Miracle-dz/UniFAR-FTUs huggingface dataset:
The role and significance of planning in the determination of house prices in Australia: Recent policy debates

On the world stage, Australian cities have been punching above their weight in global indexes of housing prices, sparking heated debates about the causes of and remedies for, sustained house price inflation. This paper examines the evidence base underpinning such debates, and the policy claims made by key commentators and stakeholders. With reference to the wider context of Australia’s housing market over a 20 year period, as well as an in depth analysis of a research paper by Australia’s central Reserve Bank, we show how economic theories commonly position land use planning as a primary driver of new supply constraints but overlook other explanations for housing market behavior. In doing so, we offer an alternative understanding of urban housing markets and land use planning interventions as a basis for more effective policy intervention in Australian and other world cities.
A computer vision algorithm for locating and recognizing traffic signal control light status and countdown time

Abstract It is of practical importance for individual vehicles to automatically identify traffic signal light status to facilitate their decision makings for traffic safety performance enhancement and operation efficiency maximization when they are approaching intersections, especially in near-future fully-or semi-autonomous vehicle-penetrated traffic systems. Traditional methods aim to directly extract information from the light emitting area of a traffic signal light head, but this can easily result in missed or false signal light status identification due to changes in detection distance and lighting conditions. To address these research gaps, an innovative computer vision-based algorithm is developed for signal control light status identification and countdown time recognition. Based on the unique characteristics of black traffic light boards, color segmentation is conducted in both RGB and HSV channels, and traffic light boards and countdown time display boards are located through cascade filtering algorithms. In the HSV color space, the H component can be extracted and highlighted to determine the current signal status by distinguishing the distribution of the pixel points of the H component. Based on the nearest neighbor interpolation algorithm, an adaptive threading method is developed to improve countdown time recognition. The experimental tests were conducted to verify the effectiveness of the developed approach under various scenarios with acceptable recognition accuracy and execution efficiency. The developed approach can be applied and integrated with other detection and control algorithms to strengthen vehicle-crossing safety performance and operational efficiency at intersections.
How does creativity influence dishonest behavior? An empirical study of Chinese students

ABSTRACT This research aims to clarify the relationship between creativity and dishonesty. Study 1 (N = 74) examined whether creativity primed by the scrambled sentence test could influence dishonesty. The results showed that primed individuals were less likely to act dishonestly. Study 2 (N = 134) investigated the association between creative style and spontaneous (using a visual perception task) and instructed dishonesty (differentiation-of-deception paradigm). Rule-followers more often implemented spontaneous dishonest behavior but showed low accuracy in instructed lying tasks. The findings expand our understanding of the correlation between creativity and unethical behavior, which may improve moral creative talents.
Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study

The study objective was to (1) develop a statistical model that creates a novel patient engagement score (PES) from electronic medical records (EMR) and health claim data, and (2) validate this developed score using health-related outcomes and charges of patients with multiple chronic conditions (MCCs). This study used 2014-16 EMR and health claim data of patients with MCCs from Sanford Health. Patient engagement score was created based on selected patients’ engagement behaviors using Gaussian finite mixture model. The PES was validated using multiple logistic and linear regression analyses to examine the associations between the PES and health-related outcomes, and hospital charges, respectively. Patient engagement score was generated from 5095 patient records and included low, medium, and high levels of patient engagement. The PES was a significant predictor for low-density lipoprotein, emergency department visit, hemoglobin A1c, estimated glomerular filtration rate, hospitalization, and hospital charge. The PES derived from patient behaviors recorded in EMR and health claim data can potentially serve as a patient engagement measure. Further study is needed to refine and validate the newly developed score.
Facile Aqueous-Phase Synthesis of Stabilizer-Free Photocatalytic Nanoparticles

One of the challenges of using nanoparticles as catalysts is the presence of reaction-disturbing stabilizers that surround the nanoparticle surface. In this report, we demonstrate a method to synthesize stabilizer-free bismuth oxychloride (BiOCl) nanoparticles to increase photocatalytic activity. This synthesis method is remarkably simple, involving only BiCl3 and deionized water. After heating an aqueous solution containing BiCl3, plate-shaped BiOCl nanoparticles were formed. The stabilizer-free BiOCl nanoplates exhibited higher photocatalytic activities compared to polyvinylpyrrolidone- and polyethyleneimine-stabilized nanoplates for the degradation of methylene blue.
Fingerprinting Movements of Industrial Robots for Replay Attack Detection

Industrial robots are prototypical cyber-physical systems widely deployed in (smart) manufacturing, which operate according to the operation code uploaded by the human operator and are monitored in real-time based on their movement data. However, industrial robots suffer from replay attacks, via which attackers can manipulate the robot operation without being observed by the monitoring system. To mitigate this vulnerability, we design a novel intrusion detection system for industrial robots using their power fingerprint, called <monospace>PIDS</monospace> (<underline>P</underline>ower-based <underline>I</underline>ntrusion <underline>D</underline>etection <underline>S</underline>ystem), and deliver <monospace>PIDS</monospace> as a <italic>bump-in-the-wire</italic> module installed at the powerline of commodity robots. The foundation of <monospace>PIDS</monospace> is the physically-induced dependency between the robot movement and the concomitant power consumption, which <monospace>PIDS</monospace> captures via joint physical analysis and (cyber) data-driven modeling. <monospace>PIDS</monospace> then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information — a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluated <monospace>PIDS</monospace> using three models of robots from different vendors — i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots — with over 2,000 operation cycles. Experimental results show that <monospace>PIDS</monospace> detects replay attacks at an average rate of 96.5 percent (up to 99.9 percent) and a 0.1s latency.
Self-Care Behavior in Heart Failure Patients: Impact on Cardiovascular Health Profile

Introduction: Heart failure is a complex syndrome with characteristic symptoms of shortness of breath, fatigue, and signs of fluid retention. Self-care is the cornerstone of successful heart failure management. The cardiovascular health profile is a direct indicator of successful self-care. Objectives: The objective of the study was to examine the association between self-care and the cardiovascular health profile including smoking status, body mass index, total cholesterol, blood pressure and blood glucose level. Methods: This study is a cross-sectional study of heart failure patients at PKU Muhammadiyah hospital of Yogyakarta between August to October 2020 with sample of 100 participants. Chi square test was used to test the relationship between the two variables. Results: The finding shows that self-care management is the dimension with the highest level of self-care (96.2%) and self-care confidence is the lowest percentage of adequate self-care (13.0%). Among five cardiovascular health profile, smoking status is the highest metric with an ideal percentage (95.0%) and the lowest percentage was blood glucose metric (14.0%). The results of the correlation test between self-care and the total cardiovascular health profile showed no significant relationship between the two variables. However, there was one dimension of self-care, namely self-care maintenance which has a significant relationship with total cholesterol levels with a value of p = 0.008. Conclusions: It is important to provide individualized health education to help the patients to improve their self-care and cardiovascular health profile in heart failure patients.
It matters how old we feel in organizations: Testing a multilevel model of organizational subjective‐age diversity on employee outcomes

This study contributes to the emerging literature on age diversity effects at the organizational level of analysis by comparing the role of chronological-age diversity versus subjective-age diversity. We hypothesize a multilevel model in which organizationallevel subjective-age diversity is negatively related to bonding social capital within organizations, which, in turn, contributes to heightened employee engagement and lowered turnover intentions. The assumed relationships are tested in a multilevel sample of 96 German small- and medium-sized companies with 16,274 employees participating. We gathered data from four different sources to circumvent common source problems and received support for most of the proposed relationships. Given the potentially detrimental effects of high subjective-age diversity in the workplace, the paper concludes with practical recommendations on how to manage subjectiveage diversity in companies proactively.
School Administrator Engagement in Teacher Induction and Mentoring: Findings from Statewide and District-Wide Programs

Research shows that school administrators’ engagement is vital in creating a structure supportive of induction and mentoring for early career teachers. This article details a mixed-method research study that examined the role and impact of school administrators’ engagement in four teacher induction programs in the United States (two statewide and two district-wide) supported by the New Teacher Center. The results identify administrator role expectations and participants’ perceptions of the programs, and they indicate how vital school administrators’ leadership and commitment are to a successful program. The article concludes with implications for theory, practice, policy, and further research.
Assessment of Pleiades Satellite Image for Mangrove Family Classification

Remote sensing technology is the most common method used in monitoring conservation and restoration at mangrove areas. This study aims to classify the mangrove family at Bagan Datuk, Perak, using object-based image analysis techniques based on Pleiades’ image with 0.63m spatial resolution obtained from the Malaysian Remote Sensing Agency (ARSM). The segmentation was done by choosing a suitable scale and merge level. Two classifiers namely support vector machine (SVM) and k-nearest neighbor (KNN) were used to classify the mangrove family. The mangrove family map was produced from the higher accuracy of the classification. The results show that the overall accuracy of SVM is 63.81% (kappa = 0.55) while KNN is 59.83% (kappa = 0.50). In conclusion, SVM outperformed K-NN for mangrove family classification.
Autonomous cars: Recent developments, challenges, and possible solutions

The development of production automobiles involved vehicles that were propelled by internal combustion engines. Improvements in the internal combustion technology continue to take place even today. However, electric vehicles are slowly making way into the bigger picture. Development in autonomous vehicle technology is also gathering pace. Although there has been remarkable progress in this domain, much needs to be accomplished. Adoption of autonomous vehicle technology has multiple benefits. Autonomous car companies have spent large amount of resources on the development of autonomous vehicle technology, with an aim to fully commercialize the technology. Several issues cause hindrance to the achievement of this goal. These issues comprise technical, non-technical and legal challenges. The future of the technology is assuring and ambitious, however, the challenges must be overcome.
Mechanism of anionic polymerization of 2-hydroxyethyl(meth)acrylates initiated by alkali metals and their alkoxides

It was shown that the polymerization of 2-hydroxyethyl (meth)acrylates initiated by alkali metals proceeds through the formation of not only an alkoxide anion resulting from the interaction of a metal with a hydroxyl group but also via an anion-radical arising from the interaction between the metal and the double bond. The process affords a polymer containing commensurable amounts of carbo- and heterochain structures in the backbone. The additives of free-radical polymerization inhibitors completely suppress the formation of carbochain polymer fragments. The initiation of the anionic polymerization of 2-hydroxyethyl (meth)acrylates with mobile hydrogen atoms in the presence of alkali-metal alkoxides occurs via the exchange interaction of the initiator and a hydroxyl group of the monomer and through the direct interaction of the initiator with the double bond. The latter reaction leads to the loss of functionality in the macromolecule being formed. The use of a tert-butoxide anion with the bulky alkyl substituent as an initiator makes it possible to avoid the occurrence of the initiation reaction due to the direct interaction of the initiator with the double bond and to preserve 100% functionality in the resulting macromonomer.
Control of radio frequency capacitively coupled plasma asymmetries using Tailored Voltage Waveforms

This thesis describes the study of capacitively coupled plasmas excited with Tailored Voltage Waveforms (TVWs) and their use in silicon thin film fabrication for photovoltaic applications. Tailoring the voltage waveforms applied on an electrode of a plasma reactor is a recent technique, which has been developed after the discovery of the electrical asymmetry effect. Compared to standard sinewaves, TVWs are composed of a fundamental frequency, in the radio frequency domain, and its first harmonics. Any waveform can then be approximated by selecting the first components of its Fourier series. It has been shown for example that the ion energy can be controlled by the amplitude asymmetry of the waveform, the ion flux being kept constant. However, at high pressure, this decoupling weakens. We further explore the reasons behind it, using both simulations and experiments. Based on this knowledge, we make use of the ability of TVWs to vary the ion energy to understand its effect on silicon thin film deposition. We find well-defined energy thresholds above which we observe, through several characterization techniques, a lower nucleation density in the case of microcrystalline silicon growth. This indicates that ions with energy above these thresholds can break the crystalline bonds. We confirm this result by studying low-temperature epitaxial growth on a crystalline silicon wafer. Above the energy threshold, epitaxy breakdown occur, and distinct amorphous columns are observed in cross-section transmission electron microscopy images, which are initiated at locations where the crystal was broken by high energy ions. Because of unexpected different nucleation densities for sawtooth-up and down waveforms, despite the similar ion energies, we further explore the effect of these waveforms on plasma discharges. We find, using simulations in argon, that the asymmetry in the sheaths motion created by these waveforms induces a strong localization of the ionization events close to one electrode, and consequently a strong ion flux asymmetry. This localization is confirmed experimentally with phase-resolved optical emission spectroscopy. Surprisingly, we find that, for a given waveform, this asymmetry can be reversed for different chemistries (namely CF4 plasmas), because of different electron heating mechanisms. This new asymmetry can be fully controlled via the rise-to-fall ratio of the waveform, i.e. its temporal asymmetry, and therefore opens the path for new applications of TVWs.
Every urologist and oncologist should know about treating sexual and gender minority prostate cancer patients: translating research findings into clinical practice

In 2016, the NIH designated sexual and gender minorities (SGM) a health disparity population. The next year, the American Society of Clinical Oncology highlighted the need to improve the suboptimal cancer and survivorship care received by SGM populations. There are currently no evidence-based training programs in culturally competent care of prostate cancer patients who are gay, bisexual and/or transgender. In this selective review, we summarize findings from the largest quantitative studies focused on sexual minority prostate cancer survivors and from 65 interviews with NIH staff, clinicians, and cancer clinics in 11 US cities. The report is divided into three parts and uses a question and answer format to address 21 questions relevant to clinicians providing care to SGM prostate cancer patients. First, we identify population-specific issues that are culturally relevant in the care of SGM patients with prostate cancer. While a body of research has emerged on sexual minority prostate cancer patients, the literature on gender minorities is limited to single case reports and inadequate to inform practice. This review covers definitions, population size, cultural and historical context, sexual behavior, population invisibility, sexual orientation and gender identity (SOGI) in the electronic medical record, disparities and evidence of discrimination in treatment provision. The second part focuses on promoting evidence-informed, patient-centered care. This includes current practices in assessing sexual orientation, management of disclosure of sexual orientation, how to address common problems sexual minority men experience post-treatment, common questions sexual minority patients have, management of urinary incontinence, HIV and STI risk during and post-treatment, and sub-groups of sexual minority patients with worse outcomes. It then identifies how male partners differ in prostate cancer support, current research on rehabilitation for sexual minority men, issues in advanced prostate cancer, and things to avoid with minority patients. Finally, we examine the cultural divide between provider and patient, advocating for cultural humility when working with minority patients. Training programs and continuing education can help providers both to become more aware of their own cultural assumptions, informed about health disparities, and able to provide quality care, and to make clinics more welcoming to SGM patients.
Facial expression recognition based on facial part attention mechanism

Abstract. In the context of facial expression recognition (FER), a lot of methods focus on feature extraction of the whole facial image, but sometimes it is equally important to concentrate on local area features that embody stronger emotions. This paper proposes an FER method based on the facial part attention mechanism (FPA). We adopt the attention mechanism to extract emotional rich local area features, which are complementary to the whole facial features for better FER. First, this paper proposes a cluster-based facial landmarks selection method to select facial landmarks that have commonality and best reflect local area emotions. Then, we design an FPA convolutional neural network, which consists of two parts. The first part is the object network, which is used to extract the whole facial features; the second part is the part attention network, and the attention component is used to extract the local part features. Finally, the two parts are merged to train the final classifier. The method was tested on the Real-World Affective Faces database and the recognition accuracy of 87.26% was obtained, which proved the effectiveness of the method.
Symptomatic Features of 932 Hospitalized Patients with COVID-19 in Wuhan

BackgroundMuch remains unknown about COVID-19 onset and rehabilitation's symptomatic features, especially the long-term health consequences of patients with COVID-19 who have been discharged from the hospital.MethodsIn this cohort study, we collected the first pandemic data of hospitalized patients in Wuhan from February 20 to March 31, 2020. All patients completed a 3-month follow-up after discharge. We carefully analyzed the detailed symptomatic characteristics of severe COVID-19 at illness onset and three months after discharge, compared it with non-severe patients, and used multiple logistic regression to determine potential symptomatic risk factors for severe COVID-19.ResultsA total of 932 hospitalized patients with COVID-19 were enrolled, including 52 severe cases and 880 non-severe cases. Fever (60%), cough (50.8%), and fatigue (36.4%) were the most common symptoms, followed by anorexia (21.8%) and dyspnea (19.2%). The median duration of fever was seven days, which was characterized by persistent low fever. The median duration of cough was 17 days, characterized by dry cough without sputum. Most dyspnea occurred on the fourth day after symptom onset, with a median duration of 16 days. The incidences of taste loss and olfactory disturbance were only 6.2% and 3.1%, respectively. Multivariate logistic regression analysis showed that age over 65 years old (OR 6.52, 95% CI 3.27-13.02, P<0.0001), male sex (3.71, 1.90-7.26, P = 0.0001), fever lasting for more than five days (1.90, 1.00-3.62, P=0.0498), anorexia at onset (2.61, 1.26-5.40, P=0.0096), and modified Medical Research Council level above grade 2 when dyspnea occurred (14.19,7.01-28.71, P<0.0001) were symptomatic risk factors for severe COVID-19. Three months after discharge from the hospital, 6.2% of patients still cough, 7.2% of patients still dyspnea, and 1.8% still fatigue, and 1.5% of patients had olfactory or taste disorders.ConclusionsCOVID-19 caused clusters of symptoms, with multiple systems involved. Specific symptomatic features at the onset of illness have predictive value for severe COVID-19. Persistent legacy symptoms are more frequent in severe COVID-19 patients.