Miracle-dz/UniFAR-FTUs huggingface dataset:
PEMANFAATAN CHAT GPT DALAM DUNIA PENDIDIKAN

Cepatnya perkembangan teknologi yang semakin maju, kini komputer dapat melakukan pengolahan yang berbasis teknologi kecerdasan buatan (Artificial Intelligence). Salah satu teknologi Artificial Intelligence yang saat ini sedang ramai diperbincangkan yaitu ChatGPT. ChatGPT adalah teknologi perangkat cerdas yang memakai metode deep learning, dan mampu memberikan output yang serupa dengan manusia ketika mengartikan dan menanggapi bahasa alami. Penelitian ini menggunakan metode study literature review terhadap 23 jurnal. Tujuan penelitian ini untuk meyakinkan apakah ChatGPT dapat meningkatkan efisiensi dan efektifivitas pada proses pembelajaran. Hasil dari penelitian ini, yaitu pemanfaatan ChatGPT dalam dunia pendidikan memiliki sisi positifnya, karena dapat membantu siswa atau mahasiswa dalam mengerjakan tugas mereka, tetapi penggunaannya perlu diberikan arahan dan diberikan peringatan agar tidak menjadikan lulusan yang kurang berpikir, menulis secara kritis, dan memiliki rasa malas hanya copy-paste saja. Penggunaan teknologi ChatGPT pada dunia pendidikan memberikan kemampuan untuk memajukan kualitas pembelajaran. Walaupun demikian, penggunaan teknologi ini diperlukan perhatian tentang aspek privasi dan etika penggunaannya. Oleh karena itu, para praktisi pendidikan harus mempertimbangkan dengan cermat bagaimana mereka menggunakan teknologi ChatGPT dalam pembelajaran dan memastikan bahwa penggunaannya sesuai dengan nilai-nilai etika dan standar privasi yang telah ditetapkan.
Prediction of corporate financial distress in the travel and tourism industry

Currently, most countries in the world endure financial distress due to the ongoing economic crisis. Thus, financial distress prediction (FDP) has become vital in speculating the business continuity for the corporate world. In the context of Sri Lanka, the travel and tourism industry has been highly exposed to political, economic and climate (weather) shocks, as well as being sensitive to the impacts of COVID-19. Along with those shakes the recent economic crisis and the need for FDP led the researchers to empirically investigate the discrimination zones, i.e., distress zone, grey zone, and safe zone of the travel and tourism industry in developed and developing countries. This study adopted Altman’s (1968) original Z-score model for data analysis. This research is based on secondary data from annual reports of 138 listed travel and tourism companies listed on the stock exchanges of developed and developing countries: the USA, Australia, Singapore, South Africa, Malaysia, and Sri Lanka during the five-years period (2016–2020). The purposive sampling method is used to obtain the secondary data for the study. The finding revealed that 96.38% of travel and tourism companies are in the distress zone, 2.90% grey zone, and 0.72% safe zone in developed and developing countries. This study recommends all the travel and tourism companies in developed and developing countries maintain enough retained earnings to avoid corporate failure. This study is significant since it evidences the FDP and signals the global economic depression.
ON QUASI-ARMENDARIZ MODULES

In this paper, we introduce the concept of a (α-) quasi-Armendariz module, principally quasi-Baer module and syudy its some properties. In particular, we show: (1) For an α-quasi-Armendariz module MR, MR is a principally quasi-Baer module if and only if M [x;α]R[x;α] is a principally quasi-Baer module. (2) A necessary and sufficient condition for a trivial extensions to be quasi-Armendariz is obtained. Consequently, new families of quasi-Armendariz rings are presented.
Prime Labeling of Small Trees with Gaussian Integers

A graph on n vertices is said to admit a prime labeling if we can label its vertices with the first n natural numbers such that any two adjacent vertices have relatively prime labels. Here we extend the idea of prime labeling to the Gaussian integers, which are the complex numbers whose real and imaginary parts are both integers. We begin by defining an order on the Gaussian integers that lie in the first quadrant. Using this ordering, we show that all trees of order at most 72 admit a prime labeling with the Gaussian integers.
Design of a Synthetic Long Peptide Vaccine Targeting HPV-16 and -18 Using Immunoinformatic Methods

Human papillomavirus types 16 and 18 cause the majority of cervical cancers worldwide. Despite the availability of three prophylactic vaccines based on virus-like particles (VLP) of the major capsid protein (L1), these vaccines are unable to clear an existing infection. Such infected persons experience an increased risk of neoplastic transformation. To overcome this problem, this study proposes an alternative synthetic long peptide (SLP)-based vaccine for persons already infected, including those with precancerous lesions. This new vaccine was designed to stimulate both CD8+ and CD4+ T cells, providing a robust and long-lasting immune response. The SLP construct includes both HLA class I- and class II-restricted epitopes, identified from IEDB or predicted using NetMHCPan and NetMHCIIPan. None of the SLPs were allergenic nor toxic, based on in silico studies. Population coverage studies provided 98.18% coverage for class I epitopes and 99.81% coverage for class II peptides in the IEDB world population’s allele set. Three-dimensional structure ab initio prediction using Rosetta provided good quality models, which were assessed using PROCHECK and QMEAN4. Molecular docking with toll-like receptor 2 identified potential intrinsic TLR2 agonist activity, while molecular dynamics studies of SLPs in water suggested good stability, with favorable thermodynamic properties.
Characteristic and distribution of grain sizes and minerals in sediments from the Vietnamese Gulf of Tonkin

Grain sizes and minerals of sediments from the Gulf of Tonkin were evaluated from 30 surface samples and 50 samples from two cores. The distribution of grain sizes and minerals from these samples were determined to help understand sediment characteristics, origins, and environmental dynamics. There were five sediment types: fine and very fine sands, very coarse, coarse, and medium silts. Minerals in the sediment were identified as decreasing in terms of the content of quartz, illite, kaolinite, chlorite, feldspar, goethite, halite, calcite, gibbsite, aragonite, and montmorillonite. Contents of major minerals such as quartz, illite, kaolinite, and chlorite varied from nearshore to offshore; quartz was higher nearshore than offshore; illite, kaolinite, and chlorite from nearshore were lower than offshore; calcite and aragonite were low nearshore and higher in the offshore; goethite was lower offshore and higher nearshore; and, finally, halite was high offshore and low nearshore. Surface sediments were divided into three groups based on sedimentary environment characteristics: Group 1 was distributed nearshore with strong dynamics, Group 2 in bays and nearshore with weak dynamics, and Group 3 was distributed offshore with quiet dynamics. The origin of the sediments was weathering and erosion from the mainland and islands under the river and sea processes in the Gulf of Tonkin with quartz, illite, kaolinite, feldspar, chlorite, and montmorillonite found present in the sediment. Geochemical processes produced goethite, gibbsite, halite, and pyrite in the sediments while biological substances produced calcite and aragonite.
HLA-DR Expression on Monocytes and Sepsis Index Are Useful in Predicting Sepsis

The reduction of mortality in patients with sepsis depends on the early identification and treatment of at-risk patients. The aim was to evaluate the HLA-DR expression on the surface of monocytes (MHLA-DR ratio), the sepsis index (CD64 expression on neutrophils/MHLA-DR ratio), and C-reactive protein (CRP) with the development of sepsis. We prospectively enrolled 77 critically ill patients, 59 with stroke and 18 with traumatic brain injuries. The biomarkers were tested at the baseline and 3, 6, 9, 12, and 15 days later. Most patients (71%) developed sepsis (4.2 ± 1.3 days after admission). On day 3, those subsequently developing sepsis had lower levels of MHLA-DR+ (81.7 ± 16.2% vs. 88.5 ± 12.1%, p < 0.05) and higher sepsis indexes (0.19 ± 0.19 vs. 0.08 ± 0.08, p < 0.01) than those not developing sepsis. The MHLA-DR ratio slowly recovered before day 6, while the sepsis index remained raised in septic patients up to day 9 (p < 0.05). To predict the development of sepsis, optimal cut-offs were CRP levels > 106.90 mg/mL (74.19% sensitivity, 69.49 specificity) and MHLA-DR expression rate < 72.80% (45.31% sensitivity, 89.47% specificity). The periodic monitoring of the MHLA-DR expression together with CRP and sepsis index may help to identify patients in the ICU at increased risk of developing sepsis.
Effect of Milk Fat Globule Membrane- and Protein-Containing Snack Product on Physical Performance of Older Women—A Randomized Controlled Trial

Introduction: Sarcopenia is common in people 70+ years of age, and its prevalence increases with further aging. Insufficient energy and protein intake accelerates muscle loss, whereas sufficient protein intake and milk fat globule membrane (MFGM) may suppress age-associated deterioration of muscle mass and strength. Our objective was to test whether a snack product high in MFGM and protein would improve physical performance in older women. Methods: In this 12-week randomized controlled trial, women ≥ 70 years, with protein intake < 1.2 g/body weight (BW) kg/day (d), were randomized into intervention (n = 51) and control (n = 50) groups. The intervention group received a daily snack product containing ≥ 23 g of milk protein and 3.6–3.9 g of MFGM. Both groups were advised to perform a five-movement exercise routine. The primary outcome was the change in the five-time-sit-to-stand test between the groups. Secondary outcomes included changes in physical performance, cognition, hand grip strength, and health-related quality of life. Results: The change in the five-time-sit-to-stand test did not differ between the intervention and the control groups. The change in the total Short Physical Performance Battery score differed significantly, favoring the intervention group (p = 0.020), and the balance test showed the largest difference. Protein intake increased significantly in the intervention group (+14 g) compared to the control group (+2 g). No other significant changes were observed. Conclusions: Our results indicate that the combination of MFGM and protein may improve the physical performance-related balance of older women.
Professional farmer collectives for effective agri-environmental management: an assessment

ABSTRACT In 2013 the EU Common Agricultural Policy (CAP) enabled groups of farmers to be applicants and final beneficiaries of Agri-environmental schemes (AES). The Dutch government went one step further, ruling that only groups of farmers (farmer collectives) could be beneficiaries of AES. The changing role of farmer groups or farmer collectives comes with a variety of challenges. Using the lens of professionalization, we investigated the professionalization of the Dutch farmer collectives after three years of coordinating AESs, with the aim of ascertaining how professional the collectives are and how any differences in professionalism can be explained. The experience of the Dutch collectives is relevant to other groups of farmers in Europe who are starting or are engaged in the process of self-organization and professionalization, as well as to the further development of the Dutch collectives. Our assessment revealed that there are different types of professional collectives, depending on the situation. Most of the collectives scored excellently on the organizational and occupational criteria with an internal focus. All the collectives have criteria that could be improved. There are differences in professionalization based on history, resources and scale.
Properties of thermally modified Scots pine (Pinus sylvestris L.), Kazdağı fir (Abies equi-trojani Asch. et Sint.), and Eastern beech (Fagus orientalis Lipsky)

Surface roughness and discoloration of thermally modified Scots pine (Pinus sylvestris L.), Kazdağı fir (Abies equi-trojani Asch. et Sint.), and Eastern beech (Fagus orientalis Lipsky) were evaluated. The surface roughness and discoloration of the samples sanded with 60, 80, and 120-grit grades were evaluated at temperature levels of 160, 180, 200, and 220 °C. Findings were subjected to multiple analysis of variance and Duncan’s homogeneity tests; using the SPSS package program, single, double, triple, and quadruple interactions were analyzed. The highest surface roughness values were found in Eastern beech samples with radial cross-section (12.4 µm) finished with 60-grit sandpaper. The lowest roughness value was found in Kazdagi fir samples that were heat-treated in 200 °C and processed with 120-grit sandpaper (2.03 µm). The lowest discoloration evaluation (∆E) value was found eastern beech samples 32.43, and highest ∆E values 46.86 found at Scots pine.
Investigations of Machine Learning Algorithms for High Efficiency Video Coding (HEVC)

The growing demand of high-resolution video on portable devices, the applications require higher coding efficiency, high throughput and low power for handling heterogenous types of video signals. This paper presents a survey on possibility of applying Machine Learning (ML) models in H.265/ HEVC video encoder unit. Higher computational complexity with respect to motion estimation, coding, and parallel processing architectures are required for HEVC. The existing HEVC algorithms are based on spatial temporal relationship which requires dynamic video sequences handling for fast changes in scenes. This paper focuses on the possible realization of machine learning algorithms for Rate Control (RC) in video sequences, Coding Unit (CU) depth decision, Neural network-based Motion Estimation and Compensation, adaptive de-blocking filter for reducing blocking artifacts and task driven semantic coding for real time video applications. The algorithms are surveyed with respect to the learning process used in various units of HEVC encoders and summarized in terms of parameters achieved and datasets used in the existing literature.
Ethical and regulatory challenges amid artificial intelligence development: an outline of the issue

Motivation: The Fourth Industrial Revolution brings with it numerous challenges for society, business and government. Its nature and pace is dictated by the increasing use of artificial intelligence (AI) technologies. The acquisition, collection, analysis, processing and informed use of data, as well as the continued development of artificial intelligence algorithms, are becoming the basis for the development of modern economies. However, its implementation is associated with a number of opportunities as well as threats. The way and extent to which artificial intelligence-based technologies are used requires constant attention, the basis of which is an appropriate regulatory system.Aim: The purpose of the paper is indicating and discussing selected challenges facing people and institutions responsible for creating regulations regarding the use and implementation of artificial intelligence. The regulators should , therefore, find a proper balance between innovation and credibility.Results: The analysis of the available literature allows to indicate at least 3 important challenges facing regulators: these are providing a precise definition of AI, ethical threats and competition in terms of creating suitable regulations regarding AI. The European Union has developed regulations on artificial intelligence with respect for freedom and human rights. This is a different approach from other regions of the world, including regulations established in the US or China.
Investigating Learners Perceptions of Completion and Certification in MOOCs

Understanding learners’ perceptions of their own learning outcomes is critical for accurately interpreting those outcomes and planning interventions to help improve them. Past research in massive open online courses (MOOCs) shows that many learners enroll in courses they do not finish, and much research has been conducted investigating the patterns and trends driving this. This paper uses a qualitative methodology to understand how learners perceive course completion and certification, and why they do or do not meet their learning goals. Data were analyzed from fifteen interviews with learners who had enrolled in at least one MOOC. The data suggests that learners have a complex understanding of completion that varies depending on their own goals and access to the material. It also shows that they see certification as distinct from completion, and will only be willing to pursue certification under certain circumstances.
Nicotinamide Prevents Diabetic Brain Inflammation via NAD+-Dependent Deacetylation Mechanisms

This study investigated the effect of nicotinamide (NAM) supplementation on the development of brain inflammation and microglial activation in a mouse model of type 1 diabetes mellitus. C57BL/6J male mice, which were made diabetic with five consecutive, low-dose (55 mg/kg i.p.) streptozotocin (STZ) injections. Diabetic mice were randomly distributed in different experimental groups and challenged to different doses of NAM (untreated, NAM low-dose, LD, 0.1%; NAM high-dose, HD, 0.25%) for 25 days. A control, non-diabetic group of mice was used as a reference. The NAD+ content was increased in the brains of NAM-treated mice compared with untreated diabetic mice (NAM LD: 3-fold; NAM HD: 3-fold, p-value < 0.05). Immunohistochemical staining revealed that markers of inflammation (TNFα: NAM LD: −35%; NAM HD: −46%; p-value < 0.05) and microglial activation (IBA-1: NAM LD: −29%; NAM HD: −50%; p-value < 0.05; BDKRB1: NAM LD: −36%; NAM HD: −37%; p-value < 0.05) in brains from NAM-treated diabetic mice were significantly decreased compared with non-treated T1D mice. This finding was accompanied by a concomitant alleviation of nuclear NFκB (p65) signaling in treated diabetic mice (NFκB (p65): NAM LD: −38%; NAM HD: −53%, p-value < 0.05). Notably, the acetylated form of the nuclear NFκB (p65) was significantly decreased in the brains of NAM-treated, diabetic mice (NAM LD: −48%; NAM HD: −63%, p-value < 0.05) and inversely correlated with NAD+ content (r = −0.50, p-value = 0.03), suggesting increased activity of NAD+-dependent deacetylases in the brains of treated mice. Thus, dietary NAM supplementation in diabetic T1D mice prevented brain inflammation via NAD+-dependent deacetylation mechanisms, suggesting an increased action of sirtuin signaling.
Effects of Dead‐End Fractures on Non‐Fickian Transport in Three‐Dimensional Discrete Fracture Networks

Understanding mechanistic causes of non‐Fickian transport in fractured media is important for many hydrogeologic processes and subsurface applications. This study elucidates the effects of dead‐end fractures on non‐Fickian transport in three‐dimensional (3D) fracture networks. Although dead‐end fractures have been identified as low‐velocity regions that could delay solute transport, the direct relation between dead‐end fractures and non‐Fickian transport has been elusive. We systematically generate a large number of 3D discrete fracture networks with different fracture length distributions and fracture densities. We then identify dead‐end fractures using a novel graph‐based method. The effect of dead‐end fractures on solute residence time maximizes at the critical fracture density of the percolation threshold, leading to strong late‐time tailing. As fracture density increases beyond the percolation threshold, the network connectivity increases, and dead‐end fractures diminish. Consequently, the increase in network connectivity leads to a reduction in the degree of late‐time tailing. We also show that dead‐end fractures can inform about main transport paths, such as the mean tortuosity of particle trajectories. This study advances our mechanistic understanding of solute transport in 3D fracture networks.
Potential for Predicting Lymph-node Metastasis in Invasive Breast Carcinoma of No Special Type Using MT1-MMP Immunohistochemistry Staining

BACKGROUND: Lymph-node metastasis (LNM) is the most frequent complication of invasive breast carcinoma (IBC). AIM: Using immunohistochemistry (IHC), this study aims to determine the role of membrane-type 1-matrix metalloproteinase (MT1-MMP) expression as a biomarker for LNM in IBC of no special type (IBC-NST). MATERIALS AND METHODS: Primary tumors from individuals with IBC-NST were preserved in paraffin and then categorized as having LNM or not. Tumor size, lymphovascular invasion (LVI), tumor grade, MT1-MMP expression, and other factors were evaluated across a range of ages. MT1-MMP expression was assessed by IHC, with supplemental data acquired from archives. Collecting and analyzing the data required the use of both bivariate and multivariate techniques. RESULTS: The odds ratio (OR) for LNM was 5.003 (95% CI: 1.68–20.61) for MT1-MMP expression, while the OR for LVI was 4.71 (95% CI: 1.57–18.8). These associations were found using the Firth penalized likelihood Logit analysis method. At an H-score cutoff of 202.22 (70.8% sensitivity and 95.8% specificity), an area under the receiver operating characteristic of 0.9130. 038 (95% CI: 0.838–0.989) was found for MT1-MMP expression in diagnosing LNM. CONCLUSION: In conjunction with LVI, MT1-MMP expression may serve as a predictor of LNM. To further assist data separation in future research, the MT1-MMP expression H-score cutoff of 202.22 could be used.
Reducing bias for maximum approximate conditional likelihood estimator with general missing data mechanism

ABSTRACT In missing data analysis, the assumption of the missing data mechanism is crucial. Under different assumptions, different statistical methods have to be developed accordingly; however, in reality this kind of assumption is usually unverifiable. Therefore a less stringent, and hence more flexible, assumption is preferred. In this paper, we consider a generally applicable missing data mechanism. Under this general missing data mechanism, we introduce the conditional likelihood and its approximate version as the base for estimating the unknown parameter of interest. Since this approximate conditional likelihood uses the completely observed samples only, it may result in large estimation bias, which could deteriorate the statistical inference and also jeopardise other statistical procedure. To tackle this problem, we propose to use some resampling techniques to reduce the estimation bias. We consider both the Jackknife and the Bootstrap in our paper. We compare their asymptotic biases through a higher order expansion up to . We also derive some results for the mean squared error (MSE) in terms of estimation accuracy. We conduct comprehensive simulation studies under different situations to illustrate our proposed method. We also apply our method to a prostate cancer data analysis.