Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This paper concentrates on low speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles. This paper presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment. The paper treats autonomous driving with real time kinematics GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with three-dimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost and hence lower accuracy GPS is desirable. Our in-house automated low speed electric vehicle is used in experimental evaluation and verification. In addition, the experimental vehicle has vehicle to everything (V2X) communication capability and utilizes a dedicated shortrange communication (DSRC) modem. It is able to communicate with instrumented traffic lights and with pedestrians and bicyclists with DSRC enabled smartphones. Before real-world experiments, our connected and automated driving hardware in the loop (HiL) simulator with real DSRC modems is used for extensive testing of the algorithms and the low level longitudinal and lateral controllers. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. Model-in-the-loop simulation, HiL simulation and experimental testing are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low speed autonomous shuttle localization and perception algorithms for control and decision making.
A Design of Ambient RF Energy Harvester with Sensitivity of −21 dBm and Power Efficiency of a 39.3% Using Internal Threshold Voltage Compensation

In this paper, a low-power reconfigurable ambient Radio Frequency to Direct Current power (RF–DC) converter using an internal threshold voltage cancellation (IVC) scheme with an auxiliary transistors block is presented. A maximum power point tracking (MPPT) algorithm is implemented in order to maintain the high efficiency by automatically selecting the number of stages. The proposed reconfigurable converter efficiently converts the RF signals to DC voltage by dynamically controlling the threshold voltage of the forward and reversed-biased transistors in the primary rectification body. During positive half-cycle, the proposed RF–DC converter reduces the voltage drop across the forward-biased transistors, which results in increased harvested power and output DC voltage. During negative half cycle, the proposed rectifier minimizes the reverse leakage current and prevents the loss of energy stored in the prior stages. A five-stage internal threshold compensated power converter is designed in 0.18 µm Complementary Metal-Oxide-Semiconductor (CMOS) technology with an active die area of 360 µm × 160 µm, while the Maximum Power Point Tracking (MPPT) occupies an active die area of 730 µm × 280 µm. The proposed scheme obtains maximum post-simulated power conversion efficiency (PCE) of 39.3% when input power level is −15 dBm and produces an output voltage of 3.3 V for a load of 1 MΩ and at a frequency of 900 MHz. The proposed scheme achieves a voltage sensitivity of 1V at a remarkably low input power of −21 dBm for a 1 MΩ load.
traffic flow prediction model based on deep belief network and genetic algorithm

Traffic flow prediction plays an indispensable role in the intelligent transportation system. The effectiveness of traffic control and management relies heavily on the prediction accuracy. The authors propose a model based on deep belief networks (DBNs) to predict the traffic flow. Moreover, they use Fletcher-Reeves conjugate gradient algorithm to optimise the fine-tuning of model's parameters. Since the traffic flow has various features at different times such as weekday, weekend, daytime and night-time, the hyper-parameters of the model should adapt to the time. Therefore, they employ the genetic algorithm to find the optimal hyper-parameters of DBN models for different times. The dataset from Caltrans Performance Measurement System was used to evaluate the performance of their models. The experimental results demonstrate that the proposed model achieved better performance in different times.
Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms

For power system disaster prevention and mitigation, risk assessment and visualization under typhoon disaster have important scientific significance and engineering value. However, current studies have problems such as incomplete factors, strong subjectivity, complicated calculations, and so on. Therefore, a novel risk assessment and its visualization system consisting of a data layer, knowledge extraction layer, and visualization layer on power towers under typhoon disaster are proposed. On the data layer, a spatial multi-source heterogeneous information database is built based on equipment operation information, meteorological information, and geographic information. On the knowledge extraction layer, six intelligent risk prediction models are established based on machine learning algorithms by hyperparameter optimization. Then the relative optimal model is selected by comparing five evaluation indicators, and the combined model consisting of five relatively superior models is established by goodness of fit method with unequal weight. On the visualization layer, the predicted results are visualized with accuracy of 1 km × 1 km by ArcGIS 10.4. In results, the power tower damage risk assessment is carried out in a Chinese coastal city under the typhoon ‘Mujigae’. By comparing predicted distribution and similarity indicator of the combined model with those of the other models, it is shown that the combined model is superior not only in quality but also in quantity.
Self-Concept and Self-Efficacy as Determinants of Colleges of Education Students’ Performance in Shorthand in North-East, Nigeria

The study examined the self-concept and self-efficacy as determinants of Colleges of Education students’ performance in shorthand in North-East, Nigeria. The study had two specific objectives, two research questions which were meant to guide the study and two null hypotheses which were tested at 0.05 level of significant. The study adopted a descriptive survey research design which is purely quantitative, using structured questionnaires which were administered to 360 randomly selected Business Education Students of Colleges of Education in North-Eastern Nigeria. Data collected was analyzed using mean and standard deviations while a simple linear regression was used in testing the hypotheses. The findings of the study revealed among others that study strategy and attitude have significant and positive influence on Performance of Business Education Students in shorthand in Colleges of Education in North-East, Nigeria. Hence, the consistent failure of Business Education Students in Shorthand particularly in Colleges of Education in North-East, Nigeria can be reduced. The present study recommend that shorthand lecturers should encourage Business Education Students in Colleges of Education to develop a good study self-concept and positive self-efficacy towards shorthand as these will improve the students’ Performance in the course and minimize the massive and consistent failure recorded among Business Education Students in shorthand. Specifically, in Colleges of Education in North-Eastern Nigeria.
Fitness Apps, Live Streaming Workout Classes, and Virtual Reality Fitness for Physical Activity During the COVID-19 Lockdown: An Empirical Study

Background Physical activity is an essential need of the human body that helps improve the physical fitness of an individual and creates a positive impact on overall wellbeing. Smartphone applications play an essential role in providing several benefits to consumers by offering various capabilities in terms of health and fitness.COVID-19 preventive measures shut down public places, and people cannot go to the gym and parks for physical activity. Smart applications for physical activity are an effective way to keep active while staying at home. Objective The objective of the present study was to assess the mediating role of the e-platforms physical activity among the Chinese people in China during the COVID-19 lockdown. Method The participants in this study were Chinese citizens living in home isolation during the early stages of the epidemic in China. The primary data was collected via an online survey using a convenience sample strategy in accordance with the study purpose. The collected data were cleaned by using the SPSS-25 statistical software. SmartPLS 3.0 software was used to investigate the suggested study framework utilizing the structural equation modeling technique. Results Descriptive statistics shows that the ratio of gender includes 49% (n = 2,626) male and 51% females in the entire sample. SEM results show that all hypotheses (H1: β = 0.497, T = 43.068, P = <0.001; H2: β = 0.498, T = 41.078, P = <0.001; H3: β = 0.498, T = 41.078, P = <0.001; H4: β = 0.471, T = 39.103, P = <0.001; H5: β = 0.468, T = 42.633, P = <0.001; H6: β = 0.251, T = 11.212, P = <0.001; H7: β = 0.367, T = 16.032, P = <0.001; H8: β = 0.170, T = 13.750, P = <0.001; H9: β = 0.125, T = 10.604, P = <0.001; H10: β = 0.173, T = 14.842, P = <0.001) were statistically confirmed. Conclusion In COVID-19, when there are limited physical activity resources, smart applications play an essential role as an alternative to gyms and change people's perspective regarding the adoption of health and fitness. Smart applications have made exercise and physical activity accessible and convenient to adopt.
The optimisation research of Blockchain application in the financial institution-dominated supply chain finance system

ABSTRACT As an information technology that could significantly improve supply chain visibility and process automation, blockchain has been extensively applied in the field of supply chain finance (SCF). However, tradeoffs among the security, the operation cost, and the efficiency of the blockchain system may cause the SCF system dominated by a financial institution to inevitably fall into the dilemmas of risky or un-economic if the blockchain technology is applied inappropriately. Therefore, the objective of this paper is to optimise the blockchain application in the financial institution-based SCF system. We first analyse the application of blockchain security in SCF, and then the performance tradeoffs of blockchain and its impact on the performance of the supported SCF system. Based on the analysis above, an optimisation approach has been proposed and a corresponding non-linear integer programming (NIP) model has been constructed to select the best blockchain design schemes for the SCF system to achieve overall optimal in terms of security, cost, and efficiency. A designed ant colony algorithm is used to solve the optimisation problem. An application case analysis is used to verify the feasibility and effectiveness of the optimisation model.
High step‐up DC–DC converter with coupled inductor suitable for renewable applications

In this study, a new non-isolated high step-up DC-DC converter is presented with high-voltage gain which is suitable for renewable applications. The proposed converter uses coupled inductor and voltage multiplier cell (diode capacitor) for increasing the voltage level. The voltage gain of the proposed converter can be increased by selecting the appropriate turns ratio of coupled inductor. Voltage multiplier cell consists of two diodes and two capacitors which are used to obtain high-voltage gain. The diode-capacitor cell is used as a clamp circuit, which leads to reducing the voltage stress across the semiconductors. The proposed converter has a single power switch which causes the control of the proposed converter is simple. Also, the power switch is used with lower ON-state resistant ( R DS-ON ). The zero-current switching of the diode is obtained in OFF state. Therefore, the conduction losses are decreased with lower normalised voltage stress across semiconductors. To prove the performance of the proposed converter, theoretical analysis and comparison with other converters are provided. To confirm the benefits of the proposed converter, a laboratory prototype with 20 V input voltage, 200 V output voltage and about 200 W power level at operating 25 kHz is built and tested.
Wideband rectangular dielectric resonator antenna for low-profile applications

A new wideband low-profile omni-directional dielectric resonator antenna (DRA) is presented. By joining back-to-back two thin identical rectangular high-permittivity dielectric resonators (DRs) and feeding the arrangement with a microstrip feed line inserted in-between the DRs, an impedance bandwidth covering the frequency range from 4.78 to 12.32 GHz, or ~88.2%, is achieved. Importantly, the measured results show that the proposed low-profile DRA also provides a stable omni-directional radiation pattern with low cross-polarisation. The proposed antenna size is 12 × 24 × 2 mm 3 or ~0.191 λ 0 × 0.384 λ 0 × 0.032 λ 0 at 4.8 GHz.
AF8c, a Multi-Kinase Inhibitor Induces Apoptosis by Activating DR5/Nrf2 via ROS in Colorectal Cancer Cells

Simple Summary AF8c, a lapatinib hybrid quinazoline-based EGFR/HER2 inhibitor, was chosen to scrutinize its antiproliferative activity in colorectal cancer (CRC) cells. We found that AF8cinduced apoptosis in CRC cells via diverse mechanisms. In addition to inhibiting the phosphorylation of the ErbB family, AF8c increased the mRNA and protein levels of death receptor 5 (DR5) in vitro and in vivo. In addition, AF8c upregulated several ER stress proteins and the redox-sensitive nuclear respiratory factor 2 alpha subunit (Nrf2) in a p53-dependent manner. We also found that the AF8c-induced increase in the levels of Nrf2, DR5, and apoptosis was diminished by p53 downregulation or knockdown. Furthermore, AF8c showed higher antiproliferative activity than lapatinib in the CRC mouse model in vivo. Therefore, our results suggest AF8c as a highly effective polypharmacological small molecule with an encouraging safety profile, both in vitro and in vivo, for further evaluation as a treatment of CRC. Abstract Our team has previously reported a series of quinazoline-based lapatinib hybrids as potent kinase-targeting anticancer agents. Among them, AF8c showed a relatively safe profile in colorectal cancer (CRC) cells. In this study, we delineate a novel anticancer activity of AF8c in CRC cells. AF8c mediated p53-dependent apoptosis of CRC cells via the generation of endoplasmic reticulum (ER) stress and reactive oxygen species (ROS), as well as activation of nuclear respiratory factor 2 alpha subunit (Nrf2) and death receptor 5 (DR5), among others. The silencing of DR5 attenuated the expression levels of Nrf2 and partially inhibited AF8c-induced apoptosis. Additionally, upregulation of Nrf2 by AF8c evoked apoptosis through a decrease in antioxidant levels. Treatment of a CRC mice model with AF8c also resulted in the upregulation of DR5, Nrf2, and CHOP proteins, subsequently leading to a significant decrease in tumor burden. In comparison with lapatinib, AF8c showed higher cellular antiproliferative activity at the tested concentrations in CRC cells and synergized TRAIL effects in CRC cells. Overall, our results suggest that AF8c-induced apoptosis may be associated with DR5/Nrf2 activation through ER stress and ROS generation in CRC cells. These findings indicate that AF8c represents a promising polypharmacological molecule for the treatment of human CRC.
Assessing the Role of Green Finance and Education as New Determinants to Mitigate Energy Poverty

Energy poverty (EP) is a problem that affects developed and developing economies, and its mitigation is of great significance to social welfare. EP affects Latin American countries, and policymakers have recently attempted to address this issue, particularly in the aftermath of the recent economic crisis. It is essential to measure and evaluate EP to implement strategies and policies effectively. Using a panel quantile regression approach, we investigate the heterogeneous impact of green finance, renewable energy (RE), and energy efficiency (EE) on EP for 33 Latin American countries from 2000 to 2018. Furthermore, certain associated control variables are incorporated into our model to avoid an omitted variable bias. According to empirical results, the impact of independent variables on EP is heterogeneous. Specifically, green finance is an essential source of alleviating EP, and it has a significant positive effect across all quantiles, but it is especially strong in the middle quantiles. RE and EE significantly mitigate EP, with the strongest effects occurring at higher quantiles. By including green finance, RE, and EE as the main explanatory determinants of EP, the findings urge policymakers in Latin American countries to design a comprehensive energy conservation policy to minimize the effects of massive EP.
The Effect of Non-Cognitive Ability on Farmer’s Ecological Protection of Farmland: Evidence from Major Tea Producing Areas in China

Ecological protection of farmland is an important means to reduce agricultural non-point source pollution and improve the quality of agricultural products. As the main body of current agricultural production and operation, the aging labor force has insufficient cognitive ability and low ability to learn actively, which is not conducive to transforming the green output. However, non-cognitive abilities closely related to the acquired environment can promote the elderly farmers’ farmland ecological protection behavior by improving life satisfaction and social adaptability. Based on the above background, using the survey data of 964 farmers in China, the bivariate Probit model was used to empirically test the influence mechanism of non-cognitive ability on the ecological protection behavior of farmland. The study found that non-cognitive ability significantly promoted farmer’s ecological protection of farmland in China. Specifically, the variables of non-cognitive ability, social communication ability, active learning ability, self-efficacy, stress resistance, altruistic tendency and individual resilience were found to significantly promote ecological protection of farmland. Mechanism analysis showed that non-cognitive ability promoted the ecological protection behavior of farmland by expanding social capital, information channels and improving technical value perception. A heterogeneity analysis revealed that non-cognitive ability had a greater impact on ecological protection behavior of farmland in the elderly and low-income groups. Therefore, government should attach importance to improving farmers’ non-cognitive abilities, further increase technical publicity, and build a communication platform for farmers in order to promote the ecological protection of farmland.
Comprehensive Analysis of Role of Cyclin-Dependent Kinases Family Members in Colorectal Cancer

Background Cyclin-dependent kinases (CDKs) are cell cycle regulators, and abnormal activation can accelerate tumor cell proliferation. However, The relation between CDKs dysregulation to colorectal cancer incidence and progression have not been examined in detail. Methods:Differences in CDKs expression between colorectal cancer and normal tissues, associations between expression and clinical prognosis, incidence and frequencies of CDKs gene mutations, and the influences of CDKs on tumor infiltration by immune cells were examined by analyses of Oncomine, Gene Expression Profiling Interactive Analysis, Kaplan-Meier plotter, cBioPortal, GeneMANIA, and TIMER databases. Results Colorectal cancer tissues showed enhanced expression levels of CDKs 1/2/4/5/6/8/12/13/19 but reduced CDK3 expression. CDK7 was highly expressed in some colorectal cancer tissues but downregulated in others. Expression levels of CDK1/3/4/7/8/10/11b/13/18/19/20 were correlated with clinical stage, and CDK 5/10/12/16 expression levels predicted prognosis and survival. Differential CDKs expression correlated with cell cycle progression, amino acid polypeptide modifications, and activation of other protein kinases. Expression levels of all CDKs except CDK16 were correlated with infiltration of CD4+T, CD8+T, B and Tregs cells. Conclusions CDK 1 and 4 could be used as diagnostic biomarkers for CRC. CDK 5/10/12/16 can be utilized as prognostic biomarkers.
On the metrological support of the long-period seismology

Modern seismological analyses require well-calibrated instruments. What is the quality of the seismic station? What is the data precision? Procedures obtaining the answers to these questions are half-finished. Seismic stations and their long-period sensors are not measuring instruments. Standard procedures of in-use testing aren't applicable to them. Nor their accuracy cannot be determined. Some Russian stations are part of the Global Seismographic Network (GSN) and International Monitoring System (IMS). These stations should use appropriate methods of verification. These methods presuppose three basic approaches.  There are two sets of long-period sensors installed at the same station and the data are compared.  They produce original calibration. They bring the reference instrument, which is considered as good, to the station and set it next to compare data.  They compare how earthquakes records of other stations correspond to records of this station. These techniques have already been partially applied. The first two approaches appear to be too expensive and time consuming for the Russian stations, which are not in the GSN and the IMS. In our opinion, in-depth development of the third approach, bringing it to a routine maintenance process stations, i.e. the creation of user-friendly programs and algorithms, is the most promising way out of this situation.