Miracle-dz/UniFAR-FTUs huggingface dataset:
Carrier phase correction for GNSS space‐time array processing based on frequency jerk detection

The signals of Global Navigation Satellite System (GNSS) are so vulnerable that can be easily jammed by intentional or unintentional interferences. One of the effective ways to protect GNSS signals from jamming is to use the Space ‐ Time Array Processing (STAP) technology. However, undesired carrier phase bias would be induced to the signals because of the STAP, which significantly influences the precision of positioning. Many algorithms have been proposed to deal with this carrier phase bias, but most of them need the directional information of signals or the pre ‐ test of antennas. In this paper, a carrier phase correction algorithm without the need of prior information is proposed. Alarmed by the jerk of carrier frequency, the al-gorithm can detect the change of jamming and calculate the induced carrier phase bias. Then it can correct this bias from the signal and alleviate its influence. The simulation results show that the algorithm can limit the bias from more than 60° to less than 10°, which verifies that the proposed algorithm is effective in the low dynamic receiver.
A tuned feed-forward deep neural network algorithm for effort estimation

ABSTRACT Software effort estimation (SEE) is a software engineering problem that requires robust predictive models. To establish robust models, the most feasible configuration of hyperparameters of regression methods is searched. Although only a few works, which include hyperparameter optimisation (HO), have been done so far for SEE, there is not any comprehensive study including deep learning models. In this study, a feed-forward deep neural network algorithm (FFDNN) is proposed for software effort estimation. The algorithm relies on a binary-search-based method for finding hyperparameters. FFDNN outperforms five comparison algorithms in the experiment that uses two performance parameters. The results of the study suggest that: 1) Employing traditional methods such as grid and random search increases tuning time remarkably. Instead, sophisticated parameter search methods compatible with the structure of regression method should be developed; 2) The performance of SEE is enhanced when associated hyperparameter search method is devised according to the essentials of chosen deep learning approach; 3) Deep learning models achieve in competitive CPU time compared to the tree-based regression methods such as CART_DE8.
GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment

Landslide susceptibility mapping is essential for a suitable land use managing and risk assessment. In this work a GIS-based approach has been proposed to map landslide susceptibility in the Portofino promontory, a Mediterranean area that is periodically hit by intense rain events that induce often shallow landslides. Based on over 110 years landslides inventory and experts’ judgements, a semi-quantitative analytical hierarchy process (AHP) method has been applied to assess the role of nine landslide conditioning factors, which include both natural and anthropogenic elements. A separated subset of landslide data has been used to validate the map. Our findings reveal that areas where possible future landslides may occur are larger than those identified in the actual official map adopted in land use and risk management. The way the new map has been compiled seems more oriented towards the possible future landslide scenario, rather than weighting with higher importance the existing landslides as in the current model. The paper provides a useful decision support tool to implement risk mitigation strategies and to better apply land use planning. Allowing to modify factors in order to local features, the proposed methodology may be adopted in different conditions or geographical context featured by rainfall induced landslide risk.
A tutorial on cooperative non-orthogonal multiple access networks

In this paper, we explore the possibilities and advantages of cooperative relaying with the addition of non-orthogonal multiple access (NOMA). First, the possibilities of NOMA for fifth-generation (5G) and beyond networks is discussed followed by the generalized structures for the cooperative NOMA. Then, advanced NOMA communication is investigated where NOMA is integrated with advanced transmission technologies for the further improvement in cooperative NOMA. Hereinafter, resource allocation is investigated and, finally, the major challenges and issues are highlighted.
Developing A Knowledge Based Index to Distributed , Disparate Data Sources

In this paper we discuss our approach, progress, and results obtained in the course of developing a set of prototypes to extract, organize and retrieve relevant doctrinal material for C2 applications. The tool under development is called the “Knowledge-Based Discovery Tool ” (KBDT*). KBDT is currently being expanded to include multiple user-defined domains instead of concentrating solely on the military doctrine domain. KBDT extracts content from documents and builds knowledge indexes based on meaning, rather than keywords. Searching the KBDT indexes provides more meaningful and accurate retrieval of information than traditional document retrieval technology. KBDT also includes visualization technology to display relevant results that can be integrated into existing applications. This paper addresses the components of KBDT that create the knowledge-based indexes, extends the domain ontology and searches the knowledge index for relevant information.
Molecular Forces Governing the Biological Function of Per-Arnt-Sim-B (PAS-B) Domains: A Comparative Computational Study

Per-Arnt-Sim (PAS) domains are evolutionarily-conserved regions found in proteins in all living systems, involved in transcriptional regulation and the response to hypoxic and xenobiotic stress. Despite having low primary sequence similarity, they show an impressively high structural conservation. Nonetheless, understanding the underlying mechanisms that drive the biological function of the PAS domains remains elusive. In this work, we used molecular dynamics simulations and bioinformatics tools in order the investigate the molecular characteristics that govern the intrinsic dynamics of five PAS-B domains (human AhR receptor, NCOA1, HIF1α, and HIF2α transcription factors, and Drosophila Suzukii (D. Suzukii) juvenile hormone receptor JHR). First, we investigated the effects of different length of N and C terminal regions of the AhR PAS-B domain, showing that truncation of those segments directly affects structural stability and aggregation propensity of the domain. Secondly, using the recently annotated PAS-B located in the methoprene-tolerant protein/juvenile hormone receptor (JHR) from D. Suzukii, we have shown that the mutation of the highly conserved “gatekeeper” tyrosine to phenylalanine (Y322F) does not affect the stability of the domain. Finally, we investigated possible redox-regulation of the AhR PAS-B domain by focusing on the cysteinome residues within PAS-B domains. The cysteines in AhR PAS-B are directly regulating the dynamics of the small molecule ligand-gating loop (residues 305 to 326). In conclusion, we comprehensibly described several molecular features governing the behaviour of PAS-B domains in solution, which may lead to a better understanding of the forces driving their biological functions.
How features embedded in eWOM predict hotel guest satisfaction: an application of artificial neural networks

ABSTRACT Electronic-word-of-mouth (eWOM) plays a crucial role not only in analyzing customer decision-making but also in enhancing the revenue and brand image of service providers in the market. Understanding guest satisfaction is one of the important aspects because it augments the guest experience and their future intention to book. Many studies carried out in past applied regression-based and qualitative methods for analyzing the satisfaction levels of guests, but data analytics methods are still lacking in the literature and how these types of approaches can be applied to benefit hoteliers has not been completely explored. The objective of this study is to identify the important features that predict hotel guest satisfaction (HGS) through the ANN-based HGS prediction model. The feature importance with respect to guest satisfaction has also been estimated and discussed. We have discussed the managerial and theoretical implications of our research. Recommendations for the hospitality industry are also provided.
Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this article, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore, we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26 percent over the baselines of 0.60 in experiment 1 and 96.1 percent over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns.
DECODING TECHNIQUE FOR LOW POWER DESIGN IN XILINX

This research paper is a survey of the current status of research and practice in various disciplines of low power VLSI developments. The paper briefly discusses the rationale of the contemporary, and concentrates on low power design, it presents the metrics and techniques that are used to access the merits of the assorted proposed for the improved energy efficiency. Power dissipation [1], [5] has become an important consideration in terms of performance and scope for VLSI chip design. The research paper describes the decoding strategies, methodology, and techniques for low power system design. Here also we have proposed the decoding technique and compared with silent coding to scale back the transition state and conserved the power which is additionally described during this research. KEYWORDS—Power minimization, decoding technique, silent coding.
An Application-Based Review of Haptics Technology

Recent technological development has led to the invention of different designs of haptic devices, electromechanical devices that mediate communication between the user and the computer and allow users to manipulate objects in a virtual environment while receiving tactile feedback. The main criteria behind providing an interactive interface are to generate kinesthetic feedback and relay information actively from the haptic device. Sensors and feedback control apparatus are of paramount importance in designing and manufacturing a haptic device. In general, haptic technology can be implemented in different applications such as gaming, teleoperation, medical surgeries, augmented reality (AR), and virtual reality (VR) devices. This paper classifies the application of haptic devices based on the construction and functionality in various fields, followed by addressing major limitations related to haptics technology and discussing prospects of this technology.
An infodemiology approach to assess the impact of unemployment on anxiety and depression in France

Abstract Mental health is a key factor for productivity and an indicator of the general well-being of the population. There has been growing evidence in past years that unemployment negatively affects the mental health of the population and, hence, reduces its overall well-being. This study assesses the unemployment and mental health correlation and, specifically, whether depression and anxiety increased due to the rise in unemployment during the 2009 debt crisis in France. We followed an infodemiology approach by first creating a distress index that accounted for all depression and anxiety-related words. Then, by mobilizing Google Trends data, we used diverse economic- and employment-related datasets for France as covariates in multiple linear regression analyses. This study is one of the first to use broad search terms through a distress index that was not restricted to the words “depression” and “anxiety,” but also considered associated terms that individuals may use when seeking information on depression and anxiety. This is also one of the first studies to introduce an infodemiology approach to health economics in France. The results showed that for each unit of additional demand for unemployment benefit, the search traffic for the distress index increased by 0.09%, a positive increase that was minor. The mild effect of demand for unemployment benefits on the distress index may be explained by aspects of social protection policies in France. This paper includes valuable policy suggestions, whereof policymakers could benefit from.
Numerical Simulation of the flow Past a Passenger Cable Car

In this paper, a numerical calculation method based on CFD is proposed to calculate the force and analyze the flow field of the cable car in static state. When the wind attack angle and the wind deflection angle are both 0°and the wind speed is 10m/s, the drag coefficient of the cable car is calculated by numerical simulation to be 0.77, which is consistent with the experimental results. Vortices with different sizes and intensities were observed on the lateral, upper and lower sides, and behind the cable car. The positions of the vortices correspond to the positions of the negative pressure zone.
A Fault Diagnosis Mechanism with Power Generation Improvement for a Photovoltaic Module Array

This paper aims to develop an online diagnostic mechanism, doubling as a maximum power point tracking scheme, for a photovoltaic (PV) module array. In case of malfunction or shadow event occurring to a PV module, the presented diagnostic mechanism is enabled, automatically and immediately, to reconfigure a PV module array for maximum output power operation under arbitrary working conditions. Meanwhile, the malfunctioning or shaded PV module can be located instantly by this diagnostic mechanism according to the array configuration, and a PV module replacement process is made more efficient than ever before for the maintenance crew. In this manner, the intended maximum output power operation can be resumed as soon as possible in consideration of a minimum business loss. Using a particle swarm optimization (PSO)-based algorithm, the PV module array is reconfigured by means of switch manipulations between modules, such that a load is supplied with the maximum amount of output power. For compactness, the PSO-based online diagnostic algorithm is implemented herein using a TMS320F2808 digital signal processor (DSP) and is experimentally validated as successful to identify a malfunctioning PV module at the end of this work.
Empathy, cyberbullying, and cybervictimization among Filipino adolescents

Purpose The purpose of this study was to gain insights into empathy, cyberbullying, and cybervictimization among Filipino adolescents. Methods The participants were 168 junior high school students in the 7th to 10th grades at a public high school in Cavite Province, Philippines. Data were collected on demographic characteristics, the Basic Empathy Scale, and Revised Cyber Bullying Inventory-II scores. The data were analyzed in SPSS using descriptive statistics (frequency and mean), the independent t-test, bivariate correlation, and one-way analysis of variance. Results The degree of empathy did not show a statistically significant relationship with cyberbullying (r=−.07, p=.359) but did show a significant relationship with cybervictimization (r=.18, p=.025). Furthermore, cyberbullying had a statistically significant association with cybervictimization (r=.60, p<.001). Conclusion Although empathy does not necessarily affect cyberbullying, higher levels of empathy were found among cyberbullying victims. However, an alarming result of this study is the possibility that victims may become cyberbullies. Therefore, we should highlight empathy as part of efforts to prevent cyberbullying and to solve various cyber-related problems. Since cyberbullying and cybervictimization are closely related, it is important to focus on this relationship and to make multilateral efforts to ensure that cyberbullying does not lead to other negative issues.
Aptamer-based biosensors for detection of lead(II) ion: a review

Lead(II) ion (Pb2+) contamination can be accumulated along the food chain and cause a serious threat to public health. Plenty of research effort has thus been devoted to the development of fast, sensitive and selective biosensors for monitoring Pb2+. Aptamer especially Pb2+ based DNAzymes (obtained from a large DNA library via in vitro selection) and DNA molecules are highly selective for a number of metal ions. Therefore, aptamer-based biosensors have been extensively studied for Pb2+ ion detection with a high sensitivity. In this review, we explain the current progress of Pb2+ aptamer-based sensors mainly in three groups: fluorescent; colorimetric; and electrochemical biosensors.
Ultrasensitive Determination of Microcystin-Leucine-Arginine (MCLR) by an Electrochemiluminescence (ECL) Immunosensor with Graphene Nanosheets as a Scaffold for Cadmium-Selenide Quantum Dots (QDs)

Abstract In order to satisfy the needs for the trace microcystins in water, a novel electrochemiluminescence (ECL) immunosensor was developed based on a dual signal amplification strategy. A MoS2-Au hybrid nanocomposite served as the matrix to add a large number of active-sites, and graphene nanosheets were employed as the scaffold to capture cadmium-selenide quantum dots (CdSe QDs). Transmission electron microscopy (TEM), scanning electron microscopy (SEM), ultraviolet-visible absorption spectroscopy and photoluminescence spectroscopy were used to characterize these materials. The fabrication of the immunosensor was performed by electrochemical impedance spectroscopy (EIS). Under the optimum conditions, a strong ECL signal was obtained from immunosensor that was approximately 7-fold higher than from an immunosensor with a pure CdSe QDs probe. The ECL immunosensor possesses a wide linear response to microcystins-LR (MCLR) from 0.005 to 100 µg/L with a low detection limit of 0.0032 µg/L. Moreover, the immunosensor exhibited good selectivity, stability, and reproducibility for MCLR.
Multi-Modal Dynamic Pricing

We consider a single product dynamic pricing with demand learning. The candidate prices belong to a wide range of a price interval; the modeling of the demand functions is nonparametric in nature, imposing only smoothness regularity conditions. One important aspect of our model is the possibility of the expected reward function to be nonconcave and indeed multimodal, which leads to many conceptual and technical challenges. Our proposed algorithm is inspired by both the Upper-Confidence-Bound algorithm for multiarmed bandit and the Optimism-in-the-Face-of-Uncertainty principle arising from linear contextual bandits. The multiarmed bandit formulation arises from local-bin approximation of an unknown continuous demand function, and the linear contextual bandit formulation is then applied to obtain more accurate local polynomial approximators within each bin. Through rigorous regret analysis, we demonstrate that our proposed algorithm achieves optimal worst-case regret over a wide range of smooth function classes. More specifically, for k-times smooth functions and T selling periods, the regret of our proposed algorithm is [Formula: see text], which is shown to be optimal via the development of information theoretical lower bounds. We also show that in special cases, such as strongly concave or infinitely smooth reward functions, our algorithm achieves an [Formula: see text] regret, matching optimal regret established in previous works. Finally, we present computational results that verify the effectiveness of our method in numerical simulations. This paper was accepted by J. George Shanthikumar, big data analytics.
The Global Blue Water Use for the Dutch Diet and Associated Environmental Impact on Water Scarcity

Agriculture is responsible for around 70% of global water withdrawal, making it the largest consumer of freshwater. Globally, freshwater is a scarce resource, and there is increasing risk that current water use is crossing the planetary boundary, meaning that humanity is using more freshwater than nature can replenish. Beside the amount of water used for the production of food, also the location of water use is of importance to identify local water scarcity. In this study we determined and analyzed the blue water use for the Dutch diet. Also, we provide an indication of the the spatial distribution in water use, related to water scarcity. The blue water use per person for the average diet of the Dutch adult population aged 19-79 years as calculated in this study was 160 l/day (=57 m3/year) for men and 153 l/day (=56 m3/year) for women. Non-alcoholic beverages were the most important contributor to water use for the diet of men as well as women. Fruits were the second most important contributor to water use for the diet of women (particularly oranges, tangerines, strawberries and bananas), and the third most important for men. Meat was the second most important contributor for men (particularly chicken fillet and beef meat) and the third for women. About one third of the blue water use for the Dutch diet takes place in countries where 30 to 70 times less available water remaining per area than the world average. This means that water scarcity is relatively high in those countries compared to the world’s average. The main countries of origin of the water used for food production for the Dutch population we identified were Spain, the Netherlands itself, South Africa, Chile, India and USA. Products with ingredients from Spain are several types of greenhouse vegetables, citrus fruits and juices. To lower the environmental impact related to blue water use, for the Dutch situation we conclude that beside reducing meat and dairy consumption, consuming more Dutch fruits like apples and pears, and reducing imports of fruits and nuts from e.g. Spain, Chile and USA, are relatively easy changes that will have a positive effect on the global water scarcity impact. Also, reducing the consumption of non-alcoholic beverages like coffee, soft drinks and juices, and increasing the use of tap water, is recommended to lower the Dutch blue water use within a healthy diet.