Miracle-dz/UniFAR-FTUs huggingface dataset:
PSFB DC-DC Converter Control Technique for DC Microgrid Application

For high power requirement in microgrid, the DC/DC converter based on phase-shift full bridge (PSFB) topology has strong application. The transformer in the circuit provides galvanic isolation as well as high voltage conversion-ratio capability. For the converter operating in voltage-mode control, a proper control strategy is required which can provide better regulation of point-of-load voltage for the dynamic changes in output side current and supply side voltage. In present study, a model based predictive control (MPC) strategy for a PSFB converter as a unidirectional interface connected between the DC bus and load has been studied. The MPC strategy provides a wide degree of flexibility to regulate the point-of-load voltage with the load fluctuations and supply voltage variation. The various results obtained from the simulation study verifies the efficacy of this control technique.
Plasmonically Induced Transparency in Graphene Oxide Quantum Dots with Dressed Phonon States

The absorption in quantum dots (QDs) embedded within a semiconductor matrix can be manipulated by resonant optical excitation of phonons. The far-field interaction of the light leading to dressed phonon states within reduced graphene oxide quantum dots has been coherently modified by a near-field optical driving field at the nanoscale limit. The near-field optical excitation was introduced by resonant excitation of localized plasmons in metal nanoparticles coupled to QDs for ultrafast light manipulation. The coherent interaction of photons due to localized plasmon induced a change in the transient absorption of graphene oxide quantum dots conjugated to silver nanoparticles. Resonant pumping of plasmons and phonons with 400 nm pump photons induces a coherent change in excitonic absorption within QDs which results in phonon-assisted plasmon induced transparency at room temperature. This novel effect can be related to the appearance of the coherent effects such as forming dark states and coherent population .
Dynamic formulations of L1 optimal transport problems

The purpose of this work is to analyse the relationships between different formulations of the Monge-Kantorovich transport problem, in the case of L 1 cost. In particular, we focus on the famous Benamou and Brenier’s dynamical formulation, adapting it to our cost function. Helped by the solutions of the other formulations, we find a way to solve the dynamical problem, with L 1 cost, and we compare the solution to the one found by Benamou and Brenier, with L 2 cost. To this aim, we also analyse the intermediate cases of L p costs, with 1 < p < 2, and work out a solution procedure based on the nonlinear coupling of a Hamilton-Jacobi equation with a transport equation. We study the qualitative behavior of the solutions to the proposed formulations on simple test cases. When explicit solutions cannot be easily recovered, we use an original numerical approach based on the Finite Volume discretization of the Hamilton-Jacobi and the transport equation and solve the nonlinearities by an ad-hoc adaptation of Newton method.
Process Design Kit and Initial Demonstration of Digital Metal-Embedded Chip Assembly for High Density IO Fan-Out Packaging

This paper describes the initial demonstration of a fan-out wafer-level package for high-density input/output (IO) digital CMOS chips using a silicon interposer with an embedded electroplated copper heat spreader. The package uses a redistribution layer (RDL) first chip-last approach featuring multi-layer interconnects that accommodate a minimum chip IO pitch of $10 \mu \mathrm{m} ((30\mu \mathrm{m}$ demonstrated). The integrated heat spreader simultaneously addresses fine-pitch chip integration, electrical shielding, and thermal management. The process development steps and challenges are discussed, and preliminary electrical results of passive test structures are presented showing $> 95{\%}$ DC connectivity. The paper also presents the development of a Process Design Kit (PDK) in the Cadence Virtuoso environment for this technology and its application in shortening design time, preventing design rule violations, and extracting RC package parasitics.
Analysis problematics of transformer noise in distribution room in office building

The article deals with the issue of noise in offices in the building directly adjacent to the distribution transformer room. Although the standard for noise exposure is satisfactory in the work environment, some parts of the transformer noise at tonal frequencies around 400 Hz disturb the psyche during the activities of office workers. The article therefore dealt with acoustic measurements on two dry distribution transformers of the same power, where one of them, the “noisier” one, disturbed the employees' noise well-being. An acoustic camera was used during the measurement to determine the cause of the noisier condition of the exposed transformer. In this way, it is possible to analyze the frequency spectrum with the possibility of 3D tracking of the transformer with the identification of the place with the highest acoustic emission. The measurement was carried out with no load and with a real load for both dry distribution transformers.
Teach Me to Explain: A Review of Datasets for Explainable NLP

Explainable NLP (E X NLP) has increasingly focused on collecting human-annotated explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a predictive task, as a loss signal to train models to produce explanations for their predictions, and as a means to evaluate the quality of model-generated explanations. In this review, we identify three predominant classes of explanations (highlights, free-text, and structured), organize the literature on annotating each type, point to what has been learned to date, and give recommendations for collecting E X NLP datasets in the future.
Direct numerical simulations of supersonic turbulent channel flows of dense gases

The influence of dense-gas effects on compressible wall-bounded turbulence is investigated by means of direct numerical simulations of supersonic turbulent channel flows. Results are obtained for PP11, a heavy fluorocarbon representative of dense gases, the thermophysics properties of which are described by using a fifth-order virial equation of state and advanced models for the transport properties. In the dense-gas regime, the speed of sound varies non-monotonically in small perturbations and the dependency of the transport properties on the fluid density (in addition to the temperature) is no longer negligible. A parametric study is carried out by varying the bulk Mach and Reynolds numbers, and results are compared to those obtained for a perfect gas, namely air. Dense-gas flow exhibits almost negligible friction heating effects, since the high specific heat of the fluids leads to a loose coupling between thermal and kinetic fields, even at high Mach numbers. Despite negligible temperature variations across the channel, the mean viscosity tends to decrease from the channel walls to the centreline (liquid-like behaviour), due to its complex dependency on fluid density. On the other hand, strong density fluctuations are present, but due to the non-standard sound speed variation (opposite to the mean density evolution across the channel), the amplitude is maximal close to the channel wall, i.e. in the viscous sublayer instead of the buffer layer like in perfect gases. As a consequence, these fluctuations do not alter the turbulence structure significantly, and Morkovin’s hypothesis is well respected at any Mach number considered in the study. The preceding features make high Mach wall-bounded flows of dense gases similar to incompressible flows with variable properties, despite the significant fluctuations of density and speed of sound. Indeed, the semi-local scaling of Patel et al. (Phys. Fluids, vol. 27 (9), 2015, 095101) or Trettel & Larsson (Phys. Fluids, vol. 28 (2), 2016, 026102) is shown to be well adapted to compare results from existing surveys and with the well-documented incompressible limit. Additionally, for a dense gas the isothermal channel flow is also almost adiabatic, and the Van Driest transformation also performs reasonably well. The present observations open the way to the development of suitable models for dense-gas turbulent flows.
Experimental Research of the Process of Masking of Digital Information Signals Using Chaotic Oscillations

The schematic implementation of digital data transfer method using chaotic masking that is based on summing of modulated carrying oscillation and chaotic oscillation was carried out in the work. It is possible to use harmonic, chaotic or noise oscillation with statistical and spectral characteristics close to the characteristics of masking signal as carrying oscillation. The phenomenon of full chaotic synchronization between chaotic oscillations of generators of transmitter and receiver is used for data recovery. The possibility of data recovery in the receiver under the conditions of noisy channel is investigated and the possibility of practical implementation of the given method is shown.
Balancing exploitation and exploration in parallel Bayesian optimization under computing resource constraint

Parameter survey with an MPI application is extensively used, where the optimality of each parameter setting is evaluated by executing the application, called a trial. Bayesian optimization can find a suboptimal parameter setting with less trials. Parallel Bayesian optimization (PBO) is to execute multiple trials in parallel to reduce the execution time. However, evaluating too many trials at once is likely to be trapped by a local minimum, and degrade the final solution. In addition, under a computing resource constraint, the more parallel trials are executed, the less computing resource each MPI application run can use. As a result, the execution time might become longer. Thus, there is a trade off in the number of parallel trials, and the parameter search generally becomes less explorative and more exploitative by decreasing the number of parallel trials. This paper proposes a method to dynamically adjust the number of parallel trials accordingly to the degree of progress in optimization. The evaluation results demonstrate that the proposed method can adapt to the optimization problems and hence is robust to the problem. As a result, the proposed method can consistently achieve both a faster improvement speed and a better final solution in many cases
Assembly-based Through-X Via (TXV) Integration Technology by Advanced Fan-Out Wafer-Level Packaging

A new formation technology of long through-hole vias we call TXV (Through-X Via, where X is polydimethylsiloxane, epoxy, or hydrogel etc.) is demonstrated without electro-less or electroplating processes. In this study, tiny copper pillars with a diameter/height of 300/300 μm are assembled by a pick-and-place method and molded with a soft epoxy as a flexible substrate based on die-first fan-out wafer-level packaging (FOWLP). Double-sided metallization is performed on the epoxy to interconnect the copper pillars. Adhesion and oxidation issues are solved with an adhesion promotor and a cleaning technique to give desired double-sided wirings connected with the copper pillars. The resulting daisy chain using the copper pillars shows relatively low resistance and good mechanical stability against repeated bending.
Aggressive Pitch Scaling (sub-0.5 μm) of W2W Hybrid Bonding Through Process Innovations

3D integration of dissimilar wafers through metal interconnect hybrid bonding has become common practice in industry from CMOS image sensors to 3D NAND memory at $> \mathbf{1}\boldsymbol{\mu} \mathbf{m}$ pitches. Advanced 3D integration nodes requiring pitch scaling to $\mathbf{sub}-\mathbf{0.5}\boldsymbol{\mu} \mathbf{m}$ face significant challenges in process control and material limitations. In this paper, through the use of advanced 300mm toolsets and process innovations we study the importance layout design, bonding dielectric, copper CMP recess control, and bonding overlay control to produce a high-quality bond interface at sub-500nm pitches. We also examined at which copper design density the material system begins to fail.
Toward Commercial Polymer Fiber Bragg Grating Sensors: Review and Applications

Interest in polymer optical fiber Bragg gratings (POFBGs) arises from the different material properties and sensing modalities brought by polymers relative to silica. Polymer fibers typically offer twice the sensitivity to temperature of conventional silica fiber and increased sensitivity to strain overall. In addition, polymer fibers have higher elastic limits and as a result a larger range of operation for physical constraints. While some polymers are effectively humidity insensitive, others present inherent humidity sensitivity. Their organic properties also allow a variety of chemical processes to create (bio)chemical sensors, with the consequences of fiber breakage in situ being less hazardous than silica. These attributes have led to the use of POFBGs for applications that remain complex using silica fibers. This review paper covers the progress toward commercialization and the increasing number of specific applications.
Optimization of Si PnC MEMS nanowires for ultra-low thermal conductivity

We report the fabrication and characterization of suspended Si nanostructures with phononic crystals (PnCs), the so called Si nanowires. Increased phonon scattering caused by nanowire edges and PnCs results in significant reduction in thermal conductivity and therefore extreme thermal isolation, which would be useful for silicon based thermoelectric devices. The thermal conductivity of the suspended silicon nanowires is characterized using Raman spectroscopy, where we measure the temperature of different locations of the fabricated Si nanowires. PnCs contributing to significantly decreased thermal conductivity are strong candidates for a range of future Micro-electromechanical systems (MEMS) device applications where extreme thermal isolation is desired. An ultra-low thermal conductivity of 2.4 W/m.K is obtained with Si PnCs nanowires in this work.
Adaptive Neuro Fuzzy Data Aggregation Model for Developing and Planning for Aquaculture Farming Practices

The dynamic nature of environmental elements limits the power and information flow of sensor-based network systems, which are critical to the performance of real-time systems, particularly in the fisheries/aquaculture sector. There is a need for a techniquewhich will improve the network information flow. As a result, a data aggregation process utilising advanced artificial intelligence methods such as Sugeno and mamdanifuzzy system, Adaptive Neuro (or Network based) -Fuzzy Inference System, deep learning, neural networks, and others to reduce the communication activity that creates a single data by aggregating information from a group of various source data in the cluster head. In this backdrop, sugeno fuzzy based adaptive neuro fuzzy data aggregation model/system was developed and validated in aquaculture systems to minimise traffic, improve sensor network efficiency, and create a cost-effective system for the predicted output. It will also be valuable for creating and planning aquaculture farming practises. Results obtained from the models were validated using four statistical parameters. In this model, 70 training dataset and 30 testing dataset were used for validation. The aggregation provides accurate result and has Rootmean square error (0.01936), Coefficient of determination (0.999999245), Mean relative percent error (0.1936)and Variance Account For (99.9837 %). the results of the developed ANFIS model and its tool reveals that will be useful for developing and planning aquaculture farming practices.
Flight Delay Prediction using GANN - An Improved Artificial Neural Network Model Integrating Genetic Algorithm

One of the difficult situations in the business world, flight planning involves a lot of unpredictability. Such a situation happens when delays happen; they have a number of root causes and cost airline companies, providers, and travelers a lot of money. Airlines, airports, and passengers all suffer considerable financial and other losses as a result of flight delays. As a result, anticipating the possibility of a delay based on the characteristics of aircraft closes a critical information gap between airlines and passengers. The prediction analysis gleaned from this research can serve as a prototype for identifying operational factors that cause delays in any situation. The flight delay analysis is based on the scheduled arrival, departure, and actual time. This study thoroughly assesses models for forecasting flight delays under this circumstance. Machine learning techniques were used in research on predicting flight delays. The best approach for anticipating flight delays has been determined by comparing the performances of various algorithms. Artificial Neural Networks (ANN), have demonstrated their high accuracy when modeling sequential data. Adam Optimization algorithm has been used to optimize the ANN algorithm. Lastly, a novel aviation delay method is optimized using an artificial neural network (ANN) in conjunction with a genetic algorithm (GA) in response to the important parameters discovered for flight delay prediction. The GANN model's performance has been confirmed, showing that it can accurately forecast delays with a score of 89%.
Personalised treatment assignment maximising expected benefit with smooth hinge loss

ABSTRACT In personalised medicine, the goal is to make a treatment recommendation for each patient with a given set of covariates to maximise the treatment benefit measured by patient's response to the treatment. In application, such a treatment assignment rule is constructed using a sample training data consisting of patients’ responses and covariates. Instead of modelling responses using treatments and covariates, an alternative approach is maximising a response-weighted target function whose value directly reflects the effectiveness of treatment assignments. Since the target function involves a loss function, efforts have been made recently on the choice of the loss function to ensure a computationally feasible and theoretically sound solution. We propose to use a smooth hinge loss function so that the target function is convex and differentiable, which possesses good asymptotic properties and numerical advantages. To further simplify the computation and interpretability, we focus on the rules that are linear functions of covariates and discuss their asymptotic properties. We also examine the performances of our method with simulation studies and real data analysis.
Characterization and manipulation of full Poincaré beams on the hybrid Poincaré sphere

We develop a hybrid Poincare sphere to characterize the so-called full Poincare beam with any polarization geometry. The two eigenstates of the hybrid Poincare sphere are defined as a fundamental-mode Gaussian beam and a Laguerre–Gaussian beam. We further establish a robust and efficient experimental setup to generate any desired full Poincare beam on the hybrid Poincare sphere via modulating the incident polarization state of light. Our research provides an alternative way for describing and manipulating the full Poincare beam and an effective method to control the polarization state of light.
Scalable Tracing of MPI Events and Performance Metrics

Tracing is a basic approach to analyzing performance and understanding MPI program behavior patterns. However, MPI event trace requires increasingly large storage space as the parallel scale grows. Besides MPI event trace, many performance analysis tasks (e.g., performance variance detection, proxy synthesis) also require detailed runtime performance metrics, which further aggravates the storage issue. In this paper, we propose a scalable tracing tool to effectively record and compress MPI event trace and related runtime performance metrics. The tool analyzes the data redundancy caused by loops and SPMD (single program multiple data) property of MPI programs. According to the analysis, the tool can compactly reorganize and store the data. Compared with existing trace compression methods, our tool can achieve generally higher compression ratio and less time cost.
Evolution of Propensity for High Strain-Rate Damage Accrual in Doped and Undoped SnAgCu Lead-free Solders in Temperature Range of -65°C to +200°C after 1-Year Sustained High Temperatures Exposure

To ensure the reliability of high-performance electronics systems, constitutive models are crucial. Predictive models are particularly necessary for electronic equipment that operates in harsh environments, where extreme temperatures ranging from -65°C to +200°C may cause significant out-of-plane deformation in board assemblies due to shock, vibration, or transient loads. Automotive under-hood and aerospace electronics may require sustained exposure to high temperatures of 150-200°C and may go through random mechanical shock and vibration. Additionally, electronics may be stored in non-climate-controlled enclosures for prolonged periods before deployment. In order to mitigate the adverse effects of sustained high-temperature exposure, the industry has experimented with adding dopants to SAC solders. This paper studies the effects of dopants, including Nickel and Bismuth, on doped alloys such as SAC-Q, SAC-R, M758, and SAC305 solder. The study examines the strain-rate range of 1–100 per second and the temperature range of -65°C to + 200°C after one year of sustained exposure to 50°C and 100°C storage temperatures. The Anand model's accuracy has been quantified by comparing experimentally measured data with predicted data computed using the model's constants. Anand parameters have been computed and implemented in an FE analysis framework to simulate drop events for a ball-grid array package on a printed circuit board assembly, determining hysteresis loops of stress vs strains and plastic work density. The study also explores the evolution in hysteresis loops and PWD.
Fuzzy and ANN based model for Test case prioritization for Regression testing

This research article performs the prioritization of the test case to test the software system after the occurrence of changes for Regression testing. The test expert here will categorize the sets as Optimistic test cases and Pessimistic test cases as formatted data for preprocessing by the Fuzzy rules. The optimistic test cases ensure that they are considered for regression testing by the tester. They are allowed to go into the next phase for deciding the prioritization. The test case is expected to have the details of case_id, case_name, case_details, predicted_result, obtained_result, seconds_time, and status. The ANN model deployed, gives the ranking to only Optimistic test cases by ensuring its capability to a dynamic environment. The efficiency of the regression testing on the proposed ANN model is evaluated by representing the faults, statements, and paths using the average percentage. The results provide a superior value above 95% when compared to the other methods taken in literature survey. The future scope of this ANN-based model can be used for prioritizing, selecting, and categorizing every cycle using reinforcement learning methods.