Miracle-dz/UniFAR-FTUs huggingface dataset:
Efektivitas Intervensi Psikoterapi ILHAM terhadap Tingkat Stres yang Dialami Mahasiswa di Universitas Paramadina

Mahasiswa merupakan salah satu kelompok yang memiliki faktor resiko untuk mengalami stres. Tuntutan akademis dan kehidupan menjadi faktor penyebab stres pada mahasiswa. Stres yang dialami mahasiswa akan berdampak negatif jika tidak ditangani dengan baik. Salah satu aspek penting yang diperhitungkan dalam penangan stres adalah spiritualitas. Salah satu psikoterapi yang mengutamakan aspek spiritual dalam prosesnya adalah Psikoterapi ILHAM. Psikoterapi ILHAM dikembangkan berdasarkan teori yang dikemukakan oleh tokoh Sufis islam Al-Ghazali. Penelitian ini bertujuan untuk melihat efektivitas psikoterapi ilham terhadap tingkat stres yang dialami mahasiswa di Universitas Paramadina. Penelitian ini menggunakan desain penelitian single subject experiment. Hasil penelitian menunjukkan responden mengalami penurunan tingkat stres setelah menjalani intervensi Psikoterapi ILHAM. Hasil wawancara juga menemukan responden merasa lebih tenang dan tidak tertekan setelah menjalani psikoterapi.
Nutritional Improvement of Bean Sprouts by Using Chitooligosaccharide as an Elicitor in Germination of Soybean (Glycine max L.)

Soybean sprouts are among the healthiest foods consumed in most Asian countries. Their nutritional content, especially bioactive compounds, may change according to the conditions of germination. The purpose of this study was to test the effect of chitooligosaccharide with different molecular weight and dosage on nutritional quality and enzymatic and antioxidant activities of soybean sprouts. The chitooligosaccharide elicitor strongly stimulated the accumulation of vitamin C, total phenolics, and total flavonoid. The stimulation effect was correlated with the molecular weight and concentration of chitooligosaccharide. With treatment of 0.01% of 1 kDa chitooligosaccharide, the nine phenolic constituents and six isoflavone compounds were significantly increased. The antioxidant capacity (DPPH radical and hydroxyl radical scavenging activity) and antioxidase activities (catalase and peroxidase) of soybean sprouts were also enhanced after treatment with chitooligosaccharide. The degree of chitooligosaccharide-induced elicitor activity increased as the molecular weight of chitooligosaccharide decreased. These results suggest that soaking soybean seeds in a solution of chitooligosaccharide, especially in 0.01% of 1 kDa chitooligosaccharide, may effectively improve the nutritional value and physiological function of soybean sprouts.
Research on Performance and Function Testing of V2X in a Closed Test Field

The V2X and cooperative vehicle infrastructure system (CVIS), which leverage the efficient information interactions through V2V, V2I, V2P, and V2N, are known as the advanced and effective technology in reducing traffic accidents and improving traffic efficiency. The complex technical characteristics of V2X and highly reliable service demand of typical V2X applications call for the test needs before the large-scale deployment of V2X. It indicates that the performance and function of V2X devices should be systematically tested and evaluated in extreme and boundary conditions of driving and communication environments before being broadly deployed and applied in infrastructures. Motivated by the previously mentioned needs, a performance and function testing scheme of V2X in a closed test field is studied. According to the analytical viewpoint from the physical layer and MAC layer, the proposed research systematically analyses the technical differences of DSRC and LTE-V, which are two typical V2X protocols, in terms of vehicle speed, communication distance, and channel adaptability. Based on the critical practical test needs from the analytical study, a function and performance test system of V2X specifically for the closed test field is proposed. The performance and typical application effectiveness in intersection environment of DSRC and LTE-V are evaluated. The limitation and proposed improvement strategies of these V2X protocols are analytically discussed.
Mind the gap: forecasting euro-area output gaps with machine learning

ABSTRACT In this paper, we use the Eurozone yield curve in an effort to forecast the deviations of the euro-area output (IPI) from its long-run trend. We use various short- and long-term interest rates spanning the period from 2004:9 to 2020:6 in monthly frequency. The interest rates are fed to three machine learning methodologies: Decision Trees, Random Forests, and Support Vector Machines (SVM). These Machine Learning methodologies are then compared to an Elastic-Net Logistic Regression (Logit) model from the area of Econometrics. According to the results, the optimal SVM model coupled with the RBF kernel outperforms the competition reaching an in-sample accuracy of 85.29% and an out-of-sample accuracy of 94.74%.
FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures

Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures or natural disasters. Though the major disturbances in such critical infrastructures are rare, the severity of such events calls for the development of effective resilience assessment strategies to mitigate relative losses. Traditional critical infrastructure resilience approaches consider that the available critical infrastructure agents are resource-sufficient and agree to exchange local data with the server and other agents. Such assumptions create two issues: (1) uncertainty in reaching convergence while applying learning strategies on resource-constrained critical infrastructure agents, and (2) a huge risk of privacy leakage. By understanding the pressing need to construct an effective resilience model for resource-constrained critical infrastructure, this paper aims at leveraging a distributed machine learning technique called Federated Learning (FL) to tackle an agent’s resource limitations effectively and at the same time keep the agent’s information private. Particularly, this paper is focused on predicting the probable outage and resource status of critical infrastructure agents without sharing any local data and carrying out the learning process even when most of the agents are incapable of accomplishing a given computational task. To that end, an FL algorithm is designed specifically for a resource-constrained critical infrastructure environment that could facilitate the training of each agent in a distributed fashion, restrict them from sharing their raw data with any other external entities (e.g., server, neighbor agents), choose proficient clients by analyzing their resources, and allow a partial amount of computation tasks to be performed by the resource-constrained agents. We considered a different number of agents with various stragglers and checked the performance of FedAvg and our proposed FedResilience algorithm with prediction tasks for a probable outage, as well as checking the agents’ resource-sharing scope. Our simulation results show that if the majority of the FL agents are stragglers and we drop them from the training process, then the agents learn very slowly and the overall model performance is negatively affected. We also demonstrate that the selection of proficient agents and allowing them to complete only parts of their tasks can significantly improve the knowledge of each agent by eliminating the straggler effects, and the global model convergence is accelerated.
Large electromagnetic field enhancement in plasmonic nanoellipse for tunable spaser based applications

We theoretically demonstrated a class of plasmonic coupled elliptical nanostructure for achieving a spaser or a nanolaser with high intensity. The plasmonic ellipse is made up of gold film substrate. The proposed structure is then trialed for various light polarizations, moreover, a simple elliptical nanostructure has been chosen primarily from which different cases have been formed by geometry alteration. The structure supports strong coupled resonance mode i.e. localized surface plasmon (LSP). The localized surface plasmon resonance (LSPR) of the investigated system is numerically examined using the finite-element method (FEM). The calculations showed that the LSPR peaks and the local field intensity or near field enhancement (NFE) of the active nanosystem can be amplified to higher values by introducing symmetry-breaking techniques in the proposed ellipse and its variants. The coupled nanostructure having different stages of wavelengths can be excited with different plasmonic resonance modes by the selection of suitable gain media. In addition, a small-sized nanolaser with high tunability range can be developed using this nanostructure. The spaser phenomena are achieved at several wavelengths by changing light polarization and structure alteration methods. Giant localized field enhancement and high LSPR values enable the proposed model to be highly appealing for sensing applications, surface-enhanced Raman spectroscopy, and much more.
A case study of post-auricular persistent mastoid cutaneous fistula closure by anteriorly based temporalis muscle flap and tempero-mastoid fascio-cutaneous-periosteal flap

Post-auricular cutaneous mastoid fistula (PAMCF) is a rare complication of chronic otitis media (COM), following complicate mastoid surgery and very rarely following congenital cholesteatoma. The failure rate with simple closure is high due to necrotic skin edges. Therefore several techniques for the closure of this fistula have been described. Post aural fistula after canal wall down (CWD) mastoidectomy can be successfully treated by fistula tract excision and obliterating the mastoid cavity. A 45 year old woman presented with discharging PAMCF, 10 years following radical mastoidectomy surgery. It was due to non-compliance of the patient, as she had not gone for follow up for 10 year following radical mastoidectomy. The temporo-mastoid fascio-cutaneous-periosteal advancement flap is effective for proper closure of the fistula and obliteration of the cavity by conchal cartilage along with anteriorly based vascularised pedicled temporalis muscle flap to ensure the viability of the flap and preventing the necrosis of the skin edges, henceforth the recurrence of the fistula.
Copper-Catalyzed direct thioetherification of Alkyl Halides with S-Alkyl Butanethioate as Thiol transfer reagent

A new and convenient copper-catalyzed synthesis of alkyl sulfides has been accomplished using S-alkyl butanethioate as a thiol source. This catalytic protocol displayed a good functional groups tolerance and high efficiency. Both secondary and primary alkyl iodides can be used in this procedure. In addition, this method features operational simplicity and a wide substrate range, providing a complementary method for alkyl sulfide synthesis without requiring toxic thiols and noble metals. GRAPHICAL ABSTRACT
Implementation and Evaluation of the Integrated Acupuncture and Moxibustion Treatment (Acupuncture, Moxibustion and Auricular Acupuncture) for Major Depressive Disorder: A Trial Protocol for a Randomized Clinical Trial

Background: Because of inadequate psychotherapy resources, antidepressants are the front-line option for Major depressive disorder, but with dissatisfied curative effect. Many patients have an interest in acupuncture and moxibustion treatment as complementary therapies. However, there is no sufficient evidence on effectiveness of the integrated treatment. The aim of this study is to evaluate the efficacy and safety of the integrated acupuncture and moxibustion treatment in treating major depressive disorder and to find out the most suitable implementation protocol.Methods: This study is a multicenter, single-blind, 2 × 2 factorial randomized trial. A total of 592 adult participants with moderate depression will be enrolled and randomized into four groups with a ratio of 2: 2: 2: 1, that is, combined group, integrated acupuncture and moxibustion treatment only group, sertraline only group, or placebo group. Participants will undergo a 12-week intervention with either 50 mg of sertraline or placebo once a day, as well as the active/sham integrated acupuncture and moxibustion treatment for three times per week. The primary outcome is assessed using the 17-item version of Hamilton Depression Scale. The secondary outcomes are anxiety and sleep-related scales that assessed using Hamilton Anxiety Scale and Pittsburgh Sleep Quality Index. Safety will be evaluated using liver and kidney function tests examined before and after treatment, as well as daily adverse events. The primary and secondary outcomes will be measured at weeks 0, 4, 8, 12 and 8-week posttreatment, and an intent-to-treat principle will be followed for outcomes analyses.Discussion: Sertraline is one of antidepressants as the front-line treatment for major depressive disorder, but with a delayed onset, high rate of inadequate response and low rate of adherence. As a traditional Chinese therapy, acupuncture and moxibustion are popular and widely accepted. This trial will provide more sufficient evidence for the efficacy and safety of the integrated treatment for major depressive disorder and the most suitable combination strategy. Thus, these finds will extend our knowledge in treating for major depressive disorder.Trial registration: Chinese Clinical Trial Registry ChiCTR2100042841; Registered on 30 January 2021.
Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification

Dictionary learning has been an important role in the success of data representation. As a complete view of data representation, hybrid dictionary learning (HDL) is still in its infant stage. In previous HDL approaches, the scheme of how to learn an effective hybrid dictionary for image classification has not been well addressed. In this paper, we proposed a locality preserving and label-aware constraint-based hybrid dictionary learning (LPLC-HDL) method, and apply it in image classification effectively. More specifically, the locality information of the data is preserved by using a graph Laplacian matrix based on the shared dictionary for learning the commonality representation, and a label-aware constraint with group regularization is imposed on the coding coefficients corresponding to the class-specific dictionary for learning the particularity representation. Moreover, all the introduced constraints in the proposed LPLC-HDL method are based on the l2-norm regularization, which can be solved efficiently via employing an alternative optimization strategy. The extensive experiments on the benchmark image datasets demonstrate that our method is an improvement over previous competing methods on both the hand-crafted and deep features.
Understanding firm-level intangible investment: a resource-based view on Korean firms

ABSTRACT The impacts of intangible assets on economic performance have increased at various levels, and the positive impacts have been well documented. However, not all firms invest in intangible assets despite the assets’ positive contribution to performance, and it is relatively less clear what determines quantity and intensity of intangible investment at firm level. Therefore, this study investigates firm-level intangible investment to find that intangible investment is determined by firm-specific internal and external resources, consistent with the resource-based view of the firm. Quantity and intensity of intangible investment are positively associated with firm-specific internal resources as innovative capability (internal R&D), cumulative capability (intellectual property as patents and trademarks), organizational resources (employee incentives to reward good performance, affiliation), but also outside resources from external R&D and outsourcing. Additionally, firm-level factors such as listing status and exporting can significantly facilitate intangible investment. Therefore, this study contributes to the literature of intangible assets and the resource-based view by highlighting the effects of heterogeneous internal and external resources on intangible assets accumulation.
The Effect of Nature Education Activities on Biophilia Levels of Science Teacher Candidates

As a result of increasing the level of interest in living and living systems (biophilia), it will be possible to educate more protectionist individuals against nature and the environment. This study was conducted to investigate the effect of nature education activities on biophilia levels of science teacher candidates. This research was conducted at public research university in Bayburt, Turkey, in the Department of Science Education during the spring semester of the 2017-2018 academic year. The activities were done in an integrated manner to the Environmental Science course. The study group included a total of 62 science teacher candidates. These students were distributed equally to the experiment and control group. The study was used a quasi-experimental method with a group of experiments and control. In the study, the Biophilia Scale was applied as a data collection tool. Independent samples t-tests and dependent samples t-tests were used for analyzing the data. As a result of the analysis, the findings indicated that nature education and in-class activities positively affected teacher candidates' biophilia levels. The findings also indicated that the knowledge and experience they gained due to students' scrutiny, touching, smelling, and even tasting the species at in-class and nature activities affected their biophilia levels.
Robust Formation Control for Nonlinear Heterogeneous Multiagent Systems Based on Adaptive Event-Triggered Strategy

In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness. Note to Practitioners—This article addresses the collaborative formation problem of multiagent systems that has potential applications in transportation and disaster relief. The design of robust and energy-saving coordination strategies is challenging in heterogeneous multivehicle systems. The proposed distributed method is suitable for large-scale uncertain heterogeneous systems, and the use of the dual adaptive event-triggered strategy reduces the data transmission rate.
Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

In higher education learning, e-learning systems have become renowned tools worldwide. The evident importance of e-learning in higher education has resulted in a prenominal increase in the number of e-learning systems delivering various forms of services, especially when traditional education (face-to-face) was suddenly forced to move online due to the COVID-19 outbreak. Accordingly, assessing e-learning systems is pivotal in the interest of effective use and successful implementation. By relying on the related literature review, an extensive model is developed by integrating the information system success model (ISSM) and the technology acceptance model (TAM) to illustrate key factors that influence the success of e-learning systems. Based on the proposed model, theory-based hypotheses are tested through structural equation modeling employing empirical data gathered through a survey questionnaire of 537 students from three private universities in Jordan. The findings demonstrate that quality factors, including instructor, technical system, support service, educational systems, and course content quality, have a direct positive influence on students’ satisfaction, perceived usefulness, and system use. Moreover, self-regulated learning negatively affects students’ satisfaction, perceived usefulness, and system use. Students’ satisfaction, perceived usefulness, and system use are key predictors of their academic performance. These findings provide e-learning stakeholders with important implications that guarantee the effective, successful use of e-learning that positively affects students’ learning.
Insulation Monitoring of Dynamic Wireless Charging Network Based on BP Neural Network

The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.
Estimation of Velocity of a Frictionless Motion of a Truck on an Infinitely Long Straight Rail

This paper is concerned with estimation of velocity of a frictionless motion of a truck on an infinitely long straight rail. For simplicity assume that the Truck is controlled only by the throttle producing an accelerative force per unit mass. A discrete dynamic model of first order difference equation is to describe the system. Kalman filtering technique is applied to the discrete dynamic model to estimate the velocity of the Truck at any particular time. A computer programme is developed to simulate the system
Do stringent environmental regulations help improve the total factor carbon productivity? Empirical evidence from China’s industrial sectors

ABSTRACT How can China fully use the limited carbon emission space to achieve the dual goal of stable economic growth and a gradual decline in carbon emissions under increasingly severe global climate change? To address this issue, this paper incorporates the carbon emission space as a production factor into the total factor analysis framework and discusses how environmental regulations affect the total factor carbon productivity. The conclusions reveal that the current environmental regulations have a direct effect on improvement in the total factor carbon productivity of industrial sectors. Environmental regulations also have an indirect effect on the total factor carbon productivity that is mainly achieved through the energy structure and technological advancement. The results of the additional analyses show that these direct and indirect effects have threshold characteristics. Increasing the total factor carbon productivity is beneficial only when the environmental regulation intensity, energy structure and technological level are within a reasonable threshold range. Therefore, the environmental regulation intensity, energy structure conditions and technological level need to be considered when environmental regulations are relied upon to improve the total factor carbon productivity.