Miracle-dz/UniFAR-FTUs huggingface dataset:
Fortifying Smart Transportation Security Through Public Blockchain

Smart vehicles-enabled intelligent transportation system (ITS) supports a wide range of applications, such as, but not limited to, traffic planning and management, collision avoidance alert system, automated road speed enforcement, electronic toll collection, and real-time parking management, to name a few. However, it suffers from various types of security and privacy issues due to insecure communication among the entities over public channels. Therefore, an efficient and lightweight security mechanism is essential to protect the data that is both at rest as well as in transit. To this direction, we propose a public blockchain-envisioned secure communication framework for ITS (PBSCF-ITS). The proposed PBSCF-ITS guarantees access control and key management among the vehicle to vehicle, vehicle to roadside unit, and roadside unit to cloud server. We analyze the security of PBSCF-ITS to prove its resilience against various types of possible attacks. Furthermore, the performance of PBSCF-ITS with other related competing schemes has been compared. The obtained results illustrate that PBSCF-ITS outperforms the existing ones. Additionally, the pragmatic study of PBSCF-ITS is conducted to check its influence on various network-related performance parameters, like the number of mined blocks and transactions per block.
Adenosine-to-Inosine RNA Editing and N6-Methyladenosine Modification Modulating Expression of Drug Metabolizing Enzymes

Interindividual differences in the expression and activity of drug metabolizing enzymes including cytochrome P450, UDP-glucuronosyltransferase, and esterases cause variable therapeutic efficacy or adverse events of drugs. As the major mechanisms causing the variability in the expression of drug metabolizing enzymes, transcriptional regulation by transcription factors, epigenetic regulation including DNA methylation, and posttranscriptional regulation by microRNA are well known. Recently, adenosine-to-inosine RNA editing and methylation of adenosine at the N6 position on RNA have emerged as novel regulators of drug metabolism potency. In this review article, the current knowledge of these two prevalent types of posttranscriptional modification mediated modulation of drug metabolism involved genes is introduced. SIGNIFICANCE STATEMENT Elucidation of the significance of adenosine-to-inosine RNA editing and N6-methyladenosine in the regulation of drug metabolizing enzymes is expected to lead to a deeper understanding of interindividual variability in the therapeutic efficacy or adverse effects of medicines.
Do psychopathic traits vary with age among women? A cross-sectional investigation

ABSTRACT Previous studies with men suggest that certain psychopathic traits vary with age. Specifically, younger men score higher on psychopathic traits measuring impulsive-antisocial behavior, including impulsivity, irresponsibility, and criminal versatility, compared to older men. On the other hand, younger and older men score comparably on psychopathic traits reflecting core personality traits of interpersonal and affective dysfunction, including conning and manipulative behavior and a lack of empathy, guilt, and remorse. However, it is currently not known whether psychopathic traits similarly vary with age among women. This study examined whether psychopathy scores (assessed via the Psychopathy Checklist – Revised [PCL-R]) varied with age among a sample of 501 incarcerated women ranging from 19 to 57 years of age. Consistent with previous studies performed with men, younger women scored higher on psychopathic traits measuring impulsive-antisocial behavior (i., PCL-R Factor 2, Facet 3, and Facet 4 scores) compared to older women. However, scores on PCL-R Factor 1, Facet 1, and Facet 2, assessing core personality traits, including interpersonal and affective dysfunction, were comparable across women in different age categories. Results obtained in this study suggest the variation of PCL-R Factor 2 traits and the stability of PCL-R Factor 1 traits across the lifespan is invariant across gender.
A Comprehensive Piston Skirt Lubrication Model Using a Mass Conserving EHL Algorithm

Elasto-Hydrodynamic Lubrication (EHL) analysis of a fully flooded piston skirt-liner conjunction is a useful methodology for design analysis of pistons. However, under typical engine operating conditions, oil present in the clearance region between the skirt and liner is sufficient to wet only a portion of the' piston skirt (partial skirt lubrication). The reduction in dampind due to partial skirt lubrication is an important consideration to address issues related to piston slap noise, liner cavitation and other noise and vibration aspects. The existing simulation methodology for EHL analysis of a fully flooded piston skirt uses a finite-difference scheme to solve the coupled Reynolds, Greenwood-Tripp and elasticity equations in order to calculate the nodal oil film pressures, contact pressures and elastic deformations respectively. Detection of cavitation zones within the oil film done via implementation of the Half-Sommerfeld boundary condition. The limitations of this boundary condition are that a) it violates the mass-conservation property of the Reynolds Equation and b) it does not rigorously track the boundaries of the cavitation zones in the oil film. As a result, this methodology cannot be applied towards partial skirt lubrication and needs to be enhanced. This is achieved via a finite-volume solution to the Reynolds Equation which takes into account a) mass conservation based on oil supply and b) more comprehensive treatment for the rupture and reattachment boundaries which define the cavitation zone. Additionally, this methodology enables to specify partial skirt lubrication by means of an oil supply to the piston skirt as a boundary condition. Results are presented from parametric studies that show comparisons between usage of the finite-difference and finite-volume schemes.
Modular framework for similarity-based dataset discovery using external knowledge

PurposeSemantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.Design/methodology/approachIn this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.FindingsThe study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.Originality/valueTo the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.
TiNNA: A Tiny Accelerator for Neural Networks With Efficient DSP Optimization

Accelerators for convolutional neural networks (CNNs) based on field-programmable gate arrays (FPGAs) have drawn much attention in recent years. Two INT-8 multiplications are often implemented on a single digital signal processor (DSP) to improve DSP efficiency and performance. However, most of these works only use DSPs to implement multiplications and do not involve accumulations, which requires a lot of look-up tables (LUTs) and consequently leads to greater energy consumption. Furthermore, these works do not detail how to obtain two signed INT-8 multiplications with a DSP. This brief thus proposes a tiny accelerator for CNNs and a DSP-based processing element (PE) that is designed to implement two signed INT-8 multiply-and-accumulate (MAC) operations to reduce the overhead of LUTs. To allow the DSP to support two signed INT-8 MAC operations and avoid the large loss of accuracy caused by data overflow of partial sums (psum), we apply an efficient DSP optimization approach and a dynamic-precision quantization method. The accelerator is implemented on a Xilinx ZC706 FPGA device and achieves a DSP utilization efficiency <inline-formula> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$7.3\times $ </tex-math></inline-formula> greater than state-of-the-art accelerators implemented on the same device. Furthermore, our accelerator offers <inline-formula> <tex-math notation="LaTeX">$3.56\times $ </tex-math></inline-formula> greater energy efficiency than other FPGA-based accelerators.
Predictive Maintenance Decision Making Based on Reinforcement Learning in Multistage Production Systems

Predictive maintenance has become increasingly prevalent in modern production systems that are challenged by high-mix low-volume production and short production life cycle. It is very helpful to prevent costly equipment failures, and reduce significant production loss caused by unscheduled machine breakdown. Although important, decision models for joint predictive maintenance and production in manufacturing systems have not been fully explored. Therefore, we propose a reinforcement learning based decision model, that brings together production system modeling and approximate dynamic programming. We start from the development of a state-based model by analyzing the dynamics of a multistage production system with predictive maintenance. It provides an approach to quantitatively evaluate the various disruptions as well as the maintenance decision’s impact on production. Then a reinforcement learning method is proposed to explore optimal maintenance policies, that optimize the production and maintenance cost. To further improve the performance of the production system, machine stoppage bottlenecks are defined. An event-based indicator is proved to identify bottlenecks with production data. We test the proposed models in simulation case studies. The proposed predictive maintenance decision model is compared with three policies, which are state-based policy (SBP), time-based policy (TBP) and greedy policy (GP). The numerical studies show that the proposed decision model outperforms the policies, and it has the lowest system cost that is 9.68%, 39.07%, and 39.56% lower than SBP, TBP, and GP, respectively. In addition, the research shows that bottleneck identification and mitigation could help manufacturing systems to achieve more than 9.00% throughput improvement.
Damping characteristics and countermeasure of DFIG sub-synchronous oscillation

SSCI(Sub-Synchronous Control Interaction),a new type of sub-synchronous oscillation,may induced by the interaction between the rotor side converter and the compensating capacitor of DFIG(Doubly-Fed Induction Generator) when large-scale wind power is transferred through the transmission lines with fixed series compensation. The working principle of DFIG converter and its output characteristics are analyzed and its equivalent simulation model based on the controlled voltage source at AC side and the controlled current source at DC side is built,which avoids the impact of high-order harmonics of switching devices on the calculative results of DFIG electrical damping to enhance the simulation efficiency. Based on the equivalent model,the complex torque coefficient method is adopted in time-domain to calculate the electrical damping characteristics of DFIG within the range of sub-synchronous frequency for analyzing the impacts of wind-speed,converter PI parameters,compensation-degree and transmission line resistance on the electrical damping characteristics. The analytical results show that,the negative electrical damping of DFIG increases along with the wind-speed decrease,inner-loop gain increase,integral time-constant decrease,compensation-degree increase and transmission line resistance decrease;the impact of outer-loop PI parameters on the damping characteristics of DFIG is not large. An additional H- SSDC( Hybrid Sub-Synchronous Damping Controller) in the rotor side converter of DFIG is proposed to restrain SSCI and its effectiveness is verified by the simulative results.
CRB Weighted Source Localization Method Based on Deep Neural Networks in Multi-UAV Network

With the advent of the Internet of Things (IoT) era, the multiunmanned aerial vehicle (UAV) networks have attracted great attention in the fields of source detection and localization. However, as the real-time signal processing performance of the UAV is limited by the computing speed and accuracy of the embedded hardware, the effectiveness of source localization is greatly reduced. Aiming at improving the accuracy and computational efficiency of source localization, a Cramer–Rao bound (CRB) weighted multi-UAV network source localization method is proposed based on the deep neural networks (DNNs) and spatial-spectrum fitting (SSF). The proposed source localization system is composed of UAVs equipped with a radar array. The source location can be achieved using the direction of arrival (DOA) of the source signals of UAVs, but the accuracy and real-time performance of the conventional DOA estimation algorithms are not satisfactory, and the data fusion strategy of the conventional cross-location framework needs further improvement. In the proposed method, a DNN-based SSF, denoted as the deep SSF (DeepSSF), is designed to achieve accurate DOA estimation. In the DeepSSF, the DOA estimation performance is guaranteed by the DNN’s strong nonlinear fitting ability and highly parallel structure. In addition, based on the obtained DOA information, the source is located once by every two UAVs. Finally, the source localization is realized based on the weighted CRB according to the principle that the more the DOA distribution deviates from zero, the lower the estimation accuracy. The simulation results verify the efficiency of the proposed method.
Let’s Give Together: Can Collaborative Giving Boost Generosity?

A growing number of people donate to charity together with others, such as a spouse, friend, or stranger. Does giving to charity collectively with another person—called collaborative giving—promote generosity? Existing data offer unsatisfactory insight; most studies are correlational, present mixed findings, or examine other concepts. Yet, theory suggests that collaborative giving may increase generosity because giving with others could be intrinsically enjoyable. We conducted two well-powered, pre-registered experiments to test whether collaborative giving boosts generosity. In Experiment 1 (N = 202; 101 dyads) and Experiment 2 (N = 310; 155 dyads), pairs of unacquainted undergraduates earned money and were randomly assigned to donate collaboratively (Experiments 1–2), individually in each other’s presence (Experiments 1–2), or privately (Experiment 2). Across studies, we observed no condition differences on generosity. However, collaborative (vs. individual) giving predicted greater intrinsic enjoyment, which, in turn, predicted larger donations, suggesting a promising potential mechanism for future research and practice.
Enhancing the Capability of Testing-Based Formal Verification by Handling Operations in Software Packages

Testing a program based on its specification is necessary to ensure that the program meets its desired functionality. Formal methods, based on some mathematical theories, are often used to enhance the quality of systems but suffer from difficulties in application. The Testing-Based Formal Verification (TBFV) is proposed as an alternative to ensure the correctness of all traversed program paths, but is limited and impractical due to the lack of the capability of dealing with operations (e.g., methods defined in classes) provided in software packages. In this paper, we provide an axiomatic approach to dealing with this problem so as to enhance the capability of the TBFV. In particular, we focus on the Vector, ArrayList, and LinkedList classes in Java. We present both an example to demonstrate how our approach works properly and two small experiments conducted to evaluate the performance of our approach by comparing it with the specification-based testing (SBT). The result shows that our approach is more than 30% superior to the SBT in bug detection.
REDUCING MOTORIST INCONVENIENCE DUE TO MAINTENANCE OPERATIONS ON HIGH-VOLUME FREEWAYS

MUDJACKING OPERATIONS ALONG THE SAN DIEGO FREEWAY REQUIRED THAT CONCRETE SLABS BE PLACED IN TWO-LANE SECTIONS. THE SCHEDULING AND TRAFFIC-CONTROL PRACTICES EMPLOYED TO MINIMIZE DELAY DURING THIS THREE-MONTH TASK ARE DESCRIBED.
Effect of interventions on activity and participation outcomes for adults with brain injury: a scoping review

ABSTRACT Objective To characterize the intervention elements associated with improvements in activity and participation outcomes for adults with brain injury. Data Sources PubMed and PsycINFO/Ovid. Study Selection We included RCTs that examined interventions for adults with acquired brain injury with an activity or participation outcome measure. Data Extraction We classified intervention elements and extracted effect sizes. We examined patterns of effect sizes associated with each intervention element based on time of follow-up and level of outcome (home versus community). Data Synthesis Thirty-nine articles were included. Outcomes focused on the performance of home and community activities. There was wide variation in effect sizes across all intervention elements, as well as by time and by outcome level (home versus community). Metacognitive interventions and daily life skills interventions showed the greatest promise for improving performance of home and community activities. Additionally, cognitive training interventions may play a role in improving home activity performance and social skills training interventions may play a role in community activity performance. Physical activity interventions showed the least promise for improving home and community activity performance. Conclusion This study highlights the importance of interventions that incorporate explicit strategies and task-specific training, rather than only addressing specific injury-related impairments.
Cell cycle and developmental control of cortical excitability in Xenopus laevis

Interest in cortical excitability – the ability of the cell cortex to generate traveling waves of protein activity – has grown considerably over the past twenty years. Attributing biological functions to cortical excitability requires an understanding of the natural behavior of excitable waves and the ability to accurately quantify wave properties. Here we have investigated and quantified the onset of cortical excitability in X. laevis eggs and embryos, and the changes in cortical excitability throughout early development. We found that cortical excitability begins to manifest shortly after egg activation. Further, we identified a close relationship between wave properties – such as wave frequency and amplitude – and cell cycle progression as well as cell size. Finally, we identified quantitative differences between cortical excitability in the cleavage furrow relative to non-furrow cortical excitability and showed that these wave regimes are mutually exclusive.
The OPC Technology and Its Application in Integration of Intelligent Building System

A new technology in process control field of OPC technology, Object Linking and Embedding (OLE) for process control is introduced in this article.According to the difficulty to get an open and seamless connection between each subsystem,it introduces the background,structure system and specification principle of OPC technology, discusses the application of OPC technology in intelligent building system integration and finally gives an example of using OPC technology in building control subsystem.
Feasibility analysis of machine learning applied to magnetized plasma diagnosis

In the paper, machine learning is demonstrated for the diagnostic enhancement of magnetized plasma probes. In the plasma experiment, the magnetic field greatly affects the properties of plasma and the performance of the probe, which limits the range of measurement parameters and reduces the accuracy of probe diagnostic results. Existing probe correction methods based on improved theory and mechanics cannot completely eliminate the influence of magnetic field and often introduce additional errors. In this paper, a novel machine learning method is proposed to improve magnetized plasma probe diagnostic based on existing methods and traditional probe correction theory. The pressure of the plasma, the total voltage of the circuit, and the magnetic induction intensity are used as input parameters, and the electron density obtained from the probe diagnostics are used as output parameters. The paper presents experiments to analyse the original probe results and the probe results revised by magnetic field probe theory through the machine learning algorithm and compare them with the results of theoretical simulations. The experimental results prove that the machine learning model based on revised data has better learning efficiency and prediction results, which can expand the application scope of traditional probe correction theory and predict results closer to the theoretical value.
Thyroid-Stimulating Hormone Favors Runx2-Mediated Matrix Mineralization in HOS and SaOS2 Cells: An In Vitro and In Silico Approach

Osteoporosis is a skeletal disease that is both systemic and silent characterized by an unbalanced activity of bone remodeling leading to bone loss. Rising evidences demonstrate that thyroid stimulating hormone (TSH) has an important role in the regulation on the metabolism of bone. However, TSH regulation on human osteoblast essential transcriptional factors has not been identified. Current study examined the role of TSH on human osteoblastic Runx2 expression and their functional genes by in vitro and in slico analysis. Human osteoblast like (HOS and SaoS-2) cells were cultured with DMEM and treated with hTSH at the concentration of 0.01 ng/mL and 10 ng/mL. After treatment, osteoblastic Runx2 and IGF-1R beta expression were studied using RT-PCR and western blot analysis. TSH treatment induced osteoblastic essential transcriptional factor, Runx2 in HOS and SaOS2 cells on 48 h duration and elevated the expression of IGF-IR β gene and Protein in SaoS-2 cells. TSH also promotes Runx2 responsive genes such as ALP, Collagen and osteocalcin in SaOS2 cells on day 2 to day 14 of 10 ng/mL of treatment and favors’ matrix mineralization matrix in these cells. In addition, TSH facilitated human osteoblastic cells to mineralize their matrix confirmed by day 21 of alizarin red calcium staining. In silico study was performed to check CREB and ELK1 interaction with Runx2. Results of in silico analysis showed that TSH mediated signalling molecules such as CREB and ELK1 showed interaction with Runx2 which involve in osteobalstic gene expression and differentiation. Present findings confirm that TSH promotes Runx2 expression, osteoblastic responsive genes and bone matrix formation.