Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Social Media Use on Young Adults’ Quality of Life During the COVID-19 Pandemic in South India

During the COVID-19 pandemic, the use of social media increased. Social media was shown to have both positive and negative effects on users. Social media use can negatively affect mental health and it can also lead to addiction. On the other hand, using social networks has helped people stay connected with friends and family during the COVID-19 pandemic. To analyze the impact of social media use among young adults during the COVID-19 pandemic, an online survey was conducted with 231 university students in South India. It was explored which type of social media use, such as using social media to connect with family and friends, as compared to other uses, was associated with an increased quality of life (QoL). However, there was a positive association between QoL and using social media to obtain information and for news. The intensity of use was not associated with the level of QoL.
Robust Design of Gradient-based Optimization in Frequency Domain

Gradient descent (GD) is a simple but efficient method in black-box optimization problems. Recently, many novel variants of conventional GD have been proposed from the perspective of control theory. In this paper, we mainly consider the robust design of GD to make the convergence performance robust to both objective function and learning rate. At first, the commonly used GDs are viewed as Lur'e system, where the main difference concentrate on the open-loop transfer function. Then, the system framework for GDs is extended to the continuous-time case, and a novel robust GD is designed motived by the robust controller design in frequency domain. Finally, a discretization method is proposed for numerical implementation, and several simulation examples are given to demonstrate the effectiveness of the proposed method.
Using Deep Learning Approach for Land-Use and Land-Cover Classification based on Satellite images

The land cover is the apparent (bio)physical cover, and land use alludes to how the actual land type is being utilized. This research is fundamental to survey the degree to which social, monetary, and natural factors influence urbanization. This will likewise assist with urban planning. As laborious process of handcrafted feature extraction has not helped obtain high accuracies, this paper proposes use of Deep Learning approach that explores different Image Recognition Models using various ML classifiers on remote sensing images classifying the images from large Landsat satellite dataset into 9 different classes. It was observed that the highest accuracy of 97.4% was achieved by the Logistic Regression algorithm coupled with Inceptionv3 model. The proposed model shows the capability of increasing the accuracy of existing state-of-art-algorithms low resolution land classification maps. Thus, the improved results will contribute to better land maps helping with the growing demand of LULC information concerning climate change and sustainable development.
Mobility Offer Allocations for Corporate Mobility as a Service.

Corporate mobility is often based on a fixed assignment of vehicles to employees. Relaxing this fixation while including alternatives such as public transportation or taxis for business and private trips could increase fleet utilization and foster the use of battery electric vehicles. Along this idea we propose a flexible booking system, leading to the introduction of the NP-hard mobility offer allocation problem which is closely related to multi-interval scheduling problems. We describe problem specific conflict graphs for representing and exploring the structure of feasible solutions. A characterization of all maximum cliques in these conflict graphs reveals symmetries which allow to formulate stronger integer linear programming models. We also present an adaptive large neighborhood search based approach which makes use of conflict graphs as well. In a computational study, the approaches are evaluated and it is demonstrated that, depending on instances and run-time requirements, either a solver for the integer linear programming model, fast greedy heuristics, or the adaptive large neighborhood search outperforms the others.
MOLECULAR AND ATOMIC LINE SURVEYS OF GALAXIES. II. UNBIASED ESTIMATES OF THEIR STAR FORMATION MODE

We make use of our “minimal” cold interstellar medium emission line model that predicts the molecular and atomic line emission per unit dense, star-forming gas mass to examine the utility of key line ratios in surveys of the so-called star formation “mode” as traced by ξSF = Mdense(H2)/Mtotal(H2). We argue that ξSF and its proxies provide very sensitive, extinction-free discriminators of rapid starburst/merger-driven versus secular quiescent/disk-like stellar mass assembly, with the most promising diagnostic to be applied in the near-future being CO J(4 → 3)/ [C i](3P1 → 3P0). These lines are accessible across nearly the full range 0 < z < 2 (thus covering the bulk of galaxy evolution) with the Atacama Large Millimeter Array. In addition to their diagnostic power, another advantage of this combination is the similar observed frequencies (Δν0 ≈ 30 GHz) of the lines, resulting in nearly spatially matched beams for a fixed aperture, thus mitigating the effects of resolution/morphology bias in the interpretation of galaxy-averaged line ratios. Finally, we discuss the capability of deep blind redshift surveys with the high-frequency component of the Square Kilometer Array (SKA) in discovering H2-rich galaxies with very low ξSF values. These could be the progenitors of starburst galaxies seen prior to the onset of star formation; such galaxies could be a class of extreme outliers from local (gas surface density)–(star formation rate) scaling laws, which would exclude them from current star formation or stellar-mass-selected samples. Our conservative model suggests that SKA could detect such systems residing at z ∼ 3 at a rate of 20–200 hr−1.
Hybrid Metasurface, Dielectric Resonator, Low-Cost, Wide-Angle Beam-Scanning Antenna for 5G Base Station Application

This work presents a novel compact, low-cost, metamaterial-based, wide-angle beam-scanning array antenna with monopole-like Huygens pattern reconfigurable element for fifth-generation (5G) base station application. A miniaturized and broadband hybrid metasurface with a compact size of <inline-formula> <tex-math notation="LaTeX">$0.28\lambda _{0} \times 0.28\lambda _{0} \times 0.11\lambda _{0}$ </tex-math></inline-formula> is first proposed by combining a dielectric resonator and a <inline-formula> <tex-math notation="LaTeX">$3\times3$ </tex-math></inline-formula> mushroom-like metasurface. By exploring a monopole-like Huygens radiation mechanism, a pattern reconfigurable hybrid metasurface antenna is implemented. It can be switched between one broadside and two tilted (<inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> = ±40°) modes. To increase the beam-scanning range, the proposed pattern reconfigurable antenna as the array element is adopted in a phased array. For demonstration, a five-element hybrid metasurface linear array antenna using the reconfigurable element is fabricated and measured. It is operated in the band of 3.30–3.80 GHz (5G-N78 band). This reconfigurable method can extend the beam-scanning angle of the five-element array symmetrically from [−35°, +35°] to [−70°, +70°]. With the merits of broadband, low-cost (simple design and fewer array elements), wide-angle beam-scanning capacity, and good radiation performance, the proposed hybrid metasurface pattern reconfigurable antenna and array are well poised for 5G multibeam base station and microcell applications.
Exact Inference Using Variable Integrating Constant Importance Distributions

In this paper a new class of importance distributions that are based on normaland t-distributions are introduced. They are labeled variable integratingconstant distributions or VIC distributions. The main innovation in thisclass of distributions is that the standard error associated with eachprameter is expanded in an exponential polynomial function of the scaleddistance from its mode, with a different polynomial used in each direction andfor each parameter. Resulting variances differ at each point in the parameterspace, thereby breaking the flexibility limitations that are inherent innormal and t-distributions allowing them to be closely matched to theposterior distribution whose moments are the goal of the inference exercise.Two illustrations are presented in which the performance of normal, t,normal-VIC, and t-VIC importance distributions are compared. Results show theVIC distributions to produce globally stable weights and substantialimprovements in the numerical efficiency of importance sampling andindependence Metropolis estimates.
XMM-Newton observations of the young open cluster around λ Orionis ,

Aims. We studied the X-ray properties of the young (∼1−8 Myr) open cluster around the hot (O8 III) star λ Ori and compared them with those of the similarly-aged σ Ori cluster to investigate possible effects of the different ambient environment. Methods. We analysed an XMM-Newton observation of the cluster using EPIC imaging and low-resolution spectral data. We studied the variability of detected sources, and performed a spectral analysis of the brightest sources in the field using multi-temperature models. Results. We detected 167 X-ray sources, of which 58 are identified with known cluster members and candidates, from massive stars down to low-mass stars with spectral types ∼M5.5. Another 23 sources were identified with new possible photometric candidates. Late-type stars have a median logLX/Lbol ∼− 3.3, close to the saturation limit. Variability was observed in ∼35% of late-type members or candidates, including six flaring sources. The emission from the central hot star λ Ori is dominated by plasma at 0.2−0.3 keV, with a weaker component at 0.7 keV, consistently with a wind origin. The coronae of late-type stars can be described by two plasma components with temperatures T1 ∼ 0.3−0.8 keV and T2 ∼ 0.8−3 keV, and subsolar abundances Z ∼ 0.1−0.3Z� , similar to what is found in other star-forming regions and associations. No significant difference was observed between stars with and without circumstellar discs, although the smallness of the sample of stars with discs and accretion does not allow us to draw definitive conclusions. Conclusions. The X-ray properties of λ Ori late-type stars are comparable to those of the coeval σ Ori cluster, suggesting that stellar activity in λ Ori has not been significantly affected by the different ambient environment.
A new edge removal strategy for complex networks based on an improved tabu search algorithm

Selecting edges of complex network to remove and thus leading to rapid disintegration of complex network can be considered as a combinatorial optimization problem. Tabu search algorithm is one kind of modern heuristic algorithm, which can be used to solve the optimal solution search problem in combinatorial optimization problems. However, the tabu search algorithm may fall into a local optimum during the solution process. This is because the initial solution is not well selected, so we first optimize the selection of the initial solution. In addition, the evaluation index of the traditional removal strategy only considers the size of the largest connected component, which is regarded as the number of nodes that can still maintain communication. While considering the size of the largest connected component, we also consider the degree centrality of edges in the search process, which is related to the importance of edges. This article improves the tabu search algorithm for these two points and compares it with the degree removal strategy, the betweenness removal strategy, the shell-max removal strategy and the traditional tabu search removal strategy. Simulations are conducted on four real-world networks and it is found that the improved tabu search removal strategy proposed in this article has the best disintegration effectiveness.
Distributed Prescribed-time Leader-following Formation Control for Second-order Multi-agent Systems via Output Feedback

This work investigates the distributed prescribed-time formation control problem for the second-order multiagent systems via output feedback in time-varying formation case. First, a novel kind of distributed prescribed-time estimators are proposed to accurately estimate the position and velocity information of the leader, since the states of the leader can only be achieved by part of the followers. Subsequently, to deal with the situation that the velocity measurements are not available for the agents, the distributed observer-based formation control protocol is presented such that the desired trajectories can be tracked and the desired patterns can be achieved within the prescribed settling time. The rigorous stability analysis is presented by Lyapunov methodology. Finally, numerical simulation results are given to illustrate the efficiency of the proposed scheme.
Experimental Research on Formation Control of UUVs Based on Behavior Rules

According to two Unmanned Underwater Vehicle(UUV) formation control tasks, formation cruising and cooperative hunting, this paper applies a formation control method via designing the behavior rules of the leader and followers. The movement of individuals in the UUV group is controlled by classified behavior fusion of the basic behaviors. Firstly, we give the kinematic model of UUV and introduce two formation tasks mentioned in this paper. Then, we present the basic behaviors which are used in the formation tasks. Moreover, we apply Motor-Schema method to combine part of the above basic behaviors, according to different tasks and roles. With the aid of the experimental platforms, we demonstrate that the presented formation control algorithms and experimental platform are suitable for the mission of the UUV group.
Simulation study of Sycomore++, a self-adapting graph-based permissionless distributed ledger

The arrival of Bitcoin [1] drove the shift to decentralized ecosystems through the exchange of transactions without intermediary. However, one of the main challenges that need to face permissionless blockchains are scalability and security. In this paper, we present a performance evaluation of Sycomore++, a permissionless graph-based distributed ledger whose main feature is to dynamically self-adapt the number of created blocks to the current number of submitted transactions, and compare them with the ones of Bitcoin and Sycomore, a graph-based distributed ledger. Our evaluation relies on agent-based simulations to evaluate the capability of these distributed ledgers to address the aforementioned challenges, within different execution contexts.
Modeling of Noise Figure for GaAs pHEMTs based on Frequency and Temperature

The noise figure modeling in microwave range for pre and post fabricated GaAs pHEMTs on multilayer technology are demonstrated in this work over a wide temperature range, e.g., -25 to 125°C. At different frequencies and temperatures, the minimum noise figure (NFmin), association gain (Ga), and equivalent noise resistance (RN) are all typical noise parameters that have been modeled. The temperature dependency of the noise-oriented parameters was also evaluated. Noise-oriented parameters of InP- and GaAs HEMTs and as well as GaN HEMTs on different substrates are comparable to the two GaAs pHEMTs technologies. According to the findings, GaAs pHEMTs have much potential in the high-temperature operation of LNAs.
Evaluation of IoT based Real-Time Pulse & Temperature Monitoring System

People’s lives have been successfully prolonged during the previous decade, thanks to the advancement in technology. With the development in healthcare technologies, the lifestyle of people has been improved through smart sensors, applications, communication systems etc. All these sensors and applications have been integrated with internet and data processing gives evolution using to IoT. In a healthcare system, continuous monitoring of vital statistics of patients plays pivotal part. Pulse rate and body temperature are essential markers that must be checked on a regular basis for those specially with heart disease. This paper proposes an IoT based system working for smart health monitoring. The system is an aggregate of sensors, Raspberry Pi (microprocessor), data collection (i.e., ThingSpeak). The system can monitor patient’s body temperature and pulse rate and displays the data in the real-time. This database is continuous updated in IoT cloud.
Chilli Identification and Grading in pre/post-harvest Environment based on Computer vision and Deep Learning approaches

Chilli, one of the spice produce, needs grading before being marketed for produce quality assurance. Manual chilli grading involves high labour cost, time-consuming, inconsistent, and expensive warranting technology intervention. In this work, a non-destructive approach to identify dry chilli images into three levels as good quality, medium quality and poor quality, using a deep learning architectures and grade them are adopted to reduce computation overload. The database of chilli grown in North Karnataka region is prepared as no standard chilli datasets are available. Dry chilli images dataset are augmented to train the dataset for transfer learning (DL) models, namely VGG16, ResNet and EfficientNet-D0 to analyse suitability of good model for the grading of chilli images. Further, work needs integration of the algorithm into automatic chilli grading tool. The proposed EfficentDet model is found suitable and yielded accuracy rate of 95.62% were in VGG16 and Resnet models accuracy was 82.67% and 83.88%. EfficientDet model out performs in terms of grading the dry chilli images.
A perturbative quantized twist embedded in Minkowski spacetime

In this article, spatially-structured gravitational waves within the paraxial approximation are investigated. Drawing upon analogies between electrodynamics and general relativity, gauge invariant paraxial ‘electric’ and ‘magnetic’ parts of the Weyl conformal tensor are derived. In this approximation, a new gauge, which we call the paraxial-traceless gauge, is found. The polarization and degrees of freedom are investigated and compared with the Eardley–Newman–Penrose classification. Paraxial gravitational waves are found to share similarities with their electromagnetic counterparts in that they possess a quantized amount of orbital angular momentum that can be transferred to matter.
Relaxation of superfluid turbulence in highly oblate Bose-Einstein condensates

We investigate thermal relaxation of superfluid turbulence in a highly oblate Bose-Einstein condensate. We generate turbulent flow in the condensate by sweeping the center region of the condensate with a repulsive optical potential. The turbulent condensate shows a spatially disordered distribution of quantized vortices, and the vortex number of the condensate exhibits nonexponential decay behavior which we attribute to the vortex pair annihilation. The vortex-antivortex collisions in the condensate are identified with crescent-shaped, coalesced vortex cores. We observe that the nonexponential decay of the vortex number is quantitatively well described by a rate equation consisting of one-body and two-body decay terms. In our measurement, we find that the local two-body decay rate is closely proportional to ${T}^{2}/\ensuremath{\mu}$, where $T$ is the temperature and $\ensuremath{\mu}$ is the chemical potential.
Modeling and analysis for network evolutionary game with reconnection mechanism

In this paper, a class of network evolutionary games(NEGs) with reconnection mechanism is studied by using the method of matrix semi-tensor product(STP). Firstly, two algorithms are proposed to describe the strategy updating rule(SUR) of the given NEGs, and the piecewise algebraic expression of the considered NEGs is calculated. This piecewise algebraic expression intuitively gives the transformation between the player's connection state and exit state in the game. Secondly, based on piecewise algebraic expression, the algebraic expression of the fusion player state is given. Moreover, the obtained algebraic expression can easily be used to analyze the withdrawal of players in the game and the reconnection of exiting players. Finally, the effectiveness of the obtained theoretical results is illustrated by a numerical example.
Q-Learning Based Bi-Objective Deadlock-Free Routing Optimization for Optical NoCs

Silicon photonics-based optical networks-on-chip (ONoCs) have been considered as a competitive alternative to electronic NoCs for chip multiprocessors (CMPs). However, the presence of thermooptic effects may prevent them from maintaining power efficiency advantages over electronic counterparts. To address the thermal issue, some routing schemes based on reinforcement learning have been proposed to reduce the optical power loss. Although thermal-related optical power loss could be minimized by routing packets along paths with the lowest thermal gradients, contention may occur on these paths. In this work, we first propose a contention-aware deadlock-free routing (CDR) for electronic-controlled ONoCs, using Q-learning model for single-objective optimization for contentions. Then we further propose a Q-learning based bi-objective deadlock-free routing scheme (BODR) for ONoCs under thermal variations, to comprehensively improve both the power efficiency and the network performance. Experimental results show that BODR routing can greatly reduce the delay with a small cost of optical power loss compared with the single-objective Q-routing for power optimization. Meanwhile, the proposed method can improve the optical power loss while simultaneously achieving a higher network performance as compared with other traditional adaptive routing schemes.
Graspable Pose Detection Approach Based on Multi-view Point Cloud Fusion

In order to solve the problem that the information collected by the monocular camera is incomplete and the graspable poses are not comprehensive, this paper proposes a graspable poses detection approach based on multi-view point cloud fusion. Firstly, the calibration method is proposed to calibrate multiple depth cameras, and the data obtained from different cameras are transformed to that in robot coordinate system for obtaining relatively complete point cloud data. Secondly, a graspable poses detection approach is proposed to generate reliable graspable poses without relying on the object model, and the neural network is trained end-to-end through a large-scale graspable dataset. At the same time, in order to improve the success rate of grasping, reachability is brought into grasping planning. Experimental results finally show that the proposed method is feasible and effective in dealing with grasping problem.