Miracle-dz/UniFAR-FTUs huggingface dataset:
Tingkat Akurasi dalam Analisis Perbandingan Metode ORESTE dengan PSI terhadap Penilai Kinerja Dosen

Teknologi yang semakin mudah untuk digunakan telah melekat diseluruh aspek kehidupan, teknologi sistem penilaian merupakan satu dari banyak sistem cerdas yang mempermudah pekerjaan manusia dalam tingkat akurasi penilaian kinerja. Kualitas kinerja dosen yang kurang baik dan berkualitas sehingga mengakibatkan berkurangya kualitas dedikasi dosen dan kualitas mahasiswa pada STMIK Triguna Dharma Medan. Oleh sebab itu dibutuhkan suatu metode untuk mengidentifikasi penyebab terhadap penilaian kinerja dosen dengan optimal dan efektif untuk memudahkan dalam meningkatkan kualitas dosen dan kualitas mahasiswa pada STMIK Triguna Dharma Medan. Peneliti menggunakan perbandingan untuk mengetahui system mana yang lebih efektif serta akurat dalam penentuan penilaian kinerja dosen menggunakan SPK (Sistem pendukung keputusan). Sistem pendukung keputusan ini adalah untuk menentukan kriteria terbaik dalam penentuan bobot nilai terbaik dari perbandingan dua metode, yaitu metode ORESTE dengan PSI (Preference selection index) dalam memecahkan materi dan menentukan tingkat akurasi penilaian kinerja terbaik pada kasus ini, yang dimana metode ORESTE menggunakan proses Besson rank yang berarti metode ini menggunakan perangkingan dan data ordinal. Metode PSI (Preference selection index) dipilih karena dapat menentukan nilai bobot untuk pada setiap atribut, dalam hal ini dapat dilanjutkan dengan perankingan yang akan bisa menyeleksi pada setiap atribut dari alternatif yang terbaik dari beberapa alternatif yang ada, sehingga di dapatkan hasil dari pemilihan bobot terbaik, yaitu kinerja dosen terbaik
GFNC: Unsupervised Link Prediction Based on Gravitational Field and Node Contraction

Currently, most existing link prediction algorithms simply study the interrelationships between node pairs without considering the interaction force and the higher order relationships between node pairs. In order to find a solution to this problem, the concept of the gravitational field is introduced in this article, and then, a novel algorithmic framework is proposed from the perspective of physics. The framework is applied to the classic link prediction algorithms to effectively enhance their prediction performance. First, the node contraction method is applied to measure the node importance, and a similarity-based link prediction algorithm is used to calculate the similarity values between node pairs. Second, the importance of nodes is introduced into the gravitational field model as the mass attribute, and the similarity values between node pairs are used as a distance metric between node pairs. Thereby, a gravitational field model of the complex network from the perspective of physics is established. Finally, the edges of the undirected complex network are assigned the weights, and a weighted local random walking-based link prediction algorithm is proposed. The link prediction method is adopted to evaluate the reasonableness and practical value of the gravitational field model. Experimental results show that most link prediction algorithms using the proposed algorithmic framework have got improvement with a minimum improvement of 2% and a maximum improvement of 33%; thus, the effectiveness and feasibility of the algorithm are verified.
Distributed and Privacy Preserving MPC With Global Constraints Over Time-Varying Communication

Distributed model predictive control (DMPC) has recently attracted extensive research attention due to its high flexibility and outstanding performance. However, most of those studies focus only on optimizing some subsystems with sets of local constraints, or on designing some schemes over time-invariant communication networks, by ignoring the cooperation of multiple subsystems with global constraints and also the privacy preservation of the subsystems in complex communication networks. Current research on DMPC is incapable to address all these challenges. Therefore, a novel distributed and privacy preserving MPC algorithm with global constraints over time-varying communication networks is proposed in this article. Firstly, we transform the MPC optimization problem that includes all the subsystems into its dual problem. Then, a fully distributed dual gradient algorithm with row-stochastic matrix is developed for solving that problem. For further reduction of the computational cost, an event-triggered communication protocol is designed, which is realized through a distributed trigger criterion. Under reasonable assumptions, we prove that the algorithm can converge to the optimal solution while assuring the recursive feasibility and exponential stability of a closed-loop system. In addition, considering that the sensitive information may be leaked or eavesdropped during the subsystem interaction, a distributed encryption algorithm with privacy preservation is presented in an updated version. We demonstrate that the proposed algorithm is secure and can effectively protect the privacy of the subsystems. Finally, the effectiveness and performance of the proposed approaches are substantiated through numerical simulations.
Defect Synthesis Using Latent Mapping Adversarial Network for Automated Visual Inspection

In Industry 4.0, internet of things (IoT) technologies are expanding and advanced smart factories are currently being developed. To build an automated visual inspection (AVI) and achieve smartization of steel manufacturing, detecting defects in products in real-time and accurately diagnosing the quality of products are essential elements. As in various manufacturing industries, the steel manufacturing process presents a class imbalance problem for products. For example, fewer defect images are available than normal images. This study developed a new image synthesis methodology for the steel manufacturing industry called a latent mapping adversarial network. Inspired by the style-based generative adversarial network (StyleGAN) structure, we constructed a mapping network for the latent space, which made it possible to compose defect images of various sizes. We discovered the most suitable loss function, and optimized the proposed method in terms of convergence and computational cost. The experimental results demonstrate the competitive performance of the proposed model compared to the traditional models in terms of classification accuracy of 92.42% and F-score of 93.15%. Consequently, the problem of data imbalance is solved, and higher productivity in steel products is expected.
Direct estimation of fibre orientations in partial volume contaminated regions using spherical deconvolution

Figure 1: in this simple 2D illustration, the voxel contains two fibre populations with distinct orientations (θ1, φ1) and (θ2, φ2) and volume fractions f1 and f2. The diffusion-weighted signal attenuation, S(θ, φ), is the sum of their contributions S1(θ, φ) and S2(θ, φ), weighted by their respective volume fractions. This can be expressed as a convolution over the unit sphere of an axially symmetric response function R(θ), describing the signal attenuation measured for a single fibre population, with a fibre orientation density function F(θ, φ), describing the fibre orientations present in the voxel (in this case, F(θ, φ) = δ(θ1, φ1) + δ(θ2, φ2)). + = ⊗ =
The Hepatitis C Care Cascade During the Direct-Acting Antiviral Era in a United States Commercially Insured Population

Abstract Background Periodic surveillance of the hepatitis C virus (HCV) care cascade is important for tracking progress toward HCV elimination goals, identifying gaps in care, and prioritizing resource allocation. In the pre-direct-acting antiviral (DAA) era, it was estimated that 50% of HCV-infected individuals were diagnosed and that 16% had been prescribed interferon-based therapy. Since then, few studies utilizing nationally representative data from the DAA era have been conducted in the United States. Methods We performed a cross-sectional study to describe the HCV care cascade in the United States using the Optum de-identified Clinformatics® Data Mart Database to identify a nationally representative sample of commercially insured beneficiaries between January 1, 2014 and December 31, 2019. We estimated the number of HCV-viremic individuals in Optum based on national HCV prevalence estimates and determined the proportion who had: (1) recorded diagnosis of HCV infection, (2) recorded HCV diagnosis and underwent HCV RNA testing, (3) DAA treatment dispensed, and (4) assessment for cure. Results Among 120,311 individuals estimated to have HCV viremia in Optum during the study period, 109,233 (90.8%; 95% CI, 90.6%–91.0%) had a recorded diagnosis of HCV infection, 75,549 (62.8%; 95% CI, 62.5%–63.1%) had a recorded diagnosis of HCV infection and underwent HCV RNA testing, 41,102 (34.2%; 95% CI, 33.9%–34.4%) were dispensed DAA treatment, and 25,760 (21.4%; 95% CI, 21.2%–21.6%) were assessed for cure. Conclusions Gaps remain between the delivery of HCV-related care and national treatment goals among commercially insured adults. Efforts are needed to increase HCV treatment among people diagnosed with chronic HCV infection to achieve national elimination goals.
The lncRNA TERC promotes gastric cancer cell proliferation, migration, and invasion by sponging miR-423-5p to regulate SOX12 expression

Background Long non-coding RNAs (lncRNAs) play critical roles in gastric cancer (GC) initiation progression. However, the biological function of the lncRNA telomerase RNA component (TERC) remains unknown in human GC. The present study sought to determine the biological function and underlying molecular mechanism of the lncRNA TERC in GC progression. Methods The expression levels of the lncRNA TERC in GC tissues and cell lines were analyzed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The effects of the lncRNA TERC on the proliferation, migration, and invasion of GC cells were determined using Cell Counting Kit-8 (CCK-8) and Transwell assays. Dual luciferase reporter and argonaute 2 (AGO2)-RNA immunoprecipitation (RIP) assays were used to detect the binding between the lncRNA TERC and microRNA-423-5p (miR-423-5p). Western blotting was performed to measure the expression levels of sex determining region Y-box 12 (SOX12), N-cadherin, E-cadherin, matrix metallopeptidase 9 (MMP9), and proliferating cell nuclear antigen (PCNA). Results The results demonstrated that the lncRNA TERC expression levels were upregulated in GC cells and tissues, while miR-423-5p expression levels were downregulated. The upregulation of the lncRNA TERC was associated with a shorter overall survival in patients with GC. The knockdown of the lncRNA TERC significantly reduced the proliferation, migration, and invasion of human GC cell lines HGC-27 and SNU-1 cells. Further, the lncRNA TERC knockdown in the HGC-27 and SNU-1 cells significantly downregulated the expression levels of SOX12, N-cadherin, MMP9, and PCNA, and upregulated the expression levels of miR-423-5p and E-cadherin. MiR-423-5p was also identified as a target of the lncRNA TERC and was found to directly bind to the lncRNA TERC. Additionally, miR-423-5p was found to directly target SOX12 to inhibit the proliferation, migration, and invasion of the HGC-27 and SNU-1 cells. Conclusions In conclusion, the findings of this study suggested that the lncRNA TERC may regulate the miR-423-5p/SOX12 signaling axis by directly sponging miR-423-5p and inhibiting SOX12 expression, thereby leading to the progression of GC. These findings may reveal novel targets for future GC therapy.
A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.
A framework for recommending tourist attractions using deep learning and association rule mining-based methods

Abstract. Many of tourism recommendation researches are based on the user rating and review data on the tourism platforms, and these approaches might be only suitable for a discrete recommendation for the tourist attractions. It is because each rating and review data on the platforms is created for a tourist place, not for multiple places on a travel itinerary. A travel blog data often contains information about the multiple places on a travel itinerary, but it is difficult to analyse the data compared to the rating and review data since it is like a text document having longer text than the review. In this paper, we introduce a framework consisting of a deep learning-based tourist-attraction extraction method from the blog text and an association rule mining-based recommendation method to recommend a list of tourist attractions that might be favourable to visit together in a travel itinerary.
Blocking Serum Amyloid-P Component from Binding to Macrophages and Augmenting Fungal Functional Amyloid Increases Macrophage Phagocytosis of Candida albicans

Candida-macrophage interactions are important immune defense responses associated with disseminated and deep-seated candidiasis in humans. Cells of Candida spp. express functional amyloids on their surfaces during the pathogenesis of disseminated candidiasis. These amyloids become decorated with serum amyloid P-component (SAP) that binds to Candida cells and macrophages and downregulates the cellular and cytokine response to the fungi. In this report, further characterization of the interactions of SAP and fungal functional amyloid are demonstrated. Blocking the binding of SAP to macrophage FcγR1 receptors increases phagocytosis of yeast cells; seeding a pro-amyloid-forming peptide on the yeast cell surface also increases phagocytosis of yeasts by macrophages; and, lastly, miridesap, a small palindromic molecule, prevents binding of SAP to yeasts and removes SAP that is bound to C. albicans thus, potentially increasing phagocytosis of yeasts by macrophages. Some, or all, of these interventions may be useful in boosting the host immune response to disseminated candidiasis.
Antioxidant Capacity and Nutritional Value of Peels and Seeds of Selected Pomegranate (Punica granatum L.) Cultivars from Sri Lanka

Purpose: Nonedible portion of Pomegranate is reported to be rich with a diverse range of phytochemicals which embrace with health promotive features. Though antioxidant power and nutritive value of fruit juice are well known, knowledge of nonedible fraction is very poor. Hence, evaluation of antioxidant power and nutritional value of fruit peel and seed of pomegranates was targeted. Research Method: IC 50 , Ferric Reducing Antioxidant Power (FRAP), Total Phenolic (TPC), Total Flavonoid (TFC), Total Anthocyanin and pro-anthocyanidin Contents (ProAC) were determined. Nutritional value was studied by proximate analysis. Findings: IC 50 , TPC, FRAP and TFC values of peels were ranged from 4.6 to 41. 1μg/mL, 318±1.77 to 478±5.62 mg Gallic acid equivalent/g, 4.270±0.83 to 6.690±0.15 mM Fe 2+ /g and 52.64±0.24 to 75.99±0.849 Rutin equivalent mg/g respectively. Antioxidant power and TFC of all the peel extracts were well above as compared with juice and seed samples, the highest IC 50 and TPC in Daya peel whereas the highest FRAP and TFC in Nimali and Kalpitiya red peels respectively. Kalpitiya red juice and peel had the highest TAC and ProAc. Proximate analysis revealed Protein, lipid and fiber contents were higher in seeds than peels. Carbohydrate all the peels higher than the seeds. yield reduction. Originality/ Value: Findings reveal that selected cultivars of pomegranate peel possess exceptionally high antioxidant power and could be applied as an excellent source of natural antioxidant in future therapeutic and medicine and as a safer natural antioxidant in food industries. High nutrient contents in pomegranate by-products facilitate to develop nutritionally valuable components such as functional food ingredients and nutraceuticals.
Using Big Data analytics tool to influence decision-making in higher education: A case of South African Technical and Vocational Education and Training colleges

a framework that allows the analysis and management of a larger amount of data (Moreno et al. 2016). Furthermore, Big Data is less about data that is big, but more of a capacity to search, aggregate, and cross-reference large data sets (Boyd & Crawford 2012). Actor network theory (ANT) is used as a lens to assess and propose how the use of Big Data Analytics (BDA) in Technical and Vocational Education and Training (TVET) environment can be used to improve decision-making. The rest of the article is organised as follows: we start with the background followed by brief review of the literature of BDA, then the discussion of the four translations of ANT, research methods and then the results and conclusions. Background: Big data analytics in education is a new concept that has the potential to change the decision-making landscape in South African Colleges. Higher institutions of learning, including Technical and Vocation Education Training (TVET) colleges like all other organisations, rely on data for their decision-making. These decisions affect the way pedagogy and student management is administered. Colleges collect huge quantities of data in different formats from students, staff and stakeholders for different reasons and occasions. Objectives: The goal of this study was to investigate how Big Data analytics and their tools may improve decision making in TVET colleges in South Africa through the lens of actor-network theory (ANT). Method: A qualitative, interpretive inquiry was undertaken. A case study using focus group was conducted. The data collected through interviews were arranged into themes and a thematic approach was employed to analyse these themes using QDA Miner Lite software. Results: The results from focus group interviews revealed that TVET colleges collect an enormous amount of data. These data are extracted for different reasons, yet there are no Analytics used for decision-making. Decisions are made by the highest-paid individuals (HiPPO) in colleges. Conclusion: This dissertation recommends that the TVET colleges invest in data science skills for their staff, and Big Data infrastructure. Big Data technologies such as Mongo DB and Hadoop are recommended as the most commonly and advanced tools that can be used for Big Data analytics.
The impact of the creative performance of agricultural heritage systems on tourists’ cultural identity: A dual perspective of knowledge transfer and novelty perception

Tourism in the Globally Important Agricultural Heritage System (GIAHS) is critical to the inheritance and innovation of excellent traditional farming cultures. Based on social identity theory, this paper explored the process by which agricultural heritage systems’ creative performance influences tourists’ cultural identity through 406 questionnaires from Chinese tourists. The results indicate that creative performance affects tourists’ cultural identity through a dual perspective of knowledge transfer and novelty perception. Furthermore, perceived authenticity acts as a moderator, weakening the impact of creative performance on tourists’ knowledge transfer, while perceived authenticity does not affect the process of tourists’ novelty perception. This research provides a fresh perspective on the sustainable development of agricultural heritage tourism. Meanwhile, it offers theoretical foundations and practical inspirations for the development of agricultural heritage’s creative tourism.
The Inter-Bristle Pressure Filed in a Large-Scale Brush Seal

Brush seals promise improvements to the widely used labyrinth seal in regulating turbomachinery leakages. Enhanced resistance to the flow is provided by a static ring of densely packed fine wire bristles that are angled in the direction of rotation and flex to accommodate rotor excursions. A large-scale brush seal was constructed to study the leakage characteristics in direct relation to the pressure field within and surrounding the bristle pack for multiple clearance conditions, therefore developing the understanding of brush seal fluid dynamic behaviour. The governing parameter controlling leakage behaviour transitioned from pressure ratio for a large clearance, to pressure load for a line-on-line configuration. In all cases, leakage flow converged to an asymptotic value once maximum levels of bristle blow-down and pack compaction were attained. For both clearance configurations, this occurred at a pressure ratio corresponding to that at which axial distributions of pressure converged; equivalent behaviour was noted for the line-on-line configuration with pressure drop. Comparatively small changes were experienced in leakage behaviour and to the inter-bristle pressure field with increasing pressure drop for the line-on-line brush seal. This indicated that brush seal performance is more influenced by changes in bristle blow-down than bristle pack compaction.
Differences in temperament and character inventory (TCI) profile between suicidal and nonsuicidal psychiatric outpatients

The purpose of this study was to identify personality traits associated with suicide attempt in a clinical sample. Temperament and character inventory (TCI) profiles of 759 patients who met the inclusion criteria among 1000 randomly selected hospital records collected. Of these 759 patients, 103 had a history of at least 1 actual suicide attempt (suicidal group) whereas 656 had no such history (nonsuicidal group). The suicidal group showed higher scores of novelty seeking (mean ± SD: 36.1 ± 1.2 vs 33.3 ± 0.5; P = .026) and harm avoidance (57.1 ± 1.5 vs 53.0 ± 0.6; P = .01) but lower scores of self-directedness (27.5 ± 1.5 vs 34.4 ± 0.6; P < .001) than the nonsuicidal group. Higher novelty seeking (OR [95% CI]: 1.031 [1.008–1.054]; P = .007) and lower self-directedness: 0.955 [0.927–0.983]; P = . 002 were also associated with suicide attempts in the analysis of 7 personality scales. These findings suggest that patients who attempt suicide differ from nonattempters in terms of personality traits, especially in novelty seeking (NS), harm avoidance (HA), and self-directedness (SD). It is noteworthy that this study contains data from actual visits to the emergency room to evaluate suicide attempts. Abbreviations: CO = cooperativeness, Ha = harm avoidance, NS = novelty seeking, PS = persistence, RD = reward dependence, SD = self-directedness, ST = self-transcendence, TCI = temperament and character inventory.