Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Validation of Indonesian Academic Resilience Scale Using Rasch Models

This study aims to develop and validate a scale to measure student academic resilience on junior high school level. The sample in this study was 865 junior high school students spreading across various regions in Indonesia. The methodology used in this research was quantitative, using a questionnaire with a 4-point Likert scale. The data analysis used the Rasch model, “i.e.” Winsteps software. Analysis shows that the respondent’s reliability index is 0.78 while the item reliability index is 0.90. The item polarity angle indicates that each item can contribute to student academic resilience because the point-measure correlation value of each item between 0.49 to 0.75. The item ideality test in the instrument shows that the outline of the item can be used. The research instruments in this study show that it measured what it should. The differential item functioning test shows that there were no items that had a bias in assessing the level of student academic resilience. The items in Indonesian academic resilience scale on junior high school level have high validity and reliability so that they can be used to measure the academic resilience of junior high school students.
MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance

In recent years, the benefits of both Augmented Reality (AR) technology and infrared thermography (IRT) have been demonstrated in the industrial maintenance sector, allowing maintenance operations to be carried out in a safer, faster, and more efficient manner. However, there still exists no solution that optimally combines both technologies. In this work, we propose a new AR system—MANTRA—with specific application to industrial maintenance. The system can automatically align virtual information and temperature on any 3D object, in real time. This is achieved through the joint use of an RGB-D sensor and an IRT camera, leading to high accuracy and robustness. To achieve this objective, a pose estimation method that combines a deep-learning-based object detection method, YOLOV4, together with the template-based LINEMOD pose estimation method, as well as a model-based 6DOF pose tracking technique, was developed. The MANTRA system is validated both quantitatively and qualitatively through a real use-case, demonstrating the effectiveness of the system compared to traditional methods and those using only AR.
How to gauge reliability of a binary classification result: a simple case

In many practical situations, we need to make a binary decision based on the available data: whether an incoming email is a spam or not, whether to give a bank loan to a company, etc. In many such situations, we can (and do) use machine learning to come up with such a decision. The problem is that while the results of a machine learning model are not 100% reliable, the existing machine learning algorithms do not allow us to decide how reliable is each result. In this paper, for simple examples, we provide a technique for gauging this reliability.
Relationships between Logistics Performance and Aspects of Sustainability: A Cross-Country Analysis

The purpose of this study is to investigate relationships between national logistics performance and dimensions of sustainability. A series of hypotheses are developed and tested using regression analysis of secondary data. The sources of data are the Sustainable Society Index (SSI) and the World Bank’s Logistics Performance Index (LPI). Fundamental aspects of social sustainability and well-being—a healthy, educated population, equality, good governance, and reasonable income distribution—are related to higher levels of logistics performance. In addition, while logistics performance is a driver of economic activity and success, it is also a contributor to environmental degradation in the form of harmful emissions. If economic growth is among a nation’s goals, its leaders are advised to support social well-being, along with technologies and practices for greater energy efficiency (and lower emissions) in transportation.
Magnetic paper – based ELISA for IgM-dengue detection

This contribution reports a novel “Magnetic Paper – Based ELISA” using core–shell magnetite@polydopamine nanoparticles supported on a Whatman paper-like new solid immunoassay platform specifically for IgM-dengue antibodies recognition as the proof-of-concept target for antibodies isotype IgM detection. Affordable procedures to deposit magnetite nanoparticles on cellulose paper sheets (Whatman type-1 and ss903) and to conjugate such nanoparticles with anti human-IgM antibodies using polydopamine as linker are reported. Structural features, magnetic behavior, coating homogeneity, and the nanoparticles/linked antibodies ratio were determined. Additionally, “Magnetic Paper – Based ELISA” for IgM-dengue antibodies detection provides a system with improved analytical response (two orders more sensitive with a 700 times lower limit of detection (LOD) than traditional ELISA or using magnetic beads without depositing), appropriate accuracy for real sample detection, low cost, easy manufacturing, and effortless and easy handling.
A Double-Blind Randomized Controlled Trial on the Effects of Montelukast on the Serum Level Of Inflammatory Factors in Patients with Chronic Obstructive Pulmonary Disease

Background: Chronic obstructive pulmonary disease (COPD) is a major noncommunicable respiratory disease with diverse pulmonary and external pulmonary clinical manifestations. This disease is one of the leading causes of mortality in the world, and about 1% of the adult population suffers from COPD. Objectives: The aim of this study was to assess the effect of Montelukast on the serum level of inflammatory factors in patients with chronic obstructive pulmonary disease (COPD). Methods: In a randomized placebo-controlled trial, 74 patients with COPD with stable conditions were followed for two months after a random assignment to the placebo and montelukast (10 mg/d) groups. All patients continued their treatment protocol irrespective of their group to evaluate the effects of the addition of montelukast on serum levels of common inflammatory factors, such as Tumor Necrosis Factor alpha (TNF-α), C-reactive protein (CRP), and Interleukin 18 (IL-18) in COPD patients. SPSS 18 software was used for data analysis. Results of quantitative data were reported as mean ± standard deviation or median (interquartile range) and qualitative data as frequency (percentage). If the data distribution was normal, the paired t-test was used to compare the mean before and after and using an independent t-test to compare the mean changes between the two groups. The Mann-Whitney U test and Wilcoxon signed-rank test were also used if the data were not assumed to be normal. A P < 0.05 was considered as the level of significance. Results: At baseline, there were no significant differences in laboratory studies between the two groups. After two months of intervention, there was no evidence of decreased TNF-α and CRP in the montelukast group. IL-18 levels were not significantly different at the end of the study between the two groups (P = 0.23), but it had a decreasing trend in the montelukast group (from 20.25 ± 5.98 ng/ml to 16.19 ± 4.17 ng/ml, P < 0.001). Conclusions: Montelukast complementary therapy in COPD patients only improve the serum IL-18 levels and has not a reducing effect on the level of TNF-α and CRP evidenced by keeping their trend from baseline to the end of the study.
Tourism, social representations and empowerment of rural communities at Langue de Barbarie National Park, Senegal

Abstract Evidence of tourism as a strategy to empower local communities around national parks remains elusive. Scholars argue that a prerequisite to empowering community members is to decipher their ideas, beliefs and perceptions. Social representations (SR) theory provides a framework to engage in such a task yet very few studies have taken on such analysis. The paper presents a study of the SR across 10 communities surrounding the Langue de Barbarie National Park in Senegal using survey and interview data. Specifically, we examine their representations towards tourism and the park as a basis to better inform their empowerment process. We conducted a cluster analysis, grouping community members responses into polemical, emancipated, and hegemonic representations. Most participants held polemical representations and perceived tourism and the park as a continuity of colonialism. Others with emancipated representations advocated for alternative tourism structures. The hegemonic representations on the other hand assessed tourism based on the direct impact it provides to individual members of their communities. For each cluster, we propose a form of empowerment that is better aligned with their social representations as a catalyst for their capacity to carve a meaningful space in tourism.
Differential Expression of Toll-Like Receptors 1 and 3 in Patients With Systemic Lupus Erythematosus and Systemic Sclerosis

Background: Systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are autoimmune diseases with some overlapping clinical manifestations. Recent studies have revealed that Toll-like receptors (TLRs) play essential roles in autoimmune diseases' pathogenesis. To investigate the expression of TLR genes in SLE and SSc that are specific to the character of these diseases' pathogenesis.Methods: We recruited patients with SLE (n = 15) and SSc (n = 9) from China Medical University Hospital hospital in Taiwan from 2016 to 2017. Included were healthy reference controls and SLE datasets from the NCBI database. RNA was extracted from peripheral blood cells of the patients for next-generation sequencing (NGS). Results in identified differentially expressed genes were compared to explore TLRs' association with SLE and SSc pathogenesis. Housekeeping genes, including ACTB, GAPDH, PGK1, PPIB, SDHA, and TBP, were used to normalize the expression levels of the TLR genes. Results: There were significant differences in raw gene expression for patients with SLE and SSc (p < 0.05). No significant difference between patients with SLE and control datasets with SLE (p >0.05). The expression levels of TLR1 (p = 0.018) and TLR3 (p = 0.031) were significantly upregulated in the patients with SSc compared with those with SLE, normalized by GAPDH. Compared with many housekeeping genes, ACTB, GAPDH, and TBP indicated a significant difference (p < 0.05) in the expression of TLR1and TLR3. Conclusions: TLR1 and TLR3 can serve as biomarkers to distinguish the gene expression between SLE and SSc, normalized with housekeeping genes such as ACTB, GADPH, and TBP.
Automatic Fetal Ultrasound Standard Plane Recognition Based on Deep Learning and IIoT

Intelligent ultrasound imaging based on deep learning is one of the important applications in the field of intelligent medical care. In this article, we propose an automatic fetal ultrasound standard plane recognition (FUSPR) model based on deep learning in the Industrial Internet of Things (IIoT) environment. We build a distributed ultrasound data processing and predicting platform by using the IIoT and high-performance computing (HPC) technology. The FUSPR model deployed in the HPC center consists of a convolutional neural network (CNN) component and a recurrent neural network (RNN) component, which learns the spatial and temporal features of the ultrasound video stream by using multitask learning, respectively. The CNN component identifies fetal key anatomical structures from each video frame and accurately recognizes the potential four fetal standard planes. The RNN component obtains the temporal information between adjacent frames, and it realizes precise localization and tracking of fetal organs across frames. In addition, we introduce two feature fusion strategies into the FUSPR model, i.e., CNN fusion and RNN fusion, to fit the spatial sequence and motion representation in the video stream, thereby effectively improving the accuracy and robustness of the model. Extensive experiments conducted on more than 1000 ultrasound videos show that the FUSPR model is superior to the competing baselines in terms of accuracy and performance.
Succession of Microbial Community in a Small Water Body within the Alluvial Aquifer of a Large River

Nitrogen is one of the essential elements limiting growth in aquatic environments. Being primarily of anthropogenic origin, it exerts negative impacts on freshwater ecosystems. The present study was carried out at the nitrate-vulnerable zone within the alluvial aquifer of the large lowland Drava River. The main aim was to investigate the ecosystem’s functionality by characterizing the bacterial and phytoplankton diversity of a small inactive gravel pit by using interdisciplinary approaches. The phytoplankton community was investigated via traditional microscopy analyses and environmental DNA (eDNA) metabarcoding, while the bacterial community was investigated by a molecular approach (eDNA). Variations in the algal and bacterial community structure indicated a strong correlation with nitrogen compounds. Summer samples were characterized by a high abundance of bloom-forming Cyanobacteria. Following the cyanobacterial breakdown in the colder winter period, Bacillariophyceae and Actinobacteriota became dominant groups. Changes in microbial composition indicated a strong correlation between N forms and algal and bacterial communities. According to the nitrogen dynamics in the alluvial aquifer, we emphasize the importance of small water bodies as potential buffer zones to anthropogenic nitrogen pressures and sentinels of the disturbances displayed as algal blooms within larger freshwater systems.
Differences between 66 Chemical Element Contents in Normal and Cancerous Prostate

Prostate cancer is an internationally important health problem in man, particularly in developed countries. The aim of this exploratory study was to clarify the differences between the prostatic levels of chemical elements in patients with malignantly transformed prostate (PCa) and healthy male inhabitance. Prostatic tissue levels of 66 chemical elements were prospectively evaluated in 60 patients with PCa and 37 healthy males. Measurements were performed using a combination of five non-destructive and destructive analytical methods. A significant increase in the mean level of Ag, Al, Au, B, Ba, Be, Bi, Br, Ce, Cr, Cu, Dy, Er, Fe, Gd, Hg, Ho, Li, Mn, Nd, Ni, Pr, Sb, Si, Sm, Sn, Sr, Tb, Th, Ti, Tl, Tm, Y and Zr accompanied a decrease in the mean level of Ca, Cd, Co, K, Mg, Na, P, Rb, S, Sc, Se, and Zn was observed in the cancerous prostates. It was not found any differences in the mean prostatic level of other chemical elements including Cs, La, Mo, Nb, P, Pb, U, and Yb between PCa patients and healthy males. This work’s results reveal that in malignantly transformed prostate the chemical element metabolism is drastically disturbed.
The effect of transformational leadership climate on employee engagement during digital transformation in Indonesian banking industry

The industrial era 4.0 requires companies to radically conduct changes related to technology. This is so that the company can follow the market's everchanging appetite and is essential to maintaining the company's sustainability. The digital transformation that occurs is fundamental, changing existing procedures and arrangements and creating a new business ecosystem. In carrying out a company transformation, it requires full support and commitment from workers. And the process needs to be conducted by competent leaders, in line with the spirit of change. In this study, the relationship between transformational leadership climate and employee engagement was measured. The research was conducted on banking service companies that have made radical changes related to technology for 2 years. The results showed that the climate created by leaders who adopt transformational leadership styles has a positive and significant effect on employee engagement. This research is a preliminary study, and has limitations, including the number of samples and types of companies studied.
Improve the Effectiveness of Image Retrieval by Combining the Optimal Distance and Linear Discriminant Analysis

In image retrieval with relevant feedback, classification and distance calculation have a great influence on image retrieval accuracy. In this paper, we propose an image retrieval method, called ODLDA (Image Retrieval using the optimal distance and linear discriminant analysis). The proposed method can effectively exploit user’s feedback from relevant and irrelevant image sets, which uses linear discriminant analysis to find a linear projection with an improved similarity measure. The experimental results performed on the two benchmark datasets have confirmed the superiority of the proposed method. Keywords—Content-based image retrieval; deep learning; similarity measures; Mahalanobis metric distance; linear discriminant analysis
Antimony Removal from Aqueous Solution Using Novel α-MnO2 Nanofibers: Equilibrium, Kinetic, and Density Functional Theory Studies

Herein, we report the synthesis and characterization of a novel α-MnO2 nanofibers (MO-2) prepared via morphological and phase transitions from δ-MnO2 nanoparticles under hydrothermal reaction in the presence of graphene oxide (GO) for the first time. The MO-2 shows long, compact, and uniform nanofiber morphology. The adsorption properties of antimonite (Sb(III)) and antimonate (Sb(V)) on MO-2 were investigated using batch experiments of adsorption isotherms and kinetics. Experimental results show that the adsorption behavior of Sb on MO-2 is spontaneous, exothermic, and pH-dependent and follows the monolayer Langmiur isotherm model, pseudo-second-order kinetic model and external mass transfer model. MO-2 has maximum Sb(III) and Sb(V) adsorption capacities of 111.70 and 89.99 mg/g, respectively. Density functional theory (DFT) calculations indicate that both Sb(III) and Sb(V) have monodentate and bidentate complexes on the (110) facet. The adsorption energies (Ead) analysis demonstrates that the formed mon.
Architectural Supports for Block Ciphers in a RISC CPU Core by Instruction Overloading

We propose a novel computer architectural concept of instruction overloading to support block ciphers. Instead of adding new instructions, we extend only the execution of some existing instructions. The proposed method allows a central processing unit core to execute different operations for the same instructions, depending on the address of the data, similar to operator overloading in object-oriented languages. We first present an extension for the AES algorithm, then we demonstrate its enhanced applicability with two further extensions supporting multiple block ciphers and hardware masking. The first extension for AES is also applicable to add/AND-rotate-XOR-based block ciphers such as SIMON. The AES and SIMON encryption speed, on this extended core, is at least doubled and is significantly less affected by memory latency. In addition, the AES encryption code requires only 18% of the memory of the previous software implementation. The second extension can further support various block ciphers defined over GF(28), and the SM4 encryption speed is increased by at least 182%. The third extension provides correlation power analysis (CPA) resistance with a 66.6% area overhead but almost no speed overhead, whereas a typical software anti-CPA AES implementation requires at least hundreds of times the execution time.
Resonant Frequency and Bandwidth of Superconducting Microstrip Antenna Fed through a Slot Cut into the Ground Plane

In this work, an efficient analysis is presented to accurately predict the resonant frequency and bandwidth of superconducting microstrip antenna fed through a slot cut into the ground plane. The effect of the superconductivity of the rectangular patch is introduced in the Full-wave analysis based on Gorter-Casimir two fluid model together with London brothers equations. In order to check the accuracy of the proposed approach, the obtained results have been compared with theoretical and experimental data reported in the literature. Finally, the influence of the slot on the resonant frequency and half-power bandwidth of the superconducting antenna has been investigated.
Stochastic Nonlinear Prescribed-Time Stabilization and Inverse Optimality

We solve the prescribed-time mean-square stabilization and inverse optimality control problems for stochastic strict-feedback nonlinear systems by developing a new nonscaling backstepping design scheme. A key novel design ingredient is that the time-varying function is not used to scale the coordinate transformations and is only suitably introduced into the virtual controllers. The advantage of this approach is that a simpler controller results and the control effort is reduced. By using this method, we design a new controller to guarantee that the equilibrium at the origin of the closed-loop system is prescribed-time mean-square stable. Then, we redesign the controller and solve the prescribed-time inverse optimal mean-square stabilization problem, with an infinite gain margin. Specifically, the designed controller is not only optimal with respect to a meaningful cost functional but also globally stabilizes the closed-loop system in the prescribed-time. Finally, two simulation examples are given to illustrate the stochastic nonlinear prescribed-time control design.
Translocation of black foot pāua (Haliotis iris) in a customary fishery management area: transformation from top-down management to kaitiakitanga (local guardianship) of a cultural keystone

Loss of access to cultural keystone species is a familiar story across the Pacific. Serial depletion of pāua (abalone, Haliotis iris), a cultural keystone for Māori in Aotearoa New Zealand, threatens Indigenous practices and values. Kāti Huirapa Rūnaka ki Puketeraki, a hapū (subtribe) of Ngāi Tahu, have struggled for over 30 years to address loss of local pāua populations. Regulations introduced by the East Otago Taiāpure, a customary fishery management area established in 1999, recognise Indigenous rights to access and manage marine resources. Despite concerted efforts to rebuild pāua populations while allowing fishing, pāua have continued to decline and the fishery was closed in 2019. A wealth of mātauranga (Māori knowledge) surrounds enhancement practices for pāua. Here, mātauranga relating to translocation was collated through interviews and applied, in conjunction with additional knowledge bases, to translocate pāua from reefs threatened by sand inundation to more accessible but historically overexploited bays. In total, 420 pāua, translocated across 21 sites, were monitored for a 2-week period, after which aggregations returned to pretranslocation densities. Total mortality reached 8.8% over this time, attributed primarily to predation by the sea star Astrostole scabra. Results suggest that translocation may not be suitable for creating artificial aggregations using the methods applied; however, it could address other management concerns, hence suggestions are made for methodological improvements. The interaction between pāua and A. scabra demonstrates the need for continued transformation in management to allow for the application of a multispecies integrated approach in the restoration of pāua.