Miracle-dz/UniFAR-FTUs huggingface dataset:
A COMPARATIVE STUDY OF DESTINATION IMAGE BETWEEN THAILAND AND INDONESIA

Tourism is an important factor for the economies in Southeast Asia because it contributes to the total annual income of these developing countries. Especially in Thailand and Indonesia, their unique and interesting features such as their landscape and culture can help to achieve a high tourist arrival. The study aims 1) to evaluate tourist perceptions of Indonesia as compared to Thailand, 2) to assess any difference in the travel destination between first time and repeat visitors as well as 3) to examine any difference in travel among tourists with different demographic profile. A total of 300 questionnaires were given to 300 convenience sampling of international travelers at Suvarnabhumi International Airport and the Temple of the Reclining Buddha (Wat Pho) located in Bangkok, Thailand. The study shows that Thailand and Indonesia have good perceptions by tourists. Both countries have good image in culture because the locals assert their cultural identities, whereas Thailand has negative image in terms of political instabilities and Indonesia still has problems of the large gap between the rich and the poor. Furthermore, this study reveals that there is a correlation between destination image and travel intention. When the tourist has a positive perception of the destination, there is a high possibility that the tourist will choose that destination. Furthermore, a destination is likely to attract people to visit if it has a good destination image. In relation to this, this research will benefit service providers, travel agencies, marketers, and tourism organizations in Thailand and Indonesia to develop or to promote their destinations
The r-map, c-map and black hole solutions

We consider various geometrical and physical aspects of the r-map and c-map, which are two maps induced by the dimensional reduction of 5d and 4d, N = 2 supergravity coupled to vector multiplets respectively. We treat reduction over a spacelike or timelike dimension on an equal footing, and prove, for the first time, that the target manifold in the image of the timelike c-map is para-quaternion Kahler. In order to do this we provide a new formulation of projective special Kahler geometry based on real Darboux coordinates, which is useful both mathematically and physically in its own right. As an application we investigate how the r-map and c-map can be used to generate new stationary black hole solutions. In four dimensions we construct new extremal non-BPS solutions, and in both four and five dimensions we construct new non-extremal solutions. We also take the first steps towards constructing new rotating solutions, though at this stage we only recover known solutions. The systematic and geometrical nature of these constructions allows us to gain a deeper understanding of many familiar properties of black holes in supergravity, such as the attractor mechanism and the transformation of BPS into non-BPS black holes using a field rotation matrix. We also observe an interesting and novel feature relating to non-extremal black holes: in order for solutions to correspond to non-extremal black holes with finite scalar fields we find that the number of integration constants must reduce by half. This suggests that non-extremal black holes always satisfy first order equations similar to their extremal counterparts. For STU-like models all calculations are performed explicitly.
Comparative Analysis of Word Embedding Methods for DSTC6 End-to-End Conversation Modeling Track

To improve the performance of the seq2seq model for the DSTC6 End-to-End Conversation Modeling Track, we make a comprehensive comparison of different word embedding techniques as the initialization methods of word vectors with the expectation that it helps the model to learn a better word representation for generating response utterances. We perform experiments using the ofﬁcial training and evaluation sets. We obtain the best result when we use fastText for the initialization. We further analyze the learned word embeddings by visualization using the t-SNE algorithm. It has been seen that there is a tendency that similar words in terms of several aspects tend to make a cluster in the embedding space when it is initialized using word2vec, while they scatter when a random initialization is used.
Influence of lead polluted soil on growth, minerals content and yield components of flax

The impact of lead toxicity on some growth characters; minerals and oil content; fiber characteristics and yield components of flax (Linum usitatissimum, cultivar Giza 5) and canola (Brassica napus, cultivar Serw 4) at different growth stages; vegetative, flowering and maturity stages was evaluated. Plants were grown in pots containing soil treated with different concentrations of lead (250, 500, 1000 and 1500 mg/kg soil) as nitrate solution. Generally, the results showed that shoot and root lengths of flax and canola were significantly increased at low concentration of lead then decreased at higher concentrations compared to control. Lead accumulated in a dose-dependent manner in both plant species which result in plant growth reduction . The concentration of minerals (N, P, K, Ca, Mg, Zn and Fe), oil content and yield components in both species as well as fiber quality of flax were decreased by increasing concentrations of Pb 2+ .
Multi-Level Trajectory Clustering for Identifying Path Choice Set

It is well known that the size and composition of the path choice sets play an essential role in the traffic assignment process. Prato (Bovy, 2009) provided an overview of choice set generation algorithms. However, the algorithms failed to capture more than 40% of the chosen route in most cases. Accordingly, no standard method exists in practice to identify all selected routes. Nevertheless, with advanced data collection techniques such as GPS and Bluetooth, vehicle trajectories (Advani et al., 2021) can be extracted and exploited to identify the observed choice set.
KOI-54: THE KEPLER DISCOVERY OF TIDALLY EXCITED PULSATIONS AND BRIGHTENINGS IN A HIGHLY ECCENTRIC BINARY

Kepler observations of the star HD 187091 (KIC 8112039, hereafter KOI-54) revealed a remarkable light curve exhibiting sharp periodic brightening events every 41.8 days with a superimposed set of oscillations forming a beating pattern in phase with the brightenings. Spectroscopic observations revealed that this is a binary star with a highly eccentric orbit, e = 0.83. We are able to match the Kepler light curve and radial velocities with a nearly face-on (i = 5.°5) binary star model in which the brightening events are caused by tidal distortion and irradiation of nearly identical A stars during their close periastron passage. The two dominant oscillations in the light curve, responsible for the beating pattern, have frequencies that are the 91st and 90th harmonic of the orbital frequency. The power spectrum of the light curve, after removing the binary star brightening component, reveals a large number of pulsations, 30 of which have a signal-to-noise ratio ≳7. Nearly all of these pulsations have frequencies that are either integer multiples of the orbital frequency or are tidally split multiples of the orbital frequency. This pattern of frequencies unambiguously establishes the pulsations as resonances between the dynamic tides at periastron and the free oscillation modes of one or both of the stars. KOI-54 is only the fourth star to show such a phenomenon and is by far the richest in terms of excited modes.
VARIABILITY ANALYSIS USING PHASE-SPACE DIAGRAMS IN AUTOMATED TEST EQUIPMENT

Integral to author’s own PhD research is the investigation on the enhancements to variability in semiconductor industry Automatic Test Equipment’s (ATE’s) equipment maintenance time. Based on industry’s case studies on product yields, ATE downtime, and ATE throughput time, ATE process variability is perceived as a real problem in semiconductor manufacturing industry. However, effective methods for addressing process variability is not available in the literature. The author proposed a relationship-based research where Independent variables (IV) designated as Production Time (PT), Idling Time (IT), Repair Time (RT), and Engineering Time (ET) with Production Yield (PY) as dependent variable (DV) are being used. A chaos theory four-quadrant phase space was plotted with coordinates in a chronological order. X-axis represents “PT changes”, “IT changes”, “RT changes” and “ET changes” signifying differences in factory shift ATE’s time, while “PY changes” illustrated differences in output on y-axis in separate charts. Quadrant in the upper-right section embodies increase in factory output with increase in ATE’s IV and DV. Quadrant in the lower-right section denotes ATE’s increase in IV but decrease in DV where ATE participates in unproductive work. Quadrant in the upper-left section symbolizes decrease in IV but increase in DV in consequence of factory improvement activities. Quadrant in the lower-left section illustrates both decrease in IV and DV proving that ATE is shut down. Analysis shows positive linear PT-PY, negative linear ET-PY, while both IT-PY and RT-PY graphs as extremely erratic. Judging on the results, cumulative ATE time characteristics can be comprehended, which provide some clarity in predictable equipment performance for support maintenance prioritization and task management, and for future research directions on prediction capability for equipment capacity improvement. In conclusion, the chaos theory’s phase space diagrams were successfully applied to simulate the chaotic characteristics and unpredictability in equipment performance in guiding maintenance teams to better prioritize maintenance tasks management, and for future research directions, to enable better prediction on equipment capacity improvement.
Visualization of Flake Knapping Sequence with Analyzing Assembled Chipped Stone Tools

For studying stone tools, repeating assembly and separation of stone tools is an inevitable process. The instructions of this process, however, are ambiguous when traditional 2D illustrations are used. The 3D exploded view is an eﬀective way for instructing the assembly, and it has been widely used in many (cid:12)elds, while it has seldom been used in archaeology. We apply this powerful presentation technique to stone tool models, and study methods of generating exploded graphs and assembly sequences with point clouds of stone tools by computer graphics algorithms. Moreover, lithic knapping meth-ods and relic excavation reports are analyzed to evaluate the diﬀerence of (cid:13)ake knapping sequences between the contents of report and automatic generation by using an algorithm. In addition to presentation of principles for restoring stone tools, we design the functions and algorithms of our system based on archaeological rules. The experiment results of stone tool assembly show eﬀectiveness and practicability of the proposed method. The user evaluation indicates that 3D visualization technology can assist in eﬃcient research of (cid:13)ake knapping sequence instruction for chipped stone tools.
GRADIENT BOOSTED DECISION TREE (GBDT) AND GREY WOLF OPTIMIZATION (GWO)BASED INTRUSION DETECTION MODEL

Intrusion Detection Systems (IDS) have become increasingly important as computer networks have grown in size and complexity over the last several years, and they have become an essential component of the system's infrastructure. Increases in the number of breaches into computer networks have been attributed to a thriving underground cybercrime market and advanced technologies that make it simpler to hack into computer networks over the past decade. Researchers in both industry and academia have been working on strategies to detect and prevent these types of security breaches for more than 40 years, and the results have been promising. They have also put in place systems to assist them in this endeavour. It is critical for an intrusion detection system (IDS) to be able to deal with situations such as a low detection rate and a large amount of work. One of the most pressing concerns facing the globe today is that of data security. The hacking of data can occur in a variety of ways, which can render any network or system less effective. Interceptor denial of service (IDS) systems that are still in use are incapable of keeping up with the continually changing and complex nature of incursion activities on computer networks. Discovering and preventing these types of attacks is one of the most difficult tasks we face today. Machine Learning has risen to become one of the most successful methods of learning about things in recent years, thanks to advances in artificial intelligence. Machine Learning approaches have the ability to generalise from known attacks to variations, or even discover new sorts of breaches, which is a beneficial thing for security professionals. In recent years, there has been a lot of research on how to combine multiple strategies in order to increase the detection rates of Machine Learning classifiers. Since the development process for intrusion detection systems has included the use of Artificial Neural Networks and Decision Trees, the technology has risen in popularity. In order to determine how well different machine learning classifiers performed with the KDD99 incursion dataset, a number of tests and evaluations were conducted. The study's main focus was on Grey Wolf optimization (GWO) models based on Gradient Boosted Decision Trees (GBDT). They were used to identify and categorise intrusions, among other things. Furthermore, this research looked into how to create attack rules by using a KDD99 dataset to look for anomalies in network audit data, which was then used to create attack rules. Performance indicators such as the false negative and false positive detection rates are important in order to increase the rate at which an intrusion detection system detects objects that have been broken into by an intruder. In the experiments, different algorithms are compared. There is evidence to suggest that the GBDT is the best since it is the most accurate and does not produce many false positives.
The Drama Provincial Accented

undesirable characteristic, and negative attitudes toward second-language speakers with regional or provincial accents in their speech are sometimes promoted even by teachers and researchers. Moreover, Sethi et al. found that foreign-accented speakers are perceived as less credible, and having this foreign-accented speech reduces the speakers' credibility. Abstract This phenomenological study looked into the lived experiences of fourth-year students exposed to the drama of provincial accented speech. Colaizzi's descriptive approach was used in the study with the purposively chosen key informants with various tools to cross-check their responses. Results showed biases on an accent because the informants thought inferiority of their accent, harassment on accented speech for they experience overt linguistic racism, probable accent discrimination, and accent intelligibility were some of the issues the students encountered. However, there were also feats to consider in using provincial accented speech to fellow second language (L2) English speakers. These were the accent sapiency and accented speech ego and pride. Based on the study’s findings, the English second language teachers and professionals are encouraged to address these identified dramas, for they have a valuable role, provided they possess the knowledge and attitudes needed to foster acceptance of linguistic diversity.
TRANSIT TIMING OBSERVATIONS FROM KEPLER. I. STATISTICAL ANALYSIS OF THE FIRST FOUR MONTHS

The architectures of multiple planet systems can provide valuable constraints on models of planet formation, including orbital migration, and excitation of orbital eccentricities and inclinations. NASA's Kepler mission has identified 1235 transiting planet candidates. The method of transit timing variations (TTVs) has already confirmed seven planets in two planetary systems. We perform a transit timing analysis of the Kepler planet candidates. We find that at least ∼11% of planet candidates currently suitable for TTV analysis show evidence suggestive of TTVs, representing at least ∼65 TTV candidates. In all cases, the time span of observations must increase for TTVs to provide strong constraints on planet masses and/or orbits, as expected based on N-body integrations of multiple transiting planet candidate systems (assuming circular and coplanar orbits). We find the fraction of planet candidates showing TTVs in this data set does not vary significantly with the number of transiting planet candidates per star, suggesting significant mutual inclinations and that many stars with a single transiting planet should host additional non-transiting planets. We anticipate that Kepler could confirm (or reject) at least ∼12 systems with multiple transiting planet candidates via TTVs. Thus, TTVs will provide a powerful tool for confirming transiting planets and characterizing the orbital dynamics of low-mass planets. If Kepler observations were extended to at least seven years, then TTVs would provide much more precise constraints on the dynamics of systems with multiple transiting planets and would become sensitive to planets with orbital periods extending into the habitable zone of solar-type stars.
Minireviews Oxidative Stress and the Central Nervous System

Biochemical integrity of the brain is vital for normal functioning of the central nervous system (CNS). One of the factors contributing to cerebral biochemical impairment is a chemical process called oxidative stress. Oxidative stress occurs upon excessive free radical production resulting from an insufficiency of the counter- acting antioxidant response system. The brain, with its high oxygen consumption and lipid-rich content, is highly susceptible to oxidative stress. Therefore, oxidative stress – induced damage to the brain has a strong potential to negatively impact normal CNS functions. Although oxidative stress has historically been considered to be involved mainly in neurodegenerative disorders such as Alzheimer disease, Huntington disease, and Parkinson disease, its involvement in neuropsychiatric disorders, including anxiety disorders and depression, is beginning to be recognized. This review is a discussion of the relevance of cerebral oxidative stress to impairment of emotional and mental well-being.
Pengaruh Jenis Plasticizer Terhadap Karakteristik Plastik Biodegradable

Rice bran contains essence usable as a material in biodegradable plastic production. The research was to identify the effects of plasticizer types and ratio of composition of plasticizer and chitosan in terms of biodegradable plastic characteristics. The biodegradable plastic is produced by solving the rice bran in aquadest and adding acetate acid. Next, the solution was heated in 65◦C temperature for 15 minutes. Then, is added with plasticizer and chitosan solution and stirred for 60 minutes in 65-70◦C temperature. Thereafter, the solution was casted and cooled in room temperature until it dried. Subsequently, the biodegradable plastic was analyzed. The variables in the research were plasticizer (glycerol, sorbitol, propilen glycol). The results of the research showed that different types of plasticizer affected the characteristics of biodegradable plastic with the best result on sorbitol as plasticizer with tensile strenght: 64,27 MPa, elongation: 3,33%, swelling: 30,77% and degradation: 63,64%. The morphological analysis on the biodegradable plastic resulted uneven and unfilled surfaces.
Can solar wind viscous drag account for coronal mass ejection deceleration?

The forces acting on solar Coronal Mass Ejections (CMEs) in the interplanetary medium have been evaluated so far in terms of an empirical drag coefficient CD ∼ 1 that quantifies the role of the aerodynamic drag experienced by a typical CME due to its interaction with the ambient solar wind. We use a microphysical prescription for viscosity in the turbulent solar wind to obtain an analytical model for the drag coefficient CD. This is the first physical characterization of the aerodynamic drag experienced by CMEs. We use this physically motivated prescription for CD in a simple, 1D model for CME propagation to obtain velocity profiles and travel times that agree well with observations of deceleration experienced by fast CMEs.
AN EVOLVING APPROACH TO DATA STREAMS CLUSTERING BASED ON CHEBYCHEV WITH FALSE MERGING

Data stream clustering is not a recent subject in data analysis and statistics. However, with the rapid deployment of the Internet of Things (IoT), this field faces new practical challenges that researchers intend to solve. These challenges can be summarized under four main categories: high-dimensionality, fast speed of data flow, real-time constraints, and evolving nature. Although numerous density-based algorithms have recently been established for data stream clustering, these algorithms are not without their problems. The quality of these existing clustering algorithms is dramatically reduced when the distance function is used. Another problem is false merging which happens when two or more clusters overlap on top of each other. This paper introduces a new online approach, which is called “Clustering of Evolving data streams based on Chebychev distance with false Merging” (CEC-Merge). The primary goal of this method is to improve the efficiency of the clusters. CEC-Merge is completely online and contains two major steps. The initial step generates a Core Micro-Clusters (CMCs) and the second step merge these CMCs to constructs Macro-clusters. The Chebychev distance function is applied to measure the distance from the new point with the existing CMCs centre. CEC-Merge uses two important parameters to avoid false merging such as “minimum links” and “time intervals”. The proposed technique was validated using various consistency measurements on real and artificial evolving data streams and compared with well-known methods. The CEC-Merge provides an effective solution for improving the efficiency of the cluster.
Implications of Microorganisms in Alzheimer’s Disease

Alzheimer’s disease (AD) is a deadly brain degenerative disorder that leads to brain shrinkage and dementia. AD is manifested with hyperphosphorylated tau protein levels and amyloid beta (Aβ) peptide buildup in the hippocampus and cortex regions of the brain. The nervous tissue of AD patients also contains fungal proteins and DNA which are linked to bacterial infections, suggesting that polymicrobial infections also occur in the brains of those with AD. Both immunohistochemistry and next-generation sequencing (NGS) techniques were employed to assess fungal and bacterial infections in the brain tissue of AD patients and non-AD controls, with the most prevalent fungus genera detected in AD patients being Alternaria, Botrytis, Candida, and Malassezia. Interestingly, Fusarium was the most common genus detected in the control group. Both AD patients and controls were also detectable for Proteobacteria, followed by Firmicutes, Actinobacteria, and Bacteroides for bacterial infection. At the family level, Burkholderiaceae and Staphylococcaceae exhibited higher levels in the brains of those with AD than the brains of the control group. Accordingly, there is thought to be a viscous cycle of uncontrolled neuroinflammation and neurodegeneration in the brain, caused by agents such as the herpes simplex virus type 1 (HSV1), Chlamydophila pneumonia, and Spirochetes, and the presence of apolipoprotein E4 (APOE4), which is associated with an increased proinflammatory response in the immune system. Systemic proinflammatory cytokines are produced by microorganisms such as Cytomegalovirus, Helicobacter pylori, and those related to periodontal infections. These can then cross the blood–brain barrier (BBB) and lead to the onset of dementia. Here, we reviewed the relationship between the etiology of AD and microorganisms (such as bacterial pathogens, Herpesviridae viruses, and periodontal pathogens) according to the evidence available to understand the pathogenesis of AD. These findings might guide a targeted anti-inflammatory therapeutic approach to AD.