Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Non-preemptive On-demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks

Recently, energy provisioning problem has become one of the leading research interests in Wireless Sensor Networks (WSNs). Deploying Mobile Charger Vehicles (MCVs) (e.g., robots, drones) equipped with high capacity-battery and Wireless Power Transfer (WPT) transmitter to charge the sensor nodes in the network wirelessly, is a promising solution. Existing scheduling policies mostly concentrated on charging tasks scheduling, but neglected the influences of network topology and energy threshold distribution. Furthermore, schedulability test of existing studies remain infeasible and we can’t guarantee implementing them for larger set of tasks, making them unsuitable for real-time WRSNs. To fill this gap, in this paper, first, we propose hexagonal clustered-based deployment of sensor nodes and MCVs to provide large coverage and a robust connected network through alternative routes. Second, we propose an efficient Non-Preemptive Priority on-Demand Charging scheduling (NPDC) scheme for the mobile chargers to schedule the charging tasks according to Earliest Deadline First EDF algorithm. Third, we propose an energy threshold distribution and algorithm to assign threshold value for nodes and optimize the arrivals of charge requests. Fourth, we propose a traffic intensity-based partial charging distribution to minimize the dead node density. Finally, extensive simulations have been conducted to evaluate the proposed algorithms. The results demonstrate that our proposed algorithms achieve good performance in terms of queue length, dead nodes density, response time, and energy efficiency.
PCIMS: Plenary Centralized Intersection Management Scheme for Heterogeneous Connected Vehicles

This paper proposes a novel plenary centralized intersection management scheme (PCIMS) for a non-signalized intersection. Our contributions are fourfold. First, we take into account vehicle heterogeneity by integrating various vehicle classes in the system. Second, we consider some key elements in the scheduling mechanism such as vehicle types, intersection rules and road priorities. Third, we discuss the significant role of different road conditions, specific vehicles behavior and safety parameters in the traffic safety. Forth, in addition to the traffic safety assurance, we demonstrate the superiority of the system performance over conventional traffic lights (TLs) in several scenarios with congested and sparse traffic in terms of average travel time (ATT), packet loss rate (PLR), channel busy rate (CBR), intersection busy time (IBT), and throughput.
Impact of Information Technology on Business Performance: Integrated Structural Equation Modeling and Artificial Neural Network Approach

In today's globalizing world, which is also called the information age, information and information technologies are becoming increasingly important for businesses, and they have become an indispensable part of economic and social life. Nowadays, it is impossible to think about a business that is far from information technology, and also it is important not only to find information but also to find the fastest and highly reliable information. The important thing is to use information technologies effectively and efficiently. Therefore, it is expected that the effective usage of information technology will have significant positive effects on business performance. The aim of this study is to examine and analyze the impact of the intensive usage of information technologies on business performance in the supply chain process. A sequential, multi-method approach integrating both structural equation modeling (SEM) and neural network analysis was employed in this research. The information technology usage performance network was formed by using the SEM model, and the ANN model was used to predict a relationship between information technology usage levels and business performance by using these network outputs. Furthermore, the validity and reliability tests of the relevant model data were performed.
High quality ECG dataset based on MIT-BIH recordings for improved heartbeats classification

Electrocardiogram (ECG) is a reliable tool for med-ical professionals to detect and diagnose abnormal heart waves that may cause cardiovascular diseases. This paper proposes a methodology to create a new high-quality heartbeat dataset from all 48 of the MIT-BIH recordings. The proposed approach computes an optimal heartbeat size, by eliminating outliers and calculating the mean value over 10-second windows. This results in independent QRS-centered heartbeats avoiding the mixing of successive heartbeats problem. The quality of the newly constructed dataset has been evaluated and compared with existing datasets. To this end, we built and trained a PyTorch 1- D Resnet architecture model that achieved 99.24 % accuracy with a 5.7% improvement compared to other methods. Additionally, downsamling the dataset has improved the model's execution time by 33% and reduced 3x memory usage.
Predicting Asset Returns with Self-Attentive Networks

Predictor interactions play a crucial role in esti-mating risk premiums. However, current methodologies might not effectively and understandably address this. Inspired by the broad success of the self-attention mechanism, we develop an attention-based asset pricing model in this paper, which we'll refer to as AAPM. This model, utilizing several stacked multi-head self-attention blocks, can efficiently capture high-order factor interactions. Empirical evidence suggests that our model surpasses all other machine learning models studied in relevant finance literature in terms of out-of-sample R2. Interestingly, our model, despite having fewer predictors, outperforms its original version that incorporates all predictors as input. We attribute this phe-nomenon to negative factor interactions that detrimentally affect the latter's result. Moreover, we identify critical predictor bundles using our model. Our results demonstrate substantial economic benefits to investors and offer a novel perspective in empirical asset pricing literature.
Simulation-based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering

V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles or roadside units, by exchanging lists of perceived objects in the 5.9 GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety, e.g., vehicles or pedestrians. The current standardization of Collective Perception in Europe considers filtering objects for transmission based on their locally perceived dynamics and freshness to preserve channel resources. However, two remaining problems of object filtering are: information redundancy and adapting object filtering to the available channel resources. In this paper, we combine redundancy mitigation and congestion control-aware filtering. We evaluate the performance of the resulting object filtering techniques by realizing realistic, large-scale simulations of a mid-size city in Germany. We assess the performance using a scoring metric. The results show better information redundancy control and adjustable channel usage for object filtering.
Biased Target-tree * Algorithm with RRT * for Reducing Parking Path Planning Time

The target-tree* algorithm, which is a variant of the optimal rapidly-exploring random tree (RRT*) has been proposed to reduce the parking path planning time. This algorithm pre-generates a set of backward paths (target-tree) around a parking spot and extends an RRT* from the initial pose until it is connected to a random sample of the target-tree. However, it is difficult to obtain the shortest (optimal) parking path within a short planning time because connected samples between the tree and the target-tree are randomly searched. To deal with this problem, this paper proposes a biased target-tree* algorithm with RRT* that searches connected random samples in a biased range near the target-tree. This range has a Gaussian distribution centered on the optimal connected sample where the shortest parking path can be obtained quickly and is obtained through supervised learning. In actual parking situations, the biased target-tree* algorithm obtained a shorter path with less length deviation than the original target-tree* algorithm within a shorter planning time.
Design and Performance Studies of a Simple Direct Digital Electronic Instrumentation System for Thermistor Interfacing

The paper proposes a direct-digital interfacing circuit for linearization of thermistors. The proposed circuit comprises a switched passive linearized network and a simple relaxation-oscillator circuit for digitization. The salient features of the proposed scheme include, 1) simple architecture for thermistor linearization, 2) direct-digital output, without requiring analog-to-digital conversion, 3) usage of a single (uni-polar) reference voltage. The working principle of the interfacing system is established using a detailed mathematical derivation. The efficacy of the proposed digitizer is evaluated using simulation and hardware experiment studies, over different range of temperatures. Results demonstrate that the interfacing system can work very well for the temperatures considered. This digital converter can find number of applications, in aviation, aerospace and other industrial sectors.
Homography Estimation for Camera Calibration in Complex Topological Scenes

Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most analysis applications. However, security cameras are susceptible to environmental conditions and small camera movements, resulting in a need for an automated re-calibration method that can account for these varying conditions. In this paper, we present an automated camera-calibration process leveraging a dictionary-based approach that does not require prior knowledge on any camera settings. The method consists of a custom implementation of a Spatial Transformer Network (STN) and a novel topological loss function. Experiments reveal that the proposed method improves the IoU metric by up to 12% w.r. t. a state-of-the-art model across five synthetic datasets and the World Cup 2014 dataset.
Targetless Extrinsic Calibration Between Event-Based and RGB Camera for Intelligent Transportation Systems

The perception of Intelligent Transportation Systems is mainly based on conventional cameras. Event-based cameras have a high potential to increase detection performance in such sensor systems. Therefore, an extrinsic calibration between these sensors is required. Since a target-based method with a checkerboard on the highway is impractical, a targetless approach is necessary. To the best of our knowledge, no working approach for targetless extrinsic calibration between event-based and conventional cameras in the domain of ITS exists. To fill this knowledge gap, we provide a targetless approach for extrinsic calibration. Our algorithm finds correspondences of the detected motion between both sensors using deep learning-based instance segmentation and sparse optical flow. Then, it calculates the transformation. We were able to verify the effectiveness of our method during experiments. Furthermore, we are comparable to existing multicamera calibration methods. Our approach can be used for targetless extrinsic calibration between event-based and conventional cameras.
Resonant damping of kink oscillations of thin expanding magnetic tubes

We study the resonant damping of kink oscillations of thin expanding magnetic flux tubes. The tube consists of a core region and a thin transitional region at the tube boundary. The resonance occurs in this transitional layer where the oscillation frequency coincides with the local Alfvén frequency. Our investigation is based on the system of equations that we previously derived. This system is not closed because it contains the jumps of the magnetic pressure perturbation and plasma displacement across the transitional layer. We calculate these jumps and thus close the system. We then use it to determine the decrements of oscillation eigenmodes. We introduce the notion of homogeneous stratification. In accordance with this condition the ratio of densities in the tube core and outside the tube does not vary along the tube, while the density in the transitional layer can be factorised and written as a product of two function, one depending on the variable along the tube and the other on the magnetic flux function. Our main result is that, under the condition of homogeneous stratification, the ratio of the decrement to the oscillation frequency is independent of a particular form of the density variation along the tube. This ratio is also the same for all oscillation eigenmodes.
Pentamaran configuration design with modeling hull form for resistance minimization

Multi-hull ship had characteristics with complex geometry configurations. In the early stage design, multi-hull ship with proper configurations can be producing both minimum resistance and power consumption. Increasing number of hull as in pentamaran will increase frictional resistance, but total resistance can be reducing by decreasing wave making resistance with a slender hull form. With CFD tool used to examine minimum resistance with modified modelling of chine hull form on variation i.e. deadrise angles, waterline section of entrance angle and longitudinal section of stem angle. All simulations were conducted with fix separation on S/L 3/16 and Froude number up to 0.7. Comparison with the original hull form, chine hull form C35 II in deadrise 35° and angle of entrance 15° more advantageous with maximum resistance drag reduction 20.38%. The results of the study had been displayed in table and charts of chine pentamaran which having the smallest total resistance expressed in coefficient of friction and wave resistance.
FARMERS’ PERCEPTION OF THE CHANGES IN LAND DISTRIBUTION IN THE BANDARBAN HILL DISTRICT OF BANGLADESH

The Chattogram Hill Tracts (CHT) are witnessing an escalating pace of land use change as a result of population dynamics, economic development, climatic change, greater accessibility, and agricultural technical advancements. The study was conducted in Rwanggchhari and Ruma upazila of Bandarban district, Bangladesh. This study explored farmers’ perception towards land use change and the factors contributing to their perception. Following a proportionate random sampling technique, a total of 120 households were sampled and survey was administered through a predesigned close ended questionnaire. A substantial number of respondents (54%) were more than 40 years old, of whom 51% were educated with an average family size was five. The respondents had an average annual income of BDT 91830. An average land holding was 3.59 acres were seen in the study area and these lands were occupied with agricultural, forest, vegetable lands and also with flat and hilly lands. Meanwhile, 98% farmers maintained medium to higher perception towards the changes of land distribution. In addition, out of eight attributes, three attributes, namely, educational attainment, agricultural training received and hill land ownership had significant influence on the perception of land use change by the respondents. The findings of the study can be utilized by the policy makers and planners of the country to formulate future land distribution policy of the CHT in future.
A SURVEY OF TREE SPECIES DIVERSITY IN AKURE FOREST RESERVE AND OKOMU NATIONAL PARK

This research aimed to survey the tree species diversity in Akure Forest Reserve and Okomu National Park. The study was carried out in Akure Forest Reserve, Ondo State and Okomu National Park, Edo State. Sample plots of (100x100 m 2 ) were demarcated in the Forest Reserves and sub-divided into smaller units of (25 by 25m 2 ). Using 50% sampling intensity, data were collected from eight (8) plots in each Forest Reserves. Tree species were identified via their botanical names. Okomu National Park had 400 trees per hectare from 53 species and 26 families, whereas Akure Forest Reserve had 388 trees per hectare from 65 species and 30 families. A J-inverse diameter distribution was observed in the two Forest Reserves. A mean Diameter at Breast Height and basal area/ha of 32.90 cm and 48.76 m 2 /ha, and 20.16 cm and 16.56 m 2 /ha, were obtained for Akure Forest Reserve, and Okomu National Park respectively. Shannon-Wiener diversity index for, Okomu and Akure Forest Reserves were 3.52 and 3.83 respectively. It could therefore be concluded that the Forest Reserves are potential biodiversity places of interest if better conservation management efforts are employed. Therefore, conservation efforts should be stepped up in the Forest Reserves as they still contain important economic species.
Parallel repetition of multi-party and quantum games via anchoring and fortification

Parallel repetition is a fundamental operation for amplifying the hardness inherent in multiplayer games. Through the efforts of many researchers in the past two decades (e.g. Feige, Kilian, Raz, Holentstein, Rao, Braverman, etc.), parallel repetition of two-player classical games has become relatively well-understood. On the other hand, games with entangled players (quantum games), crucial to the study of quantum non-locality and quantum cryptography, and multi-player games were poorly understood until recently. In this thesis, we resolve some of the major problems regarding the parallel repetition of quantum and multi-player games by establishing the first exponential-rate hardness amplification results for these games and hence extend the classes of games where exponential decay rates is known considerably. We consider two different methods for obtaining these hardness amplification results. For our first method, we draw from the recent work of Moshkovitz on parallel repetition of fortified games. We introduce an analytic reformulation of Moshkovitz's fortification framework. This reformulation allows us to expand the scope of the fortification method to new settings. In particular, we prove parallel repetition and fortification theorems for games with players sharing quantum entanglement, and games with more than two players in this new framework. An important component of our work is a variant of the fortification transformation, called ordered fortification, that preserves the entangled value of a game. For our second method, we introduce a class of games we call anchored. Anchoring is a simple transformation on games inspired in part by the transorfmation proposed in the pioneering work of Feige-Kilian. Unlike the Feige-Kilian transformation, our anchoring transformation is completeness preserving. We prove an exponential-decay parallel repetition theorem for anchored games that involve any number of entangled players. We also prove a threshold version of our parallel repetition theorem for anchored games. Thesis Supervisor: Madhu Sudan Title: Gordon McKay Professor of Computer Science, Harvard University
A comparison of Mode‐S Enhanced Surveillance observations with other in situ aircraft observations

Observations from aircraft are an important element of the global observing network. A promising new observation source, deriving wind and temperature measurements from air traffic management data, has previously been reported on by a small number of groups. This article further investigates the error characteristics by comparing a year's worth of in situ observed winds and temperatures from a commercial British Airways Boeing 747 (B747) with the derived Mode‐Selective (Mode‐S) Enhanced Surveillance (EHS) observations from the Met Office network of Mode‐S receivers. It is shown that, whilst the winds and high‐altitude temperatures are of good quality, they show error profiles with altitude that are different for ascent and descent. The data show that the situation of the aircraft is critical to understand the biases; this is dependent on the aircraft, operator and airport, making corrections infeasible. Further understanding is gained by an intercomparison flight with the UK Facility for Airborne Atmospheric Measurement (FAAM) BAe 146 aircraft. This showed a good comparison with Mode‐S EHS winds, with a RMS difference between the FAAM data and B747 Mode‐S EHS data of 1.5 and 0.9 m/s for the u and v components respectively. Comparing the FAAM data and B747 flight data recorder (FDR) provided RMS differences of 0.6 and 1.4 m/s respectively. This suggests that the data quality from Mode‐S EHS is similar to that which can be achieved from a commercial aircraft. The temperature RMS differences were found to be 1.6 and 0.5 K when the FAAM data were compared to the Mode‐S EHS data and FDR data from the B747 respectively, suggesting that the temperature Mode‐S EHS data are of an inferior quality.
DETERMINATION OF FLORAL DEVELOPMENT STAGES IN ‘CABERNET

SUMMARY Despite relatively intensive work on the development of inflorescence primordia during grapevine growth in season one, some informational gaps are present in the flower and floral organ development in the season two. In addition, concurrents events of phenology and formation of flowers and floral parts has not been dealt with. With the aid of digital imaging, this research had three objectives; a) describe the developmental events that take place during and after bud break in the buds and in the individual flowers in terms of differentiation, b) match these events with phenological stages, and c) determine size-related growth of the floral organs. After careful dissecting and examination of the samples under microscopy, taken ever 5-10 days between March 20 and May 10 in 2016, the results indicated that highly esteemed works regarding the reproductive anatomy of grapevines needed some additional stages to fully describe events in the stamen and pistil primordia
Equilibrium Grading Policies with Implications for Female Interest in STEM Courses∗

Substantial earnings differences exist across occupations, with STEM occupations paying especially well. We show that stricter grading policies in STEM courses reduce STEM enrollment, especially for women. To show this, we estimate a model of student demand for courses and optimal effort choices of students given professor grading policies. Professor grading policies are treated as equilibrium objects that in part depend on student demand for courses. Restrictions on grading policies that equalize average grades across classes reduce the STEM gender gap and increase overall enrollment in STEM classes.
Stimulated Anti-Stokes Raman Emission Generated by Gold Nanorod Coated Optical Resonators

Plasmonic nanomaterials and nanostructured substrates have made a significant impact in sensing and imaging due to their ability to improve optical field confinement at interfaces. This improved confinement increases the optical field intensity, enabling numerous nonlinear effects to be revealed. However, one challenge is effectively coupling light into and out of the plasmonic nanomaterial. One approach is to directly integrate the plasmonic nanomaterials onto the surface of light emitting optical devices. In this work, the highly nonlinear complex of organic small molecule functionalized gold nanorods is coated on the surface of optical resonators. The evanescent tail of the optical field circulating inside the resonator directly interacts with the nanoparticle complex, creating a hybridized plasmon-whispering gallery mode. Due to the strong field localization by the nanorods and the large circulating power within the resonator, Stimulated Anti-Stokes Raman Scattering is generated with only 14 mW of inp.