Miracle-dz/UniFAR-FTUs huggingface dataset:
EMET: Embeddings from Multilingual-Encoder Transformer for Fake News Detection

In the last few years, social media networks have changed human life experience and behavior as it has broken down communication barriers, allowing ordinary people to actively produce multimedia content on a massive scale. On this wise, the information dissemination in social media platforms becomes increasingly common. However, misinformation is propagated with the same facility and velocity as real news, though it can result in irreversible damage to an individual or society at large. Solving this problem is not a trivial task, considering the reduced size of the text messages usually posted on these communication vehicles. This paper proposes an end-to-end framework called EMET to classify the reliability of small messages posted on social media platforms. Our method leverages text-embeddings from multilingual-encoder transformers that take into consideration the semantic knowledge from preceding trustworthy news and the use of the reader’s reactions to detect misleading content. Our findings demonstrated the value of user interaction and prior information to check social media post’s credibility.
Alternative balanced scorecards built from paradigm models in strategic HRM and employment/industrial relations and used to measure the state of employment relations and HR system performance across U.S. workplaces

This paper constructs alternative balanced scorecards based on high‐performance work system (HPWS) and employment relations system (ERS) models. The models are depicted and compared in diagrams and used as framework skeletons for building separate HPWS and ERS scorecards, intended to provide a detailed data picture of the operational health and performance of an organization's employment/HR system and its operations, processes, and inputs/outputs. The scorecards are filled in with nationally representative data from 2,000+ U.S. workplaces using more than 50 employment/HR indicators, as reported by separate panels of managers and employees. The indicators for each workplace are aggregated into an overall HR/employment system score, ranked from low‐to‐high, and graphed as frequency distributions. These distributions provide a unique snapshot picture of the mean and dispersion of the state of employment relations and HR system performance for companies across the United State. They also reveal that “models matter” since the HPWS and ERS scorecards provide distinctly different evaluation assessments.
Analysis of a Stochastic Optimal Control for Pension Funds and Application to Investments in Lower Middle-Income Countries

One of the major problems faced in the management of pension funds and plan is how to allocate and control the future flow of contribution likewise the proportion of portfolio value and investments in risky assets. This work considers the management of a pension plan by means of a stochastic dynamic programming model based on Merton's model. The model is analyzed such that the conditions of optimal contribution and investment in risky assets are determined and sensitized. The case study of Nigeria, Ghana, Kenya is considered for various periods in the model simulation. Thus, the volatility condition obtained is used to estimate the efficiency of some important parameters of the model.
Like ships passing in the night? Nonstandard work schedules and spousal satisfaction in Finland, the Netherlands and the United Kingdom

This study examined the associations of work schedules and spousal satisfaction among Finnish (n = 347), Dutch (n = 304) and British (n = 337) parents. In addition to comparing parents with nonstandard schedule with parents in regular day work, the study examined separately the roles played in spousal satisfaction of morning, evening, night and weekend work and other working time-related variables (working hours, changes in and influence over one’s work schedules and spouse’s work schedule). The three-country data were analyzed using structural equation modeling with a multigroup procedure in Mplus. Little evidence for negative associations of work schedules and related factors with spousal satisfaction was found among the present European employees. No between-country differences were found in the examined associations. The quantitative analyses were supplemented with content analysis of parents’ descriptions of the experienced effects of their working times on their spousal relationship. Parents described their working times mostly as having a negative influence on their possibilities of being together, organization of daily life, psychological and physiological reactions and spousal communication. Some, however, also mentioned benefits and finding solutions to problems related to their working times.
Design Method of Dual Active Bridge Converters for Photovoltaic Systems with High Voltage Gain

In this paper, a design method for a photovoltaic system based on a dual active bridge converter and a photovoltaic module is proposed. The method is supported by analytical results and theoretical predictions, which are confirmed with circuital simulations. The analytical development, the theoretical predictions, and the validation through circuital simulations, are the main contributions of the paper. The dual active bridge converter is selected due to its high efficiency, high input and output voltages range, and high voltage-conversion ratio, which enables the interface of low-voltage photovoltaic modules with a high-voltage dc bus, such as the input of a micro-inverter. To propose the design method, the circuital analysis of the dual active bridge converter is performed to describe the general waveforms derived from the circuit behavior. Then, the analysis of the dual active bridge converter, interacting with a photovoltaic module driven by a maximum power point tracking algorithm, is used to establish the mathematical expressions for the leakage inductor current, the photovoltaic current, and the range of operation for the phase shift. The design method also provides analytical equations for both the high-frequency transformer equivalent leakage inductor and the photovoltaic side capacitor. The design method is validated through detailed circuital simulations of the whole photovoltaic system, which confirm that the maximum power of the photovoltaic module can be extracted with a correct design of the dual active bridge converter. Also, the theoretical restrictions of the photovoltaic system, such as the photovoltaic voltage and power ripples, are fulfilled with errors lower than 2% with respect to the circuital simulations. Finally, the simulation results also demonstrate that the maximum power point for different environmental conditions is reached, optimizing the phase shift factor with a maximum power point tracking algorithm.
Female Entrepreneurship: Can Cooperatives Contribute to Overcoming the Gender Gap? A Spanish First Step to Equality

To date, entrepreneurship research has rarely focused on cooperatives. Recent research has suggested there is a positive association between cooperatives and women’s entrepreneurial activity based on the assumption that this model is especially suitable for women’s expectations and is favourable to their economic entrepreneurial development. However, few studies have empirically tested these propositions. This research, which analysed data from 264 Spanish cooperatives, confirms these findings. A mixed-methods approach was used. In the first stage, a Delphi study was run with 11 cooperative entrepreneurship experts to ask what factors may be decisive for women preferring a cooperative model. In the second stage, the factors derived from the Delphi study were used in a survey targeting female cooperative associates. The findings confirmed that female cooperative owners perceived this business model to be aligned with their values and best fit their work and lifestyle. This research contributes to the existing social entrepreneurship literature by providing empirical evidence from the Spanish context that highlights the role of women in cooperatives. The findings also emphasize the need for responsive policies and programmes that promote the cooperative model since it promotes female employment and women’s entrepreneurial activity even in periods of economic austerity.
Prothonotary Warbler (Protonotaria citrea) Plumage as an Indicator for Infection: the Relationship between Haemosporidia Infection and Breast Feather Reflectance in a Neotropical Migrant Passerine

PROTHONOTARY WARBLER (PROTONOTARIA CITREA) PLUMAGE AS AN INDICATOR FOR INFECTION: THE RELATIONSHIP BETWEEN HAEMOSPORIDIA INFECTION AND BREAST FEATHER REFLECTANCE IN A NEOTROPICAL MIGRANT PASSERINE By Robert Fithian, B.S. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2009 Major Director: D. C. Ghislaine Mayer Assistant Professor, Department of Biology Yellow avian plumage is a direct result of carotenoid pigments obtained in a bird’s diet and may act as an indicator for individual health, parasite resistance, and status. This study describes breast feather reflectance of adult Prothonotary Warblers (Protonotaria citrea) (n=169), insectivorous Neotropical migrant passerines, throughout the Ultraviolet (UV) and human visible light spectra and examines the relationship between Haemosporidia (pathogen causing Avian Malaria) infection and feather reflectance (n=41). Reflectance was characterized using a Principle Component Analysis evaluating Intensity, Brightness, Hue, UV Intensity, UV Brightness, and UV Chroma. UV and visible light reflectance was higher in birds sampled
Optimized Sensor Selection for Joint Radar-communication Systems

Sensor array-based joint radar-communication (JRC) systems exploit adaptive beamforming to transmit radar and communication signals in their respective directions. Optimal sensor selection is anticipated as an attractive means to achieve superior performance with a low hardware cost because of the ever-decreasing cost of the sensor deployment compared to the radio frequency (RF) chains and processors. In this paper, we address optimal sensor selection for adaptive beamforming-based JRC systems by exploiting a constrained re-weighted ℓ1-norm minimization with low computational complexity. We argue that, compared to the individual approaches, the grouped counterpart eases the hardware implementation by mollifying the unnecessary sensor switching and enables effective utilization of up-conversion chains. Simulation results clearly demonstrate the superior performance of the proposed strategies.
Scenario-Wise Distributionally Robust Optimization for Collaborative Intermittent Resources and Electric Vehicle Aggregator Bidding Strategy

The increasing penetrations of renewable energy in the electricity sector and plug-in electric vehicles (PEVs) in the transportation sector have increased the interests in introducing new methods to deal with uncertainties in power system studies. In this paper, a new distributionally robust optimization (DRO) via scenario wise ambiguity set is proposed to develop a collaborative bidding strategy for intermittent resources such as hydroelectric generation, wind farms, solar farms and electric vehicle aggregator in the day-ahead energy market. The proposed scenario wise ambiguity set is based on Wasserstein distance and is capable of considering both distributional information and statistical distance metric information in the ambiguity set. In this context, the robust counterpart of proposed DRO applying scenario based affine recourse approximation is developed in this paper. The proposed methodology is applied on a 3-bus test system as well as IEEE 118-bus test system to corroborate the effectiveness of the novel DRO model.
Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.
Application Technology of Wireless Computer Network in Intelligent Driving

With the continuous development of wireless network and Internet technologies, research on the application technology of wireless computer networks in intelligent driving has also entered the blowout development stage. The purpose of this paper is to study the application technology of wireless computer networks in intelligent driving. This paper also proposes an optimization solution for wireless vehicle networking in view of the many disadvantages of the traditional traffic law enforcement management system. This article first briefly introduces the concepts of the Internet of Things, the Internet of Vehicles, and wireless systems, analyzes the necessity and feasibility of the fusion of the two technologies, and then proposes a framework for the integration of wireless computer network technology and Internet of Things technology. Various innovative applications of the Internet of Vehicles based on this architecture in intelligent transportation are analyzed and demonstrated. Wireless networked car networking has demonstrated its superiority to a certain extent in the application of several special scenarios, alternative analysis of ETC systems, and optimization of traditional traffic law enforcement management. At the end of this paper, a GA algorithm based on wireless networked vehicle networking applications is proposed. Through the results of the algorithm, it is concluded that the system performance and economic performance of wireless vehicle networking are superior. According to the experimental results, the system developed in this paper can not only solve the vehicle congestion situation, but also reduce the occurrence of traffic accidents.
EFFICACY OF A VIDEO-BASED PHYSICAL THERAPY AND REHABILITATION SYSTEM IN PATIENTS WITH POST-STROKE HEMIPLEGIA: A RANDOMIZED, CONTROLLED, PILOT STUDY

1 Bezmialem Vakif University , Department of Physical Medicine and Rehabilitation, Istanbul, TURKEY. 2 Baltalimanı Metin Sabancı Bone Diseases Training And Research Hospital, Department of Physical Medicine and Rehabilitation, Istanbul, TURKEY. 3 Haydarpasa Numune Education and Research Hospital, Department of Physical Medicine and Rehabilitation, Istanbul, TURKEY. 4 Gebze Technical University, Department of Computer Engineering, Kocaeli, TURKEY. 5 Avrasya Hospital, Department of Physical Medicine and Rehabilitation, Istanbul, TURKEY. EFFICACY OF A VIDEO-BASED PHYSICAL THERAPY AND REHABILITATION SYSTEM IN PATIENTS WITH POST-STROKE HEMIPLEGIA: A RANDOMIZED, CONTROLLED, PILOT STUDY RESEARCH
Magnet-induced monostable nonlinearity for improving the VIV-galloping-coupled wind energy harvesting using combined cross-sectioned bluff body

This study proposes a novel wind energy harvester that exploits both vortex-induced vibration (VIV) and galloping phenomena with a hybrid cross-sectioned bluff body. Magnetic-induced monostable nonlinearity is then introduced in the harvester to improve its energy harvesting performance. The bluff-body cross section uses the combination of 3/4 round and 1/4 square to introduce the coupled VIV and galloping, and the magnetic repulsive force of the two magnets is employed to realize the monostable nonlinearity. Governing equations bringing together the VIV, galloping and magnet-induced monostable nonlinearity are established. Wind tunnel experiment is first conducted to validate the aerodynamic model of the governing equations without the monostable nonlinearity, which reveals the advantage of the coupled VIV and galloping for performance improvement. Consequently, the numerical simulations are performed to reveal the benefit of the magnet-induced monostable nonlinearity for wind energy harvesting. Results show that compared to the coupled VIV-galloping harvester without the monostable nonlinearity, the proposed monostable harvester can reduce the cut-in speed of galloping, which boosts the voltage output in a wider wind speed range and further promotes the advantages of the combination of VIV and galloping.
Similarity Learning For Cover Song Identification Using Cross-Similarity Matrices of Multi-Level Deep Sequences

In recent years, several deep learning models have been proposed for cover song identification and they have been designed to learn fixed-length feature vectors for music tracks. However, the aspect of temporal progression of music, which is important for measuring the melody similarity between two tracks, is not well represented by fixed-length vectors. In this paper, we propose a new Siamese network architecture for music melody similarity metric learning. The architecture consists of two parts. One part is a network for learning the deep sequence representation of music tracks, and the other is a similarity estimation network which takes as input the cross-similarity matrices calculated from the deep sequences of a pair of tracks. The two networks are jointly trained and optimized to achieve high melody similarity prediction accuracy. Experiments conducted on several public datasets demonstrate the superiority of the proposed architecture.
Illex argentinus, Argentine shortfin squid

Illex argentinus, a close relative of the other North Atlantic Illex species, concentrates on the Patagonian shelf edge and spreads over the shelf from Southern Brazil to the Falkland Islands. It supports one of the world’s largest squid fisheries. The fishery mostly exploits the winter spawning, south Patagonian stock, which is more abundant, grows to a larger body size and extends further south than the summer spawners. Spawning takes place in the northern part of the range and the eggs hatch into a typical ommastrephid rhynchoteuthon paralarva. I. argentinus is short-lived and semelparous living approximately one year, at the end of which the squid die after spawning. Growth is rapid and the largest individuals - females from the South Patagonian, winter spawning stock - reach a mean mantle length of 330 mm (maximum of 400 mm ML) when fully grown. Large scale migrations between the spawning grounds and the feeding grounds, and back again, over the one year life cycle are accompanied by diel vertical migrations. Immature and maturing squid occur 3 - 20 m off the bottom during the day but at night they ascend to 5 - 20 m over the shelf and to 20 - 200 m beyond the shelf break. Mature squid of the South Patagonian Stock migrate northward along the continental slope to the spawning grounds in May – July remaining close to the sea bed (500 - 900 m) at night and ascend to 200-300 m above the bottom during the day. The paralarvae are thought to feed using their proboscis, on body mucus enriched with microorganisms and other food particles that stick to it. Juveniles and adults are opportunistic predators feeding on fishes, crustaceans, and cephalopods including smaller conspecifics. The diet generally reflects the plankton and nekton available. Small quid 50% of annual food intake and is also consumed by hoki, red cod, dogfish, and various species of penguin, albatross, some petrels and several seals and dolphins. In the vicinity of the Falkland Islands the southern Patagonian stock has few important predators because squid have grown to a relatively large size before they arrive there. I. argentinus is host to 21 known species of parasite, mostly the larval stages of helminths and trematodes. The fishery is pursued by trawlers but mostly by jiggers which attract the squid with powerful lights that can be seen in visible band satellite imagery. The largest annual catch to date was 1.153 tonnes taken in 1999. There are substantial variations in recruitment and stock size driven by environmental variability.
Assessment of Enhanced Dempster-Shafer Theory for Uncertainty Modeling in a GIS-Based Seismic Vulnerability Assessment Model, Case Study - Tabriz City

Earthquake is one of the natural disasters which threaten many lives every year. It is impossible to prevent earthquakes from occurring; however, it is possible to predict the building damage, human and property losses in advance to mitigate the adverse effects of the catastrophe. Seismic vulnerability assessment is a complex uncertain spatial decision making problem due to intrinsic uncertainties such as lack of complete data, vagueness in experts’ comments and uncertainties in the numerical data/relations. It is important to identify and model the incorporated uncertainties of seismic vulnerability assessment in order to obtain realistic predictions. Fuzzy sets theory can model the vagueness in weights of the selected criteria and relationships of the criteria with building damage. Dempster’s combination rule is useful for fusion of information on the vulnerability of the buildings which leads to decreased uncertainty of the results. However, when there is a conflict among information sources, classical Dempster rule of combination is not efficient. This paper analyses the uncertainty sources in a geospatial information system (GIS)-based seismic vulnerability assessment of buildings and then focuses on assessing the efficiency of Dempster rule of combination in the fusion of the information sources for the seismic vulnerability assessment. Tabriz, a historical and earthquake prone city in the north west of Iran was selected as the study area. The results verified that some inconsistencies among information sources exist which are important to be considered while proposing a method for the fusion of the information in order to obtain vulnerability assessments with less uncertainty. Based on the assessed building damage, the number of probable victims was estimated. The produced physical and social seismic vulnerability maps provide the required information for urban planners and administrators to reduce property and human losses through pre-earthquake mitigation and preparedness plans efficiently.