Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Holographic Metasurface Aided Wideband OFDM Communications Against Beam Squint

The future sixth-generation wireless communications are expected to realize an intelligent network of low powered and high throughput transmissions. In response to such demands, the reconfigurable holographic metasurface (RHS) is evolving into a promising solution owing to its flexible beam steering capability with low power and hardware costs. Unlike traditional phased antenna arrays with independent phase shifters, the RHS can generate desired beams by radiation amplitude control of the reference wave propagating along the metasurface. This so-called holographic interference technique enables an ultra-thin, small-sized planar antenna structure, which is suitable for integrated implementation without any complex phase-shifting or power-division circuits. In this paper, we consider an RHS-aided wideband communication system with an RHS-equipped base station. We aim to maximize the achievable data rate via joint digital beamforming (DBF) and holographic beamforming (HBF). To defeat the beam squint loss brought by the frequency-selective nature, we leverage the additivity of holographic interference patterns to design a novel amplitude control algorithm for solving the HBF subproblem, which contributes to our proposed joint DBF and HBF scheme. Simulation results validate the effectiveness of the proposed scheme against the beam squint issue in terms of the achievable rate.
Causal identification of infectious disease intervention effects in a clustered population

Causal identification of treatment effects for infectious disease outcomes in interconnected populations is challenging because infection outcomes may be transmissible to others, and treatment given to one individual may affect others’ outcomes. Contagion, or transmissibility of outcomes, complicates standard conceptions of treatment interference in which an intervention delivered to one individual can affect outcomes of others. For example, a vaccine given to an individual may affect their risk of infection given exposure to an infectious source as well as their infectiousness if they become infected. Several statistical frameworks have been proposed to measure causal treatment effects in this setting, including structural transmission models, mediation-based partnership models, and randomized trial designs. However, existing estimands for infectious disease intervention effects are of limited conceptual usefulness: Some are parameters in a structural model whose causal interpretation is unclear, others are causal effects defined only in a restricted two-person setting, and still others are nonparametric estimands that arise naturally in the context of a randomized trial but may not measure any biologically meaningful effect. In this paper, we describe a unifying formalism for defining nonparametric structural causal estimands and an identification strategy for learning about infectious disease intervention effects in clusters of interacting individuals when infection times are observed. The estimands generalize existing quantities and provide a framework for causal identification in randomized and observational studies, including situations where only binary infection outcomes are observed. A semiparametric class of pairwise Cox-type transmission hazard models is used to facilitate statistical inference in finite samples. A comprehensive simulation study compares existing and proposed estimands under a variety of randomized and observational vaccine trial designs.
Self-Guided Curriculum Learning for Neural Machine Translation

In supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible. Inspired by this, we formulate a difficulty criterion based on the recovery degrees of training examples. Motivated by the intuition that after skimming through the training corpus, the neural machine translation (NMT) model “knows” how to schedule a suitable curriculum according to learning difficulty, we propose a self-guided curriculum learning strategy that encourages the NMT model to learn from easy to hard on the basis of recovery degrees. Specifically, we adopt sentence-level BLEU score as the proxy of recovery degree. Experimental results on translation benchmarks including WMT14 English-German and WMT17 Chinese-English demonstrate that our proposed method considerably improves the recovery degree, thus consistently improving the translation performance.
First-principle investigation of structural, electronic and magnetic properties of Co2VIn and CoVIn Heusler compounds

Investigation of the structural, electronic and magnetic properties of full-Heusler Co2VIn as well as half-Heusler CoVIn Cobalt based Heusler compounds using density functional theory (DFT) leads to the general conclusion that Co2VIn and CoVIn are half-metallic materials with a gap at the Fermi level in the minority states and majority states respectively. A Hubbard-like Coulomb correlation term U has been included in the DFT (DFT+U) for the computation of the electronic and magnetic properties of the compounds. The structural properties have been calculated for the paramagnetic and ferromagnetic phases, and both Co2VIn and CoVIn are found to be stable in the ferromagnetic phase. The calculated magnetic moments are 2 μB and 0.9 μB per formula unit for Co2VIn and CoVIn respectively.
Evaluation of diagnostic accuracy of physical examination and MRI for ligament and meniscus injuries

Surgical planning shall be based on thorough physical examination and radiological findings during patient evaluation. The use of computational methods in the evaluation of diagnostic tests may facilitate the decision-making process as well as reduce treatment costs. Conventional X-ray is a modality of choice for bone visualisation however, it is not suitable for soft tissue evaluation. MRI is a tool designated for soft tissue examination however, its accuracy is debated in the literature. This study was performed to evaluate the diagnostic accuracy of MRI in most common knee injuries in comparison to physical examination. The evaluation of selected diagnostic methods used in the evaluation of ligaments and meniscus was carried out based on statistical indicators and ROC (Receiver Operating Characteristic) curves. The indicators were determined using Statistica and Matlab software. 69 patients were enrolled in the study, who were previously scheduled for arthroscopic knee surgery. Each patient underwent a closed protocol physical evaluation prior to surgery. MRI reports were collected and stored. Diagnostic accuracy was evaluated with the use of ROC curves. Physical examination presented greater diagnostic accuracy than MRI in ACL tears. The same dependency was found in lateral and medial meniscal tears. MRI showed AUC of 0.835 for MM, 0.788 for LM, and 0.870 for ACL, respectively. The physical examination had the greatest diagnostic accuracy in comparison with ACL tears, where AUC for the Lachman test reached 0,902. Apley test was most accurate for LM tears with AUC reaching 0,842, while the McMurray test was the most accurate for MM lesions. MRI is a preferable addition to a thorough physical examination.
A Parameter-free Clustering Algorithm based K-means

Clustering is one of the relevant data mining tasks, which aims to process data sets in an effective way. This paper introduces a new clustering heuristic combining the E-transitive heuristic adapted to quantitative data and the k-means algorithm with the goal of ensuring the optimal number of clusters and the suitable initial cluster centres for k-means. The suggested heuris-tic, called PFK-means, is a parameter-free clustering algorithm since it does not require the prior initialization of the number of clusters. Thus, it generates progressively the initial cluster centres until the appropriate number of clusters is automatically detected. Moreover, this paper exposes a thorough comparison between the PFK-means heuristic, its diverse variants, the E-Transitive heuristic for clustering quantitative data and the traditional k-means in terms of the sum of squared errors and accuracy using different data sets. The experiments results reveal that, in general, the proposed heuristic and its variants provide the appropriate number of clusters for different real-world data sets and give good clusters quality related to the traditional k-means. Furthermore, the experiments conducted on synthetic data sets report the performance of this heuristic in terms of processing time.
Personalized Prognosis of Oncosurgical Patients Using Standard Tool Microsoft Access

The most common treatment for oncological diseases is using of surgery. Despite successful methods of surgery, the problem of a personalized prediction of the outcome of hospitalization is an important and fundamental problem in the treatment of a noncosurgical patient with five or more concomitant diseases. This is due to the fact that at present the choice of an unified approach to the decision to conduct a planned surgical intervention in the case of oncological pathology with appropriate recommendations puts the physician before a difficult choice of treatment tactics for each case separately. There are many prognostic models, both implemented and not implemented in the form of various computer and mobile applications that allowedphysician to assess the severity of the patient’s condition and predict the outcome of treatment. Therefore, to support the physician of a medical decision, simple and accessible tools are needed, allowing divide patients according to individual selection of the treatment regimen. Nevertheless, the introduction of specific models for predicting therapeutic measures (for example, surgical intervention) in patients, in particular elderly patients, in clinical practice often remains at the level of basic research and is used only in a few clinics related to that studies.The purpose of our work is to implement a decision rule as the Microsoft Access software, which allowed ranking patients with oncological diseases by the probability of lethal outcome before surgical intervention.The software implementation methodology was implemented using elements of the standard Access database.The result of our research was the implementation of a decision rule in the form of Microsoft Access software Oncoprognosis 1.0, which allows physician to rank oncosurgical patients according to the likelihood of death in oncology.
Development of In Situ Gelling Inserts by Lyophilization for Nasal Delivery of Terbutaline Sulfate

ABSTRACT The aim of present study was development and in vitro characterization of sponge like, in situ gelling inserts of terbutaline sulfate by lyophilization for nasal administration to avoid first-pass metabolism. The inserts were investigated for thickness, water uptake, moisture absorption, drug content, in vitro drug release, histopathology, scanning electron microscopy analysis, and powder X-ray diffraction. The developed inserts were smooth in appearance with uniform thickness and drug content. The inserts exhibited satisfactory water uptake and extended drug release up to 10 h. Hence, inserts could be used as a viable alternative to improve therapeutic efficacy of terbutaline sulfate. GRAPHICAL ABSTRACT
Challenges Associated With The Construction And Operation Of Seasonal Storage For A Small Solar District Heating System: A Multi-Objective Optimization Approach

Solar assisted district heating systems (SDHS) is a promising pathway towards sustainable transaction to clean energy production. However, the technical performance variation due to the misestimation of the seasonal storage tank (SST) combined with inadequate financial assessment and insufficient environmental impact data associated with the deployment of those innovative systems represents a big challenge for the broad implementation of SDHS in Europe. This paper evaluates the feasibility of deploying the SDHS at a small urban community sizes with tracing its techno-economic failures as well as its environmental impact. Furthermore, this study attempts to focus on the technical failure of the SST through including its geometry as well as its design parameters in the optimization framework. The capabilities of this approach are illustrated through a case study of a SDHS located in Madrid (Spain), to satify the heating demand for an urban community of 10 building with total heating demand of 1913.4 MWh/a. The optimization results show that including the SST geometry and its construction properties can effectectively enhance the SDHS feasibility, especially from the technical and environmental point of view. Keywords— Solar district heating system, seasonal storage, Life cycle cost, Life cycle assessment, Multi-objective optimization
Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

: Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing.In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.
Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

The main challenge in the implementation of long-lasting vibration monitoring systems is to tackle the constantly evolving complexity of modern “mesoscale” structures. Thus, the design of energy-aware solutions is promoted for the joint optimization of data sampling rates, onboard storage requirements, and communication data payloads. In this context, the present work explores the feasibility of the rakeness-based compressed sensing (Rak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data. In particular, a novel model-assisted variant (MRak-CS) is proposed, which is built on a synthetic derivation of the spectral profile of the structure by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the wavelet packet transform operator is conceived, which aims at maximizing the signal sparsity while allowing for a precise time-frequency localization. The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the rakeness-based compression strategies are superior to conventional eigenvalue approaches and to standard CS methods. The achieved compression ratio is equal to seven and the quality of the reconstructed structural parameters is preserved even in presence of defective configurations.
A Fokker-Planck Approach for Modeling the Stochastic Phenomena in Emerging Non-Volatile Memory Devices

—Embedded non-volatile memory technologies such as resistive random access memory (RRAM) and spin-transfer torque magnetic RAM (STT MRAM) are increasingly being researched for application in neuromorphic computing and hardware accelerators for AI. However, the stochastic write processes in these memory technologies affect their yield and need to be studied alongside process variations, which drastically increase the complexity of yield analysis using the Monte Carlo approach. Therefore, we propose an approach based on the Fokker-Planck equation for modeling the stochastic write processes in STT MRAM and RRAM devices. Moreover, we show that our proposed approach can reproduce the experimentally results for both STT-MRAM and RRAM devices.
The Consolation of Network Coding and Partial Protection Techniques to Optical Transport Networks in Data, Data, Data Era

—The age of acceleration is taking place, driven by the revolutionary digital transformation creating basically a digital version of our physical world and the currency in that digital space is data. Massive amount of data has been generated ranging from wearable devices monitoring our physical health every single millisecond to autonomous vehicles generating roughly 5Tb hourly to even astronomical activities producing an order of Exabytes on daily basis and then ultra-broadband Internet comes into play, moving such data to the cloud. Internet trafﬁc therefore has been experiencing explosive growth and in this context, optical transport networks forming the backbone of the Internet are pushed for transformation in system capacity. While the intuitive solution of deploying multiple ﬁbers can address the pressing demand for increased capacity, doing so does not bring improvement in economic of scales in terms of cost, power consumption and spectral efﬁciency. This necessitates for a different approach so that the ﬁber capacity could be utilized in a more efﬁcient manner. In this paper, we focus on innovative techniques, that is, network coding and partial protection, to reduce the effective trafﬁc load in order to achieve greater capacity efﬁciency for optical transport networks. Speciﬁcally, the application of network coding is examined by upgrading the functionalities of intermediate nodes with processing (i.e., encoding and decoding) capabilities. Besides, partial protection relying on the premise of providing just enough bandwidth in case of failure events is investigated for saving the redundant protection capacity. What is more interesting arises when combining both network coding and partial protection and we present insights on how to derive compounding gains in such unique prospect.
Influence of Polyvinylidene Fluoride Polymorphs in Membrane-Assisted Crystallization: NaCl Crystals Nucleation and Growth

Membrane-assisted crystallization is a new unit operation for separation and/or promotion of pure crystals formation, where microporous hydrophobic membranes are used not as selective barriers but to promote water vapor transfer between phases inducing supersaturation in solution. Polyvinylidene fluoride (PVDF) is one of the most utilized polymers due to its excellent combination of properties and processability and has been used in a wide plethora of applications in membrane technology including membrane crystallization. PVDF has different polymorphs and this property is important in membrane technology because different phases might significantly affect the final membrane properties and performances, e.g., on membrane fouling and membrane wetting. A clear correlation between the dominating crystalline phases in PVDF and the performance in membrane contactor applications, and in particular in membrane crystallization is still missing and necessary. In this work, molecular dynamics simulation was utilized to analyze the relationship and influence of different polymorphs of PVDF membranes on crystals formation via membrane crystallization process. Atomistic simulations were used to study the crystal nucleation and growth of sodium chloride in contact with amorphous, α and β PVDF hydrophobic polymer surfaces at a supersaturated concentration of salt. Predictions about the ability of α and β polymorphs to influence the nucleation and growth of salts were obtained. The results specified the crystals (as cluster) size distributions, the size of critical nuclei, and the nucleation rate. The amorphous PVDF led to the formation of smaller but more regular clusters in reference to the other samples; α PVDF produced much more inhomogeneous crystals and of small dimensions; βPVDF produces crystals of larger dimensions than α PVDF.
Role of Majorana fermions in spin transport of anisotropic Kitaev model

We study a quantum spin Kitaev model with zigzag edges to clarify the effects of anisotropy in the exchange couplings on the spin propagation. We simulate the spin and Majorana dynamics triggered by a magnetic pulse, using the real-space time-dependent Majorana mean-field theory. When the anisotropy is small, the dispersion of the itinerant Majorana fermions remains gapless, where the velocity of the spin propagation matches the group velocity of the itinerant Majorana fermions at the nodal points. On the other hand, in the gapped system with a large anisotropy, the spin propagation is strongly suppressed although its nature depends on the shape of the pulse. The spin transport in the junction system described by the Kitaev models with distinct anisotropies is also addressed.
Procedimientos actuales para la extracción y purificación de flavonoides cítricos Current procedures for extraction and purification of citrus flavonoides

In the food industry, antimicrobial agents are used for preserving the quality and safety of processed food. Flavonoids found in citrus extracts inhibit cell growth of a large group of infectious microorganisms, therefore, these compounds may be useful as antiviral, antifungal and antibacterial agents. Hesperetin, hesperidin, luteolin, naringenin, naringin, narirutin, neohesperidin, nobiletin and tangeretin are some of the main flavonoids found in various citrus fruits. The processes used in recent years to extract, purify and analyze typical flavonoids from citrus species are reviewed.
Low-Profile Wideband Millimeter-Wave Antenna-in-Package Suitable for Embedded Organic Substrate Package

An antenna-in-package solution is proposed based on embedded organic substrate package. RF front-end module is integrated with an antenna considering the electrical and thermal performance, dimensions, manufacturing as well as cost. A millimeter-wave low-profile wideband antenna is designed, analyzed, and verified experimentally. The radiator of the proposed antenna consists of three groups of patches with different dimensions and it is fed by a stripline through an aperture etched on a ground plane. Input impedance analysis is utilized to accurately explore resonance characteristics of the aperture-coupled radiator. As a comparison, characteristic mode (CM) analysis is applied to calculate CMs of the source-free radiator. Different results obtained by two methods are analyzed. Over the operating band, three resonance modes, respectively, dominated by quasi-TM<sub>10</sub> mode of different patches are excited in turn, which contributes to wideband performance. Measured results demonstrate that the proposed antenna with only <inline-formula> <tex-math notation="LaTeX">$0.039\lambda _{0} (\lambda _{0}$ </tex-math></inline-formula> is wavelength of 28 GHz in free space) substrate thickness achieves an impedance bandwidth (<inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert \le -10$ </tex-math></inline-formula> dB) of 37.5% (22.8–33.3 GHz). The peak gain is up to 10.7 dBi and 3 dB gain bandwidth is 23.7% (25.7–32.6 GHz), while 6 dBi gain bandwidth is 33.6% (23.5–33 GHz).