Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Effects of Minimum Wages on Employment, Unemployment and Labour Participation in Java: A Dynamic Spatial Panel Approach

Using district-level data for urban areas in Java, we assess the impact of minimum wages on formal- and informal-sector employment, unemployment and labour participation. We employ the spatial Durbin model for our estimation and allow labour markets to be spatially correlated across districts. Our findings suggest that a minimum-wage rise affects mainly the local labour market where the minimum-wage increase occurs. The spillover effects of a minimum-wage increase on neighbouring districts are negligible, except for the impact on labour participation. The results of this study highlight the need to account for spatial dependence when modelling formal-sector employment, unemployment and labour participation, as we found those variables to be geographically correlated, at least across districts in Java.
The fourth scientific discovery paradigm for precision medicine and healthcare: Challenges ahead

Abstract With the progression of modern information techniques, such as next generation sequencing (NGS), Internet of Everything (IoE) based smart sensors, and artificial intelligence algorithms, data-intensive research and applications are emerging as the fourth paradigm for scientific discovery. However, we face many challenges to practical application of this paradigm. In this article, 10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.
Improved Pd/CeO2 Catalysts for Low-Temperature NO Reduction: Activation of CeO2 Lattice Oxygen by Fe Doping

Developing better three-way catalysts with improved low-temperature performance is essential for cold start emission control. Density functional theory in combination with microkinetics simulations is used to predict reactivity of CO/NO/H2 mixtures on a small Pd cluster on CeO2(111). At low temperatures, N2O formation occurs via a N2O2 dimer over metallic Pd3. Part of the N2O intermediate product re-oxidizes Pd, limiting NO conversion and requiring rich conditions to obtain high N2 selectivity. High N2 selectivity at elevated temperatures is due to N2O decomposition on oxygen vacancies. Doping CeO2 by Fe is predicted to lead to more oxygen vacancies and a higher N2 selectivity, which is validated by the lower onset of N2 formation for a Pd catalyst supported on Fe-doped CeO2 prepared by flame spray pyrolysis. Activating ceria surface oxygen by transition metal doping is a promising strategy to improve the performance of three-way catalysts.
Distributed denial‐of‐service attack mitigation in network functions virtualization‐based 5G networks using management and orchestration

The fifth generation (5G) technology is expected to allow connectivity to billions of devices, known as Internet of Things (IoT). However, IoT devices will inevitably be the main target of various cyberattack types. The most common one is known as distributed denial‐of‐service (DDoS) attack. In order to mitigate such attacks, network functions virtualization (NFV) has a great potential to provide the benefit of elasticity and low‐cost solutions for protecting 5G networks. In this context, this study proposes a new mechanism developed to mitigate DDoS attacks in 5G NFV networks. The proposed mechanism utilizes intrusion prevention system's (IPS) virtual machines (VMs) to intercept the queries. Based on the volume of DDoS traffic, IPS's VMs are dynamically deployed by means of management and orchestration (MANO) in order to balance the load. To evaluate the effectiveness of the mechanism, experiments are conducted in a real 5G NFV environment built by using 5G NFV environment tools. To our best knowledge, this is the first time that NFV‐based mechanism is experimentally tested in a real 5G NFV environment for mitigating DDoS attacks in 5G networks. The experimental results verify that the proposed mechanism can mitigate DDoS attacks effectively.
Estimation under matrix quadratic loss and matrix superharmonicity

We investigate estimation of a normal mean matrix under the matrix quadratic loss. Improved estimation under the matrix quadratic loss implies improved estimation of any linear combination of the columns under the quadratic loss. First, an unbiased estimate of risk is derived and the Efron–Morris estimator is shown to be minimax. Next, a notion of matrix superharmonicity for matrix-variate functions is introduced and shown to have properties analogous to those of the usual superharmonic functions, which may be of independent interest. Then, it is shown that the generalized Bayes estimator with respect to a matrix superharmonic prior is minimax. We also provide a class of matrix superharmonic priors that includes the previously proposed generalization of Stein’s prior. Numerical results demonstrate that matrix superharmonic priors work well for low-rank matrices.
A Survey on Over-The-Air Linearization Methods for MIMO Systems

Transmitter antenna arrays are typically coupled to several RF chains, which imposes stringent requirements on the linearization of each power amplifier (PA) present in the system. For this and other reasons discussed in this work, Over-the-air (OTA) linearization methods are considered to linearize transmitter antenna arrays in 5G scenarios. However, several factors need to be considered when applying OTA linearization methods. In this paper, an extended critical review of validated OTA linearization methods is presented. The main goal is to point out and discuss the most prominent methods, in order to determine which one is the most suitable for a specific application. In particular, analysis for each method is performed and, subsequently, their benefits and the disadvantages are systematically discussed. This is sought to fill-in a gap in the scientific literarure and, thus, to help radio-frequency engineers in the implementation of OTA digital pre-distortion (DPD) techniques for multiple input multiple output (MIMO) systems.
A high gain 79-GHz low noise amplifier using inductor-embedded neutralization technique

This paper presents a 79 GHz low noise amplifier (LNA) design featuring high gain fabricated in a 40-nm CMOS process. To make better use of active devices, we propose an inductor-embedded neutralization technique. The implemented prototype consists of four-stage common-source amplifiers using the proposed technique and transformer-based matching networks. The measurement results show that the amplifier realizes a peak gain of 23 dB at 79 GHz with 14.4 mW power dissipation and 0.4 mm2 area occupation. The LNA achieves a minimum noise figure (NF) of 6.3 dB.
Effects of Attitude, Self-efficacy Beliefs, and Motivation on Behavioural Intention in Teaching Science

Article History: Purpose: Teacher’s behavioural intention in teaching science is one of the key determinants of students’ learning outcomes. Thus, it is crucial to study which affective domains are contributed to teacher’s behavioural intentions in teaching science. The present study aims to investigate the mediating effect of the dimensions of attitudes toward teaching science (i.e., cognitive beliefs, affective states, and perceived control) in the relationship between teacher self-efficacy beliefs and teaching motivation on behavioural intention in teaching science. Method: A quantitative research design was employed upon 127 pre-service science teachers in Malaysia and the PLS-SEM approaches were used for Received: 23 Apr. 2020 Received in revised form: 26 Jul. 2020 Accepted: 13 Mar. 2021 DOI: 10.14689/ejer.2021. 93.11
Evidence for Pre-printing Interventions: A Scoping Review

ABSTRACT An integral part of occupational therapy practice in early intervention involves assessment and treatment of pre-printing challenges and determination of printing readiness; however, the evidence to inform best practice has not been thoroughly investigated. This paper is part of a larger scoping review that focused on theoretical frameworks for printing readiness and evidence for pre-printing development and skills. This companion paper undertook a critical evaluation of pre-printing interventions and programs, based on a new proposed integrated theoretical framework outlined in Klein et al. (2021, same issue). Of the 98 articles included in the scoping review, 12 were intervention-based studies. Most of these intervention studies were rated at a low level of evidence and often utilized a bottom-up approach for intervention, which is maligned with current best practice principles. Direct task-based learning is considered best practice for handwriting interventions; however, there is a paucity of research studies at higher levels of evidence that evaluate this approach for pre-printing intervention. Commercially available pre-printing programs that incorporate tenets of an integrated theoretical framework for printing readiness exist; however, there is a gap in the literature evaluating their effectiveness. Empirically evaluated pre-printing interventions and programs that are rooted in an integrated approach are needed, drawing on principles from emergent literacy and neurodevelopmental frameworks, embracing top-down, task-based learning. Development and use of these programs in early intervention will facilitate collaborative partnerships between occupational therapists, educators, and parents for developmentally appropriate pre-printing intervention that fit within curriculum expectations. As so few evidence-based pre-printing programs exist, once developed, there will be a critical need to research their effectiveness.
Automatic Roadway Features Detection with Oriented Object Detection

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.
Out‐of‐Phase Decadal Change in Drought Over Northeast China Between Early Spring and Late Summer Around 2000 and Its Linkage to the Atlantic Sea Surface Temperature

Based on the standardized precipitation evaporation index (SPEI) and empirical orthogonal function (EOF) analysis, this article presents that the primary mode (i.e., a monosign pattern) of drought over Northeast China (NEC) in early spring (February‐March‐April, FMA) and late summer (July‐August‐September, JAS) shows an out‐of‐phase decadal change, which is characterized with a wetting polarity in FMA while a drying polarity in JAS approximately after 2000 as compared to the counterparts before that time. Such an out‐of‐phase decadal change is generally supported by the decadal changes in atmospheric circulations. Compared to the former period, during the latter period, the equivalent barotropic cyclonic (anticyclonic) anomalies dominate northeastern Asia and an anomalous ascending occupies NEC in FMA (JAS), which help to alleviate (aggravate) drought over NEC. In addition, decreases in water vapor transport toward the NEC region act in the aggravation of drought in JAS, while the variation of drought in FMA is not closely connected to the change in water vapor transport. Further analyses indicate that the shifts of the sea surface temperature (SST) in the tropical and mid‐high latitude Atlantic Ocean from a cold phase to a warm phase also play a role in the decadal change of drought in FMA and JAS, respectively, through their influences on the above atmospheric circulations with the aid of downstream propagation of wave trains. Hence, the seasonal dependence of the Atlantic SST and atmospheric circulations contributes to the out‐of‐phase decadal change of drought in NEC.
Determination of Near Surface Shear‐Wave Velocities in the Central Los Angeles Basin With Dense Arrays

In this study, we investigate the shallow shear wave velocity structure of the Los Angeles Basin in southern California, using ambient noise correlations between 5 dense arrays and 21 broadband stations from the Southern California Seismic Network (SCSN). We observe clear fundamental mode and first overtone Rayleigh waves in the frequency band 0.25–2.0 Hz, and obtain group velocity maps through tomography. We further derive a 3D shear wave velocity model, covering a large portion of the central LA Basin for the depths shallower than 3 km. We found that the small scale shallow velocity structure heterogeneities are better resolved compared with the SCEC Community velocity models. Our model captures the presence of the Newport‐Inglewood fault by a NW–SE trending high velocity belt. Our model provides more accurate constraints on local ground motion predictions with detailed mapping of structural heterogeneities.
The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles.
An Evaluation of the Core Competency Suicide Prevention Training Program for University Health Service Providers

ABSTRACT Despite advances in suicide risk assessment and intervention, evidence-based suicide prevention training for university health service providers is still lacking. This study evaluated the core competency model of suicide prevention training for an interdisciplinary sample of university counseling and student health providers (N = 21). Training was delivered in a half-day workshop format. Participants completed a pre-post training assessment survey. Student health provider risk screening practices were also assessed in the time-period preceding and following training implementation. Consistent with expectations, training yielded significant and robust improvements in providers’ perceived suicide prevention skills and suicide prevention knowledge. However, suicide prevention attitudes and clinical response recognition did not reliably improve. Training was also associated with a greater frequency of student health service providers’ mental health screening administration. The core competency model demonstrates promise as an evidence-informed training program for university counseling center and student health providers.
Funded-capitalized pension designs and the demand for minimum pension guarantee

Using funded and unfunded pillars, the optimal pension structure is estimated using an over-lapping generation model, calibrated to the average OECD countries. While simulating different pillar sizes, a socio-economic characteristic was revealed in which low-earning groups are prone to unexpected market risks than high-earning cohorts and support a larger contribution than better-off individuals. This led to high contribution rates for funded pillars and low contributions rates for social security pillars. This suboptimal allocation leads to inefficient hedging capability for the pension portfolio. An alternative is a minimum pension guarantee as an efficient system stabilizer as it rebalances the economic cost among different earning cohorts. However, the guarantee might be expensive to implement if not capitalized early in the working phases in an era of aging populations, low birth rates, and deep financial crisis.
Groundwater contributions of flow and nitrogen in a headwater agricultural watershed

Nonpoint sources of nitrogen (N) and other nutrients are a major source of water pollution within the Chesapeake Bay watershed and other basins around the world. Human activities associated with agricultural practices can account for a large percentage of N loadings delivered to streams and rivers. This work aims to improve understanding of N transport from groundwater to surface waters, quantifying the principal hydrological processes driving water and N fluxes into and out of a headwater agricultural stream reach. The study site is a 175‐m stream reach in a heavily cultivated 40‐ha watershed in east‐central Pennsylvania. This subwatershed is underlain by fractured shale bedrock, and receives most of its baseflow from groundwater, either by diffuse matrix discharge through the streambed or by localized discharge through riparian seeps. Samples of stream, seep, and shallow groundwater were collected approximately monthly under steady hydrologic conditions in 2017. Calculated matrix flow from hydraulic head and conductivity measurements paired with differential stream gauging was used to solve for the riparian seep flux using a mass balance approach. Riparian seep fluxes ranged from 45 to 217 m3/d, transporting 0.6–4.2 kg N d−1 of nitrate‐N from the fractured bedrock aquifer to the stream. Hydrochemical data suggest that the stream is mainly disconnected from the underlying aquifer and that seeps supply essentially all water and N to the system. Seeps are likely sourced with N in nearby agricultural fields and accelerated through the system with shorter residence times than shallow groundwater. Water isotope data reinforced this notion. This study underscores the importance of agriculture as a source of N to ground and surface waters. Identifying source areas that are causing groundwater enrichment of N and seep areas where N discharges to streams is beneficial for developing N pollution mitigation strategies and implementing management practices that aim to reduce nutrient loads to the Chesapeake Bay.
A successful method of transplanting adult Ecklonia radiata kelp, and relevance to other habitat‐forming macroalgae

The ability to successfully transplant adult kelp has applications not only for ecological experiments, but also for habitat conservation and restoration projects. However, approaches to the long‐term transplanting of adult kelp (especially for stalked or ‘stipitate’ species), and the communication of these methods, has been relatively haphazard, often due to poor results and excessive mortality. Here, we provide a brief communication to describe a method to effectively transplant the stipitate kelp Ecklonia radiata—the most widespread and abundant kelp in Australasia—and which allowed the transplanting of >1,000 adult kelp over 1.5 ha. We also discuss additional observations relevant to the success of transplanting kelp (such as donor age/size), and the applicability of this method to other habitat‐forming macroalgae. Our method involved securing the adult Ecklonia to the substratum using large bands made from recycled rubber, which held the holdfast firmly but gently against the substratum. Re‐attachment of the adult kelp typically occurred within 3–6 weeks, while rates of survivorship were approximately 75% and not affected by the density nor size of the transplanted patch. This method could also readily be adapted to suit different substratum types and other species of kelp and habitat‐forming algae. Ultimately, this transplanting method adds to the collection of effective techniques for restoring habitat‐forming macroalgae, especially for stipitate species where few methods have been communicated.
High-speed sampling strategy for photoacoustic tomography using ROMP compressed sensing algorithm

Photoacoustic tomography technology is a new imaging technology based on photoacoustic effect. It has become one of the most important techniques in biomedical imaging field because of its non-invasive, non-ionized and high resolution. The imaging data of photoacoustic imaging is complex. The traditional Nyquist sampling consumes much time and resources. And it requires high equipment. In order to improve the sampling efficiency and reduce the equipment requirements, Compression Sensing (CS) theory has been used to collect photoacoustic data. Compressed Sensing theory can break through the limitation of Nyquist sampling law and reduce the data redundancy greatly so that the desired imaging results can be reconstructed with less time and resources. In this paper, the K-wave simulation toolbox of MATLAB is used to set up the virtual photoacoustic field and collect the photoacoustic signal of blood vessel. The results show that the MATLAB virtual Compressed Sensing photoacoustic tomography simulation platform based on k-wave can achieve high quality photoacoustic tomography with less data. The superiority of Compressed Sensing theory and the high efficiency and stability of k-wave virtual platform are verified. Also, the Compressed Sensing reconstruction algorithm OMP and ROMP are compared in this paper and the result shows that the ROMP algorithm works better.