Miracle-dz/UniFAR-FTUs huggingface dataset:
A Literature Review of Human, Organization, Technology (HOT) – Fit Evaluation Model

An evaluation model that is used to evaluate the electronic service system came to prominence a decade ago with the appearance of Yusof 2007 article on Human, Organization, Technology (HOT) and Net Benefit specifically on health informatic systems. Since that time, the approach has attracted many researchers to use the framework in evaluating electronic system services in several sectors. This paper systematically examines cases of HOT fit evaluation model practices comport with the use of the evaluation model integrating the HOT fit component by reviewing 30 articles conducting the evaluation model. The data figured out by classifying the article onto the sector that had been criticised through the evaluation model by using the help of Atlas.ti software program. Evidence derived from this review to repudiate or substantiate many of the claims put both by critics of and advocates for evaluation model are presented as an agenda for future research on the future research. Further research is recommended to overcome the development of technological development.
Short-Term Stock Price Forecasting Based on an SVD-LSTM Model

Stocks are the key components of most investment portfolios. The accurate forecasting of stock prices can help investors and investment brokerage firms make profits or reduce losses. However, stock forecasting is complex because of the intrinsic features of stock data, such as nonlinearity, long-term dependency, and volatility. Moreover, stock prices are affected by multiple factors. Various studies in this field have proposed ways to improve prediction accuracy. However, not all of the proposed features are valid, and there is often noise in the features—such as political, economic, and legal factors—which can lead to poor prediction results. To overcome such limitations, this study proposes a forecasting model for predicting stock prices in a short-term time series. First, we use singular value decomposition (SVD) to reconstruct the features of stock data, eliminate data noise, retain the most effective data features, and improve the accuracy of prediction. We then model the time-series stock data based on a long short-term memory (LSTM) model. We compare our proposed SVD-LSTM model with four state-of-the-art methods using real-world stock datasets from two Chinese banks: Ping an Bank and Shanghai Pudong Development Bank. The experimental results show that the proposed method can improve the accuracy of stock price predictions.
Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented.
A Review on Object Tracking Across Real-World Multi Camera Environment

Object identification, tracking and monitoring are significant and testing assignments in numerous computer vision applications, for example, surveillance, vehicle navigation and self-governing robot navigation. Video reconnaissance in a powerful environment, particularly for people and vehicles, is one of the momentum testing research points. It is a key innovation to battle against illegal activity, offence, public security and for effective administration of traffic. The work includes planning of the proficient video observation framework in complex conditions. In video observation, recognition of moving items from a video is significant for object discovery, target tracking, and behavior understanding. Recognition of moving items in video transfers is the primary significant advance of data and background subtraction is a famous methodology for frontal segmentation. In this paper, after surveying ongoing advances of online article, we do enormous scope tries different things with different assessment standards to see how these algorithms perform. By breaking down quantitative outcomes, we distinguish viable methodologies for powerful following and give potential future examination bearings in this field.
Predictive programmatic re-targeting to improve website conversion rates

In the era of programmatic advertising, the advertisers have huge amount of first party data to leverage on enabling them to do highly granular re-targeting. Programmatic re-targeting is the ability to use data to show an ad to a user who has demonstrated an interest in your product offerings before. Re-targeting ads are a powerful conversion optimization tool and are typically known to outperform conventional targeting in terms of performance. As per 99 Firms, 41% of marketing allocation in 2018 to paid display spend was on re targeting and for most of the websites, only 2% of web-traffic converts on the first time visit. In this paper, a conversion is referred as a purchase made and a converting user is one who made the purchase on the website. The question that arises is - “should we be re-targeting all the users who have landed on the site? ”. In ad campaigns which has low budgets or in campaigns where the conversion rate is really low even though a huge volume of users visit the site, it may not make complete sense to simply re-target all those users, instead we would want to re-target those who are clearly showing an intent to make a purchase either through their on-site browsing behaviors or their past conversion patterns. Through this paper we present the use of first party privacy preserving data to do predictive programmatic re-targeting of users who are going to make a conversion in the next few days given their past site-browsing and conversion behavior using a structured data science and advanced ML based framework. Additionally, this project allows to tie the model results to real time programmatic activation by the creation of user segments depending on whether the user is going to make a conversion for the first time, or is converting again. The final outputs are these user segments, which are going to be used by in house ad-traders who would be able to bid deferentially for a specified period of time against each of the segments on a demand side platform. We have successfully tested this model on 2 advertising clients and were able to capture 80-85% of the actual converts happening over the next few days of them landing.
Preparation and Characterization of Pt–Ru Bimetallic Catalysts Synthesized by Electroless Deposition Methods

Two series of Pt@Ru/C and Ru@Pt/C bimetallic catalysts have been prepared by an electroless deposition (ED) method. For Pt@Ru/C compositions, a new ED bath was developed using Ru(NH3)6Cl3 as the Ru precursor and HCOOH as the reducing agent. For Ru@Pt/C preparations, a standard bath using H2PtCl6 and DMAB as the Pt precursor and reducing agent, respectively, was employed. The Pt@Ru/C and Ru@Pt/C bimetallic catalysts have been characterized by temperature-programmed reduction (TPR), selective chemisorption, X-ray photoelectron spectroscopy (XPS), and scanning transmission electron microscopy (STEM) with X-ray energy dispersive spectroscopy (XEDS). TPR and selective chemisorption (H2 titration of oxygen-precovered surfaces) experiments have confirmed the existence of strong surface interactions between Pt and Ru, as evidenced by hydrogen spillover of Pt to Ru (Pt-assisted reduction of oxygen precovered Ru). XPS analyses also showed e– transfer from Pt to Ru on the bimetallic surface, again indicating strong .
Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network and exponential smoothing

Tuberculosis (TB) morbidity rate in Indonesia shows the number of population in Indonesia who suffer from TB. The TB morbidity rate can be used by insurance companies to predict a person’s risk of TB so that insurance companies can determine the premiums that will be charged to insurance applicants based on the risks. Thus, the ability to estimate the TB morbidity rate accurately is essential for insurance companies to be able to determine the right premium amount while remaining competitive. This study compared two models that can be used to predict TB morbidity rate in Indonesia. The model was built using the temporal convolutional neural network (TCNN) and exponential smoothing methods. The data analyzed in this study are data obtained from the official website of the Ministry of Health of the Republic of Indonesia. Before the model was built, the data used in this study were compiled into training and validation datasets. The model is built using a training dataset and validated using the validation dataset. The results of the model’s validation are then evaluated and compared based on the value of the mean squared error (MSE). The result of this study shows that the TCNN model provides lower MSE compared to exponential smoothing.
iTSP-PseAAC: Identifying Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC

The uncontrolled growth due to accumulation of genetic and epigenetic changes as a result of loss or reduction in the normal function of Tumor Suppressor Genes (TSGs) and Pro-oncogenes is known as cancer. TSGs control cell division and growth by repairing of DNA mistakes during replication and restrict the unwanted proliferation of a cell or activities, those are the part of tumor production. This study aims to propose a novel, accurate, user-friendly model to predict tumor suppressor proteins, which would be freely available to experimental molecular biologists to assist them using in vitro and in vivo studies. The predictor model has used the input feature vector (IFV) calculated from the physicochemical properties of proteins based on FCNN to compute the accuracy, sensitivity, specificity, and MCC. The proposed model was validated against different exhaustive validation techniques i.e. self-consistency and cross-validation. Using self-consistency, the accuracy is 99%, for cross-validation and independent testing has 99.80% and 100% accuracy respectively. The overall accuracy of the proposed model is 99%, sensitivity value 98% and specificity 99% and F1-score was 0.99. It concludes, the proposed model for prediction of the tumor suppressor proteins can predict the tumor suppressor proteins efficiently, but it still has space for improvements in computational ways as the protein sequences may rapidly increase, day by day.
A review of the gas hydrate distribution offshore Chilean margin

In last decades, the Chilean margin has been extensively investigated to better characterize the complex geological setting through the acquisition of geophysical data and, in particular, seismic lines. The analysis of seismic lines allowed identifying the occurrence of gas hydrates and free gas in many places along the margin. Clearly, the gas hydrate reservoir could be a strategic energy reserve for Chile, but, on the other hand, the dissociated of gas hydrate due to climate change could be an issue to face. Moreover, this region is characterized by large and mega-scale earthquakes that may contribute to gas hydrate dissociation and consequent submarine slides triggering. In this context, Chilean margin should be considered a natural laboratory to study the hydrate system evolution.
High Yield Super-Hydrophobic Carbon Nanomaterials Using Cobalt/Iron Co-Catalyst Impregnated on Powder Activated Carbon

Synthesis of super-hydrophobic carbonaceous materials is gaining a broader interest by the research community due to its versatile application in separation processes, special coating technologies, and membrane distillation. Carbon nanomaterials (CNMs) may exhibit stable super-hydrophobic character due to their unique physio-chemical features which can be further controlled based on customer requirements by optimizing the process variables. This study deals with the application of a bimetallic catalyst composed of iron (Fe) and cobalt (Co) to synthesize CNMs from powder activated carbon as a precursor. The process parameters were optimized to ensure super-hydrophobic surfaces. Chemical vapor deposition was utilized for the growth of carbon nanomaterials. The impact of input variables on the desired output of yield and contact angle was analyzed. The chemical vapor deposition process was optimized using the response surface methodology based on Box-Behnken design. The proportion of the catalysts and reaction time were the three input explanatory variables whereas the desired response variables were selected as the carbon yield (CY) and contact angle (CA). The synthesized super-hydrophobic materials were characterized using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), Raman spectroscopy, thermogravimetric analysis (TGA), and contact angle analysis. The comprehensive statistical study of the results led to a significant model and optimization. The highest CY (351%) and CA (173°) were obtained at the optimal loading of 2.5% Fe and 2% Mo with a reaction time of 60 min. The images obtained from FESEM and TEM revealed the presence of two types of CNMs including carbon nanofibers and multiwall carbon nanotubes. Thermogravimetric analysis was carried out to observe the temperature degradation profile of the synthesized sample. Raman spectroscopic analysis was also used to observe the proportion of ordered and disordered carbon content inside the synthesized samples. The improved catalytic super-hydrophobic carbon nanostructured materials production process proposed by this study assures the stability and high yield of the product.
Efficient multi-level lung cancer prediction model using support vector machine classifier

This paper aims at the requirement for an interactive learning framework which empowers the successful checking of disorder in a patient. Principal component analysis stands out as an outstanding algorithm to significantly classify the target classes. PCA blends associated characteristics and makes a dissipated showcase of its components well. Scree plot examination gives solidarity of how many principal components are to be retained. Support Vector Machines (SVM ) is a fast and dependable classification algorithm that outperforms other techniques with a limited amount of data. The obtained components will be served to Support Vector Machine for further classification. The pre-dangerous stage will remind the clinical experts to give additional consideration to those patients. The expectation ability is estimated in terms of the confusion matrix. The model developed gives a high and uncompromising accuracy in early detection of different levels of malignancy
Neutral-Point-Clamped Five-Level Inverter With Self-Balanced Switched Capacitor

In this article, a new five-level inverter is developed by inserting a switched-capacitor (SC) unit into the traditional three-level neutral-point-clamped (NPC) inverter phase leg. The SC unit consists of two capacitors and one bidirectional switch, all of which withstand a quarter of the dc input voltage. While increasing the output levels, the performance is also improved in terms of power loss, common-mode voltage, switching stress dv/dt, and output filter. Compared to other five-level inverters like the conventional NPC and active NPC five-level topologies, the new solution not only reduces the number of components and simplifies the design but also has the advantage of self-balanced capacitor voltages. The analysis, simulation, and experiment indicate that the proposed inverter is suitable for a wide range of applications like renewable source grid-connected interfaces and motor drivers. Simulation and experimental results of grid-connected operation verify that the new inverter is capable of providing both active and reactive power to the grid. Its excellent performance is also experimentally evaluated by a 1.2-kW prototype and the measured efficiency is above 97% for a wide range of load.
An overview of compute first networking

: Edge computing has become an important innovative business model in the 5G era, especially its low latency feature, which is considered to be not available in traditional solutions. Therefore, edge computing can provide more service capabilities and has a wider range of application scenarios. However, the collaboration of computing power between edge computing and cloud computing in the central position has become a new technical problem. That is, it is necessary to achieve cloud-to-network collaboration, cloud-to-edge collaboration, or even edge-to-edge collaboration among edge computing, cloud computing, and network to achieve the optimisation of resource utilisation. On the basis of studying the compute distribution and scheduling requirements of edge computing, this paper introduces a compute network scheme based on the deep fusion of cloud, edge and network, compute first networking. Firstly, we introduce the basic concept and related work of compute first networking. Then, we mainly discuss the framework and key technology of compute first networking. After that, we present some applications with respect to compute first networking. Finally, we discuss the challenges and opportunities in the area of compute first networking.
Current status and perspective of remote sensing application in crop management

Knowledge of the spatial distribution of agricultural crops and crop rotation is necessary for the understanding of the farming practices concerning the long-term sustainability of agricultural production. Agricultural crops are increasingly subject to drought due to the effects of global climate change, and the same is true for Croatia due to the constant rise in mean annual temperatures and uneven rainfall distribution. Remote sensing methods have proven to be superior in the detection and monitoring of drought compared to conventional methods of observation from meteorological stations. Information on the condition of crops in the early stages of development indicates potential irregularities in the development of agricultural crops. The objective of this paper is to provide a perspective for the application of remote sensing in crop management using state-of-the-art methods. Analysis of the possible implementation of these methods in Croatia was performed on a macro- and micro-level. Spatial classification, cropland suitability multicriteria analysis, drought assessment, weed detection and crop density calculation were evaluated according to the necessary equipment and data processing segments. Remote sensing application in crop management offers a potential basis for better crop management both at the macro-level for land use planning and at the micro-level for family farms.
Genetic Purity of Cacao Criollo from Honduras Is Revealed by SSR Molecular Markers

The cultivation of cacao represents an income option and a source of employment for thousands of small producers in Central America. In Honduras, due to the demand for fine flavor cacao to produce high-quality chocolate, the number of hectares planted is increasing. In addition, cacao clones belonging to the genetic group named Criollo are in great demand since their white beans lack of bitterness and excellent aroma are used in the manufacturing of premium chocolate. Unfortunately, the low resistance to pests and diseases and less productive potential of Criollo cacao leads to the replacement with vigorous new cultivars belonging to the other genetic groups or admixture of them. In this study, 89 samples showing phenotypic traits of Criollo cacao from four regions of Honduras (Copán, Santa Bárbara, Intibucá, and Olancho) were selected to study their genetic purity using 16 SSR molecular markers. The results showed that some samples belong to the Criollo group while other accessions have genetic characteristics of “Trinitario” or other admixtures cacao types. These results confirm the genetic purity of Criollo cacao in Honduras, reaffirming the theory that Mesoamerica is a cacao domestication center and also serves to generate interest in the conservation of this genetic wealth both in-situ and ex-situ.
Pedestrian Motion Trajectory Prediction in Intelligent Driving from Far Shot First-Person Perspective Video

Pedestrian motion trajectory prediction is an important task in intelligent driving, and it can provide a valuable reference for the subsequent path decision of intelligent driving. However, so far, there are only a few models in the field of specific pedestrian motion track prediction in intelligent driving from far shot first-person perspective video. To accomplish this task, we proposed a deep learning model for pedestrian motion trajectory prediction from far shot first-person perspective video with four key innovations: a) A macroscopic pedestrian trajectory prediction module is established under the close correlation between neighboring frames to estimate the pedestrian motion track on the whole; b) A relative motion transformation module of vehicle-mounted camera is designed to consider the effect of vehicle-mounted camera’s ego-motion on the pedestrian motion track; c) We set up a circular training module to maintain the number of parameters in our model to simplify and reduce the size of model; d) A new far shot first-person pedestrian motion dataset under intelligent driving is specifically established to train and test the proposed model. The above four modules are integrated into the proposed deep learning model, which achieves state-of-the-art results for predicting pedestrian motion trajectory from both far and close shot first-person perspective video.
Green inclusive leadership and green creativity in the tourism and hospitality sector: serial mediation of green psychological climate and work engagement

Abstract Green creativity contributes to green innovation and green sustainability in both the manufacturing and services sectors. However, academic research offering more nuanced insights about the drivers of green creativity (GCRT) is deficient, particularly within the tourism and hospitality sector. The present study thus theorized a model based on the Componential Theory of Creativity (CTC) to examine green inclusive leadership (GIL), green psychological climate (GPC), and green work engagement (GWE) as antecedents of GCRT. The model not only posits the direct associations but also accommodates a more complex interaction of variables by anticipating the mediation effect of GPC and GWE on the association of GIL and GCRT. The data collected through Prolific from 302 employees in the tourism and hospitality sector is analyzed using PROCESS macro. Findings confirmed a positive association of GIL with GPC and GCRT, GPC with GWE, and GWE with GCRT. In addition, the sequential mediation effect of GPC and GWE is supported. These results are useful for the tourism and hospitality sector, where a relational leadership style, such as inclusive leadership, is more relevant for fostering creativity, given the small size of business units, the sector's service-oriented nature, and the power of employees at the point of interface with the customers.