Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint optimal multi-connectivity enabled user association and power allocation in mmWave networks

The millimeter wave (mmWave) spectrum has been involved in the fifth generation wireless systems (5G) for its enormous spectrum resources. However, signals over mmWave band suffer from severe path loss and are vulnerable to be blocked by obstacles due to the extremely high frequency, which greatly degrades the quality of transmission and connection reliability. One of the promising ways to overcome this problem is the multi-connectivity technology, which allows a user in an mmWave network to associate with multiple mmWave base stations (mBSs). And for multi-connectivity, there are two essential challenges: one is the optimal user association, that is, which are the most appropriate mBSs a user should be connected to, and the other is the optimal power allocation of each connection. In view of this, this paper aims to jointly optimize the user association and optimal power allocation for multi-connectable mmWave networks. Different from most existing works, the optimization objectives in our work are three-fold, namely maximizing the overall energy-efficiency and meanwhile balancing the achievable rates among all users and the load among all mBSs, respectively, under the QoS constraint. Considering that this multi-objective problems is a mixed integer programming and NP-hard, this paper proposes a method based on the novel non-dominated sorting genetic algorithm II (NSGA-II) to obtain the near-optimal solutions. Simulation results show that the proposed scheme enables optimal multi-connectivity enabled user associations and power allocations simultaneously in ultra-dense mmWave networks and yields EE-fairness tradeoff.
Expanding the Maker Movement by Recentering "Building for Others" in Construction Activities

The goal of the Bots for Tots project is to develop theory around the role of activity framing on diverse participation in making and construction practices. In Bots for Tots, a designbased research project in three locations over two years, builders create a “dream toy” for a younger member of their local community. We hypothesized that building for others would appeal to young girls that may not typically see building robots and racecars as aligning with their goals and values. Data indicates participating girls consistently considered their “client” throughout the design process and were interested in continuing to build for others after the project ended. We show how the degree to which the “other” impacts the construction process depends on the quality of the relationship between builder and client. We propose recentering “women’s ways of knowing” in maker practices to broaden participation among women in STEM domains.
Push yoUr Password: Secure and Fast WiFi Connection for IoT Devices

Internet of things (IoT) is an indispensable paradigm in today’s industrial change. IoT interconnects appliances around us through the Internet, and user’s private information is deeply placed inside the network. Nonetheless, security vulnerabilities in IoT have not been addressed appropriately so far due to various reasons. Even in the initial WiFi connection procedures of IoT devices, WiFi credentials can be leaked, making the WiFi access point (AP) a hacking path. We propose a secure connection scheme, termed PUP, that aims to securely and quickly connect an IoT device to the AP in proximity while improving the user experience. PUP shows perfect security performance, enabling connection time within 11 s.
A Tutorial of Bland Altman Analysis in A Bayesian Framework

ABSTRACT There are two schools of thought in statistical analysis, frequentist, and Bayesian. Though the two approaches produce similar estimations and predictions in large-sample studies, their interpretations are different. Bland Altman analysis is a statistical method that is widely used for comparing two methods of measurement. It was originally proposed under a frequentist framework, and it has not been used under a Bayesian framework despite the growing popularity of Bayesian analysis. It seems that the mathematical and computational complexity narrows access to Bayesian Bland Altman analysis. In this article, we provide a tutorial of Bayesian Bland Altman analysis. One approach we suggest is to address the objective of Bland Altman analysis via the posterior predictive distribution. We can estimate the probability of an acceptable degree of disagreement (fixed a priori) for the difference between two future measurements. To ease mathematical and computational complexity, an interface applet is provided with a guideline.
Tourism destination competitiveness with a particular focus on sport: the current state and a glance into the future – a systematic literature analysis

ABSTRACT This literature review focuses on tourism destination competitiveness in the field of sport (TDC sport) or, more specifically, on mapping literature dealing with factors influencing and measuring TDC sport. Existing literature has mainly focused on factors influencing tourism destination competitiveness (TDC) and on measuring TDC in general, paying no attention to two factors: First, there is an emerging line of research and theory building that bridges the fields of leisure, sport, and tourism; and second, when tourism and sport are concerned, generic models in their original form may not be specific enough and some of their dimensions and elements may not be suitable. Thus, this systematic literature analysis (a) focused on TDC in the context of sport, taking into account that sport has unique characteristics, (b) reviewed the current state of empirical research by framing a conceptual model of factors influencing TDC in a sport context, and (c) revealed gaps for future research. Results showed that no single universal set of indicators is applicable to all destinations and issues at all times. Furthermore, approaches in the existing literature have suggested that sport contributes to the construction of a country's brand and provides a unique advantage. Moreover, the analysis highlighted sport-specific factors and addressed the debates about ‘soft’ factors and the notion of a destination as ‘the’ place to be.
Thermal Conductivity and Pressure-Dependent Raman Studies of Vertical Graphene Nanosheets

Thermal and mechanical properties of graphene sheet are of significant importance in the areas of thermal and stress management, respectively. Here, we report the thermal conductivity and high-pressure behaviors of unsupported vertical graphene nanosheets (VGNs) grown by electron cyclotron resonance-plasma enhanced chemical vapor deposition method. Structural morphology of the as-grown VGNs on SiO2/Si substrate suggests a homogeneous, uniformly interconnected network of graphene sheets standing vertically on a basal nanographitic layer. On examination of edges of exfoliated sheets using transmission electron microscopy, seven layers of graphene is estimated. The frequency of the G-band (E2g-in plane mode) is found to vary linearly with temperature. The first-order temperature coefficient for G-band is found to be 1.47(1) × 10–2 cm–1 K–1. Using the G-band temperature coefficient and its position dependence on excitation laser power, the thermal conductivity of the VGNs at room temperature is estimated to b.
Fuzzy-Logic-Based Handover Algorithm for 5G Networks

A traditional 4G handover algorithm that performs well in a macro-cell-only network could not be employed in the 5G network with the random distribution of small base stations due to the irregular change of the signal-to-interference-plus-noise ratio (SINR) of a moving user equipment (UE). Besides, the fuzzy logic is a well-known method to translate the domain knowledge of a human expert into a set of basic rules and to formalize the uncertainty judged by the human expert. In this paper, based on the fuzzy logic, we make the first attempt to propose a handover algorithm for a UE in 5G networks. Simulations show that our algorithm has a good performance in terms of the radio link failure (RLF) rate and the ping-pong rate in a 5G network, as compared with the state-of-the-art methods.
Nyström Method-Based Hybrid Precoding for mmWave Full-Duplex Integrated Access and Backhaul Systems

Integrated Access and Backhaul (IAB) systems, millimeter-wave (mmWave), and full-duplex (FD) are the critical technologies for the 5G and beyond cellular networks. The capacity and efficiency are significantly enhanced, thanks to the large available bandwidth and simultaneous transmission. However, self-interference (SI) becomes significant, which needs to be canceled in different stages. In this paper, under the mmWave-FD-IAB systems, we propose a fast hybrid precoding algorithm using the Nyström method for the wideband single-user scenario. SI and residual SI (RSI) is canceled by RF precoder and minimum mean squared error combiner at the IAB-node transmitter and receiver, respectively. RF insertion loss with different kinds of phase shifters (PSs) and channel estimation error are considered for accounting the impact of the RF components impairment and channel uncertainty. Simulations show that the proposed design can enhance the backhaul link spectral efficiency and cancel the SI efficiently at the IAB-node. Moreover, passive PSs can tolerate more RSI than active PSs.
Phosphate fertilization in potato: productivity of Ágata and Atlantic

Abstract The goal of this study was to evaluate the influence of phosphate rates on productivity of potatoes tubers of cultivars Atlantic and Ágata. The experiments were carried out in Unaí-MG, Brazil (Ágata and Atlantic) and Mucugê-BA, Brazil (Ágata). The rates of phosphate was 0, 150, 300, 600 and 900 kg ha−1 of P2O5. All macro and micronutrient contents increased by increasing P rates in Atlantic-MG. In Ágata-MG, only S was reduced and in Ágata-BA, higher rates of P reduced N, Cu, Mn and Zn contents. Despite this reduction, only Zn was below the range considered suitable for potato. The maximum rates estimated for total yield in Atlantic-MG (38 t ha−1), Ágata-MG (47.6 t ha−1) and Ágata-BA (59.7 t ha−1) were, respectively: 606, 730 and 535 kg ha−1 of P2O5. The P response of Ágata-BA was 25.4% lower than Ágata-MG. This can be related to medium clay content and low CEC of Mucugê-BA soils, compared to Unaí-MG soils, which are more clayey and have a higher CEC, consequently, they have a higher P retention capacity. However, rates of P should be oriented based on knowledge of soil attributes and dynamics of its constituents, cultivar requirements and interactions with regional climatic conditions.
Analytical quality by design approach versus conventional approach: Development of HPLC-DAD method for simultaneous determination of etizolam and propranolol hydrochloride

Abstract Considering the importance of robust analytics in the pharmaceutical industry, an analytical quality by design (AQbD) based reversed phase liquid chromatographic method was developed for the quantification of propranolol hydrochloride (PRO) and etizolam (ETZ). The developed method was validated and compared with an analytical method of conventional approach. In AQbD approach, analytical target profile (ATP) and risk assessment were tailored to identify critical method variables (CMVs) as inputs. Accordingly, pH (X1) % aqueous composition (X2), flow rate (X3) and % triethylamine (X4) were identified as CMVs and the respective retention time (Y1 and Y2) and theoretical plate (Y3 and Y4) for PRO and ETZ were set as method responses. The central composite design (CCD) predicted the scientific relationship between CMVs (Xn) and method responses (Yn). Then, the method operable design region (MODR) was arrived from systematic simulation and contour plots. There were six candidate methods selected from MODR and they demonstrated the r 2 value > 0.9, thus the model proved its consistency for prediction. The robust range for the selected CMVs was derived from MODR, thus the ranges were 3–4 for pH, 30–35 for % aqueous composition, 0.9–1.1 mL min−1 for flow rate and 0.1–0.2 for % TEA buffer. Finally, the target AQbD method was optimized with pH; 3.5 (X1), 30% of aqueous composition in mobile phase with acetonitrile (X2), 0.9 mL min−1 of flow rate of (X3) and 0. 15% trimethylamine as buffer (X4) and further validated as per ICH Q2 guidelines. The result was compared with the conventional LC method with respect to specificity, accuracy, precision and robustness. The conventional HPLC method failed for robustness and showed relatively higher % RSD values than AQbD method. Graphical Abstract
Evaluation of toxicity levels of micronutrient strengths on regeneration of grass pea under in vitro conditions

Abstract Grass pea is very sensitive to the deficiency or excessive amount of micronutrient elements like Fe, Zn, B, Mo that could disturb metabolic activities of the plants including inhibition in growth and their death. There is lack of information about the influence of these elements increased concentration in grass pea growth under in vitro conditions. Therefore, the present study aimed to investigate the availability and regeneration capacity of grass pea cv. Gurbuz using 1× (control), 2×, 3× 4× and 5× concentration of Fe, Zn, B and Mo in MS medium containing 1 mg/L thidiazuron and 0.25 mg/L naphthaleneacetic acid. The results demonstrated significant variations in regeneration capacity of the cultivar in respective treatments and showed inhibited response to enhanced concentration of Fe, Zn, B, and Mo supplies. All enhanced concentrations of Fe, Zn, B, and Mo treatments in MS medium resulted in poor shoot induction and reduced shoot counts per plants as compared to control. Each increase in concentration of Fe and B significantly increased shoot length, number of nodes/plants and internode lengths. Whereas, significant decrease in same traits were noted on MS medium containing enhanced concentrations of Zn and Mo. Root induction was completely inhibited using enhanced concentration of Fe and B. Whereas, root induction was promoted by Zn and Mo micronutrients in MS medium. The rooted shoots from Fe and Zn micronutrients content induced early flowering and maturity during hardening. The results showed that optimum micronutrient supply are key factors in successful regeneration of grass pea under culture conditions.
Home-in-language: examining Tibetan migrants’ narratives of homeplace amid China’s urbanization

ABSTRACT This study examines Tibetan migrants’ well-being by exploring their understanding of home and how they remake its meaning in the middle of a migration into intensive urbanization in China. We look at outmigration as a language-learning and -imposing process. Grounding Heidegger’s notion of home-in-language in Tibet and its people, this study found: (a) Tibetan language speaks of a geo-cultural home; (b) a shared language and language alienation both bring home near, even as it is still far away in reality; and (c) language remakes home as auspicious connections arises among Heidegger’s four elements of earth, sky, mortals and divinities.
Critical literacy, schooling, and social justice: a review of Allan Luke’s collected essays (2018)

ABSTRACT In the wider context of ways that Literacy works to enforce control by elites, the maintenance of high culture, racial stratification, national identity and social injustice through education, this review essay reflects on Allan Luke’s collected essays republished by Routledge in 2018 as Critical literacy, schooling, and social justice: The selected works of Allan Luke. The article engages with some of the book’s key concerns, investigating: scholarship advancing the politics and practice of language and literacy education; debates around the transformation of schooling for social justice; implications of changes to the political economy of the contemporary communications order; and finally how the book challenges us to consider the purposes and practices of academic scholarship for critical education. As a collection the book challenges all readers to consider how best to contribute original radical theory to the study of Education.
Nonlinear One-bit Precoding for Massive MIMO Downlink: Minimizing the Maximum Mean Squared Error

In this paper, we study the nonlinear one-bit pre-coding in the massive multiple-input multiple-output downlink system. Different from the conventional criterion of minimizing the sum of mean squared error (min-sum MSE) of all users, the criterion of minimizing the maximum MSE (min-max MSE) is considered here which is more suitable for communications in block and slow fading channels. Based on this min-max MSE criterion, two methods are proposed to optimize the precoding vector and precoding factor. In the first semi-definite relaxation (SDR) method, the min-max MSE problem is first formulated as a semi-definite programming problem with non-convex rank-1 constraint, and then SDR is employed to relax this constraint. The output of SDR is further processed by Gaussian randomization procedure, where, for each candidate precoding vector generated according to the output of SDR, the precoding factor is solved by the quadratically-constrained quadratic programming. To reduce the complexity, the second method employing two-stage processing strategy is further proposed. In the two-stage precoding, the initial solution is first acquired by the efficient alternating direction method of multipliers for the min-sum MSE problem, and then refined to achieve better min-max MSE performance by the symbol-flipping (SF). Finally, the proposed methods are demonstrated by computer simulations.
Joint Traffic Signal and Connected Vehicle Control in IoV via Deep Reinforcement Learning

In this paper, we propose to exploit the interconnection in the Internet of Vehicles (IoV) to realize efficient traffic network control, which is indispensable in building intelligent transportation systems (ITS). In addition to control the traffic signals as in conventional traffic network control schemes, we propose to control the detouring behavior of the connected vehicles as well, with an objective to further enhance the traffic efficiency. Specifically, we formulate the joint traffic signal and connected vehicle control problem as a reinforcement learning (RL) problem, the action and state spaces of which are specifically designed to take into account the connected vehicles. To characterize the detouring behavior of the connected vehicles while keeping the decision process simple, we introduce a new concept termed as detouring ratio, which is defined as the fraction of connected vehicles that detour. Moreover, we also design an effective rewarding mechanism that takes into account the impact of the detouring on the network traffic efficiency. By utilizing tools from deep RL, we put forward an efficient algorithm to jointly control the traffic signals and the connected vehicles. Numerical results demonstrate the validity of our proposed models and show that the proposed joint control algorithm can significantly enhance the network traffic efficiency in terms of the travel time and the waiting time.
Indoor Real-Time Localization by Mitigating Multipath Signals

Indoor localization using WiFi signal has received great attentions since it is ubiquitous. So, in this paper we propose the design, implementation and evaluation of a real-time indoor localization system using commodity WiFi signal. The proposed system can localize the target by using Angle of Arrival (AOA) without any hardware modification and large localization latency. The contributions of this paper are follows. Firstly, an Angle estimation algorithm based on WiFi signals is proposed, which can quickly estimate AOA of Line of Sight (LOS) path in the case of fewer antennas and packets, ensuring real-time localization. Secondly, the influence of multipath signals on the energy spectrum of direct signals is analyzed by using the IEEE 802.11 Saleh-Valenzuela (S-V) channel model. Then, in order to improve localization accuracy, we propose a method of antenna selection. Finally, to deliver a real-time localization system we realize the proposed system by developing a software framework including a localization sever and a web-based location displayer. We have implemented the system on the commodity WiFi APs and the experimental results show that it can achieve accurate localization and has less the time cost.
Peer Tutor Matching for Introductory Programming: Data-Driven Methods to Enable New Opportunities for Help

: The number of students that can be helped in a given class period is limited by the time constraints of the class and the number of agents available for providing help. We use a classroom-replay of previously collected data to evaluate a data-driven method for increasing the number of students that can be helped. We use a machine learning model to identify students who need help in real-time, and an interaction network to group students who need similar help together using approach maps. By assigning these groups of struggling students to peer tutors (as well the instructor), we were able to more than double the number of students helped.
Deep Reinforcement Learning based Handoff Algorithm in End-to-End Network Slicing Enabling HetNets

End-to-end network slicing, as a key technology in 5G and B5G mobile communication systems, is to enable traditional wireless networks to support different services in vertical industries. In a heterogeneous cellular network (HetNets) with network slicing functions, due to the dense deployment of base stations (BSs) and the mobility of user equipments (UEs), dynamically switching network slices (NSs) is necessary for better system performance. This paper models the handoff problem of end-to-end NS as a Markov decision process (MDP) maximizing the utility related to the UE’s profit of being served, the handoff cost and the outage penalty. Both the states of the radio access network resources and the core network resources of each end-to-end NS are considered. The deep reinforcement learning (DRL) is adopted as the solution, and a double deep Q network (DQN) based NS handoff algorithm is designed. Numerical results confirm the convergence of the DQN used to make handoff decisions and show that compared with typical handoff algorithms, the algorithm we proposed performs the best from the aspect of the cumulative reward designed in this paper.
A New Approach on Task Offloading Scheduling for Application of Mobile Edge Computing

In mobile edge computing(MEC), application partitioning can split the executions into local and edge server parts. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Edge Computing (MEC). In this paper, a new approach on task offloading scheduling for application of mobile edge computing is proposed. We divide the computing task into several subtasks. Then, we analyze the overhead of the system, and propose a fine-grained strategy for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption and delay, the computation offloading is considered as a constrained multi-objective optimization problem (CMOP), which can be solved by an improved NSGA-II algorithm. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.
Improving Connectivity via Multi-User Scheduling in 5G and Beyond Networks

In this paper, a novel, yet efficient, multi-user scheduling scheme based on mode selection and power allocation is presented for 5G and beyond networks. The proposed scheme schedules the users in co-existence (CE) and non coexistence (NCE) modes to maximize the connectivity under signal separability and reliability constraints. In addition, two multi-user scheduling mechanisms; check requirements before scheduling (CRBS) and check requirements after scheduling (CRAS), are proposed for practical scenarios to study the impact of quality of service (QoS) on multi-user scheduling in terms of users’ reliability requirements. Analytical results show that CRBS outperforms CRAS and orthogonal multiple access (OMA) schemes in terms of connectivity and system throughput with a complexity trade-off.