Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of a Field-Modulated PM Transverse Flux Linear Generator With Tilted Translator

Over the past two decades, linear generators have been widely used in direct drive wave energy converters (DD-WECs), thus simplifying the system structure and improving conversion efficiency. While these linear generators are desirable for wave energy converter because of their high reliability, they must be very large to generate significant electrical power from such low-speed motion. These linear generators have demerits of large volume, low power density and low conversion efficiency when they are adopted in DD-WEC. Magnetically geared machine and field-modulated permanent magnet linear generator can significantly decreases the volume and cost of the required generator, also the speed of travelling magnetic field is enhanced and high power density capability is ensured by the ‘magnetic gearing effect’. A field-modulated PM transverse flux linear generator (FM-PMTFLG) is proposed and analyzed in this article. The field modulation mechanism in FM-PMTFLG with tilted translator is introduced in detail. The electromagnetic characteristics of different combinations of stator slots and translator poles and different tilt angle of magnetic tilted bars and nonmagnetic tilted bars are comparatively investigated. The FM-PMTFLG prototype is manufactured to validate the results of the selected design parameters and the relationship between magnetic gearing effect and tilted translator. A fundamental experiment was conducted. A good agreement between 3D-FEA and experiment results proved the validity of the generator design.
The Effect of Blockchain Technology on Hospital Supply Chain Performance

There is an ongoing challenge in the healthcare sector to meet increased demand. Deployment of innovative technologies is one of the ways to enhance the level of service level in this sector. Blockchain technology has several advantages, such as immutability, security and transparency, which can address major issues in the healthcare supply chain. This research investigates the impact of blockchain technology on hospital supply chain performance in the Kingdom of Saudi Arabia. Underlined by task-technology fit theory, a conceptual framework of blockchain technology utilisation and its impact on hospital performance is developed. Responses are collected from managers working in both public and private hospitals. A fuzzy-set qualitative comparative analysis (fs-QCA) is used to analyse the impact of blockchain technology on hospital supply chain performance. The results revealed that causal conditions task characteristics (TC), blockchain characteristics (BC), Individual characteristics (IC), task-technology fit (TTF) and blockchain utilisation (BU) are necessary and sufficient for higher levels of hospital performance. The study findings also contribute to an in-depth understanding of how blockchain technology can enhance the healthcare supply chain system by improving its privacy, safety and transparency.
3D printing of cell-delivery scaffolds for tissue regeneration

Abstract Tissue engineering strategy that combine biomaterials with living cells has shown special advantages in tissue regeneration and promoted the development of regenerative medicine. In particular, the rising of 3D printing technology further enriched the structural design and composition of tissue engineering scaffolds, which also provided convenience for cell loading and cell delivery of living cells. In this review, two types of cell-delivery scaffolds for tissue regeneration, including 3D printed scaffolds with subsequent cell-seeding and 3D cells bioprinted scaffolds, are mainly reviewed. We devote a major part to present and discuss the recent advances of two 3D printed cell-delivery scaffolds in regeneration of various tissues, involving bone, cartilage, skin tissues etc. Although two types of 3D printed cell-delivery scaffolds have some shortcomings, they do have generally facilitated the exploration of tissue engineering scaffolds in multiple tissue regeneration. It is expected that 3D printed cell-delivery scaffolds will be further explored in function mechanism of seeding cells in vivo, precise mimicking of complex tissues and even organ reconstruction under the cooperation of multiple fields in future.
Large eddy simulation of wall bounded turbulent flows with heat transfer

In this thesis work. large eddy simulation was used to study a variety of wallbounded turbulent flows using a compressib le finite vo lume formulation. The features of this fornm lation include time derivative preconditioning, an implicit lower-upper symmetric Gauss-Seidel scheme, and parallelization using themcssage pass ing interface (MPl). Subgrid scale terms in both momentum and energy equations were modeled dynamically. Furthermore, due to the inhomogeneity of wall-bounded flows, the model was further localized to better represent the physics of the problem. The localized dynamic model was first applied to study the incompressible turbulent flow through a duct wi th square cross-section. Mean flow, law of the wall, and turbulence statistics were compared with the benchmark results of direct numerical simulation and excellent agreement was ach ieved. The secondary flow of Prandtl 's second kind in the cross-section was captured through the simulation. lt is composed of four pairs of counter-rotating cells. The interaction between the mean flow field and the secondary flow fie ld creates some important features and they were studied in this work. Based on the work of incompressible duct flow, system rotation was applied to investigate the effects of rotation on the turbulent flow field. The spanwise system rotation was found to suppress turbulence production and reduce the turbulence level on the leading side. whi le enhancing turbulence production and increasing the turbulence level on the trailing side. ln the global sense, rotation decreases the turbulent kinetic energy signifi cantly. Because of the rotation, the secondaty flow field in non-rotati ng duct was found to be d iminished at weaker rotation and even eliminated at stronger rotation. Instead, a pair of counter rotating cells called Taylor-
Harmonizing CT images via physics-based deep neural networks

The rendition of medical images influences the accuracy and precision of quantifications. Image variations or biases make measuring imaging biomarkers challenging. The objective of this paper is to reduce the variability of computed tomography (CT) quantifications for radiomics and biomarkers using physics-based deep neural networks (DNNs). The proposed framework used the knowledge of ground truth through a virtual imaging trial (VIT) methodology to harmonize the different renditions of CT scans across variations in reconstruction kernel and dose. This harmonization was done by developing a generative adversarial network (GAN) model, informed by pixel values and patient-based modulation transfer function (MTF) estimates. To train the network, the VIT platform was used to acquire CT images from a set of forty computational human models (XCAT). Models included varying levels of pulmonary diseases including lung nodules and emphysema. The patient models were imaged with a validated CT simulator (DukeSim) modeling a commercial CT scanner operating across a range of dose (20-100 mAs) and reconstruction kernels (15 from smooth to sharp). The harmonized virtual images were evaluated in three different ways: 1) visual assessment of the images, 2) bias and variation in density-based biomarkers, and 3) bias and variation in morphological-based biomarkers. The harmonization improved the bias and variability of the test set images yielding a structural similarity index of 95±1%, a normalized mean squared error of 10.2±1.5%, and a peak signal-to-noise ratio of 31.8±1.5 dB compared to variations in these metrics when no harmonization was applied (87±9%, 14.2±4%, and 29.6±2, respectively). Emphysema-based imaging biomarkers of LAA-950 (-1.5±1.8%), Perc15 (13.65±9.3 HU), and Lung mass (0.1±0.3 g) had more precise and consistent quantifications compared to values when images were not harmonized (9.9±12%, -36.0±53 HU, and 0.2±0.4g, respectively). These results suggest that the method can be promising to improve consistency in multi-center studies when reliable and consistent quantification of data from multiple systems are required.
Segmentation and Tracking of Marine Cellular Clouds observed by Geostationary Satellites

ABSTRACT Marine stratocumulus (MSC) are shallow marine boundary layer clouds that have a significant cooling contribution to the Earth’s radiative balance. The amplitude of this cooling effect strongly depends on the properties of closed and open cells comprising MSC cloud fields. Systematic study of the underlying processes associated with cloud cell properties requires accurate and reliable cell characterization. Here we propose a method for cell segmentation of MSC clouds as observed from geostationary satellite images. The method, which is based on watershed transformation, is found to be highly efficient in segmentation of both open and closed MSC scenes. Application of the suggested methodology over a Lagrangian framework that track the clouds as they are advected by the wind and comparison of the results between pairs of consecutive images indicate that the resulted segmentation is robust and consistent. The methodology developed in this work opens the way to systematic investigation of spatiotemporal changes in MSC cloud field properties, which will improve our understanding of MSC clouds and their role in regulating Earth’s radiative budget.
Semi-centralized federated learning network for low-dose CT imaging

Federated learning method shows great potential in computed tomography imaging field by utilizing a decentralized strategy with data privacy-preserving for local medical institutions. However, directly aggregating the parameters of each local model would degrade the generalization performance of the updated global model. In addition, well paired centralized training datasets can be collected in real world, which are not included in the current federated learning methods. To address the issue, we present a semi-centralized federated learning method to promote the generalization performance of the learned global model. Specifically, each local model is firstly trained locally at a fixed round, then, the parameters are aggregated on server to initialized the global model. After that, the global model is further trained with a standard dataset on the server, which contains well paired training samples to stabilize and standardize the global model. For shorten, we call the presented semi-centralized federated learning method as “SC-FL”. Experimental results on different local datasets demonstrate the presented SC-FL outperforms the competing methods.
Molecular Diagnosis Of Bovine Genital Mycoplasma And Ureaplasma Infections By PCR

Abstract Genital Mycoplasmalar (Mycoplasma spp. and Ureaplasma diversum), which are commensals of the genital tract, are activated due to genetic and environmental factors, stress, immune system failure, wrong antibiotic treatment, and thus to genital canal infection and loss in cattle enterprises cause economic problems in the country. The lack of effective antibiotic treatment and vaccination is one of the most important causes of loss in the meat and dairy industry. In a study conducted in sheep by Gregory et al. (2012), the results obtained according to the Mollicute infection rates in the semen samples showed that the diagnosis of these bacteria should be a routine procedure. In addition, many studies have reported that PCR has a higher sensitivity than other methods in order to accelerate the identification of mycoplasma (Razin, 2002). With this study, it was aimed for the first time to diagnose Mycoplasmas in the genital materials of healthy and infertile cattle in Ankara and its provinces by PCR. At the same time, the materials were examined by bacteriological isolation methods and the results obtained by both methods were evaluated comparatively. In the study, positivity in terms of mycoplasma and ureaplasma in the genital material of 221 healthy and 135 infertile cattle was found to be 61.2 % by isolation methods and 67.7% by PCR. It was determined that PCR gave accurate results in a shorter time. And findings suggest that high positivity in healthy animals may be caused under stress or in the initial stage of infection. ÖZET Genital yol kommensalleri olan Genital Mikoplazmalar (Mycoplasma spp. ve Ureaplasma diversum), genetik ve çevresel faktörler, stres, bağışıklık sistemi yetmezliği, yanlış antibiyotik tedavisi gibi nedenlerle aktive olmakta ve bu nedenle sığır işletmelerinde genital kanal enfeksiyonu ve ülke açısından da ekonomik sorunlara sebep olmaktadır. Etkili antibiyotik tedavisi ve aşılama eksikliği, et ve süt endüstrisindeki kayıpların en önemli nedenlerinden biridir. Gregory ve ark. (2012) tarafından semen örneklerinde Mollicute enfeksiyon oranlarına göre elde edilen sonuçlar, bu bakterilerin teşhisinin rutin bir işlem olması gerektiğini göstermiştir. Ayrıca birçok çalışmada mikoplazma teşhisini hızlandırmak için PCR'nin diğer yöntemlere göre daha yüksek duyarlılığa sahip olduğu bildirilmiştir (Razin, 2002). Bu çalışma ile Ankara ve illerinde ilk kez sağlıklı ve infertil sığırların genital materyallerinde mikoplazmaların PCR ile teşhis edilmesi amaçlandı. Aynı zamanda materyaller bakteriyolojik izolasyon yöntemleri ile incelendi ve her iki yöntemle elde edilen sonuçlar karşılaştırmalı olarak değerlendirildi. Çalışmada 221 sağlıklı ve 135 infertil sığırın genital materyalinde mikoplazma ve üreaplazma açısından pozitiflik izolasyon yöntemleri ile %61,2 ve PCR ile %67,7 olarak bulundu. PCR'ın daha kısa sürede doğru sonuçlar verdiği belirlendi ve bulgular, sağlıklı hayvanlarda yüksek pozitifliğin stres altında veya enfeksiyonun ilk aşamasında ortaya çıkabileceğini düşündürdü. Anahtar kelimeler: Büyükbaş hayvan, Mikoplazma, PCR, Üreplazma
Impact of Shopping Website Design on Customer Satisfaction and Loyalty: The Mediating Role of Usability and the Moderating Role of Trust

In a purchase situation, customer satisfaction and loyalty are primarily determined by usability, trust, and web design. However, the nature of their relationship remains unclear. According to the literature, trust can generate customer loyalty. Consumers’ cognitive and affective processes in online shopping are well discussed in the literature. However, the role of trust in website design has yet to be thoroughly investigated. Given the above knowledge gaps, we studied 96 Chinese youths using two shopping websites. Structural equation modeling was considered to validate the hypothesized relationships, focusing on three key website design features. We discovered that the three design elements predicted usability and satisfaction differently. In addition, website usability and customer satisfaction mediated the relationship between navigation/information design and loyalty, and satisfaction mediated the relationship between visual design and loyalty. Furthermore, the effects of website design and usability on customer satisfaction are strengthened or weakened depending on customer trust. When trust is high, the effect of website design on satisfaction is strengthened, while the effect of usability on satisfaction is weakened. To retain customers, designers should pay more attention to website design and establish trust. These findings offer crucial insights for online retailers in promoting and capitalizing on the positive effects of various website design elements on customers’ shopping experiences.
Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models

Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting.
Nonparametric Estimation of the Number of Drug Users in Hong Kong Using Repeated Multiple Lists

We update a previous approach to the estimation of the size of an open population when there are multiple lists at each time point. Our motivation is 35 years of longitudinal data on the detection of drug users by the Central Registry of Drug Abuse in Hong Kong. We develop a two‐stage smoothing spline approach. This gives a flexible and easily implemented alternative to the previous method which was based on kernel smoothing. The new method retains the property of reducing the variability of the individual estimates at each time point. We evaluate the new method by means of a simulation study that includes an examination of the effects of variable selection. The new method is then applied to data collected by the Central Registry of Drug Abuse. The parameter estimates obtained are compared with the well known Jolly–Seber estimates based on single capture methods.
A Compact Sensor Integrated in a Single Hollow Core Bragg Fiber for Simultaneous Measurement of Temperature and Strain

A compact sensor integrated in a single hollow core Bragg fiber (HCBF) is proposed and demonstrated for simultaneous measurement of strain and temperature. The sensor is manufactured by splicing a 3-mm-long homemade HCBF sandwiched between two segments of single-mode fibers (SMFs) with a lateral offset at one end. Experimental results demonstrate that both Mach–Zehnder interferometer (MZI) and Bragg resonant mechanism are coexisting in the HCBF-based sensor. Two sensing mechanisms have different strain and temperature sensitivities with good linearity. The axial strain sensitivities of −0.4 and 0.33 pm/ $\mu \varepsilon $ and the temperature sensitivities of 40.8 and 26.5 pm/°C are obtained by monitoring the wavelength shifts with axial strain and temperature in temperature range of 60°–80°. Therefore, simultaneous measurement of temperature and strain can be achieved by using a characteristic matrix approach. Due to the large refractive index (RI) difference between the core and cladding of the Bragg fiber, the length of HCBF for forming mode interference in the sensor could be greatly reduced. This makes the sensor more compact than most previous reports for simultaneous measurement of temperature and strain.
On negative polarity items and free choice items in English with special reference to inherently negative predicate constructions

Sohng, Hong-Ki. 2014. On negative polarity items and free choice items in English with special reference to inherently negative predicate constructions. Linguistic Research 31(1), 135-163. This paper explores the uses of any that has a free choice interpretation in generic contexts and an existential interpretation in polarity contexts, with special focus on the uses of any in inherently negative predicate constructions. Following the approaches by Giannakidou (2001:673) and Montague (1969), I argue that inherently negative predicates are verbs with negation inherent in them, and that they are nonveridical operators. Considering any as a unitary lexical item, I have put forth the Licensing Conditions on Any, modifying Giannakidou’s Condition and incorporating Chung’s (2010) Free Choice Condition. I have shown that the uses of any in inherently negative predicate construction as well as the other uses of any in this paper can be adequately handled via the Licensing Conditions proposed. I have shown that the c-command requirement of the Licensing Conditions follows from the structural relationship between a nonveridical operator and any, and that the Condition on free choice of the Licensing Conditions is derived from the lexical semantics of any. (Korea Aerospace University)
Generation of femtosecond soliton tweezers using a half-panda system for modeling the trapping of a human red blood cell

Femtosecond optical tweezers are an effective tool for handling individual human red blood cells, as they allow for comparatively easy single cell manipulation with consequent diverse applications in biological science. Optical trapping at low power with short exposure time is desired in order to minimize damage in the vicinity of targeted tissue, and such a requirement can be achieved by using micro-ring resonators in an optical tweezers configuration. This paper describes a modified add/drop multiplexer system, known as half-panda ring resonator, which is suitable for human blood cells trapping and deployment during investigations on deformation and vibration behavior of biological cells. Dark-bright solitons and Gaussian beams are propagated within the proposed system, while the output features can be controlled via manipulation of specific parameters. This study details optical tweezers of FWHM of 33 and 100, and 90 and 152 femtoseconds that are generated at the drop and through ports of the system respectively
Generation of RGB EVG stained image from hyperspectral H&E stained image using generative adversarial network (GAN)

Quantifying elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. In conventional pathology, special staining technique such as EVG (Verhoeff’s Van Gieson) is applied physically for this purpose which is expensive and time-consuming procedure. Though H&E (Hematoxylin and Eosin) staining is routinely used, less expensive and most common tissue staining technique, elastic and collagen fibers cannot be differentiated using it. This study proposes a modified CycleGAN based unsupervised method for the computerized generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Our proposed method is designed to utilize the sufficient spectral information provided by the H&E hyperspectral image (HSI) without reducing the spectral dimension. For doing so, we have faced challenges to calculate one of the training losses (identity loss) of CycleGAN that requires reducing the channel dimension of H&E HSI to be the same as RGB EVG stained image. We have addressed the issue by adopting intentionally designed three basis functions that can reduce the channel dimension of HSI into three without losing the essential color of elastic fibers. The set of this function includes Linear Discriminant Function (LDF) and the transmittance spectrum of Eosin and Hematoxylin which has proved to best preserve the underlying important features of EVG stained image while reducing the dimensionality of hyperspectral H&E. The experimental result proves the feasibility of our proposed method to generate realistic EVG stained image from its corresponding H&E stained one.