Miracle-dz/UniFAR-FTUs huggingface dataset:
Proline Isomerization: From the Chemistry and Biology to Therapeutic Opportunities

Simple Summary Proline isomerization influences protein folding and function tremendously and serves as a unique type of post-translational modification that regulates multiple biological pathways. Although impactful, the importance and prevalence of proline isomerization as a regulation mechanism in biological systems has not been fully understood or recognized. Aiming to fill gaps and bring new awareness, in this wholistic review, we attempt to connect various aspects of proline isomerization from its chemistry, historic discovery, and biological function, to related diseases. Therapeutic opportunities opened up by this unique behavior of proline and future urgent needs pertinent to the topic are clearly communicated. Abstract Proline isomerization, the process of interconversion between the cis- and trans-forms of proline, is an important and unique post-translational modification that can affect protein folding and conformations, and ultimately regulate protein functions and biological pathways. Although impactful, the importance and prevalence of proline isomerization as a regulation mechanism in biological systems have not been fully understood or recognized. Aiming to fill gaps and bring new awareness, we attempt to provide a wholistic review on proline isomerization that firstly covers what proline isomerization is and the basic chemistry behind it. In this section, we vividly show that the cause of the unique ability of proline to adopt both cis- and trans-conformations in significant abundance is rooted from the steric hindrance of these two forms being similar, which is different from that in linear residues. We then discuss how proline isomerization was discovered historically followed by an introduction to all three types of proline isomerases and how proline isomerization plays a role in various cellular responses, such as cell cycle regulation, DNA damage repair, T-cell activation, and ion channel gating. We then explore various human diseases that have been linked to the dysregulation of proline isomerization. Finally, we wrap up with the current stage of various inhibitors developed to target proline isomerases as a strategy for therapeutic development.
Grain Size-Dependent Thermal Expansion of Nanocrystalline Metals

In the present work, we have used classical molecular dynamics and quantum mechanical density functional theory modeling to investigate the grain size-dependent thermal expansion coefficient (CTE) of nanocrystalline Cu. We find that the CTE increases by up to 20% with a gradually decreasing grain size. This behavior emerges as a result of the increased population of occupied anti-bonding states and bond order variation in the grain boundary regions, which contribute to the reduced resistance against thermally-induced bond stretching and dictate the thermal expansion behavior in the small grain size limit. As a part of the present work, we have established a procedure to produce ab initio thermal expansion maps that can be used for the prediction of the grain size-dependent CTE. This can serve as a modeling tool, e.g., to explore the impact of grain boundary impurity segregation on the CTE.
CT dose estimations using the ICRU/AAPM phantom

A proposed method to obtain the dose for Computed Tomography was evaluated on long a phantom, made of polyethylene. This phantom is 600 mm large. Adopting an empirical characterized function , a tendency of the asymptotical grow of the dose by the increase of the scanning length can estimate the dose in an infinite scanning length for a single axial rotation of the x-ray tube. The curve of was estimated for different clinical protocols: tension, pitch, head/body, and center/peripheral measurements. The ultrahigh molecular weight polyethylene phantom of 600 mm length, named ICRU/AAPM phantom, was proposed by a partnership between ICRU and AAPM (Task Group 200). This length provides a higher scanner length then PMMA phantom for , whose measurement is 100 mm. The measures of ICRU/AAPM phantom were performed on General Electric, Toshiba, and Philips scanners. The measurement was performed on a thimble ion chamber inserted in the center or periphery of the phantom. Using the measurements, functions (equilibrium dose, equilibrium length and scatter fraction) were obtained. A single scan has been demonstrated to be sufficient to obtain the function.
Analysis of the MU-MIMO System from the Perspective of Orthogonal and Non-Orthogonal Access

Many advanced technologies, including MIMO (Multiple Input Multiple Output) technology, have been proposed to achieve the data rates defined by today's wireless standards and to improve spectral efficiency. With this technology, multiple users were allowed to use the same spectrum. MU-MIMO stands for multi-user and represents a significant advance over single-user MIMO. Multiple access technologies can be divided into orthogonal and non-orthogonal methods. NOMA (Non-Orthogonal Multiple Access) technology currently includes many varieties and classifications and the only criterion by which a variety of multiple access technologies is classified as NOMA is that it is not an orthogonal multiple access technology. Also with the development of MIMO technology, it is possible to use spatial resources to organize multiple access. This paper shows that a communication system with MU-MIMO technology can also be considered as a NOMA system.
Paddy Disease Classification Based on the Lightweight MobileNet-V2

Paddy, a critical global staple food and economic resource, faces significant challenges in cultivation due to diseases that have devastating effects on crop yield and quality. Accurate and valid classification of these diseases is essential for their effective prevention, management, and timely treatment. Traditional identification methods, which are manual, time-consuming, labor-intensive, and prone to misclassification, fail to efficiently address these concerns. In response to these limitations, this research focuses on designing a lightweight neural network model for rice disease classification, leveraging the power of MobileNet-V2. This deep separable convolution-based neural network architecture is optimized for efficiency and accuracy in image classification tasks, making it well-suited for mobile devices. The approach enables real-time identification of rice diseases in the field, facilitating prompt intervention and treatment, ultimately minimizing the impact on crop yield and quality. The proposed model has undergone rigorous testing and benchmarking against state-of-the-art methods in paddy disease classification. Sufficient and multifaceted results demonstrate that the designed method achieves superior performance, outperforming the state-of-the-art in terms of accuracy and efficiency. The utilization of MobileNet-V2 in this research offers a valuable solution for the rapid and precise diagnosis of rice diseases, significantly contributing to the minimization of their spread and impact on crop yield and quality.
A Hybrid Adaptive Beamforming Algorithm for SINR Enhancement in Massive MIMO Systems

With the extreme density of devices and fast change of their directions in massive MIMO networks, a fast adaptive beamforming algorithm is required to provide high directivity and an enhanced signal-to-interference and noise ratio (SINR). Blind adaptive beamforming is suitable but less efficient, while non-blind adaptive beamforming is more efficient but requires significant training time. This study proposes a hybrid adaptive beamforming algorithm that addresses these issues. The algorithm integrates an improved direction-finding method to estimate the directions of arrival (DoAs) of incident signals at the antenna array, even in coherent signals cases, and a cascading combination of a blind and non-blind algorithms. The proposed algorithm generates an accurate main beam toward the desired direction and deep nulls in the direction of interfering signals, resulting in enhanced SINR. Compared to other algorithms, our approach achieves better performance without requiring additional antenna elements.
Problème de complémentarité linéaire semi‐défini; méthodes de points intérieurs; algorithmesprimal‐dual de trajectoire; complexité polynomiale des algorithmes

Dans cette these est consacree a l’etude de la construction des codes Cortex auto-duaux a base auto-dual ou bien non. Les codes Cortex introduis fournissant un moyen simple de construire des codes auto-duaux binaires lorsque le code de base l’est [2.1]. Ils offrent de plus une methode effcace pour construire des codes extremaux lorsque le code de Hamming etendu H8 de longueur 8 est le code de base. Cette construction permet en effet d’obtenir des codes auto-duaux extre- maux de type II. Par la suite, En utilisant un algorithme qui calcule la suite de permutation, le code de base non auto-dual et le code Cortex auto-dual obtenu
Gleason’s problem associated to the fractional Cauchy-Riemann operator, Fueter series, Drury-Arveson space and related topics

Abstract. In this paper we present the building blocks for a function theory based on fractional Cauchy-Riemann operators. We are going to construct basic monogenic powers and Fueter series. With these tools we are going to study Gleason’s problem and reproducing kernel spaces, like the DruryArveson space and de Branges-Rovnyak spaces. We end with a statement on Schur multipliers in this setting.
An open spatial capture-recapture framework for estimating the abundance and seasonal dynamics of white sharks at aggregation sites

: The present study provides the first estimate of abundance for the white shark at a new ag - gregation site in the western North Atlantic, which re quired the development of a novel modeling framework to accommodate the species’ migratory behavior. Estimates of abundance are needed to evaluate the performance of existing conservation measures for white shark populations worldwide but have historically been infeasible to obtain in the region. Following the recent emergence of Cape Cod, Massachusetts, USA, as a seasonal aggregation site, we con ducted a photographic capture−recapture survey and identified 393 individual white sharks from 2015− 2018. As conventional capture−recapture models do not adequately represent the species’ migratory be - havior, we extended an existing open spatial capture− recapture framework to allow for movements into and out of the surveyed area and accommodate variation in residency and habitat use among individuals. Using simulations, we demonstrated that failing to account for these processes resulted in biased estimates of abundance that would be misleading if used as the basis for management advice. We applied the model developed to describe the seasonal dynamics of the Cape Cod aggregation site and estimated a superpopulation size of 800 (393−1286) individuals, which provides an important baseline for this species of conservation concern. Because it directly links changes in abundance over time to the demographic processes underpinning them, the model described provides a more mechanistic understanding of the dynamics of white shark aggregations and improves the applied relevance of the results for the conservation and management of the species.
Relationship between honesty-credit, specialty identity, career identity, and willingness to fulfill the contract among rural-oriented tuition-waived medical students of China: a cross-sectional study

Background The fulfillment of contractual obligations by rural-oriented tuition-waived medical students (RTMSs) to work in rural medical institutions after graduation directly impacts the improvement of rural health quality. This study aimed to not only quantitatively measure the direct impact of honesty-credit, specialty identity, and career identity on willingness to fulfill the contract of RTMSs but also to quantify the intermediary role of specialty identity and career identity between honesty-credit and willingness to fulfill the contract. The research results provided recommendations for the rural-oriented tuition-waived medical education (RTME) program to achieve its goal of training rural primary healthcare personnel. Methods From March to May 2022, 1162 RTMSs were selected as the research objects. The honesty-credit, specialty identity, career identity, and willingness to fulfill the contract were quantitated using a self-completed questionnaire. Pearson's correlation analysis and structural equation modeling were used for statistical analysis and mediating effect evaluation. Results A total of 455 (42.3%) RTMSs had high willingness to fulfill the contract, and honesty-credit had a significant direct positive effect on willingness (β = 0.198, P < 0.001), specialty identity (β = 0.653, P < 0.001), and career identity (β = 0.180, P < 0.001). In the intermediary path between honesty-credit and willingness, career identity [95% confidence interval (CI): 0.007–0.051] had significant mediating effects. Career identity (95% CI: 0.030–0.149) also had significant mediating effects between specialty identity and willingness, and specialty identity (95% CI: 0.465–0.760) had significant mediating effects between honesty-credit and career identity. These results strongly confirmed that honesty-credit, specialty identity, and career identity are early and powerful predictors of the willingness to fulfill the contract of RTMSs. Conclusion The honesty-credit of RTMSs can predict their willingness to fulfill the contract early, significantly and positively. For the students who fail to pass the credit assessment for many times and have a strong tendency to default, their training qualifications should be canceled in time, so that students who are truly willing to serve rural areas can enter the project, and finally achieve the policy goal of “strengthening the rural primary medical and health system”.
Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage

Microgrids are an effective solution to decentralize electrical grids and improve usage of distributed energy resources (DERs). Within a microgrid there are multiple active players and it can be computationally expensive to consider all their interactions. An optimal scheduler ensures that the needs within the microgrid are met without wasting electricity. With higher requirements for electric vehicle charging stations (EVCSs), schedulers are essential to ensure EV charging demands are met while being profitable and flattening peak load on the main power grid (MPG). This paper introduces two novel microgrid models, combining energy generated by a DER, the possibility of storage with an energy storage system (ESS), a load entity in the form of an EVCS and electricity trading with the MPG. The model incorporates all important environment parameters created by these players in an intelligent way that keeps the action space relatively small and thus avoiding the problems associated with a high computational complexity. These models are proven to successfully shift the load from the MPG, while still providing high customer satisfaction and throughput, in a profitable way, despite costs incurred by the DER. Instead of relying on models, real data is used, ensuring that the model is robust. Additional real world stress tests are carried out with respect to electricity costs, wind energy generation, and charging rates. Reinforcement learning is implemented to find the optimal scheduler by maximizing overall profits. In all cases considered a self-sustaining system is established, that is a more profitable and reliable EVCS.
78.12 The family tree of the Pythagorean triplets revisited

1. Bibby, N.L. , "Wherefore ""plug and chug"? : computer algebra vs A level mathematics, Gazette, March 1991. 2. Schumway, R J., "Graphics calculators: skills vs concepts", in Senior secondary school mathematics education, eds. J. de Lange and M. Doorman, OW & OC, Utrecht 1988. 3. Derive, a mathematical assistant. Soft Warehouse Inc. 3615 Harding Av. , Suite SOS, Honolulu HI 96816, USA. 4. Etchells, T.A. , Computer algebra systems and the consequences for the teaching of mathematics, Uunpublished MSc thesis. Centre for Studies in Science and Mathematical Education, University of Leeds (1992).
A fourth-order finite volume method for direct numerical simulation of turbulence at higher Reynolds numbers

At ECCOMAS 94 we have presented results of a direct numerical simulation (DNS) of turbulent flow in a lid-driven cubical cavity at a Reynolds number Re = 10,000. The 3D, unsteady, incompressible Navier-Stokes equations were solved using a second-order finite volume method on a 100(8) (stretched) grid. The outcomes agreed well with the experimental data of Prasad and Koseff. Here, we will discuss a fourth-order finite volume method which yields a solution on a 50(3) grid that agrees better with the experimental data, for less than one twentieth of the CPU-time. This savings of costs allows for simulations at higher Reynolds numbers. With the improved simulation method, a DNS of a turbulent driven cavity flow at Re = 50,000 is performed using a 192(3) grid. Time-mean quantities and power spectra are computed. Mean velocities at Re = 50,000 will be compared with mean velocities at Re = 10,000.
A GAN-Based Defense Framework Against Model Inversion Attacks

With the development of deep learning, deep neural network (DNN)-based application have become an indispensable aspect of daily life. However, recent studies have shown that these well-trained DNN models are vulnerable to model inversion attacks (MIAs), where attackers can recover their training data with high fidelity. Although several defensive strategies have been proposed to mitigate the impact of such attacks, existing defenses will inevitably compromise the model performance and are ineffective against more sophisticated attacks, such as Mirror (An et al., 2022). In this paper, we introduce a novel GAN-based defense approach against model inversion attacks. Unlike previous works that perturb the prediction vector of the model, we manipulate the training procedure of the victim model by incorporating carefully-designed GAN-based fake samples. We also adjust the loss of the inversed samples to inject misleading features into the protected label of the victim model. Additionally, we adopt the concept of continual learning to improve the utility of the model. Extensive experiments conducted on the CelebA, VGG-Face, and VGG-Face2 datasets demonstrate that our proposed method outperforms existing defenses against state-of-the-art model inversion attacks, including DMI (Chen et al., 2021), Mirror (An et al., 2022), Privacy (Fredrikson et al., 2014), and AMI (Yang et al., 2019). It is shown that our proposed method can also retain a high defense performance in black-box scenarios.
Evaluation of Deep Learning Benchmarks in Retrieving Outfalls Into Rivers With UAS Images

Outfall surveys are critical to basin environmental and ecosystem protection. High-spatial-resolution remote sensing has been a key technology in outfall surveys. However, manual visual interpretation of outfalls from unmanned aircraft system (UAS) images is inefficient. Recently, the convolutional neural network (CNN) deep learning method has been demonstrated to be an efficient and accurate method for retrieving specific objects from remote sensing imagery. However, CNN’s architecture and features are often proposed to detect common objects from universal images that are less complex than the specific remote sensing objects. Thus, an optimal CNN benchmark is significant to the performance of CNN-based approaches in the field of remote sensing. In this study, we evaluate the performance of several state-of-the-art CNN architectures and features for improving the geo-deep learning CNN for outfalls (GDCNN-outfalls) we proposed. We found that Faster R-CNN combined with feature pyramid network (FPN) and generalized intersection over union (GIoU) or complete intersection over union (CIoU) are the most suitable benchmarks, with <inline-formula> <tex-math notation="LaTeX">${AP}_{{50}}$ </tex-math></inline-formula> values of 68.7 and 67.0, respectively, which are better than those of cascade R-CNN (<inline-formula> <tex-math notation="LaTeX">${AP}_{{50}}$ </tex-math></inline-formula> of 64.6) and Libra R-CNN (<inline-formula> <tex-math notation="LaTeX">${AP}_{{50}}$ </tex-math></inline-formula> of 62.7). You only look once (YOLO)-v4 can be an alternative with an <inline-formula> <tex-math notation="LaTeX">${AP}_{{50}}$ </tex-math></inline-formula> of 66.8 and comprehensively considers retrieval accuracy, the current GPU performance, and the application needs. This result indicates that the latest CNN models in the field of computer vision are not always the most suitable benchmarks for specific object retrievals from remote sensing imagery. Our study also provides a typical workflow for the relevant CNN-based model designs and applications.
An optically transparent metasurface for broadband microwave antireflection

Metamaterial absorbers and diffusers provide powerful routes to decrease the backward reflection significantly with advantages of ultrathin profile and customized bandwidth. Simultaneous control of the absorption and scattering behaviors of the metamaterials which helps to improve the suppression capabilities of backward reflection, however, still remains a challenge. Aiming at this goal, we propose a metasurface constituted by two kinds of elements in a pseudorandom arrangement. By the use of indium tin oxide with moderate sheet resistance in the meta-atoms, enhanced absorption of energy can be achieved in a broad spectrum when interacted with illuminated waves. In the meanwhile, electromagnetic diffusion will be invoked from the destructive interference among the elements, giving rise to significant reduction of specular reflection as a result. Excellent agreements are observed between simulation and experiment with pronounced reflection suppression from 6.8 GHz to 19.4 GHz. In addition, the optical tra.
Charmonium Production and HF decay properties at ATLAS

The ATLAS collaboration has measured the production of heavy flavoured bottom and charm hadrons in proton--proton collisions at the Large Hadron Collider (LHC) at centre of mass energies of $\sqrt{s}=5.02,7,8$ and $13~\rm TeV$. These measurements serve as an important test of QCD calculations. Production measurements of $J/\psi, \psi(2S), X(3872)$ charmonium states, as well as $b$-hadron and $J/\psi$ pairs are presented.
Association of Dietary Inflammatory Index and Dietary Oxidative Balance Score with All-Cause and Disease-Specific Mortality: Findings of 2003–2014 National Health and Nutrition Examination Survey

To clarify the effects of dietary inflammatory and pro-oxidative potential, we investigated the impact of the Dietary Inflammation Index (DII) and the Dietary Oxidative Balance Score (DOBS) on all-cause and disease-specific mortality. For DII and DOBS, 17,550 and 24,527 participants were included. Twenty-six and seventeen dietary factors were selected for scoring. Cox proportional hazards regression models were used. DII and DOBS were significantly associated with all-cause, CVD, and cancer mortality in this nationally representative sample of American adults. Compared with the lowest DII, the multivariable-adjusted hazard ratios (95% CI) of all-cause, CVD, and cancer mortality for the highest were 1.49 (1.23–1.80), 1.58 (1.08–2.33), and 1.56 (1.07–2.25). The highest quartile of DOBS was associated with the risk of all-cause death (HR 0.71, 95% CI 0.59–0.86). Pro-inflammatory and pro-oxidative diets were associated with increased risk for all-cause (HR 1.59, 95% CI 1.28–1.97), and CVD (HR 2.29, 95% CI 1.33–3.94) death compared to anti-inflammatory and antioxidant diets. Similar results were observed among the stratification analyses. Inflammation-reducing and oxidative-balancing diets are linked to lower all-cause and CVD mortality. Diets impact health by regulating inflammation and oxidative stress.