Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization and in vitro antitumor, antibacterial and antifungal activities of green synthesized silver nanoparticles using cell extract of Nostoc sp. strain HKAR-2

In the present study we have made an attempt to develop an eco-friendly, cheap and convenient biological (green) method for the synthesis of silver nanoparticles (AgNPs) using the cell extract of the cyanobacterium Nostoc sp. strain HKAR-2. Their anticancerous, antifungal and antibacterial properties were also studied against MCF-7 cells, two fungal strains (Aspergillus niger and Trichoderma harzianum) and two plant bacterial strains (Ralstonia solanacearum and Xanthomonas campestris), respectively. The structural, morphological and optical properties of green synthesized AgNPs were determined by UV-VIS spectroscopy, Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction, transmission electron microscopy selected area electron diffraction (TEM-SAED) and scanning electron microscopy (SEM). Spectroscopic analysis showed the peak at 419 nm due to the reduction of AgNO3 into silver ion by cyanobacterial extract indicating surface plasmon resonance (SPR) of the synthesized AgNPs. The XRD pattern of AgNPs showed the characteristic Bragg peaks at (111), (200), (220) and (311) facets of the face centre cubic (fcc) confirming their crystalline nature. FTIR analysis revealed that proteins and amino acids are responsible for the reduction of AgNO3 into Ag + as well as for the stability of nanoparticles. Zeta potential confirmed that the charge on the nanoparticles is 1.80 mV which indicates the presence of stable nanoparticles. The results of SEM and TEM confirmed the large agglomerated shape of AgNPs with size ranging between 51-100 nm. The AgNPs showed a dose-dependent cytotoxic activity against human breast cancer MCF-7 cells with IC50 of 27.5 μg/ml. They also exhibited excellent antibacterial and antifungal activities.
Enhanced Failure Recovery Mechanism Using OpenState Pipeline in SDN

In Software Defined Networking (SDN), link failure situations require switches to make optimized rerouting decisions locally i.e. without controller intervention. However, such optimized rerouting decisions must be made after the link failure situation has been confirmed. The mechanisms that confirm such link failure situations require a certain amount of processing time which may introduce a bottleneck in the performance. This paper proposes an enhanced approach that employs a mechanism to make the rerouting decision without the confirmation of link failure. The approach uses the OpenState Pipeline to manage the network in failure situations, and the experiment results show notable enhancements in packets loss rate and usage of Ternary Content Addressable Memory (TCAM).
Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM

Nowadays, most of the objective speech quality assessment tools (e.g., perceptual evaluation of speech quality (PESQ)) are based on the comparison of the degraded/processed speech with its clean counterpart. The need of a "golden" reference considerably restricts the practicality of such assessment tools in real-world scenarios since the clean reference usually cannot be accessed. On the other hand, human beings can readily evaluate the speech quality without any reference (e.g., mean opinion score (MOS) tests), implying the existence of an objective and non-intrusive (no clean reference needed) quality assessment mechanism. In this study, we propose a novel end-to-end, non-intrusive speech quality evaluation model, termed Quality-Net, based on bidirectional long short-term memory. The evaluation of utterance-level quality in Quality-Net is based on the frame-level assessment. Frame constraints and sensible initializations of forget gate biases are applied to learn meaningful frame-level quality assessment from the utterance-level quality label. Experimental results show that Quality-Net can yield high correlation to PESQ (0.9 for the noisy speech and 0.84 for the speech processed by speech enhancement). We believe that Quality-Net has potential to be used in a wide variety of applications of speech signal processing.
Astrocytic Coverage of Dendritic Spines, Dendritic Shafts, and Axonal Boutons in Hippocampal Neuropil

Distal astrocytic processes have a complex morphology, reminiscent of branchlets and leaflets. Astrocytic branchlets are rod-like processes containing mitochondria and endoplasmic reticulum, capable of generating inositol-3-phosphate (IP3)-dependent Ca2+ signals. Leaflets are small and flat processes that protrude from branchlets and fill the space between synapses. Here we use three-dimensional (3D) reconstructions from serial section electron microscopy (EM) of rat CA1 hippocampal neuropil to determine the astrocytic coverage of dendritic spines, shafts and axonal boutons. The distance to the maximum of the astrocyte volume fraction (VF) correlated with the size of the spine when calculated from the center of mass of the postsynaptic density (PSD) or from the edge of the PSD, but not from the spine surface. This suggests that the astrocytic coverage of small and larger spines is similar in hippocampal neuropil. Diffusion simulations showed that such synaptic microenvironment favors glutamate spillover and extrasynaptic receptor activation at smaller spines. We used complexity and entropy measures to characterize astrocytic branchlets and leaflets. The 2D projections of astrocytic branchlets had smaller spatial complexity and entropy than leaflets, consistent with the higher structural complexity and less organized distribution of leaflets. The VF of astrocytic leaflets was highest around dendritic spines, lower around axonal boutons and lowest around dendritic shafts. In contrast, the VF of astrocytic branchlets was similarly low around these three neuronal compartments. Taken together, these results suggest that astrocytic leaflets preferentially contact synapses as opposed to the dendritic shaft, an arrangement that might favor neurotransmitter spillover and extrasynaptic receptor activation along dendritic shafts.
State-of-the-Art Power Management Circuits for Piezoelectric Energy Harvesters

Piezoelectric energy harvesters scavenging energy from the environment have been a promising solution to satisfy the increasing power demand in wireless sensor nodes. Power conditioning circuits for piezoelectric energy harvesters are a burgeoning technology widely disclosed in literature since 2001; however, there is still no complete overview available, which exhaustively describes the cutting-edge status of the research on power conditioning for piezoelectric energy harvesters. This paper aims at providing a more complete review on the state-of-the-art piezoelectric energy harvesting interfaces. A systematic categorization of the piezoelectric energy harvesting approaches is presented, based on the qualitative working principle, adopted control scheme, and employed components. The presented power conditioning interfaces are commented and discussed in detail, addressing the strengths and weaknesses based on the achieved results. Furthermore, this paper performs a critical comparison of state-of-the-art techniques by employing seven criteria to evaluate the performance of an energy harvesting system. The observed trend in this field is to design harvesting rectification schemes by employing Maximum Power Point Tracking (MPPT) algorithms to achieve a high Power Conversion Efficiency (PCE) and, resonant rectification techniques to harvest efficiently energy from wide-band excitations.
Second-Order Asymptotics of Universal JSCC for Arbitrary Sources and Additive Channels

We consider a universal joint source channel coding (JSCC) scheme to transmit an arbitrary memoryless source over an arbitrary additive channel. We adopt an architecture that consists of Gaussian codebooks for both the source reproduction sequences and channel codewords. The natural minimum Euclidean distance encoder and decoder, however, need to be judiciously modified to ensure universality as well as to obtain the best (highest) possible communication rates. In particular, we consider the analogue of an unequal error (or message) protection scheme in which all sources are partitioned into disjoint power type classes. We also regularize the nearest neighbor decoder so an appropriate measure of the size of each power type class is taken into account in the decoding strategy. For such an architecture, we derive ensemble tight second-order asymptotics.
A Perspective for the Implementation of a Path Towards the Factory of the Future: The Italian Case

This work presents a case study analysis aiming at depicting a perspective for the implementation of a path towards the Factory of the Future (FoF) and Industry 4.0 (14.0) in organizations of the Italian manufacturing value chain. Starting from the presentation and framing of related supporting initiatives at national and regional level and a review of current literature on digitalization of manufacturing, the paper analyses and discusses good and best practices along three specific dimensions: investments in FoF/14.0 technologies, ability in perceiving and facing the path towards FoF/14.0, knowledge transfer. A set of business cases addressing different actors of the manufacturing supply chain (large companies and SMEs, technology providers and research and innovation players) has been selected for investigating the challenges related to the implementation of the FoF/14.0 paradigm. The main contribution of this work relates to the identification of best practices highlighting enablers and barriers for the adoption of FoF/14.0 technologies based on a theoretical framework on three dimensions, which have to be considered in close connection for an integrated vision of the digital transformation path.
An instrumental variable method for closed-loop identification of coreless linear motors

This paper presents an instrumental variable (IV) method dedicated to identification of coreless linear motors (CLMs) operating in closed-loop. The dynamics of a CLM can be described as a linear dynamical system preceded by a static input gain that is nonlinearly dependent on the output. The nonlinear dependency on the output makes it challenging to find an appropriate predictor for identification. In this paper, we introduce a linear-in-the-parameter predictor for the CLM dynamics, which is a modification of the nonlinear autoregressive exogenous (NARX) model. It is proven that the IV method using the introduced predictor results in a consistent estimate. In addition, we show that in many applications, the simple NARX predictor, which does not require knowledge of the statistical properties of the output measurement noise, can provide an estimate that is very close to the true parameter. A numerical example is shown for demonstration.
Selective Decrease in Allodynia With High‐Frequency Neuromodulation via High‐Electrode‐Count Intrafascicular Peripheral Nerve Interface After Brachial Plexus Injury

Kilohertz high‐frequency alternating current (KHFAC) electrical nerve stimulation produces a reversible nerve block in peripheral nerves in human patients with chronic pain pathologies. Although this stimulation methodology has been verified with nonselective extrafascicular electrodes, the effectiveness of producing a selective nerve block with more‐selective intrafascicular electrodes has not been well documented. The objective of this study was to examine whether intrafascicular electrodes can block painful stimuli while preserving conduction of other neural activity within the implanted nerve.
Practical Proactive DDoS-Attack Mitigation via Endpoint-Driven In-Network Traffic Control

Volumetric attacks, which overwhelm the bandwidth of a destination, are among the most common distributed denial-of-service (DDoS) attacks today. Despite considerable effort made by both research and industry, our recent interviews with over 100 potential DDoS victims in over 10 industry segments indicate that today’s DDoS prevention is far from perfect. On one hand, few academical proposals have ever been deployed in the Internet; on the other hand, solutions offered by existing DDoS prevention vendors are not silver bullet to defend against the entire attack spectrum. Guided by such large-scale study of today’s DDoS defense, in this paper, we present MiddlePolice, the first readily deployable and proactive DDoS prevention mechanism. We carefully architect MiddlePolice such that it requires no changes from both the Internet core and the network stack of clients, yielding instant deployability in the current Internet architecture. Further, relying on our novel capability feedback mechanism, MiddlePolice is able to enforce destination-driven traffic control so that it guarantees to deliver victim-desired traffic regardless of the attacker strategies. We implement a prototype of MiddlePolice and demonstrate its feasibility via extensive evaluations in the Internet, hardware testbed, and large-scale simulations.
Dual-Loop Controller for LLC Resonant Converters Using an Average Equivalent Model

LLC resonant converters have gained popularity in a wide number of industrial applications due to their high efficiency and power density. Common applications of these converters are battery chargers, electric vehicles, and high-efficiency power supplies, which require tight output voltage regulations. However, traditional averaging techniques employed in pulse width modulation converters cannot be employed for LLCs. As a consequence, designing linear controllers for these types of converters requires complex analysis or applying empirical methods. This paper proposes a simple and straightforward methodology for designing linear controllers for LLC resonant converters. A dual-loop control scheme including an inner current loop and an outer voltage loop is introduced. A simplified second-order equivalent circuit is employed to derive all the relevant equations for designing the compensators. Simulations and experimental results using a 150-W platform are employed to validate the theoretical analysis.
Using Introspection to Collect Provenance in R

Data provenance is the history of an item of data from the point of its creation to its present state. It can support science by improving understanding of and confidence in data. RDataTracker is an R package that collects data provenance from R scripts (https://github.com/End-to-end-provenance/RDataTracker). In addition to details on inputs, outputs, and the computing environment collected by most provenance tools, RDataTracker also records a detailed execution trace and intermediate data values. It does this using R’s powerful introspection functions and by parsing R statements prior to sending them to the interpreter so it knows what provenance to collect. The provenance is stored in a specialized graph structure called a Data Derivation Graph, which makes it possible to determine exactly how an output value is computed or how an input value is used. In this paper, we provide details about the provenance RDataTracker collects and the mechanisms used to collect it. We also speculate about how this rich source of information could be used by other tools to help an R programmer gain a deeper understanding of the software used and to support reproducibility.
Soft Transparency for Point Cloud Rendering

We propose a novel rendering framework for visualizing point data with complex structures and/or different quality of data. The point cloud can be characterized by setting a per-point scalar field associated to the aspect that differentiates the parts of the dataset (i.e. uncertainty given by local normal variation). Our rendering method uses the scalar field to render points as solid splats or semi-transparent spheres with non-uniform density to produce the final image. To that end, we derive a base model for integrating density in (intersecting) spheres for both the uniform and non-uniform setting and introduce a simple and fast approximation which yields interactive rendering speeds for millions of points. Because our method only relies on the basic OpenGL rasterization pipeline, rendering properties can be adjusted in real-time by user. The method has been tested on several datasets with different characteristics, and user studies show that a clearer understanding of the scene is possible in comparison with point splatting techniques and basic transparency rendering.
A Low Overhead Feedback Scheme of Channel Covariance Matrix for Massive MIMO Systems

In this paper, we propose a feedback scheme of a channel covariance matrix with low overhead for massive multiple-input multiple-output systems. The proposed scheme decomposes the channel covariance matrix into the phase and amplitude parts. For the phase part, the element-wise uniform scalar quantization is performed. For the amplitude part, the following feedback information is generated: a bitmap which denotes a sign of difference between adjacent elements, the first value, an increment, and a decrement. To calculate the elements of the amplitude part, starting from the first value, when a bitmap is 1, the increment is added, otherwise the decrement is added. Simulation results show that the feedback overhead of the proposed scheme can be significantly reduced from 1.92% to 48.33% while the performance of mean square error can be maintained with that of the conventional scheme.
Yield Approximation of Analog Integrated Circuits Under Time-Dependent Variability

In this paper an approximation technique for the time-dependent yield of analog integrated circuits is presented. Due to the interaction of aging effects and process variations the yield approximation in early design stages can become very time consuming and hampers the quantity of explorative analysis. The here proposed method is based on the pre-computed analyses of basic analog blocks and uses the small signal behavior in order to approximate the yield. The method is evaluated at the example of a two-stage Miller-OTA.
Movement-Oriented Objectified Organization and Retrieval Approach for Heterogeneous GeoVideo Data

With the wide deployment of the video sensor network and the rapid development of video spatialization technology, the large volume of complex GeoVideo data necessitates improvements in the application efficiency of the GeoVideo database and GeoVideo surveillance system. Traditional storage management approaches focus on the optimization of access to the GeoVideo stream. However, they suffer from poor management of the diverse movement processes contained within it; for example, they cannot support associative queries or comprehensive analysis of multi-type GeoVideo data in complex geographic environments. This paper takes physical movement process in GeoVideo as a new type of object and carries out an objectified organization of the heterogeneous GeoVideo data around it (including the video stream, spatial references, interpretations of the video data, and the overall scene) in a Not only SQL-Structured Query Language (NoSQL-SQL) hybrid GeoVideo database. This paper systematically explores the hybrid spatiotemporal indexes and multi-modal retrieval methods around movement processes, which enrich the query modes of the GeoVideo data. A prototype implementation and experimental analysis are presented to prove the feasibility and effectiveness of this organization and retrieval approach.
Quantifying Coordination in Human Dyads via a Measure of Verticality

Working towards the goal of understanding complex, interactive movement in human dyads, this paper presents a model for analyzing motion capture data of human pairs and proposes measures that correlate with features of the coordination in the movement. Based on deep inquiry of what it means to partner in a motion task, a measure that characterizes the changing verticality of each agent is developed. In parallel a naïve human motion expert provides a qualitative description of the features and quality of coordination within a dyad. Analysis on the verticality measure, the cross-correlation of verticality signals, and deviation of those verticality signals from the trend over time, provides quantitative insight that corroborates the naïve expert's analysis. Specifically, the paper shows that, for four samples of dyadic behavior, these measures provide information about 1) whether two agents were involved in the same dyadic interaction and 2) the level of "resistance" found in these interactions. Future work will test this model over a larger dataset and develop human-robot coordination schemes based on this model.
Fault diagnosis design for heating, ventilation and air conditioning systems

This work presents a framework for fault detection and isolation (FDI) of variable air volume (VAV) boxes, a common component of heating, ventilation and air conditioning (HVAC) systems. To this end, a novel linear causal model based framework for FDI of multiple actuator and multiple sensor faults is designed and implemented. It is shown that the proposed framework produces superior results compared with an alternative statistical model based approach.
The Control Technique for Cascaded H-Bridge Multilevel Converter with Faulty Cells

This paper presents a novel control technique for a cascaded H-bridge (CHB) multilevel converter with faulty cells. The control strategy is based on the neutral shift method with a simple modification of a control block to the CHB faulty operation. The implementation of the method is provided by the line-to-line voltage balance by shaping unbalanced phase shifts of the output voltages. Simulation results confirm the reliable operation of the CHB multilevel converter with faulty cells.
Estimating External Disturbances in UAVs Using Linear and Spin Momenta

This paper deals with the problem of estimating external disturbances acting upon aerial vehicles. First, a dynamic model based on the linear and spin momentum equations of rigid bodies is presented. This model renders the system linear, which is innovative in the area of disturbance estimation affecting UAV. Next, two estimators following the line of already published external disturbance methods are reviewed and adapted to the new model. Also, two Kalman observers that exploit the linearity of the derived model are presented. Finally, all the methods are compared in simulation using two case studies that consider ideal and realistic scenarios.