Miracle-dz/UniFAR-FTUs huggingface dataset:
Customer Segmentation Based on Recency Frequency Monetary (RFM) and User Event Tracking (UET) Using K-Means Algorithm

Business competition in e-commerce today is very tight, so every company is competing to increase sales. One is by providing the best customer service, so customer loyalty is formed to continue transacting again. Therefore, companies are required to be able to recognize and establish closeness with their customers. Customer segmentation is an effort to group customers based on customer characteristics and behavior in conducting transactions. With customer segmentation, companies can provide more targeted treatment and services. However, producing the proper customer segmentation requires a complicated data analysis process. In this study, customer segmentation was carried out using the K-Means clustering method based on the Recency, Frequency, and monetary (RFM) model combined with the User Event Tracking (UET) parameter. Based on the results of tests that have been carried out on 1,447,984 transaction data and 932,021 user tracking data, the resulting customer segmentation is divided into 3 (three) groups, namely Platinum (43.9%), Gold (9.5%), and Silver (46.6%). Companies can run different marketing strategies for each of these customer groups.
HD 53975 : AN O-TYPE SPECTROSCOPIC BINARY WITH A LARGE MASS RATIO

We present radial velocities and line profiles of the O-type star HD 53975, which we find to be a spectroscopic binary with a period of 6.0173 days. Although there are no obvious signatures of the secondary in the line profiles, we have used the merit function of Shafter et al. (1980) to show that a secondary component is present in lines found in B-type spectra. We have compared the observed merit functions with merit functions for model profiles constructed assuming a main-sequence secondary, and this comparison consistently indicates a companion with a mass ratio of q = M(sub 2)/M(sub 1) = 0.23 +/- 0.04 and a magnitude difference Delta m = 3.3 +/- 0.4. We present a tomographic reconstruction of the spectrum of the secondary for these parameters. We suggest that other large mass ratio binaries may be found among the O stars when observed with high-resolution and high signal-to-noise ratio spectroscopy.
Dynamic Power Control for Delay-Optimal Replicated Edge Computing

The recently advocated replicated edge computing can effectively exploit the rich ambient computing resource to fulfill better edge intelligence applications. Specifically, in replicated edge computing, the same task is replicated and offloaded to multiple edge nodes so as to allow them to perform collaborative transmission to reduce communication latency. Nonetheless, existing pioneering works in this regard often assume the ideal high signal-to-noise ratio (SNR) scenarios and transmit power control is often ignored. In practice not only the edge nodes may have limited power budget but also the wireless channels may experience random fading. With this consideration, transmit power control is studied in this work for effective operations of replicated edge computing in more general dynamic wireless environments. However, highly nontrivial non-convex optimization is involved and existing solutions for conventional wireless communications may not suit due to the joint influence of both communication and computing on the long-term energy consumption. To address this technical challenge, by exploiting the underlying structural property, a novel partition-based iterative optimization method is developed, which, together with the generic Lyapunov optimization framework, allows us to obtain the closed-form expression for the optimal dynamic power control strategy. In addition, simulations are conducted to validate the effectiveness of the proposed scheme.
Modulation for Massive Unsourced Random Access Based on Tensor Block Term Decomposition

In this paper, we propose a novel tensor-based modulation scheme for massive unsourced random access. The proposed modulation can be deemed as a summation of three-dimensional tensors, where the symbols should be invariant with scaling and rotation. We provide two encoding methods for the proposed modulation: QPSK encoding and Grassmann encoding. The essential uniqueness of tensor decomposition provides a theoretical guarantee for active user separation. Simulation results show that our proposed method outperforms the state-of-the-art tensor-based modulation scheme for unsourced random access.
Ultra-wideband Supercontinuum Generation through Liquid Core Photonic Crystal Fiber

This paper presents and evaluates a Borosilicate glass photonic crystal fiber with a carbon disulfide core for the generation of supercontinuum(SC). The proposed design has a high nonlinear coefficient of = 3.934 W-1m-1 and = 2.856 W-1m-1 at operating wavelengths of =1300 nm and = 1550 nm, respectively. The SC is simulated with the Split-Step-Fourier (SSF) method by solving the Nonlinear Schrödinger Equation (NLSE), and the modal analysis of the given design is done with the Full-Vectorial Finite-Element Method (FV-FEM). The performance of the SC generation has been thoroughly investigated in terms of suggested fiber length, pumping peak power, and operational wavelength. It has been demonstrated that the proposed design has a short zero-dispersion wavelength (ZDW), allowing pumping at 1550 nm. Additionally, the reported design provides an ultra-broadband SC spectrum of 8182 nm and 3009 nm at the wavelengths of 1550 nm and 1300 nm, respectively, with an incredibly compact device length of 5 mm. The achieved ultra-broadband spectra had the highest bandwidth with an incredibly short device length for the two operational wavelengths, 1550 nm, and 1300 nm.
Convolutional Neural Network based Human Emotion Recognition System: A Deep Learning Approach

Recent research focuses towards Expression recognition. Variety of applications is now available ranging from security cameras to detecting emotions. Facial recognition is an important activity in emotion detection Convolutional Neural Networks (CNN) are used for facial recognition. Images are taken as input and facial expressions are produced as outcome like Happy, Sad, Disgust, Angry, Fear, Surprise and neutral. In this paper, we propose an Artificial Intelligence (AI) which recognizes the facial emotions using the different layers in the CNN. Thorough examination of deep Face Expression Recognizer (FER), including datasets and methods that shed light on these underlying difficulties. First, the FER scheme, which includes pertinent background information, is implemented for seeking advice for each level. The dataset used for experimentation is FER challenge dataset available in kaggle repository. The implementation environment includes keras, tensorflow, cv2 python packages. The results include the comparison of accuracy of emotion detection between training and testing phase. The average accuracy achieved was 84.50%.
Digital Banking Features and Customer Satisfaction

The various types of banking services have provided many choices and alternatives to bank users. In this case, there is fierce competition between the banks' services. The profitability of a bank is based on the number of loyal customers. It means to obtain loyal customers; the bank should provide services that match their demands and satisfy them. Due to the advancing technology, traditional banking gradually had been replaced by electronic banking. Therefore, it is crucial to understand the level of customer satisfaction with electronic banking. The primary data were collected through administrating questionnaire. Convenient sampling procedure was used to obtain 220 responses from customer of banking services. Multiple regressions were used to investigate the relationship between dependent and independent variables. The results of the regression test showed that internet banking, point of sale and website design have positive impact customer satisfaction.
Approximation error for invariant density calculations

Let $T:[0,1]\rightarrow[0,1]$ be an expanding piecewise-onto map with bounded distortion and countably many intervals of monotonicity. We prove a uniform bound on the rate of exponential convergence to equilibrium for iterates of the Perron-Frobenius operator. The quantitative information thus obtained is applied to prove explicit error bounds for Ulam's method for approximating invariant measures. The approach also yields rates of mixing for the matrix representations of Ulam approximation. "Monte-Carlo" type simulations of the scheme are discussed. The method of proof is applicable to multi-dimensional transformations, although the only generalisations presented here are to a limited class of "non-onto" one-dimensional transformations.
Fire Detection In Wetland Using YOLOv4 And Deep Learning Architecture

One of the problems in wetlands is fire. Apart from material loss, land fires also cause vegetation degradation which has an impact on biodiversity and indirect effects such as haze. Current fire detection approach is utilizing Wireless Sensor Networks (WSN), but its use has disadvantages, namely the possibility of false alarms, limited space, and high-power consumption when using many sensors and additional cameras. Based on the shortcomings of WSN, several studies have suggested the use of Computer Vision. This is because the use of computer vision has advantages in terms of direct verification and only requires a camera device. One of the computer vision techniques is object detection, where fire and smoke are objects in an image. The current development of object detection that has good accuracy performance is YOLOv4. Therefore, YOLOv4 is used for detection and classification of wetland fires with image and video input. YOLOv4 model training using the transfer learning method resulted in 92.39 mAP performance on the CSPDarknet53-PAN-SPP architecture and experimental results on CSPResnet50-PAN-SPP (Full Mish Activation) resulted in mAP 92.13. In testing images and videos, CSPDarknet53-PAN-SPP has a higher accuracy result of 94% on images and 90% on video. The YOLOv4 average speed of the CSPResnet50-PAN-SPP model reaches a higher average value of 275.63 milliseconds in the image and 14.7 FPS in the video than CSPDarknet53-PAN-SPP. Therefore, the YOLOv4 model CSPDarknet53-PAN-SPP is better to serve as a prototype for detection and classification of fires in wetlands in term of accuracy.
Stability of cellular neural networks with opposite-sign templates

Cellular neural networks (CNNs) with opposite-sign templates have been successfully applied by T. Matsumoto et al. in connected component detection (CCD) in feature extraction (see ibid., vol.37, p.633-5, 1990). A stability analysis of this class of nonreciprocal CNN is provided by L.O. Chua et al. (see ibid., vol.37, p.1520-7, 1990). In this paper, a thorough stability analysis of this type of CNNs which shows the dependence of complete stability on the template values is presented. Parameter regions for complete stability and instability are determined, and the parameter region for the functionality of CCD is also given based on this investigation. Simulation examples verify that the complete stability of CNN with opposite-sign templates is not always preserved. >
Deep Learning-based Channel Estimation in 5G MIMO-OFDM Systems

The primary air interface of fifth-generation (5G) wireless communication is multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM). In addition, channel estimation holds a major role in achieving effective system performance in 5G wireless networks. Importantly, deep learning (DL) techniques have the ability to improve system performance and reliability and to reduce the computational complexity of 5G communication systems. Hence, DL techniques have been applied in MIMO-OFDM systems to improve the channel estimation quality and reduce the bit error rate compared to traditional channel estimation methods. This paper offers a comprehensive survey of DL-based channel estimation in MIMO-OFDM receivers in terms of DL algorithms, 5G channel models, and error performance. In conclusion, the application of DL techniques in MIMO-OFDM shows a significant potential for performance enhancement in 5G networks.
Synchronization Techniques for Multi-threaded Web Server: A Comparative Study

With the exponential growth and advancement of the internet and network technologies, the number of active internet users is expected to exceed 5 billion worldwide by 2025. As a result, designing high performance multi-threaded web server capable of concurrently handling thousands of incoming requests per second is in high demand. To achieve this goal, it is essential for the multi-threaded web server to utilize synchronization algorithm to coordinate the simultaneous use of resources by clients; avoid data inconsistency and still provide timely responses. The aim of this study is to compare and evaluate the performance of different traditional and modern synchronization algorithms adopted by multi-threaded web servers. An empirical experiment is conducted that simulates a file synchronization problem in distributed systems to evaluate how the performance of a multi-threaded web server is affected by those synchronization algorithms. Mutex locks, Spinlocks, Semaphore, and WebR2sync are evaluated, and the performance of each synchronization algorithm is measured using three performance metrics: Synchronization Time, Failure Rate, and Scalability. Results show that Semaphore has the fastest synchronization time, WebR2sync has the lowest failure rate, and Mutex excels at scalability. Such findings help with the decision of which synchronization algorithms to utilize for multi-threaded web servers based on the requirements.
Technical, Economical, Environmental feasibility of Solar PV System for Sustainable Shrimp Aquaculture: A Case Study of a Circular Shrimp Pond in Indonesia

The shrimp aquaculture industry in Indonesia has continued to expand, leading to a higher energy demand. The new circular shrimp pond technology has gained much attention; this method uses paddle wheel aeration and an air blower to satisfy the dissolved oxygen in the pond. The energy consumption by both devices is intensive and associated with greenhouse gas emissions. This study demonstrates an optimal configuration for a solar PV system to satisfy the electrical requirements for circular pond technology in shrimp aquaculture. This paper presents a technical, economic, and environmental analysis for achieving a cost-effective configuration using actual data from a supply and demand perspective. The results show that integrating 28.4-kW solar PV panels, a 5.52-kW DC-AC inverter, and 114 strings of 1-kW lead-acid battery storage banks is the configuration that leads to a minimum cost of energy (COE) of 0.468 $/kWh. Such a configuration can reduce emissions by 7,349 kg of carbon dioxide, 31.9 kg of sulfur dioxide, and 15.6 kg of nitrogen oxide per year. Using a solar PV system reduces the negative environmental impact and allows sustainable shrimp aquaculture.
Chemical generation of atomic iodine for a COIL via atomic fluorine

Results of experimental investigation of the chemical generation of atomic iodine for a Chemical Oxygen-Iodine Laser (COIL) are presented. The work was focused on the reaction system with atomic fluorine as an intermediate species produced by the chemical way from gaseous reactants. At the first step, atomic fluorine is produced in reaction of molecular fluorine with nitrogen oxide. Then F atoms react with gaseous hydrogen iodide producing atomic iodine. The efficiency of this two-step process was studied thoroughly in dependence on mixing conditions, flow rate of reacting gases and pressure in the reactor. The results obtained on the small-scale device under experimental conditions simulating pressure and flow conditions in a COIL show that atomic iodine is generated by this alternative, advantageous method with rather high concentrations sufficient for operation of the supersonic COIL.
Stability of parabolic difference approximations to certain mixed initial boundary value problems

We consider the equation t- a(x, t)u. - b(x, t)u. - c(x, t)u = f(x, t) in a region 0 ? x ? 1, t > 0, with inhomogeneous initial and boundary data. We are con- cerned with stability and estimates on divided differences in the maximum norm for solutions of consistent implicit, multistep, parabolic difference approximations to this problem. Using a parametrix approach, we give sufficient conditions for certain estimates to be valid. 1. Introduction. In this paper, we shall consider invertibility, stability, and smoothness up to the boundary of the solutions of a general class of implicit multistep approximations to heat-type equations in one space variable in regions with bound- aries. In an earlier paper (3), we proved a stability theorem for more general problems but allowed only constant coefficients. Here, we require mild smoothness of the coefficients, and we then estimate not only the solution, but certain of its divided differences. The results extend those of Varah (11). In his paper, he considered only explicit one-step approximations to the constant coefficient equation with no lower order terms. The main results in both involve the kind of normal mode analysis which was discussed in (2), (3), (4), (12). The parametrix technique was used by Widlund (8) in his paper on approximations to the initial-value problem for more general parabolic systems. Many of the estimates for this mixed problem are similar to those he obtained for the analogous initial- value problem. The invertibility technique modifies some of the ideas of Strang (6), (7) in his papers on difference schemes for which the solution is assumed identically zero outside the region. Our accuracy assumptions at the boundary and the normal mode condition at z = 1 seem to be necessary in order to obtain the appropriate estimates on the first divided differences up to the boundary. The example (9.1) below indicates the diffi- culties involved in weakening these assumptions. This last example was due to Bj6rn Engquist, and the author would like to thank him for several stimulating discussions on these and related problems.
Role of Noroviruses in Sporadic Acute Gastroenteritis Cases from Children Attending a Large Referral Children’s Hospital in Buenos Aires City, Argentina

Background: Noroviruses (NVs) are recognized as the leading cause of sporadic and epidemic acute gastroenteritis worldwide, in all age groups. Although there is increasing knowledge that NVs are responsible for many acute gastroenteritis outbreaks in Argentina, studies to estimate prevalence in sporadic cases are scarce. Methods: A descriptive, observational and cross-sectional study was conducted with children under 5 years with acute gastroenteritis attending the outpatient department at the “Ricardo Gutiérrez” Children’s Hospital (RGCH) in Buenos Aires City between June 2017 and June 2021. Sociodemographic, clinical and epidemiologic data were recorded. Stool samples were tested and genotyped for norovirus. Association between norovirus detection and demographic and clinical variables was assessed. Results: A total of 350 children with acute diarrhea were included, of which stool samples were collected for 332 (94.9%). Norovirus was detected in 81 cases (24.4%). Vomiting and moderate/severe diarrhea were more frequent in norovirus-positive than norovirus-negative children. However, the presence of watery diarrhea and a history of rotavirus vaccination were significantly associated with norovirus etiology. GII and GII.4 were the most frequently detected genogroup and genotype, respectively. Conclusions: NVs were detected with high frequency, mostly in children between 6 months and 2 years old, reinforcing the hypothesis of a newly updated scenario of norovirus predominance over rotavirus. Watery diarrhea, complete vaccination against rotavirus and vomiting are 3 key parameters that should raise suspicion of possible norovirus gastroenteritis. Continuous and active norovirus surveillance in this age group is important because children represent a priority group for norovirus vaccine design and development.
Dual beam Doppler FD-OCT system with integrated Dynamic Vessel Analyzer and rotatable beams to measure total retinal blood flow

We present a method capable of measuring the total retinal blood flow in arteries and veins based on dual beam Fourierdomain Doppler optical coherence tomography (OCT) in combination with a fundus camera based Dynamic Vessel Analyzer. Incorporating a Dynamic vessel analyzer into the system not only gives a live image of the fundus – it also allows determining the vessels’ diameter precisely during the OCT measurement, which is necessary for the determination of the blood flow. While dual beam systems with fixed detection plane allow only vessels with certain orientations to be measured, the detection plane of our system can be rotated by 90°. This ensures that the blood’s velocity can be measured in all vessels around the optic nerve head. The results of the total blood flow measurements are in the same range as previously published data. Additionally, the high degree of conformity between the measured venous and arterial flow corroborated the system’s validity. For larger vessels, the logarithmic values of vessel diameter and blood flow were found to be related linearly with a regression coefficient of around 3, which is in accordance with Murray’s law. For smaller vessels (diameter below 60 μm), the values diverge from the linear dependence. The high sensitivity and the good agreement with published data suggest a high potential for examining the retinal blood flow in patients with ocular diseases.
FORCED VIBRATIONS OF ELASTIC BENDING–TORSION COUPLED BEAMS

The objective of the present paper is to analyze coupled bending and torsional vibrations of distributed-parameter beams. The governing coupled set of partial differential equations is solved by separating the dynamic response in a quasistatic and in a complementary dynamic response. The quasistatic portion that may also contain singularities or discontinuities due to sudden load changes is determined in a closed form. The remaining complementary dynamic part is non-singular and can be approximated by a truncated modal series of fast accelerated convergence. The solution of the resulting generalized decoupled single-degree-of-freedom oscillators is given by means of Duhamel's convolution integral, whereby the acceleration of the loads is the driving term. The proposed procedure is illustrated for a dynamically loaded simply supported beam with channel cross-section, and the improvement in comparison to the classical modal analysis is demonstrated.
Planetary nebulae as standard candles. I. Evolutionary models

Models of PNs are used here to simulate observations of an ensemble of PNs in order to study luminosity distributions in the emission lines of H-beta and forbidden O III 5007 A. The resulting luminosity distributions agree very well with the observational data, thus supporting the use of the constancy of the shape of the forbidden O III luminosity distribution to derive distances to galaxies. The sharp bright-end cutoff seen in the data can be reproduced in the models only if the mass distribution of central stars is narrow and a substantial fraction of bright PN central stars are burning helium. It is concluded that either there are very few high-mass central stars or the true evolutionary track differs from those published. The sharp cutoff further suggests that the brightest PNs in a galaxy may be used with some confidence as a standard candle. 65 refs.