Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Design of the Novel Class-F/E Power Amplifier With Series Output Filter

In this brief, a new lumped-element Class-F<sub>3</sub>/E and transmission-line (TL) Class-F<sub>3</sub>/E<sub>2</sub> power amplifiers are presented. It is shown that employing the series output filter (inverse Class-E) combined with a third harmonic control circuit (Class-F) leads to the new Class-F/E power amplifier. Compared with other switch-mode Class-E, Class-F/E, and Class-E/F power amplifiers, the proposed circuit has a higher maximum operating frequency (<inline-formula> <tex-math notation="LaTeX">${f} _{\max }$ </tex-math></inline-formula>), higher output power capability (<inline-formula> <tex-math notation="LaTeX">${C} _{\mathrm{ p}}$ </tex-math></inline-formula>), and lower peak drain voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{\max }$ </tex-math></inline-formula>). The capability of the Class-F/E mode with a series filter has been demonstrated by two design examples, one with lumped elements at 100 MHz and the other with transmission lines at 2.21 GHz. Two test boards of power amplifiers were measured, and high-performance results have been obtained. The simulated and measured drain efficiency (DE) of 86.4/85 %, output power of 41/40.6 dBm, and gain of 15.9/15.6 dB for the lumped-element Class-F<sub>3</sub>/E power amplifier have been obtained. On the other hand, the simulated/measured DE of 83/81%, power added efficiency of 79/78%, and output power of about 40/39.8 dBm for Class-F<sub>3</sub>/E<sub>2</sub> power amplifier at an input power of 27 dBm were obtained.
FPGA-Embedded Smart Monitoring System for Irrigation Decisions Based on Soil Moisture and Temperature Sensors

The basic need common to all living beings is water. Less than 1% of the water on earth is fresh water and water use is increasing daily. Agricultural practices alone require huge amounts of water. The drip technique improved the efficiency of water use in irrigation and initiated the introduction and development of fertigation, the integrated distribution of water and fertilizer. The past few decades have seen extensive research being carried out in the area of development and evaluation of different technologies available to estimate/measure soil moisture to aid in various applications and to facilitate the use of drip irrigation for users and farmers. In this technology, plant moisture and temperature are accurately monitored and controlled in real time over roots in the form of droplets, by developing smart monitoring system to save water and avoid water waste using drip irrigation technology. Water is delivered to the roots drop by drop, which saves water as well as prevents plants from being flooded and decaying due to excess water released by irrigation methods such as flood irrigation, border irrigation, furrow irrigation, and control basin irrigation. Drip irrigation with an embedded intelligent monitoring system is one of the most valuable techniques used to save water and farmers’ time and energy. In this paper, we design an embedded monitoring system based in the integrated 65 nm CMOS technology in agricultural practices which would facilitate agriculture and enable farmers to monitor crops. Hence, to demonstrate the feasibility, a prototype was constructed and simulated with modelsim and validated with nclaunch the both tools from Cadence, as well as implementation on the FPGA board, was be performed.
Self-referential processing in false recognition and source monitoring: Self-other differences

ABSTRACT The present experiment examined the effect of self, close other, and unknown other-references on false recognition and source monitoring in the Deese-Roediger-McDermott (DRM) paradigm. In the present study, participants were shown items with their own name, the name of a closely known other, an unknown other, or alone. Then, they were asked to recognise the items and the reference conditions. The results showed that the items paired with oneself and a closely known other lead to higher true recognition scores compared to other two conditions. Moreover, the source of the items in the self and the close other-reference conditions were more likely to be attributed to the correct sources than the latter two. However, the self and the close other-referenced items increased false recognition and source misattribution scores. The findings indicate that referencing the self and close others may lead to memory illusions via distorting source monitoring abilities.
Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

The way people travel, organise their time, and acquire information has changed due to information technologies. Artificial intelligence (AI) and machine learning (ML) are mechanisms that evolved from data management and developing processes. Incorporating these mechanisms into business is a trend many different industries, including education, have identified as game-changers. As a result, education platforms and applications are more closely aligned with learners’ needs and knowledge, making the educational process more efficient. Therefore, AI and ML have great potential in e-learning and higher education institutions (HEI). Thus, the article aims to determine its potential and use areas in higher education based on secondary research and document analysis (literature review), content analysis, and primary research (survey). As referent points for this research, multiple academic, scientific, and commercial sources were used to obtain a broader picture of the research subject. Furthermore, the survey was implemented among students in the Republic of Serbia, with 103 respondents to generate data and information on how much knowledge of AI and ML is held by the student population, mainly to understand both opportunities and challenges involved in AI and ML in HEI. The study addresses critical issues, like common knowledge and stance of research bases regarding AI and ML in HEI; best practices regarding usage of AI and ML in HEI; students’ knowledge of AI and ML; and students’ attitudes regarding AI and ML opportunities and challenges in HEI. In statistical considerations, aiming to evaluate if the indicators were considered reflexive and, in this case, belong to the same theoretical dimension, the Correlation Matrix was presented, followed by the Composite Reliability. Finally, the results were evaluated by regression analysis. The results indicated that AI and ML are essential technologies that enhance learning, primarily through students’ skills, collaborative learning in HEI, and an accessible research environment.
Pharmacist-led strategy to address the opioid crisis: The Medication Assessment Centre Interprofessional Opioid Pain Service (MAC iOPS)

Background One in 5 Canadians experience chronic pain, with increased prevalence in older adults, females, Indigenous peoples and populations affected by social inequities. Chronic pain is a complex biopsychosocial condition, and management is influenced by the intricate relationship between pain, mental health and physical well-being. Unmanaged chronic pain negatively affects all aspects of an individual’s life and society as a whole. Chronic pain management is challenging due to the number of people affected and the significant time and resources required to provide comprehensive, integrated care. Specialized care may be offered in interprofessional chronic pain clinics, but wait lists in many regions are exceptionally long, and in some areas, these clinics are nonexistent. The management of chronic pain has been complicated by the opioid crisis and the subsequent increased societal awareness of opioid use. Opioids play an important role in treating pain, but the risk of harm related to opioid use is also high. Between January 2016 and June 2020, there were more than 17,602 opioid-related deaths within Canada, and at least 1 pharmaceutical opioid was involved in 33% and 16% of opioid toxicity deaths among females and males, respectively. The 2017 Canadian Guideline for Opioids for Chronic NonCancer Pain provides recommendations regarding the safe and appropriate use of opioids to manage chronic pain. In addition, other strategies have been implemented to address the opioid crisis. Unfortunately, some of these strategies have created barriers for patients with chronic pain to access care, including difficulty obtaining opioid prescriptions. Health professionals must work collaboratively and in a patientcentred manner to develop individualized treatment plans, to provide education on how to minimize opioid risk and to offer support and follow-up. In an effort to provide better access to interprofessional chronic pain management, the Medication Assessment Centre (MAC) has recently launched the Medication Assessment Centre Interprofessional Opioid Pain Service (MAC iOPS), using an innovative, pharmacist-led, interprofessional approach. This new service aims to use pharmacists’ unique knowledge and skills to implement and evaluate a new way to manage chronic pain in Saskatchewan. This article provides an overview of the MAC iOPS and offers insight into the establishment of this unique model of care.
UniPredict: A GRE-score based University Recommender System using Hybrid Model

In recent times, it is seen that many graduate students are willing to learn in foreign universities. Various factors like better opportunities of research, post-graduation, PhD and wider exposure to grab work in a plethora of jobs drive fresh graduates and experienced people to apply for different universities. This situation is predominant in students from Indian sub-continent and Asian countries. These students aim to get admissions in many top universities in the USA. According to the data obtained, the scores of exams like GRE, IELTS, TOEFL and, GPA of UG along with the work experience play a pivotal role in the university admissions. The aim of the web based recommendation system is to suggest the users - top 3 recommended colleges based on their profiles and inputs. As students spend huge amounts of money on counseling for obtaining university recommendations, our UniPredict system acts as a complete cost affordable platform for accurate results and user preferences. Collaborative filtering and content-based filtering is used to form a hybrid model that will be in turn used with ensemble learning to predict the universities. This system can be financially very affordable and helpful for the test takers to send 4 universities free applications after taking their test according to the GRE policy as of 2021.
Probabilities of unranked and ranked anomaly zones under birth-death models.

A labeled gene tree topology that is more probable than the labeled gene tree topology matching a species tree is called anomalous. Species trees that can generate such anomalous gene trees are said to be in the anomaly zone. Here, probabilities of unranked and ranked gene tree topologies under the multispecies coalescent are considered. A ranked tree depicts not only the topological relationship among gene lineages, as an unranked tree does, but also the sequence in which the lineages coalesce. In this article, we study how the parameters of a species tree simulated under a constant rate birth-death process can affect the probability that the species tree lies in the anomaly zone. We find that with more than five taxa, it is possible for species trees to have both anomalous unranked (AGTs) and ranked (ARGTs) gene trees. The probability of being in either type of anomaly zone increases with more taxa. The probability of AGTs also increases with higher speciation rates. We observe that the probabilities of unranked anomaly zones are higher and grow much faster than those of ranked anomaly zones as the speciation rate increases. Our simulation shows that the most probable ranked gene tree is likely to have the same unranked topology as the species tree. We design the software PRANC, which computes probabilities of ranked gene tree topologies given a species tree under the coalescent model.
A Parallel Tracking of Salivary and Gut Microbiota Profiles Can Reveal Maturation and Interplay of Early Life Microbial Communities in Healthy Infants

In this study, the onset and shaping of the salivary and gut microbiota in healthy newborns during the first period of life has been followed, evaluating the impact of salivary microbiota on the development of early fecal microbial communities. The microbiota of 80 salivary and 82 fecal samples that were collected from healthy newborns in the first six months of life, was investigated by 16S rRNA amplicon profiling. The microbial relationship within and between the saliva and gut ecosystems was determined by correlation heatmaps and co-occurrence networks. Streptococcus and Staphylococcus appeared as early commensals in the salivary microbiota, dominating this ecosystem through the time, while Fusobacterium, Prevotella, Porphyromonas, Granulicatella, and Veillonella were late colonizers. Enterobacteriaceae, Staphylococcus and Streptococcus were gut pioneers, followed by the anaerobic Bifidobacterium, Veillonella, Eggerthella, and Bacteroides. Streptococcus, Staphylococcus, and Veillonella were shared by the gut and saliva ecosystems. The saliva and gut microbiota seem to evolve independently, driven by local adaptation strategies, except for the oral Streptococcus and Veillonella that are involved in gut microbiota development as seeding species. This study offers a piece of knowledge on how the oral microbiota may affect the gut microbiota in healthy newborns, shedding light onto new microbial targets for the development of therapies for early life intestinal dysbiosis.
EFFECT OF VOLATILITY IN THE NAIRA - DOLLAR EXCHANGE RATE ON THE VOLUME OF IMPORTS TO, AND EXPORTS FROM NIGERIA

Economics ABSTRACT We analyzed the effect of volatility in the Naira-Dollar exchange rate on the volume of imports to and exports from Nigeria between 1990 and 2019. Data for all variables, except volatility, were sourced from the Central Bank of Nigeria (CBN), the National Bureau of Statistics (NBS), and the International Financial Statistics. The volatility of the naira exchange rate was calculated from the model. The study employed both the autoregressive distributed lagged (ARDL) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models to test for the short and long-run relationships between changes in the Naira-Dollar exchange rate and the volume of imports and exports. Volatility in the Naira-Dollar exchange rate was found to be related to the volume of imports to and exports from Nigeria in the long run with no short-run effects because of the pass-through effect to domestic inflation. The study recommends that government should sustain efforts at promoting exports and reducing imports to improve the trade balance. The study brings into perspective another way of looking at the long-run relationship between the Naira-Dollar exchange rate and Nigeria’s trade balance.
A wait-free universal construction for large objects

Concurrency has been a subject of study for more than 50 years. Still, many developers struggle to adapt their sequential code to be accessed concurrently. This need has pushed for generic solutions and specific concurrent data structures. Wait-free universal constructions are attractive as they can turn a sequential implementation of any object into an equivalent, yet concurrent and wait-free, implementation. Existing universal constructions either maintain a per-thread replica of the object, or make a new copy of the object on every operation, with both approaches yielding low performance. To overcome these limitations, we have designed CX, a multi-instance-based wait-free universal construction that shares replicas among threads using reader-writer locks. CX performs significantly better than existing wait-free constructions for our tested sequential data structures. Moreover, on read-oriented workloads, CX rivals hand-written lock-free and wait-free data structures. Our universal construction is the first to provide integrated wait-free memory reclamation.
Examining Terrain Effects on an Upstate New York Tornado Event Utilizing a High-Resolution Model Simulation

The complex terrain at the intersection of the Mohawk and Hudson valleys of New York has an impact on the development and evolution of severe convection in the region. Specifically, previous research has concluded that terrain-channeled flow in the Mohawk and Hudson valleys likely contributes to increased low-level wind shear and instability in the valleys during severe weather events such as the historic 31 May 1998 event that produced a strong (F3) tornado in Mechanicville, New York.The goal of this study is to further examine the impact of terrain channeling on severe convection by analyzing a high-resolution WRF model simulation of the 31 May 1998 event. Results from the simulation suggest that terrain-channeled flow resulted in the localized formation of an enhanced low-level moisture gradient, resembling a dryline, at the intersection of the Mohawk and Hudson valleys. East of this boundary, the environment was characterized by stronger low-level wind shear and greater low-level moisture and instability, increasing tornadogenesis potential. A simulated supercell intensified after crossing the boundary, as the larger instability and streamwise vorticity of the low-level inflow was ingested into the supercell updraft. These results suggest that terrain can have a key role in producing mesoscale inhomogeneities that impact the evolution of severe convection. Recognition of these terrain-induced boundaries may help in anticipating where the risk of severe weather may be locally enhanced.
A Hierarchical Autonomous Driver for a Racing Car: Real-Time Planning and Tracking of the Trajectory

The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver.
The devil is in the details: pentagonal bipyramids and dynamic arrest

Colloidal suspensions have long been studied as a model for atomic and molecular systems, due to the ability to fluorescently label and individually track each particle, yielding particle-resolved structural information. This allows various local order parameters to be probed that are otherwise inaccessible for a comparable molecular system. For phase transitions such as crystallisation, appropriate order parameters which emphasise 6-fold symmetry are a natural choice, but for vitrification the choice of order parameter is less clear cut. Previous work has highlighted the importance of icosahedral local structure as the glass transition is approached. However, counting icosahedra or related motifs is not a continuous order parameter in the same way as, for example, the bond-orientational order parameters Q6 and W6. In this work we investigate the suitability of using pentagonal bipyramid membership, a structure which can be assembled into larger, five-fold symmetric structures, as a finer order parameter to investigate the glass transition. We explore various structural and dynamic properties and show that this new approach produces many of the same findings as simple icosahedral membership, but we also find that large instantaneous displacements are often correlated with significant changes in pentagonal bipyramid membership, and unlike the population of defective icosahedra, the pentagonal bypyramid membership and spindle number do not saturate for any measured volume fraction, but continue to increase.
Parameter settings in a compact laser‐nitriding system for titanium composed of a focused pulsed Nd:YAG laser and nitrogen gas blow

The aim of the present study was to form a nitride layer on a titanium (Ti) substrate through a compact laser‐nitriding system comprising a focused pulsed Nd:YAG laser and nitrogen gas blow. To obtain a high‐quality layer, the effects of pulse frequency and gas flow rate on the surface characteristics were investigated by using plasma emission analysis as well as X‐ray analyses. Optical emission spectra from the laser‐induced plasma mainly consisted of ionic Ti lines, and their intensities when the pulse frequency was 15 Hz were much higher than those for 8 Hz. Similarly, the reflections from the δ‐TiN phase in the X‐ray diffraction (XRD) pattern were enhanced when using 15 Hz. On the other hand, the flow rate of nitrogen gas blow had a significant effect on the thickness of the thin oxide layer that formed above the nitride layer. Using a lower flow rate resulted in the formation of a thicker oxide layer. The higher pulse frequency and the faster flow rate were beneficial for obtaining a higher‐quality layer because of the enhancement of nitridation and the suppression of oxidation, respectively.
Growth of Lithium Dendrites and Globules through a Solid Block Copolymer Electrolyte as a Function of Current Density

The uncontrollable nonplanar electrodeposition of lithium is a significant barrier to the widespread adoption of high energy density rechargeable batteries with a lithium metal anode. A promising approach for preventing the growth of lithium dendrites is the use of solid polymer electrolytes with a high shear modulus. Current density is the key variable in the electrodeposition of lithium. The present study is the first attempt at quantifying the effect of current density on the geometry and density of dendrites and other protrusions during electrodeposition through a solid polymer electrolyte. The geometry and density of defects formed on the lithium electrode were determined by X-ray microtomography. The tomograms revealed protrusions on the electrodeposited lithium electrodes that were either globular or dendritic, or void defects. The range of current densities over which stable, planar deposition was observed is quantified. At higher current densities, globular protrusions were observed. At the highe.