Miracle-dz/UniFAR-FTUs huggingface dataset:
The Place of Information and Communication Technologies in Curriculum Design and Development

With the introduction of more modern instructional practices, the traditional approach to learning and teaching is becoming ever more obsolete. Education practitioners all over the world have been calling for educational institutions, in both developed and developing countries, to improve pedagogical practices as a means of significantly enhancing learner experiences and outcomes in the learning-teaching process. There is evidence that rapid technological changes are taking place in the educational landscape and that the utilisation of Information and Communication Technologies in the instructional process is becoming more and more mandatory. Bearing in mind the afore-mentioned, the objective of this discussion is to underscore the place of Information and Communication Technologies in curriculum design and development. The focus of this discussion and review of literature is on curriculum and what it entails, the importance of curriculum, curriculum design and development, and ICTs and curriculum design and development (which looks at learning theories and ICT-based curricula, integration of ICTs into educational curricula, integration of ICTs into foreign language curricula, and the need for ICT-based curricula). Special consideration is given to ICT-based foreign language curricula because of the increasing importance of language didactics and foreign language education today. Information and Communication Technologies should be considered and integrated into educational curricula because they can revolutionise instructional practices and stimulate pedagogical innovation; foster learner engagement; cater for student learning diversity; promote learner independence and autonomy; increase learner interaction and communication, and support intellectual expressiveness and creativity of learners, application, and lifelong learning.
A 2–18 GHz Compressed Sensing Receiver With Broadband LO Chain in 0.13- $\mu$ m BiCMOS

We present an integrated 2–18 GHz four-channel compressed sensing receiver (CSRX), which performs a <inline-formula> <tex-math notation="LaTeX">$128.5\times $ </tex-math></inline-formula> sub-Nyquist acquisition with 36-Gb/s pseudorandom bit sequence (PRBS) generators in 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> BiCMOS. The integrated four-channel CSRX consists of broadband receiver chains, each of which includes a distributed RF buffer that operates as an active balun at 2–18 GHz, followed by a double-balanced passive mixer for high local-oscillator (LO)-to-intermediate frequency (IF) isolation at the IF band, and a 700-MHz variable gain amplifier (VGA). Four independent 36-Gb/s PRBS generators are integrated to perform the compressed sensing by mixing the PRBS signal as the random modulator. To deliver the generated wideband PRBS signal to the mixer, the LO driver conducts band-equalization by applying a shunt-shunt feedback amplifier to compensate for the high-frequency degradation of the PRBS LO tones through the LO generation stage. The chip consumes 195 mA per channel with a 2.5-V supply.
Enabling Computational Storage Through FPGA Neural Network Accelerator for Enterprise SSD

Computational storage is an emerging concept in big data scenario where the demand to process ever-growing storage workloads is outpacing traditional compute server architectures. To enable this paradigm there is a call for developing accelerators that off-load some of the management routines that are usually demanded to the smartness inside the storage. For enterprise solid-state drives (SSD) this translates into a dedicated hardware that exploits the interconnection fabric of the host with the goal of improving SSD reliability/performance. In this brief, we have developed an field-programmable gate array-based neural network accelerator for the moving read reference shift prediction in enterprise SSD. The accelerator high prediction accuracy (up to 99.5%), low latency (6.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> per prediction), and low energy consumption (19.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula>) opens up unprecedented usage models in the storage environment.
Seismic loss and damage in light‐frame wood buildings from sequences of induced earthquakes

Activities related to oil and gas production, especially deep disposal of wastewater, have led to sequences of induced earthquakes in the central United States. This study aims to quantify damage to and seismic losses for light‐frame wood buildings when subjected to sequences of induced, small to moderate magnitude, events. To conduct this investigation, one‐ and two‐story multifamily wood frame buildings are designed, and their seismic response dynamically simulated using three‐dimensional nonlinear models, subjected to ground motion sequences recorded in induced events. Damage is quantified through seismic losses, which are estimated using the FEMA P‐58 methodology. Results show that at levels of shaking experienced in recent earthquakes, minor damage, consisting of cracking of interior finishes and nonstructural damage to plumbing and heating, ventilation, and air conditioning systems, is expected, which is consistent with observed damage in these events. The study also examines how expected losses and building fragility will accumulate and/or change over a sequence of earthquakes. Results indicate that damage quantified in terms of absorbed hysteretic energy tended to accumulate over the sequences; this damage corresponds to elongation or widening of cracks. However, fragility is not significantly altered by damage in a preceding event, meaning structures are not becoming more vulnerable due to existing damage. In addition, sequences of events do not change losses if the building is only repaired once at the end of the sequence, as the worsening of damage does not alter repair actions. If repairs are conducted after each event, though, total seismic losses can increase greatly from the sequence.
Investigation Into Multi-Layer Fractional-Slot Concentrated Windings With Unconventional Slot-Pole Combinations

Fractional-slot concentrated windings (FSCWs) are an attractive option for the design of synchronous permanent-magnet machines. It is commonly assumed in the existing literature that a symmetrical three-phase FSCW is feasible only on a condition that the number of slots Z is an integer multiple of three times the maximum common divisor between Z and the number of pole pairs p. Slot-pole combinations satisfying this rule can be defined conventionally, the others unconventionally. In contrast to the common belief, this paper shows that, using a multi-layer arrangement, it is possible to synthetize a symmetrical FSCW having unconventional slot-pole combinations. A general design methodology for this purpose is presented and validated by finite element analysis. The pros and contras of FSCWs with unconventional slot-pole combinations are examined. Finally, the application of an unconventional FSCW to a shipboard surface permanent-magnet machine prototype is presented to illustrate the possible practical convenience of this kind of winding and tests on the prototype are reported for experimental validation.
O-Band Silicon Photonic Transmitters for Datacom and Computercom Interconnects

Today, the datacenter ecosystems are fueling the demand for novel transmitter (ΤΧ) technologies complying with the off-board, on-board, and chip-to-chip computing needs. This has set a new class of requirements for the TX infrastructure that should now offer multiple credentials, namely: high-speed, O-band operation for avoiding dispersion compensation in long distances, wavelength-division multiplexing (WDM) capabilities for higher throughput and multicasting/broadcasting support, and tight co-packaging with low-power electronics. Silicon (Si) photonic TXs have been extensively studied toward high-speed and WDM TX engines targeting mainly C-band. Only a limited number of Si-Pho O-band TXs have been reported, however with ≤32 Gb/s/channel line-rate capabilities and with a WDM portfolio that has not been fully explored yet. In this paper, we introduce a novel silicon photonic high-speed O-band TX hardware platform that can meet the current datacom and computercom interconnect requirements. We demonstrate a ring modulator (RM) based four-channel WDM TX at 4 × 40 Gb/s non-return-to-zero (NRZ) operation that supports wavelength parallelism in unicast operation but can also pave the way toward WDM TX engines for the post-100 GbE TX era. Moreover, we present a broadband Si Mach–Zehnder modulator employed in a WDM modulation scheme of 2 × 25 Gb/s NRZ signals and demonstrate multicasting when combined with a 8 × 8 passive arrayed waveguide grating router (AWGR) wavelength router, addressing the broadcasting needs of traffic usually encountered in cache-coherent multisocket settings. Finally, we further demonstrate the tight synergy of O-band Si-RM modulators with high-speed CMOS electronics, presenting an RM-based TX assembly prototype employing a fully depleted silicon-on-insulator CMOS driver, delivering 50-Gb/s NRZ operation.
Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.
Analysis and Calculation of Radial Electromagnetic Force of Circular Winding Brushless DC Motor

The circular winding brushless direct current (CWBLDC) motor is a special type of multiphase permanent magnet motors with nonsinusoidal electromotive forces and circularly connected phase windings. The motor does not rely on conventional pulsewidth-modulation inverters and has high torque density and low torque ripple, making it a promising choice for high-power and high-performance applications, such as marine propulsions. This paper studies the radial electromagnetic force of CWBLDC motors. The effect of slot-pole combinations on harmonic contents of the radial force density is analytically derived. Moreover, two computationally efficient numerical methods to calculate the radial electromagnetic force density distribution are proposed based on the space-time symmetry of CWBLDC motors and the superposition principle of the magnetic field, respectively. In order to validate the analysis, vibration testing of a 10-kW CWBLDC prototype is performed.
Fault Diagnosis of Induction Motor Using Convolutional Neural Network

Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.
Uncertainty Analysis Using Fuzzy Transformation Method: An Application in Power-Flow Studies

This paper is concerned with a fuzzy analysis of power-flow (PF) involving uncertainties of load demands and network parameters. The crux of this paper is to propose an advanced fuzzy arithmetic. Fuzzy transformation method merges with backward–forward sweep in order to evaluate the contribution and propagation of uncertainty in IEEE 33-bus and 69-bus distribution systems. Results are validated by true intervals and random ranges. To determine true intervals, Global Optimization Problems (GOPs) are defined and solved through derivative-based and free techniques. To estimate random ranges, Monte-Carlo Simulations (MCSs) are employed. Our findings confirm that the sharpness of fuzzy intervals, tractability of computations, and applicability of possibility distributions. Following scenario-based evaluations, this paper discusses new implications of power losses, voltage profiles, optimal re-configuration, feeder extension, and reactive power compensation so that results would be beneficial to system planners and operators. Altogether, this paper provides a blueprint for a new way to handle uncertainties in a wide variety of power system problems without global optimization, linearization, and randomized simulations.
Ontology-based model-driven design of distributed control applications in manufacturing systems

Integration of system design models with software design has been employed as a common paradigm for model-driven design of control applications of distributed automation systems. However, there is still a lack of efficient methods to automatically project the system design to the control dimension. In this study, ontology modelling and reasoning is exploited to bridge the system design in SysML and control application design in IEC 61499 specific to the manufacturing domain. First, a manufacturing ontology (MFO) is formally defined, based on which the system design can be enriched and verified. Second, a distributed control ontology (DCO) is proposed to correlate the enriched system design and control application design such that automated inference between them can be achieved. Finally, an IEC 61499 model is generated from the platform-independent control application design in DCO. The approach is implemented by an ontology-based framework leveraging semantic web technologies. A CNC bending machine is used as a case study to illustrate the workflow and potential usage of the generated models.
An Analytical Model of Single-Event Transients in Double-Gate MOSFET for Circuit Simulation

In this paper, a physics-based bias-dependent model of single-event transients (SETs) in double-gate (DG) MOSFET suitable for circuit simulation is presented. The existing approaches that use double exponential and dual double-exponential current sources to emulate these transient currents in the circuit simulators depend on the parameters extracted from TCAD device simulations. In order to capture the essential physics behind these current transients in the circuit simulations, there is a need for a physics-based bias-dependent SET current model that considers the electrostatics in the chosen device. The proposed SET current model is developed from the solution of 2-D Poisson’s equation with proper boundary conditions of DG MOSFET. It takes into account the dependence of the transient potential and drain current on linear energy transfer (LET), strike positions, drain and gate biases, device dimensions, and channel doping. The results from the model are validated with the simulation results from TCAD. The SET current model is integrated in Cadence circuit simulator and observed through simulations the voltage perturbation at the output of the CMOS inverter due to heavy ion strike on nMOS transistor in OFF state for different LETs and loads. The proposed model captures the current plateau region effect in CMOS inverter.
Double-Sided Linear Iron-Core Fine-Tooth Motor for Low Acoustic Noise and High Acceleration

This paper presents the design, construction, and testing of a low-noise high-force double-sided linear iron-core motor for high-precision and high-throughput system applications such as in semiconductor photo-lithography machines. Linear iron-core permanent-magnet motors can emit significant vibration and acoustic noise due to high spatial-frequency force harmonics, causing harmonic vibrations of a moving stage. As a design solution to such motor noise, we presented magnetic designs of a new linear iron-core motor having fine teeth, narrow slots with high aspect ratio, and a moving skewed Halbach magnet array in an earlier article. In this paper, we present a further noise reduction method of directly canceling out the normal-direction force harmonics by using a symmetric double-sided motor. In the conventional iron-core motor design, the double-sided configuration achieves more than 70% overall noise reduction relative to a single-sided configuration. The new fine-tooth motor even in the single-sided configuration reduces the acoustic noise further by 79% from the double-sided conventional motor, which corresponds to 95% noise reduction compared to the single-sided conventional motor. By configuring the fine-tooth motors in a double-sided manner, we achieve more than 35 dB noise reduction in sound pressure level during acceleration regions, which corresponds to a 50:1 reduction in units of Pascals. In this paper, we also present the high-shear-stress performance of the double-sided fine-tooth motor, achieving 8.2 G with a 12.3 kg stage using only two magnet pole pairs each for the upper and lower motors.
Reducing root‐mean‐square current stress on the DC link capacitor of a five‐phase electric drive through multiple carrier pulse‐width modulation technique

This study investigates the angle of phase shift between interleaved carriers in pulse-width modulation, as an additional design parameter to reduce the DC link stress of a five-phase drive. Several phase shift strategies are studied and defined with a focus on the DC link root-mean-square current. The effect of adjusting the phase shift between interleaved carriers on the DC link stress is analysed and a general mathematical model is proposed to estimate the effect for any phase shift strategy. The trends predicted by simulations and analytical models have been validated through the application of the control algorithm on a five-phase motor drive system.
An Inference Model of Medical Insurance Fraud Detection: Based on Ontology and SWRL

: Medical insurance fraud is common in many countries’ medical insurance systems and represents a serious threat to the insurance funds and the benefits of patients. In this paper, we present an inference model of medical insurance fraud detection, based on a medical detection domain ontology that incorporates the knowledge base provided by the Medical Terminology, NKIMed, and Chinese Library Classification systems. Through analyzing the behaviors of irregular and fraudulent medical services, we defined the scope of the medical domain ontology relevant to the task and built the ontology about medical sciences and medical service behaviors. The ontology then utilizes Semantic Web Rule Language (SWRL) and Java Expert System Shell (JESS) to detect medical irregularities and mine implicit knowledge. The system can be used to improve the management of medical insurance risks.
Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing

Supervisory Control and Data Acquisition (SCADA) is a technology for monitoring and controlling distributed processes. SCADA provides real-time data exchange between a control/monitoring centre and field devices connected to the distributed processes. A SCADA system performs these functions using its four basic elements: Field Instrumentation Devices (FIDs) such as sensors and actuators which are connected to the distributed process plants being managed, Remote Terminal Units (RTUs) such as single board computers for receiving, processing and sending the remote data from the field instrumentation devices, Master Terminal Units (MTUs) for handling data processing and human machine interactions, and lastly SCADA Communication Channels for connecting the RTUs to the MTUs, and for parsing the acquired data. Generally, there are two classes of SCADA hardware and software; Proprietary (Commercial) and Open Source. In this paper, we present the design and implementation of a low-cost, Open Source SCADA system by using Thinger.IO local server IoT platform as the MTU and ESP32 Thing micro-controller as the RTU. SCADA architectures have evolved over the years from monolithic (stand-alone) through distributed and networked architectures to the latest Internet of Things (IoT) architecture. The SCADA system proposed in this work is based on the Internet of Things SCADA architecture which incorporates web services with the conventional (traditional) SCADA for a more robust supervisory control and monitoring. It comprises of analog Current and Voltage Sensors, the low-power ESP32 Thing micro-controller, a Raspberry Pi micro-controller, and a local Wi-Fi Router. In its implementation, the current and voltage sensors acquire the desired data from the process plant, the ESP32 micro-controller receives, processes and sends the acquired sensor data via a Wi-Fi network to the Thinger.IO local server IoT platform for data storage, real-time monitoring and remote control. The Thinger.IO server is locally hosted by the Raspberry Pi micro-controller, while the Wi-Fi network which forms the SCADA communication channel is created using the Wi-Fi Router. In order to test the proposed SCADA system solution, the designed hardware was set up to remotely monitor the Photovoltaic (PV) voltage, current, and power, as well as the storage battery voltage of a 260 W, 12 V Solar PV System. Some of the created Human Machine Interfaces (HMIs) on Thinger.IO Server where an operator can remotely monitor the data in the cloud, as well as initiate supervisory control activities if the acquired data are not in the expected range, using both a computer connected to the network, and Thinger.IO Mobile Apps are presented in the paper.