Miracle-dz/UniFAR-FTUs huggingface dataset:
Studi Kelayakan Produk Baru : Ban 12.00 R24 di PT Gtr

PT GTR merupakan salah satu pabrik ban di Indonesia yang memproduksi ban Penumpang Radial, ban Bias yang saat ini menjadi satu-satunya pabrik ban di Indonesia yang mampu untuk memproduksi ban Truk dan Bus dengan konstruksi radial. Pangsa pasar ban PT. GTR adalah domestik dan ekspor. Sebagai perusahaan ban, PT GTR dituntut untuk selalu mengikuti permintaan pasar agar tetap diminati dan laku dipasaran. Namun mengembangkan suatu model ban yang baru sangat beresiko, karena bisa saja ban baru tidak mampu bersaing dengan kompetitor, atau bisa pula investasi yang diperlukan untuk pengembangan model ban baru tersebut ternyata sangatlah besar. Solusi untuk mengatasi persoalan tersebut di atas adalah dengan terlebih dahulu melakukan studi kelayakan terlebih dahulu. Studi kelayakan yang meliputi studi pasar, studi kualitas ban kompetitor, studi biaya dan kebutuhan teknologi adalah apa yang dilakukan oleh GTR untuk menilai apakah produk baru tersebut direalisasikan. Berdasarkan hasil studi tersebut, maka disimpulkan bahwa target kualitas, target biaya dan kebutuhan teknologi bisa terpenuhi. Sedangkan dari hasil perhitungan peramalan kebutuhan dengan Metoda Konstan, menunjukkan bahwa besarnya kebutuhan produk baru berada di bawah permintaan pasar. Begitu pula dari segi keuangan, nilai IRR berada di atas bunga 12% yang berlaku, nilai PP yang rendah dibandingkan nilai ekonomis mold dan NPV yang positif, menunjukan bahwa investasi yang ditanam untuk pengembangan produk baru tersebut adalah layak. Kata Kunci : Ban, Studi Kelayakan, Pengembangan Produk
A Multiplex PCR for Detection of hlyA, papC, and traT genes in multidrug resistance Escherichia coli isolated from pregnant women

Urinary tract infection (UTI) in pregnancy is a common clinical problem which causes morbidity and, in a small minority of cases, renal damage and chronic renal failure. Urine samples from three hundred pregnant women were collected from Baghdad Teaching Hospital, were examined between January to June 2012 for the presence of bacteria. Their ages ranging from 15 to 50 years. Up to 32.6% (98) of women have a urinary tract infection (UTI). Escherichia coli was detected in 27.5% (27) of all the urine samples. All isolates were screened for some virulence factors: α-haemolysin, adhesion factor type P fimbriae and serum resistance. Multiplex PCR systems were used to detect genes encoding α-hemolysin (hlyA), adhesion P(papC), and traT associated with serum resistance.The results indicated that the occurrence of papC was detected in (18.5%), while traT was detected in (96.2%) of isolates on the other hand hlyA can't detected in any E. coli isolates. All the isolates were also studied for antibiotic susceptibility pattern using a disc diffusion method.The rate of resistance of E. coli Amoxicillin (92.5%), Amoxicillin/Clavulanic acid (89%), Cefotazidime, Cefotaxime and Cefepime (78%), kanamycin (59%), levofloxacin (55.5%), Ciprofloxacin (52%) and showed high degree of sensitivity toward Cefoxitin and Imipenem (7% and 0%) respectively. Additionally,17 (62.9%) isolates were multidrugresistant.The finding of this study indicated that the traT widespread among pathogenic E. coli in Iraq. Furthermore it concludes that E. coli is one of the important multidrug resistance causative agents of urinary tract infection in young women especially during the state of pregnancy.
Mediators of Amylin Action in Metabolic Control

Amylin (also called islet amyloid polypeptide (IAPP)) is a pancreatic beta-cell hormone that is co-secreted with insulin in response to nutrient stimuli. The last 35 years of intensive research have shown that amylin exerts important physiological effects on metabolic control. Most importantly, amylin is a physiological control of meal-ending satiation, and it limits the rate of gastric emptying and reduces the secretion of pancreatic glucagon, in particular in postprandial states. The physiological effects of amylin and its analogs are mediated by direct brain activation, with the caudal hindbrain playing the most prominent role. The clarification of the structure of amylin receptors, consisting of the calcitonin core receptor plus receptor-activity modifying proteins, aided in the development of amylin analogs with a broad pharmacological profile. The general interest in amylin physiology and pharmacology was boosted by the finding that amylin is a sensitizer to the catabolic actions of leptin. Today, amylin derived analogs are considered to be among the most promising approaches for the pharmacotherapy against obesity. At least in conjunction with insulin, amylin analogs are also considered important treatment options in diabetic patients, so that new drugs may soon be added to the only currently approved compound pramlintide (Symlin®). This review provides a brief summary of the physiology of amylin’s mode of actions and its role in the control of the metabolism, in particular energy intake and glucose metabolism.
Determinants of Destination Choices of Traveling for Old-age Resources: Evidence from Urban Older Adults

ABSTRACT Traveling for old-age resources (TOR) is an effective means of satisfying urban older adults’ needs for healthcare and improving their quality of life. Understanding TOR destination preferences helps manage and alleviate old-age resource shortages in cities. This study examined Chinese urban older adults’ preferences for TOR destinations, exploring the effects of motivations, constraints, satisfaction, quality of life, frequency, and length. The Chinese city tier system was considered in classifying the destinations. Multinomial logistic regression modeling was employed for data analysis on 367 urban older adults from China. The results suggested that TOR destination choices regarding general/specific destination types are subject to the identified factors. This study helps policymakers and practitioners understand how these factors affect seniors’ TOR decisions when developing, managing, and marketing these destinations. More theoretical and practical implications are discussed within the realms of senior living and senior tourism.
The pandemic politics of cultural work: collective responses to the COVID-19 crisis

ABSTRACT The scope, unevenness, and severity of the COVID-19 pandemic’s impact on cultural work has been widely acknowledged. This article turns to how sections of the cultural industries responded to the onset of this crisis. Our account is based on document research completed during the first wave of the pandemic. We gathered news reports, impact survey results, policy recommendations, open letters, event announcements, and other grey literature generated by a range of organizations in the cultural sector, including trade unions, professional associations, and activist groups, among others. Framed by the concepts ‘labouring of culture’ and ‘policy from below’, our thematic analysis of this material reveals that cultural workers responded to the pandemic by surfacing the idea of cultural production as work; by enacting practices of care and mutual aid; and by proposing policy changes. These collective responses are marked by multiple tensions, particularly between rehabilitating the status quo in the cultural sector and radically reimagining it for a post-COVID-19 world.
A Comparative Study of Two-Sample Tests for High-Dimensional Covariance Matrices

The equality of covariance matrices is an essential assumption in means and discriminant analyses for high-dimensional data. The performance of tests for covariance matrices may vary substantially depending on the covariance structure, so using inappropriate methods to verify the assumption will result in worse performance. The purpose of this study is to assess and compare the performance of three tests for two-sample high-dimensional covariance matrices: Schott’s (2007), Srivastava and Yanagihara’s (2010), and Li and Chen’s (2012) under various covariance structures. A simulation study was conducted when the covariance structures were spherical, compound symmetric, block-diagonal, and first-order autoregressive with homogenous variances. The results show that Li and Chen’s test outperforms the others with a sample size of at least 10 under particular covariance structures. When the number of variables is increased with a fixed sample size, Li and Chen’s test still performs well, whereas Schott’s performance deteriorates. Some recommendations for selecting appropriate tests are also provided in this paper.
Livestock Management With Unmanned Aerial Vehicles: A Review

The ease of use and advancements in drone technology is resulting in the widespread application of Unmanned Aerial Vehicles (UAVs) to diverse fields, making it a booming technology. Among UAVs’ several applications, livestock agriculture is one of the most promising, where UAVs facilitate various operations for efficient animal management. But the field is characterized by multiple environmental, technical, economic, and strategic challenges. However, the use of advanced technological techniques like Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), Deep Learning (DL), advanced sensors, etc., along with the assurance of animal welfare while operating the UAVs, can lead to widespread adoption of drone technology amongst livestock farmers. This paper discusses livestock management research where UAVs monitor farm animals via detection, counting, tracking animals, etc. In this article, an attempt has been made to elucidate different aspects and broader issues around livestock management while highlighting the associated challenges, opportunities, and prospects. This work is the first review paper on the subject matter with all the necessary information and analysis, to the best of our knowledge. Therefore, the article promises to provide interested researchers with detailed information about the field, guiding future research.
Process Analysis of Recommendations for a Touristic Service Provision

In order to make the consumers’ decision-making process easier with regard to the choice of a provision of tourism services, it is paramount to understand how to better use the different types of social information (eg, product popularity, critics and advice of community members virtual). In this context, the purpose of our research is to explore and to dissect the different phases of the recommendation process. By doing so, we have conducted qualitative interviews to explore the reflected opinions and ideas so as to monitor the behavior of the virtual communities’ members. We have converted the traditional making process that is made of two steps into another one more accurate and composed of three steps while identifying users' information search trends at each stage. In the light of the results of our study, suggestions are made to the related recommendation systems, implications are presented and future perspectives are reviewed.
Companion Animals—An Overlooked and Misdiagnosed Reservoir of Carbapenem Resistance

The dissemination of antimicrobial-resistance is a major global threat affecting both human and animal health. Carbapenems are human use β-lactams of last resort; thus. the dissemination of carbapenemase-producing (CP) bacteria creates severe limitations for the treatment of multidrug-resistant bacteria in hospitalized patients. Even though carbapenems are not routinely used in veterinary medicine, reports of infection or colonization by carbapenemase-producing Enterobacterales in companion animals are being reported. NDM-5 and OXA-48-like carbapenemases are among the most frequently reported in companion animals. Like in humans, Escherichia coli and Klebsiella pneumoniae are the most represented CP Enterobacterales found in companion animals, alongside with Acinetobacter baumannii. Considering that the detection of carbapenemase-producing Enterobacterales presents several difficulties, misdiagnosis of CP bacteria in companion animals may lead to important animal and public-health consequences. It is of the upmost importance to ensure an adequate monitoring and detection of CP bacteria in veterinary microbiology in order to safeguard animal health and minimise its dissemination to humans and the environment. This review encompasses an overview of the carbapenemase detection methods currently available, aiming to guide veterinary microbiologists on the best practices to improve its detection for clinical or research purposes.
Sparse Feature Clustering Network for Unsupervised SAR Image Change Detection

In this paper, we propose a sparse feature clustering network (SFCNet) for change detection in synthetic aperture radar (SAR) images. One of the principle problems in dealing with SAR images is to reduce the impact of speckle noise. Therefore, based on a neural network framework for change detection, we introduce the multi-objective sparse feature learning model where the sparsity of representation is adaptively learned in order to increase the robustness to different levels of noise. For learning the semantic information of changed and unchanged pixels, the network is fine-tuned by the correctly labeled samples selected from coarse results. The selection criterion influences the change detection result a lot. Therefore, we construct a novel cross-entropy clustering loss (CEC) by introducing a clustering regularization term to learn the discriminative representations. Experiments on simulate and real SAR images demonstrate the superiority of the proposed method over compared methods.
Evaluation of PPG Feature Values Toward Biometric Authentication Against Presentation Attacks

In this study, we examined information leakage in photoplethysmogram (PPG)-based biometric authentication and assessed an attack against authentication based on the information leakage. Several approaches have been proposed to apply PPG to biometric authentication using a wearable device; however, there may be several attacks against PPG-based authentication. One of the attacks is a “presentation attack” (PA), which utilizes the information leakage originating from the various PPG measurement sites on a body. The PA records the victim’s PPG stealthily on non-genuine measurement sites and transmits it to the PPG sensor to break the authentication. We examined the information leakage and assessed the PA by evaluating feature values extracted from the PPG signals. We recorded the PPG signals of 12 participants on their fingertips and wrists. We compared the feature values extracted from the recorded PPG signals by computing the differences, correlation coefficients, and mutual information to examine the leakage of information required for the PPG-based authentication. We then assessed the feasibility of a PA based on existing PPG-based authentication algorithms and evaluated the contribution of each value to authentication and PA by computing the permutation importance of all feature values. The experimental results indicated that there might be information leakage and selection of feature values to reduce the feasibility of the PA up to 62.8 %.
Analysis of the Mechanical Properties and Damage Mechanism of Carbon Fiber/Epoxy Composites under UV Aging

The UV durability of carbon fiber composites has been a concern. In this work, UV irradiation on carbon fiber-reinforced polymer (CFRP) materials was performed using an artificial accelerated UV aging chamber to investigate the effect of UV exposure on carbon fiber composites. UV aging caused some of the macromolecular chains on the surface resin to break, resulting in the loss of small molecules and loss of mass. After 80 days of UV irradiation exposure, a significant decline in the macroscopic mechanical properties occurred in the longitudinal direction, with the largest decrease of 23% in longitudinal compressive strength and a decreasing trend in the transverse mechanical properties at the later stage of aging. The microscopic mechanical properties of the CFRP specimens were characterized using nanoindentation, and it was found that UV aging had an embrittlement effect on the matrix, and its hardness/modulus values were higher than the initial values with UV exposure. The fibers were less affected by UV irradiation.
Insulin-Like Growth Factor 1 (IGF- 1) Levels And Left Ventricular Meridional End Systolic Stress (LVMESS) Interrelationship In Middle- Aged And Elderly Obese Subject With Type 2 Diabetes Mellitus

Background: Left ventricular meridional end systolic stress (LVMESS) is a quantitative determinant index of LV afterload which is an essential determinant of LV performance. The key factors influencing LVMSS, notably ventricular wall thickness, chamber size and configuration are shown to be altered by both obesity and type2 diabetes mellitus (T2DM). There exist reports implicating high free insulin-like growth factor 1 (IGF-1) but not growth hormone (GH) levels, with increased left ventricular mass index (LVMI), abnormal LV geometric remodeling, and A1c levels. Objective: to explore whether a meaningful link exists between LVMESS and free IGF-1 plasma levels, in obese middle-aged and elderly individuals with or without T2DM. Methodology: In this cross sectional study, a total of 145 participants Involved. Middle aged and elderly obese individuals with or without T2 DM were matched with healthy lean subjects of the same age group, with or without T2 DM. LVMESS and IGF-1 levels were compared between groups; group differences were analyzed using unpaired t test and linear correlation test. Data were expressed as the mean ± S.D, using SPSS version 18). Results: LVMESS values showed significant decrease in diabetic patients compared to control in both middle and old aged subjects. IGF-1, concentration levels were lower in the elderly obese diabetics compared to control, with significant (r=0.462; p= 0.04) negative correlation with LVMESS in healthy obese old aged, and a negative but non-significant correlation with LVMESS in lean old aged with or without diabetics Conclusion: Nonlinear correlation of IGF-1 levels with LVMESS obtained. It could be hypothesized that because of its negative significant correlation with LVMESS in the elderly non diabetic obese, IGF-1 free level could be utilized to serve as a predictor of impaired LV systolic function in these subjects. In addition the results could shed some light on IGF-1- LVMESS relationship, when considering IGF-1 therapy in certain conditions. Recommendation: It is suggested that using IGF-1 plasma levels in the elderly with or without T2DM could aid as predictor for LV global systolic dysfunction . However, larger sample size are to be considered.
The what and why of supplementary schooling in Flanders: purposes and underlying motives as perceived by initiators

ABSTRACT Supplementary schooling can play an important role in the educational trajectory of minoritised youth. Yet, our knowledge of the communities’ motives for organising education and the purposes the schools pursue remains limited. Existing literature tends to understand supplementary schools either as resisting ongoing inequity in mainstream society or as transmitting the heritage language. We argue that it is valuable to transcend this fragmented view by taking a more comprehensive approach to the diversity of purposes of supplementary schools and by revealing how purposes respond to various motives. Methodologically, semi-structured in-depth interviews with initiators of 12 supplementary schools in Flanders took place to learn about the pursued purposes and the motives underlying them. Rather than the fragmented view often presented in the literature, the initiators show a strong sensitivity to the variety of needs and are deeply invested to support and strengthen youth in navigating the different educational and socio-cultural contexts they are part of. These findings shed light on the variety of educational needs and desires of minoritised communities in continental Europa, highlighting opportunities to tackle enduring inequalities experienced by minoritised youth in mainstream school
Fine-Grained Job Salary Benchmarking with a Nonparametric Dirichlet Process-Based Latent Factor Model

As a key decision-making process in compensation and benefits (C&B) in human resource management, job salary benchmarking (JSB) plays an indispensable role in attracting, motivating, and retaining talent. Whereas the existing research mainly focuses on revealing the essential impacts of personal and organizational characteristics and economic factors on labor costs (e.g., C&B), few studies target optimizing JSB from a practical, data-driven perspective. Traditional approaches suffer from issues that result from using small and sparse data as well as from the limitations of linear statistical models in practice. Furthermore, there are also important technical issues that need to be addressed in the small number of machine learning–based JSB approaches, such as “cold start” issues when considering a brand-new type of company or job or model interpretability issues. To this end, we propose to address the JSB problem with data-driven techniques from a fine-grained perspective by modeling large-scale, real-world online recruitment data. Specifically, we develop a nonparametric Dirichlet process–based latent factor model (NDP-JSB) to jointly model the latent representations of both company and job position and then apply the model to predict salaries based on company and position information. Our model strengthens the usage of data-driven approaches in JSB optimization by addressing the aforementioned issues in existing models. For evaluation, extensive experiments are conducted on two large-scale, real-world data sets. Our results validate the effectiveness of the NDP-JSB and demonstrate its strength in providing interpretable salary benchmarking to benefit complex decision-making processes in talent management. Summary of Contribution: This paper bridges the cutting-edge machine learning techniques to their implementation in a practical operation research problem in human resources. We focus on optimizing the salary-matching work to help the companies to seek reasonable salaries for their positions by proposing a data-driven approach to capture hidden patterns from user and company profiles. The contributions of this work reside in both operation research and computing. We (1) formulate the JSB optimization problem and (2) solve it by developing a data-driven method along with an effective algorithm optimization. Moreover, the proposed methodology has strengths in addressing the issues of data sparseness and result interpretability.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Oct 25, 2021 Revised Feb 9, 2022 Accepted Feb 27, 2022 Sentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches.