Miracle-dz/UniFAR-FTUs huggingface dataset:
LoRaWAN Adaptive Data Rate With Flexible Link Margin

LoRaWAN is a remarkable network protocol to support the deployment of low-power wide-area network applications for the Internet of Things. Its modulation scheme, LoRa, uses different transmission parameters to allow long-range bidirectional communication by trading-off range for Time on Air (ToA). The LoRaWAN specification suggests using an adaptive data rate (ADR) algorithm to assign transmission parameters to nodes dynamically. The ADR algorithm used by The Things Network, a global open network, selects transmission parameters for communication without disconnection while lowering ToA and energy consumption. However, its often optimistic link quality estimate drives the assignment of inadequate parameters, especially in lossy channels. More precise channel quality estimates would improve resource allocation at the cost of increased complexity at the network server (NS). Through simulations, we observe that we can use the ADR link margin parameter to compensate for inaccurate link quality estimates, providing better service to nodes and maintaining low levels of energy consumption. In this work, we propose a modification to ADR that allows the selection of the link margin at runtime, increasing the network performance without the need of any prior knowledge and with no increase in the NS computational complexity.
Effects of Corruption on Tertiary School Enrolment in Africa

This paper empirically analyses the effects of control of corruption on tertiary school enrolment in a panel of 44 African countries over the period 1996-2016. Using system Generalized method of moments and controlling for a large number of other determinants of tertiary school enrolment, it ﬁnds that, over the sample period, control of corruption has a substantial positive effect. This is probably because control of corruption increases public spending level and effectiveness on tertiary education, increases the return on tertiary education, and ultimately fosters the access to tertiary education specially the poorer strata in the African economies. In the light of this result, an intensification of control of corruption in Africa is imperative to improve the tertiary school enrolment. Keywords: Control of corruption, tertiary school enrolment, System GMM, Africa DOI: 10.7176/JEP/11-29-06 Publication date: October 31 st 2020
NFV and Blockchain Enabled 5G for Ultra-Reliable and Low-Latency Communications in Industry: Architecture and Performance Evaluation

5G networks are expected to provide cost-efficient, reliable, and flexible services for industrial productions and applications potentially, by introducing emerging network technologies like blockchain and network functions virtualization (NFV), which virtualizes network functions and runs them on standard infrastructure rather than customized hardware. However, how to deal with the emerging security challenges and fulfil the requirement of ultra-reliable and low-latency communications (URLLC) has not been fully resolved. In this article, we present an NFV-enabled 5G paradigm for the industry with the guarantee of URLLC through service chain acceleration and dynamic blockchain-based spectrum resource sharing among a variety of industry applications running in NVF-based equipment. First, we elaborate the benefits and shortcomings of NFV for industry, by executing an industry application experiment in virtualized and nonvirtualized data center networks. Then, we illustrate an NFV-enabled 5G paradigm for URLLC in detail, with a special focus on the service chain acceleration and spectrum sharing built on NFV, blockchain, software-defined networking, and mobile edge computing. Finally, we establish a mathematical model to study the worst-cast transmission latency of NFV-enabled 5G with the input of the bursty traffic. The proposed model can be exploited to support the plan, management, and optimization of NFV-enabled 5G URLLC systems for industry.
Separation of dinitrophenyl-derivatized amino acids from plant tissue by high-performance liquid chromatography

Abstract A high‐performance liquid chromatographic (HPLC) method for separation of amino acids derivatized with dinitrophenyl (DNP) is presented. The separation was on a Nova‐Pak C18 column. The DNP‐amino acids were eluted in a linear gradient from 20% to 75% acetonitrile in 1 % glacial acetic acid in water over 60 min. They were detected at 254 nm. The separation was good and 250 ng of a DNP‐amino acid was detectable. This method was used effectively to separate amino acids in the leaf tissue of boronia.
Measurement of stress factors and residual stress of a film by in situ X-ray diffraction during four-point bending

A method is proposed for the simultaneous measurement of the stress factors and residual stress state of a film by in-situ X-ray diffraction during four-point bending. The externally applied load acts as an additional degree of freedom in the stress–strain relation, which allows the problem to be solved for both residual stress and elastic moduli without assuming any grain interaction model. The procedure was tested on a galvanic nickel deposit and the predictions of existing grain interaction models were compared with the experimental results.
Monitoring of batch processes through state‐space models

The development of a state-space framework for monitoring batch processes that can complement the existing multivariate monitoring methods is presented. A subspace identification method will be used to extract the dynamic and batch-to-batch trends of the process and quality variables from historical operation data in the form of a “lifted” state-space stochastic model. A simple monitoring procedure can be formed around the state and residuals of the model using appropriate scalar statistical metrics. The proposed state-space monitoring framework complements the existing multivariate methods like the multi-way PCA method, in that it allows us to build a more complete statistical representation of batch operations and use it with incoming measurements for early detection of not only large, abrupt changes but also subtle changes. In particular, it is shown to be effective for detecting changes in the batch-to-batch correlation structure, slow drifts, and mean shifts. Such information can be useful in adapting the prediction model for batch-to-batch control. The framework allows for the use of on-line process measurements and/or off-line quality measurements. When both types of measurements are used in model building, one can also use the model to predict the quality variables based on incoming on-line measurements and quality measurements of previous batches. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1198–1210, 2004
Designing a Health Coach-Augmented mHealth System for the Secondary Prevention of Coronary Heart Disease Among Women

While the number of women with Coronary Heart Disease requiring cardiac rehabilitation (CR) continues to increase, lack of access and other barriers to center-based CR present significant challenges. A mobile phone and wearable device-based technological system can offer home-based secondary prevention program for CHD. We have designed a women-specific digital health intervention system for use in a home-based CR program. The system that we have proposed combines human-expert intelligence with machine intelligence to enhance the decision-making capability of a health coach. We have evaluated the prototype of our system with eight women with CHD for over 13 weeks. The evaluation has shown a significant positive impact of preprogrammed intervention messages on participants’ walking performance on the same and the next day. Since increased walking activities directly improve heart health, we can directly infer that our proposed system is beneficial as a tool for secondary prevention among women with CHD.
Merging of the Dielectric α and β Relaxations in Glass-Forming Polymers

The merging of the main and the secondary dielectric relaxations in a series of six polymers has been investigated by using broadband dielectric spectroscopy (10-2−109 Hz). The data have been analyzed in two different ways. In one case, the whole relaxation process has been modeled by the simple addition of the relaxation functions corresponding to the main and secondary relaxations. In the other case, we have used the approach first proposed by Williams and Watts (Williams, G.; Watts, C.D. In NMR, Basic Principles and Progress; Diehl, P., Fluck, E., Kosfeld R., Eds.; Springer-Verlag, Berlin, 1971; Vol. 4, pp 271−285). This approach can be formally reduced to the addition of two terms, one corresponding to the main relaxation and the other to a combination of the functions corresponding to the main and secondary relaxations extrapolated from low temperatures. Both methods of analysis happen to describe the experimental data successfully in the merging region. In the framework of the Williams and Watts app.
Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition

Article history: Received: 28 June, 2020 Accepted: 19 August, 2020 Online: 09 September, 2020 Suppose we want to classify a query item Q with a classification model that consists of a large set of predefined classes L and suppose we have a knowledge that indicates to us that the target class of Q belongs to a small subset from L. Naturally, this filtering will improve the accuracy of any classifier, even random guessing. Based on this principle, this paper proposes a new classification approach using convolutional neural networks (CNN) and computational geometry (CG) algorithms. The approach is applied and tested on the recognition of isolated handwritten Arabic characters (IHAC). The main idea of the proposed approach is to direct CNN using a filtering layer, which reduces the set of possible classes for a query item. The rules of the relative neighborhood graph (RNG) and Gabriel’s graph (GG) are combined for this purpose. The choice of RNG-GG was based on its great capacity to correctly reduce the list of possible classes. This capacity is measured by a new indicator that we call "the appearance rate". In recent years and due to strong data growth, CNNs have performed classification tasks very well. On the contrary, CG algorithms yield limited results in huge datasets and suffer from high computational time, but they generally reach high appearance rates and do not require any training phase. Consequently, the proposed approach uses an optimal architecture to exploit the advantages of the two techniques and overcome the computational time issue. Experiments carried out on the IFHCDB database have shown that the suggested approach outperforms a normal CNN and yield satisfactory results.
Theoretical analysis of new wavelength‐division solar cells

The new wavelength‐division solar cell is proposed and its conversion efficiency is calculated. The proposed diode structure can be fabricated by the usual epitaxial growth technique. The considered materials are the combination of InP and In0.58Ga0. 42As0.84P0. 16 having the band‐gap energy of 0.827 eV. The calculated maximum conversion efficiency is 19.5% at air‐mass‐zero (AM0) condition which can be improved by the growth of a CdS window layer on the InP surface. With thick CdS on InP, 22.2% efficiency is obtainable, and when the thickness of CdS is as thin as 0.2 μm, 27% efficiency will be attainable. Fabrication of the proposed structure with the GaAlAs/GaAs system gives a conversion efficiency of 23.5% which is about the same as conventional heteroface or graded‐band‐gap solar cells. The optimum layer thicknesses that give maximum efficiency are also determined in this paper.
Strain–Stress Formation During Stationary and Intermittent Drying of Deformable Media

The goal of this work is to study the effect of different drying conditions on the induced stresses within deformable media, the drying kinetics, and the energy consumption. A comparison between stationary and intermittent drying with periodically changing air temperature was performed. A theoretical formulation of the coupled heat, mass, and momentum transfers in saturated porous media was established. The model is based on the averaging theory. The thermo-hydro-mechanical coupling was closed using the effective stress theory of Terzaghi. In this approach, the viscoelastic behavior of the medium was considered. A bi-dimensional-shaped bentonite sample was used for numerical tests. The evolution of drying kinetics and stresses within the material during drying at constant and intermittent conditions was presented. It was observed that a non-stationary drying with smaller period applied at the end of the constant drying rate phase has the best effects on the product quality and energy gain without considerably extending the drying time.
This paper is included in the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation

Abstract Emerging big-data workloads with massive I/O processing require fast, scalable, and ﬂexible storage virtualization support. Hardware-assisted virtualization can achieve reasonable performance for fast storage devices, but it comes at the expense of limited functionalities in a virtualized environment (e.g., migration, replication, caching). To restore the VM features with minimal performance degradation, recent advances propose to implement a new software-based virtualization layer by dedicating computing cores to virtual device emulation. However, due to the dedication of expensive general-purpose cores and the nature of host-driven storage device management, the proposed schemes raise the critical performance and scalability issues with the increasing number and performance of storage devices per server. In this paper, we propose FVM, a new hardware-assisted storage virtualization mechanism to achieve high performance and scalability while maintaining the ﬂexibility to support various VM features. The key idea is to implement (1) a storage virtualization layer on an FPGA card (FVM-engine) decoupled from the host resources and (2) a device-control method to have the card directly manage the physical storage devices. In this way, a server equipped with FVM-engine can save the invaluable host-side resources (i.e., CPU, memory bandwidth) from virtual and physical device management and utilize the decoupled FPGA resources for virtual device emulation. Our FVM-engine prototype outperforms existing storage virtualization schemes while maintaining the same ﬂexibility and programmability as software implementations.
Making friends with uncertainty: experiences of developing a transdiagnostic group intervention targeting intolerance of uncertainty in IAPT. Feasibility, acceptability and implications

Abstract Co-morbid mental health diagnoses present challenges for services structured to provide disorder-specific models of treatment, such as those structured around Improving Access to Psychological Therapies (IAPT). Intolerance of uncertainty (IU) has been identified as both disorder specific and transdiagnostic, although little research explores transdiagnostic approaches to treatment of IU alone. A cognitive behavioural therapy treatment targeting IU previously established for individual therapy was adapted for a group within an IAPT primary care service: the ‘Making Friends with Uncertainty’ (MFWU) group. Twenty patients presenting with symptoms of a range of disorders including generalised anxiety disorder, obsessive compulsive disorder and depression completed the intervention in three groups. Measures of IU, anxiety and depression administered in the first and final sessions of the group have been analysed as well as examining common themes in participant feedback and facilitators’ own reflections. The results showed that nine participants (45%) showed reliable change in IU following the group and 16 (80%) showed reliable change on a measure of depression or anxiety. A thematic analysis identified universality, tolerance and acceptance and the manifestation of IU in the group process as key. The results suggest that the MFWU group provides an effective and feasible option for individuals presenting to IAPT services, including those reporting co-morbid difficulties. Implications for future practice are discussed as iteration and evaluation of the framework and content will continue. Key learning aims (1) To gain an overview of a transdiagnostic model of IU. (2) To develop understanding of a group intervention for building tolerance to uncertainty. (3) To describe some group processes observed and learn from the delivery of a transdiagnostic group intervention. (4) To consider applications of transdiagnostic treatment in a group setting in IAPT. (5) To understand how to structure a group treatment for IU, and which factors to consider.
Prediction of Melting Points of Organic Compounds Using Extreme Learning Machines

Nonlinear regression methods such as artificial neural networks have been extensively used in prediction of properties of compounds from their molecular structure. Recently a new fast algorithm for training artificial neural networks known as the extreme learning machine was developed. In this paper we apply a simple ensemble of extreme learning machines to a large data set of melting points of organic molecules. The results obtained by extreme learning machines (cross-validated test set root-mean-square error = 45.4 K) are slightly better than those obtained using k nearest neighbor regression with genetic parameter optimization (cross-validated test set error = 46.2 K) and significantly better than those obtained by artificial neural networks trained using gradient descent (test set error = 49.3 K). The training of the extreme learning machine involves only linear regression resulting in faster training. Ensembling the extreme learning machines removes the dependence of results on initial random weights.
Visually Impaired Mentally Sighted: An Inclusive Education Case

This research was carried out as part of an Erasmus + project called “Visually Impaired Mentally Sighted” targeting to improve the conditions and materials used in the educational environment and to develop personal and professional skills of the school staff. This qualitative study employed descriptive case study design. The study involved 16 participants in total including visually impaired students, teachers and the principal from the high school which is the coordinator institution of the project. The researchers conducted two semi-structured focus group interviews with the participant teachers, one focus group interview with participant students and a face-to-face interview with the school principal. The data were analysed according to content analysis. The researchers created four themes as follows; ‘consciousness’, ‘professional development’, ‘teaching materials’ and ‘motivation’. That means students in this case study increased their motivation at school. Also, teachers realized that there is a key area for their professional development. At the same time, the importance of using inclusive educational materials that enable disabled students to access education became apparent.
Absolute H atom density measurement in pure methane pulsed discharge

Absolute atomic hydrogen and methyl radical production resulting from direct electron impact dissociative excitation of CH4 has been measured in pulsed discharge by two-photon allowed laser induced fluorescence. For 20 Torr pure methane discharge with 480 ns pulse duration, 4–6 kV voltage applied, and 6–11.5 mJ input electrical energy yield of 10–22×1015 cm−3 hydrogen atoms, i.e., up to 4.5% dissociation of CH4 is achieved by direct electron impact with 50% energy conversion efficiency.
Disperse Dyeing of Nylon 6 Fibers with 1,4-Diaminoanthraquinone in the Presence of Didodecyldimethylammonium Bromide

The dyeing behavior of 1,4-diaminoanthraquinone (1,4-DAA) on nylon 6 fiber in the presence of didodecyldimethylammonium bromide (DDDMAB) is investigated. Both dyeing rate and dye saturation value of 1,4-DAA improve considerably with DDDMAB, compared with a dyehouse grade of 1,4-DAA, Miketon Fast Red Violet R. There is no evidence for a reduction in T g of the fiber pretreated with aqueous DDDMAB solution. The apparent solubility of 1,4-DAA in aqueous DDDMAB solution as well as the amount of DDDMAB adsorbed onto nylon 6 fiber is large. The enhanced dyeing properties of the fiber in the presence of DDDMAB are attributed to the formation of the concentrated layer of monomeric dye molecules on the fiber surface.
Thermally Initiated Free-Radical Oxidation of Cellulose

Purified cotton cellulose was heated in the resonant cavity of the electron spin resonance spectrometer in static nitrogen, flowing nitrogen, and flowing air and in vacuum. The formation of free radicals in the samples of cotton was monitored at temperatures below 300°C. Cotton was treated with chemical additives and then heated in static nitrogen at atmos pheric pressure. At the same temperature of heating, a much greater concentration of free radicals was formed in purified cotton heated in static nitrogen than in cotton heated in flowing nitrogen or in vacuum. The rate of free-radical forma tion in cotton heated in flowing nitrogen was constant throughout the heating period. Cotton, heated at the same tem peratures and times in flowing air or in static nitrogen, showed a greater concentration of free radicals than cotton heated in flowing nitrogen. The effects of additives or the application of vacuum on the apparent energy of activation for the formation of free radicals in heated cotton were also investigated. The amount of char formed during pyrolysis, as indi cated by the intensity of the free-radical signal, was much greater for samples which contained additives or which were allowed to trap decomposition products than for untreated cotton purged free of volatile decomposition products, as they were formed.
Time-Aware Tensor Decomposition for Missing Entry Prediction

Given a time-evolving tensor with missing entries, how can we effectively factorize it for precisely predicting the missing entries? Tensor factorization has been extensively utilized for analyzing various multi-dimensional real-world data. However, existing models for tensor factorization have disregarded the temporal property for tensor factorization while most real-world data are closely related to time. Moreover, they do not address accuracy degradation due to the sparsity of time slices. The essential problems of how to exploit the temporal property for tensor decomposition and consider the sparsity of time slices remain unresolved. In this paper, we propose TATD (Time-Aware Tensor Decomposition), a novel tensor decomposition method for real-world temporal tensors. TATD is designed to exploit temporal dependency and time-varying sparsity of real-world temporal tensors. We propose a new smoothing regularization with Gaussian kernel for modeling time dependency. Moreover, we improve the performance of TATD by considering time-varying sparsity. We design an alternating optimization scheme suitable for temporal tensor factorization with our smoothing regularization. Extensive experiments show that TATD provides the state-of-the-art accuracy for decomposing temporal tensors.