Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis, characterisation and application of carbonised metal-organic frameworks

The main purpose of this project was to synthesise, characterise and evaluate metal organic frameworks (MOFs) and MOF-derived carbons for different applications. Carbon is widely used in the structure of electrodes of electrochemical energy conversion/storage devices. In order to control the pore size distribution of such carbons, the pyrolysis of MFOs under inert atmosphere was considered. MOFs are highly porous crystalline materials constructed from metal ions linked together by different organic ligands. When MOFs are pyrolysed, the resulting carbon material inherits the pore structure of the precursor MOF. By combining different metal ions and organic linkers, it is possible to create MOFs with different pore sizes. In this research, we aimed to take advantage of this property and derive carbons from MOFs to produce porous carbons with controlled pore size distributions and evaluate their behaviour in electrochemical energy conversion/storage devices to improve the performance of the latter. Three different MOFs were selected in terms of their pore sizes (ZIF-8, MOF-5 and MIL-100(Fe)) and were hydrothermally synthesised and characterised with Brunauer–Emmet–Teller (BET) and powder X-ray diffraction. These MOFs were then carbonised under an argon atmosphere and characterised with BET and energy dispersive spectroscopy analysis. The electrocatalyst was made by loading platinum on the carbonised ZIF-8 and characterised with EDS to measure the amount of platinum loaded and assess the homogeneity of the platinisation process for fuel cell application. The Pt/CZIF-8 was electrochemically characterised by cyclic voltammetry, oxygen reduction reaction and durability tests to evaluate its electrochemical behaviour. For the supercapacitor applications, all MOF-derived carbons were characterised by cyclic voltammetry and the cyclic charge–discharge curve. The separation ability of the MOFs was also assessed by immersion calorimetry, and when any unexpected behaviour was observed, the assessment was applied to the replica MOF (carbonised MOFs), and their behaviour was observed. After evaluating the results, the best MOF/carbonised MOF for each application was introduced. Four different MOFs, ZIF-8, HKUST, UiO-66 and UiO-67, were synthesised and characterised with BET and X-ray diffraction, and their separation ability was investigated using immersion calorimetry in xylene isomers. Since HKUST and UiO-66 had unexpected adsorption behaviour, the replicas of these MOFs were also produced under argon, and their separation ability was evaluated in m-xylene.
Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO

In this paper, we propose a deep-learning-based for joint antenna selection and hybrid beamformer design problem in mmWave massive MIMO systems. In this respect, we treat both problems as a classification problem. We design two convolutional neural networks (CNNs) which accept the input as the channel matrix and it yields the output as the optimum antenna subarray. The selected part of channel matrix is fed to the second CNN which gives the output as the analog and baseband beamformers. We evaluate the performance of the proposed approach through numerical simulations and show that our CNN framework provides significantly better performance as compared to the conventional techniques such as orthogonal matching pursuit.
A 3D Printed Lens Antenna for 5G Applications

A switchable multi-antenna architecture for lens-assisted beamsteering in the Ka-band region allocated for 5G communications (24-30 GHz) is proposed. The radiating elements are bow-tie antennas mounted with a 3D printed high-permittivity dielectric lens (εr = 10). A 5-element array is simulated showing switchable 30° beam steering over a 120° sector with 19 dB maximum gain. Single antenna measurements of a scaled prototype with a Polylactic Acid (PLA) lens (εr = 2.7) show gains around 11 dB, in agreement with projected performances.
Effect of different binders on reconstitution behaviors and physical, structural, and morphological properties of fluidized bed agglomerated yoghurt powder

Abstract Fluidized bed agglomeration is a widely used technique in the production of instant food powders. In this process, primary particles are agglomerated to give a granule-shaped product with improved instant characteristics compared to the original primary particles. Instant products are easy to use by the consumers and hence are favored over the unagglomerated particles. Depending on the type of binder used and process conditions, agglomerates exhibit different shapes, structures and instant properties. In this study, spray-dried yoghurt powders were agglomerated in a top sprayed fluid bed granulator. The influence of different binders (distilled water, ionic, and sugar-based solutions) on agglomeration growth mechanism, particle morphology, and chemical bonds were investigated. The results showed that sugar-based binders caused large particle size and porosity compared to water binders (p < 0.05). Hydrophilic sugars also improved wettability. b* value of powders enhanced with sugar binders. Protein and stretching vibration of N–H groups were affected by acidity of water and resulted in an improvement of powder solubility. Circularity of particles decreased with the usage of hydrophilic sugar solutions while elongation increased.
Examination of nutritional knowledge levels of physical education and sports stakeholders in gender variable: A systematic review and meta-analysis

Background and Study Aim. Nutrition knowledge is related to dietary behavior in athletes. Therefore, it may also have an impact on performance. Athletes with better nutrition knowledge have more healthy dietary habits. This meta-analysis study focused on the impact of gender on the nutrition knowledge levels of physical education and sports stakeholders. Material and Methods. This study adopted a meta-analysis research design, which is used to analyze, synthesize, and interpret quantitative findings from an array of studies through advanced statistical techniques. A meta-analysis involves combining the findings of studies carried out in different places and at different times on the same topic and obtaining a quantitatively accurate result based on a large sample. This study employed the Comprehensive Meta-Analysis (CMA, v. 2.0) to determine effect sizes and the variance of each study and to compare groups. Cohen’s kappa intercoder reliability and outlier tests were performed using the Statistical Package for Social Sciences (SPSS). Results. We focused on 31 studies with a total sample size of 4575. We calculated the effect size of each study. We found a statistically significant effect size in favor of female stakeholders (d = 0.15; 95% CI -0.22 -0.09) in the fixed effects model, which was a weak result according to Cohen’s classification. We determined a statistically significant effect size in favor of female stakeholders (d = 0.15; 95% CI -0.29-0.01) in the random-effects model. These results suggest a slight difference in nutrition knowledge levels between male and female physical education and sports stakeholders. This result can pave the way for further research. Conclusions. It is understood from the physical education and sports stakeholders that there is a weak difference in the nutritional knowledge levels of women compared to men. It is thought that people who study on sports nutrition and nutrition programs will benefit from the present finding. In addition, it is estimated that the researches to be carried out on the relevant subject will take the current study as a reference.
Neural Net-Based Anomaly Detection System in Substation Networks

Important components of the electric energy distribution systems are primary and secondary substations. Due to the incorporation of legacy communication infrastructure in these systems, they often have inherent cyber-security vulnerabilities. Further, traditional intrusion defence strategies for IT systems are often not applicable. In order to improve cyber-security in substation networks, this paper presents a neural net-based monitoring system. Further, to evaluate the applicability of the system, all experiments were conducted on a real test bed, which represents the substation domain as close as possible to reality. The proposed monitoring system covers several tasks. First, relevant network packets are acquired from network traffic and analysed. Based on these packets statistical features are extracted. Then, classes are defined, and a normal behaviour model of the network is trained by the neural net. New network traffic is compared to the model, in order to determine the nature of the traffic and identify potential anomalies. Finally, the monitoring system is evaluated by conducting several supervised and unsupervised network attacks against the test bed.
A study on the impact of positive and negative comments on consumers' perceived usefulness

With the rapid development of e-commerce, online comments has become an important reference for consumers when choosing, purchasing products or services. This paper takes the number, quality, form, timeliness and credibility of the comment as the starting point, adopts the questionnaire survey method, takes the college students as the main research object, and uses the factor analysis and regression analysis methods to study the differences in the impact of positive and negative comments on consumer perceived usefulness who purchase electronic digital products online. This study found that the impact of different types of comments on consumer perceived usefulness is quite different. Different commentary factors have different effects on consumer perceived usefulness, and corresponding countermeasures and suggestions are proposed.
Demystifying Fixed  $k$ -Nearest Neighbor Information Estimators

Estimating mutual information from independent identically distributed samples drawn from an unknown joint density function is a basic statistical problem of broad interest with multitudinous applications. The most popular estimator is the one proposed by Kraskov, Stögbauer, and Grassberger (KSG) in 2004 and is nonparametric and based on the distances of each sample to its <inline-formula> <tex-math notation="LaTeX">$k^{\mathrm{ th}}$ </tex-math></inline-formula> nearest neighboring sample, where <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is a fixed small integer. Despite of its widespread use (part of scientific software packages), theoretical properties of this estimator have been largely unexplored. In this paper, we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> error as a function of a number of samples. We argue that the performance benefits of the KSG estimator stems from a curious “correlation boosting” effect and build on this intuition to modify the KSG estimator in novel ways to construct a superior estimator. As a by-product of our investigations, we obtain nearly tight rates of convergence of the <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> error of the well-known fixed <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor estimator of differential entropy by Kozachenko and Leonenko.
Stabilization of Walking with Walking-Aid Cane Robot Applying Light Touch Effect

This paper describes a cane type single-wheel inverted pendulum robot for walking aid, which is lightweight and compact. In order to verify the effectiveness of the stabilization by this robot, we conducted an experiment observing trunk sway of a volunteer while he walked with/without our robot. As a result of this experiment, it was confirmed that our robot could reduce the trunk sway of the volunteer by about 20 [%] comparing one without our robot.
Image-Guided Observer-Based Control for Needle Steering

In robot-assisted needle-insertion procedures, in order to steer a beveled-tip needle toward a target location, the needle tip pose is required to be used as feedback. Mathematically, the needle tip pose can be expressed as a position vector and a rotation matrix that represents the orientation of the needle. Using the traditional 2-D ultrasound imaging modality, since the images are low in resolution and the needles are small in diameter, only the position of the needle tip can be measured, and the orientation information cannot be directly extracted. This brief presents a nonlinear observer for estimating the needle tip orientation based on needle 3-D kinematic equations and the position data measured from ultrasound images. Assuming bounded inputs for the needle model, it is shown that the observation error remains bounded. The performance of the observer is shown in an observer/controller feedback loop. The stability and convergence of the observer/controller combination are shown by solving linear matrix inequality (LMI) optimizations. Experiments performed for different tissue types and different needle insertion goals verify the effectiveness of our method.
Liquid-Crystal-Based Reconfigurable Holographic Metamaterial Antenna with Stable Phase Center

In this paper, a novel reconfigurable holographic metamaterial antenna (RHMA) based on electrically controlled liquid crystal (LC) is proposed. A novel unit cell is designed, and the resulting antenna presents a large beam scanning range rising from −62.7° to +59.7°. Moreover, method to obtain stable phase center (PC) is presented. The simulation results demonstrate that the variation in PC is significantly suppressed based on the proposed method. The proposed antenna is a good candidate for the satellite communication.
New Pattern Reconfigurable Antenna with 4 U-Slots for MIMO Applications

New compact and low profile pattern reconfigurable antenna with four U-slots is proposed. The size of the antenna is 42 mm * 40 mm * 0.72 mm, and the used substrate is FR-4 with a dielectric constant of 4.4 and a loss tangent of 0.02. The antenna has four pattern configurations which are directed (in HFSS simulation) to phi = 85, 120, 245 and 285 degrees, with simulated peak gains ranging between – 0.11 dB to 2.7 dB at theta = 90 degrees, and with a simulated overlapped 10-dB bandwidth from 3.65 GHz to 3.752 GHz (Overlapped bandwidth is 102 MHz). The simulated efficiencies for all configurations are ranging from 50% to 59%.
Frequency‐Division Multiplexing in Magnonic Logic Networks Based on Caustic‐Like Spin‐Wave Beams

Wave‐based data processing by spin waves (SW) and their quanta, magnons, is a promising technique to overcome the challenges which CMOS‐based logic networks are facing nowadays. The advantage of these quasi‐particles lies in their potential for the realization of energy efficient devices on the micro‐ to nanometer scale due to their charge‐less propagation in magnetic materials. In this paper, the frequency dependence of the propagation direction of caustic‐like spin‐wave beams in microstructured ferromagnets is studied by micromagnetic simulations. Based on the observed alteration of the propagation angle, an approach to spatially combine and separate spin‐wave signals of different frequencies is demonstrated. The presented magnetic structure constitutes a prototype design of a passive circuit enabling frequency‐division multiplexing (FDM) in magnonic logic networks. It is verified that spin‐wave signals of different frequencies can be transmitted through the device simultaneously without any interaction or creation of spurious signals. Due to the wave‐based approach of computing in magnonic networks, the technique of FDM can be the basis for parallel data processing in single magnonic devices, enabling the multiplication of the data throughput.
Flower Classification with Convolutional Neural Networks

Computer software that is able to identify the plant type given the image can be very beneficial tool in the area of botany, horticulture and agriculture. Besides improving the research possibilities in the mentioned fields, it can be used as entertaining learning tool or it can be applied to other similar domains. Flower classification can be quite challenging task, since the majority of flowers have highly similar main features. This paper proposes using convolutional neural networks for flower classification. The first step in this research was preparing the dataset for network training. Numerous of network models were implemented during this research, but the main focus is on LeNet and AlexNet models. AlexNet model with Sigmoid Uniform function for allocating initial weights provided the best classification results in this research. Network training, as well as the data preprocessing was conducted in deeplearning4j Java library.
Indonesian Defense Diplomacy in Achieving Cyber Security Through ADMM-Plus 2014-2019

Cyber ? security issues began to emerge along with advances in the field of information technology and the rapid increase in internet use. However, the greater the need for the internet in the daily activities of the world community, the more potential international security threats arise from activities within the cyber scope. Given the "borderless" nature of cybercrime, it is necessary to deal with other countries. Indonesia's participation in ADMM-Plus illustrates Indonesia's efforts to achieve national interests, namely cyber security through the external environment. This study aims to discuss Indonesia's defense diplomacy in achieving cyber security through ADMM-Plus 2014-2019. By using defense diplomacy theory and qualitative methods, this research illustrates that Indonesia's diplomacy in ADMM-Plus is still at an early stage, so it is difficult to say that the diplomacy has not been maximized. However, the presence of ADMM-Plus has a positive impact on Indonesia, especially in terms of cyber security.
Plutonium in the Presence of Natural Organic Ligands: Sorption Kinetics, Surface Complex Stability, and Subsurface Transport

It has been suggested that strongly sorbing ions such as plutonium may become irreversibly bound to mineral surfaces over time which has implications for nearand farfield transport of Pu. Batch adsorption–desorption data were collected as a function of time and pH to study the surface stability of Pu on goethite. Pu(IV) was adsorbed to goethite over the pH range 4.2 to 6.6 for different periods of time (1, 6, 15, 34 and 116 d). Following adsorption, Pu was leached from the mineral surface with desferrioxamine B (DFOB), a complexant capable of effectively competing with the goethite surface for Pu. The amount of Pu desorbed from the goethite was found to vary as a function of the adsorption equilibration time, with less Pu removed from the goethite following longer adsorption periods. This effect was most pronounced at low pH. Logarithmic desorption distribution ratios for each adsorption equilibration time were fit to a pH-dependent model. Model slopes decreased between 1 and 116 d adsorption time, indicating that overall Pu(IV) surface stability on goethite surfaces becomes less dependent on pH with greater adsorption
A Multi-Objective Metaheuristic Approach Based Adaptive Clustering and Path Selection in IoT Enabled Wireless Sensor Networks

– The application-oriented Internet of Things (IoT) systems that exhibit the use of wireless sensor networks (WSNs) have energy constraint issues. The nodes in the WSN are driven by batteries that cannot be used for a very long time and thus the network is unable to combat the energy efficiency challenge. Also, the energy of the nodes drains rapidly with time as a result of a steady sensing task. Moreover, there are several intermediate tasks performed by the wireless network from sensing to sending the data to the destination. The traditional wireless models can accomplish the task of sensing and transmitting but are unable to avoid the tradeoff between many quality-of-service matrices such as network latency and throughput. So, there is a need to employ optimization techniques with a multi-objective paradigm. In this paper, a model for both choosing the cluster head and selecting the efficient path in a WSN for IoT applications has been proposed. The cluster head selection which is a part of clustering is done using a multi-objective rider optimization algorithm (ROA) which considers 3 objectives namely energy, distance, and delay. The routing is performed by selecting efficient and optimal paths using the multi-objective sailfish optimization algorithm (SFO). The results reveal that the proposed model proves itself superior to other similar existing works when compared based on execution time, energy depletion, network delay, throughput, packet delivery ratio, alive nodes in the network, and increase in dead nodes. The experimentation is done on a dense sensor network and it is observed that the proposed work can mitigate up to 30-40% of energy utilization and 40-60% of delay when compared with similar multi-objective techniques for routing and clustering. The intensification in the network lifespan and throughput is also marked by the proposed multi-objective technique which makes it profitable to be used in various IoT applications.
Leading from Behind? Gender Equality in Germany During the Merkel Era

Whilst maintaining its status as an economic engine in Europe, Germany has historically been a laggard in adopting gender equality measures. The European Gender Equality Index, however, now ranks Germany relatively high and shows substantial progress since 2005. While this has gone mostly unnoticed, Germany has passed far-reaching legislation in major policy fields relevant for gender equality. This expansive policy adoption occurred during the chancellorship of Angela Merkel, but we still know little about the actors and processes that explain it. The divergent perceptions of Germany in relation to gender invite deeper scholarly investigation. In general, we lack knowledge about how conservative women and their parties lead on gender policy. More specifically, we need to know more about how policy measures introduced by conservative governments affect gender norms and gender culture, and if they ultimately lead to effective implementation and greater equality. Investigating the effects of Merkel's tenure on gender equality, we assess policy output and outcomes with a focus on internal power dynamics in Germany, as well as international and EU-level pressures in the policy domains of political representation, LGBTI rights, migration, the labour market, and care.
Mass Performance of Adapted Physical Activity: A “Population Strategy to Prevent Frailty”: Setting-Up and Running the Service-8 Years of Experience

Lack of physical activity has been postulated to be the cause of a significant loss of healthy life expectancy and in much greater costs, both for the National Health Service and for the individual. On the other side, the beneficial effects of Adapted Physical Activities have been demonstrated in many of the pathologies prevalent among older persons. Our experience, arising from a test performed 8 years ago, has demonstrated both the beneficial effects on the participants and the substantial reduction of costs for the Health Service. Nowadays, more than 2,500 senior citizens from the area covered by our Local Health Service take part in several Physical Activity courses that include both Adapted Physical Activity and fitness programs. The courses are managed by a private, nonprofit entity under the supervision of the Local Health Service and have involved also the local Social Services and the Municipalities. By all accounts the approach is successful and worth applying on a larger scale. The applied criteria and organization are described and guidelines proposed for further development.