Miracle-dz/UniFAR-FTUs huggingface dataset:
Realizing the opportunities of the platform economy through freedom of association and collective bargaining

This study provides empirical evidence from different regions of the world to identify avenues for platform economy workers to access freedom of association and collective bargaining. It shows that collective protests, the establishment of new organizations of workers and platforms, social dialogue and, to a limited extent, collective bargaining are taking place in the platform economy. The experiences from the ground described in this study indicate ways and a demand to create an even more enabling environment for freedom of association and collective bargaining in order to realize the opportunities of the platform economy for workers and employers.
Proportional Odds Models for Survival Data and Estimates Using Ranks

Survival data are analysed where it is assumed that the logarithm of the odds against survival beyond a certain time is equal to a linear term, involving covariates in a regression model, plus a function of time. It is shown that estimates for the regression parameters can be obtained by replacing survival times by their ranks. Comparisons are made with estimates found by Bennett (1983a, b), who used the same model but different techniques of estimation.
Palmprint Recognition Based on Statistical Local Binary Orientation Code

A novel coding based method named as local binary orientation code (LBOCode) for palmprint recognition is proposed. The palmprint image is firstly convolved with a bank of Gabor filters, and then the orientation information is attained with a winner-take-all rule. Subsequently, the resulting orientation mapping array is operated by uniform local binary pattern. Accordingly, LBOCode image is achieved which contains palmprint orientation information in pixel level. Further we divide the LBOCode image into several equal-size and nonoverlapping regions, and extract the statistical code histogram from each region independently, which builds a global description of palmprint in regional level. In matching stage, the matching score between two palmprints is achieved by calculating the two spatial enhanced histograms' dissimilarity, which brings the benefit of computational simplicity. Experimental results demonstrate that the proposed method achieves more promising recognition performance compared with that of several state-of-the-art methods.
Identification of the Functional Modules of SlPP2C.D—SlSAUR and Their Roles in Abscisic Acid-Mediated Inhibition of Tomato Hypocotyl Elongation

The plant hormone ABA regulates various physiological processes, such as promoting stomatal closure and inhibiting hypocotyl elongation by mediating de-phosphorylation of H+-ATPase. However, the mechanism acting on ABA-induced de-phosphorylation of H+-ATPase remains largely unknown. SMALL AUXIN UP RNAs (SAURs), the largest family of early auxin-response genes, were well-reported to bind to and inhibit PP2C.D phosphatases to maintain plasma membrane H+-ATPase activity. In this study, we aimed to investigate whether SAUR-PP2C.D functional modules were involved in ABA-mediated inhibition of hypocotyl elongation. Here, we show that ABA suppresses hypocotyl elongation in both light-grown and dark-grown tomato seedlings in a dose-dependent manner. Hypocotyl elongation of dark-grown seedlings was more sensitive to ABA compared to that of light-grown seedlings. ABA upregulates seven SlPP2C.D genes. SlPP2C.D1 was highly expressed in hypocotyl and upregulated by light. Y2H data showed SlPP2C.D1 interacted with SlSAUR2, 35, 40, 55, 57, 59, 65, and 70. The other four SlPP2C.Ds were also associated with a subset of SAUR proteins. Our findings have provided new insights for further examination on the SAUR-PP2C.D modules that regulate outputs of ABA and other phytohormones controlling plant growth and development.
Synthesizing the evidence of nitrous oxide mitigation practices in agroecosystems

Nitrous oxide (N2O) emissions from agricultural soils are the main source of atmospheric N2O, a potent greenhouse gas and key ozone-depleting substance. Several agricultural practices with potential to mitigate N2O emissions have been tested worldwide. However, to guide policymaking for reducing N2O emissions from agricultural soils, it is necessary to better understand the overall performance and variability of mitigation practices and identify those requiring further investigation. We performed a systematic review and a second-order meta-analysis to assess the abatement efficiency of N2O mitigation practices from agricultural soils. We used 27 meta-analyses including 41 effect sizes based on 1119 primary studies. Technology-driven solutions (e.g. enhanced-efficiency fertilizers, drip irrigation, and biochar) and optimization of fertilizer rate have considerable mitigation potential. Agroecological mitigation practices (e.g. organic fertilizer and reduced tillage), while potentially contributing to soil quality and carbon storage, may enhance N2O emissions and only lead to reductions under certain pedoclimatic and farming conditions. Other mitigation practices (e.g. lime amendment or crop residue removal) led to marginal N2O decreases. Despite the variable mitigation potential, evidencing the context-dependency of N2O reductions and tradeoffs, several mitigation practices may maintain or increase crop production, representing relevant alternatives for policymaking to reduce greenhouse gas emissions and safeguard food security.
Hybrid Learning Methods for Accurate Prediction of
Cardiovascular Disease

Death from heart disease ranks high among the world's leading killers. Healthcare data analysis has a significant difficulty in the domain of cardiovascular disease forecast. The vast amounts of data generated by the healthcare business present significant opportunities for machine learning to aid in decision making and prediction. Additionally, recent innovations in various IoT domains have made use of deep - learning approaches (IoT). Predicting cardiovascular illness with machine learning techniques is only partially explored in existing research. In this study, we offer a new approach to heart disease diagnosis that makes use of methods of machine learning to identify critically important traits. Numerous feature combinations are introduced, together with several well-established classification methods, to serve as the basis for the prediction method. By combining Hybrid Randomized Forest as well as a Linear Model in cardiac disease predictions, we achieve performance improvements with an accuracy level of 86.47 percent.
Explicit mapping between a two-dimensional quantum Hall system and a one-dimensional Luttinger liquid. I. Luttinger parameters

We study a model of a quantum Hall system with the electrons confined to a narrow, linear channel. This system is mapped to a one-dimensional (1D) system which in the low-energy approximation has the form of a Luttinger liquid with different interactions between particles of equal and of opposite chiralities. The mapping is studied at the microscopic level, and we show in detail how the form of the 1D Luttinger liquid is detemined by interactions with particles at the edges and in the bulk of the quantum Hall system. We focus, in particular, on how the parameters are renormalized by the interactions between the two edges and show that the velocity parameter of the current is not modified. The paper is Paper I in a sequence of two papers, where Paper II [M. Horsdal and J. M. Leinaas, Phys. Rev. B 76, 195322 (2007)] gives a derivation of the two-dimensional electron correlation function.
Enhancing Students’ Online Learning Experiences with Artificial Intelligence (AI): The MERLIN Project

The COVID-19 pandemic led to all institutions of education having to transition to fully online learning almost immediately. However, research showed that online learning still lacked adequate interactions with students. This is even more problematic when students are online learning on their own, when adequate online scaffolding activities are absent. This study investigated the impact of chatbots as a scaffolding agent to assist student learning during their independent online learning times. A total of 62 Diploma level students participated in this mixed method research study and presented with a multimedia-based AI chatbot named MERLIN. Data was collected on their attitudes towards using it. Results showed that students were motivated to learn more using MERLIN, improved their learning, and wanted more chatbots in their other courses. These findings have important implications for using AI chatbots as a scaffolding and instructional tool in 21st-century learning environments. © 2022, International Journal of Technology. All Rights Reserved.
A blind and militant attachment: Russian patriotism in comparative perspective

ABSTRACT Much of the literature on patriotic sentiment in post-Soviet Russia leans on public opinion surveys administered exclusively to Russian citizens. Absent a comparison group, such evidence, while helpful, can leave one adrift in trying to assess the meaning of a particular polling result. Drawing on multiple waves of from the International Social Survey Program and the World Values Survey, we benchmark Russians’ patriotic sentiment to that of citizens in a diverse group of middle- and high-income countries. This exercise highlights that while Russians are not unusual in the degree to which they have a benign attachment to and/or pride in their country, they stand out for espousing a patriotism that has remained consistently blind and militant since at least the mid-1990s. We speculate as to the underlying cause and highlight a potential consequence: the nature of Russian patriotism has lowered the cost to the Russian leadership of military aggression.
Object Removal by Hierarchical Super-Resolution Based Inpainting

This paper introduces novel frame work for Object Removal by means of inpainting. In this method, first the object in the required target area is removed by inpainting. The output thus obtained is given as input to a super-resolution algorithm to recover details on missing areas. Exemplar-based inpainting is used to remove objects that are not required. It is desirable to use a Super-resolution algorithm since inpainting produces a low resolution (LR) image. In this paper a regularization method based on morphologic operations is used for SR image reconstruction. It is always desirable to generate a high resolution (HR) image as it shows more intricate details.
An IoT enabled smart healthcare system using deep reinforcement learning

Smart healthcare systems do exist with a variety of architectures. However, the hunt for better smart healthcare systems is more predominant. The cutting‐edge field of IoT (internet of things) and technological developments provide better solutions for smart healthcare systems using Sensor–Body Area Networks. Thus, the patient's sensor data can be collected, stored, analyzed, and suitable treatments can be offered, over the inter‐network, anytime, anywhere. The most complex part in such systems is the physician analysis of the huge volume of patient's data, to handle and prepare suitable diagnose and treatment for humanity. This article reveals a methodology of Deep Reinforcement Learning for smart healthcare decisions in an IoT interfaced Smart Healthcare–intelligent monitoring system. The system incorporates four layers, patient data collection, Edge computing, patient data transmission and Cloud computing. IoT is employed for automatic collection of Patient's data and for transmission of data, to data centers. Artificial intelligence techniques are used to analyze these data to provide suitable decisions, diagnosis, and treatment for those patients and humanity. Deep Reinforcement Learning provides the platform for smart decisions, diagnosis, and treatment. The investigation was experimented with synthetic simulated data of various BAN sensors. We developed a data set of size 286, which contains 21 different health parameters. After pre‐processing, these data were stored in the Amazon web services (AWS) cloud server using (message queue telemetry and transport) MQTT–IoT protocol. Initially, the Deep Q‐Network (DQN) was imposed to the training algorithm. The methodology was examined in PyTorch using a single GTX 1080 Ti X GPU with the training data sizes from 27 to 1536. The training time was about 10,000 to 90,000 s for training 500 epochs. In the high dimensional action space environment, the algorithm responded slowly to analyze, explore, and determine effective healthcare strategies. The systems convergence response of estimated hidden health state (g') and the actual health state (g) for the 21 different health parameters were estimated, whose values range from 0 to 1. The system responded with smart decisive interventions, which were good and close to that of a Physician's decision. The proposed methodology is definitely a promising solution for a smart and economic telemedicine.
Functional profile in Parkinson’s disease and predictors of gait speed

Abstract Purpose: To improve the social participation of people with Parkinson’s disease (PD), it is important to establish the functional profile and variables that influence gait, as gait is a predictive factor for independence. Based on the biopsychosocial approach of the International Classification of Functioning, Disability and Health (BPS-ICF), this paper aimed to characterise the functional profile of people with PD and correlate measures related to gait speed. Methods: Cross-sectional research with 50 people with PD, systematically assessed with functional measures, analysed based on the ICF dimensions. From this framework, multiple linear regression analyses were used to determine the gait speed predicting variables. Results: The participants’ mean age was 63.16 ± 12.68 years; 58% (n = 29) were men; 30 participants (60%) were classified into Hoehn & Yahr stages 1 and 2. Multiple linear regression analysis indicates that gait speed is significantly influenced by Mini BESTest, 6-Minute Walk Test, and handgrip strength. Conclusions: The functional profile based on the BPS-ICF model was outlined, and findings related to personal and environmental factors were described. The triad – body balance, aerobic conditioning, and muscle strength – was identified as predictive variables for gait speed in people with PD.
Cost-Driven Design of Printed Wideband Antennas with Reduced Silver Ink Consumption for the Internet of Things

The Internet of Things (IoT) accelerates the need for compact, lightweight and low-cost antennas combining wideband operation with a high integration potential. Although screen printing is excellently suited for manufacturing conformal antennas on a flexible substrate, its application is typically limited due to the expensive nature of conductive inks. This paper investigates how the production cost of a flexible coplanar waveguide (CPW)-fed planar monopole antenna can be reduced by exploiting a mesh-based method for limiting ink consumption. Prototypes with mesh grids of different line widths and densities were screen-printed on a polyethylene terephthalate (PET) foil using silver-based nanoparticle ink. Smaller line widths decrease antenna gain and efficiency, while denser mesh grids better approximate unmeshed antenna behavior, albeit at the expense of greater ink consumption. A meshed prototype of 34.76×58.03mm with almost 80% ink reduction compared to an unmeshed counterpart is presented. It is capable of providing wideband coverage in the IMT/LTE-1/n1 (1.92–2.17 GHz), LTE-40/n40 (2.3–2.4 GHz), 2.45 GHz ISM (2.4–2.4835 GHz), IMT-E/LTE-7/n7 (2.5–2.69 GHz), and n78 5G (3.3–3.8 GHz) frequency bands. It exhibits a peak radiation efficiency above 90% and a metallized surface area of 2.46 cm2 (yielding an ink-to-total-surface ratio of 12.2%).
Real-Time Marker-Based Tracking and Pose Estimation for a Rotating Object Using High-Speed Vision

Object tracking and pose estimation have always been challenging tasks in robotics, particularly for rotating objects. Rotating objects move quickly and with complex pose variations. In this study, we introduce a marker-based tracking and pose estimation method for rotating objects using a high-speed vision system. The method can obtain pose information at frequencies greater than 500 Hz, and can still estimate the pose when parts of the markers are lost during tracking. A robot catching experiment shows that the accuracy and frequency of this system are capable of high-speed tracking tasks.
Don't Panic: Remaining El Capitan While Navigating Unpreparedness in Response to Extreme Events

Company leaders are frequently confronted with highly uncertain and risky situations for which they are often ill-prepared, and consequently, in which they often panic. Based on an exploratory case study of extreme rock climber Alexander Honnold, we have developed propositions that help decision-makers to learn to avoid panic in crises. Our findings suggest that gradual exposure to incrementally more challenging tasks by adjusting individual learning goals allows decision-makers to leverage existing skills, and to develop their physical, mental and emotional states simultaneously. Deliberate confrontation with the challenges that may trigger panic through real-life simulation and imagination helps decision-makers to retrain and transform triggers for panic responses, and to build systemic confidence. This organic and holistic growth provides decision-makers with the simultaneous preparation and mastery of their physical, mental, and emotional states to ensure the lightness and calmness necessary not to panic when facing a crisis.
Leveraging Artificial Intelligence to Analyze Consumer Sentiments within Their Context: A Case Study of Always #LikeAGirl Campaign

Abstract Because practitioners and scholars are increasingly using artificial intelligence (AI) to analyze consumer sentiments toward social media–based campaigns, we compared various supervised machine-learning (ML) algorithms (four traditional ML-based algorithms and two proprietary deep-learning-based models from Amazon and Google) in terms of their performances in classifying user comments into a sentiment category. We adopted the #LikeAGirl campaign by Always feminine hygiene brand as a case and analyzed the sentiments of 19,198 YouTube comments on the campaign using different supervised ML algorithms. Results indicate that the two proprietary models from Amazon and Google performed better than traditional supervised ML algorithms. We then used unsupervised ML to reveal the hidden topics from positive and negative consumer comments. The study has important methodological implications for advertising practitioners and scholars.
Monte Carlo methods for phase equilibria of fluids

This article presents an overview of Monte Carlo methods for simulations of the phase behaviour of fluids. The Gibbs ensemble method and histogram-reweighting Monte Carlo techniques are described in detail. The Gibbs ensemble method is based on simulations of two regions coupled via volume change and particle transfer moves so that the conditions for phase coexistence are satisfied in a statistical sense. Histogram-reweighting methods obtain the free energy of a system over a broad range of conditions from a small set of grand canonical Monte Carlo calculations. The histogram methods can produce highly accurate data, especially in the vicinity of critical points. Other methods described briefly include interfacial simulations, the NPT + test particle method, Gibbs-Duhem integration and pseudo-ensembles. Configurational-bias sampling techniques and expanded ensembles can be used for multisegment molecules to increase the efficiency of the simulations. The last section of the review covers applications to both model and realistic systems that have appeared since 1995.
Accessibility to Integrated Community Case Management of Childhood Pneumonia Services among Caregivers in Kisii County, Kenya

Pneumonia is the leading cause of childhood illness and mortality worldwide. The number of children under five with pneumonia in Kisii County, Kenya, was 10% compared to the national average of 9% despite the integrated Community Case Management (iCCM) roll out. In order to identify factors associated with caregivers' access to iCCM services, a study was conducted. Materials and Methods: Qualitative and quantitative approaches were used in this descriptive cross-sectional study. Purposive sampling was utilized to choose 10 informants while multistage and random sampling was applied to select 330 caregivers. Thematic content analysis and discourse analysis techniques were used to analyse qualitative data, while Statistical Package for Social Science (SPSS) version 22 was used in quantitative data. Results: Only 33.6% of caregivers accessed iCCM for childhood pneumonia services. In 49% of cases, CHVs was the first point of contact after onset of pneumonia symptoms, with only 31.2% seeking help within 24 hours. 96% of CHVs did not follow the recommended iCCM diagnostic protocols, and only 34.8% prescribed antibiotics. Age (0.011), religion (0.007), and marital status (0.024) were significantly associated with access to iCCM. Business persons were 2.97 times (OR 2.972; CI 1.232,7.167; P= 0.015) more likely to access iCCM than farmers. Caregivers who perceived pneumonia to be severe were 3.03 more likely to access ICCM (OR 3.039;CI 1.703,5.424; P< 0.001) than opposite peers. Respondents who didn't think pandemics affected access were 10% (OR 0.119; CI .058,0.246; P<.0.001) more likely to access iCCM. Conclusion: Access to iCCM was low and Community health volunteers (CHVs) were not the first point of care for pneumonia in children. Evidence of poor performance by CHVs in the areas of diagnosis, treatment, and the administration of antibiotics suggests that iCCM needs more monitoring and oversight.
From Structure to Function: Understanding Synthetic Conditions in Relation to Magnetic Properties of Hybrid Pd/Fe-Oxide Nanoparticles

Heterostructured magnetic nanoparticles show great potential for numerous applications in biomedicine due to their ability to express multiple functionalities in a single structure. Magnetic properties are generally determined by the morphological characteristics of nanoparticles, such as the size/shape, and composition of the nanocrystals. These in turn are highly dependent on the synthetic conditions applied. Additionally, incorporation of a non-magnetic heterometal influences the final magnetic behavior. Therefore, construction of multifunctional hybrid nanoparticles with preserved magnetic properties represents a certain nanotechnological challenge. Here, we focus on palladium/iron oxide nanoparticles designed for combined brachytherapy, the internal form of radiotherapy, and MRI-guided hyperthermia of tumors. The choice of palladium forming the nanoparticle core is envisioned for the eventual radiolabeling with 103Pd to enable the combination of hyperthermia with brachytherapy, the latter being beyond the scope of the present study. At this stage, we investigated the synthetic mechanisms and their effects on the final magnetic properties of the hybrid nanoparticles. Thermal decomposition was applied for the synthesis of Pd/Fe-oxide nanoparticles via both, one-pot and seed-mediated processes. The latter method was found to provide better control over morphology of the nanoparticles and was therefore examined closely by varying reaction conditions. This resulted in several batches of Pd/Fe-oxide nanoparticles, whose magnetic properties were evaluated, revealing the most relevant synthetic parameters leading to promising performance in hyperthermia and MRI.