Miracle-dz/UniFAR-FTUs huggingface dataset:
A 6-bit CMOS Active Phase Shifter for Ku-Band Phased Arrays

A 6-bit high-accuracy active phase shifter is developed in 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology for <italic>Ku</italic>-band phased arrays. Improved transformer balun with wriggly stub is applied to split the single input to differential and achieve high balance with reduced chip area. Two-stage polyphase filter (PPF) generates broadband I/Q signals with high accuracy. A main digital-to-analog convertor (DAC) controls the I/Q amplitude to achieve 6-bit phase resolution, while an auxiliary DAC compensates the amplitude/phase error introduced by balun and PPF. Consequently, high resolution along with low phase/gain error can be achieved. The phase shifter shows measured rms phase error of <4° at 12–18 GHz and <3° at 13–18 GHz. The measured mean gain is −2.5–1 dB, and the rms gain error is <0.9 dB at 12–18 GHz. The total power consumption is 37.5 mW, and chip size is <inline-formula> <tex-math notation="LaTeX">$0.75 \times 0.32$ </tex-math></inline-formula> mm<sup>2</sup> excluding pads.
A Study of the Hybrid Recurrent Neural Network Model for Electricity Loads Forecasting

In the electric industry, electricity loads forecasting has become more and more important, because electricity demand quantity is a major determinant factor in electricity supply strategy. Furthermore, accurate regional electricity loads forecasting is one of the principal factors for the electric industry to improve the energy management performance. Recently, statistical methods and artificial intelligence have been developed for electricity loads forecasting. However, there are two drawbacks in the past electricity loads forecasting models: (1) conventional statistical methods, such as regression models are unable to deal with the nonlinear relationships well, because electricity loads forecasting are known to be the nonlinear relationships; and (2) artificial intelligence technologies (i.e., support vector machines (SVM) and convention artificial neural networks (ANNs)) do not take into account time series causality for regional electricity loads forecasting. Based on these reasons above, this study proposes a new electricity loads forecasting model, which incorporates one step-ahead concept into recurrent neural network (RNN) to build a hybrid RNN model. The time series method considered from RNN model, which can be fitted to time series electricity loads datasets, and the neural network in hybrid RNN model can deal with the nonlinear relationships. For evaluating electricity loads forecasting performance, six different electricity loads forecasting models (RSVMG, ANN, Regression, HEFST, ANFIS and AR(2)) are used as comparison models. The research results indicate that the proposed electricity loads forecasting model is superior to the listing comparison models in terms of mean absolute percentage errors (MAPE).
Elevation patterns of plant diversity and recent altitudinal range shifts in Sinai's high‐mountain flora

Questions: Is there evidence of recent altitudinal range shifts in a hyper-arid Middle Eastern desert mountain flora? How do the directions of shift for upper and lower altitudinal range limits of plants vary? Location: Hyper-arid mountain desert, St Katherine Protectorate, South Sinai, Egypt. Method: We tested for shifts in both upper and lower altitudinal range limits by comparing a 1970s dataset of recorded species’ limits with recent surveys using altitudinal transects across 36 sites. Altitudinal limits between 63 paired upper-limit and 22 paired lower-limit values from the 1970s and 2014 were compared using paired t-tests; binomial tests were used to indicate the dominant direction of change. The upper and lower limits of 22 species were considered together to allow assessment of overall altitudinal range-size changes. In order to avoid the potential effect of yearly environmental fluctuations on the distributions of annual species, subsets of upper and lower limit shifts were taken for perennials, and trees and shrubs. Results: Our results show significant overall upslope shifts in mean upper altitudinal limits and significant overall downslope shifts in mean lower altitudinal limits. A majority of assessed species expanded their altitudinal ranges, but the responses of individual species varied. Since perennial herbs/graminoids, and trees and shrubs, show strong patterns of change, we suggest there has been a long-term shift in altitudinal range in South Sinai’s mountain flora. Greater research effort needs to be focussed upon the drivers of range-shift responses in arid regions.
Subjective Experience of Illness Among Adolescents and Young Adults With Diabetes

Diabetes mellitus type 1 is a chronic illness that has its onset in childhood or adolescence. It is determined by the insufficient production of insulin. The main therapies consist of replacement of the lacking insulin, diet, exercise, and glycemic control. If treatment is managed by parents in childhood, adherence seems to be a difficult issue during adolescence. In order to understand the subjective experience of the illness and treatment, the present study aims to investigate the patients’ subjective perspective by addressing a group of adolescents and young adults in pediatric care. A qualitative research design was adopted with semi-structured interviews, and later software thematic analysis was performed. Six core issues were deepened: family and health-care professional’s mediation support for self-management, adherence to treatments as a boring responsibility, disclosure problems in peer relationships, difficulty in becoming autonomous in a period of socioeconomic crisis, illness as concern for the future, and seeking a normal life in daily activities. The knowledge can be useful for designing more appropriate interventions that favor the active engagement of patients.
Compact Data Structures for Location-Based Forwarding in NDN Networks

Since data obtained by Internet of Things devices is correlated with their locations, location-based services (LBSs) of exploiting data locality are promising. We design a location- based forwarding mechanism to retrieve location data in Named Data Networking networks to support such LBSs. A key feature is that a name of location is used as a destination rather than a physical address. This paper proposes a naming scheme for location data and data structures for location-based forwarding.
UAV-Assisted Uplink Transmission for Ultra-Reliable and Low-Latency Communications

In this work, we investigate the potential of using unmanned aerial vehicles (UAVs) in supporting ultra- reliable and low-latency communications (URLLC). The key idea is to leverage better link qualities provided by high possibility of line-of-sight (LoS) links in UAV communication systems. We first characterize the latency, the reliability and the network availability of UAV communication systems. We show that the probability of the existing of LoS links and the network availability are strictly concave in terms of the distance between the user and the UAV. Then, given the density of UAVs, we optimize the altitude of UAV and bandwidth allocation for minimizing the required total bandwidth of URLLC. Numerical results show that increasing the density of UAVs can remarkably reduce the total bandwidth required by URLLC in suburban areas. It is also shown that, in urban areas, using a single ground-to-air wireless link is not sufficient for ensuring the QoS and the network availability of URLLC, implying the needs of deploying multiple ground-to-air or ground-to-ground wireless links in order to support URLLC.
Effect of pre-sowing electromagnetic treatment on seed germination and seedling growth in maize (Zea mays L.)

The application of magnetic fields appears to produce changes in some physiological processes of plants, including encouraging their development. This study reports the effects of different intensities of electromagnetic fields generated by a solenoid with a variable horizontal magnetic field on the germination and growth of maize seeds. We worked with the experimental seed hybrid San Jose, which was exposed to electromagnetic fields of 0, 2, 4 and 6 mT for 3 min, using a completely randomized design with two seed conditions: imbibed and non-imbibed. Analysis of variance was performed for the variables germination and growth, with a level of significance of 95%; increases were observed in germination, vigor index I, vigor index II and root length, 3, 20, 34 and 23% respectively, compared to the control seeds (untreated) on the eighth day. Similarly, the presence of chlorophyll pigments and carotenoids was assessed and an increase was found in the concentration of chlorophyll a, chlorophyll b and carotene in corn seedlings grown from electromagnetically treated seeds. The response varied depending on the magnetic induction, without any particular trend, the best treatment was 4 μ T for 3 min of exposure. The improvement of the evaluated functional variables suggests that the seeds may perform better with an electromagnetic field treatment.
Transform-Data-by-Example (TDE): An Extensible Search Engine for Data Transformations

Today, business analysts and data scientists increasingly need to clean, standardize and transform diverse data sets, such as name, address, date time, and phone number, before they can perform analysis. This process of data transformation is an important part of data preparation, and is known to be difficult and time-consuming for end-users. Traditionally, developers have dealt with these longstanding transformation problems using custom code libraries. They have built vast varieties of custom logic for name parsing and address standardization, etc., and shared their source code in places like GitHub. Data transformation would be a lot easier for end-users if they can discover and reuse such existing transformation logic. We developed Transform-Data-by-Example ( TDE ), which works like a search engine for data transformations. TDE "indexes" vast varieties of transformation logic in source code, DLLs, web services and mapping tables, so that users only need to provide a few input/output examples to demonstrate a desired transformation, and TDE can interactively find relevant functions to synthesize new programs consistent with all examples. Using an index of 50K functions crawled from GitHub and Stackoverflow, TDE can already handle many common transformations not currently supported by existing systems. On a benchmark with over 200 transformation tasks, TDE generates correct transformations for 72% tasks, which is considerably better than other systems evaluated. A beta version of TDE for Microsoft Excel is available via Office store 1 . Part of the TDE technology also ships in Microsoft Power BI.
Impact of Female Education on Fertility in Bangladesh

This study aims to assess the extent to which female education affects cumulative fertility and progression to parity-specific fertility (up to fourth birth) in Bangladesh. It also aims to quantify the contribution of selected socio-demographic and cultural covariates to the fertility differentials between various categories of education. Fertility differentials across various categories of female education were examined using data from the 2011 Bangladesh Demographic and Health Survey. Poisson regression estimates, decomposition analysis (for cumulative fertility) and event history analysis (for parity-specific fertility) were done. The findings show that lower educated females are more likely to have higher cumulative fertility and higher progression to parity-specific fertility compared to their counterparts with higher education even after adjusting for selected socio-demographic and cultural characteristics. The findings suggest that the government should take necessary initiatives to ensure higher education for all women to reduce fertility rate in Bangladesh.
Junior medical students’ knowledge about and attitudes towards electroconvulsive therapy in a South African setting

Background Although electroconvulsive therapy (ECT) is a safe and effective treatment modality with a long history of use in psychiatry, it remains controversial owing to misconceptions and negative attitudes among the public and medical profession. The aim of this study was to explore the state of knowledge and attitudes towards ECT among a sample of South African medical students. Method Prior to their theoretical psychiatry module, 131 second-year medical students responded to an anonymous online survey designed to assess the source and extent of their ECT knowledge as well as their attitude towards ECT and psychiatry in general. Results The Internet (46.6%) and TV and/or movies (30.5%) were the principal sources of knowledge of ECT while ‘professional publication’ was the least common (0%). The students’ attitudes towards psychiatry were generally positive and nearly one-third (29.8%) would consider specialising in the field. Overall, perception towards ECT was mixed, with many respondents approving of its use albeit only as a last resort. Notably, low ECT knowledge scores were associated with more negative attitudes towards this treatment modality and a lower perception of psychiatry as a medical speciality. Conclusion The findings indicate that for these students, media is the main source of ECT knowledge. While they are generally knowledgeable about ECT, they still harbour some misconceptions and negative attitudes about the treatment. Knowledge appears able to amend these attitudes, thus underlining the importance of integrating accurate information about ECT into the preclinical medical curriculum rather than leaving it to mass media to forge warped perceptions and attitudes for these future clinicians.
An Efficient Approach Based on UAV Orthographic Imagery to Map Paddy With Support of Field-Level Canopy Height From Point Cloud Data

Unmanned aerial vehicle technology which is capable of acquiring the centimeter-level orthographic images provides a promising way to map the paddy rice extent. However, crop spectral mixture is still the major that limits the performance of paddy rice identification when using the spectral information alone. In order to eliminate the influence of spectral mixture on crop mapping accuracy, an innovative method was proposed to produce field-level canopy height data by calculating the elevation difference between the vegetated and nonvegetated parcels. We applied support vector machine on four types of datasets: 1) Pixel-based Spectral Features (PSF); 2) PSF and Canopy Height Features; 3) Object-based Spectral Features (OSF); and 4) Object-based Spectral Features and Canopy Height Features (OSCHF). The results showed that the full-class classification accuracy from OSCHF was the highest with an overall accuracy of 94.04% and Kappa of 0.91, which was significantly higher than result using OSF, and the accuracy of PSF was the worst. The OSF could eliminate speckle noise problem to some extent, but grapes and trees were still to some extent misclassified as paddy rice due to similar spectrum. Fortunately, these confusions were effectively avoided by including the canopy height.
Synthesis and Characterization of Graphene Oxide and Reduced Graphene Oxide Thin Films Deposited by Spray Pyrolysis Method

Graphene Oxide (GO) was chemically synthesized from Natural Flake Graphite (NFG). The GO was chemically reduced to Reduced Graphene Oxide (RGO) using hydrazine monohydrate. Thin films of GO and RGO were also deposited on sodalime glass substrate using spray pyrolysis technique (SPT). The samples were characterized using Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM) with Energy Dispersive X-Ray (EDS) facility attached to it, UV-Visible Spectrometry and Four-Point probe. The FTIR spectra showed the addition of oxygen functionality groups in GO while such groups was drastically reduced in RGO. SEM micrograph of GO thin film showed a porous sponge-like structure while the micrograph of RGO thin film showed evenly distributed and well connected graphene structure. The EDX spectrum of RGO showed that there was decrease in oxygen content and increase in carbon content of RGO when compared to GO. The optical analysis of the GO and RGO thin films gave a direct energy bandgap of 2.7 eV and 2.2 eV respectively. The value of sheet resistance of GO and RGO films was determined to be 22.9 × 106Ω/sq and 4.95 × 106Ω/sq respectively.
When and why do eyewitnesses make identification errors ?

Lorraine Hope PhD is a Reader in Applied Cognitive Psychology in the Department of Psychology at the University of Portsmouth. Her research interests focus on the performance of human cognition in applied contexts, including witness and victim memory, stress, and decision making under challenging conditions. In particular, her work has focused on developing theoretically-informed approaches to investigative interviewing for use in policing and wider investigative contexts, such as the Self-Administered Interview (SAI©). She is currently an Associate Editor for the British Psychological Society (BPS) journal, Legal and Criminological Psychology and a Consulting Editor for the American Psychological Association (APA) Journal of Experimental Psychology: Applied.
Adaptive Cooperative Tracking Control of Multi-Agent Systems With Unknown Actuators Hysteresis

This paper considers cooperative tracking control of nonlinear multi-agent systems with actuators hysteresis over digraphs. Each agent is modeled by a higher-order nonlinear system in strict-feedback form with the generalized Prandtl–Ishlinskii hysteresis input and unknown time-varying virtual control coefficients. An online adaptive law is introduced for compensating the unknown hysteresis effect. A special Nussbaum-type function is developed to handle unknown time-varying virtual control coefficients. A switching mechanism is utilized to combine the neural networks approximation with an extra robust term, which can take over the authority outside the neural active region. In this sense, the globally uniformly ultimately bounded stability is guaranteed by the proposed distributed adaptive control law. Moreover, all agents ultimately synchronize to the leader node with bounded residual errors. Simulation results justify the proposed algorithm.
Early and persistent motor difficulties in infants at-risk of developing autism spectrum disorder: A prospective study

Analyses were conducted in order to investigate motor development in younger siblings of children diagnosed with autism spectrum disorder (ASD). Infants at familial risk and low risk of developing ASD were tested longitudinally between the ages of 7 and 36 months. Data were analysed from motor scales on the Mullen Scales of Early Learning and the Vineland Adaptive Behaviour Scales at each age point. Significantly lower motor scores in at-risk infants were evident from the age of 7 months compared to the low-risk group. Infants who were later diagnosed with ASD demonstrated significantly poorer Fine Motor skills at 36 months than at-risk infants without any developmental difficulties. In addition, Gross Motor scores were highly correlated across the two measures for low-risk infants and infants who later developed ASD. Early motor difficulties may be an early indicator of a number of neurodevelopmental disorders, including ASD.
Prevalence and factors associated with burnout among frontline primary health care providers in low- and middle-income countries: A systematic review

Background: Primary health care (PHC) systems require motivated and well-trained frontline providers, but are increasingly challenged by the growing global shortage of health care workers. Burnout, defined as emotional exhaustion, depersonalization, and low personal achievement, negatively impacts motivation and may further decrease productivity of already limited workforces. The objective of this review was to analyze the prevalence of and factors associated with provider burnout in low and middle-income countries (LMICs). Methods: We performed a systematic review of articles on outpatient provider burnout in LMICs published up to 2016 in three electronic databases (EMBASE, MEDLINE, and CAB). Articles were reviewed to identify prevalence of factors associated with provider burnout. Results: A total of 6,182 articles were identified, with 20 meeting eligibility criteria. We found heterogeneity in definition and prevalence of burnout. Most studies assessed burnout using the Maslach Burnout Inventory. All three dimensions of burnout were seen across multiple cadres (physicians, nurses, community health workers, midwives, and pharmacists). Frontline nurses in South Africa had the highest prevalence of high emotional exhaustion and depersonalization, while PHC providers in Lebanon had the highest reported prevalence of low personal achievement. Higher provider burnout (for example, among nurses, pharmacists, and rural health workers) was associated with high job stress, high time pressure and workload, and lack of organizational support. Conclusions: Our comprehensive review of published literature showed that provider burnout is prevalent across various health care providers in LMICs. Further studies are required to better measure the causes and consequences of burnout and guide the development of effective interventions to reduce or prevent burnout.
Non-Production Benefits of Education: Crime, Health, and Good Citizenship

A growing body of work suggests that education offers a wide-range of benefits that extend beyond increases in labor market productivity. Improvements in education can lower crime, improve health, and increase voting and democratic participation. This chapter reviews recent developments on these 'non-production' benefits of education with an emphasis on contributions made by economists.
Subsurface of a Nuclear Legacy Waste Site Communities in the Highly Contaminated Rhodanobacter Dominate Bacterial Denitrifying Bacteria from the Genus

Published Ahead of Print 16 December 2011. 10.1128/AEM.06435-11. 2012, 78(4):1039. DOI: Appl. Environ. Microbiol. Brooks and Joel E. Kostka Tiedje, David B. Watson, Christopher W. Schadt, Scott C. Overholt, Erick Cardenas, Daniela Hubbard, James M. Stefan J. Green, Om Prakash, Puja Jasrotia, Will A. Subsurface of a Nuclear Legacy Waste Site Communities in the Highly Contaminated Rhodanobacter Dominate Bacterial Denitrifying Bacteria from the Genus
Hardness of improper one-sided learning of conjunctions for all uniformly falsifiable CSPs

We consider several closely related variants of PAC-learning in which false-positive and false-negative errors are treated differently. In these models we seek to guarantee a given, low rate of false-positive errors and as few false-negative errors as possible given that we meet the false-positive constraint. Bshouty and Burroughs first observed that learning conjunctions in such models would enable PAC-learning of DNF in the usual distribution-free model; in turn, results of Daniely and Shalev-Shwartz establish that learning of DNF would imply algorithms for refuting random k-SAT using far fewer constraints than believed possible. Such algorithms would violate a slight strengthening of Feige’s R3SAT assumption, and would violate the RCSP hypothesis of Barak et al. We show here that actually, an algorithm for learning conjunctions in this model would have much more far-reaching consequences: it gives refutation algorithms for all predicates that are falsified by one of the uniform constant strings. To our knowledge, this is the first hardness result of improper learning for such a large class of natural average-case problems with natural distributions.