Miracle-dz/UniFAR-FTUs huggingface dataset:
A practical review of electroencephalography’s value to consumer research

Electroencephalography (EEG) offers insights into the neural responses of consumers to marketing stimuli which can be used to evaluate the effectiveness of such stimuli in directing favorable consumer behavior in real-world settings. This practical review provides guidelines for using EEG in consumer and marketing research. It provides recommendations for how EEG may effectively be employed in neuromarketing in the future. EEG requires careful experimental design, and as such, we outline current recommendations for the collection, processing, analysis, and interpretation of EEG evoked potentials (i.e., event related potentials; ERP) and spectral content (i.e., EEG frequency). By providing an introductory overview of EEG measures in marketing and consumer research, this practical review extends previous literature that is primarily focused on other neuroimaging techniques (e.g., functional magnetic resonance imaging) and other disciplines (e.g., economics and organizational behaviour). Furthermore, by reviewing how EEG has been used throughout psychophysiological and neuromarketing research, we provide recommendations on how EEG can be used to measure marketing-related outcomes. These include processes relating to perception, attention, memory, emotion, and cognitive load, demonstrating the unique value of considering the neural responses captured by EEG in understanding and predicting consumer behavior.
Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds

This paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point clouds, there are few practical engineering applications in the construction industry. However, in the process of building digitization, the level of manual participation has significantly reduced the efficiency of digitization and increased the application restrictions. To address this problem, we propose a robust classification method using deep learning networks, which is combined with traditional shape features for the point cloud of construction components. The proposed method starts with local and global feature extraction, where global features processed by the neural network and the traditional shape features are processed separately. Then, we generate a feature map and perform deep convolution to achieve feature fusion. Finally, experiments are designed to prove the efficiency of the proposed method based on the construction dataset we establish. This paper fills in the lack of deep learning applications of point clouds in construction component classification. Additionally, this paper provides a feasible solution to improve the construction digitization efficiency and provides an available dataset for future work.
Optimal configuration of energy storage system based on frequency hierarchical control in ship power system with solar photovoltaic plant

This study presents the results of hybrid energy storage system (HESS) and improved maximum power point tracking (MPPT) algorithm simulation analysis undertaken to improve the smoothing performance of solar photovoltaic (PV) plant installed in the ship power system. Frequency hierarchical control algorithm is used to assign high-frequency oscillation part to the supercapacitor and low-frequency oscillation part to the battery. Meanwhile, improved MPPT algorithm is also used to limit the maximum output power of PV plant, which can reduce the necessary installed capacity of HESS further. The effectiveness of the proposed method was verified under a typical ship power system model.
Student perceptions of model-based estimating

ABSTRACT The use of building information modeling (BIM) is well established in architecture, engineering and construction. However, previous studies note a lack of BIM expertise in academia, leading to slow adoption and limited knowledge on which to base BIM-related content. To improve course development, it is important to understand students’ perceptions of BIM-related subject matter. This study exposed estimating students (n = 214) to a model-based quantity takeoff (QTO) tutorial using Revit. Students’ perceptions of a Revit-based BIM’s usefulness for easily providing reliable and accurate material quantities were measured using a pre-test, tutorial and post-test design. Results revealed significant differences in students’ mean levels of agreement that an accurate QTO is completed for an estimator with the click of a button when a design team generated BIM model is available. Results also revealed significant differences in students’ level of agreement that a Revit-based BIM makes performing QTO by hand obsolete. This study provides educators’ insight into students’ perceptions of BIM use in QTO. Understanding student perceptions is paramount for construction management educators when designing a course and attempting to convey the value of foundational estimating skills while acknowledging the existence of more technologically advance estimating methods. Limitations and opportunities for further research are discussed.
Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the mapping relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shijingshan District, Beijing is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model.
TOOLS FOR SELECTING A SOFTWARE DEVELOPMENT METHODOLOGY TAKING INTO ACCOUNT PROJECT CHARACTERISTICS

Context. In the software development process, the choice of a software development methodology is one of the important stages that significantly affects the success/failure of the project. The choice of the optimal development methodology depends on many factors and is a time-consuming and nontrivial task. Objective. Therefore, there is a need to develop an effective and flexible software tool for selecting the best software development methodology that would automate this process and take into account the key characteristics of the project. Method. This article presents an algorithm for selecting a software development methodology using methods of multi-criteria analysis and expert evaluation, which provides for gathering of the expert evaluation and implements the process of selecting the methodology using such methods as AHP, TOPSIS and Weighted Sum. Results. Using the above-mentioned algorithm, a software system was developed for selecting the best software development methodology depending on the characteristics of the project, where the criteria weights provided by experts were taken into account and the AHP method was applied to determine user priorities regarding the criteria for the methodology comparison. The TOPSIS and Weighted Sum method were chosen to calculate the estimates of the methodology selection. The software tool provides for the output of useful details of the selection results, namely, an expert evaluation of the specified parameter values in relation to all methodologies, and it can be used to improve the efficiency of the software development process in terms of automating the provision of recommendations to IT project managers. Conclusions. The algorithm for selecting a software development methodology was developed, which, unlike the existing ones, provided for gathering of expert evaluation, taking into account the values of the criteria set by a user independently, and implemented the process of selecting the methodologies using such methods of multi-criteria analysis as AHP, TOPSIS and weighted sum. Using the above algorithm, a software system was developed for selecting the best software development methodology, depending on the characteristics of the project, where the criteria weights provided by experts were taken into account, and the AHP method was applied to determine user priorities for methodology comparison criteria. TOPSIS and weighted sum methods and were chosen to calculate the scores of methodology choice. The software tool provides for the output of useful details about the selection results, namely, an expert evaluation of the set parameter values regarding all methodologies.
Probabilistic Fleet Sizing and Routing Problem to Minimize Mobility Disparities

The objective of this paper is to develop an analytical model to analyze the mobility behavior of a target population and to minimize the disparities. The analysis is conducted using household travel survey data collected from 7993 residents of five boroughs of New York City, where citizens with mobility impairments are considered as our target population. The study starts by quantifying the existing gap in the patterns of the mobility-impaired individuals and the base population followed by the generation of new activities in the agenda of the target population using the patterns of their nearest neighbors in the base population. Activities are generated based on copula sampling method. Attributes of activities forming the new agenda of the target population are the inputs that will be used for the design of an optimum ridesharing system. By integrating probabilistic models with the existing routing models, we develop a methodology to identify the optimum fleet size, optimum route, and optimum schedule such that all requests of the physically impaired travelers are fulfilled within a reasonable wait time for ride requesters.
Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm

A remodeled sperm swarm optimization (RMSSO) algorithm for a home energy management (HEM) system is proposed, and its real-time efficacy was evaluated using a hardware experimental model. This home environment comprised sixteen residential loads, a smart meter and a Raspberry Pi controller to optimize the energy consumption cost (ECC) in response to the Indian day-ahead pricing (DAP) scheme. A wired/wireless communication network was considered to communicate with the smart meter and controller. To address this optimization problem, the sperm swarm optimization (SSO) algorithm’s constriction coefficient was remodeled to improve its global searching capability and proposed as RMSSO. For the first time, salp swarm optimization (SSA), SSO, and RMSSO algorithms were employed to schedule home appliances in the Indian scenario. To validate the proposed technique’s outcome, the results were compared to those of the conventional SSO and SSA algorithms. This problem was solved using the Python/GUROBI optimizer tool. As a consequence, consumers can use this control strategy in real-time to reduce energy consumption costs.
Effective Hospital Care Delivery Model for Older People in Nigeria with Multimorbidity: Recommendations for Practice

The importance of developing an effective action-based model of care for multimorbid patients has become common knowledge, but it remains unclear why researchers in Nigeria have not paid attention to the issue. Hence, this study assessed the quality of health services using the Donabedian model and aimed to recommend an effective hospital care delivery model for older people in Nigeria with multimorbidity. A cross-sectional study using face-to-face data was conducted between October 2021 and February 2022. The reported data were collated, checked, coded, and entered into JISC online survey software and then exported to IBM Statistical Package for Social Science (SPSS) version 27 for analysis, sourced from the University of West London, London, United Kingdom. The data were collected from the outpatient department of four high-volume public secondary hospitals in Niger State (the largest hospital in the three senatorial zones and that of the state capital). Systematic random sampling was used to select 734 patients with two or more chronic diseases (multimorbidity) aged 60 years and above who presented for routine ambulatory outpatient and consented to participate in the study. A Service Availability and Readiness Assessment (SARA) tool was used to assess the structure, and the process quality was assessed by the patients’ experiences as they navigated the care pathway, whereas the outcome was measured using the patients’ overall satisfaction. Using Spearman’s correlation, no statistically significant association was observed between satisfaction level with the healthcare that was received and the five domains of health facility readiness (Total score Basic Amenities, Total score Basic Equipment, Total score infection control, Total score diagnostic capacity, Total score essential drugs), and the general facility readiness. Finally, the process component superseded the structure as the determinant of the quality of healthcare among multimorbid patients in Niger State. The emphasis of the process should be on improving access to quality of care, improving patient–physician relationships and timing, reducing the financial burden of medical care, and building confidence and trust in medical care. Therefore, these factors should be incorporated into designing the healthcare model for multimorbid patients in Nigeria.
Flow Behavior Analysis of Emc in Molded Underfill (Muf) Encapsulation for Multi Flip-Chip Package

This paper present a numerical analysis of molded underfill (MUF) for multi flip chip package during encapsulation. Castro Macosko model is used in ANSYS fluent to describe the flow behavior of the EMC. The physical quantities such as viscosity, shear rate and velocity is plotted to analyzed the flow behavior of EMC during encapsulation. The EMC flow behavior at flip chip (FC) and free passage (FP) region in cavity are been compared. The viscosity profile of EMC is inversely proportional to the shear rate and velocity profile at FC and FP region The tendency of “frozen skin” layer occur in cavity package have been predicted with velocity profile during encapsulation.
Time Series Analysis and structural break detection: A case of Zambia’s CPI.

Purpose: By empirically examining Zambia’s CPI between 2010 and 2020, the study attempts to determine the structural change in the time series. The CPI is one of the most important variables for analyzing inflation in macroeconomics, therefore any change in the dynamic must be determined. In this paper change points and dates are highlighted and statistical analysis methods have been employed to explore and discover the underlying patterns and trends of Zambia’s CPI for the past 10 years. Methodology/approach: Secondary Data from Zambia Statistics Agency (ZamStats.gov.zm) was used for the Study. From 132 elements of observations of time series for 10 years, the detection methods of structural change were employed. The Cumulative Sum Tests (CUSUM test) of Ordinary Least Squares (OLS), Andrew Sup F test, Bai and Perron test, and Chow test were used to detect the model stability and verify the hypothesis using P-value. Results: The results show that there were five (5) Structural changes or breaks in mean and variance and these were February 2012, February 2014, October 2015, October 2017, and May 2019. The structural breaks are highly suggestive as they appear to broadly coincide with readily identifiable macroeconomic events, increased stock of external debt following the issuance of Eurobonds in 2012, 2014, and 2015, rise increased food prices arising from the adverse impact of erratic rainfall on agricultural output and the pass-through from the depreciation of the Kwacha. Policy Implication: Based on the study, strong and sound macroeconomic policies are needed to be implemented: Such as debt management and diversification of foreign exchange sources, and increased earnings.
JSW IMPROVEMENTS IN BOF LANCE BY SLAGLESS CLEAN UP® TECHNOLOGY

Steel Melting Shop One (SMS-I) has 03 BOF converters with 135 tons capacity each. The SMS-II has 04 BOF converters with 180 tons capacity each and SMS-III with 1x160 ton EAF Furnace. India’s largest integrated complex intend in the near future to build SMS-IV and will reach 16 million tons of steel per year. The other side, common problems during operations of BOF happens like: Jam’s formation of blowing lance and converters mouth with dirty skirt. Efforts are made every day to avoid and reduce these effects and increase productivity and reduce costs. This present paper, the focus was enhanced blowing lance operation, reduction of lance jam and converters mouths jam of BOF shops. A new concept of lance tips was introduced for the same to improve more stable conditions during long life lance tips called “Slagless Clean Up cartridge”. The target of this work is shown new conditions to reach like: increase cartridges life, reduce jams around lance and mouth contribute for stable operations.
Clinical variables for predicting type-1 and type-2 non-ST segment elevation myocardial infarction in those presenting with ischemic symptoms

Introduction The accuracy of detecting myocardial infarction (MI) has greatly improved with the advent of more sensitive assays, and this has led to etiologic subtyping. Distinguishing between type 1 and type 2 non-ST-segment elevation myocardial infarction (NSTEMI) early in the clinical course allows for the most appropriate advanced diagnostic procedures and most efficacious treatments. The purpose of this study was to investigate the predictive effect of demographic and clinical variables on predicting NSTEMI subtypes in patients presenting with ischemic symptoms. Material and methods We performed a single institution retrospective cohort study of patients who presented to the emergency department (ED) with ischemic signs and symptoms consistent with non-ST-segment myocardial infarction, for whom results of coronary angiography were available. We analyzed demographic, laboratory, echocardiography and angiography data to determine predictors of NSTEMI sub-types. Results Five hundred and forty-six patients were enrolled; 426 patients were found on coronary angiography to have type 1 acute MI (T1AMI), whereas 120 patients had type 2 acute MI (T2AMI). Age (OR per year = 1.03 (1.00, 1.05), p = 0.03), prior MI (OR = 3.50 (1.68, 7.22), p = 0.001), L/H > 2.0 (OR = 1.55 (1.12, 2.13), p = 0.007), percentage change in troponin I > 25% (OR = 2.54 (1.38, 4.69), p = 0.003), and regional wall motion abnormalities (RWMA) (OR = 3.53 (1.46, 8.54), p = 0.004) were independent predictors of T1AMI, whereas sex, race, body mass index, hypertension, end-stage renal disease (ESRD), heart failure, family history (FH) of coronary artery disease (CAD), HbA1c, and left ventricular ejection fraction (LVEF) were not. Conclusions Key clinical variables such as age, prior MI, L/H ratio, percentage change in troponin I, and presence of RWMA on echocardiogram may be utilized as significant predictors of T1AMI in patients presenting with ischemic symptoms to the ED.
Classification of Heart Sound Signal Using Multiple Features

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”.
Fast and nanoscale-accuracy surface recovery of white-light scanning interferometry based on GPU-accelerated FFT-LMA-PSI algorithm

White-light scanning interferometry (WLSI) has been widely in the field of Nano-Micro topography measurement. In this study , to improve accuracy and robust of surface recovery, the Fast Fourier Transform-Levenberg Marquardt Algorithm-Phase Shift Interference (FFT-LMA-PSI) algorithm is first proposed to position zero optical path difference (OPD), and the FFT-LMA-PSI algorithm is a cascaded algorithm that involves FFT and multiple iterative optimization steps. In order to improve the computational efficiency, the GPU-accelerated FFT-LMA-PSI is developed and designed via compute unified device architecture (CUDA) C language. The speedup ratio of the proposed method are 28.6756×, 36.6950×, 41.9732×, 33.1928× for CCD pixel sizes of 128 × 128, 256 × 256, 512 × 512, 1024 × 1024. The standard deviation (σ) error are 7.34 nm and 9.79 nm and the peak-valley (PV) error are 87.04 nm and 90.36 nm for the Step and Sphere, respectively. The experiment shows that the proposed method has good performance in calculation accuracy and efficiency.
The European Metrology Programme for Innovation and Research project: Implementing the new kelvin 2 (InK2)

The International System of Units (SI) is to be redefined with implementation set to be on World Metrology Day (20th May 2019). Under the auspices of the Consultative Committee of Thermometry (CCT) the world thermometry community has been working together to ensure a smooth and effective redefinition of the kelvin. A large part of that activity has been coordinated through the European Metrology Programme for Innovation and Research: “Implementing the new kelvin” projects. This paper describes the InK2 project contribution.