Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunoinformatics-Based Proteome Mining to Develop a Next-Generation Vaccine Design against Borrelia burgdorferi: The Cause of Lyme Borreliosis

The tick-borne bacterium, Borrelia burgdorferi has been implicated in Lyme disease—a deadly infection, formerly confined to North America, but currently widespread across Europe and Asia. Despite the severity of this disease, there is still no human Lyme disease vaccine available. A reliable immunoinformatic approach is urgently needed for designing a therapeutic vaccine against this Gram-negative pathogen. Through this research, we explored the immunodominant proteins of B. burgdorferi and developed a novel and reliable vaccine design with great immunological predictability as well as low contamination and autoimmunity risks. Our initial analysis involved proteome-wide analysis to filter out proteins on the basis of their redundancy, homology to humans, virulence, immunogenicity, and size. Following the selection of proteins, immunoinformatic tools were employed to identify MHC class I & II epitopes and B-cell epitopes, which were subsequently subjected to a rigorous screening procedure. In the final formulation, ten common MHC-I and II epitopes were used together with a suitable adjuvant. We predicted that the final chimeric multi-epitope vaccine could invoke B-cell responses and IFN-gamma-mediated immunity as well as being stable and non-allergenic. The dynamics simulations predicted the stable folding of the designed molecule, after which the molecular docking predicted the stability of the interaction between the potential antigenic epitopes and human immune receptors. Our studies have shown that the designed next-generation vaccine stimulates desirable immune responses, thus potentially providing a viable way to prevent Lyme disease. Nevertheless, further experimental studies in a wet lab are needed in order to validate the results.
Development of a Reliable High-Performance WLP for a SAW Device

In this paper, we present wafer-level packaging technology for surface acoustic wave (SAW) filters with higher long-term reliability and better electrical performance. This article focuses on the package structure, fabrication processes, and reliability for the SAW filter wafer-level package (WLP). The key processes, including cavity wall (CW) dam formation through non-photosensitive film vias development using a laser drilling process, a redistribution layer (RDL), and ball-grid array formation are developed. In addition, a numerical study based on the finite element model has been conducted to analyze the stress distribution of Cu RDL traces. In addition, the CW dam and the roof layer are covered with polymer, which solves the delamination problem between the CW dam and the substrate. Meanwhile, after practical verification, the SAW filter WLP was resistant to encapsulating pressure using a high elastic modulus capping material, which solved the collapse problem. Additionally, a comparison of the RF filter package’s electrical performance following the preconditional level 3 and unbiased highly accelerated stress test revealed no differences in insertion attenuation across the passband (<0.2 dB, standard value: 1 dB). The final packages passed the reliability tests in the field of consumer electronics.
Introduction to black hole entropy and supersymmetry

In these lectures we introduce some of the principles and techniques that are relevant for the determination of the entropy of extremal black holes by either string theory or supergravity. We consider such black holes with N=2 and N=4 supersymmetry, explaining how agreement is obtained for both the terms that are leading and those that are subleading in the limit of large charges. We discuss the relevance of these results in the context of the more recent developments.
Data Loss in RAID-5 and RAID-6 Storage Systems with Latent Errors

—Storage systems employ redundancy and recovering schemes to protect against device failures and latent sector errors, as well as to enhance reliability. The effectiveness of these schemes has been evaluated based on the Mean Time to Data Loss ( MTTDL ) and the Expected Annual Fraction of Data Loss ( EAFDL ) metrics. The reliability degradation due to device failures has been assessed in terms of both these metrics, but the adverse effect of latent errors has been assessed only in terms of the MTTDL metric. This article addresses the issue of evaluating the amount of data losses caused by latent errors. It presents a methodology for obtaining MTTDL and EAFDL of RAID-5 and RAID-6 systems analytically in the presence of unrecoverable or latent errors. A theoretical model capturing the effect of independent latent errors and device failures is developed, and closed-form expressions are derived for the metrics of interest.
Anomaly Composition and Decomposition Network for Accurate Visual Inspection of Texture Defects

Texture defect inspection remains challenging due to the extreme variations in various textures and defects. Current unsupervised-learning-based texture defect inspection methods cannot simultaneously inspect a wide variety of texture defects because they lack an explicit mechanism to encourage the model to create large anomaly scores for defects. In this study, we propose a novel anomaly composition and decomposition network (ACDN) for accurate inspection of various texture defects. In the proposed ACDN, a Gaussian-sampling-based anomaly composition (GSAC) method is proposed to perform the anomaly composition procedure, which composites a large number of defective images for training. Then, a novel anomaly decomposition network (ADN) is proposed to perform the anomaly decomposition procedure, which decomposes the defective images into texture background images and anomaly images by forcing the intrinsic texture features of abnormal images to share a common distribution with those of defect-free images. Through the GSAC and ADN, ACDN learns not only to accurately reconstruct texture background images to cause large reconstruction errors for defect regions but also accurately segment defects. In the testing phase, a defective image is decomposed into a texture background image and an anomaly image through the trained ADN. The residual image between the defective image and the texture background image is then fused with the anomaly image to obtain the defect inspection result. Extensive experimental results on mainstream texture defect datasets demonstrate that ACDN achieves the state-of-the-art texture defect inspection accuracy.
Un algoritmo de contacto para el análisis explícito de procesos de embutición

En el presente trabajo se presentan aspectos algoritmicos para el tratamiento del problema de contacto friccional entre herramientas y chapa en la simulacion de procesos de embuticion de laminas. El objetivo principal del trabajo es obtener un algoritmo de busqueda efectivo y computacionalmente economico para la determinacion de los puntos en contacto. Para ello se consideran las caracteristicas propias de este tipo de procesos y del esquema explicito de integracion. Por otro lado se pretende que el algoritmo sea suficientemente general como para tratar superficies de herramientas definidas a traves de triangularizaciones "no conformes" y que puedan presentar solapamientos o brechas (pequenas). Se muestra en un ejemplo el tipo de mallas tratadas y los resultados obtenidos.
Zirconia versus Titanium Implants: 8-Year Follow-Up in a Patient Cohort Contrasted with Histological Evidence from a Preclinical Animal Model

Zirconia ceramic (ZC) implants are becoming more common, but comparisons between preclinical histology and long-term clinical trials are rare. This investigation comprised (1) 8-year clinical follow-up of one-piece ZC or titanium (Ti) implants supporting full overdentures and (2) histomorphometric analysis of the same implants in an animal model, comparing implants with various surface treatments. Methods: (1) Clinical trial: 24 completely edentulous participants (2 groups of N = 12) received 7 implants (one-piece ball-abutment ZC or Ti; maxilla N = 4, mandible N = 3) restored with implant overdentures. Outcomes after 8-years included survival, peri-implant bone levels, soft-tissue responses, and prosthodontic issues. (2) Preclinical trial: 10 New Zealand sheep received 4 implants bilaterally in the femoral condyle: Southern Implants ZC or Ti one-piece implants, identical to the clinical trial, and controls: Southern ITC® two-piece implants with the same surface or Nobel (NBC) anodised (TiUnite™) surface. %Bone-implant contact (%BIC) was measured after 12 weeks of unloaded healing. Results: 8 of 24 participants (33%) of an average age of 75 ± 8 years were recalled; 21% of original participants had died, and 46% could not be contacted. 80.4% of implants survived; excluding palatal sites, 87.5% of Ti and 79% of ZC implants survived. All failed implants were in the maxilla. Three ZC implants had fractured. Bone loss was similar for Ti vs. ZC; pocket depths (p = 0.04) and attachment levels (p = 0.02) were greater for Ti than ZC implants. (1.7 ± 1.6 mm vs. 1.6 ± 1.3 mm). All implants in sheep femurs survived. %BIC was not statistically different for one-piece blasted surface Ti (80 ± 19%) versus ZC (76 ± 20%) or ITC® (75 ± 16 mm); NBC had significantly higher %BIC than ITC (84 ± 17%, p = 0.4). Conclusion: Short-term preclinical results for ZC and Ti one-piece implants showed excellent bone-implant contact in unloaded femoral sites. This differed from the long-term clinical results in older-aged, edentulous participants. While ZC and Ti implants showed equivalent performance, the risks of peri-implantitis and implant loss in older, completely edentulous patients remain a significant factor.
A novel mechanism of regulation of the oncogenic transcription factor GLI3 by toll-like receptor signaling

The transcription factor GLI3 is a member of the GLI family and has been shown to be regulated by canonical hedgehog (HH) signaling through smoothened (SMO). Little is known about SMO-independent regulation of GLI3. Here, we identify TLR signaling as a novel pathway regulating GLI3 expression. We show that GLI3 expression is induced by LPS/TLR4 in human monocyte cell lines and peripheral blood CD14+ cells. Further analysis identified TRIF, but not MyD88, signaling as the adapter used by TLR4 to regulate GLI3. Using pharmacological and genetic tools, we identified IRF3 as the transcription factor regulating GLI3 downstream of TRIF. Furthermore, using additional TLR ligands that signal through TRIF such as the TLR4 ligand, MPLA and the TLR3 ligand, Poly(I:C), we confirm the role of TRIF-IRF3 in the regulation of GLI3. We found that IRF3 directly binds to the GLI3 promoter region and this binding was increased upon stimulation of TRIF-IRF3 with Poly(I:C). Furthermore, using Irf3−/− MEFs, we found that Poly(I:C) stimulation no longer induced GLI3 expression. Finally, using macrophages from mice lacking Gli3 expression in myeloid cells (M-Gli3−/−), we found that in the absence of Gli3, LPS stimulated macrophages secrete less CCL2 and TNF-α compared with macrophages from wild-type (WT) mice. Taken together, these results identify a novel TLR-TRIF-IRF3 pathway that regulates the expression of GLI3 that regulates inflammatory cytokines and expands our understanding of the non-canonical signaling pathways involved in the regulation of GLI transcription factors.
On the symmetry of the quantum-mechanical particle in a cubic box

In this paper we show that the point-group (geometrical) symmetry is insufficient to account for the degeneracy of the energy levels of the particle in a cubic box. The discrepancy is due to hidden (dynamical symmetry). We obtain the operators that commute with the Hamiltonian one and connect eigenfunctions of different symmetries. We also show that the addition of a suitable potential inside the box breaks the dynamical symmetry but preserves the geometrical one.The resulting degeneracy is that predicted by point-group symmetry.
Interact or counteract? Behavioural observation of interactions between vulnerable road users and autonomous shuttles in Oslo, Norway

The current paper presents the results of behavioural observations in a field experiment with automated shuttles in Oslo, Norway. Video observations were conducted at five fixed locations along a challenging 1.2 km automated shuttle line with varying traffic conditions. Observed interactions between vulnerable road users and automated shuttles were coded using a predefined codebook, which allowed a structured quantitative analysis. The paper identified several potentially risky types of situations in which the automated shuttles did not always behave according to the traffic rules. Generally, the automated shuttles failed to give way to pedestrians at pedestrian crossings in 26%–50% of the interactions. Right-turning shuttles failed to yield to cyclists going straight in 38% of the interactions at observation Site 1 (the only location where the automated shuttle takes a right turn). In majority of same direction interactions between cyclists and automated shuttles, the interactions resulted in the cyclist overtaking the automated shuttle, usually on the left-hand side. Generally, the paper found little evidence of road users trying to bully or otherwise take advantage of the defensive driving style of the automated shuttles and identified only a limited number of interactions in which a vulnerable road user behaved ignorant or aggressive towards the automated shuttles. In addition, the paper found very little indication of temporal effects that suggest changes in the interaction patterns over time.
Deterministic Evolution of Strength in Multiple Comparisons Models: Who is the Greatest Golfer?

We present a statistical methodology for fitting time‐varying rankings, by estimating the strength parameters of the Plackett–Luce multiple comparisons model at regularly spaced times for each ranked item. We use the little‐known method of barycentric rational interpolation to interpolate between the strength parameters so that a competitor's strength can be evaluated at any time. We chose the time‐varying strengths to evolve deterministically rather than stochastically, a preference that we reason often has merit. There are many statistical and computational problems to overcome on fitting anything beyond ‘toy’ data sets. The methodological innovations here include a method for maximizing a likelihood function for many parameters, approximations for modelling tied data and an approach to the elimination of secular drift of the estimated ‘strengths’. The methodology has obvious applications to fields such as marketing, although we demonstrate our approach by analysing a large data set of golf tournament results, in search of an answer to the question ‘who is the greatest golfer of all time? ’
THE AMAZING COLORS OF PERUVIAN BIODIVERSITY: SELECT PERUVIAN PLANTS FOR USE AS FOOD COLORANTS

La creciente demanda de los consumidores de alimentos más nutritivos, ingredientes de origen natural y etiquetas más limpias está empujando a las industrias de alimentos y cosméticos a pasar del uso de colorantes artificiales a alternativas de origen natural. En este contexto, la industria busca continuamente fuentes de colorantes más estables, con especial interés en las fuentes vegetales. La vibrante biodiversidad que se encuentra en el Perú representa una emocionante oportunidad económica. En esta revisión, destacamos cultivos peruanos seleccionados con excelente potencial para su uso como fuentes de colorantes para aplicaciones industriales, con algunas fuentes que se estudian ampliamente y otras que reciben atención en años más recientes. El maíz morado, un cultivo nativo de la región de los Andes, es una rica fuente de pigmentos con gran estabilidad y una larga historia de uso en diferentes aplicaciones alrededor del mundo. Las papas de pulpa coloreada, cultivos andinos subutilizados, pueden expresar diferentes colores debido a sus perfiles de pigmentos variados. Sauco, la baya del saúco peruana, tiene una fuerte actividad antioxidante y un perfil de pigmento único que le da su característico color negro-púrpura. Las especies de Berberis, una clase diversa de arbustos con bayas altamente pigmentadas, se pueden usar directamente como aditivos de color sin necesidad de procedimientos de extracción. Huito, una fruta poco estudiada originaria del Amazonas es naturalmente incolora, pero puede volverse azul cuando se expone al oxígeno o a los aminoácidos y puede expresar diferentes tonalidades según la fuente del grupo de aminas primarias. En general, el maíz morado, las papas de pulpa coloreada, el sauco, las especies de Berberis y el huito son fuentes peruanas prometedoras de colorantes naturales para aplicaciones alimentarias y cosméticas debido a su versatilidad, estabilidad y atractivas características de color.
Feasibility of Hyperspectral Single Photon Lidar for Robust Autonomous Vehicle Perception

Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200–1570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics.
Rapid fault extraction from seismic images via deep learning

Abstract. Using deep learning to automatically and quickly extract faults from seismic images is of practical significance. An improved U-Net algorithm is proposed by reducing convolutional layers, designing skip connections, enforcing deep supervision, and improving the loss function and learning rate to build a new model. In the operation, the feature map parameters in the network are further revised, the number of training iterations is increased, a callback function is added, and the parameter adjustment training consumes less time and space and has higher accuracy. Experiments on real public datasets show that the improved network can limit the time required to extract a 128 × 128 × 128 three-dimensional image within 200 ms, which not only requires less time and computing power than existing methods but also has an extraction accuracy as high as 97.6%.
Monocular Camera based Trajectory Tracking of 3‐DOF Helicopter

The vision based flight control problems of unmanned micro aerial vehicles (MAVs) have attracted much attention in recent years. This paper presents a new solution to the trajectory tracking problem of a three‐degrees‐of‐freedom (3‐DOF) helicopter by utilizing only an onboard monocular camera without any artificial marker. First, the parallel tracking and mapping (PTAM) algorithm, which is a famous solution to the visual simultaneous localization and mapping (vSLAM) problem, is employed to estimate the attitude angles of the 3‐DOF helicopter. Then the calibration puzzle of the mapping between the onboard camera and helicopter coordinate systems is turned into an optimization problem. A robust cost function is applied to get the accurate estimate of the mapping. Finally, for the purpose of alleviating the influences of nonlinearity and coupling between channels, the feedback linearization (FL) and the linear quadratic regulation (LQR) techniques are employed to design the controller. Experimental results show that the proposed method can ensure that the helicopter hovers without drift and has good tracking performance.
Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment analysis. A survey of the work that has been done till now is conducted observing the results and outcomes concerning varying parameters of various researchers who worked on previously existing as well as novel and hybrid algorithms opting legion methodologies. The fundamental algorithms like Support Vector Machine (SVM), Bayesian Networks (BN), Maximum Entropy (MaxEnt), Conditional Random Fields (CRF) and Artificial Neural Networks (ANN) are also discussed to achieve practice percentage and accuracy score in the field of NLP, sentiment analysis and text analytics. Various other novel approaches and algorithms like CNN, LSTM, KNN, K*, K‐means, K‐means++, SOM and ENORA, along with their limitations and the performance metrics providing accuracies for major open data sets are also analyzed.
Characterizing Computational Thinking in the Context of Model-Planning Activities

Computational thinking (CT) is a critical skill needed for STEM professionals and educational interventions that emphasize CT are needed. In engineering, one potential pedagogical tool to build CT is modeling, an essential skill for engineering students where they apply their scientific knowledge to real-world problems involving planning, building, evaluating, and reflecting on created systems to simulate the real world. However, in-depth studies of how modeling is done in the class in relation to CT are limited. We used a case study methodology to evaluate a model-planning activity in a final-year undergraduate engineering classroom to elicit CT practices in students as they planned their modeling approach. Thematic analysis was used on student artifacts to triangulate and identify diverse ways that students used CT practices. We find that model-planning activities are useful for students to practice many aspects of CT, such as abstraction, algorithmic thinking, and generalization. We report implications for instructors wanting to implement model-planning activities into their classrooms.