Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic variation of Austropuccinia psidii in some species of Myrtaceae as host plants in Java, Indonesia based on simple sequence repeats (SSR) markers

Abstract. Faradilla FA, Prihatini I, Suranto, Susilowati A. 2022. Genetic variation of Austropuccinia psidii in some species of Myrtaceae as host plants in Java, Indonesia based on simple sequence repeats (SSR) markers. Biodiversitas 23: 256-263. Austropuccinia psidii is pathogenic rust with a wide host and is considered a biosecurity threat to the Myrtaceae family in many countries. The genetic variation of this rust in Java, Indonesia is poorly reported. Therefore, this study aimed to determine the genetic variation among A. psidii from different Myrtaceae hosts, namely Syzygium polyanthum, S. myrtifolium, and Melaleuca cajuputi from three different locations in Java using seven simple sequence repeats (SSR) markers. Data were collected from 28 A. psidiisamples from three different hosts and locations in Java. The genetic variations of A. psidii were found in six isolates in locus USYD_Pp168, PpSSR161, and PpSSR195*. The results showed that the expected heterozygosity value among 12 isolates of A. psidii is 0.312. The dendrogram illustrates two clusters constructed with cluster I consisting of subcluster IA (S1, S3, S4, S5, K1, K2, K3, K4, and P1) and IB (P2); and cluster II consists of isolates S2 and S6. Principal Coordinate Analysis (PCoA) was used to demonstrate the similarity and dissimilarity among isolates based on microsatellite sites. Axis 1 and 2 explained 87.7 % of the total variations and sep a rated isolate S1, S2, S3, S4, S6 from other isolates, and grouping the rest of isolates together. The dendrogram and PCo analysis demonstrated A. psidii isolates tend to have close genetic similarity based on their host.
Research on the Application of Deep Learning in Automatic Vehicle

In recent years, various kinds of applications of artificial intelligence technology to the automatic driving vehicle were widely reported by the media, also aroused public interest. According to a survey by the Ministry of Public Security, there are more than 500 million motor vehicles in China in 2021 and more than 200 thousand traffic accidents occurred in 2021, which is a great challenge to traffic safety protection. Human reaction time, which consists of perception time and judgment time, is too long to take measures. Besides, human drivers may find it difficult to even make the right response in some complex road conditions. To improve this situation, the Intelligent Transportation System (ITS) may transform the traditional passive safety that takes protective measures after accidents into active safety that focuses on prevention. Deep learning network receives information through the vehicle-loaded sensor then makes judgments by its computing unit and prompts drivers the possible danger. This article reviewed the performance of different deep learning models in autonomous driving vehicles.
Contents and determinants of CSR website communication toward international stakeholders: evidence from Chinese MNCs

Abstract With Chinese companies increasingly transforming into powerful social and economic institutions in the global market, they have begun to face progressive pressure to sustain communication with international stakeholders in a socially responsible fashion. This study represents a preliminary attempt to explore Chinese MNCs’ corporate social responsibility (CSR) communication to their international audiences through corporate websites, as well as its company-level determinants. The CSR information posed by 149 leading Chinese MNCs on their official English websites was analyzed in terms of their visibility, extent, and themes, and the impact of four company-level factors on the communication was tested. The findings indicated that Chinese MNCs generally presented CSR information in a visible and readily accessible way, yet the extent of the communication was relatively low. The communication focused on issues related to consumers and stakeholders, followed by the community and employees, with inadequate addressing of suppliers. The positive impact of company-level determinants (i.e. degree of internationalization, overseas experience of top management, and industry affiliation to polluting sectors) on communication was confirmed. Theoretical and practical implications for online CSR communication in the global context are subsequently discussed.
Agile project management in a public context: case study on forms of organising

Agile development methods were developed to enhance innovations and productivity in software projects, increase customer collaboration and flexibility, and enable dynamic approach to change management. These key principles of agile approach responded to the challenges that project management faced with software projects. The agile methods are increasingly adopted by the public sector which traditionally is not considered as agile but control-oriented and bureaucratic. In this paper, a single case study method is used to explore how the adoption of agile methods is managed in the context of a large governmental agency. This study examines a public software development project utilising agile methods and analyses the form of organising in the agile project using a framework focusing on the universal problems of organising: task division, task allocation, reward distribution and information flows. As a result, the paper presents the case project's solutions to the problems of organising and discusses the differences between agile project setup and the traditional project management approach to manage a project organisation in the public sector context. In the case project, task division was centralised and owned by a project owner, task allocation was done by an autonomous agile team, reward distribution was not used to create additional incentives, and information flows were based on virtual communication tools and occasional meetings.
Does Poor Mental Health Impair the Effectiveness of Complementary Food Hygiene Behavior Change Intervention in Rural Malawi?

Mental disorders have the potential to affect an individual’s capacity to perform household daily activities such as water, sanitation, and hygiene (food hygiene inclusive) that require effort, time, and strong internal motivation. However, there is limited detailed assessment about the influence of mental health on food hygiene behaviors at household level. We conducted a follow-up study to detect the effects of mental health on food hygiene behaviors after food hygiene intervention delivery to child caregivers in rural Malawi. Face-to-face interviews, based on the Risk, Attitude, Norms, Ability, and Self-regulations (RANAS) model, were conducted with 819 participants (control and intervention group) to assess their handwashing and food hygiene-related behaviors. Mental health was assessed using the validated Self-Reporting Questionnaire. Study results showed a significant negative relationship between mental health and handwashing with soap behavior (r = −0.135) and keeping utensils in an elevated place (r = −0.093). Further, a significant difference was found between people with good versus poor mental health on handwashing with soap behavior (p = 0.050) among the intervention group. The results showed that the influence of the intervention on handwashing with soap behavior was mediated by mental health. Thus, integration of mental health in food hygiene interventions can result in improved outcomes for caregivers with poor mental health.
Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network

Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management.
Density Functional Theory Study on NiNx (x = 1, 2, 3, 4) Catalytic Hydrogenation of Acetylene

In this study, using the application of density functional theory, the mechanism of graphene-NiNx (x = 1, 2, 3, 4) series non-noble metal catalysts in acetylene hydrogenation was examined under the B3LYP/6-31G** approach. With the DFT-D3 density functional dispersion correction, the effective core pseudopotential basis set of LANL2DZ was applied to metallic Ni atoms. The reaction energy barriers of NiNx catalysts are different from the co-adsorption structure during the catalytic hydrogenation of graphene-NiNx (x = 1, 2, 3, 4). The calculated results showed that the energy barrier and selectivity of graphene-NiN4 for ethylene production were 25.24 kcal/mol and 26.35 kcal/mol, respectively. The low energy barrier and high activity characteristics showed excellent catalytic performance of the catalyst. Therefore, graphene-NiN4 provides an idea for the direction of catalytic hydrogenation.
Quality Assurance in Distance Education through Data Mining

Learning Management Systems (LMS) are software applications that facilitate the management and monitoring of online teaching courses and/or training programs, workshops, webinars, forums, and other similar learning activities. The LMS provides learning and teaching benefits and possibilities for synchronous, asynchronous, and hybrid training. For instance, learning management systems (LMS) can store a wide variety of large-scale educational data. The stored data can be analyzed by employing educational data mining methods. Educational data mining (EDM) is a new discipline that deals with methods for exploring the unique and large-scale data generated by digital platforms to better understand students’ learning progress and the learning environment itself. In this study, the data stored in the LMS used by Balıkesir University during the fall semester of the 2021–2022 academic year were analyzed by using educational data mining methods in order to reveal the current status of distance education activities and make suggestions to improve the quality.
Band gap tuning of armchair silicene nanoribbons using periodic hexagonal holes

The popularity of graphene owing to its unique and exotic properties has triggered a great deal of interest in other two-dimensional nanomaterials. Among them silicene shows considerable promise for electronic devices with a carrier mobility comparable to graphene, flexible buckled structure, and expected compatibility with silicon electronics. Using first-principle calculations based on density functional theory, the electronic properties of armchair silicene nanoribbons perforated with periodic nanoholes (ASiNRPNHs) are investigated. Two different configurations of mono-hydrogenated (:H) and di-hydrogenated (:2H) silicene edges are considered. Pristine armchair silicene nanoribbons (ASiNRs) can be categorized into three branches with width W = 3P − 1, 3P, and 3P + 1, P is an integer. The order of their energy gaps change from “EG (3P − 1) < EG (3P) < EG (3P + 1)” for W-ASiNRs:H to “EG (3P + 1) < EG (3P − 1) < EG (3P)” for W-ASiNRs:2H. We found the band gaps of W-ASiNRs:H and (W + 2)-ASiNRs:2H are slight.
Research on the derivation of AS hidden links and the Discovery of Critical AS

AS relationships are the basis for studying Internet security, route hijacking, route leakage, etc. To obtain more complete AS relationships, using machine learning (ML) models to learn the similarity between adjacent link groups and predict hidden links is a method that can obtain more complete AS relationships. The features selected by the ML model have a large impact on the accuracy of the prediction results, and we extract 10 ML features by combining the actual geographic location information of AS. After our optimization, the accuracy of the prediction model reaches 91.57%. In the classification of hidden link types, we oversample the small sample type data and optimize the classifier, and the classification accuracy of hidden link categories reaches 97.42%. The recall rate of p2c and c2p links improved by 24.29% and 7.17%, respectively. We found that the hidden links caused the change of network traffic transmission routes by the change of "Critical AS" in the AS network. AS 3549 has the highest number of effective paths, and the network traffic prefers to choose the AS with a lower hierarchy for forwarding.
User Behavior of a Publicly Available, Free-to-Use, Self-guided mHealth App for Depression: Observational Study in a Global Sample

Background Reducing the burden of depression is a global health concern. Self-guided mobile health (mHealth) apps are one approach to address this problem. However, there is little research on self-guided mHealth apps in a global sample or on how they are used in the real world. These gaps in our knowledge must be addressed to bring the promise of mHealth apps for reducing the global burden of depression closer to reality. Objective The purpose of this study is to examine the naturalistic user behavior of MoodTools, a publicly available, free-to-use, self-guided mHealth app designed to improve symptoms of depression, in a global community sample. Methods Mobile analytics data were collected from all unique downloads of the Android version of MoodTools between March 1, 2016, and February 28, 2018. Due to the deidentification and data aggregation process, no demographic or personal identifying information was tied to individual user data. All information was stored in aggregated, anonymized data files on Google Analytics’ storage database. Google’s software development kit was used to securely capture data about the number of downloads, location of downloads, number of app sessions, frequency and duration of app sessions, time between app sessions, and user retention, allowing for examination of which app’s tools were viewed and for how long, including Information (psychoeducation), Test (self-monitoring using the Patient Health Questionnaire [PHQ-9]), Thought Diary (targeting negative cognitions), Activities (behavioral activation), Videos (curated from YouTube), and Safety Plan (safety plan development and links to quickly access crisis management resources). Results MoodTools was used by 158,930 people from 198 countries, including countries where English was not the primary language and in low- and middle-income countries. After the initial download, 51.14% (n=81,277) of users returned to the app after the initial download, and retention rates decreased with each subsequent app session. The typical person used the app for 3 sessions for a total of 12 minutes over 90 days. The most frequently visited tools were Test and Thought Diary (n=393,549, 24.32%). On average, users completed and reviewed the results of the PHQ-9 for 49 seconds and 53 seconds, respectively, and spent 3 minutes and 5 seconds on Thought Diary. Conclusions Self-guided mHealth apps could be one approach (among the many needed) to reduce the burden of depression. Observational data collected in this study show a global interest in MoodTools, including in low- and middle-income countries and countries where English is not the primary language. Future research is needed to determine whether people who use self-guided apps experience improvement in depressive symptoms, and if so, what “dosage” provides a meaningful benefit.
A Compact Gap-Waveguide Dual-Polarized Ka-Band Feed for 50dBi Reflector Antennas With Tracking Function

A dual-polarized Ka-band feed based on gap waveguide (GW) technology for an ultra-high-gain reflector antenna is presented. The feed provides SUM-beams for data transfer and DIFF-beams for tracking. The whole reflector antenna is composed of the feed and a dual-reflector Cassegraine antenna. The feed has been prototyped, and the measured reflection coefficients for the horizontally and the vertically polarized SUM-beam ports are below −10 dB and −8 dB over 30.8–38 GHz, respectively. The measured feed radiation patterns agree well with the simulated ones. The dual-reflector Cassegraine antenna has been designed and simulated by using GRASP with the simulated far-field function of the proposed feed, showing that the reflector antenna achieves SUM-beam gains above 50 dBi and the null depth of the DIFF-beams are more than 30 dB below the maximum of SUM-beams.
A thesis to probe unique exoplanet regimes with micro-arcsecond astrometry and precision closure phases at CHARA and VLTI

In this thesis work, we exploit the unique capabilities of long baseline interferometry to fill two gaps in exoplanet parameter space: 1) the discovery of new planets around stars more massive than the Sun (Project ARMADA), and 2) the characterization of known planets that are extremely close to their host star (Project PRIME). Current detection methods struggle to find exoplanets around hot (A/B-type) stars. We are pushing the astrometric limits of ground-based optical interferometers to carry out a survey of sub-arcsecond A/B-type binary systems with ARMADA. We are achieving astrometric precision at the few tens of micro-arcsecond level in short observations at CHARA/MIRC-X and VLTI/GRAVITY. This incredible precision allows us to probe the au-regime for giant planets orbiting individual stars of the binary system. We present the status of our survey, including our newly implemented etalon wavelength calibration method at CHARA, detection of new stellar mass companions, and non-detection limits down to a few Jupiter masses in some cases. With Project PRIME, we show that ground-based optical interferometry can be used to measure the orbit-dependent spectra of close-in “hot Jupiter”-type exoplanets with precision closure phases. Detecting the infrared spectra of such planets allows us to place useful constraints on atmosphere circulation models. We perform injection tests with MIRC-X and MYSTIC at CHARA for the hot Jupiter exoplanet Ups And b to show that we are reaching down to a contrast of 2e-4. The promise of both these methods demonstrate that optical interferometers are a valuable tool for probing unique regimes of exoplanet science.
Evolutionary Algorithm-based Methods to Choose Fixed Charge Transportation Plan

In this paper, we consider a fixed-charge transportation problem (fcTP) and the comparison of tree-based genetic representation methods which can be employed as representation methods to solve fcTP, and investigate the usability of the genetic representations by comparing the performance of each genetic representation method to solve this problem. And in this paper, aim is to seek the best genetic representation. The fcTP seeks determination of a minimum-cost transportation plan for a homogeneous commodity from a number of plants to a number of warehouses. This problem is a well-known NP-hard problem. Many practical transportation and distribution problems such as the minimum cost network flow problem with fixed charge in logistics can be formulated as fcTP. In this study, among meta-heuristic approaches, a genetic algorithm will be used to solve fcTP. Moreover various chromosome representations will be applied, and the results through the numerical experiments will be compared. To solve the problem, we applied the Degree based ( d-based), Edge based (e-based), Link and Node Biased (LNB), Network Random Key (Netkey) and the Prufer number representation, and assess and compare the performance of these algorithms.
Pricing strategies for information products with network effects and complementary services in a duopolistic market

In this paper, we model a fully covered duopoly market in which two firms offer a differentiated information product that exhibits positive network effects and a complementary premium service to consumers. For each firm, there are two marketing strategies: the freemium strategy and the bundling strategy. We find that, under the market equilibrium, a firms’ decision whether to employ the freemium strategy or not depends largely on the quality of the information product compared to its rival. When the information product quality is similar and the products’ intrinsic values are sufficiently large, both firms will be better off by adopting the freemium strategy, while the bundling strategy will prevail if the products’ intrinsic values are sufficiently small. Additionally, when the magnitude of complementary effects or network effects exceeds a given threshold, both firms’ profit can be enhanced by an increase in the degree of product complementarity or in the intensity of network effects. We also demonstrate that a firm can benefit from an increasing market size only if the intrinsic value of its information product is sufficiently large. Finally, we extend our model to the uncovered market and derive the equilibrium prices and profits.
Elastic Tactile Sensor Skin on Double-Curved Surfaces for Robots and Wearables

Tactile perception is an important modality for dexterous manipulation, but faces unique challenges in that the sensor has to wrap around the system. Biological skin solves these problems extremely well. Not only can it detect contacts, but it also grows on complex three-dimensional shapes, stretches with underlying soft tissue and around joints, and leaves enough space inside for other organs. Previous artificial sensors could only achieve different subsets of these features. We present a novel tactile sensor skin which is completely made from elastic silicone rubber and can stretch to multiple times its original length without damage. It can be produced in three-dimensional double-curved shapes, is thin enough to accommodate other robot components or human body parts, and it can measure forces and locations of multiple simultaneous contacts. We develop production methods and material formulations, algorithms for computer-aided design, compact readout electronics using small, low-power FPGAs, firmware, and software.