Miracle-dz/UniFAR-FTUs huggingface dataset:
MISPhyl: Mutual Information Supermatrix Phylogenomics pipeline

Classification of species, particularly pathogens are important in understanding mode of infections, their origins and even treatments. The advancement of sequencing technologies has offered a parallel advancement in species classification with the used of phylogenomics. The success of phylogenomics requires a series of bioinformatics methods and procedures, which would be a challenge for most clinical analysts or researchers. Herein, we present MISPhyl (Mutual Information Supermatrix Phylogenomics), an automated pipeline developed to facilitate the reconstruction of high confidence supermatrix tree using the median-ranked single-copy core orthologous genes (SCGs) evaluated via mutual information in annotated genome sets. In house accessory scripts for sequence quality assessments, renaming FASTA headers and subsampling based on mutual information are distributed along with MISPhyl, made publicly available (https://github.com/pendy05/MISPhyl).
Opportunities for DERs in Ancillary Services Markets: analysis and deployment strategies in a national framework

Ancillary Services Markets (ASMs) in Europe are evolving to welcome Distributed Energy Resources (DERs). For such a goal, several issues have to be addressed; among them, market transparency and economic efficiency is one of the most sensitive, especially within central dispatch systems such as the Italian one. This is crucial not to favor market incumbents, and to ensure an adequate learning curve for newcomers. In this paper an analysis of Italian ASM dynamics is provided to highlight possible business opportunities for DERs. In particular, a spatial analysis of accepted and presented market bids has been performed. Results point out a heterogeneous statistic for awarded bids: many offers are awarded even if their price is largely not convenient (so-called “extreme” bids), thus in contrast with an economic efficiency principle. The paper studies the topology of these strategic bidders and the consequent potential benefit coming from DERs deployment in strategic areas. Results allow to draw some suggestions for both market operators and regulatory authorities.
Understanding The Factors Influencing Consumer Purchase Intentions via Instagram Shopping Feature

This study aims to examine the influence of factors that influence product purchase intention on Instagram shopping feature. This study developed a conceptual model with 6 hypotheses from 7 variables: social motive, escapism motive, value motive, hedonic enjoyment, utilitarian value, information quality, and purchase intention. For this study, therefore, data on Instagram shoppers was collected and analyzed involving 616 respondents with a 6-point Likert scale using the online survey form. We used the snowball sampling technique and the SmartPLS 3.0 application to evaluate the research model. The analysis results found four variables affecting the interest to purchase intention: escapism motive, hedonic enjoyment, information quality, and value motive. However, two factors showed no association with purchase intention: social motive and utilitarian value. The findings of this study have several theoretical contributions and practical implications for marketing for evoking purchase intention using the Instagram shop feature.
Factors Influencing Online Shopping Intention in Indonesia

The purpose of this study is to determine the factors that influence the Indonesian people so that they decide to shop online through e-commerce. Data was collected in May 2nd, 2022 until June 2nd, 2022 through a google form questionnaire which was collect as many as 526 respondents from various regions in Indonesia using purposive technique sampling. Data processing uses the SmartPLS application to measure convergent validity, discriminant validity, and path coefficients. The results of the analysis show that Payment Options, Online Shopping Awareness, Online Shopping Perception, Privacy, and Security affect Online Shopping Intention variable. Meanwhile, Website Quality does not have a positive relationship with Online Shopping Intention variable because most consumers prefer to shop through applications on smartphones than through websites. The conclusion is that Indonesian people tend to choose to shop online compared to shopping in-store because shopping online, flexible, practical, competitive prices, quality comparable to offline purchases, and of course shopping can be done online and at any time via internet.
Conditional quenching: a detailed look at the SFR−density relation at $z$
∼ 0.9 from ORELSE

We present a study of the star-formation rate (SFR)-density relation at z ~ 0.9 using data drawn from the Observations of Redshift Evolution in Large Scale Environments (ORELSE) survey. We find that SFR does depend on environment, but only for intermediate-stellar mass galaxies (10^10.1 < M* / Msol < 10^10.8) wherein the median SFR at the highest densities is 0.2-0.3 dex less than at lower densities at a significance of 4 sigma. Interestingly, mass does not drive SFR; galaxies that are more/less massive have SFRs that vary at most by ~20% across all environments showing no statistically significant dependence. We further split galaxies into low-redshift (z ~ 0.8) and high-redshift (z ~ 1.05) subsamples and observe nearly identical behavior. We devise a simple toy model to explore possible star-formation histories (SFHs) for galaxies evolving between these redshifts. The key assumption in this model is that star-forming galaxies in a given environment-stellar mass bin can be described as a superposition of two exponential timescales (SFR ~ e^(-t/tau)): a long-tau timescale with tau = 4 Gyr to simulate "normal" star-forming galaxies, and a short-tau timescale with free tau (between 0.3 < tau/Gyr < 2) to simulate galaxies on a quenching trajectory. In general we find that galaxies residing in low/high environmental densities are more heavily weighted to the long-tau/short-tau pathways respectively, which we argue is a signature of environmental quenching. Furthermore, for intermediate-stellar mass galaxies this transition begins at intermediate-density environments suggesting that environmental quenching is relevant in group-like halos and/or cluster infall regions.
On comparing partitions

Rand (1971) proposed the Rand Index to measure the stability of two partitions of one set of units. Hubert and Arabie (1985) corrected the Rand Index for chance (Adjusted Rand Index). In this paper, we present some alternative indices. The proposed indices do not assume one set of units for two partitions. Here, one set of units can be a subset of the other set of units. According to the purpose of the comparison of two partitions, the merging and splitting of clusters in two partitions can have different impact on the value of the indices. Therefore, we proposed different modified Rand Indices.
Deep 3D Model Optimization for Immersive and Interactive Applications

The growing diffusion of immersive and interactive applications is posing new challenges in the multimedia processing chain. When dealing with AR and VR applications, the most relevant aspects to consider are the (1) quality of the visualized 3D objects and (2) the fluidity in the visualization in case the user is moving in the environment. In this framework, we propose a deep learning based approach that estimates the optimal model parameters to be used in relation to the viewer’s movement and the model characteristics and quality. The performed tests show the effectiveness of the proposed approach.
Robot-based Environmental Monitoring in Automated Life Science Laboratories

Processes in classic life sciences laboratories are characterized by the handling of a wide variety of chemicals and solvents. Leaks or accidents when handling the samples and labware can result in the uncontrolled escape of these often harmful or explosive substances. The monitoring of the gas concentrations in the room air of the laboratories is therefore of particular importance. In this research paper, the integration and testing of two metal oxide semiconductor gas sensors are presented. The sensors have been combined with a low-cost IoT-based microcontroller in a small sensor node to transfer the collected sensors data from the sensor node to the IoT cloud which is responsible for data processing, storing, and monitoring. The sensor node was attached to a mobile robot. The sensor node was tested with six different volatile organic solvents (VOCs). Two gas leakage positions were used to evaluate the sensor’s performance in different leakage situations and distances. Five solvent volumes were investigated to differentiate the sensor’s response and to select the active detecting thresholds for triggering an alarm in case of a dangerous level of toxic/ hazardous gases level detected.
Analysis of Factors Affecting the Intensity of Mobile Transportation Applications Usage in Pekanbaru

This study was made to analyzes the factors that affects the intensity of mobile transportation applications usage in Pekanbaru. This study is intended to find the correlation between behavioral intention to use main factor such as service value, service quality, and satisfaction. In this study, researcher is using quantitative research as the research method. Purposive sampling was used to collect the data. The data collection of this study is survey. While the survey's link was up from 12th June to 29th June 2022, it collected a final amount of 238 respondents. The survey is made with google forms and shared through online groups on Line, WhatsApp, and Discord that is populated with Pekanbaru residents. This study target respondent is for those who has stayed in Pekanbaru and has already used mobile transportation application in Pekanbaru. 2 hypotheses are rejected in this study as the result of this study. The hypotheses are perceived usefulness and perceived enjoyment has a positive relationship, satisfaction and behavioral intention has a positive relationship. This study found that service value and service quality are the main driver of behavioral intention to use the service. Although satisfaction doesn't serve as main driver of behavioral intention, it still affects the usage of the application. Limitation of this studies is the sample size that can be improved by further studies that add new variables such as accessibility and security.
Aspect Based Multilabel Text Classification for Identifying Dangerous Speech Twitter Text

As part of hate speech, dangerous speech is any expression that can increase the risk of committing violence against other people. So far, hate speech research only explains whether some sentences is categorized as hate speech. It does not explain aspects of the sentences that make them called dangerous speech. Aspects of dangerous speech are social context, historical context, dehumanization, the accusation in the mirror, women and children attack, loyalty to the group, and group threat. This study uses the multi-label text classification method to determine dangerous speeches on Twitter texts based on seven aspects. Then, we assign a weighted score from those aspects to differentiate dangerous and hate speech. Based on the test results show the best performance is the Naive Bayes method with label-based subset accuracy (±36 %), instance-based (average) accuracy (±86%) and classification accuracy (±77%). However, even though Naive Bayes has the best performance in terms of instance based (average) accuracy, the average difference between all methods with Naive Bayes is only ± 0.014, this indicates that other methods also produce quite good performance.
J- AND H-BAND IMAGING OF AKARI NORTH ECLIPTIC POLE SURVEY FIELD

We present the J- and H-band source catalog covering the AKARI North Ecliptic Pole field. Filling the gap between the optical data from other follow-up observations and mid-infrared (MIR) data from AKARI, our near-infrared (NIR) data provides contiguous wavelength coverage from optical to MIR. For the J- and H-band imaging, we used the FLoridA Multi-object Imaging Near-ir Grism Observational Spectrometer on the Kitt Peak National Observatory 2.1 m telescope covering a 5.1 deg2 area down to a 5σ depth of ∼21.6 mag and ∼21.3 mag (AB) for the J and H bands with an astrometric accuracy of 0. ″14 and 0. ″17 for 1σ in R.A. and decl. directions, respectively. We detected 208,020 sources for the J band and 203,832 sources for the H band. This NIR data is being used for studies including the analysis of the physical properties of infrared sources such as stellar mass and photometric redshifts, and will be a valuable data set for various future missions.
The ICT Paradox in Indonesia: ICT Investment and Firm Profitability

This study examines the ICT paradox using data from Indonesian firms. Specifically, it aims to investigate the impact of investing in ICT capital on the profitability of Indonesian firms. We use data from companies listed on the LQ45 index for the research sample, with a five-year observation from 2014 to 2018. Firm profitability is measured using ROA, while ICT capital is proxied using the ICT investment and intensity. Control variables included in this study are firm size, age, and leverage. We then employ a Cobb-Douglas process and fixed-effect panel data regression to analyze the data. Our findings suggest that ICT investment affects firms' performance positively. However, companies could not as well invest too much in ICT as we found that the square of ICT investment variable has a significant negative coefficient, indicating decreasing benefits of ICT investment after a certain amount. Furthermore, the relationship between ICT investment and performance was reinforced by the negative sign of the ICT intensity dummy variable. Based on this finding, we encourage firms to invest in ICT capital but be mindful not to over-invest as outstretching ICT investment could adversely affect profit.
Fatigue Level Prediction of Lower Extremity Knee Flexor and Extensor Muscles using Neural Network

The purpose of this paper is to build a model to predict the fatigue level of lower extremity knee flexor and extensor muscles based on sensor data. We used maximum voluntary contraction (MVC) to assess the fatigue level of muscles. We collected walking data from six healthy adults at four MVC levels. These data come from sixteen pressure sensors on the insoles and two inertial measurement units (IMUs) on the two feet. Fifty-one gait features were extracted from these data. Through using the ReliefF Algorithm, the fourteen features most relevant to the MVC level were extracted. The accuracy of neural network classifier is 72.7%. This indicates that the selection of features and the use of neural networks is reasonable. This study used fewer sensors and predicted more subdivided fatigue levels than previous studies. Findings from this study can help better understand how fatigue of knee flexor and extensor muscles affect gait characteristics, and further aid in developing an early warning system to prevent knee injuries.
Personal Chatbot Evaluation with Elaboration Likelihood Model on Social Messaging Application

This study proposes a chatbot technology adoption model with an elaboration likelihood model theory approach. The theory of technology acceptance model (TAM) is used as a reference for the development of models that not only evaluate models functionally such as perceived ease-of-use and perceived usefulness but also socially such as perceived sociability. Hypothesis testing was carried out by analyzing the respondent's data with a total of 392 data using the CB-SEM method. It was found that the ELM theory can be applied in the context of the adoption of chatbot technology. The peripheral route (visual attractiveness, language mastery, and reputation) has a positive correlation with perceived sociability. The central route has a strong positive correlation to perceived usefulness and a moderate positive correlation to perceived ease of use. Perceived sociability and perceived usefulness were found to strongly predict user intentions to interact. Meanwhile, perceived ease-of-use does not have a significant effect on user intentions for continuous interaction. This research reinforces previous studies which found that chatbots need not only functional acceptance but also social acceptance. With these results, developers can find out how priorities must be arranged in the development of chatbots that are used sustainably by their users.
Voting Neural Network (VNN) for Endoscopic Image Segmentation

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the recent few years. Recent studies show that 80%–95% of the endoscopic abnormalities are found in the intestine which would be the leading cause of cancer-related deaths globally. However, the conventional method of screening and diagnosis in colonoscopy is highly dependent on the expert endoscopist’s skills and experience. To automate colonoscopy analysis, several computer vision based techniques have been proposed for polyps detection and segmentation. However, polyp segmentation is a challenging research problem due to polyp morphology variabilities and intrinsic image appearance. The main goal of this research is an early and accurate segmentation of polyps to accurately determine the polyp region. In this paper, a heterogeneous ensemble deep learning model is proposed that combines the knowledge of three state-of-the-art deep learning architectures including UNet, ResUNet, and ResUNet++. Experiments are conducted on two real world datasets: Kvasir-SEG, and CVC-Clinic [1] and compared with state-of-the-art techniques. The proposed approach resulted in an accuracy of 0.93 on test data.
Real-time Style Transfer for Videos to Enhance the Realism of Simulation of Laparoscopic Surgeries

Surgical simulation has repeatedly proven its potential, but is limited by the lack of realism to the surgical experience. For laparoscopic surgery simulation, an image processing method known as style transfer can improve the realism by transferring the style of a picture from surgery onto the video of the simulator using a stylisation network. In this article, we propose an adjustable style transfer algorithm for videos to improve the realism of silicone based models. The results show that our method can successfully implement multiple stylisations in real-time onto the video of the simulator while maintaining temporal consistency and overall smoothness of the video. Comparing to other style-transfer methods, our technique can offer multiple stylisations and, except from the method of Johnson et al. [6], achieves better realness score when evaluated by surgeons. This method can also be trained on a generic database in only 3h43 contrary to the other image processing techniques such as image translation which require specific training datasets.
Analysis of Intention and Use of Audit Tools Linked Archive System (ATLAS) at Public Accounting Firms

The goal of this study is to explore the influence of performance expectation, effort expectation, social influence, and facilitating conditions on the intention to continue using Audit Tools and Linked Archive System (ATLAS) by the external auditor at a public accounting firm in Indonesia. This study adopted the Unified Theory of Acceptance and Use of Technology. This study collected data from 126 external auditors using the purposive sampling method. The empirical data indicate that performance expectations, effort expectations, and social influence have a substantial impact on continuation intent. Moreover, facilitating conditions have no appreciable positive impact. This study suggests that an accounting firm needs to provide better IT support and improve the quality of information about the parties that can be contacted if there are difficulties and obstacles in using ATLAS.
A Nonuniform Sampling Lifetime Estimation Technique for Luminescent Oxygen Measurements

This paper presents a nonuniform sampling technique for measuring the lifetime of luminescent materials for oxygen sensing. The system features a switched-capacitor circuit to implement fixed-voltage steps for quantization, enabling long integration times without saturating the front-end amplifier. A control circuit automatically tunes the light emitting diode (LED) excitation pulses to avoid overpowering or starving the front end as photodiode current varies with changes in the partial pressure of oxygen. Time gating of the front-end integrator removes the need for optical filtering. The analog front end (AFE) has a gain bandwidth product of 10 MHz and an input-referred noise of 124 $\mu V_{rms}$ (measured 200 Hz - 100 kHz). The circuit was realized in 180 nm CMOS technology. The AFE and LED driver consume a maximum of 16 $\mu J$ per calculation. We have demonstrated the entire system's functionality by measuring oxygen concentrations from 0 to 240 mmHg in a controlled gas vessel. The results indicate satisfactory linearity on a Stern-Volmer plot covering the human-relevant range of 50 to 150 mmHg.
Stable wave segments in a discrete-continuous medium

Nervous tissue simultaneously has the properties of both a discrete set of individual neurons that transmit an electrical signal to each other, and diffusion-binding intercellular space connecting it. We have found stable wave segments that occur naturally in such a discrete-continuous medium and are resilient to scaling. Unlike the previously described wave segments, they do not require constant noise exposure or feedback to maintain. The discovered SWS are stable to small perturbations expressed in spatial inhomogeneities and scaling. We have described possible scenarios for their emergence and evolution and analyzed the main properties of the observed structures.
The Effect of Image Dimension and Exposure Fusion Framework Enhancement in Pneumonia Detection Using Residual Neural Network

This study tries to find the performance of ResNet 50, 101 and 152 version 1 architecture to classify normal and pneumonia using chest X-ray. For the second goal, the effect of image dimension is investigated. Furthermore, the different distribution of training, validation and testing images in the dataset is explored, i.e. dataset A and B. 5,838 chest X-ray images are used in dataset A. For dataset B, 5,856 images are used. These images are distributed randomly as training, validation and testing images with compositions of 80%, 10% and 10%. For each dataset, 12 image dimensions are used, i.e., 96 × 96; 128 × 128; 160 × 160; 192 × 192; 224 × 224; 256 × 256; 288 × 288; 320 × 320; 352 × 352; 384 × 384; 416 × 416 and 448 × 448 pixels. Moreover, this study used exposure fusion framework as image enhancement in the preprocessing stage. In general, ResNet 152 architecture has better performance than ResNet 50 and 152. However, the best performance is achieved by dataset A using ResNet 50 and an image dimension of 384 × 384 pixels. This model has an accuracy, sensitivity, specificity, precision, F1-score and ROC of 97.6%, 99.3%, 93.1%, 97.5%, 98.4% and 96.2%. The pneumonia detection using ResNet is affected by the distribution of training, validation and testing images. Furthermore, there is no correlation between the image dimension and the detection performance.