Miracle-dz/UniFAR-FTUs huggingface dataset:
MM-wave Initial Access: A Context Information Overview

The attractive features of millimeter-wave (mm-wave) technologies in the forthcoming 5G networks entail a rich set of network access challenges. These technologies are characterized by high-gain array antennas to overcome the huge attenuations, this requires to resort to directional transmissions during every network operation. The initial access phase is one of the most critical, because, if not properly managed, it can introduce a non-negligible access delay caused by multiple transmission attempts along several directions. We believe that contextual information about user and network conditions can boost this discovery phase. In this paper, we investigate how differently-rich context information can impact on the duration of the initial cell access. We propose several initial access procedures that can exploit different available information and cope with the presence of obstacles within the service area. Finally, relying on the contextual information on past access attempts, we develop a recommendation system based on machine-learning techniques, which, by processing this information, can derive the best directions to explore to connect incoming users.
Emergence of Monkeypox as the Most Important Orthopoxvirus Infection in Humans

Monkeypox is an emerging zoonotic disease recognized as the most important orthopoxvirus infection in humans in the smallpox post-eradication era. The clinical presentation of monkeypox is similar to the one of smallpox. The case fatality rate of monkeypox (10%) lies between the case fatality rate of variola major (30%) and variola minor (1%). The disease is endemic in the Democratic Republic of the Congo, but other countries of Central and West Africa either reported cases of monkeypox in humans or circulation in wildlife. The disease was also imported once into the USA. The disease has always been considered rare and self-limiting, however recent sporadic reports suggest otherwise. Unfortunately, the collected data is limited, dispersed and often incomplete. Therefore, the objective of this review is to trace all reported human monkeypox outbreaks and relevant epidemiological information. The frequency and geographical spread of human monkeypox cases have increased in recent years, and there are huge gaps in our understanding of the disease's emergence, epidemiology, and ecology. The monkeypox virus is considered a high threat pathogen causing a disease of public health importance. Therefore, there is an urgent need to focus on building surveillance capacities which will provide valuable information for designing appropriate prevention, preparedness and response activities.
Design of Nanostructured Graphene Based RF Complementary Split Ring Resonator Gas Sensor for Detection of Nitrogen Dioxide

The complementary split ring resonator (CSRR) incorporated with the nanostructured graphene is proposed to design an efficient RF gas sensor. The nanostructured graphene is synthesized using the nitrogen rich cow urine, which can also be termed as the nitrogen doped graphene (NGn). The CSRR is etched on back side of the microstrip line designed on a FR4 substrate, and is excited by the electric field from top of the line. In this study, the nitrogen dioxide (NO2) gas is detected by tracking the shift in the resonant frequency of the measured transmission coefficient (S21) of the designed sensor when it is exposed to the gaseous environment. The gas sensing capability of designed NGn-CSRR sensor is tested at room temperature using 50–200 ppm of NO2. A shift in the resonant frequency of 51 MHz is observed even for as low as 50 ppm of NO2 molecules interacting with NGn surface of the designed sensor. The detection of very low traces of NO2 by the proposed NGn-CSRR RF sensor shows its application for NO2 sensing in harsh environment.
Toxic Action Reevaluation of Okadaic Acid, Dinophysistoxin-1 and Dinophysistoxin-2: Toxicity Equivalency Factors Based on the Oral Toxicity Study

Background/Aims: Okadaic acid (OA) and the structurally related compounds dinophysistoxin-1 (DTX1) and dinophysistoxin-2 (DTX2) are marine phycotoxins that cause diarrheic shellfish poisoning (DSP) in humans due to ingestion of contaminated shellfish. In order to guarantee consumer protection, the regulatory authorities have defined the maximum level of DSP toxins as 160 µg OA equivalent kg-1 shellfish meat. For risk assessment and overall toxicity determination, knowledge of the relative toxicities of each analogue is required. In absence of enough information from human intoxications, oral toxicity in mice is the most reliable data for establishing Toxicity Equivalence Factors (TEFs). Methods: Toxins were administered to mice by gavage, after that the symptomatology and mice mortality was registered over a period of 24 h. Organ damage data were collected at necropsy and transmission electron microscopy (TEM) was used for ultrastructural studies. Toxins in urine, feces and blood were analyzed by HPLC-MS/MS. The evaluation of in vitro potencies of OA, DTX1 and DTX2 was performed by the protein phosphatase 2A (PP2A) inhibition assay. Results: Mice that received DSP toxins by gavage showed diarrhea as the main symptom. Those toxins caused similar gastrointestinal alterations as well as intestine ultrastructural changes. However, DSP toxins did not modify tight junctions to trigger diarrhea. They had different toxicokinetics and toxic potency. The lethal dose 50 (LD50) was 487 µg kg-1 bw for DTX1, 760 µg kg-1 bw for OA and 2262 µg kg-1 bw for DTX2. Therefore, the oral TEF values are: OA = 1, DTX1 = 1.5 and DTX2 = 0.3. Conclusion: This is the first comparative study of DSP toxins performed with accurate well-characterized standards and based on acute toxicity data. Results confirmed that DTX1 is more toxic than OA by oral route while DTX2 is less toxic. Hence, the current TEFs based on intraperitoneal toxicity should be modified. Also, the generally accepted toxic mode of action of this group of toxins needs to be reevaluated.
Can routine peripheral blood counts like neutrophil-to-lymphocyte ratio be beneficial in prediagnosis of testicular cancer and its stages?

Background: The purpose of this study was to assess the diagnostic role of preoperative hematological parameters, especially neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume (MPV) in germ cell testicular malignancies and their prediagnostic role in staging of testicular cancer. Materials and Methods: In this cross-sectional retrospective study, we analyzed 39 patients who underwent radical orchiectomy due to a testicular cancer (Group 1) and 82 patients on whom varicocelectomy procedure was performed as control group (Group 2) between January 2006 and January 2016 in our clinic. Evaluation of the preoperative hematological parameters in both groups and also the subgroups in malignancy group according to histopathological stages was conducted in this study. Results: When the hematological parameters were compared, a statistically significant difference was found between the two groups in terms of neutrophil counts, NLR, PLR, and MPV. NLR and PLR were significantly higher and MPV was significantly lower in testicular cancer group compared to the control group. NLR was 3.1 ± 1.4 and 2.0 ± 1.5, PLR was 141.3 ± 53.2 and 115.7 ± 44.8, and MPV was 8.9 ± 1.0 and 9.3 ± 1.1 for testicular cancer and control groups, respectively (P < 0.05). Furthermore, differences were observed between only mean corpuscular volume, mean corpuscular hemoglobin, and MPV (P < 0.05) in different stages of malignancy. Conclusion: In accordance with these findings, NLR, PLR, and MPV may be helpful for prediagnosis of testicular malignancies. Hematological parameters will become important in the preoperative assessment for those patients.
An Intelligence-Driven Security-Aware Defense Mechanism for Advanced Persistent Threats

Combined with many different attack forms, advanced persistent threats (APTs) are becoming a major threat to cyber security. Existing security protection works typically either focus on one-shot case, or separate detection from response decisions. Such practices lead to tractable analysis, but miss key inherent APTs persistence and risk heterogeneity. To this end, we propose a Lyapunov-based security-aware defense mechanism backed by threat intelligence, where robust defense strategy-making is based on acquired heterogeneity knowledge. By exploring temporal evolution of risk level, we introduce priority-aware virtual queues, which together with attack queues, enable security-aware response among hosts. Specifically, a long-term time average profit maximization problem is formulated. We first develop risk admission control policy to accommodate hosts’ risk tolerance and response capacity. Under multiple attacker resources, defense control policy is implemented on two-stage decisions, involving proportional fair resource allocation and host-attack assignment. In particular, distributed auction-based assignment algorithm is designed to capture uncertainty in the number of resolved attacks, where high-risk host-attack pairs are prioritized over others. We theoretically prove our mechanism can guarantee bounded queue backlogs, profit optimality, no underflow condition, and robustness to detection errors. Simulations on real-world data set corroborate theoretical analysis and reveal the importance of security awareness.
An implementation of graph mining to find the group evolution in communication data record

In this paper, we show how to utilize communication transaction data to generate useful knowledge using graph mining. Knowledge in question is a model that can be used to know the group contained in an organization and the evolution of the group from time to time. We also show how to choose appropriate algorithms to deal with problems related to data conditions, how to analyze data, how to perform suitable preprocessing, graphs modeling that represents issues and some adaptations to produce the best models. The results of the implementation using the Facetnet algorithm, show that graph mining algorithm can be used to process communication data and build a meaningful group evolution model.
Optical coherence tomography angiography analysis of macular flow density in glaucoma

Modifications in ocular blood flow may play a significant role in glaucoma development. Optical coherence tomography angiography (OCT‐A) is based on the detection and analysis of the reflection behaviour of motion in a static environment and therefore is able to quantify the retinal flow density. We used this new technology to examine the density of the active flow vasculature in the macular area in glaucoma patients compared to healthy patients.
Switching Stochastic Nonlinear Systems With Application to an Automotive Throttle

This paper presents results to assure the almost sure stability of switching stochastic nonlinear systems. The switching rule governing the parameters of the system is driven by independent and identically distributed random variables. In this scenario, we prove that the switching nonlinear system is almost surely stable when appropriate matrices have spectral radius less than one. The result is particularly useful for applications, as shown in the paper by the application for an automotive electronic throttle device. The stability result was used to design a real-time controller for the automotive throttle device, and the experimental data confirm the usefulness of our approach.
A History of Drug Discovery for Treatment of Nausea and Vomiting and the Implications for Future Research

The origins of the major classes of current anti-emetics are examined. Serendipity is a recurrent theme in discovery of their anti-emetic properties and repurposing from one indication to another is a continuing trend. Notably, the discoveries have occurred against a background of company mergers and changing anti-emetic requirements. Major drug classes include: (i) Muscarinic receptor antagonists–originated from historical accounts of plant extracts containing atropine and hyoscine with development stimulated by the need to prevent sea-sickness among soldiers during beach landings; (ii) Histamine receptor antagonists–searching for replacements for the anti-malaria drug quinine, in short supply because of wartime shipping blockade, facilitated the discovery of histamine (H1) antagonists (e.g., dimenhydrinate), followed by serendipitous discovery of anti-emetic activity against motion sickness in a patient undergoing treatment for urticaria; (iii) Phenothiazines and dopamine receptor antagonists–investigations of their pharmacology as “sedatives” (e.g., chlorpromazine) implicated dopamine receptors in emesis, leading to development of selective dopamine (D2) receptor antagonists (e.g., domperidone with poor ability to penetrate the blood-brain barrier) as anti-emetics in chemotherapy and surgery; (iv) Metoclopramide and selective 5-hydroxytryptamine3 (5-HT3) receptor antagonists–metoclopramide was initially assumed to act only via D2 receptor antagonism but subsequently its gastric motility stimulant effect (proposed to contribute to the anti-emetic action) was shown to be due to 5-hydroxytryptamine4 receptor agonism. Pre-clinical studies showed that anti-emetic efficacy against the newly-introduced, highly emetic, chemotherapeutic agent cisplatin was due to antagonism at 5-HT3 receptors. The latter led to identification of selective 5-HT3 receptor antagonists (e.g., granisetron), a major breakthrough in treatment of chemotherapy-induced emesis; (v) Neurokinin1 receptor antagonists–antagonists of the actions of substance P were developed as analgesics but pre-clinical studies identified broad-spectrum anti-emetic effects; clinical studies showed particular efficacy in the delayed phase of chemotherapy-induced emesis. Finally, the repurposing of different drugs for treatment of nausea and vomiting is examined, particularly during palliative care, and also the challenges in identifying novel anti-emetic drugs, particularly for treatment of nausea as compared to vomiting. We consider the lessons from the past for the future and ask why there has not been a major breakthrough in the last 20 years.
A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays

The wide proliferation of drones has posed great threats to personal privacy and public security, which makes it urgent to monitor the drones in the sensitive areas. Drone surveillance using acoustic arrays, as a promising method, could localize and track the drones effectively. In this paper, we develop a systematic method for drone localization and tracking by using acoustic arrays. Specifically, we develop a time difference of arrival (TDOA) estimation algorithm based on Gauss priori probability density function to overcome the multipath effect and the low signal-to-noise ratio (SNR), and design a localization method by making full use of the TDOA estimation results, followed by tracking the drone by Kalman filter. Finally, field experiments are carried out to verify the effectiveness and performance of the proposed methods. The results show that they can achieve good performance in drone localization and tracking.
Energy Routers for Residential AC Nano-PV Systems: Control and Security Considerations

Residential AC Nano-PV Systems (NPV) are less than 10 kW solar PV systems that can generate, store, control and import or export electrical power from/to the Grid. At the core of these systems are Energy Routers which are hardware/software components that allow control of the system's generators (e.g., solar PV panels), energy storage devices, and loads. In this paper, we review AC NPV design considerations for residential systems, including short-term load prediction for constant power generation, and security issues. Also, we note some of the upcoming technologies of interest in this application such as blockchain.
An In-Vehicle Infotainment Platform for Integrating Heterogeneous Networks Interconnection

In order to effectively manage the information exchange between telematics equipment and various terminal equipment in a vehicle. This paper proposes a platform, which uses an in-vehicle infotainment (IVI) system as a heterogeneous networks interconnection gateway. The proposed platform integrates the power line communication (PLC), the visible light communication (VLC), the CAN Bus, and the other related heterogeneous networks. Hence, the platform can be used as a heterogeneous networks coordinator, which is a key role of the intelligent transportation system applications. As a result, with the proposed platform, a flexible network can be organized to achieve vehicle-to-vehicle (V2V) communication, and therefore to provide traffic safety related application services.
Learning to Hash for Large-Scale Medical Image Retrieval

Hashing is a popular approach for performing computationally efficient approximate nearest neighbor search by means of encoding data items into sequence of bits, such that the nearest neighbor search in the coding space is efficient and accurate. This thesis explores aspects of code-consistent training of hashing forests and deep learning models for end-to-end learning of hash codes and demonstrates that such hashing models can be leveraged to perform efficient and accurate large-scale content-based medical image retrieval.
Secondary Frequency Regulation Strategy With Fuzzy Logic Method and Self-Adaptive Modification of State of Charge

Battery storage has been adopted to coordinate with generators in automatic generation control (AGC). In this paper, a control strategy of battery storage involved in secondary frequency regulation is proposed. Through the analysis of the control mode based on area control error signal and the control mode based on area regulation requirement signal in AGC, the integrated control strategy was developed, and the switch timing and the output depth of the battery storage were defined. That is, when the system frequency is deteriorated and the frequency deviation $\vert \Delta f \vert $ is large, according to the fuzzy logic method, the output power of the battery storage will be smoothened on the basis of the state of charge (SOC). When the system frequency is normal, the frequency deviation $\vert \Delta f \vert $ will be small. The remaining frequency regulation capacity of generators can be used to improve the SOC. Furthermore, according to the defined frequency regulation evaluation indexes, MATLAB/Simulink was used to simulate the battery storage coordinating generators for secondary frequency regulation. A typical continuous disturbance was selected in the simulation. The results of the simulation showed that the proposed strategy could enhance the effect of frequency regulation, improve the SOC of the battery storage, and increase the utilization rate of the generator set.
Disaggregation for Networked Power Systems

Electricity data such as supply, demand, prices, and line flows, are sensitive. Utility companies, understandably do not want to, in fact often cannot, make this data publicly available. However, such data is critical in many fundamental areas in power systems research. As a compromise, aggregated data sets are sometimes made available. In such a setting it may be the case that data is aggregated over a geographical region and time. This forces researchers to try and “invert” the data to obtain dis aggregated data sets. In this paper we rigorously formulate the disaggregation problem for networked power systems and present two algorithms that provide solutions to the DC version of the problem. We show that it is possible to invert the data, but that does not imply that ground truth solutions are obtained, thus the utility companies maintain a notion of privacy. The aim of this paper is to highlight the potential benefits to both the research community and utility companies of releasing aggregated data.
Passive ISAR for Maritime Target Imaging: Experimental Results

The present work illustrates the Fraunhofer FHR contribution to the MAPIS project field trials held in Livorno in June 2017. The MAPIS project, funded by EDA, aims at demonstrating radar imaging capability of passive radar systems in a (military) maritime scenario. In particular, two different Passive Bistatic Radar (PBR) systems, one exploiting Digital Video Broadcasting - Terrestrial (DVB-T) signals, the other one exploiting Digital Video Broadcasting - Satellite (DVB-S) signals have been used to experimentally assess the imaging performance. This paper describes the systems developed by Fraunhofer FHR and used during the trials. In addition, a small overview of the trial itself is provided together with some preliminary results concerning the passive radar imaging.
Underwater multi-focus image fusion based on sparse matrix

Due to the limited focus range of optical imaging system, and the locations or focus of different objects in the same scene are different, multiple objects cannot be focused at the same time. In order to solve this problem and make the underwater image clearer, we propose a fusion method based on the sparse matrix in this paper. Firstly, we transform the source image into sparse image by sparse transform and get the clearity of the image based on the sparsity. Then, the clearity image will be segmented into focus regions. After that, the focus regions and non-focus regions are fused respectively based on different fusion algorithms. Finally, the focus regions and non-focus regions are combined to get the enhanced image. The experiments in the end show that the fusion method we proposed in this paper has higher information entropy, correlation entropy, standard deviation, and average gradient, so it can enhance the underwater multi-focus image and can be applied to the underwater object detection.
Utilizing Power Management and Timing Slack for Low Power in High-Level Synthesis

As the design size continues to increase, low power has become a very important concern. In the high-level synthesis stage, operation scheduling is critical for circuit performance. Previous algorithms do not consider power management and timing slack (operation delay selection) at the same time. Different from previous works, in this paper, we utilize power management and timing slack as possible to reduce the total power under the overall latency constraint. We propose an integer linear programming (ILP) approach to combine operation scheduling, power management, and timing slack selection in order to reduce the total power. Benchmark circuits show that our ILP approach has a significant improvement.
Hand Shape Recognition Using Very Deep Convolutional Neural Networks

This work examines the application of modern deep convolutional neural network architectures for classification tasks in the sign language domain. Transfer learning is performed by pre-training the models on the ImageNet dataset. After fine-tuning on the ASL fingerspelling and the 1 Million Hands datasets the models outperform state-of-the-art approaches on both hand shape classification tasks. Introspection of the trained models using Saliency Maps is also performed to analyze how the networks make their decisions. Finally, their robustness is investigated by occluding selected image regions.