Miracle-dz/UniFAR-FTUs huggingface dataset:
Global analysis for periodic variations in gravity wave squared amplitudes and momentum fluxes in the middle atmosphere

Abstract. Atmospheric gravity waves (GWs) are an important coupling mechanism in the middle atmosphere. For instance, they provide a large part of the driving of long-period atmospheric oscillations such as the Quasi-Biennial Oscillation (QBO) and the semiannual oscillation (SAO) and are in turn modulated. They also induce the wind reversal in the mesosphere–lower thermosphere region (MLT) and the residual mean circulation at these altitudes. In this study, the variations in monthly zonal mean gravity wave square temperature amplitudes (GWSTAs) and, for the first time, absolute gravity wave momentum flux (GWMF) on different timescales such as the annual, semiannual, terannual and quasi-biennial variations are investigated by spectrally analyzing SABER observations from 2002 to 2015. Latitude–altitude cross sections of spectral amplitudes and phases of GWSTA and absolute GWMF in the stratosphere and mesosphere are presented and physically interpreted. It is shown that the time series of GWSTA and GWMF at a certain altitude and latitude results from the complex interplay of GW sources, propagation through and filtering in lower altitudes, oblique propagation superposing GWs from different source locations, and, finally, the modulation of the GW spectrum by the winds at a considered altitude and latitude. The strongest component is the annual variation, dominated in the summer hemisphere by subtropical convective sources and in the winter hemisphere by polar vortex dynamics. At heights of the wind reversal, a 180∘ phase shift also occurs, which is at different altitudes for GWSTA and GWMF. In the intermediate latitudes a semiannual variation (SAV) is found. Dedicated GW modeling is used to investigate the nature of this SAV, which is a different phenomenon from the tropical SAO also seen in the data. In the tropics a stratospheric and a mesospheric QBO are found, which are, as expected, in antiphase. Indication for a QBO influence is also found at higher latitudes. In previous studies a terannual variation (TAV) was identified. In the current study we explain its origin. In particular the observed patterns for the shorter periods, SAV and TAV, can only be explained by poleward propagation of GWs from the lower-stratosphere subtropics into the midlatitude and high-latitude mesosphere. In this way, critical wind filtering in the lowermost stratosphere is avoided and this oblique propagation is hence likely an important factor for MLT dynamics.
Trajectory Planning of a Mobile Robot using Enhanced A-Star Algorithm

Objectives: This paper presents an optimization technique for a robot to identify the best path for traversing from a start state to a goal state without colliding with any obstacles. Methods: The existing A-star algorithm is modified to make the robot to move in an unknown scenario in which static obstacles are located. The robot is expected to travel to the goal position without colliding with any of the obstacles present. The Enhanced A* algorithm guides the robot to reach the target following an optimal path. By adding a new parameter namely number of turnings (p(n)) the robot makes during its traverse, currently existing A* algorithm is improved. This is done in order to improve the algorithm for optimal motion of the robot. Findings: The proposed Enhanced A* algorithm will be shown to remove certain drawbacks, which are found in the currently existing algorithms, for optimal travel of the robot. Simulation experiments are carried out to compare the performances of the proposed Enhanced A* algorithm and the original A* algorithm. The outcome of the simulation experiments is expected to provide better performance as compared to the A* algorithm concerning Elapsed time of travel. It is expected that the Enhanced A* algorithm is able to provide an effective contrivance for exploring path planning in robotics education and path control of robot. Improvements: The Future Enhancement is to make the algorithm work in an unknown environment with dynamic obstacles present and implement the same in real time using an autonomous mobile robot.
Modeling of volatility-linked financial products

This thesis is the collation of four papers, adapted from their original versions as to form here four distinct chapters. In the first chapter we illustrate and solve the pricing problem of a target volatility option (TVO) using three different methodologies. In the second chapter we study the pricing PDE for a general contingent claim involving an asset and its realized volatility, and then solve it for a variety of actual models and payoffs. The third chapter introduces a class of time-changed stochastic processes based on which a martingale asset price evolution can be devised. Pricing equations for volatility-linked derivatives are also obtained in this framework. In the final chapter we analyze one specific model of this class; we conclude that it does show high flexibility in explaining the forward volatility skew dynamics and that it can capture certain interesting stylized facts.
Energy Collected and Delivered by PV Modules

This chapter deals with two different questions: “How much solar radiation reaches the surface of a photovoltaic (PV) collector? ” and “What is the conversion efficiency of this solar radiation into electricity? ” Both questions are analysed from the PV-system designer’s point of view, whose ultimate interests, depending on the type of application, are the determination of the PV collector surface needed to provide a certain required service, and the estimation of the average electricity yield from a certain PV array.
Near-surface phytoplankton distribution in the western Intra-Americas Sea: The influence of El Niño and weather events

The space-time variation of phytoplankton pigments in the western Intra-Americas Sea (IAS), in the vicinity of the island of Cuba, is examined using digital images obtained with the Coastal Zone Color Scanner sensor flown aboard the Nimbus 7 satellite from 1978 to 1986. The results are compared to historical in situ hydrographic observations. A marked seasonality in pigment concentration was observed in waters around Cuba, with an average of 0.07 mg m−3 in summer (April-September) and 0.13 mg m−3 during winter (October–March). The range of variation in pigment concentration was larger in the Gulf of Mexico relative to the western Caribbean Sea. We identified four biogeographical areas on the basis of groups of pixels with similar patterns of time variability. These are area I: southwest of Cuba, Yucatan Channel, and Florida Strait; area II: central Gulf of Mexico; area III: east of Cuba; and area IV: central Caribbean Sea, south of Jamaica and Hispaniola. Two major meteorological events led to anomalies in the seasonal cycle of pigment concentrations. During El Nino-Southern Oscillation (ENSO) of 1982–1983, positive anomalies were observed in the pigment concentration in the western IAS during winter months. This was associated with intense mixing of the water column by higher-frequency and stronger winds associated with cold fronts. ENSO 1982–1983 therefore had a fertilizing effect on the IAS region. Another positive anomaly was observed in 1980–1981, a non-ENSO period that featured higher hurricane and extratropical low-pressure activity.
Overview of the European “Particulates” Project on the Characterization of Exhaust Particulate Emissions from Road Vehicles: Results for Heavy Duty Engines

This paper presents an overview of the results on heavy duty engines collected in the “PARTICULATES” project, which aimed at the characterization of exhaust particle emissions from road vehicles. The same exhaust gas sampling and measurement system as employed for the measurements on light duty vehicles [1] was used. Measurements were made in three labs to evaluate a wide range of particulate properties with a range of heavy duty engines and fuels. The measured properties included particle number, with focus separately on nucleation mode and solid particles, particle active surface and total mass. The sample consisted of 10 engines, ranging from Euro-I to prototype Euro-V technologies. The same core diesel fuels were used as in the light duty programme, mainly differentiated with respect to their sulphur content . Additional fuels were tested by some partners to extend the knowledge base. Engine tests were mainly conducted over the standard European regulatory cycles (ESC and ETC), although additional steady state conditions, including some offcycle points, were also assessed. All data (both real time and integrated) were collected in a common data base and centrally analyzed, using common formats and methodologies in order to eliminate inconsistencies and optimize comparability. As for light duty vehicles, the results show that particulate emissions from heavy duty engines are markedly reduced by advanced technologies, most notably by the combination of particulate traps and sulphur-free fuels. However, particulate emissions patterns are also shown to be influenced by operating conditions; in particular fuel sulphur effects are most obvious under high temperature operation. The study provides evidence that particulate number measurement offers the potential for greater sensitivity in evaluating particulate emissions. It demonstrates that the “PARTICULATES” dedicated sampling procedure is capable of delivering repeatable results even in the case of the unstable nucleation mode, also for heavy duty engines. The data provide a first step towards emission factors based on particle size and number for heavy duty engines. However it should not be forgotten that nucleation mode particles are highly dependent on sampling conditions. Further research continues to be needed on the health relevance of measurements of “nucleation” mode particles, their chemical composition and their fate in the atmosphere.
Hydrogel-Shielded Soft Tactile Sensor for Biocompatible Drug Delivery Monitoring

Tactile sensing is an emerging technological advancement in surgical robotics in order to probe interactions between confined tissue environments and instruments based on touch information. The tactile sense can assist in improving the efficiency of the whole practice and hence enhance precision, control, and safety during surgery. This paper demonstrates a distinct proof-of-concept therapeutic device equipped with a soft tactile sensor. The tactile sensor was custom-made using flexible piezoresistive materials and conductive ink, wrapped with a biocompatible hydrogel polymer matrix for safer human–tissue interactions. The proposed tactile sensor was then calibrated and its performance was compared with gold standard sensors. It was further tested with a continuous force (5 N) for an extended period of time (about 6 h) to address robustness and repeatability. The sensor showed a sensitivity of 0.833 N−1 and a drift of ≤1%. Successful cadaver experiment demonstrates the efficiency of tactile sensing assistance to clinicians.
Critical Factors Causing Contractor's Business Failure in Gaza Strip

The construction industry remains a major player of the Palestinian economy. Business failure is an important issue for companies in the Gaza Strip uncertain environment. This paper aimed to explore the critical factors that have the potential to cause contractor's business failure and to determine their level of severity from contractor's viewpoint. This paper has considered. Critical factors were listed under five groups: financial and political, contractual, managerial, organizational, and economical causes. Contractors have been advised to consider the most critical factors that have the potential to cause business failure. The most critical factors include: cost of materials, lack of resources, delay in collecting dibs from clients, monopoly, changing funding sources, dealing with suppliers and traders and Israeli attacks.
The use of mobile learning resources to enhance physics learning for engineering students: a six year study

The use of mobile devices for learning purposes has increased in recent years, but there has been little effort to measure itsimpact on student outcomes. The goals of this work are: to quantify the impact of using mobile learning resources onacademic performance and to know the student perception about these resources. Our hypothesis is that mobile learningresources have a positive effect on the learning processes. We evaluate the effectiveness of two mobile learning resources forengineering students regarding free body diagram and conservation of linear momentum. We gave pre and post-tests toexperimental and control groups during a 6-year time span and analyzed the differences in the learning gains for bothgroups. We also gave perception questionnaires to our students about the use of mobile learning resources. With a sampleofN= 645 students, we found that the experimental group obtained learning gains 7–10 points higher (on a 0–100 scale)than the control group. We found robust evidence regarding the effectiveness of our mobile learning resources throughlinear regressions (p= 0.001–0.053) andt-Student tests (p= 0.002–0.045). However, the observed effect size was onlyES=0.28. The student perception questionnaires indicate that students found the implementation of mobile learning resourcesto be very useful. Our results are indicative of a positive impact of the use of mobile learning resources to enhance studentconcept comprehension and problem-solving skills in undergraduate Physics courses.
Impact of 5G Technology on IoT Applications in Construction Project Management?

– IoT based platforms are enhancing decision-making capabilities in many sectors. The impact of IoT in construction has not been significant because of the unstructured nature of the process and the project-based approach to construction. Further, the technology platforms available today do not support high data flow from distributed locations as required for construction. However, it is anticipated that the use of IoT in construction will increase significantly when standards such as 5G are implemented for widespread usage. The first objective of this paper is to identify the potential usage of IoT technology for various construction project processes based on the PMBOK framework for construction. Several works on the applications of IoT which have been published are reviewed, and a framework for construction domain is proposed. The second objective is to identify and discuss the barriers raised by connectivity issues within this framework and influence of 5G technology in overcoming this barrier is elaborated. A comparative quantitative analysis of sample processes is presented to show the potential advantage that 5G technology will bring over 4G with the help of an example.
Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing

We investigate the problem of scheduling a sequence of cars to be placed on an assembly line. Stations, along the assembly line install options (e.g. air conditioning), but have limited capacities, and hence cars requiring the same options need to be distributed far enough apart. The desired separation is not always feasible, leading to an optimisation problem that minimises the violation of the ideal separation requirements. In order to solve the problem, we use a large neighbourhood search (LNS) based on mixed integer programming (MIP). The search is implemented as a sliding window, by selecting overlapping subsequences of manageable sizes, which can be solved efficiently. Our experiments show that, with LNS, substantial improvements in solution quality can be found.
Coordinated Attitude Synchronization and Tracking Control of Multiple Spacecraft Over a Communication Network With a Switching Topology

This paper investigates the coordinated attitude synchronization and tracking problems of multiple rigid-body spacecraft subject to inaccurate inertial parameters. Two adaptive distributed control algorithms are developed based on communications among spacecraft connected through a switching communication network, which allow each switching subgraph to be disconnected. In the first place, by introducing an adaptive strategy for the inertial parameters, a distributed attitude synchronization control algorithm is designed. In particular, a gain selection criteria is specified for the asymptotic attitude synchronization under uniform joint connectivity. In the second place, for the coordinated attitude tracking problem, we first develop a distributed observer for each spacecraft to estimate the reference information associated with a leader over a switching network with uniform joint quasi-strong connectivity. Then, we propose an adaptive tracking scheme such that each spacecraft tracks the estimated information. Finally, numerical examples are given to validate the effectiveness of two proposed adaptive distributed control algorithms.
The range of the (n + 1)th moment for distributions on [0, 1]

Let p denote the class of all probability measures defined on the Borel subsets of the unit interval I = [0, 1]. For each positive integer n, take Mn is convex, closed, bounded, and n-dimensional; the convex hull of the space curve {(t,t2, …, tn ): 0 ≦ t ≦ 1}; e.g., see Theorems 7.2, 7.3 of [1]. At each point (c1, C2, …, cn ) of Mn , define Note that v −, v + depend only on C1, C2, …, Cn− 1; Vm only on cn ; We shall as notational convenience dictates and as will be apparent from the context regard v ± n as functions on Mn− 1 or on higher order moment spaces and also regard Vn as a function on moment spaces of order higher than n.
Comparing the safety climate of the Indonesian and Australian construction industries

Purpose The purpose of this paper is to assess, compare and explain safety climate differences between the Indonesian and Australian construction industries. Design/methodology/approach The paper reports a comparative safety climate survey of 415 construction personnel working in the Australian and Indonesia construction industries. Findings Surprisingly, the results show that the safety climate in Indonesia and Australia are similar and that the differences in safety performance, safety climate must be interpreted within the context of wider health and safety norms, regulations, awareness, knowledge and typical work environments to make any sense. Research limitations/implications This research contributes a missing international comparative dimension to the emerging research on construction safety climate. Indonesian studies are notably absent from this literature, despite the size of the country and the poor health and safety record of its construction industry. Similar comparisons between safety climate in other countries need to be made. Practical implications This research allows construction managers operating across international boundaries to better understand the cultural and institutional context in which safety climate is developed. This will assist in the development of more culturally sensitive safety management strategies. Social implications The construction industry’s poor safety record has serious implications for both individuals working in the industry, their immediate families and the communities in which they live. By improving the safety record of the industry these impacts can be reduced. Originality/value This research reveals, for the first time, the cultural and institutional complexities of comparing safety climate across different countries. The results contribute to safety climate research by highlighting the importance of cultural and institutional relativity in making international comparisons of health and safety research.
Cooperative and Dynamically Weighted Model Predictive Control of a 3-Level Uninterruptible Power Supply With Improved Performance and Dynamic Response

In this paper, we propose a 3-level double conversion uninterruptible power supply with a new cooperative and dynamically weighted model predictive control technique. A 3-level dc–dc converter is used to improve the dc bus balance and the overall response of the uninterruptible power supply (UPS). A new cooperative model predictive control principle is proposed to enhance the response of multiconverter systems. New controllers are proposed for all converters of the UPS, based on the dynamically weighted model predictive control technique and using the presented cooperative principle. A new power management technique is also proposed for the UPS. The proposed 3-level UPS system presents improved dynamic response and steady-state performance in distinct operating points, when compared to the conventional system. Experimental results demonstrate the advantages of each proposed novelty.
A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a feasible way to perform software defect prediction (SDP) when lacking historical data. Recent CPDP approaches have employed deep learning techniques to better exploit the information from the program’s abstract syntax trees (ASTs). However, the granularity of the AST nodes and the data distribution difference between projects may have negative impacts on the prediction performance, which many CPDP studies didn’t take into consideration. To handle these issues, this paper explores a better AST node granularity and proposes a CPDP framework based on multi-kernel transfer convolutional neural networks. Specifically, for AST node granularity, we explore the difference of three AST node granularities and then compare the prediction performance of each granularity on several prediction models. For the CPDP framework, we first parse the program source code into ASTs and then encode the AST nodes into numerical vectors using the embedding technique. Secondly, to mine transferable semantic features, the encoded ASTs are fed into a convolutional neural network, in which a multi-kernel matching layer is added to minimize the data distribution divergence between the source and target project. Finally, to make use of the information from the handcrafted features, the semantic features mined from the AST are joint with handcrafted features to form the joint features for CPDP. We evaluate our approach on 110 CPDP tasks formed by 11 open-source projects and results show that the proposed CPDP method outperforms most deep learning-based approaches.
Cooperative Communications With Relay Selection Based on Deep Reinforcement Learning in Wireless Sensor Networks

Cooperative communication technology has become a research hotspot in wireless sensor networks (WSNs) in recent years, and will become one of the key technologies for improving spectrum utilization in wireless communication systems in the future. It leverages cooperation among multiple relay nodes in the wireless network to realize path transmission sharing, thereby improving the system throughput. In this paper, we model the process of cooperative communications with relay selection in WSNs as a Markov decision process and propose DQ-RSS, a deep-reinforcement-learning-based relay selection scheme, in WSNs. In DQ-RSS, a deep-Q-network (DQN) is trained according to the outage probability and mutual information, and the optimal relay is selected from a plurality of relay nodes without the need for a network model or prior data. More specifically, we use DQN to process high-dimensional state spaces and accelerate the learning rate. We compare DQ-RSS with the Q-learning-based relay selection scheme and evaluate the network performance on the basis of three aspects: outage probability, system capacity, and energy consumption. Simulation results indicate that DQ-RSS can achieve better performance on these elements and save the convergence time compared with existing schemes.