Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of SNS Social Capital on E-Service Quality and Sustained Referral Intentions of E-Fitness Apparel: Comparative Body Image Satisfaction Analysis

Fitness apparel companies target consumers with easy access to social media (e.g., Facebook, Instagram, and Pinterest). However, fitness apparel companies have struggled to incorporate social interactivity into their marketing strategies due to a lack of knowledge about consumers’ social media behaviors and different country contexts. The purpose of this study was to investigate (1) comparison of college students’ body image satisfaction in both the United States (U.S.) and South Korea and (2) how their body satisfaction influences consumer communication and the sustained referral intentions of fitness apparel in social media. The findings from 1144 survey responses of U.S. and South Korean college students reveal that student body satisfaction differs between the two countries. Body-dissatisfied U.S. and South Korean students with social capital on social networking websites are directly influenced by word-of-mouth regarding online fitness apparel purchases. Furthermore, perceived e-service quality, including website design and website responsiveness, is a significant mediator in both cultures, affecting the word-of-mouth for fitness-related purchases. This study provides evidence for marketers of fitness apparel, particularly e-marketers, to consider the cultural differences in customer preferences and customer body satisfaction, so as to enhance service performance.
Analyzing the Performance of Intrusion Detection Model Using Weighted One-Against-One Support Vector Machine and Feature Selection for Imbalanced Classes

: Imbalanced class is one of the main problems for intrusion detection models that use machine learning methods. The classifiers generally are designed to minimize the global error rates, which have not considered the condition of imbalanced class. The amount of training data for each type of imbalanced attack can cause those intrusion detection models to have high accuracy but can also lead to difficulty in identifying minority class attacks. In this research, we propose an intrusion detection model using a combination of the feature selection method for imbalanced class and weighted support vector machine classifier. We apply a composite performance index in the features selection and the optimization of the weight of the minority class. The experimental result using the NSL-KDD dataset shows that this model produces overall accuracy, sensitivity, and specificity that reaches more than 99%, with false alarms below 0.5% and false-negative rates below 0.7%. The sensitivity of the U2R and R2L classes are 56% and 92%.
Evaluation of Production Preferences and Production Costs of Agricultural Enterprises in Çanakkale

This study was conducted in Kumkale Plain of Canakkale Province. In this research, it is aimed to determine which factors the regional farmers take into consideration in their production and the costs of the products they obtain. The data of the study consisted of primary and original questionnaires provided through face-to-face interviews. The sample volume was determined by stratified sampling method and 99 questionnaires were conducted. The enterprises are ranked according to land size and divided into three layers as 1-50, 51-100, 101 decare and above. Analytical Hierarchy Process (AHS) method was used to determine producer preferences as well as simple cost calculations. The criteria of the study were determined as mechanization, irrigation facilities, supply of inputs, marketing opportunities, aquaculture information, product prices, input prices and labor force. According to the results of AHS obtained from the study, wheat ranked first with 0.213 points and sunflower ranked second with 0.196 points. The remaining alternatives are paddy, tomato, corn (seed) and silage maize respectively. According to AHS results, wheat, sunflower, paddy, tomato, corn and silage maize are preferred. Product costs are calculated as follows; 0.52 TL/kg for tomatoes, 1.56 TL/kg for paddy , 1.16 TL/kg for grain corn, 0.14 TL/kg for silage maize, 0.69 TL/kg for wheat and 1.44 TL/kg for sunflower.
Speech Workload Estimation for Human-Machine Interaction

Performing tasks quickly and accurately in dynamic and intense environments is critical, such as supervising a remotely piloted aircraft; however, these environments contain periods of low and high workload, which can decrease task performance. A system capable of intelligently adapting its interaction modality based on the human’s workload state may mitigate these undesirable workload states: underload and overload. Such a system requires mechanisms to determine accurately the human’s overall workload state and each workload component state (i.e., cognitive, physical, visual, speech, and auditory) in order to understand the current workload state’s underlying cause effectively. Existing work estimates multiple workload components, but no method estimates speech workload. This manuscript presents an algorithm for accurately estimating a human’s speech workload level using methods suitable for real-time workload assessment. The algorithm is an essential component to future adaptive human-machine interfaces.
Laplacian Regularized Nonnegative Representation for Clustering and Dimensionality Reduction

Self-representation methods, such as low-rank representation (LRR), sparse subspace clustering (SSC) and their variants may generate negative coding coefficients since there is no explicit nonnegative constraint. These negative coefficients lack physical meaning. To be specific, it is unreasonable to allow a query sample encoded by heterogeneous samples to cancel each other out with subtractions. In this paper, we propose a novel model named Laplacian regularized nonnegative representation (LapNR). The new model improves its physical interpretability by ensuring that the query sample should be approximated from homogeneous samples and irrelevant to heterogeneous ones. More importantly, it captures the geometric information of input data by imposing the graph Laplacian to the nonnegative representations. As a result, the representation matrix generated by our LapNR model becomes sparse and discriminative. Based on the alternating direction method of multipliers (ADMM), an efficient optimization procedure is developed for LapNR. The extensive experiments on clustering and dimensionality reduction tasks show the effectiveness and efficiency of our LapNR.
BİST Altın Fiyatları Serisinin Markov Rejim Değişim Modeli İle Analizi

Altin, fiziksel ve kimyasal ozellikleri nedeniyle, endustriyel kullaniminin yaninda yatirim araci olarak da kullanilan degerli bir madendir. Merkez bankalari tarafindan rezerv araci olarak kullanilan altin, ayni zamanda borsalarda yogun bir sekilde islem gormektedir. Bu calismada, Borsa Istanbul (BIST) altin getirilerinin dogrusal olmayan yapisi, Markov Rejim Degisim Modeli ile analiz edilmistir. Analizde getiri donemleri uc rejimli bir model ile belirlenmistir. Analiz sonuclarina gore, altin getirilerinin en yuksek oldugu donemler, volatilitenin en yuksek oldugu donemlerin oncesinde ve sonrasinda yer almaktadir. Ayrica altin getiri serisinde volatilitenin en dusuk oldugu rejim, en buyuk sure degiskenine sahiptir. Bu da altinin bir yatirim araci olarak oynakliginin cok yuksek olmadigini ve bu nedenle uzun donem icin guvenilir bir yatirim araci oldugunu gostermektedir.
Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images

Pixel-level classification for very high resolution (VHR) images is a crucial but challenging task in remote sensing. However, since the diverse ways of satellite image acquisition and the distinct structures of various regions, the distributions of the same semantic classes among different data sets are dissimilar. Therefore, the classification model trained on one data set (source domain) may collapse, when it is directly applied to another one (target domain). To solve this problem, many adversarial-based domain adaptation methods have been proposed. However, these methods only consider the source and the target domains independently in the adversarial training, where only the target domain is explicitly contributed to narrow the gap between the distributions of both domains. Unlike previous methods, we propose a triplet adversarial domain adaptation (TriADA) method that jointly considers both domains to learn a domain-invariant classifier by a novel domain similarity discriminator. Specifically, the discriminator takes a triplet of segmentation maps as input, where two segmentation maps from the same domain are to be distinguished from the two maps from the different domains during the adversarial learning. Consequently, it explicitly considers both domains’ information to narrow the distribution gap across domains. To enhance the discriminability of the classifier on the target domain, a class-aware self-training strategy, which depends on the output of the discriminator, is proposed to assign pseudo-labels with high adapted confidence on target data to retrain the classifier. Extensive experiments on several VHR pixel-level classification benchmarks demonstrate the effectiveness of our method as well as its superiority to the-state of the art.
Stable Polymer Nanoparticles with Exceptionally High Drug Loading by Sequential Nanoprecipitation

Poor solubility often leads to low drug efficacy. Encapsulation of water‐insoluble drugs in polymeric nanoparticles offers a solution. However, low drug loading remains a critical challenge. Here, we report a simple and robust sequential nanoprecipitation technology to produce stable drug‐core polymer‐shell nanoparticles with high drug loading (up to 58.5%) from a wide range of polymers and drugs. This technology is based on tuning the precipitation time of drugs and polymers using a solvent system comprising multiple organic solvents, which allows the formation of drug nanoparticles first followed by immediate precipitation of one or two polymers. This technology offers a new strategy to manufacture polymeric nanoparticles with high drug loading having good long‐term stability and programmed release, and opens a unique opportunity for drug delivery applications.
Effect of adenosine deaminase activity on lipid profile in patients of type 2 diabetes mellitus

Background: Type 2 diabetes mellitus is a worldwide endemic disease. Dyslipidemia is also a frequent disorder associated with diabetic patients. The present study is an attempt to link that elevated levels of adenosine deaminase in Type 2 diabetes mellitus patients augment hyperlipidemia. Material and Methods: The subjects included in this study were divided into 3 groups. Group A consisted of 30 normal healthy individuals who served as controls with no history of DM. Group B consisted of 30 patients of Type 2 Diabetes Mellitus both males & females in the age group of 40-65 years on oral hypoglycaemic drugs with HbA1c 7 %. Serum levels of fasting blood sugar, HbA1c, ADA and lipid profile were estimated in all the subjects under study. Results: All the three parameters, FBS, HbA1c and ADA levels were found to be increased in the patients of Type 2 DM as compared to controls. The mean S. Triglyceride and S. VLDL-C levels were significantly elevated in group C as compared to Group A. The mean serum levels of S. HDL-C showed a decreasing trend with increase in levels of HbA1c. Conclusion: From the present study, it was concluded that dyslipidemia was found to be higher in diabetic patients with respect to healthy controls. The lipid abnormality (dyslipidemia) associated with type 2 diabetes typically consists of elevated triglyceride and decreased HDL-C levels. Keywords: Diabetes Mellitus type 2, Adenosine deaminase (ADA), Glycated haemoglobin (HbA1c), Fasting blood Sugar (FBS)
Beneficial and Detrimental Effects of Cyberloafing in the Workplace

Orgutlerdeki calisanlar cok degisik tutumlar ve davranislar sergileyebilirler. Bunlarin bazilari orgut iklimi ve verimliligini artirmak icin olumlu ve yararli olabilirken, bazilari da calisanlarin iliskileri ve performanslari acisindan olumsuz ve zararli olabilir. Olumlu tutum ve davranislar cogunlukla toplumsal acidan onaylanmistir ve kendini adama, ozdeslesme, gomulme, katilim, canlilik, baglilik, arkadaslik, yenilikcilik, yaraticilik vb. sekillerde yansiyabilir. Buna karsilik, olumsuz tutum ve davranislar genellikle verimsizdir ve hirsizlik, baltalama, cekilme, kacma, nezaketsizlik, dislama, bencillik, onemsememe, sahtekârlik, zorbalik, saldirganlik, siddet, yildirma, intikam, cinsel taciz vb. bicimlerde ortaya cikabilir. Bu gorece geleneksel olan davranislarin yani sira, isyerlerinde gittikce artan bir sekilde gozlemlenen guncel davranis turlerinden biri de siber aylakliktir. Bu kavram, Internet’in calisma saatleri icinde kasitli ve surekli olarak kisisel amaclar icin kullanmasi olarak tanimlanabilir. Siber aylakligin bazi gostergeleri bulunmaktadir. Bunlar arasinda kisisel iletiler gonderme ve alma, gunluk gazeteleri okuma, spor programlarini izleme, sanal oyunlar oynama, cevrimici alisveris, borsayi takip etme, bankacilik islemleri yapma, forumlara katilma, blog yazma, cevrimici kumar, yetiskin sitelerine grime, muzik indirme, populer videolari izleme, sanal topluluklara katilma, sosyal aglarda arkadaslarla iletisimde bulunma, isle ilgili olmayan icerik uretme ve paylasma sayilabilir. Alanyazin genellikle Internet’in kisisel amaclarla kullanimi ile isyerinde siber aylaklik arasindaki cizginin cok belirgin olmadigini gostermektedir. Dahasi, siber aylakligin hem bireysel kurumsal nedenleri vardir. Sonuc olarak da, siber aylakligin isyerinde hem yararli hem zararli etkileri olabilmektedir. Bu makele, once siber aylakligin ayirt edici ozelliklerini betimlemekte, ardindan yaygin nedenleri aciklamakta ve son olarak isyerlerinde hangi kosullar altinda siber aylakligin yararli ve zararli etkilerinin ortaya ciktigini tartismaktadir.
AN ITERATIVE THRESHOLD ALGORITHM BASED ON LOG-SUM NORM REGULARIZATION FOR MAGNETIC RESONANCE IMAGE RECOVERY

This paper considers the class of Iterative Shrinkage Threshold Algorithm (ISTA) to solve the linear inverse problem that occurs in magnetic resonance (MR) image recovery. The ISTA algorithm adheres to the principle of minimizing the L1 norm. This method can be considered as an extension of the classical gradient algorithm. However, it is known that the ISTA algorithm converges slowly, and the accuracy of the algorithm is not sufficient. In many MR image recovery problems, using nonconvex log-sum norm minimization can often obtain better results than the l1-norm minimization. In this paper, we firstly transform the MR image recovery into a non-convex optimization problem with log-sum norm regularization and combine it with a faster global convergence method. Then a Log-sum generalized iterated shrinkage threshold algorithm (LISTA) for solving the MR image recovery problem is proposed. Finally, numerical experiments are conducted to show the superiority of our algorithm.
Pengaruh Regulasi, Politik Anggaran, Perencanaan Anggaran, Sumber Daya Manusia Dan Pengadaan Barang/ Jasa Terhadap Penyerapan Anggaran Belanja Pada OPD Provinsi Sumatera Barat

This study aims to prove empirically the effect of regulation, budget politics, budget planning, human resources and the procurement of goods/services on budget absorption. This research is classified as causative research. The population in this study are 39 Regional Organizations (OPD) of West Sumatra Province. The sample in this study used the Total Sampling method. The type of data used in this study is primary and secondary data. Data collection techniques using a questionnaire consisting of 4 respondents in each OPD so that the questionnaire distributed was 156 questionnaires. The analytical method used is Multiple Regression Analysis using the SPSS version 25.00 program. The result of the study showed regulation, budget planning and the procurement of good/services has a significant positive effect on budget absorption. However, budget politics and human resources have no effect on budget absorption.
The complete chloroplast genome of the invasive and Cd-hyperaccumulator herb Bidens pilosa L. (Asteraceae)

Abstract Bidens pilosa is an annual invasive and Cd-hyperaccumulator herb. The complete chloroplast genome sequence of the B. pilosa is 150,542 bp in length, which is composed of a large single-copy region of 83,542 bp, a small single-copy region of 17,624 bp and a pair of inverted repeat regions of 24,688 bp. It encodes a set of 114 genes, consisting of 80 protein coding, 30 tRNA and 4 rRNA genes. Among all of these genes, 2 genes possess double introns, and 16 genes have a single intron. Phylogenetic analysis showed that B. pilosa clustered together with Marshallia obovata.
Zooplankton diel vertical migration and downward C flux into the oxygen minimum zone in the highly productive upwelling region off northern Chile

Abstract. Diel vertical migration (DVM) can enhance the vertical flux of carbon (C), and so contributes to the functioning of the biological pump in the ocean. The magnitude and efficiency of this active transport of C may depend on the size and taxonomic structure of the migrant zooplankton. However, the impact that a variable community structure can have on zooplankton-mediated downward C flux has not been properly addressed. This taxonomic effect may become critically important in highly productive eastern boundary upwelling systems (EBUSs), where high levels of zooplankton biomass are found in the coastal zone and are composed by a diverse community with variable DVM behavior. In these systems, presence of a subsurface oxygen minimum zone (OMZ) can impose an additional constraint to vertical migration and so influence the downward C export. Here, we address these issues based on a vertically stratified zooplankton sampling at three stations off northern Chile (20–30 ∘ S) during November–December 2015. Automated analysis of zooplankton composition and taxa-structured biomass allowed us to estimate daily migrant biomass by taxa and their amplitude of migration. We found that a higher biomass aggregates above the oxycline, associated with more oxygenated surface waters and this was more evident upon a more intense OMZ. Some taxonomic groups, however, were found closely associated with the OMZ. Most taxa were able to perform DVM in the upwelling zone withstanding severe hypoxia. Also, strong migrants, such as eucalanid copepods and euphausiids, can exhibit a large migration amplitude ( ∼500 m), remaining either temporarily or permanently within the core of the OMZ and thus contributing to the release of C below the thermocline. Our estimates of DVM-mediated C flux suggested that a mean migrant biomass of ca. 958 mg C m −2 d −1 may contribute with about 71.3 mg C m −2 d −1 to the OMZ system through respiration, mortality and C excretion at depth, accounting for ca. 4 % of the net primary production, and so implies the existence of an efficient mechanism to incorporate freshly produced C into the OMZ. This downward C flux mediated by zooplankton is however spatially variable and mostly dependent on the taxonomic structure due to variable migration amplitude and DVM behavior.
A Mixed Reality Simulation Tool for In-Flight Evaluations

For more than a decade, the promise of augmented, virtual, and mixed reality technologies has been to provide the means to enhance training, while significantly reducing costs. While incremental improvements have been made, it is the recent advances in high capability hardware such as stereo cameras, head-mounted displays, and motion tracking that bridge the technology gap encountered in previous approaches. Furthermore, these technologies are now available commercial-off-the-shelf (COTS) at much lower cost than a decade ago. Using this new low-cos hardware, Systems Technology, Inc. (STI) has developed Fused Reality® Flight, a blended-reality solution for in-flight pilot evaluations and training based on STI’s patented Fused Reality® technology. Building upon successful in-flight Fused Reality® demonstrations conducted for NASA, the current in-flight system applies new low-cost COTS hardware to create an in-flight experience that allows for virtual objects including other aircraft to be placed out-of-the-cockpit-window as part of combined virtual and real-world video scene viewed through a head-mounted display. Pilots maintain situational awareness via a virtual head-up “display. ” Furthermore, the combined video image support scans of the actual cockpit instruments and inceptors. This paper describes the application of the mixed reality simulation solution to the in-flight environment.
Gastronomi Eğitimi Üzerine Bir Değerlendirme

In the shortest definition, the gastronomy as “the art and science of good eating,” or “the art and science of fine eating. ” The gastronomy is a comprehensive matter that focuses on human being and aesthetics. The moment witnessing to the consumption has a meaning more what is being eaten. As it is known, beside the gastronomy is science good food and eating, it necessitates an artistic and cultural philosophy. The first examples of Gastronomy Departments were opened at the Faculty of Fine Arts in Turkey. This should be seen a true extension of the terms of a philosophical approach. However, by opening the gastronomy departments at Tourism Faculties, the philosophical and cultural aspects have been ignored in the following years. Today, there are 30 gastronomy departments in Turkey. Despite this satisfactory number of the gastronomy departments in the graduate level, it is still a matter of discussions whether the departments have training programs as targeted. The basic needs ranking that was found at the end of Gastronomy Education Search Conference on February 26th, 27th 2015 in Antalya supports this concern. According to the results of the search conference, the experts have identified the curriculum as primary problem in gastronomy. When the current situation analysed, there are limits in finding and training the academic staff that will be occupied. Professionals are those holding the pulse of the community about gastronomy habits and driving people on some issues such as health, fashion, nutrition, environment and diet. However, it has been concluded in the conference as secondary problems that the professionals are not willing to contribute to the gastronomy education and the authorities of these schools do not spend so many efforts for the cooperation. The education of gastronomy should be considered as a practice education. The best way to learn the sector and to gain experience for students is the internship. The matter of internship is a problem waiting for one of solutions. In this study, it has been tried to give information about existing departments while concentration on what should be done in the gastronomy education. Because this study was planned as conceptual framework, primarily the literature review was conducted. Then, the outcomes of Search Conference contributed by the field experts and academicians. The study was constructed in the base of qualitative study based on secondary data. From the evolutions and the results that will be conducted at