Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of submergence ratio on flow pattern around short T-head spur dike in a mild bend with rigid bed using numerical model

Abstract Spur dikes are hydraulic structures frequently applied to protect rivers’ banks. Since flow pattern around a spur dike located in a rivers’ bend is particularly complicated due to the existence of secondary flow and helical flow, the effect of submergence ratio on flow patterns around a short T-head spur dike located in a 90° bend has been monitored numerically in this study. The numerical data are verified with experimental data obtained under non-submerged conditions, which indicate the numerical results are in accordance with experimental data. In this research, the numerical simulation based on various submergence ratios is done using FLOW-3D software to analyze. The results showed an increase in dimensions and the number of vortices associated with an increase in submergence ratio so that for the amount of 50% submergence, two vortices form around outer wall and an area downstream of the wing of spur dike, at bed level. Moreover, the length and width of separation zones were estimated to be 1.6 and 1.5 times larger compared to non-submerged condition, respectively.
The Marine-Atmospheric Emitted Radiance Interferometer: A High-Accuracy, Seagoing Infrared Spectroradiometer

Abstract The Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) is described, and some examples of the environmental variables that can be derived from its measurements and the types of research that these can support are briefly presented. The M-AERI is a robust, accurate, self-calibrating, seagoing Fourier-transform interferometric infrared spectroradiometer that is deployed on marine platforms to measure the emission spectra from the sea surface and marine atmosphere. The instrument works continuously under computer control and functions well under a very wide range of environmental conditions with a high rate of data return. Spectral measurements are made in the range of ∼3 to ∼18 μm wavelength and are calibrated using two internal, National Institute of Standards and Technology–traceable blackbody cavities. The environmental variables derived from the spectra include the surface skin temperature of the ocean, surface emissivity, near-surface air temperature, and profiles of temperature and h.
Impact of Emotional Intelligence and Artificial Intelligence on Employee Retention: A Review of the Service Industry

Employee retention is one of the biggest challenges in today’s work environment. Organizations are constantly working on different tactics and strategies at organizational and individual levels to retain their talent. To handle the scenario, Artificial intelligence (AI) and Emotional Intelligence (EI) are emerging as significant dimensions to be taken up for the research. Thus, this study aims to examine the role of emotional and artificial intelligence in retaining employees. To investigate the association between these two important variables, a review of the literature was conducted. The findings demonstrate that much research has validated the association between AI and EI and the impact thereof on employee retention.
Analysis of Factors Influencing The Use of QRIS Through Mobile Banking in Jakarta and Tangerang

The growth of Indonesia’s payment via mobile phones has increased lately. In 2019, QRIS was launched by Bank Indonesia and it has been used to make payments using every application that utilizes the QRIS system. By the year of 2025, Bank Indonesia expects to focus on digital finance, including banking digitalization. However, the number of QRIS transactions are still less than other payment methods. Therefore, in order to better understand users’ acceptance of QRIS as a payment method through mobile banking applications, this research is conducted. This study uses the TAM (Technology Acceptance Model) with other independent variables using a quantitative method by distributing questionnaires to people that uses QRIS on mobile banking applications in Jakarta and Tangerang with 355 respondents. The data was examined using the Partial Least Square (PLS) approach of the Structural Equation Model (SEM) using the analytical tool SMART PLS. The findings in this study show that perceived compatibility affects both perceived usefulness and perceived ease of use. Effort expectancy affects perceived ease of use, all three factors of perceived security, perceived usefulness, and perceived ease of use affects attitude toward using, and attitude toward using affects behavioral intention to use. However, social influence does not affect both perceived usefulness and perceived ease of use. The result of this study may give the QRIS payment system room for improvements to be more impactful in their users’ daily lives therefore they are able to influence other people to use QRIS as a payment system.
Performance Analysis of Machine Learning - Semantic Relational Approach based Job Recommendation System

The rapid growth of software computing and web technology has broadened users' horizons, allowing them to investigate web methods for obtaining goods or services. It has opened up tremendous opportunities for corporates to explore users' search patterns or behaviours to make personalized business decisions and recommendations. The research paper aims to illustrate the machine learning methods based recommendation framework that extracts competencies from the website and matches individuals with jobs whose descriptions closely match their profiles. On LinkedIn and Facebook sets of data, this experiment is carried out. Word-2vec idea was used to retrieve semantic meaning, then processed using a multi-phased or multiplexed feature search algorithm that included Wilcoxon Significant Test, Significant Predictor Test and Cross-Correlation Test. To ensure jobs with the highest (recommendable/prediction) scores, the framework included Majority Voting Ensemble (MVE) concept. It uses several machine learning methods and the findings demonstrate that the ensemble classifier achieves the expected result compared to other mechanisms.
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and a New Benchmark

To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR lacks visual results for model interpretation and FAS compromises downstream recognition due to artifacts. Therefore, we propose a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. Specifically, we propose an attention-based feature decomposition to decompose the mixed face features into two uncorrelated components—identity- and age-related features—in a spatially constrained way. Unlike the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, which can improve the age smoothness of synthesized faces through a weight-sharing strategy. Benefiting from the proposed multi-task framework, we then leverage those high-quality synthesized faces from FAS to further boost AIFR via a novel selective fine-tuning strategy. Furthermore, to advance both AIFR and FAS, we collect and release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children. Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance than state-of-the-art methods for both AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, obtaining competitive performance on face recognition in the wild. The source code and datasets are available at http://hzzone.github. io/MTLFace.
A Nudity Detection Algorithm for Web-based Online Networking Platform

Social media and online platforms have become an integral part of the day-to-day life of the people and the organizations, which shows a lot of diversity. Many online platforms allow users to share media extensively, like images, videos, and audio but not all the media content shared on the internet is safe for work or should be restricted from particular platforms. Images with nudity are of big concern when it comes to online media sharing and social platforms must take serious measures to restrict users to share images with nudity. The rate of growth of nudity content over the internet or social media is very high. It affects the mental and physical health of individuals. In this paper, a novel method for nudity detection in images is presented. We aim to develop a Machine Learning algorithm for nudity detection in a community networking platform and how it should be integrated with such a platform so that posted images can be thoroughly checked for nudity and if detected how it should be dealt with.
Performance Analysis of Machine Learning Approaches for Stroke Prediction in Healthcare

A stroke is a situation that happens due to insufficient blood supply to the brain. Nowadays, there is an increased number of deaths which can be caused due to many reasons including stroke as one of the major reasons. Other impacts of stroke are facial paralysis and one side of body paralysis. This is a big concern as it could lead to an increased death rate. But, due to the advancements in the field of technology, it is easy to make predictions about a stroke. Various machine-learning techniques are used for this purpose. In this article, four major techniques are used Naive Bayes, KStar, multilayer perceptron, and random forest. To apply these classifiers the dataset is taken from Kaggle and by using the WEKA tool the performance is evaluated in the form of accuracy, recall, precision, F-measure, and execution time. In the proposed work, it is found that random forest works best with the highest accuracy of 95.02%. Further, the performance of the proposed model has also been compared with the existing works and it has been found that the proposed model exhibits better performance. Additionally, the work may contribute to the healthcare sector to provide assistance to clinicians for the early prediction of strokes.
Performance Comparison of Full Adder Design using Different Adiabatic Logic Techniques

In arithmetic logic units (ALUs), one-bit full adder cells are the most widely used digital circuit elements. Arithmetic operations are performed using FA. They are also the core functionality of all computational circuits. This paper compares the performance of a full adder using different adiabatic logic techniques. Power dissipation during switching events is reduced by adiabatic logic. A simulation of the circuits has been performed using SPICE in 0.18um CMOS technology. Analysis of ECRL, PFAL is compared with conventional CMOS logic at different supply voltages varying from 1 V to 2 V. The ECRL full adder indicates less power dissipation of 90.719pW with a delay of 37.351ns compared to CMOS and PFAL full adder at a supply voltage of 1.8V. Comparative analysis indicates ECRL adder is the better outcome with PDP variations from [4.228 - 1.116]*10-21 J.
The Heyde Theorem on a Group R n × D , Where D is a Discrete Abelian Group

Heyde proved that the Gaussian distribution on the real line is characterized by the symmetry of the conditional distribution of one linear form given another. The present article is devoted to a group analogue of the Heyde theorem. We describe distributions of independent random variables ξ 1 and ξ 2 with values in a group X = R n × D , where D is a discrete Abelian group, which are characterized by the symmetry of the conditional distribution of the linear form L 2 = ξ 1 + δξ 2 given L 1 = ξ 1 + ξ 2 , where δ is a topological automorphism of X such that Ker ( I + δ) = { 0 } .
Sentiment Analysis of Audio Files Using Machine Learning and Textual Classification of Audio Data

Sentiment Analysis has an increasing implication in solving Human-Machine collaboration issue. It’s a tough task to be able to know how an individual feels but it seems even worse to recognize these sentiments using a machine. As we all know in today’s world with day-to-day advancement in technologies people are searching for more and more easy and convenient ways to operate, with subsequent growth in the applications of Artificial Intelligence (AI), it now has generated a need to spontaneously identify the sentiments of the person involved in the Human Computer Interaction (HCI). The demand for sentiment analysis is increasing and it now is applied in various parts of industry. This research paper discusses the methods to identify various sentiments from human conversation using textual classification of audio data.
A/B Testing and Audience Creation for Effective Digital Marketing: Evidences from Facebook Analytics

Digital marketers use a number of strategies, including custom audiences, lookalike audiences, ad creative, and A/B testing, to organize and enhance how they present advertisements to consumers. The purpose of this research is to show an effective method and process for running advertisements on Facebook more efficiently. The study has taken a case study of a company- Great Deal Tires and analyzed the Facebook analytics data and insights with A/B testing and audience creation approach to assess the digital marketing impact. The study has implemented three test strategies for designing and placing Facebook ads for Great Deal Tires in order to assess the marketing impact. In the first test, a custom audience was chosen, and an identical Facebook video ad’s impact was evaluated based on the duration of the ad. In the second test, A/B testing was used to test Facebook ads, and the results of the tests were compared across three different audience groups to determine how well the ads performed. The third test evaluated on the audience conversion in the terms of subscribing to the Page, in which three different ads were shown to three distinct audience groups in order to compare the effects of the various ads on the diverse audiences. The study’s findings provided important information about Facebook ad placement, its effects on various audience segments, and digital marketing strategies to increase audience outreach. The study is important in demonstrating that Facebook advertisements are an effective marketing medium and a digital marketer can achieve good outreach with a modest investment of funds.
River Channel Relocation: Problems and Prospects

River relocation is the diversion of a river into an entirely new channel for part of their length (often called river diversions). Relocations have been common through history and have been carried out for a wide range of purposes, but most commonly to construct infrastructure and for mining. However, they have not been considered as a specific category of anthropogenic channel change. Relocated channels present a consistent set of physical and ecological challenges, often related to accelerated erosion and deposition. We present a new classification of river relocation, and present a series of case studies that highlight some of the key issues with river relocation construction and performance. Primary changes to the channel dimensions and materials, alongside changes to flow velocity or channel capacity, can lead to a consistent set of problems, and lead to further secondary and tertiary issues, such as heightened erosion or deposition, hanging tributaries, vegetation loss, water quality issues, and associated ecological impacts. Occasionally, relocated channels can suffer engineering failure, such as overtopping or complete channel collapse during floods. Older river relocation channels were constructed to minimise cost and carry large floods, and were straight and trapezoidal. In some countries, modern relocated channels represent an exciting new challenge in that they are now designed to replicate natural rivers, the success of which depends on understanding the characteristics, heterogeneity, and mechanisms at work within the natural channel. We discuss shortcomings in current practice for river relocation and highlight areas for future research for successful rehabilitation of relocated rivers.
Traditional Indian Textiles Classification using Deep feature fusion with Curvelet transforms

With the increasing demand for online shopping in the competitive fashion market, new designs come as early birds, and fabric texture plays a crucial role in selecting the correct fabric design. The Indian traditional textile patterns are varied and vibrant, and it exhibits the culture of the area of its origin. They are widely in demand in the international market. Unfortunately, with the incursion of mechanization and the low yield of handmade textiles, artisans for such art forms are dwindling. Generating new designs in sync with the market stream is time-consuming, and the art must be learned meticulously. Hence, every single attempt at developing technology to conserve these art forms is the moment’s need. This paper proposes a Traditional Indian Textiles Classification based on a fusion of deep features and Curvelet transforms. The traditional motif images are complex. Rather than straight lines, it contains curves; hence, the curvelet transform is designed to handle it. Compared with other transforms, Curvelet transforms allow a more systematic representation of other singularities and edges along lines. This work was tested with the Indian art forms datasets. It utilized the pre-trained CNN architecture (InceptionresNetV2, VGG16) as a feature extractor and concatenated these features with curvelet features. In this experiment, the XGB classifier provided the best results (precision 98.24%, recall 97.15%, F1Score 97.15%, accuracy 97.15%, and specificity 99.52%) with 4scale Curvelet and InceptionResNetV2 feature sets. These initial results are promising and motivate further work on larger and more complex datasets.
A Concise Survey On Predicting Stock Prices

Stocks price prediction is a current highly profitable area of research where future stock value predictions are made utilising different deep learning models and various machine learning algorithms. Due to the erratic price changes, market participants have exhibited interest in the subject of stock prediction. Since stock prices are effected by various factors, it becomes difficult to predict the trend in which the direction of price moves. This forecasting of prices helps investors reduce risk and help them to take informed decision. This papers reviews previously proposed model ranging since 2015 to 2022. All papers are classified into different segments that are then compared and analyzed, this research also reviews the data sets and the models proposed in this paper. This research compares hybrid machine models and through this survey the authors conclude that machine learning techniques are rapidly used for financial modeling.
Direct Writing Target Structures by Two-Photon Polymerization

Abstract Two-photon polymerization (2PP) offers an attractive option for direct writing micron- to millimeter-sized laser target components that support high-energy-density plasma physics research. 2PP was used to deterministically print a number of common targetlike structures including tubes, spatially periodic Rayleigh-Taylor–like surfaces, and low-density foams. The structures were printed using commercially available acrylic photoresins. The elemental compositions are reported for comparison with other polymers used for making target components. A number of foamlike structures ranging in size from tens to hundreds of microns and varying in density from 600 to 60 mg/cm3 were readily printed in times ranging from several seconds to a few hours depending on the size. In addition, direct printing was demonstrated to fabricate graded-density foam comprising 12 individual layers with a vertical density gradient of 600 to 80 mg/cm3. Control of shrinkage and deformation during development and subsequent drying remains a challenge for certain structures and a focus of ongoing research.
How IoT-Enabled Smart Manufacturing Affect Firm Performance through Internationalization: A Systematic Literature Review

Internet of Things is a global network infrastructure consisting of aspects of “Things” that are connected to an information network and have identity, physical form, virtual personality, and intelligent appearance. In the implementation of smart manufacturing, IoT has become key enabling factor that help its optimalization. Smart manufacturing is a collection of manufacturing activities that use integrated information and communication technology in the process of managing manufacturing operations. The purpose of making this paper is to produce a systematic literature review that can help discuss and examine what Internet of Things is and how it is implemented in smart manufacturing, how IoT enabled smart manufacturing can support internationalization efforts, and the impact that internationalization efforts can have towards firm performance. The steps taken in making a systematic literature review include three main steps, namely SLR planning, process, and report. The report shows that implementation of smart manufacturing could help bring competitive advantage for firm and support internationalization effort that would affect firm’s performance. The success of internationalization process has a varied impact on the company’s performance according to various reference results, where there is a common concept, which is a decrease in performance at the beginning of the internationalization process due to the capital expansion costs required.
Tunable plasmonic-lattice mode sensors with ultrahigh sensitivities and figure-of-merits

We study the application of plasmonic lattice modes of arrays of closely packed large metallic nanodisks for chemical and biological sensors with ultrahigh sensitivity and refractive index dynamic range. Our results show that by changing the refractive index of the environment the narrow spectral features associated with these collective modes can be shifted by about 250 nm, going from visible (∼650 nm) to infrared (∼900 nm) range without any mode degradation. We attribute this shift to the refractive-index enhancement of the superstrate collective modes of these arrays. This index enhancement allows the arrays to preserve their mode integrity within this range. We show that, because of this feature and the ultra-long range fields of these modes, such structures can offer a unique platform for biosensors based on dielectric-coated metallic nanoparticle arrays. In such structures, the dielectric layers are used to tune the collective modes of the arrays, protect them against environmental degradation, and .