Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic Beam-Scanning Antenna Based on a Reconfigurable Phase-Modulated Metasurface

Metasurfaces (MSs) have enabled the emergence of new ideas and solutions in the design of antennas and for the control of electromagnetic waves. In this work, we propose to design a directional high-gain reconfigurable planar antenna based on a phase-modulated metasurface. Reconfigurability is achieved by integrating varactor diodes into the elementary meta-atoms composing the metasurface. As a proof of concept, a metasurface prototype that operates around 5 GHz is designed and fabricated to be tested in an antenna configuration. The metasurface is flexibly controlled by different bias voltages applied to the varactor diodes, thus allowing the user to control its phase characteristics. By assigning judiciously calculated phase profiles to the metasurface illuminated by a feeding primary source, different scenarios of far-field patterns can be considered. Different phase profiles are tested, allowing us to, firstly, achieve a highly directive boresight radiation and, secondly, to steer the main radiated beam towards an off-normal direction. The whole design process is verified by numerical simulations and is validated experimentally by far-field antenna measurements. The proposed metasurface enables the design of directive flat antennas with beam-scanning characteristics without complex feeding systems and power-consuming phase shifters, and thus provides potential interests for next generation antenna hardware.
TASP: Enabling Time-Triggered Task Scheduling in TSN-Based Mixed-Criticality Systems

Distributed mixed-criticality system (DMCS) has been widely used in various critical domains such as self-driving cars and space crafts. To guarantee the end-to-end QoS (i.e., deadline/jitter requirements) of sensing-controlling-actuating control loops (CL) applications, DMCS adopts Time-Sensitive Networking (TSN), an emerging real-time Ethernet technology, for communication between end systems (ES). TSN provides a synchronized global clock and guarantees bounded delay for time-critical traffic in CLs, making it possible for DMCS to collaboratively schedule the computation (on ES) and communication (in TSN) to meet the Quality of Service (QoS) requirement. However, as modern DMCS tends to use fully-fledged Linux distributions (rather than a custom real-time OS) on ES to enjoy Linux’s mature ecosystem, it is challenging for DMCS to realize TSN-based QoS guarantees because the event-triggered scheduling of Linux on ES is incompatible with TSN.This paper proposes TAsk Scheduling Puppeteer (TASP), a mechanism that schedules CL tasks based on TSN without modifying the Linux OS. The key idea of TASP is to manipulate task scheduling by controlling the timing of CL packet submissions at the interface between TSN and ES. Specifically, TASP extracts two parameters: Fore Guardband (ForeGB) and Back Guardband (BackGB). During the ForeGB period before a CL packet’s submission, TASP forbids any packets’ submission; while during the BackGB period after a CL packet’s submission, TASP forbids any other CL packets’ submission. ForeGB and BackGB can ensure that there is at most one schedulable task on the ES at any time, and thus Linux has no choice but to schedule the only task, making the Linux scheduler a puppet. We have deployed TASP and evaluated it in real-world TSN switches based on an open-source TSN project, OpenTSN. The TASP-enabled ES can achieve task scheduling precisely based on TSN’s global clock, which outperforms the original ES by reducing end-to-end jitter from milliseconds to microseconds.
Design Optimization of Dual-Pole Permanent Magnet Machine

This paper aims to optimize the steady-state and the transient response of the dual-pole permanent magnet (PM) machine. The objective functions are defined as the reduction of cogging torque, and the improvement of the startup capability. First, the machine is modeled in Finite Element (FE), and simulated to generate a sufficient number of data. Next, the generated data are fed into an optimization framework based on a Genetic Algorithm (GA) to identify the optimal structure. To determine the efficacy of the presented solution, the response of the optimized machine is compared against the baseline structure.
Prototype Design of Automatic Switching Speed of Exhaust Fan For Air Quality Control Based On IoT

Bad indoor air quality is dangerous for humans body health because many people tend to do indoor activities so that it makes a house become a micro environment that related to air pollution risk. Therefore, this research has a goal to make an automatic air quality control system that can be online monitored where exhaust fan rotation speed is controlled by exhaust fan switching speed form a few dimmer that has a different power to decrease indoor air pollutant concentration instead. The system has been made, is using MG-811 as CO2 concentration detector placed in the component box and Opto-interrupter sensor to measure exhaust fan airflow rate. The analysis results showed that MG-811 has error average is 0.93% while the accuracy is 99.07% and airflow rate has average error is 1.65% while the accuracy is 98.35%. The experiment result showed that the prototype able to respond the change of CO2 concentration by switching the speed level of the exhaust fan but the monitoring system still has few a data loss because it is disconnected from the surrounding wifi.
THE CORRELATIONS BETWEEN INFLUENCE TACTICS AND LEADERSHIP STYLE AMONG LECTURER IN HIGHER EDUCATION INSTITUTE

Mastering the tactics of influence in leadership practices is important in able to impact the culture and relationships of fellow citizens in the organization. Hence, leaders need to know how to use appropriate tactics of subordinates to reduce conflict in leadership, especially in higher education. The purpose of this study is to identify the most frequently used influence tactics as well as to identify the relationship of influence tactics and leadership styles. This descriptive study involve sample of 154 polytechnic lecturers in Kelantan. The quantitative data of the study were collected by cross-sectional use of Influence Behavior Questionnaire and Multifactor Leadership Questionnaire. The findings show that soft influence tactics are most often used than hard influence tactics. The findings also show that the tactics of influence have a significant relationship with the transformational leadership compared to the transactional leadership and the laissez faire. It is important for a leader of an educational institution to identify appropriate influence tactics based on the situation and environment.
An Energy Analysis of Rapeseed Production for Biodiesel in Croatia

Before starting with biodiesel production from rapeseed oil in Croatia, an energy analysis of the production was made. The data for the energy analysis were gathered from investigations on farms in the Slavonia region, the main arable production area in Croatia, in the period 2001 to 2003. For the calculation of energy outputs and inputs, the results of this investigation were used together with energy values from the literature. The average rapeseed yield and oil content was 3195 kg/ha and 42.5%, respectively. Considering rapeseed oil for biodiesel and meal for animal feed, the total energy outputs were 74.01 GJ/ha. Total energy inputs for the rapeseed production were 23.44 GJ/ha, and the major energy inputs were fertilizers and fuel. The energy ratio of the rapeseed production was 3.16, the net energy gain was 50.56 GJ/ha, and the energy productivity was 49.23 L/GJ. Some possibilities for energy saving in rapeseed production were also suggested.
Anti-Survival Effect of SI306 and Its Derivatives on Human Glioblastoma Cells

Glioblastoma (GBM) is the most common adult brain tumor and, although many efforts have been made to find valid therapies, the onset of resistance is the main cause of recurrence. Therefore, it is crucial to identify and target the molecular mediators responsible for GBM malignancy. In this context, the use of Src inhibitors such as SI306 (C1) and its prodrug (C2) showed promising results, suggesting that SI306 could be the lead compound useful to derivate new anti-GBM drugs. Therefore, a new prodrug of SI306 (C3) was synthesized and tested on CAS-1 and U87 human GBM cells by comparing its effect to that of C1 and C2. All compounds were more effective on CAS-1 than U87 cells, while C2 was the most active on both cell lines. Moreover, the anti-survival effect was associated with a reduction in the expression of epidermal growth factor receptor (EGFR)WT and EGFR-vIII in U87 and CAS-1 cells, respectively. Collectively, our findings demonstrate that all tested compounds are able to counteract GBM survival, further supporting the role of SI306 as progenitor of promising new drugs to treat malignant GBM.
Pattern diversity and isolation enhancement of UWB MIMO antenna based on characteristic modes for mobile terminals

Abstract A printed ultra-wideband (UWB) multiple input multiple output antenna with isolation enhancement and pattern diversity is presented, which is based on characteristic modes (CM) for upcoming wireless mobile platforms. The presented antenna is composed of dual UWB antenna elements (AEs) – an antipodal Vivaldi and a circular-shaped monopole positioned on the two opposite edges of the antenna's rectangular-shaped ground surface. The two AEs are capable to stimulate distinct modes in the ground surface and therefore obtaining the required distinct radiation (far-field) patterns and highly isolated AEs without introducing any decoupling structure (DS). The measured results demonstrate that both AEs are UWB with an operating frequency from 2.6 to 11 GHz. The measured isolation is more than 30.1 dB with a maximum value of 55 dB in UWB. The peak realized gain is over 0.83 dBi. The radiation efficiency is greater than 75%. The Envelope Correlation Coefficient is <0.08.
Improving Human Activity Recognition Integrating LSTM with Different Data Sources: Features, Object Detection and Skeleton Tracking

Over the past few years, technologies in the field of computer vision have greatly advanced. The use of deep neural networks, together with the development of computing capabilities, has made it possible to solve problems of great interest to society. In this work, we focus on one such problem that has seen a great development, the recognition of actions in live videos. Although the problem has been oriented in different ways in the literature, we have focused on indoor residential environments, such as a house or a nursing home. Our system can be used to understand what actions a person or group of people are carrying out. Two of the approaches used to solve the problem have been 3D convolution networks and recurrent networks. In our case, we have created a model that accurately combines several recurrent networks with processed data from different techniques: image feature extraction, object detection and people’s skeletons. The need to integrate these three techniques arises from the search to improve the detection of certain actions by taking advantage of the best recognition offered by each of the methods. In a complete experimentation, where several techniques have been evaluated against different datasets, the classification of the actions has been improved with respect to the existing models.
The two-component model of behavior factors: The phenomenon of anticipatory engagement of a situationally dominant factor

One of the debatable issues in the theory of social action is the interaction of explicit and implicit factors in their influence on human behavior. The ‘parallel’ influence models oppose the ‘sequential’ models. The former argue that one factor determines another, and the last one influences behavior. The latter argue that factors are of an independent nature and affect behavior each in their own way. In practical terms, agreeing with one of these models means rejecting or accepting the idea of a conflict of factors and of behavior as a result of this conflict. In the previous article, we showed that the influence of ideologically biased statements on the explicit and implicit components of the attitude towards the object of these statements can be exactly the opposite. It is necessary to check whether this phenomenon is stable. If so, it will be necessary to recognize the independent nature of the factors of behavior. Such a check was carried out and (within the limits of the empirical data) confirmed the correctness of the ‘parallel’ models. This conclusion is all the more convincing because it was obtained by observing the dynamic reaction of the associations between the components of the attitude and the assumed factors of its formation. As the criticality of respondents increases, the nature of this relationship predictably changes. But it changes differently for the explicit and implicit component of the attitude. Moreover, we identified a phenomenon of the “anticipatory engagement of a situationally dominant factor”. If we are not mistaken in understanding its nature, the very foundations of theories of the ‘sequential’ influence are under a question.
Partial Scene Reconstruction for Close Range Photogrammetry Using Deep Learning Pipeline for Region Masking

3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of-the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method that reduces the need for a controlled environment and allows the capture of multiple objects with independent motion. We achieve this by creating a preprocessing pipeline that uses deep learning to transform a complex scene from an uncontrolled environment into multiple stationary scenes with a black background that is then fed into existing software systems for reconstruction. Our pipeline achieves this by using deep learning models to detect and track objects through the scene. The detection and tracking pipeline uses semantic-based detection and tracking and supports using available pretrained or custom networks. We develop a correction mechanism to overcome some detection and tracking shortcomings, namely, object-reidentification and multiple detections of the same object. We show detection and tracking are effective techniques to address scenes with multiple motion systems and that objects can be reconstructed with limited or no knowledge of the camera or the environment.
Neural Network Algorithm for Detection of New Word Meanings Denoting Named Entities

Lexical semantic change detection has been a rapidly developing field of science in recent years. Existed algorithms of lexical semantic change detection face difficulties when they are used to work with words denoting named entities. This paper proposes a method that allows one to reveal a word in a large corpus that started being used as a named entity, as well as to date the first usage of this word as a proper name. To solve this problem, firstly, we offer an algorithm that allows for detecting words in a large corpus denoting named entities. The recognizer is based on an analysis of co-occurrences with the most frequent words and was trained on data from the English subcorpus of the Google Books Ngram corpus. The achieved recognition accuracy of named entities is 98.44% on the test sample. Secondly, we test the possibility of applying the trained recognizer to diachronic data. The analysed cases show that the recognizer initially trained using the total bigram frequencies for a long time interval, at least for any frequent word, provides stable results for the annual frequency values. This can make the recognizer a good tool for language evolution studies, especially for detecting new meanings of words. The analysed cases show that the proposed method allows revealing new word meanings associated with named entities, as well as detecting genericized meaning of words that were earlier used as proper names.
Request for Information (RFI) on an Implementation Plan for a National Artificial Intelligence Research

Applications of artificial intelligence and machine learning (AI/ML) to the sciences are already ubiquitous but have not reached their full potential, with scalability and access holding back researchers across the spectrum. This response focuses on Question 2, Topic D , concerning opportunities and specific steps that will enable AI/ML methods to increase their impact on the sciences and support researchers throughout the US.
An Interplay of Light and Smoke Compounds in Photoblastic Seeds

Light increases the germinability of positively photoblastic seeds and inhibits the germination of negative ones. In an area where plant-generated smoke from fire is a periodically occurring environmental factor, smoke chemicals can affect the germination of seeds, including those that are photoblastically sensitive. Moreover, as smoke and its compounds, mostly karrikin 1, KAR1, have been used for priming the seeds of many species, including photoblastic ones, a systematic review of papers dealing with the phenomenon was conducted. The review indicates that the unification of experimental treatments (light spectrum, intensity and photoperiod, and KAR1 concentration within the species) could improve the quality of global research on the impact of smoke chemicals on photoblastic seeds, also at the molecular level. The review also reveals that the physiologically active concentration of KAR1 varies in different species. Moreover, the physiological window of KAR’s impact on germination can be narrow due to different depths of primary seed dormancy. Another concern is the mode of action of different smoke sources and formulations (aerosol smoke, smoke-saturated water), or pure smoke chemicals. The reason for this concern is the additive or synergetic effect of KARs, cyanohydrins, nitrates and other compounds, and the presence of a germination inhibitor, trimethylbutenolide (TMB) in smoke and its formulations. Obviously, environmental factors that are characteristic of the local environment need to be considered. From a practical perspective, seeds germinating faster in response to smoke chemicals can outcompete other seeds. Hence, a thorough understanding of this phenomenon can be useful in the restoration of plant habitats and the protection of rare species, as well as yielding an improvement in plants that are sown directly to the field. On the other hand, the application of smoke compounds can induce “suicidal germination” in the photoblastic seeds that are buried in the soil and deplete the soil seed bank of the local population of unwanted species.
Competency analysis and educational strategies to meet the demand for a learning health system workforce

Learning Health Systems (LHS) require a workforce with specific knowledge and skills to identify and address healthcare quality issues, develop solutions to address those issues, and sustain and spread improvements within and outside the organization. Educational programs are tasked with designing learning opportunities that can meet these organizational needs. This manuscript explores different mechanisms for addressing challenges to creating educational programs to prepare individuals who can work in and lead LHS. Strategies and recommendations for educational programs to support the LHS include the creation of a new program, collaborating across existing programs, and producing a set of instructional materials.
ESG features in financial instruments: A challenge for the accounting treatment

The volume of financial instruments including environmental, social, and governance (ESG) features is rapidly increasing with a result that the scale of the issue continues to increase in the lack of a specific accounting rule. This situation creates a deep debate referring to the possibility of financial instruments with an ESG factor to pass the solely payments of principal and interest (SPPI) test according to the current requirements in International Financial Reporting Standards (IFRS) 9. The debate is not only present in Europe but also in the US. The current accounting standards are not able to define a unique accounting solution for instruments that incorporate ESG factors and when these factors are material for the market, it is not clear which may be the proper solution to present them in the financial statements. The main issue is if it needs to separate ESG features from the basic financial instruments. Existing different positions on this issue, European Financial Reporting Advisory Group (EFRAG) proposed to International Accounting Standards Board (IASB) the introduction of more guidance and examples to apply in a consistent way the current provisions set forth by IFRS 9. In a dynamic market characterized by strong growth and the introduction of new complex instruments, the solution proposed by the EFRAG appears minimal. The introduction of a specific section of IFRS 9 addressed to this issue may be more appropriate in the light of the existing attention on the ESG features disclosure and the possibility to provide specific metric that permits measurement of the ESG features separately from the basic lending instrument
Identifying Candidate Reference Chemicals for In Vitro Testing of the Retinoid Pathway for Predictive Developmental Toxicity

Evaluating chemicals for potential in vivo toxicity based on their in vitro bioactivity profile is an important step toward animal-free testing. A compendium of reference chemicals and data describing their bioactivity on specific molecular targets, cellular pathways, and biological processes is needed to bolster confidence in the predictive value of in vitro hazard detection. Endogenous signaling by all-trans retinoic acid (ATRA) is an important pathway in developmental processes and toxicities. Employing data extraction methods and advanced literature extraction tools, we assembled a set of candidate reference chemicals with demonstrated activity on ten protein family targets in the retinoid system. The compendium was culled from Protein Data Bank, ChEMBL, ToxCast/Tox21, and the biomedical literature in PubMed. Finally, we performed a case study on one chemical in our collection, citral, an inhibitor of endogenous ATRA production, to determine whether the literature supports an adverse outcome pathway explaining the compound’s developmental toxicity initiated by disruption of the retinoid pathway. We also deliver an updated Abstract Sifter tool populated with these reference compounds and complex search terms designed to query the literature for the downstream consequences to support concordance with targeted retinoid pathway disruption.