Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Design and Noise Analysis of High-Performance DBR-Integrated Lateral Germanium (Ge) Photodetectors for SWIR Applications

This work presents the high-performance Si/SiO<sub>2</sub> distributed Bragg reflector (DBR)-integrated lateral germanium (Ge) <italic>p-i-n</italic> photodetectors (PDs) for atmospheric gas sensing and fiber-optic telecommunication networks in the short-wave infrared (SWIR) regime. In addition, this study also proposes an optoelectronic compact small-signal noise equivalent circuit model (SSNECM) of the designed device to compute the noise performance at the detectors’ output. Various figure-of merits including current under dark and illumination, responsivity, detectivity, bandwidth, and the noise of the proposed device are estimated at the room temperature (RT) for an incident optical power of <inline-formula> <tex-math notation="LaTeX">${0}. {5} {\mu }{W}$ </tex-math></inline-formula>. Furthermore, the impact of width and height scaling on dark current, responsivity, and bandwidth are investigated to optimize the proposed device. The validation of the proposed model is done by comparing various parameters including dark current, responsivity, and detectivity of the designed device with other Ge PDs. The estimated results show the reduced <italic>trade-off</italic> between responsivity and bandwidth of the designed device. At <inline-formula> <tex-math notation="LaTeX">${\lambda }=1550 nm$ </tex-math></inline-formula>, the proposed device achieves a high detectivity and SNR of <inline-formula> <tex-math notation="LaTeX">$>2\times {10}^{11}$ </tex-math></inline-formula> Jones and 120 dB (at 3 THz), respectively, with the bias voltage of −2V. These encouraging results pave the path for the future development of low-noise and high-speed detectors.
Possibilities and limitations of antidepressant use to correct depressive and negative symptoms in schizophrenia

The purpose of this review is to analyze approaches to the treatment of depressive and negative disorders in schizophrenia in terms of their level of efficacy and safety. Materials and Methods: A search was conducted for full-text articles published over the last 10 years in PubMed, Springer, Wiley Online Library, Taylor & Francis Online, APA PsycInfo, CORE, Science Direct, and eLIBRARY.RU databases. Several articles published previously to this period were also included into the review due to their high scientific value. Results: Our review suggests that antidepressants (ADs) are effective medications and they can be prescribed to correct depressive disorders and negative symptoms in patients with schizophrenia when used in combination with antipsychotics (АPs). However, when administering ADs and АPs combinations, it is important to consider the safety profile of these combinations as well as their tolerance. Negative symptoms of schizophrenia, including those induced by a number of АP, are less amenable to correction by АDs monotherapy, which requires a long period of АPs (on average - 8 weeks), which can be limited in the real life of the patient outside the hospital. Current approaches to the therapy of depressive disorders in patients suffering from schizophrenia vary from country to country. However, most of АDs used in clinical psychiatric practice are widely used in the comorbid state under consideration. Conclusion: The efficacy and safety of АDs of the different classes considered in this review depends on their mechanisms of action, duration of admission, type of АPs taken, and specific clinical situation (acute depressive disorder, major depressive episode, or chronic depressive episode). Most promising in clinical practice are serotonin–norepinephrine reuptake inhibitors (SNRIs) and dual ADs. The use of tricyclic antidepressants (TCAs) is limited due to a higher risk of adverse drug reactions (ADRs). The use of most selective serotonin reuptake inhibitors (SSRIs) is limited due to the risk of aggravation of hallucinations (this risk being higher for patients with visual hallucinations, and lower for those with auditory hallucinations) and\or iatrogenic psychosis. These ADRs may probably occur in patients suffering from schizophrenia due to their ideal "poor metabolizer" pharmacogenetic profile, since most of the drugs considered in this review have hepatic metabolism.
Dihydrotanshinone-Induced NOX5 Activation Inhibits Breast Cancer Stem Cell through the ROS/Stat3 Signaling Pathway

Cancer stem cells (CSCs) are known to mediate metastasis and recurrence and are therefore a promising therapeutic target. In this study, we found that dihydrotanshinone (DHTS) inhibits CSC formation. DHTS inhibited mammosphere formation in a dose-dependent manner and showed significant tumor growth inhibition in a xenograft model. This compound reduced the CD44high/CD24low- and aldehyde dehydrogenase- (ALDH-) expressing cell population and the self-renewal-related genes Nanog, SOX2, OCT4, C-Myc, and CD44. DHTS induced NOX5 activation by increasing calcium, and NOX5 activation induced reactive oxygen species (ROS) production. ROS production reduced the nuclear phosphorylation levels of Stat3 and secreted IL-6 levels in the mammospheres. DHTS deregulated the dynamic equilibrium from non-stem cancer cells to CSCs by dephosphorylating Stat3 and decreasing IL-6 secretion and inhibiting CSC formation. These novel findings showed that DHTS-induced ROS deregulated the Stat3/IL-6 pathway and induced CSC death. NOX5 activation by DHTS inhibits CSC formation through ROS/Stat3/IL-6 signaling, and DHTS may be a promising potential therapeutic agent against breast CSCs.
Adaptation and validation of the Psychological General Well-Being Index: confirmatory factor analysis of the short version

Background: Psychological well-being is a personal perception which can impact students’ health behaviors. To evaluate psychological well-being, instruments should be preferably short and have adequate psychometric properties. Objective: To validate the Questionario Geral de Bem-Estar Psicologico, versao reduzida (QGBEP-R) and verify its psychometric properties, after translation of the 6-item short version of the Psychological General Well-Being Index (PGWB-S). This Index was chosen because of the excellent psychometric properties of its original version. Methodology: The original factor structure was validated using confirmatory factor analysis through structural equation modeling and maximum likelihood estimation. A total of 300 students were randomly selected from a total sample of 1,400 Portuguese university students. Results: The confirmatory factor analysis revealed a fit 6-item one-factor model. Conclusion: The QGBEP-R has good psychometric properties and is valid for use in Portuguese university students
Use of Plastic Waste in Bitumen

There has been a sudden increase in population and in the establishment of different industries due to which we are having waste pollution problems, including plastic waste. Discarding of plastic waste is a major problem, as it is non-biodegradable. When we mix plastic waste with bitumen, we are able to enhance bitumen's physical properties for a specific road mix. Then, bitumen's stability and water resisting capacity is upgraded. Plus, it acts as a more superior binding material than without the addition of plastic waste. Through this work, we are able to find the optimum percentage of bitumen to be substituted with plastic waste. This will also act as a part of smart waste disposal in smart cities and improve the air quality with increase environmental, economic parameters of the place leading to the improvement in human health in urban areas.
A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.
Velocity-Based Gait Planning for Underactuated Bipedal Robot on Uneven and Compliant Terrain

This article develops a gait planning method for underactuated bipedal robot on uneven and compliant terrain. First, we employ a linear spring-damper model to describe foot-ground compliant contact, and establish a decoupled robot–ground three-dimensional dynamic mode. Second, based on a velocity-based bipedal stability definition and human walking characteristics, we propose a gait planning method to realize underactuated bipedal walking on uneven and compliant terrain. We decouple bipedal gait planning into sagittal and lateral master-slave ones. By planning the motion state of Center-of-Mass (CoM) of a robot, we make the movement of lateral and sagittal coincident such that bipedal walking is realized. Finally, underactuated bipedal walking with an average walking speed of 0.216 m/s and a step length of 183.9 mm is realized on uneven terrain where the maximum height of unevenness is 32 mm. The experimental results show that underactuated bipedal walking can be realized on uneven and compliant terrain by using the proposed method to control robot CoM and track its desired velocity.
Machine Learning Based Prediction of Croatian 2017 Local Elections

Internet development enabled intensive applications of new ways of communication. In today's digital age, social media became fundamental mean of communication. Political parties are increasingly using social media for the purpose of advertising and voters` mobilization. An extensive literature review has identified multiple benefits of social media usage, such as gaining publicity, spreading messages and mobilizing voters, but also the need to monitor the content that is published, and moreover to analyze the impact of that content on potential voters. This paper examines the influence of political candidates' activities on social media on elections outcome. By means of decision tree methodology, predictive model of election outcome is developed based on dataset consisting of candidates` characteristics, but also data of the candidates` activity on the social network Facebook. The predictive model is developed on dataset consisting of candidates in the local elections at large Croatian cities held in Croatia in 2017. The research identified the most important factors of political communication on the social network Facebook for election outcome and provides guidelines for the effective use of Facebook in political campaigns
Plasma Level of MicroRNAs, MiR-107, MiR-194 and MiR-210 as Potential Biomarkers for Diagnosis Intestinal-Type Gastric Cancer in Human

Background: Timely and sensitive diagnosis of gastric cancer is crucial for efficient treatment and survival of the patients. microRNAs have been considered as diagnostic biomarkers in different type of cancers including gastric cancer. In the present study, the expression profile of four microRNAs, miR-103, miR-107, miR-194 and miR-210 were evaluated in patients with intestinal-type of gastric cancer (IGC) in order to assess their diagnosis utility as noninvasive biomarkers. Methods: A total number of 100 plasma samples from patients with gastric cancer and healthy controls were obtained and total RNA was extracted using a commercial monophasic solution of phenol and guanidium thiocyanate. Reverse transcription (RT) reactions were performed by specific stem-loop RT primers and M-MuLV RT-enzyme. The expression patterns of microRNAs were assessed using reverse transcription quantitative real-time PCR (RT-qPCR) method and the expression of SNORD47 RNA was used as the reference for normalization. Results: The results indicate that the plasma levels of miR-107, miR-194, and miR-210 were significantly lower in patients. Receiver operating characteristic (ROC) curve analysis showed that the patients could be distinguished from healthy individuals at the cutoff levels of 0.504, 0.266, and 0.394 of miR-107, miR-194, and miR-210, respectively. On the other hand, the expression levels of these miRNAs were not significantly different in different clinicopathological stages of the disease. Conclusion: These findings suggest that the plasma levels of miR-107, miR-194 and miR-210 were downregulated in patients with ICG and propose these molecules as potential non-invasive biomarkers for detection of IGC.
Expanding the Disorder-Function Paradigm in the C-Terminal Tails of Erbbs

ErbBs are receptor tyrosine kinases involved not only in development, but also in a wide variety of diseases, particularly cancer. Their extracellular, transmembrane, juxtamembrane, and kinase folded domains were described extensively over the past 20 years, structurally and functionally. However, their whole C-terminal tails (CTs) following the kinase domain were only described at atomic resolution in the last 4 years. They were shown to be intrinsically disordered. The CTs are known to be tyrosine-phosphorylated when the activated homo- or hetero-dimers of ErbBs are formed. Their phosphorylation triggers interaction with phosphotyrosine binding (PTB) or Src Homology 2 (SH2) domains and activates several signaling pathways controling cellular motility, proliferation, adhesion, and apoptosis. Beyond this passive role of phosphorylated domain and site display for partners, recent structural and function studies unveiled active roles in regulation of phosphorylation and interaction: the CT regulates activity of the kinase domain; different phosphorylation states have different compaction levels, potentially modulating the succession of phosphorylation events; and prolines have an important role in structure, dynamics, and possibly regulatory interactions. Here, we review both the canonical role of the disordered CT domains of ErbBs as phosphotyrosine display domains and the recent findings that expand the known range of their regulation functions linked to specific structural and dynamic features.
Utilizing External Knowledge to Enhance Semantics in Emotion Detection in Conversation

Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. We propose KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets.
Fuzzy Rule-Based Classification Method for Incremental Rule Learning

Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due to the diversity of data types, how to improve the readability of the extracted granular rules while ensuring efficiency is always a challenge. Since granular reduct in granular computing (GrC) can simplify real complex problem and dataset, this article carries out granular rule learning from the perspective of granular reduct by taking formal concept analysis (FCA)-based GrC method as a framework. Specifically, for achieving classification task, we first propose a method to update the granular reduct, and then explore the updating mechanism of fuzzy granular rule in a reduced dataset. Second, a novel fuzzy rule-based classification model named FRCM is presented for fuzzy granular rule learning. In order to verify the effectiveness of the proposed model, some numerical experiments for incremental learning and fuzzy rule mining are conducted to demonstrate that FRCM can achieve the state-of-the-art classification performance.
Considering Airport Planners’ Preferences and Imbalanced Datasets when Predicting Flight Delays and Cancellations

A key part of efficient airport operational planning is to have insight into potential flight delays and cancellations. For airport planners, it is important to obtain flight delay or cancellation predictions with a high degree of certainty, i.e. a high precision. This allows planners to make sound decisions based on these predictions. To obtain such predictions, machine learning classification techniques are often applied. An important issue for classification problems is that of imbalanced class distributions: the number of actually cancelled/delayed flights is low. In general, the imbalance is addressed by resampling the data using one or more sampling techniques. However, resampling does not necessarily correspond to an imbalance ratio that leads to the best classification results. In this paper a systematic approach is presented to deal with imbalanced data for classification problems, while taking into account the preferences of airport planners. A range of feasible imbalance ratios, together with several classification algorithms and sampling techniques, are considered. An optimal imbalance ratio is identified with respect to relevant performance metrics. The approach is illustrated by performing binary classification of flight cancellations and delays at a large European airport. The results show that the highest prediction precision is obtained using a base imbalance ratio, whereas a higher imbalance ratio is needed to obtain the highest F1-score. Specifically, the cancellation prediction performance is increased by up to 243%, while its optimal imbalance ratio does not correspond to resampling. In general, the results underline the need to investigate the influence of varying data imbalance ratios on the performance of classification algorithms.
Personlike intelligent systems architectures for robotic shared control and automated operations

There are many types of tasks in space where operations with robotics can play a significant role, including: (1) Tasks that are dangerous, boring, fatiguing for persons [extravehicular activity (EVA) crewmembers]; (2) Tasks where a division of labor between EVA crewmembers and robotic equipment is desirable. Current notions involve a succession of robotic capabilities: (1) Teleoperations (where the robotic system is controlled remotely to the level of maneuvers); (2) Telerobotics [where the robotic system can carry out a set of maneuvers on its own, with full-time monitoring from an EVA or intravehicular activity (IVA) crewmember]; (3) Supervised autonomy (with control and monitoring functions on the part of persons provided on a less intense basis) with occasional traded control or shared control. Of these, only the first can be considered state of the art for space applications. In considering how to achieve shared control and autonomous capability, there is a tendency to invoke terms like `cognition,' `perception,' `learning,' etc., thereby constituting wish lists of `what is needed. ' By way of contrast, the thrust of this paper is to outline an approach whereby robotic systems become as `person-like' as possible to achieve needed capabilities. This approach makes use of formulations in the Person Concept, pioneered by one of the present authors, Dr. Peter G. Ossorio. These include: (1) The state of affairs (SA) system; (2) The intentional action (IA) system.
Thermospheric Composition and Solar EUV Flux From the Global‐Scale Observations of the Limb and Disk (GOLD) Mission

Observations of far‐ultraviolet (FUV) dayglow by the Global‐scale Observations of Limb and Disk (GOLD) mission provide a new opportunity to monitor relative composition changes in the upper atmosphere as well as solar extreme ultraviolet (EUV) variability. Relative composition changes are quantified by ΣO/N2, the column density ratio of atomic oxygen to molecular nitrogen, while QEUV provides a measure of the solar EUV energy flux from 1 to 45 nm into the upper atmosphere. This spectral range provides the ionizing radiation which ultimately results in FUV airglow emission produced by photodissociation and photoelectron impact. The quantities ΣO/N2 and QEUV are derived from GOLD FUV observations through lookup tables that are constructed using a first‐principles photoelectron transport model. The two FUV emissions used are O I 135.6 nm and the N2 Lyman‐Birge‐Hopfield (LBH) bands. We present an overview of the theoretical basis for the algorithms and practical considerations for application to GOLD data. The effects of uncertainties in electron impact cross sections, off‐nadir viewing, and instrument artifacts are reviewed. We also discuss GOLD Level 1C DAY, Level 2 data products ON2 and QEUV, and present representative samples of each.
Effects of dietary levels of calcium, phosphorus, and 1-alpha-hydroxycholecalciferol on digestibility, retention of calcium and phosphorus, and concentration of metabolizable energy in diets fed to sows in late-gestation

Abstract: The apparent total tract digestibility and retention of calcium (Ca) and phosphorus (P) and concentrations of digestible energy and metabolizable energy in diets fed to late-gestating sows were not affected by Ca and P levels, but they were increased by dietary supplementation with 1-alpha-hydroxycholecalciferol. There was no interaction between dietary Ca and P and supplementation with 1-alpha-hydroxycholecalciferol.
Multicarrier techniques for 4G mobile communications

From the Publisher: As research for future fourth generation (4G) mobile communication systems is underway worldwide in major companies and academic institutions, forward-thinking professionals are striving to gain a thorough understanding of the cutting-edge technologies and design techniques that will make these systems work. This book helps readers do just that by: providing a comprehensive introduction to multicarrier techniques for 4G mobile communications with a special focus on the analytical aspects; explaining radio channel characteristics and phenomena and discussing the advantages and disadvantages of the OFDM scheme; featuring new multicarrier-related techniques, MC-CDMA, research on several 4G systems, and a look at several problems to be overcome with these systems; examining the concept and detail of the ODFM scheme and how to carry out theoretical analysis on the performance of transmission systems in radio channels; showing how ODFM has been successfully adopted as a modulation scheme in communications systems and broadcasting systems such as ADSL, wireless LANs, and DVB-T.