; the depth of the ridge gap relative to the ridge depression 0.08 ± 0.01 m.
Evidence for a Physiological Role of T-Type Ca Channels in Ventricular Cardiomyocytes of Adult Mice

T-type Ca channels are strongly expressed and important in the developing heart. In the adult heart, these channels play a significant role in pacemaker tissues, but there is uncertainty about their presence and physiological relevance in the working myocardium. Here, we show that the T-type Ca channel isoforms Cav3.1 and Cav3.2 are expressed at a protein level in ventricular cardiomyocytes from healthy adult C57/BL6 mice. Myocytes isolated from adult wild-type and Cav3.2 KO mice showed considerable whole cell T-type Ca currents under beta-adrenergic stimulation with isoprenaline. We further show that the detectability of basal T-type Ca currents in murine wild-type cardiomyocytes depends on the applied experimental conditions. Together, these findings reveal the presence of functional T-type Ca channels in the membrane of ventricular myocytes. In addition, electrically evoked Ca release from the sarcoplasmic reticulum was significantly impaired in Cav3.2 KO compared to wild-type cardiomyocytes. Our work implies a physiological role of T-type Ca channels in the healthy adult murine ventricular working myocardium.
RLCharge: Imitative Multi-Agent Spatiotemporal Reinforcement Learning for Electric Vehicle Charging Station Recommendation

Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find the proper spots for charging, because of the limited charging infrastructures and the spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve the charging experience from various aspects over a long-term horizon. In this paper, we propose an Imitative Multi-Agent Spatio-Temporal Reinforcement Learning (RlCharge) framework for intelligently recommending public accessible charging stations by jointly considering various long-term spatio-temporal factors. Specifically, by regarding each charging station as an individual agent, we formulate the problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with centralized training decentralized execution. Particularly, we propose a tailor-designed centralized attentive critic to coordinate the recommendation between geo-distributed agents, and introduce a delayed access strategy to exploit the knowledge of future charging competition during centralized training. Moreover, to handle the partial observability problem during decentralized execution in the large-scale multi-agent system, we propose the spatio-temporal heterogeneous graph convolution module, including (1) a dynamic graph convolution block to generate real-time representations for observable forthcoming EVs, and (2) a spatial graph convolution block to share the agent observations by message propagation between spatially adjacent agents. After that, to effectively optimize multiple divergent learning objectives, we extend the centralized attentive critic to multi-critics, and develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction. In addition, we propose an adaptive imitation learning scheme to further accelerate and stabilize the policy convergence. Finally, extensive experiments on two real-world datasets demonstrate that RlCharge achieves the best comprehensive performance compared with ten baseline approaches.
Interaction between wind speed and net radiation controls reference evapotranspiration variance in the inland river basin of Northwest China

Exploring the variance in reference evapotranspiration (ET0) and its dominant influencing factors is important for climate change, hydrological cycles and water management. Temperature (T), wind speed (U2), net radiation (Rn) and actual vapour pressure (ea) are the major climatic input variables in Penman–Monteith equation. Previous studies have successfully applied the total differential method to determine the relative contributions of changes in these variables to variation in ET0 on different timescales. However, the interaction of climatic variables has not been incorporated into this method. Taking the inland river basin of Northwest China as the study area, we extended the total differential method to decompose the ET0 variance into temporal variance and covariance of T, U2, Rn and ea on intra‐annual and annual scales during 1960–2017. The results indicated that the variance in ET0 on the intra‐annual scale was larger than that on the annual scale. Among the four single climatic variables, U2 variance made larger contributions to intra‐annual and annual ET0 variance with relative contributions of 6.8% and 1.1%, respectively. However, the interaction of climatic variables played a dominant role in the variation in ET0. Specifically, on intra‐annual scale, coupled U2 and Rn primarily controlled the ET0 variance (76.1%), followed by coupled Rn and ea (9.6%); but their positive effects were weakened by the negative effects of coupled T and ea (−2.3%). On annual scale, coupled U2 and Rn still governed the variance in ET0 (97.3%), but the effects were also weakened by other groups of interaction effects. Furthermore, ET0 was highly related to NDVI on an intra‐annual scale (R2 = 0.68, p = 0.00), indicating the strong effect of seasonal vegetation dynamics on ET0 variance. This study provides new insights to quantitatively assess the interaction effects of environmental factors on key hydrometeorological variables.
DNA Barcoding to Enhance Conservation of Sunshine Coast Heathlands

Conservation priorities and decisions can be informed by understanding diversity patterns and the evolutionary history of ecosystems, and phylogenetic metrics can contribute to this. This project used a range of diversity metrics in concert to examine diversity patterns in the Sunshine Coast heathlands, an ecosystem under intense pressure. The species richness and composition of 80 heathland sites over nine regional ecosystems of heathland on the Sunshine Coast were enhanced with phylogenetic metrics, determined by barcoding 366 heath species of the region. The resulting data were added to an existing phylogeny of regional rainforest species. The diversity metrics for sites and regional ecosystems were compared using univariate and multivariate statistics. The phylogeny from this study, and the low phylogenetic diversity of the heathlands, is consistent with the theory that heath species evolved on the fringes on a wider Australian rainforest flora. Distinctive heathland communities were highlighted, and the existence of geographically scattered, but compositionally similar, phylogenetically even sites points to a possible “refugial environment”, characterised by moisture and instability. This suggests contrasting conservation implications: the protection of distinctive communities but also the management of the dynamic processes in other wet and alluvial “refugial environments”. The potential for more focused conservation priorities is enhanced.
Comparative Analysis of Intravitreal Ranibizumab versus Laser Therapy for Retinopathy of Prematurity

The therapeutic effects of intravitreal Ranibizumab injection (IVR) and the efficacy of laser photocoagulation for the treatment of retinopathy of prematurity (ROP) are compared. The screening criteria for ROP were infants born with less than 32 weeks gestational period and/or weighed less than 1500 grams or unstable clinical course. After ethical approval from the Institutional Review Board, this retrospective study was conducted. The study considered 1315 infants and an observation was made that 36/42 (85.7%) eyes showed regression of ROP in the IVR group whereas 27/27 (100%) eyes that were treated with laser therapy showed regression of ROP (p=0.09). A considerable discrepancy in habituation of ROP in both groups with 12 eyes (28.6%) has been noted which is showing recurrence of ROP in the IVR group compared to 1 eye (3.7%) in the Laser group (P=0.01. In terms of anisometropia and refractive error, both groups made no notable difference. Squint was found remarkably higher in the laser group (29.6%) than in the IVR group (4.8%) (P=0.01). It was found that treating ROP eyes with laser treatment leads to greater improvement in infants than when treated by IVR. Moreover, a significant recurrence of ROP was discovered after IVR treatment in comparison with the laser treatment.
The effect of early enrollment in dual-language immersion programs on children’s English reading development: findings from a 5-year longitudinal study

ABSTRACT The present study examined the effect of children’s enrollment in U.S. dual-language immersion (DLI) programs in first grade on English development across five years, using the Early Childhood Longitudinal Study, Kindergarten Class of 2011 (ECLS-K:2011) database. Propensity score matching was used to create comparable groups of DLI and non-DLI students based on students’ kindergarten reading performance and a series of student-, family-, and school-level characteristics. Growth curve models demonstrate that first-grade DLI enrollment had a positive effect on children’s English reading growth from Grade 1 to 5. Children who enrolled in DLI experienced greater improvements by Grade 5. We also found that first-grade teacher judgment was related to children’s initial reading performance but not their reading growth. Implications related to DLI programs, teacher practices, and bilingual educational policies are discussed.
Proapoptotic Effect and Molecular Docking Analysis of Curcumin–Resveratrol Hybrids in Colorectal Cancer Chemoprevention

Different hybrids based on curcumin and resveratrol were previously synthesized and characterized by spectroscopic techniques. The most active molecules (3a, 3e, 3i, and 3k) were evaluated in vitro as an approach to determine the possible mechanism of action of the hybrids. The results indicated that the evaluated curcumin/resveratrol hybrids induce mitochondrial instability in SW620 and SW480 cells. Moreover, these molecules caused a loss in membrane integrity, suggesting an apoptotic process mediated by caspases after the treatment with compounds 3i (SW480) and 3k (SW620). In addition, the results suggest that the mechanism of action of the hybrids could be independent of the p53 status. Furthermore, hybrids 3e and 3i caused G0/G1 phase arrest, which highlights the potential of these molecules not only as cytotoxic but also as cytostatic compounds. Hybrids 3e and 3i caused a negative modulation of the matrix metalloproteinase 7 (MMP7) on SW480 cells. These curcumin resveratrol hybrids could be potential candidates for further investigations in the search for potential chemopreventive agents, even in those cases with resistance to conventional chemotherapy because of the lack of p53 expression or function. Molecular docking simulations showed that compounds 3e, 3i, and 3k bind efficiently to proapoptotic human caspases 3/7 proteins, as well as human MMP-7 and p53, which, in turn, could explain at the molecular level the in vitro cytotoxic effect of these compounds in SW480 and SW620 colon cancer cell lines.
Unpacking the Effect of Social Media Infodemic on Consumers’ Panic Banking Behaviour during the Covid-19 Pandemic. Evidence from the Banking Sector in Oman

During the Covid-19 pandemic, some government restrictions to curb the spread of corona virus rendered print media obsolete. Social media became a convenient channel of communication. However, social media was awash with false and alarming information about the pandemic and government initiatives. This led to infodemic as consumers accessed misrepresented information on social media. As a result of social media infodemic, some consumers engaged in panic banking through withdrawal rush due to uncertain future expectation. This study aimed to examine the effect of social media infodemic during the Covid-19 pandemic on consumers’ panic intention behaviour in the banking industry. Data for the study was collected from 230 consumers of the baking industry in Oman using a questionnaire. A social media infodemic model was developed using a deductive approach. The study found out that social media infodemic was responsible for panic banking intention behaviour in Oman. The four determinants of social media panic behaviour were all statistically significantly impacting on panic banking behaviour. The study concluded that social media infodemic is a key determinant of panic banking in the banking sector. In light of the above findings, the banking industry should monitor social media so as to dilute misinformation with factual corporate communications so as to minimise panic banking behaviour.
Enabling Long-term Predictions and Cost-benefit Analysis Related to Housing Adaptation Needs for a Population Aging in Place: Protocol for a Simulation Study

Background Policies that promote aging in place are common in Sweden and many other countries. However, the current housing stock cannot sufficiently accommodate a population aging in place considering how functional capacity and housing needs change as people age. To be suitable for all regardless of their functional ability, housing should be designed or adapted to facilitate the performance of activities of daily living. Long-term planning and plausible projections of development 20 to 30 years into the future are needed. Objective The overall aim is to develop simulation models that enable long-term predictions and analysis of potential consequences in terms of societal gains and costs for different large-scale measures and interventions in the ordinary housing stock. Methods This study is designed as a simulation study and will broadly apply health impact assessment methods in collaboration with five municipalities in Sweden. Individual interviews and research circles were used to identify current and prioritize potential new policies to improve the accessibility of the housing stock. We will run a series of simulations based on an estimated willingness to pay from discussions with the municipalities. Two to three different prioritized policies will be compared simultaneously using Markov cohort analysis to estimate the potential costs and health impact on the population. Using data from a systematic review and existing population-based data sets with individual-level data on home and health variables, we will calculate parameter estimates for the relations between housing accessibility and health outcomes. The potential impact of selected policy interventions will be estimated in several microsimulations representing people living in the community. Sensitivity analyses will be conducted for each simulation. Results As of April 2022, open access data was collected, and a systematic review was underway and expected to be completed by November 2022. Collaboration with five municipalities was established in autumn 2020. In spring 2021, the municipalities developed a list of prioritized policy interventions to be tested and used in the simulation models. Inventories of barrier frequencies in ordinary housing started in spring 2022 and are expected to be completed in autumn 2022. Data gathering and analyses for simulation inputs will be completed during 2022 followed by the simulation modeling analyses to be completed in 2023. Conclusions Improved accessibility of the ordinary housing stock has the potential to maintain or improve the health of the aging population. This study will generate tools that enable long-term predictions and reliable cost-benefit estimates related to the housing adaptation needs for a population aging in place, thus providing support for the best-informed policy decisions. International Registered Report Identifier (IRRID) DERR1-10.2196/39032
An Automated Rail Extraction Framework for Low-Density LiDAR Data Without Sensor Configuration Information

Around one-third of the U.S. rail network is owned and operated by shortlines (Class II and III railroads). The infrastructure conditions of these railroads are marginal, and their revenue and number of employees are insufficient. To conduct proper and timely infrastructure management, it is critical for shortlines to establish a reliable and cost-effective inventory of their existing rail tracks. Significant efforts have been made to develop and employ automatic rail extraction methods as it is a critical step to establishing a rail infrastructure inventory. However, existing methods rely heavily on high-density point cloud datasets with known sensor properties and configurations or sophisticated imaging sensors. These requirements prevent their deployments in shortlines because point cloud data available to shortlines are often in low-density and with unknown specifications constrained by their financial situations. To address those limitations, we propose an automatic, configuration-independent coarse-to-fine extraction method for low-density LiDAR data. The proposed extraction framework is only developed based on high-level geometric features of the rail that can even be captured by low-density point cloud data with unknown sensor properties and configurations. The proposed method is evaluated on the existing grade-crossing dataset collected by the Federal Railroad Administration for grade-crossing safety evaluation with a point cloud density of only 293 points/ $\text{m}^{{2}}$ . The overall performance shows average completeness of 96.97%, correctness of 99.71%, and quality of 96.67% for all extraction scenarios. The promising results provide shortlines a capability to extract rails for any geometry measurements based on any low-density LiDAR data available to them.
A Novel Quaternary Multiplexer using CNTFET

Multiple-valued logic (MVL) is a propositional calculus in which there are more than two truth values. In binary system due to difficulty in interconnection problems MVL preferred to solve and enhance in VLSI circuits. To work with MVL, Carbon Nano Tube Field-Effect Transistors (CNTFET) will be used. Quaternary multiplexer is one of the important circuit to be used in the design quaternary arithmetic and logic circuits. In this paper, the quaternary 4:1 Multiplexer designed using pass transistor logic to reduce the number of transistors. The proposed quaternary multiplexer design simulated using cadence Spectre with a CNTFET technology of 32-nm. The performance of these proposed design is tested for different parameters including power, delay and power delay product, with various supply voltages. The simulation results confirmed that the proposed quaternary multiplexer is better than existing one.