Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Technology to Create Mindful Intercultural Learning Experiences in Undergraduate Education

Abstract This chapter describes the special advantages of globally networked learning experiences (GNLE) for engendering cognitive complexity as a means for developing mindful global citizenship among undergraduate students. Practitioners discover pedagogical approaches that take advantage of the possibility of direct communication with the cultural “other” afforded by recent advances in cost-free, user-friendly, robust, and reliable technologies. Examples of effective pedagogical practices, ideas for building successful faculty-to-faculty partnerships, suggestions for preparing participants, guidelines for selecting and implementing appropriate technologies, and resources for further exploration are provided.
Comparison Of A Novel Non-Conventional Multi-level Inverter For Different Levels

2 Abstract: Multi-level inverters play a key role in present days as the output obtained from it is of high quality as it contains less harmonic distortion and almost similar to sine-wave . Multi-level inverters are used at high power and voltage purposes which are required by the industries, hence the growth of Multi-level inverters. In this paper, a novel non-conventional Multi-level inverter topology is developed with less number of voltage sources and switching devices, and this developed topology is compared for different levels. Asymmetric condition is verified for this proposed novel non-conventional Multi-level inverter topology by using MATLAB SIMULINK software.
How national integrated air quality models can be used in defining environmental policies: the revision of the NEC directive

L’inquinamento atmosferico e il principale fattore di rischio ambientale per la salute umana in Europa e l’Agenzia Internazionale per la Ricerca sul Cancro ha classificato il particolato atmosferico tra le sostanze accertate come cancerogene per l’uomo. Molte misure e politiche di contrasto all’inquinamento atmosferico sono state adottate negli ultimi decenni, dal livello europeo a quello locale, ma gran parte della popolazione e degli ecosistemi europei e tuttora esposta ad elevate concentrazioni di inquinanti che eccedono i limiti di qualita dell’aria stabiliti nelle direttive europee e dall’Organizzazione Mondiale della Sanita. In questo contesto, nel 2013 la Commissione Europea ha adottato il Clean Air Policy Package con lo scopo di ridurre gli impatti sulla salute umana e sugli ecosistemi, il cui strumento principale per la riduzione delle emissioni e rappresentato dalla proposta di una nuova Direttiva NEC (National Emission Ceilings – Tetti Nazionali di Emissione) che pone degli obiettivi al 2030, espressi come percentuali di riduzione rispetto ai livelli registrati nel 2005. Tali obiettivi riguardano le emissioni di SO2, NOX, NMVOC, NH3, come nella versione vigente della direttiva, cui si aggiungono anche il PM2.5 e il CH4. L’elaborazione di possibili scenari di riduzione delle emissioni e stata realizzata con il modello GAINS-Europa attraverso un’analisi costi-benefici che ha consentito alla Commissione europea di formulare la proposta di una nuova direttiva NEC contenente gli obiettivi di riduzione delle emissioni per il 2020 e il 2030 rispetto all’anno base 2005. Nel processo di negoziazione per la nuova Direttiva NEC, l’Italia ha elaborato diversi scenari di riduzione delle emissioni utilizzando il sistema modellistico integrato nazionale sulla qualita dell’aria, sviluppato nell’ambito del progetto MINNI. Nel presente lavoro tutti gli scenari e le differenti proposte di riduzione delle emissioni per l’Italia verranno presentate e discusse con l’obiettivo di evidenziare come, durante un processo negoziale, un sistema modellistico nazionale possa influenzare la definizione di politiche di contrasto all’inquinamento atmosferico.
Analysis of the Power Conditioning System for a Superconducting Magnetic Energy Storage Unit

Superconducting Magnetic Energy Storage (SMES) has branched out from its application origins of load leveling, in the early 1970s, to include power quality for utility, industrial, commercial and military applications. It has also shown promise as a power supply for pulsed loads such as electric guns and electromagnetic aircraft launchers (EMAL) as well as for vital loads when power distribution systems are temporarily down. These new applications demand more efficient and compact high performance power electronics. A 250 kW Power Conditioning System (PCS), consisting of a voltage source converter (VSC) and bi-directional two-quadrant DC/DC converter (chopper), was developed at the Center for Power Electronics Systems (CPES) under an ONR funded program. The project was to develop advanced power electronic techniques for SMES Naval applications. This thesis focuses on system analysis and development of a demonstration test plan to illustrate the SMES systems’ ability to be multitasked for
What's next for the airliner? : historical analysis and future predictions of aircraft architecture and performance

Air travel has advanced greatly since the inception of the first commercially viable airliner in the 1930s, the Douglas DC-3. In the 80 years since then, the number of annual air passengers in the US has increased from 6000 to over 800 million. In order to accommodate this demand growth, civil passenger aircraft architectures have changed over time, investment in aircraft technologies has rapidly increased, and aircraft performance has improved. The historical context of aircraft architectures are analyzed in order to establish trends and precedents for architectural change. This historical analysis is carried out based on a database of 157 architectures from the DC-3 to the Boeing 787, and concludes that historical aircraft architecture changes are mainly driven by selection of engine type. More recently aircraft performance has experienced diminishing returns in terms of efficiency, on the order of 1% reduction in fuel consumption annually since 2010. Meanwhile, according to projections by Airbus and Boeing, air passenger traffic is expected to increase 3.5-4.6% per annum. ICAO has recommended that overall energy efficiency be reduced by 2% annually. The rate of increase in demand and decrease in fuel consumption, raises the question of how this goal can be met. Much prior work has been done to optimize design variables within the context of a single aircraft architecture for maximum performance. Additionally optimization of point designs at the corners of the architecture design space has also been extensively examined. Despite the abundance of work in this domain, there has been limited work done to understand the potential trajectories that would cause current architectures to evolve into these potential future architectures. Therefore, the goal of this thesis is to analyze the conditions that could break the current architecture of commercial aircraft. and identify the stringency of policies that could invoke such a disruption. To answer this research question, the major drivers of increasing engine performance are identified including increasing bypass ratio, increasing overall pressure ratio and turbine inlet temperature, and increasing component efficiency. A hybrid
A NOVEL HYBRID 2D AND 3D AUGMENTED REALITY BASED METHOD FOR GEOMETRIC PRODUCT DEVELOPMENT

A method that uses the interaction of two and three dimensional environments to carry out the geometric modeling and refinement steps of the product development process is presented. The 2D environment is an interactive computer screen, where the initial sketching of the product takes place. This environment provides a direct one-to-one interaction and the feeling of using traditional pen and paper. Next, a characteristic feature of the 2D sketch is selected to be exported to a 3D environment, where 3D vision and manual interaction over an immersive augmented reality environment is provided to carry out the refinement steps. The 3D environment provides fully 3D visual feedback and geometry manipulation interaction. In this way, the advantages of 2D and 3D environments are used for a smooth transition between the sketching and the modeling tasks at the early stages of the design process. The goal of this ongoing work is solving some limitations of the 2D nature of sketching, the speed constrains imposed by the transition between a 2D sketch and its digital 3D model representation and the limitations of current CAD systems to easily modify freeform surfaces under form development.
Configurable Open-source Data Structure for Distributed Conforming Unstructured Homogeneous Meshes with GPU Support

A general multi-purpose data structure for an efficient representation of conforming unstructured homogeneous meshes for scientific computations on CPU and GPU-based systems is presented. The data structure is provided as open-source software as part of the TNL library (https://tnl-project.org/). The abstract representation supports almost any cell shape and common 2D quadrilateral, 3D hexahedron and arbitrarily dimensional simplex shapes are currently built into the library. The implementation is highly configurable via templates of the C++ language, which allows avoiding the storage of unnecessary dynamic data. The internal memory layout is based on state-of-the-art sparse matrix storage formats, which are optimized for different hardware architectures in order to provide high-performance computations. The proposed data structure is also suitable for meshes decomposed into several subdomains and distributed computing using the Message Passing Interface (MPI). The efficiency of the implemented data structure on CPU and GPU hardware architectures is demonstrated on several benchmark problems and a comparison with another library. Its applicability to advanced numerical methods is demonstrated with an example problem of two-phase flow in porous media using a numerical scheme based on the mixed-hybrid finite element method (MHFEM). We show GPU speed-ups that rise above 20 in 2D and 50 in 3D when compared to sequential CPU computations, and above 2 in 2D and 9 in 3D when compared to 12-threaded CPU computations.
Breaking Cellular IoT with Forged Data-plane Signaling: Attacks and Countermeasure

We devise new attacks exploiting the unprotected data-plane signaling in cellular IoT networks (a.k.a. both NB-IoT and Cat-M). We show that, despite the deployed security mechanisms on both control-plane signaling and data-plane packet forwarding, novel data-plane signaling attacks are still feasible. The attacker can forge both uplink and downlink data-plane signaling messages that pass the current security checks used by the receiver. With the capability of forging messages, the attacker can launch attacks that exhibit a variety of attack forms beyond simplistic packet-blasting, denial-of-service (DoS) threats, including location privacy breach, packet delivery loop, prolonged data delivery, throughput limiting, radio resource draining, connection reset, and multicast disabling. Our testbed evaluation and operational network validation have confirmed the attack viability. To combat the threat, we further propose a new defense solution within the 3GPP C-IoT standard framework. It leverages the synchronized timer clock information to protect the data-plane signaling messages with low overhead.
Eryngium Species as a Potential Ally for Treating Metabolic Syndrome and Diabetes

Medicinal plants possess natural compounds that can be used as an alternative for synthetic medicines that may cause long-term side effects on patients such as neurocognitive effects, muscular and hepatic toxicity. Metabolic Syndrome is associated with increased risk of several diseases such as diabetes, cardiovascular disease, dyslipidemia, and hypertension thus, becoming the greatest challenge as a growing public health concern worldwide. Latin-American countries possess a wide diversity of medicinal plants that have been used to treat different health conditions since pre-Hispanic times. Eryngium spp. has been studied due to their beneficial properties mainly to treat diabetes, dyslipidemia, blood pressure, and digestive problems. This review gives an update mainly on the pharmacological activities of the Eryngium spp. , summarizing the biological activities and plausible mechanism of action of their bioactive components toward metabolic syndrome. For instance, flavonoids and tannins proved to increase the levels of HDL and reduced the levels of VLDL, LDL. On the other hand, phenolic acids improved glucose metabolism through the inhibition of phosphoenolpyruvate carboxykinase and glucose 6-phosphatase (G6Pase) and reestablished the impaired activity of enzymes related to glucose oxidation and glycogen synthesis. The terpenes and sesquiterpenes like β-farnese, β-pinene, and calamenene exhibited a protective effect by reducing the oxidizing damage by the regulation of the Reactive Oxygen Species (ROS). Saponins controlled the dyslipidemia by reducing the serum concentrations of lipids, triglycerides and total cholesterol. Finally, the aerial parts of Eryngium had the capacity of inhibiting dietary carbohydrate digestive enzymes, thus controlling glucose levels. The Eryngium plant is normally consumed as an infusion to obtain the benefits of the plants, however novel technologies such as cavitation, ultrasound assisted, microwave assisted, and supercritical fluid have been applied to improve the extraction yields and selectivity of bioactive compounds. The common treatment to control diabetic dyslipidemia are synthetic medicines such as metformin and ezetimibe, which allows the regulation of glucose, cholesterol and insulin resistance. However, patients that take these medications may present side effects such as muscular toxicity, hepatic toxicity, neurocognitive effects, just to name a few. More studies regarding the efficacy and safety of the use of traditional medicinal herbs are required. However, these materials may be used in the treatment of diabetes related conditions to ensure life quality and reduce side effects among the diabetic population
The Language of Brands in Social Media: Using Topic Modeling on Social Media Conversations to Drive Brand Strategy

This article highlights how social media data and language analysis can help managers understand brand positioning and brand competitive spaces to enable them to make various strategic and tactical decisions about brands. The authors use the output of topic models at the brand level to evaluate similarities between brands and to identify potential cobrand partners. In addition to using average topic probabilities to assess brands’ relationships to each other, they incorporate a differential language analysis framework, which implements scientific inference with multi-test-corrected hypothesis testing, to evaluate positive and negative topic correlates of brand names. The authors highlight the various applications of these approaches in decision making for brand management, including the assessment of brand positioning and future cobranding partnerships, design of marketing communication, identification of new product introductions, and identification of potential negative brand associations that can pose a threat to a brand's image. Moreover, they introduce a new metric, “temporal topic variability,” that can serve as an early warning of future changes in consumer preference. The authors evaluate social media analytic contributions against offline survey data. They demonstrate their approach with a sample of 193 brands, representing a broad set of categories, and discuss its implications.
Modelling the oceanic partial pressure of carbon dioxide in the North Indian Ocean

Accurate information about sea surface pCO2 distribution is essential to understand the variability of the ocean’s ability to uptake excess CO2 in the atmosphere. Here, we present a machine learning algorithm based pCO2 model customised for the north Indian ocean. The model makes use of Sea Surface Temperature (SST), Sea Surface Salinity (SSS) and Chlorophyll-a (Chl-a) to incorporate the thermodynamic, physical and biological effect that controls the air-sea exchange. Our results show that the model is able to capture the pCO2 seasonal variations and are consistent with available observations.
HyperX: A Hybrid RRAM-SRAM partitioned system for error recovery in memristive Xbars

Memristive crossbars based on Non-volatile Memory (NVM) technologies such as RRAM, have recently shown great promise for accelerating Deep Neural Networks (DNNs). They achieve this by performing efficient Matrix-Vector-Multiplications (MVMs) while offering dense on-chip storage and minimal off-chip data movement. However, their analog nature of computing introduces functional errors due to non-ideal RRAM devices, significantly degrading the application accuracy. Further, RRAMs suffer from low endurance and high write costs, hindering on-chip trainability. To alleviate these limitations, we propose HyperX, a hybrid RRAM-SRAM system that leverages the complementary benefits of NVM and CMOS technologies. Our proposed system consists of a fixed RRAM block offering area and energy-efficient MVMs and an SRAM block enabling on-chip training to recover the accuracy drop due to the RRAM non-idealities. The improvements are reported in terms of energy and product of latency and area ${\left(ms\,\times \,mm^{2}\right)}$, termed as area-normalized latency. Our experiments on CIFAR datasets using ResNet-20 show up to 2.88 × and 10.1 × improvements in inference energy and area-normalized latency, respectively. In addition, for a transfer learning task from ImageNet to CIFAR datasets using ResNet-18, we observe up to 1.58 × and 4.48 × improvements in energy and area-normalized latency, respectively. These improvements are with respect to an all-SRAM baseline.
Physics-Based Magnetic Modeling of a Three-Port Transformer in a Triple-Active-Bridge Converter with Decoupled Power Flow Regulation

Triple-active-bridge (TAB) converter is a promising solution in the smart distributed power management applications, where the three-port high-frequency transformer is the most critical component. This work firstly presents a physics-based magnetic modeling approach for a three-port transformer to investigate its intrinsic magnetic characteristics, especially the high-fidelity magnetic flux density estimation. Then a decoupled power flow control strategy is developed. The proposed magnetic model and the control strategy are evaluated on a 150-kW TAB converter with a high-frequency three-port transformer. The decoupled power control method can effectively reduce the cross coupling among multi-port power regulation. In addition, the transformer flux density dc bias is also identified and it can be reduced significantly using a slew rate control approach.
Impacts of Switching Parameters on Thermal and Mechanical Characteristics in IGBT Modules

This paper presents a comprehensive investigation of the influence of switching parameters on thermal and mechanical characteristics in insulated gate bipolar transistor (IGBT) modules. Through finite element method (FEM) simulation, the impacts of switching frequency, current amplitude, and duty cycle on the temperature field and deformation field of IGBT modules are studied. It is found that time-varying temperature and deformation are composed of transient process and steady-state process. During transient process, both temperature and deformation shows a fluctuating upward trend. Then it comes to steady-state process, temperature and deformation fluctuate around a certain average value. The average values of temperature and deformation are positively correlated with current amplitude and duty cycle, while independent of switching frequency. Meanwhile, the fluctuations of temperature and deformation are negatively correlated with the switching frequency, positively correlated with the current amplitude, and shows a trend of first increasing and then decreasing.
Reliability of DC-link Capacitors in Three-Level NPC Inverters under different PWM Methods

Pulse-width modulation (PWM) method plays an important role to ensure high efficiency and reliability of the three-level neutral-point-clamped (NPC) inverter. However, a comparison of the PWM methods in previous research has only considered power loss and reliability of power devices, while other reliability-critical components such as dc-link capacitors are not taken into account in the analysis. In this paper, the impact of PWM methods on the reliability of the dc-link capacitors in three-level NPC inverters is investigated by considering the capacitor power loss and their thermal stress. The evaluation results indicate that the PWM method which aims at minimizing the switching loss may introduce a significant power loss in the dc-link capacitors due to high amplitude of the low-frequency neutral-point (NP) voltage fluctuation. In that case, the thermal stress of the dc-link capacitors can be significantly increased, especially when the discontinuous modulation scheme is applied, affecting the reliability of the dc-link capacitors.
Design enablement of CFET devices for sub-2nm CMOS nodes

Novel devices that optimize their structure in a three-dimensional fashion and offer significant area gains by reducing standard cell track height are adopted to scale silicon technologies beyond the 5nm node. Such a device is the Complementary FET (CFET), which consists of an n-type channel stacked vertically over a p-type channel. In this paper we review the significant benefits of CFET devices as well as the challenges that arise with their use. More specifically, we focus on the standard cell design challenges as well as the physical implementation ones. We show that to fully exploit the area benefits of the CFET devices, one must carefully select the metal stack used for the physical implementation of a large design.
High-accuracy seabed sediment classification using multi-beam acoustic backscatter data

The seabed sediment classification map has been widely applied in marine science and engineering for providing high-resolution and high-accuracy sediment type. However, some factors degrade the resolution and identification of sediment type, which include calculation of initial incidence angle of beam, backscattering intensity extraction method and coordinate calculation for the echo samples, echo intensity correction, wavelet-BP neural network (WBPNN) improvement and so on. To generate a highly accurate seabed sediment map, a five-step sediment classification method using multi-beam acoustic backscatter data was proposed. First, initial incidence angle of beam was calculated accurately taking into account vessel attitude and beam assigned angle, and echo samples were extracted in the way of grid within the beam footprint, meanwhile, echo samples coordinate was calculated according to the geometrical relationship between echo samples and pointing echo sample in beam, footprint. Second, samples intensities were corrected based on Time Varying Gain (TVG), Angle Varying Gain (AVG). Third, a critical step was proposed for determining the image sample width and preferred characteristic parameters based on the relevant principles. In addition, an improving the wavelet-BP neural network with self-adaptive learning rate and momentum factor, and the network parameters initialization was introduced. Ultimately, the selected image samples were trained to classify the seabed sediment map of a selected test area in Jiaozhou Bay. The experimental results indicated that sample identification accuracy and image identification accuracy reached 93.3% and 98.8% respectively, the proposed method can significantly improve the resolution of the seabed sediment map and identification accuracy of sediment classification.