Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised Structure-Preserved Image Registration Framework for Multimodal Retinal Images

Multi-modal image registration of ophthalmology images is vital for disease diagnosis and treatment plans. However, it is challenging as the divergences of image appearance, resolution, and different transformations among different modal images. Therefore, we propose an image registration framework for multimodal retinal images, which directly solves both rigid and deformable transformation. Considering the blood vessel should be consistent among different modal images, we propose a Structure-preserved registration network (SPR-Net) in the framework. Specifically, SPR-Net adopts structure-preserved modal transformation to provide generated multimodal images for the training of the registration network. We also propose a smooth loss function for the constraint of the predicted deformation field. Extensive experiments prove the effectiveness of our proposed registration framework.
Consumers' attitudes on downscale extensions of a luxury automotive brand

PurposeThis study aims to understand how consumers evaluate downscale vertical line extensions of a prestige/luxury original equipment manufacturer (OEM) in the European automotive market. The authors investigate the moderator effects of innovativeness and the need-for-status traits on the relationships between consumers' extension perceived fit (EPF), extension attitude (EA) and extension perceived value (EPV). Design/methodology/approachExperimental design with quantitative analyses based on a sample of 419 participants. Participants were randomly assigned to two treatments: low-fit and high-fit extension simulations.FindingsThe purchase intention of the downscale vertical extension of a luxury OEM brand is directly influenced by EPV and indirectly influenced by consumer EA and EPF with the parent brand. Findings also suggest that parent brand equity is transferable to extensions that present closeness and consistency with the brand’s heritage. Moreover, the need for status strengthens the relationship between the EPF and the extension perceived social value (EPSV). Originality/valueThe authors developed a realistic simulation of a downscale model of a well-known prestige/luxury car brand. The authors test the influence of innovativeness and need-for-status personal traits on consumer extension acceptance.
B2 or Not B2: A Systematic Literature Review on Migraine Headaches and Riboflavin Deficiency

ObjectiveTo assess the prevalence of riboflavin deficiency in patients with migraines or headaches.MethodsSystematic review of the literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.ResultsTwo articles met the inclusion and exclusion criteria, and neither of these discussed riboflavin deficiency prevalence in those patients who suffer from migraines.ConclusionThe prevalence of riboflavin deficiency in migraineurs and in headache patients is unknown.
Does Size Matter? Small and Large Larvae of Pikeperch (Sander lucioperca) in a Comparative Gene Expression Analysis

Size differences are common in the aquaculture of fishes. In the larviculture of cannibalistic species such as pikeperch, they majorly influence mortality rates and consequently provoke losses in the aquaculture industry. With this study, we aim to reveal molecular differences between small and large pikeperch of the same age using a set of 20 genes associated with essential developmental processes. Hereby, we applied a general study design to early and late larval pikeperch before the onset of piscivory to explore the causes of growth differences in these developmental groups. The analysis of the expression levels showed developmental but not size-related differences in PGC1A, TGFB1, MYOD1, MRF4, and the collagens COL1A1 and COL1A2. Furthermore, increased head lengths were found in larger late larvae compared to their smaller conspecifics. While no uniquely size-related expression differences were found, the expression patterns of PGC1A in combination with TGFB1 as regulators of the citric acid cycle indicate a possible influence of mitochondrial energy metabolism. Furthermore, expression differences of MYOD1 and MRF4 point out possible temporal advantages of myogenetic processes in the larger late larval group and hypothesise growth advantages of the larger late larvae resulting from various influences, which provide a promising target for future research.
Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm

Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%.
Californian drought: The processes and factors controlling the 2011-2016 drought and winter precipitation in California

The factors and processes controlling winter precipitation in California and the 2011-2016 California drought have been examined in a literature study. The study showed that mid-latitude cyclones are the systems that generate winter precipitation in California. The location of high- and low-pressure systems in the northeast Pacific Ocean have a huge impact on the winter precipitation as they largely determine the direction of the jet stream, which transports the moisture-holding cyclones in over the North American west coast. Baroclinic instability and warm sea surface temperatures determines the intensity of cyclones. Orographic precipitation in mountainous regions is a significant mesoscale process, through which snowpack accumulates. Furthermore, the phases of the El Nino-Southern Oscillation and the Pacific decadal oscillation generally affects Californian winter precipitation to a degree. The El Nino-Southern Oscillation probably affected the drought, but the Pacific decadal oscillation influence on the drought is unclear. Global warming and climate change surely influence the aridity in California, but the drought was probably mainly caused by natural variability. (Less)
A Context-Supported Deep Learning Framework for Multimodal Brain Imaging Classification

Over the past decade, “content-based” multimedia systems have realized success. By comparison, brain imaging and classification systems demand more efforts for improvement with respect to accuracy, generalization, and interpretation. The relationship between electroencephalogram (EEG) signals and corresponding multimedia content needs to be further explored. In this paper, we integrate implicit and explicit learning modalities into a context-supported deep learning framework. We propose an improved solution for the task of brain imaging classification via EEG signals. In our proposed framework, we introduce a consistency test by exploiting the context of brain images and establishing a mapping between visual-level features and cognitive-level features inferred based on EEG signals. In this way, a multimodal approach can be developed to deliver an improved solution for brain imaging and its classification based on explicit learning modalities and research from the image processing community. In addition, a number of fusion techniques are investigated in this work to optimize individual classification results. Extensive experiments have been carried out, and their results demonstrate the effectiveness of our proposed framework. In comparison with the existing state-of-the-art approaches, our proposed framework achieves superior performance in terms of not only the standard visual object classification criteria, but also the exploitation of transfer learning. For the convenience of research dissemination, we make the source code publicly available for downloading at GitHub (https://github.com/aneeg/dual-modal-learning).
The Representation of Hedonism Lifestyle on the Islamic Media Platform Dream.co.id

Web dream.co.id claims itself to be a Muslim news platform in Indonesia. However, upon further scrutinizing its contents, not all information uploaded is news with Islamic values. The website contains a lot of information that may lead to jealousy and envy by promoting hedonism. This research aims to determine the representation of the hedonistic lifestyle published by the Muslim online platform dream.co.id. This research also explores how media represents good news or information content that should invite people to do good according to Islamic teachings and how a media claiming to promote Islamic values disseminates and provides information to the public. This research uses a qualitative method with library research using Roland Barthez’s Semiotic Theory which contains a system model for analyzing a meaning, including denotative meaning (true meaning), connotative meaning (figurative meaning), and mythical meaning. Based on the results of data processing, this study shows that the contents of dream.co.id represent the public figure’s hedonistic lifestyle. The hedonistic lifestyle represented in the article shows public figures who like to spend money, use branded products, and live extravagantly. Web dream.co.id indicates that those public figures’ hedonistic lifestyle is a form of fulfilling personal satisfaction and pleasure, which indicates a hedonistic lifestyle.
Radar Observations of Dendritic Growth Zones in Colorado Winter Storms

X-band polarimetric radar observations of winter storms in northeastern Colorado on 20-21 February, 9 March, and 9 April 2013 are examined. These observations were taken by the Colorado State University-University of Chicago-Illinois State Water Survey (CSU-CHILL) radar during the Front Range Orographic Storms (FROST) project. The polarization radar moments of reflectivity factor at horizontal polarization (ZH), differential reflectivity (ZDR), and specific differential phase (KDP) exhibit a range of signatures at different times near the -15° C temperature level favored for dendritic ice crystal growth. Generally, KDP is enhanced in dendritic ice crystal growth region with ZDR decreasing and ZH increasing towards the ground, suggestive of aggregation (or riming). The largest ZDR values (~3.5-5.5 dB) are found during periods of significant low-level upslope flow. Convective features observed when the upslope flow was weaker have the highest KDP (> 1.5 deg km ) and ZH (> 20 dBz) values. Vertically-pointing observations of radial velocity (VR) taken by NCAR’s X-band polarimetric radar (NCAR-XPOL) are analyzed along with the polarimetric observations from the CSU-CHILL radar. The magnitude of VR is found to decrease towards the ground near the -15 °C temperature level, indicating a decrease in the mean particle fall speed. ZH increases towards the ground in this region, indicating an increase in the mean particle size. These signatures are found to be consistent with the growth and subsequent aggregation of dendrites. Electromagnetic scattering calculations using the Generalized Multi-particle Mie (GMM) method are used to determine whether these radar signatures are consistent with dendrites. Particle size distributions (PSDs) are retrieved for a variety of cases using these scattering calculations and the corresponding radar observations. The PSDs are found to be reasonably consistent with previous observations of ice particle size distributions. Observations of enhanced KDP, decreasing
The Development of a Low-Cost High-Resolution Ultrasonic ISAR Imaging System for Educational Purposes

This paper presents the design and implementation of an ultrasonic radar system using a reusable radar design to generate inverse synthetic aperture radar(ISAR) images of small objects uniformly rotating on a turntable. The system was developed to create a low-cost radar to obtain inexpensive radar data for ISAR education purposes in indoor environments. Simulations were done to generate ISAR images of small rotating objects, and results were used to select suitable waveform parameters for the ultrasonic radar measuring real-world targets. The results from the experiments with measuring rotating corner reflectors and toy models demonstrate the imaging systems' effectiveness in producing high-resolution ISAR images, which can be used in various applications ranging from academic to industrial settings. The proposed system is expected to contribute to the field of radar imaging, providing a reliable and practical solution for imaging objects of interest and testing various algorithms for ISAR imaging. The system's design allows for further expansion and reuse in different forms of ISAR imaging, such as bi-static ISAR.
A Copula-Based Bivariate Composite Model for Modelling Claim Costs

This paper aims to develop a new family of bivariate distributions for modelling different types of claims and their associated costs jointly in a flexible manner. The proposed bivariate distributions can be viewed as a continuous copula distribution paired with two marginals based on composite distributions. For expository purposes, the details of one of the proposed bivarite composite distributions is provided. The dependence measures for the resulting bivariate copula-based composite distribution are studied, and its fitting is compared with other bivariate composite distributions and existing bivariate distributions. The parameters of the proposed bivariate composite model are estimated via the inference functions for margins (IFM) method. The suitability of the proposed bivariate distribution is examined using two real-world insurance datasets, namely the motor third-party liability (MTPL) insurance dataset and Danish fire insurance dataset.
Engineered T cells from induced pluripotent stem cells: from research towards clinical implementation

Induced pluripotent stem cell (iPSC)-derived T (iT) cells represent a groundbreaking frontier in adoptive cell therapies with engineered T cells, poised to overcome pivotal limitations associated with conventional manufacturing methods. iPSCs offer an off-the-shelf source of therapeutic T cells with the potential for infinite expansion and straightforward genetic manipulation to ensure hypo-immunogenicity and introduce specific therapeutic functions, such as antigen specificity through a chimeric antigen receptor (CAR). Importantly, genetic engineering of iPSC offers the benefit of generating fully modified clonal lines that are amenable to rigorous safety assessments. Critical to harnessing the potential of iT cells is the development of a robust and clinically compatible production process. Current protocols for genetic engineering as well as differentiation protocols designed to mirror human hematopoiesis and T cell development, vary in efficiency and often contain non-compliant components, thereby rendering them unsuitable for clinical implementation. This comprehensive review centers on the remarkable progress made over the last decade in generating functional engineered T cells from iPSCs. Emphasis is placed on alignment with good manufacturing practice (GMP) standards, scalability, safety measures and quality controls, which constitute the fundamental prerequisites for clinical application. In conclusion, the focus on iPSC as a source promises standardized, scalable, clinically relevant, and potentially safer production of engineered T cells. This groundbreaking approach holds the potential to extend hope to a broader spectrum of patients and diseases, leading in a new era in adoptive T cell therapy.
Spherical Easel: An Open-Source Web Application Modeling Spherical Geometry

Spherical Easel (https://easelgeo.app) is a free Vue-based application for researching, teaching, and learning spherical geometry written in Typescript. It offers an abundant suite of tools that allow users to dynamically explore the geometry of the sphere by drawing and controlling the behavior of spherical objects including points, lines, circles, and parametric curves. The authors have created an open-source code base that innovatively combines several design patterns, global data structures, and cloud based storage. At the same time we have invited a variety of students (including from Art and Design) and community members to collaborate, build and suggest additional features, and add different languages to this internationally accessible tool for learners, educators, and researchers at all levels.
Charting the Course of Ship Track Prediction: A Novel Approach for Maritime Traffic Analysis and Enhanced Situational Awareness

Accurate ship track prediction plays a pivotal role in maritime operations, enabling proactive decision-making, enhancing safety, and optimizing vessel routing. We propose ship trajectory prediction - a threefold technique for facilitating accurate predictions, which involves clustering historical AIS trajectories into maritime de facto routes, classifying new trajectory to one of these routes, and conducting predictions along the identified route. To overcome the challenges of capturing the latent structure in high-dimensional and heterogeneous space imposed by AIS data, we introduce a new similarity technique that automatically determines the number of clusters. Furthermore, we introduce a method to automatically annotate the feature space, enhancing the efficiency of data analysis tasks like clustering. This not only improves performance but also ensures transparency, allowing for effective performance evaluation. Our approach demonstrates an accuracy of over 88% and an accuracy of 78% in predicting routes for tanker vessels.
Adolescent Depression Prevalence and Factors: A Jordanian Cross-Sectional Study

Objective There is a great deal of variation in the prevalence of adolescent depression among nations, but overall, it is a severe and common mental illness that is a major contributor to disability on a global scale. This study aimed to determine the prevalence of depression, the severity of its symptoms, and its characteristics among adolescents residing in Amman, Jordan, in 2023. Method A cross-sectional design was used to collect descriptive data from 784 adolescents from Jordan, aged between 14 and 17 years. Participants were administered the Patient Health Questionnaire-9 to assess the prevalence and severity of depression as well as measures of socio-demographics and health history. Significance was set at p ≤. 05. Results The mean age was 15.56 +/- 1.077 with 55% female and 45% male participants, with a prevalence of moderate to severe depression of 32.4%. This study identified four significant predictors of depression: female sex, cigarette smoking, history of mental health problems, and family history of psychiatric disorders. Conclusion Our findings highlight the importance of directing prevention and intervention efforts toward adolescents, considering the projected increase in depression rates across the Arab region. This serves as a warning for policymakers and healthcare professionals to prioritize this demographic in their strategies.
Accounting for Centennial Scale Variability when Detecting Changes in ENSO: a study of the Pliocene.†

The El Nino Southern Oscillation (ENSO) is the dominant mode of interannual climate variability. However, climate models are inconsistent in future predictions of ENSO, and long term variations in ENSO cannot be quantified from the short instrumental records available. Here we analyse ENSO behaviour in millennial-scale climate simulations of a warm climate of the past, the mid-Pliocene Warm Period (mPWP; ∼3.3 − 3.0Ma). We consider centennial-scale variability in ENSO for both the mPWP and the preindustrial, and consider which changes between the two climates are detectable above this variability. We find that El Nino typically occurred 12% less frequently in the mPWP but with a 20% longer duration, and with stronger amplitude in precipitation and temperature. However low frequency variability in ENSO meant that Pliocene-preindustrial changes in El Nino temperature amplitude in the NINO3.4 region (5° N-5° S, 170° W-120° W) were not always detectable. The Pliocene-preindustrial El Nino temperature signal in the NINO4 region (5° N-5° S, 160° E-150° W) and the El Nino precipitation signal are usually larger than centennial scale variations of El Nino amplitude, and provide consistent indications of ENSO amplitude change. The enhanced mPWP temperature signal in the NINO4 region is associated with an increase in Central Pacific El Nino events similar to those observed in recent decades and predicted for the future. This study highlights the importance of considering centennial scale variability when comparing ENSO changes between two climate states. If centennial scale variability in ENSO has not first been established, results suggesting changes in ENSO behaviour may not be robust.
Composite Right/Left Handed Leaky Wave Antenna Using Complementary Omega Shape on Half Mode Substrate Integrated Waveguide

In this paper, a half-mode substrate integrated waveguide (HMSIW) by using complementary omega slots to provide HMSIW Composite right/left handed (CRLH) Leaky wave antenna (LWA) is presented. Omega shaped slots provide the series capacitance required for the CRLH transmission line, whereas the vias on one side of the proposed structure provides the shunt inductance. The unit cell configuration has been analyzed and an array of eight-unit cells is used for presenting HMSIW CRLH LWA exhibiting continuous beam scanning from the angle -30 to 20 between the frequency range of 14.5 - 19 GHz.
Regression Analysis Based Circuit Power Estimation Technique

Recent advances in Very Large Scale Integration technology(VLSI) enable the integration of millions of transistors on a single chip. Early power estimation is essential in VLSI circuits because it greatly impacts the reliability of VLSI circuits. Therefore, signal statistics obtained from functional simulations can be used to perform behavioral-level performance estimation and calculate energy consumption. Accuracy and efficiency of power estimation technique is always have trade-off. Power consumption of VLSI circuits can be reduced by properly estimating power during design phase. This has greatly increased circuit complexity, resulting in a need for power estimation techniques that are time-saving and cost-effective. High-level power estimation has many advantages over low-level abstraction power estimation techniques. This reduces both time constraints and computational resource requirements. Among the training functions assessed, 'traingd' emerges as the most fitting choice for power estimation in combinational circuits. The experimental results shows that proposed method has minimal deviation from the ideal power estimation, with deviations of only 0.77% and 1.04% observed. Specifically, for NAND circuits, the Root Mean Square Error (RMSE) stands at 0.0341, accompanied by a correlation coefficient (R) of 0.960. Similarly, in the case of NOR circuits, the RMSE and R values are 0.0387 and 0.973, respectively. Regression analysis using Back Propagation Neural Network is an attractive tool for solving complex, nonlinear and time-constrained problems and is therefore suitable for power estimation in CMOS VLSI circuits.