Miracle-dz/UniFAR-FTUs huggingface dataset:
Trilayer Stackelberg Game Approach for Robustly Power Management in Community Grids

Multiuncertainties in community grids bring enormous challenges to system scheduling. To reply to renewable uncertainties on the prosumer side, this article proposes a trilayer stackelberg game (SG) based robust power management method for a typical community grid. Firstly, in the upper-layer, the supplier acts as a leader to determine the trading prices according to prosumers' power demands in the normal scenario. The scheduling of prosumers before and after uncertainties is formulated as an inner-loop SG model in the middle- and lower-layers, and a linear robust counterpart model is developed to address the nonlinearity and nonconvexity of traditional robust models, thereby ensuring the convergence of the robust equilibrium solution. Second, to solve the trilayer SG model effectively, the existence and uniqueness of the equilibrium solution are proved based on a multistep backward induction method. Third, a novel two-stage distributed iterative algorithm is further exploited to avoid the nested iterations in existing solution methods. Furthermore, the possible oscillation in iterative optimization is also prevented to improve the computational efficiency. Case studies illustrate the effectiveness of the proposed trilayer SG model, and the developed distributed algorithm requires fewer iterations and less solution time comparing to other algorithms. Besides, this method shows better scalability and practicality with the expansion of the grid-scale.
Automatic lung segmentation in CT images using mask R-CNN for mapping the feature extraction in supervised methods of machine learning using transfer learning

According to the World Health Organization, severe lung pathologies bring about 250,000 deaths each year, and by 2030 it will be the third leading cause of death in the world. The usage of (CT) Computed Tomography is a crucial tool to aid medical diagnosis. Several studies, based on the computer vision area, in association with the medical field, provide computational models through machine learning and deep learning. In this study, we created a new feature extractor that works as the Mask R-CNN kernel for lung image segmentation through transfer learning. Our approaches minimize the number of images used by CNN’s training step, thereby also decreasing the number of interactions performed by the network. The model obtained results surpassing the standard results generated by Mask R-CNN, obtaining more than 99% about the metrics of real lung position on CT with our best model Mask + SVM, surpassing methods in the literature reaching 11 seconds for pulmonary segmentation. To present the effectiveness of our approach also in the generalization of models (methods capable of generalizing machine knowledge to other different databases), we carried out experiments also with various databases. The method was able, with only one training based on a single database, to segment CT lung images belonging to another lung database, generating excellent results getting 99% accuracy.
Develop Load Shape Dictionary Through Efficient Clustering Based on Elastic Dissimilarity Measure

Load shape dictionary (LSD) is a useful tool for utilizing the enormous amount of smart meter data to understand customers’ electricity consumption behaviors. To tackle the big data challenge as well as to better capture the load shape features, this article develops a bilevel LSD generation framework to cluster and index the residential load profiles into a neat local LSD and a global LSD based on the Derivative Dynamic Time Warping (DDTW) elastic dissimilarity measure. Different from the classic Dynamic Time Warping (DTW), DDTW works on the derivative of the raw data to avoid DTW’s problem of pathological alignments. To reduce the computational cost, a fast DDTW (FDDTW) is proposed to speed up the DDTW calculation. Based on the generated bilevel LSD, analytic approaches are proposed to extract features from the data indexed by the LSD to reveal useful information of customers’ electricity consumption behaviors. Numerical experiments on real premise data verify the effectiveness of the proposed methodology in terms of clustering performance and computational efficiency. Our analysis suggests that the proposed methodology can be applied to improve load forecasting, tariff design and demand response (DR) customer targeting.
Dynamics Analysis of Two-Dimensional Systems of the Hodgkin-Huxley Model on Propagatsion of Nerve Cell Impulse

This research aims to assess the environmental quality of Jodipan, Malang through the Comprehensive Assessment System for Built Environment Efficiency (CASBEE) Tools. Jodipan is one of the urban villages in Malang city which stands along in the Brantas riverbanks. It is a high-density settlement with the majority of the population work as a merchant. At 2016, the settlement in Jodipan riverbanks painted colorfully, and it made Jodipan called “Kampung Warna Warni” or Colourful Kampong. Jodipan now became one of the new community-based tourism destinations in Malang and succeeded to attract domestic and international tourist. The existence of this kampong gave a big impact on environmental quality especially river since their communities’ activities are very depending on the river. The method based on the triplebottom-line approach that adopts three classifications of sustainable development which are the environment, society, and economy. The result of environmental quality in Jodipan kampong riverbank was 2.1. This score indicates a low value and below the average of the environmental quality standards.
Self-Mutual Information-Based Band Selection for Hyperspectral Image Classification

Due to significant inter-band correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is commonly used to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with crucial self-retained spectral information, while also avoiding highly correlated bands to be selected. This article presents a novel approach, referred to as self-mutual information-based band selection (SMI-BS) for hyperspectral image classification (HSIC) to address these two issues. It first constructs a hyperspectral band channel from a hyperspectral image and then takes advantage of such a band channel to coin a new concept of SMI, which is defined as the mutual information (MI) between a selected band, b, and the set of full bands, <inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\Omega}} $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula>(b;<inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\Omega}}$ </tex-math></inline-formula>). As a result, a curve plotted as a function of <inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula>(b;<inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\Omega}}$ </tex-math></inline-formula>) versus individual band b, called SMI curve, can be used as a BS criterion which selects those bands with large <inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula>(b;<inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\Omega}}$ </tex-math></inline-formula>) values as desired bands. Since such selected bands may be highly correlated, another new concept, called prominent band (PB), which is defined as a band corresponding to a prominent peak of an SMI curve, is further introduced to avoid selecting highly inter-correlated spectral bands. To validate the utility of SMI-BS in HSIC, experiments are conducted to compare existing state-of-the-art BS methods for performance evaluation. The results demonstrate that SMI-BS is indeed a very effective BS method and also performs better than other test BS methods.
Occupational stress among library personnel in public universities in Nigeria

Occupational stress, which is stress experienced by employees at the workplace, is increasingly becoming a challenge. It is often the case that when employees are confronted with work demands and pressures beyond their knowledge and abilities, stress may be the outcome. Due to their role expectations, paraprofessional and professional librarians are susceptible to occupational stress which could lead to a decreasing commitment to the organization. The study adopted a survey research design to investigate occupational stress among paraprofessional and professional librarians in public universities in South-West, Nigeria. Total enumeration was used to cover all professional and paraprofessional librarians in the study area. Out of the 280 copies of questionnaire administered, 268 (95.7%) were retrieved for analysis. The findings revealed that male library personnel experience more stress in public universities than their female counterpart. Specifically, the precursors to stress among library personnel, with high mean scores, include sourcing of funds for career development (x the quest for promotion (x , excessive workloads (x , and broken interpersonal relationships with management (x . The study, therefore, recommends equity in the sharing of responsibilities between the male and female genders to partly reduce pressure due to excessive workloads. Also, library personnel should have access to research funds to facility upward mobility in their career. In addition, interpersonal relationship workshops should be regularly conducted for allcadres of staff to promote team spirit.
Investigation on Torque Characteristic and PM Operation Point of Flux-Intensifying PM Motor Considering Low-Speed Operation

In this article, a new type of flux-intensifying permanent magnet (FI-PM) motor is designed, analyzed, and optimized. It focuses on the low-speed operation condition of electric vehicles (EVs). The key characteristic of this motor is the feature of the reverse saliency, so that it not only utilizes the positive reluctance under the positive $d$ -axis current but also improves the PM operation point under maximum torque per ampere (MTPA) control strategy. The relationships among the key parameters, reverse saliency, and torque performance are analyzed and investigated. The response surface method is used to optimize the motor performances. Then, for a better presentation of the investigated motor, the motor performances are evaluated and compared with the traditional interior permanent magnet motor (IPM) in torque characteristic and irreversible demagnetization ability. Finally, for the experimental validation, the prototyped motor is manufactured and tested.
A Score Level Fusion Method on Fingerprint and Finger Vein

In this paper, we represent a score level fusion method on fingerprint and finger vein. Each unimodal identification system carries out processes of image preprocessing, feature extraction and feature matching to generate a vector of score. And we apply clustering analysis to split the score range into zones of interest. Then a decision tree and weighted-sum approach are used to make the decision. We test the proposed method on standard biometric database. Three metrics, namely, False Accept Rate, False Reject Rate, Recognition Rate, are used to evaluate experimental results. And experimental results show that the fusion system has a better performance than unimodal identification system.
Equivariance and Invariance for Robust Unsupervised and Semi-Supervised Learning

Although there is a great success of applying deep learning on a wide variety of tasks, it heavily relies on a large amount of labeled training data, which could be hard to obtain in many real scenarios. To address this problem, unsupervised and semi-supervised learning emerge to take advantage of the plenty of cheap unlabeled data to improve the model generalization. In this dissertation, we claim that equivariant and invariance are two critical criteria to approach robust unsupervised and semi-supervised learning. The idea is as follows: the features of a robust model ought to be sufficiently informative and equivariant to transformations on the input data, and the classifiers should be resilient and invariant to small perturbations on the data manifold and model parameters. Specifically, features are learnt via auto-encoding the transformations on the input data, and models are regularized through minimizing the effects of perturbations on features or model parameters. Experiments on several benchmarks show the proposed methods outperform many state-of-the-art approaches on unsupervised and semi-supervised learning, proving importance of the equivariance and invariance rules for robust feature representation learning.
Accelerated Degradation of IGBTs Due to High Gate Voltage at Various Temperature Environments

In this study, degradation phenomena due to gate bias stress at elevated temperature for the reliability test of punch-through IGBTs, which are widely used in DC circuit systems for automotive applications, were investigated. For the reliability tests of various temperatures, 4000 times temperature cycle stress was applied in IGBTs between −40°C and 200°C to accelerate the degradation process using the wide range temperature. And the delamination of the passivation layer and the metal layer in IGBTs was observed using the scan acoustic microscopy. In addition, the positive bias stress with 48 V at elevated temperature of 150°C for 60 min was applied to the gate, the threshold voltage increased up to 6.07 V due to charge trapping at gate oxide. We also compared the recovery of the devices, but the IGBTs that stressed with high DC field and elevated temperature slowed down the recovery process after 24 hours without reverse voltage. This work was focused to find the device recovery considering real conditions. Therefore, the various temperature stress and high electric field stress are important in the reliability test for IGBTs, it was reported the IGBT degradation process and failure symptoms using the accelerated and various reliability tests.
MODELING THE DISPERSION AND EVAPORATION OF SPRAYS IN AERONAUTICAL COMBUSTION CHAMBERS

Combustion still represents about 90% of the energy production in the world. Most industrial burners are fuelled with liquid hydrocarbons. However, most studies have been dedicated to gaseous sames and the impact of liquid spray is still misunderstood. The purpose of this study is to improve the modelisation of two main phenomena occurring between atomization and combustion, i.e. the droplet dispersion in the turbulent gaseous flow and the evaporation process, in the context of Large Eddy Simulation (LES) of complex configurations. First, the mesoscopic Euler-Euler approach (Fevrier et al. (2005)) based on a conditioned ensemble averaging and implemented in AVBP is improved. The closure model (Simonin et al. (2001), Kaufmann (2004)) for the second-order moments appearing in the transport equations solved fails in mean-sheared configurations (Riber (2007)). Several new models proposed by Masi (2010) and a priori tested in a particle-laden slab are tested a posteriori in the same configuration. A quantitative analysis based on several calculations varying the Stokes number, the gaseous Reynolds number and the grid resolution allows to retain a non-linear model using the particle rate-of-strain tensor as timescale and called 2EASM3. The second part consists in improving the evaporation model implemented in AVBP which assumes infinite conduction in the liquid and spherical symmetry in the gas phase along with simplified thermodynamics and transport properties calculation. A new model is proposed, where the dependence of gaseous mixture viscosity on local composition is accounted for, and the Prandtl and Schmidt numbers are fixed by a reference equilibrium calculation using complex thermodynamics and transport properties. This method shows good agreement with experimental measurements in the configuration of an isolated droplet evaporating in quiescent N2 without further increasing the computational cost. Finally, the impact of the new models is analysed in the LES of the MERCATO semi-industrial configuration (Garcia-Rosa (2008)). Although the experimental data are not sufficient to confirm the results, both the droplet distribution and the fuel mass fraction are significantly affected, which would eventually affect the ignition process.
Single-Chip CMOS Reconfigurable Dual-Band Tri-Mode High-Efficiency RF Amplifier Design

This brief presents a new design methodology of single-chip reconfigurable CMOS RF power amplifier (PA) supporting both dual-band and multi-power-mode operation. The proposed architecture utilizes a novel switched-capacitor based output network to facilitate differential signal combining and impedance transformation at two widely separated frequency bands. It can offer three distinct output power levels (saturation) to be selected for multi-power-mode applications. Besides, closed-form explicit equations are readily available for the evaluation of component values. For verification, a PA operating at 1.8/2.6 GHz (non-concurrent), with three distinct saturation power levels (21, 23, 26 dBm) and peak efficiency of about 30%, is designed and fabricated using $0.35~{\mu }\text{m}$ CMOS process.
Continuous processing of urban refuse to oil using carbon monoxide

Experimental results obtained in the production of oil in a small continuous unit by treating sucrose with carbon monoxide and water at elevated temperatures and pressure were reported. The runs were made on 30 percent aqueous sucrose solution containing 5 percent sodium carbonate as catalyst. The effects of temperature, pressure, and residence time were determined. A preliminary run with a slurry of urban refuse gave significant oil yields, but not as good as predicted by the sucrose runs. Preliminary cost estimates for the process were made. (JSR)
Exploring Participants’ Views on a Supported Work Internship Program for Autistic and Learning Disabled Young People

Individuals with additional needs, such as learning disabilities, face a crisis of unemployment in the United Kingdom (UK). Many of these individuals encounter few adult services that are in place to address their support needs. Supported work internship programs are one attempt to address this crisis. One such program for young people with learning disabilities is the Project SEARCH model. Though a number of research studies have explored the efficacy of the Project SEARCH model, the reported markers of success (e.g., subsequent employment rates) have not taken into account first-hand participant experiences. The current study aimed to establish whether participant views were consistent with the previously reported, positive, quantitative measures. We explored the views of seven interns, aged 17 to 24 years, with various learning difficulties, and three job coaches who were involved in the program from September 2017 to June 2018. Semistructured interviews were conducted and analysed using thematic analysis. Participants revealed meaningful experiences for those involved, positive self-development, and renewed aspirations for the future. The value of a strong and consistent support network involving family members, job coaches, coworkers, and supervisors was highlighted. The study also found ongoing challenges associated with the scheme. These centred on concerns that interns’ abilities were being underestimated, the impact of a reduction/withdrawal of support at the conclusion of the program, and the barriers to finding subsequent competitive employment for interns. The research highlights the value of eliciting participant voice within research, and discusses how the findings can be used to further develop supported work internship programs.
Context Effects on Recognition Memory for Words

The process by which different kinds of information are encoded and retrieved by memory is still unclear, despite the current knowledge of distinct memory systems (Murnane, Phelps, & Malmberg, 1999; Oberauer, 2018). The dual process model of recognition memory states that two separate processes govern recognition: familiarity and recollection (Norman & O’Reilly, 2003; Opitz, 2010; Rugg & Yonelinas, 2003). Familiarity involves recognizing a piece of information or stimulus without accessing other information about the context in which the material was learned (Opitz, 2010). Recollection involves retrieving all the information about the stimulus and the context in which it was presented (Opitz, 2010). The dual process model of recognition shows the complexity of recognition memory and the retrieval process (Jacoby & Dallas, 1981; Mulligan, Smith, & Spataro, 2016; Murnane, Phelps, & Malmberg, 1999). This study replicates and extends an experiment that tested the dual process model’s predictions about the influence of context and repetition on recognition memory (Opitz, 2010). Our study replicates the behavioral portion of that study’s methodology, which measured reaction time and accuracy, and extends the previous work to examine the influence of word frequency on ABSTRACT. The present study examined whether word frequency, study context, and word repetition produce differences in word recognition and context recognition. We also tested a prediction of the dual process model where recognition memory has two individual processes, namely recollection and familiarity. Participants studied lists of words presented in contexts defined by background color, screen position of the word, and study question during encoding. Word frequency, study context, and word repetition were manipulated during the encoding phase. During the subsequent retrieval phase, participants performed two memory tasks: a word recognition task involving old judgments or new judgments and a context recognition task involving remember judgments and know judgments for words reported as an old judgment. We found effects of each of the manipulated factors on recognition memory. False alarms were higher for common words than uncommon words (ηp = .38, p < .001). Repeated words were remembered better than nonrepeated words (ηp = .76, p < .001). Words repeated across contexts during the study were recognized better than words repeated in the same context during encoding (ηp = .18, p = .03). We also found effects of repetition and word frequency on reaction time. Repeated words were recognized faster than nonrepeated words (ηp = .23, p = .01), and uncommon words were recognized faster than common words (ηp = .38, p = .001). However, we did not find evidence to support the dual process model’s predictions about the impact of context on remember and know judgments.
Management of Timber and Nontimber Forest Products: Evidence from a Framed Field Experiment in Benin, West Africa

Across sub-Saharan Africa, rapid deforestation threatens the economic and environmental benefits forests provide to communities and individuals. These losses are especially salient when forest users benefit from multiple forest products and not just timber resources. Using a common-pool resource (CPR) game in rural Benin framed around a threatened species, Afzelia africana, this paper tests how different payments for ecosystem services (PES) programs may induce conservation behavior among individuals who harvest multiple forest products (e.g., timber and leaves). PES treatments vary along two dimensions: the level of the payment and how the payment is divided among group members. We find evidence that all implemented PES treatments reduce the harvest of timber; however, a high PES payment evenly divided among group members also significantly reduced the harvest of leaves. Our findings suggest that a single-product PES focused only on whole trees might not be effective for multipurpose species.