Miracle-dz/UniFAR-FTUs huggingface dataset:
Panel-to-Substring PWM Differential Power Processing Converter and Its Maximum Power Point Tracking Technique for Solar Roof of Plug-In Electric Vehicles

In photovoltaic (PV) panels comprising multiple substrings, a mismatch in substring characteristics is known to reduce energy yield significantly. Substring characteristics in solar roofs of plug-in hybrid vehicles (PHVs) are always mismatched to some extent due to the curved surface of the panel. This paper proposes a PWM differential power processing (DPP) converter based on a panel-to-substring power redistribution architecture and its dual maximum power point tracking (MPPT) control technique for PHEVs’ solar roofs. Depending on the degree of characteristic mismatch, the proposed DPP converter adjusts its output current to maximize the energy yield of the panel. With the proposed dual MPPT control, a duty cycle <inline-formula> <tex-math notation="LaTeX">$d_{DPP}$ </tex-math></inline-formula> of the DPP converter is manipulated to optimize the panel characteristic while an external boost converter manipulates its duty cycle <inline-formula> <tex-math notation="LaTeX">$D_{boost}$ </tex-math></inline-formula> to track an MPP of the panel. A prototype for 180-W solar roofs comprising seven substrings was built, and laboratory testing was performed using solar array simulators to emulate characteristic mismatch conditions. Thanks to the dual MPPT control technique, the proposed DPP converter operated with the optimal <inline-formula> <tex-math notation="LaTeX">$d_{DPP}$ </tex-math></inline-formula> while the external boost converter tracked the MPP by manipulating <inline-formula> <tex-math notation="LaTeX">$D_{boost}$ </tex-math></inline-formula>, realizing the enhanced energy yield.
Behavior from Phase Factor Approximate Upon the Beam Propagation in Bessel Beam Angular Spectrum Expansion

In this study, the effect of approximate order numbers of phase factor on beam propagation in Bessel beam angular spectrum characterization is studied. Based on angular spectrum expansion, the analytical expression for beam propagation are presented under the zero, first, and second-order approximation of the phase factor. The corresponding analysis and discussion for the results are conducted.
Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

The movie is one of the integral components of our everyday entertainment. The worldwide movie industry is one of the most growing and significant industries and seizing the attention of people of all ages. It has been observed in the recent study that only a few of the movies achieve success. Uncertainty in the sector has created immense pressure on the film production stakeholder. Moviemakers and researchers continuously feel it necessary to have some expert systems predicting the movie success probability preceding its production with reasonable accuracy. A maximum of the research work has been conducted to predict the movie popularity in the post-production stage. To help the movie maker estimate the upcoming film and make necessary changes, we need to conduct the prediction at the early stage of movie production and provide specific observations about the upcoming movie. This study has proposed a content-based (CB) movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description. Using RS output and movie rating and voting information of similar movies, we created a new feature set and proposed a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. We also proposed a system to predict the popularity of the upcoming movie among different audience groups. We have divided the audience group into four age groups junior, teenage, mid-age and senior. This study has used publicly available Internet Movie Database (IMDb) data and The Movie Database (TMDb) data. We had implemented a multiclass classification model and achieved 96.8% accuracy, which outperforms all the benchmark models. This study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decisions.
Rapid Calculation and Optimization of Vibration and Noise of Permanent-Magnet Synchronous Motors for EVs Based on Equivalent Structural Network

Optimizing electromagnetic performance and vibration noise performance simultaneously is important when designing the drive motor for electric vehicles (EVs). This has not been fully explored, and there are only a few relevant studies. To achieve simultaneous optimization, this paper proposes an equivalent structural network (ESN) of stator assembly to calculate the modal distribution and harmonic response transfer functions. Based on the ESN model, the motor’s electromagnetic vibration noise harmonic response, such as acceleration and equivalent radiated power level (ERPL), can be quickly calculated. The feasibility of the established ESN model is verified by structural-field finite-element method (FEM) and modal hammer tests. Based on the modern optimization algorithm and the ESN model, an improved multi-physics and multi-objective optimization design approach is proposed for an optimized design of a 30 kW interior permanent-magnet synchronous machine (IPMSM). The motor’s maximum output torque and ERPL were selected as optimization objectives, and then the ERPL and acceleration were recalculated using structural-field FEM to validate the accuracy of the optimal design. Finally, vibration acceleration tests were carried out on a manufactured prototype motor to verify the feasibility and validity of the proposed optimization design method.
A new pedestrian recognition system based on edge detection and different census transform features under weather conditions

Pedestrian detection has so far achieved great success in normal illumination, while pedestrians captured in extreme weather are often ignored. This paper investigates the importance of studying the effects of weather conditions on the recognition task, such as blurring and low contrast. Many image restoration techniques have recently been proposed, but are still insufficient to remove weather effects from images. We present our strong new pedestrian recognition system against climate situations, which is based on locating contours cues by applying multiple edge filters and extracting multiple features from images such as census transform (CT), modified census transform (MCT), and local gradient pattern (LGP) without performing any image restoration algorithm. The next stage involves finding the most discriminative characteristics using feature selection (FS) techniques. Finally, we use the final feature vector as an input to a radial basis function-based support vector machine classifier (RbfSVM) for pedestrian recognition. Experiments are performed on the daimler pedestrian classification benchmark dataset. Results show that the area under the curve (AUC) and the detection rate of our model are less affected by weather conditions compared to other common models like histogram of oriented gradients (HOG) and gabor filter bank (GFB) detectors.
Cancers: What Are the Costs in Relation to Disability-Adjusted Life Years? A Systematic Review and Meta-Analysis

Cancers currently represent a leading cause of morbidity and mortality, and precisely estimating their burden is crucial for evidence-based decision-making. This study aimed at understanding the average costs of cancer-related disability-adjusted life years (DALYs) and highlighting possible differences in economic estimates obtained with diverse approaches. We searched four scientific databases to identify all the primary literature simultaneously investigating cancer-related costs and DALYs. In view of the different methodologies, studies were divided into two groups: those estimating costs starting from DALYs, and those independently performing cost and DALY analyses. The latter were pooled to compute costs per disease-related DALY: meta-analytic syntheses were performed for total costs and indirect costs, and in relation to the corresponding gross domestic product (GDP) per capita. The quality of included studies was assessed through the Quality of Health Economic Studies instrument. Seven studies were selected. Total and indirect pooled costs per DALY were, respectively, USD 9150 (95% CI: 5560–15,050) and USD 3890 (95% CI: 2570–5880). Moreover, the cost per cancer-related DALY has been found to be, on average, 32% (95% CI: 24–42%) of the corresponding countries’ GDP per capita. Costs calculated a priori from DALYs may lead to results widely different from those obtained after data retrieval and model building. Further research is needed to better estimate the economic burden of cancer in terms of costs and DALYs.
University Instructors’ Perceptions about the Prominence of Culturally Responsive Teaching Pedagogy

This qualitative study occurs in linguistically and culturally varied Moroccan higher educational contexts. It includes interviewing (10) University instructors from different universities in Morocco. The aim is to get their views and perceptions about the importance of implementing culturally responsive teaching pedagogy in their teaching methods and approaches to enhance learning opportunities for students from linguistically and culturally diverse backgrounds. The notable objective of this study is to inaugurate an appropriate teaching pedagogy to teach students from varied backgrounds and to effectively satisfy the learning and teaching processes of these culturally and linguistically diverse students. The data collected from all the participants were studied and organized according to similarities and differences. The analysis of all instructors’ answers revealed their agreement that culturally responsive teaching methods and approaches are paramount to engaging culturally and linguistically diverse students in the classroom for more academic achievements.
The removal of iron from waterlogged archaeological wood: efficacy and effects on the room temperature wood properties

ABSTRACT Present results provided new information on the practice of removing iron from waterlogged wood using chelating agents. Two series of samples were selected: small waterlogged wood samples of Quercus spp. , Pinus sylvestris L. and Pinus pinaster Aiton, and a portion from the dismantled shipwreck Huguangjiao I. Iron extractions were carried out using disodium ethylenediaminetetraacetic acid (EDTA) and diethylenetriaminepentaacetic acid (DTPA). Samples were characterized by environmental scanning electron microscopy coupled with dispersive energy spectrometry, micro-Raman spectroscopy, atomic absorption spectroscopy, Fourier transform infrared spectroscopy and dynamic mechanical analysis (DMA), the latter used to evaluate viscoelastic properties. The extraction efficiency of both products was good, although it depended on the wood state of preservation, being higher in more decayed material. Still, the extraction procedures induced a limited effect on the decayed wood tissue, affecting both cell wall chemical composition and physical characteristics. Moreover, a significant difference existed between the two agents. In decayed hardwoods, they had an impact on both E’ (decrease) and tanδ (increase), mostly apparent for DTPA. The difference between DTPA and EDTA was also more evident when the frequency was varied in DMA tests. In contrast, the well-preserved softwood was not affected by the considered treatments.
Discrimination of tectonic influence from volcano‐sedimentary successions: An example from the Janggi Basin, south‐eastern part of the Korean Peninsula

In extensional and transtensional basins, the dual action of basin‐forming tectonism and explosive volcanic activity results in deposition of complex volcano‐sedimentary successions. Since both aspects contemporaneously affect sediment‐dispersal systems, it is difficult to distinguish one from the other by analyzing volcano‐sedimentary successions. To discriminate tectonic influence, basin fills of the Miocene, pull‐apart Janggi Basin were investigated. Here, the contemporaneous influence of tectonic subsidence and explosive volcanism formed <966 m thick, conformable volcano‐sedimentary successions composed of laterally extensive, pumice‐bearing, unwelded pyroclastic deposits and nonmarine sedimentary rocks formed under humid climatic conditions. Based on Laser Ablation – Multiple Collector – Inductively Coupled Plasma Mass Spectrometry zircon U–Pb age data, these successions were deposited from 21.1 ± 0.2 to 20.1 ± 0.1 Ma. After deposition of basinwide, tens of metres thick, and unwelded pyroclastic deposits, the directly overlying facies successions (FS‐1 and FS‐2) show contrasting depositional features to the classical model of volcaniclastic sedimentation. Facies succession‐1 is represented by fluvial conglomerates composed mostly of (sub)rounded pebble and cobble sourced from basement. The supply of basement‐derived clasts through fluvial systems resulted from development of physiographic relief during or soon after the eruption by syndepositional tectonic subsidence. Facies succession‐2 occurs directly on pyroclastic deposits that cover FS‐1 and show coarsening‐upward trends, and is composed of basal lacustrine mudstones and overlying resedimented volcaniclastic sandstones showing a progradational geometry, interpreted as a result of progressive filling of the lake by remobilized volcaniclastic sediments. Occurrence of basal lacustrine laminated mudstones indicates that syndepositional creation of the accommodation by tectonic subsidence exceeded forceful input of pyroclastic and remobilized volcaniclastic sediments, resulting in a delayed sedimentary response to explosive volcanic eruptions. This study shows that, despite voluminous production of volcaniclastic sediments, tectonic activity controlled sediment type and stacking patterns. Therefore, these depositional features allow to discriminate tectonic effects from complex volcano‐sedimentary successions, enabling to reconstruct basin evolution.
Output-correlated adversarial attack for image translation network

Abstract. Maliciously forged images generated by image translation networks can cause significant security threats to personal privacy and national security. An emerging solution for forged images is preventing image forgery models from tampering with user images through adversarial attacks. Currently, conventional adversarial generation algorithms use random noise as the starting point, which makes the final adversarial output similar to the original output but does not prevent image tampering. The output-correlated initialization is applied to improve the adversarial attack algorithm for the image translation network and improve the visual effect of adversarial attacks. Moreover, a comparative experiment is performed on multiple loss functions, and the loss function with the best performance is selected as the adversarial loss function to complete the adversarial attack on the image translation network. The selected initialization method makes the search process of adversarial examples more comprehensive and makes the generation results of adversarial examples more diverse. The analysis of the visual effects of the attack reveals how the proposed adversarial attack methods affect the forgery results of different image translation frameworks and generate more chaotic images. Comparison of multiple indicators demonstrates that the proposed method has a high attack success rate and expands the image distance between the adversarial output and the original output, thereby improving the attack efficiency, preventing malicious tampering with the image, and protecting the user image.
A fully integrated 2TX–4RX 60-GHz FMCW radar transceiver for short-range applications

ABSTRACT A fully integrated 2-transmitter–4-receiver (2TX–4RX) multiple-input multiple-output (MIMO) radar front-end in the 57–64-GHz band for short-range applications is presented in this paper. The proposed architecture with 8 elements within a one-dimensional virtual array enables theoretical angular resolution down to . The TX chains operate in time-division multiplexing (TDM) mode, and each delivers 12 dBm of saturated output power with output-referred 1-dB compression point (OP1dB) larger than 5 dBm over 7-GHz bandwidth. The millimeter-wave (mm-wave) part of the RX chains has a noise figure lower than 13 dB, and a conversion gain of 17 dB in the 60-GHz unlicensed band. The receiver chain shows a maximum conversion gain of 77 dB, while achieving an input-referred 1-dB compression point (IP1dB) greater than – 12.5 dBm, including baseband circuitry. The programmable baseband processing chain with 60-dB gain allows beat frequency selection in the frequency range from 100 kHz up to 1.3 MHz. Linear chirps are synthesized using a fractional-N phase-locked loop (PLL) with a fundamental voltage-controlled oscillator (VCO). The integrated system includes a programmable multiplying delay-locked loop (MDLL) which scales-up external high purity 40-MHz frequency reference to a referent 240-MHz clock used in the frequency-modulated continuous-wave (FMCW) synthesizer. The front-end is implemented in a 130-nm SiGe:C BiCMOS process. The chip occupies an area of 12 mm2 including pads and consumes 930 mW, combined from 1.2-V and 3.3-V supplies.
Understanding Online Consumer Behavior at E-commerce Portals Using Eye-Gaze Tracking

Abstract E-commerce websites are doing extremely well despite the global slowdown in the traditional brick-and-mortar retail industry. Web page design, search options, and graphical layout may greatly influence the customer base of any e-commerce website. This paper intends to identify the most influential elements of a graphical interface and their effect on consumers’ visual attention. Visual layouts having major influence on customers’ decisions during online shopping were investigated using eye-gaze tracking. Data from 76 respondents were collected to identify parts of the graphical interface attaining maximum user attention in terms of first fixation of eye-gaze and fixation count. Heat maps were used to visualize the results. The results revealed a clear impact of graphical interface on gaining user attention. Further, the experiment also revealed gender-specific visual preferences toward various parts of graphical user interface. The results may further be extended to evaluate product design, packaging design, promotional design, and graphical interfaces of websites.
Extraction of human understandable insight from machine learning model for diabetes prediction

Explaining the reason for model’s output as diabetes positive or negative is crucial for diabetes diagnosis. Because, reasoning the predictive outcome of model helps to understand why the model predicted an instance into diabetes positive or negative class. In recent years, highest predictive accuracy and promising result is achieved with simple linear model to complex deep neural network. However, the use of complex model such as ensemble and deep learning have trade-off between accuracy and interpretability. In response to the problem of interpretability, different approaches have been proposed to explain the predictive outcome of complex model. However, the relationship between the proposed approaches and the preferred approach for diabetes prediction is not clear. To address this problem, the authors aimed to implement and compare existing model interpretation approaches, local interpretable model agnostic explanation (LIME), shapely additive explanation (SHAP) and permutation feature importance by employing extreme boosting (XGBoost). Experiment is conducted on diabetes dataset with the aim of investigating the most influencing feature on model output. Overall, experimental result evidently appears to reveal that blood glucose has the highest impact on model prediction outcome.
A Fast Multipoint Expected Improvement for Parallel Expensive Optimization

The multipoint expected improvement (EI) criterion is a well-defined parallel infill criterion for expensive optimization. However, the exact calculation of the classical multipoint EI involves evaluating a significant amount of multivariate normal cumulative distribution functions, which makes the inner optimization of this infill criterion very time consuming when the number of infill samples is large. To tackle this problem, we propose a novel fast multipoint EI criterion in this work. The proposed infill criterion is calculated using only univariate normal cumulative distributions; thus, it is easier to implement and cheaper to compute than the classical multipoint EI criterion. It is shown that the computational time of the proposed fast multipoint EI is several orders lower than the classical multipoint EI on the benchmark problems. In addition, we propose to use cooperative coevolutionary algorithms (CCEAs) to solve the inner optimization problem of the proposed fast multipoint EI by decomposing the optimization problem into multiple subproblems with each subproblem corresponding to one infill sample and solving these subproblems cooperatively. Numerical experiments show that using CCEAs can improve the performance of the proposed algorithm significantly compared with using standard evolutionary algorithms. This work provides a fast and efficient approach for parallel expensive optimization.
The Relationship Between Disaster Risk Perception and Multiple Deprivation: A Study on Rangpur City, Bangladesh, Using Geospatial and Statistical Approaches

Rapid urbanization and multiple deprivation are making cities more prone to numerous disasters. This study examines the relationships between disaster risk perception and multiple deprivation in the Rangpur city Corporation area in Bangladesh. Enhanced understanding of this relationship would bring valuable insights for planning and policies, especially pertinent for vulnerable communities in the Global South. Rangpur city is located in a deprived zone of Bangladesh that is highly prone to earthquake and fire hazards, and the population’s low level of risk perception might exasperate their vulnerability to earthquakes and fire. This study’s research methods include an index of multiple deprivation, earthquake and fire risk perception index, geographic information system (GIS)-based mapping, statistical analysis and questionnaire surveys. The statistical methods are correlation analysis, factor analysis, Kaiser–Meyer–Olkin test and cross-table analysis. The results of the study show that there is no statistically significant relationship between disaster risk perception and multiple deprivation; however, cross-table analysis revealed a relationship between risk perception and deprivation. The study identified the hot spots of hazard risks and deprivation in Rangpur city. To the best of our knowledge, this is the first effort to investigate the relationship between disaster risk perception and multiple deprivation.