Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence

A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable “smart” nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this.
Active Shielding of MWCNT Bundle Interconnects: An Efficient Approach to Cancellation of Crosstalk-Induced Functional Failures in Ternary Logic

This study presents an efficient geometry for active shielding of multiwalled carbon nanotube (MWCNT) bundle interconnects which cancels the crosstalk-induced functional failures in ternary logic. In this geometry, MWCNT bundles are used innovatively as shields on both sides of the signal transmission line to reduce the crosstalk effects while maintaining the minimum interconnect pitch. Simulations are performed using HSPICE for intermediate and global interconnects in ternary logic at 14-nm node. The results indicate that the crosstalk-induced functional failure in ternary logic is omitted by using the proposed structure. Moreover, according to the results, our proposed geometry reduces the crosstalk noise peak as compared to the widened MWCNT bundles, distanced MWCNT bundles and Cu interconnects on average by 41%, 44%, and 61% for the intermediate interconnects and 61%, 46%, and 74% for the global interconnects, respectively, without any considerable power consumption penalties.
High temperature stable polymer planar waveguide Bragg grating sensors

We review our work on polymer planar waveguide Bragg grating (PPBG) sensors and demonstrate their potential for high temperature applications. Our sensors are fabricated with a rapid UV-based single writing step (SWS) technique, which allows for the simultaneous inscription of an integrated waveguide and a Bragg grating in polymer planar substrates. The developed SWS technique is applicable to different polymer materials with different material properties and allows a free selection of the planar substrate’s geometry. In this contribution, we demonstrate the SWS technique and report on different PPBG sensor designs and application scenarios for high temperature sensing of up to 160°C as well as temperature referenced strain sensing.
Development of an Interactive System for the Visualization of a Cardiac Model activated by the R-Wave of ECG in a 3D CAVE

Interactive systems have been used to design chemical structures, physical devices, to train the use of surgical instruments, to monitor rehabilitation therapies or to provide educational information through animations. In the present work, an interactive application is proposed for the animated visualization of the functioning of the heart, for educational purposes. A 3D model of the human heart was adopted, and the R-wave of the electrocardiographic signal, registered online, was used to activate the pattern of movement of the model. The animation is displayed in a CAVE system for immersive virtual reality to provide interaction with the user.
An iterative two‐step heuristic for the parallel drone scheduling traveling salesman problem

A recent evolution in urban logistics involves the usage of drones. In this article, we address a heuristic solution of the parallel drone scheduling traveling salesman problem, recently introduced by Murray and Chu. In this problem, deliveries are split between a vehicle and drones. The vehicle performs a classical delivery tour, while the drones are constrained to perform back and forth trips. The objective is to minimize completion time. We propose an iterative two‐step heuristic, composed of: a coding step that transforms a solution into a customer sequence, and a decoding step that decomposes the customer sequence into a tour for the vehicle and trips for the drones. Decoding is expressed as a bicriteria shortest path problem and is carried out by dynamic programming. Experiments conducted on benchmark instances confirm the efficiency of the approach and give some insights on this drone delivery system.
Short-Term and Long-Term Dielectric Characteristics of Molded Compound According to Various Portion of Alumina Trihydrate Micro-Filler

The demand on electric power is expected to increase continuously due to the development of industry and the congestion of cities which requires large capacity and high voltage transmission for electric power system. In addition, electric power of good quality, compacted electric power device, reliable and secure electric system are also required. For these reasons, solid insulation materials are preferred due to its advantage on size reduction and weight lightening. Solid dielectric materials improve the electric breakdown strength by suppresses the initiation and propagation of breakdown. However, the characteristics of solid dielectric materials are apt to influenced by operation voltage, period of use and the condition of internal solid insulation material. Especially, the internal deterioration of solid dielectric due to the increment of operation period in rated voltage condition could cause breakdown of the electric power apparatuses. In this paper, short term and long-term breakdown tests for Bulk Molded Compound (BMC) with various portion of Alumina Trihydrate (ATH) micro filler were carried out to find out the dielectric performance of solid dielectric introduced in Vacuum Circuit Breaker (VCB) product. The long-term breakdown test corresponds to consideration of the operation voltage of VCB to establish the lifetime and insulation reliability of dielectric material. The short-term breakdown tests were conducted in mineral oil with sphere - sphere electrodes for BMC with 0, 25, 40% of ATH. The long-term breakdown test was carried out for the voltage level of 3, 5, 10% of one - minute step - up tests based on the results from the short test breakdown test and the comparison analysis on the time to breakdown has been performed. From the test results, the breakdown voltage of BMC with 25% of ATH specimen was increased compared to specimen with 0% one, and the breakdown voltage of BMC with 40% ATH specimen was slightly higher than 25% one. In case of long term breakdown test, the time to breakdown was reduced in case of BMC with 40% ATH compared to 25% one.
Comparison of muscular strength and balance in athletes with visual impairment and hearing impairment

This study was conducted to compare muscular strength and balance of athletes with visual and hearing impairment. The study was carried out with 20 athletes of national Olympic level sports goalball team and 20 athletes of national Olympic level sports hearing-impaired karate team. Isokinetic muscular strength was assessed by IsoMed 2000 device as concentric-concentric at 60°/sec and 240°/sec. Balance assessment was carried out with the Human Body Equilibrium 360 device. There was no significant difference between groups regarding age, height, weight and body mass index (P>0.05). There were no differences between the dominant and nondominant knee flexion and extension peak torque (PT), % of flexion/extension PT ratio, % of dominant/nondominant PT differences at 60°/sec and 240°/sec velocities (P>0.05). There was no difference between the groups regarding of both leg static balance (P>0.05). However, single leg standing balance was significantly different between groups in favor of athletes with hearing impairment (P<0.001). As a result of our study it was determined that muscular strength and static balance of athletes with visual and hearing impairment were similar, but athletes with visual problems are likely to have lower levels of single leg balance. Strategies to promote single leg balance in athletes with visual impairments are warranted.
Modelling and Performance Analysis of Wireless LAN Enabled by RF Energy Transfer

RF signals can be relied upon for transferring energy to those power-thirsty wireless devices. Thanks to the broadcast nature of wireless channels, dedicated RF signals for delivering information to specific devices can also be received by others for energy harvesting. Coordinating the conventional wireless information transfer and the wireless energy transfer (WET) requires a systematic design in all OSI layers, which yields data and energy integrated communication networks. Based on the classic carrier-sense-multiple-access with collision avoidance protocol, we originally propose a distributed multiple access protocol in the medium access control (MAC) layer for the indoor wireless-local-area-network (WLAN) powered by RF signal-based WET. The operation of our proposed protocol in the WET powered indoor WLAN is modeled by a multi-dimensional Markov chain, which is exploited for deriving the closed-form throughput of the WLAN studied. The simulation results demonstrate the accuracy of our theoretical analysis, which pave the way for the future optimization design in the MAC layer of WET powered indoor WLAN.
Access Control on Internet of Things based on Publish/Subscribe using Authentication Server and Secure Protocol

This study proposes a prototype model from access control security system in the Internet of Things (IoT) that uses the Message Queuing Telemetry Transport (MQTT) protocol as its communication and fog computing as its architecture with the authentication server and secure protocol. In the MQTT protocol, there are security mechanism issues such as publisher (device) authentication and data privacy protection is still not good. This causes the integrity and confidentiality of a data used by the subscriber to be less secure. Device nodes and gateway devices in IoT that become publishers will be authenticated to obtain tokens using authentication server via HTTPS. Tokens are embedding with sensor data inside secure payload format and publish to MQTT broker. Authentication server will perform management and validation credentials on all publishers and secure payload in MQTT broker. In addition, SSL certificates are applied to the MQTT protocol to secure their communications. Based on evaluation and security analysis, the application of access control mechanisms can be implemented on the MQTT protocol and can secure the integrity and confidentiality of data that sent from the device to the cloud over the internet. In the overhead analysis, there is a significant increase in payload because of the payload data sent with the token, but the latency of delivery time, CPU, memory usage is still up to a reasonable limit of not more than 50%.
Türkiye Denizlerinde Kullanılan Pelajik Kılıç Paragatları.

Bu calisma, Turkiye denizlerinde kilic baliklari icin kullanilan pelajik paragatlarin teknik ozellikleri ve avlama yontemlerini rapor etmektedir. Turk kilic balikciliginda toplam 11 farkli pelajik paragat tespit edilmistir. Toplam tekne ve igne sayisi ile paragat uzunlugu sirasiyla 52 tekne, 15448 igne ve 508 km olarak hesaplanmistir
Condition Monitoring and Diagnosis of Outer Raceway Bearing Fault using Support Vector Machine

Fault detection at initial stage and follow-up the monitoring progress are mandatory to avoid the damage of induction motors. If fault not detected at initial stage, it may lead to economic loss or even shutdown of the industry. Some of the commonly occurring bearing faults are scratch, fluting and pitting. From industrial point of view, the possibility of multiple fault must be considered, as a progressive stage of initial single failure. Thus, in the present study, three types of bearing conditions (hole, scratch, hole and scratch) are discussed. First, fast Fourier transform (FFT) is performed using the load current of stator. Next, features of frequency spectrum are extracted and finally diagnosis is carried out using Support Vector Machine (SVM) algorithm. The algorithm is trained using the features of FFT analysis. Finally, accuracy of the diagnosed results is discussed.
Learning to predict the cosmological structure formation

Significance To understand the evolution of the Universe requires a concerted effort of accurate observation of the sky and fast prediction of structures in the Universe. N-body simulation is an effective approach to predicting structure formation of the Universe, though computationally expensive. Here, we build a deep neural network to predict structure formation of the Universe. It outperforms the traditional fast-analytical approximation and accurately extrapolates far beyond its training data. Our study proves that deep learning is an accurate alternative to the traditional way of generating approximate cosmological simulations. Our study shows that one can use deep learning to generate complex 3D simulations in cosmology. This suggests that deep learning can provide a powerful alternative to traditional numerical simulations in cosmology. Matter evolved under the influence of gravity from minuscule density fluctuations. Nonperturbative structure formed hierarchically over all scales and developed non-Gaussian features in the Universe, known as the cosmic web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and use a large ensemble of computer simulations to compare with the observed data to extract the full information of our own Universe. However, to evolve billions of particles over billions of years, even with the simplest physics, is a daunting task. We build a deep neural network, the Deep Density Displacement Model (D3M), which learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel’dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Our extensive analysis demonstrates that D3M outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. We also show that D3M is able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. Our study proves that deep learning is a practical and accurate alternative to approximate 3D simulations of the gravitational structure formation of the Universe.
Fundamental Study on Ambiguity Suppression of Automotive Squint-Mode MW-SAR by Using SIMO Radar

Automatic operating system for vehicle has been widely studied. Especially, Millimeter wave (mmWave) automotive radar has been attracting attentions. Synthetic Aperture Radar (SAR) by using mmWave radar can obtain high-resolution 2-D images. Depending on the speed of the vehicle and the performance of the radar, grating lobes are generated in observing area. Using Single Output Multiple Output (SIMO) radar is a method of solving this problem. In this paper, we provide some computer simulation results and show ambiguity suppression of grating lobes by using SIMO radar.
MicroRNA‑940 promotes cell proliferation and invasion of glioma by directly targeting Kruppel‑like factor 9.

MicroRNA‑940 (miR‑940) has been extensively studied in the pathogenesis of numerous types of human cancer; however, the expression pattern, roles and molecular mechanisms underlying the regulatory actions of miR‑940 in glioma remain unknown. The present study aimed to further investigate miR‑940 by studying its expression, roles and mechanisms of action in glioma. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was used to detect miR‑940 expression in glioma tissues and cell lines. The regulatory effects of miR‑940 in glioma cell proliferation and invasion were determined using MTT and cell invasion assays. Bioinformatics analyses was performed to identify the potential target of miR‑940, which was further confirmed by luciferase reporter assay, RT‑qPCR and western blot analysis. In the present study, significantly increased miR‑940 expression levels were observed in glioma tissues and cell lines compared with normal brain tissues and normal human astrocytes, respectively. Decreased miR‑940 expression levels attenuated glioma cell proliferation and invasion in vitro. Kruppel‑like factor 9 (KLF9) was predicted as a potential target of miR‑940. Further assays demonstrated that miR‑940 negatively regulated KLF9 expression in glioma cells by directly targeting the 3'‑untranslated regions of KLF9. Additionally, KLF9 expression was downregulated in glioma tissues and was inversely correlated with miR‑940. Furthermore, KLF9 knockdown was able to rescue the effects of miR‑940 on glioma cell proliferation and invasion. The results of the present study suggest that miR‑940 may function as an oncogene in glioma by targeting KLF9 and may be a considered a therapeutic target for the treatment of gliomas.
Primary bone lymphoma: tunisian multicentric retrospective study about 32 cases.

AIM To report the epidemiologic, clinical features, treatment modalities and prognosis of primary bone lymphomas (PBL) within a retrospective Tunisian series. METHODS We collected cases of histologically confirmed PBL in 3 medical oncology departments from northern and central Tunisia andwe analyzed their characteristics. RESULTS From January 1990 to July 2014, we collected 32 patients with histologically proven PBL, having a median age of 53 years. They affected mainly the long bones and diagnosed at early stages. 91% of the PBL were large cell B lymphoma. All patients received CHOP or CHOP-likeChemotherapy (CT), associated to Rituximab in the last 9 cases, with 14/32 patients received loco-regional radiotherapy and one patient had a resection-reconstruction surgery. We observed 90% of objective responses after primary CT.With a median follow-up of 38.5 months (1 to 192), the 5-year overall survival OS rate was 63%. 18 patients relapsed and 14 remain alive in complete remission. CONCLUSION PBL remains a rare disease lately diagnosed in Tunisia. However, most of the patients had early stages tumors. Furthermore, the efficacy of CT and introduction of Rituximab leads to a high rate of complete/objective responses, improving the survival rate.
Axes and Coordinate Systems Representations for Immersive Analytics of Multi-Dimensional Data

Axes are the main components of coordinate systems representations. They play a critical role for the visual analysis of multi-dimensional data. However their representation seems to have always be considered self evident, with oriented lines crossing at an origin, completed with labels such as ticks and names. Such classical representation show limits when it comes 3D visualization and immersive analytic (IA), mainly because orthogonal projection of points on linear axes is hard in a 3d environment, and because the user can move therefore the axes can get out of his field of view. In this paper we propose a task-based definition of axes and coordinate systems representation, as well as a tentative design space for coordinates systems representation in immersion. We also present an exploratory user study we carried out to compare three grid-based representations of coordinate systems for multidimensional data analysis with 3D scatterplots.
Sparse Signal Processing and Statistical Inference for Internet of Things

Data originating from many devices within the Internet of Things (IoT) framework can be modeled as sparse signals. Efficient compression techniques of such data are essential to reduce the memory storage, bandwidth, and transmission power. In this thesis, I develop some theory and propose practical schemes for IoT applications to exploit the signal sparsity for efficient data acquisition and compression under the frameworks of compressed sensing (CS) and transform coding. In the context of CS, the restricted isometry constant of finite Gaussian measurement matrices is investigated, based on the exact distributions of the extreme eigenvalues of Wishart matrices. The analysis determines how aggressively the signal can be sub-sampled and recovered from a small number of linear measurements. The signal reconstruction is guaranteed, with a predefined probability, via various recovery algorithms. Moreover, the measurement matrix design for simultaneously acquiring multiple signals is considered. This problem is important for IoT networks, where a huge number of nodes are involved. In this scenario, the presented analytical methods provide limits on the compression of joint sparse sources by analyzing the weak restricted isometry constant of Gaussian measurement matrices. Regarding transform coding, two efficient source encoders for noisy sparse sources are proposed, based on channel coding theory. The analytical performance is derived in terms of the operational rate-distortion and energy-distortion. Furthermore, a case study for the compression of real signals from a wireless sensor network using the proposed encoders is considered. These techniques can reduce the power consumption and increase the lifetime of IoT networks. Finally, a frame synchronization mechanism has been designed to achieve reliable radio links for IoT devices, where optimal and suboptimal metrics for noncoherent frame synchronization are derived. The proposed tests outperform the commonly used correlation detector, leading to accurate data extraction and reduced power consumption.
A Data-Driven Metric of Hardness for WSC Sentences

The Winograd Schema Challenge (WSC) — the task of resolving pronouns in certain sentences where shallow parsing techniques seem not to be directly applicable — has been proposed as an alternative to the Turing Test. According to Levesque, having access to a large corpus of text would likely not help much in the WSC. Among a number of attempts to tackle this challenge, one particular approach has demonstrated the plausibility of using commonsense knowledge automatically acquired from raw text in English Wikipedia. Here, we present the results of a large-scale experiment that shows how the performance of that particular automated approach varies with the availability of training material. We compare the results of this experiment with two studies: one from the literature that investigates how adult native speakers tackle the WSC, and one that we design and undertake to investigate how teenager non-native speakers tackle the WSC. We find that the performance of the automated approach correlates positively with the performance of humans, suggesting that the performance of the particular automated approach could be used as a metric of hardness for WSC instances.