Miracle-dz/UniFAR-FTUs huggingface dataset:
Stereochemical Study of the Super Large Tetrakis Alkaloid Alasmontamine A by Means of an Advanced Computational NMR

1H and 13C NMR chemical shifts of the tetrakis monoterpene indole alkaloid alasmontamine A, with a molecular formula of C84H91N8O12, have been calculated within the DFT framework. Six minimum energy conformers of this alkaloid were identified, and three key configurations that contribute to its NMR shielding constants were established. Several ambiguities in the reported assignment of the NMR chemical shifts of alasmontamine A have been resolved.
Effects of urbanisation on urban residents’ perception of vegetable production in Yaoundé, Cameroon

Abstract In the context of rapid and unplanned urbanisation in many Sub-Saharan African cities, the social and political context of urban and peri-urban vegetable production is becoming unfriendly despite its multifunctionality in achieving human development. This paper aims at measuring the effects of urbanisation on urban residents’ perception of vegetable production in urban and peri-urban areas of Yaoundé, Cameroon. Data from a survey conducted by the World Vegetable Centre among urban residents living within and around vegetable production areas was employed for the study. The results show that urban residents agree with vegetable production in their vicinity, but depending on the extent of urbanisation, the magnitude of their positive perception varies significantly from one production area to another. More specifically, while proximity to the city centre increases the likelihood of urban residents’ ability to agree with local vegetable production, the density of the population decreases this positive perception instead.
Manufacturing acceleration of free-form micro-optical arrays (FMOAs) with CAD algorithms

Freeform micro-optical arrays (FMOAs) can overcome some limitations of rotationally symmetric optical components. The manufacturability assessment for FMOA designs containing many freeform elements is demanding. We present computer-aided design (CAD) tools that analyze FMOA designs for manufacturing constraints. They 1) extract the height (Sag) and 2) local slope while highlighting areas beyond manufacturing limits, 3) extract the minimum tool radius and 4) convert CADs into ray traceable solids using NURBS for performance comparison of the actual micro-structure to the design. Critical parameters can be identified early, reducing time-consuming and costly adjustments later.
Continuous and highly accurate multi-material extrusion-based bioprinting with optical coherence tomography imaging

Extrusion-based bioprinting is a widely used approach to construct artificial organs or tissues in the medical fields due to its easy operation and good ability to combine multimaterial. Nevertheless, the current technology is limited to some printing errors when combining multi-material printing, including mismatch between printing filaments of different materials and error deposited materials (e.g., under-extrusion and overextrusion). These errors will affect the function of the printed structure (e.g., mechanical and biological properties), and the traditional manual correction methods are inefficient in time and material, so an automatic procedure is needed to improve multimaterial printing accuracy and efficiency. However, to the best of our knowledge, very few automated procedure can achieve the registration between printing filaments of different materials. Herein, we utilized optical coherence tomography (OCT) to monitor printing process and presented a multi-material static model and a time-related control model in extrusion-based multi-material bioprinting. Specifically, the multi-material static model revealed the relationship between printed filament metrics (filament size and layer thickness) and printing parameters (printing speeds or pressures) with different materials, which enables the registration of printing filaments by rapid selection of printing parameters for the materials, while time-related control model could correct control parameters of nozzles to reduce the material deposition error at connection point between nozzles in a short time. According to the experimental results of singlelayer scaffold and multi-layer scaffold, material deposition error is eliminated, and the same layer thickness between different materials of the same layer is achieved, which proves the accuracy and practicability of these models. The proposed models could achieve improved precision of printed structure and printing efficiency.
ZenFS+: Nurturing Performance and Isolation to ZenFS

This paper proposes ZenFS+, a new storage backend of RocksDB for small-zone ZNS SSD. RocksDB has complicated internal operations such as flush and compaction. Flush and compaction run in separate threads, and it is well known that they closely interact around the sstables. Due to the concurrent storage I/O between flush and compaction, ZenFS presents unsatisfactory performance for small-zone ZNS SSD. We believe that ZenFS+ presents how ZNS SSD can support the performance and isolation of modern key-value stores. Leveraging the ZNS SSD, ZenFS+ intelligently identifies the independent zones group (IZG), which reveals the device’s internal parallelism. With the IZG information, ZenFS+ effectively isolates the flush workloads of RocksDB from compaction. Besides, ZenFS+ spreads the sstables to multiple IZGs so that the storage write can leverage the hardware parallelism. ZenFS+ presents up to 4.8x higher storage throughput for write-intensive microbenchmark and stabilizing 99.9P tail latency by 1/51 from existing ZenFS. Further, ZenFS+ implements proactive garbage collection, and presents sustainable performance over longer lifespan of the device.
Test Resource Allocation in Hierarchical Systems Using Bayesian Networks

This paper develops analytical methods for test resource allocation that aid in reducing the uncertainty in the system model prediction for multilevel and multidisciplinary systems. The various component, subsystem, and system-level model predictions; the corresponding inputs and calibration parameters; test data; and model and measurement errors are connected efficiently using a Bayesian network. This provides a unified framework for uncertainty analysis where test data can be integrated along with computational simulations. The Bayesian network is used in an inverse problem where the model parameters of multiple subsystems are calibrated simultaneously. This leads to a decrease in the variance of the model parameters, and hence, in the variance of the overall system performance prediction. An optimization-based procedure is then used for test resource allocation using the Bayesian network, and those tests that can effectively reduce the uncertainty in the system model prediction are identified. The prop.
The Effect of Hydroxytyrosol in Type II Epithelial-Mesenchymal Transition in Human Skin Wound Healing

Skin wound healing is a multiphase physiological process that involves the activation of numerous types of cells and is characterized by four phases, namely haemostasis, inflammatory, proliferative, and remodeling. However, on some occasions this healing becomes pathological, resulting in fibrosis. Epithelial mesenchymal transition (EMT) is an important process in which epithelial cells acquire mesenchymal fibroblast-like characteristics. Hydroxytyrosol (HT) is a phenolic compound extracted from olive oil and has been proven to have several health benefits. The aim of this study was to determine the effect of HT in type II EMT in human skin wound healing via cell viability, proliferation, migration, and proteins expression. Human dermal fibroblasts (HDF) isolated from skin samples were cultured in different concentrations of HT and EMT model, induced by adding 5 ng/mL of transforming growth factor-beta (TGF-β) to the cells. HT concentrations were determined via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cells’ migrations were evaluated using scratch and transwell migration assay. Protein expressions were evaluated via immunocytochemistry. The result showed that HT at 0.2% and 0.4% significantly increased the proliferation rate of HDF (p < 0.05) compared to control. Scratch assay after 24 h showed increased cell migration in cells treated with 0.4% HT (p < 0.05) compared to the other groups. After 48 h, both concentrations of HT showed increased cell migration (p < 0.05) compared to the TGF-β group. Transwell migration revealed that HT enhanced the migration capacity of cells significantly (p < 0.05) as compared to TGF-β and the control group. In addition, HT supplemented cells upregulate the expression of epithelial marker E-cadherin while downregulating the expression of mesenchymal marker vimentin in comparison to TGF-β group and control group. This study showed that HT has the ability to inhibit EMT, which has potential in the inhibition of fibrosis and persistent inflammation related to skin wound healing.
Infrared image-based remote target detection for maritime rescue utilizing a deep learning network and data augmentation

In this paper, a fast and robust infrared remote target detection network is proposed based on deep learning. Furthermore, we construct our own IR image database imitating humans in remote maritime rescue situations using FLIR M232 IR camera. First, IR image is preprocessed with contrast enhancement for data augmentation and to increase Signal-to-Noise Ratio (SNR). Second, multi-scale feature extraction is performed combined with fixed weighted kernels and convolutional neural network layers. Lastly, the feature map is mapped into a likelihood map indicating the potential locations of the targets. Experimental results reveal that the proposed method can detect remote targets even under complex backgrounds surpassing the previous methods by a significant margin of +0.62 in terms of mIOU.
Asymptotic Expansions for Random Walks with Normal Errors

The asymptotic distribution of the least-squares estimators in the random walk model was first found by White [17] and is described in terms of functional of Brownian motion with no closed form expression known. Evans and Savin [5,6] and others have examined numerically both the asymptotic and finite sample distribution. The purpose of this paper is to derive an asymptotic expansion for the distribution. Our approach is in contrast to Phillips [12,13] who has already derived some terms in a general expansion by analyzing the functionals. We proceed by assuming that the errors are normally distributed and expand the characteristic function directly. Then, via numerical integration, we invert the characteristic function to find the distribution. The approximation is shown to be extremely accurate for all sample sizes ≥25, and can be used to construct simple tests for the presence of a unit root in a univariate time series model. This could have useful applications in applied economics.
Huge CH4, NO2 and CO Emissions from Coal Mines in the Kuznetsk Basin (Russia) Detected by Sentinel-5P

The coal industry is the largest global emitter of carbon dioxide (CO2). However, recent data suggests that coal mine methane (CH4) emissions worldwide are higher than those of the oil and gas industry. Furthermore, the coal industry is less active in reducing methane emissions than the oil and gas sectors due to lower profitability. Although uncertainties remain in quantifying methane emissions from mines, the use of satellite observations is revolutionizing the process of monitoring and improving the accuracy of emission accounting. The methodology presented here allows us to determine background CH4 concentrations and improve our ability to detect emission events using Sentinel-5P data. Knowing the background concentrations for the area of interest provides us the opportunity to track seasonal and annual variations and trends, as well as quickly detect periodic or accidental emissions from unregulated sources, etc. The methodology and systematic research applied in this paper for the period of May 2018 to the end of 2022 enables us to detect hundreds of large-scale emissions of CH4, NO2, and CO from the coal mines in the Kuznetsk Basin (the Kemerovo region), the largest coal mining area in Russia. We estimated that the amount of these emissions is significantly higher than the emissions reported by various authors for other coal mining regions such as Poland and Australia. We found that in cases of high methane quantity there is a positive correlation between NO2 and CO emissions in time and location. The source of emissions in the study area is homogeneous, which allows it to be used as a benchmark for building models to estimate and track emissions in heterogeneous areas.
Seasonal changes in spider diversity in subtropical riparian forests: what drives the seasonality of the araneofauna?

Abstract. Spiders are usually found through all seasons in the subtropics, but little is known about their seasonality patterns and how they respond to abiotic factors, especially in species-rich regions such as Brazil. We investigated the seasonal variation in spider communities and the possible influence of abiotic factors (e.g., temperature, rainfall) on spider abundance, species richness and composition in subtropical riparian forest in four river basins. Changes in spider abundance among seasons differed between ontogenetic stages: there were more spiderlings in the autumn, and similar numbers of adults year round. Species richness, on the other hand, was highest in spring and summer. Species composition differed between seasons and river basins. Several factors may be behind diversity changes in the araneofauna among seasons, however, none of the abiotic factors evaluated showed a strong direct influence. Seasonal patterns varied, with different species having peaks in all three seasons but autumn. Seasonality is clear in these spider communities but not all patterns follow predictions from simple temperature changes.
Study on the dynamic performance of tilt rotor supported by spherical roller bearings

Tilt rotor supported by rolling bearings is often used in mechanical equipment. In the past, researches on dynamic performance of bearing-rotor were mainly carried out for horizontal rotor, but there is insufficient reported research on the tilting rotors. In this paper, the dynamic performance of a tilt rotor supported by spherical roller bearings is studied. Through the dynamics analysis of the rotor, a transfer matrix dynamics model for the tilt rotor is established. The stiffness and damping calculation model of spherical roller bearings is built. With a calculation example, the effects of tilting angles and some rotor structure parameters on the system’s dynamic performance are studied numerically. The results obtained show that with the increase in the tilt angle, the critical speed of the rotor decreases, and the amplitude increases in the vertical direction of the shaft. With the same tilting angle, the smaller the distance from the disc to the middle point of the rotating shaft, the larger the amplitude of the vibration mode and the amplitude difference compared to that of the rotor without inclination. The unbalanced response amplitude curves of the rotor distribute asymmetrically because of the rotor’s inclination. The bigger the inclination angle, the more obvious is the asymmetry distribution.
Georges Matheron: Founder of Spatial Statistics

Originally a geologist, Professor Georges Matheron (1930-2000) became increasingly mathematical in his approach to problem-solving. He made fundamental contributions to science by establishing new theoretical frameworks in spatial statistics, random sets, mathematical morphology, and the physics of random media. He inspired numerous other scientists first in France and later worldwide. His ideas and methods have been tested extensively and continue to be applied widely both inside and outside of geoscience. With Ronald Fisher and John Tukey, Georges Matheron belongs to the most influential mathematical statisticians of the twentieth century.
Genetic Analyses of Seed Longevity in Capsicum annuum L. in Cold Storage Conditions

Seed longevity is the most important trait in the genebank management system. No seed can remain infinitely viable. There are 1241 accessions of Capsicum annuum L. available at the German Federal ex situ genebank at IPK Gatersleben. C. annuum (Capsicum) is the most economically important species of the genus Capsicum. So far, there is no report that has addressed the genetic basis of seed longevity in Capsicum. Here, we convened a total of 1152 Capsicum accessions that were deposited in Gatersleben over forty years (from 1976 to 2017) and assessed their longevity by analyzing the standard germination percentage after 5–40 years of storage at −15/−18 °C. These data were used to determine the genetic causes of seed longevity, along with 23,462 single nucleotide polymorphism (SNP) markers covering all of the 12 Capsicum chromosomes. Using the association-mapping approach, we identified a total of 224 marker trait associations (MTAs) (34, 25, 31, 35, 39, 7, 21 and 32 MTAs after 5-, 10-, 15-, 20-, 25-, 30-, 35- and 40-year storage intervals) on all the Capsicum chromosomes. Several candidate genes were identified using the blast analysis of SNPs, and these candidate genes are discussed.
ReclaimingSTEM: A healing-centered counterspace model for inclusive science communication and policy training

The dominant U.S. cultural norms shape science, technology, engineering, and math (STEM), and in turn, these norms shape science communication, further perpetuating oppressive systems. Despite being a core scientific skill, science communication research and practice lack inclusive training spaces that center marginalized identities. We address this need with a healing-centered counterspace grounded in the key principles of inclusive science communication: ReclaimingSTEM. ReclaimingSTEM is a science communication and science policy training space that centers the experiences, needs, and wants of people from marginalized communities. ReclaimingSTEM problematizes and expands the definitions of “what counts” as science communication. We organize ReclaimingSTEM with intentionality, emphasizing inclusion at every part of the process. Since initiating in 2018, five ReclaimingSTEM workshops have been held in multiple locations, both in-person and virtually, reaching more than 700 participants from all over the globe. In this paper, we share our model for ReclaimingSTEM, reflections of workshop participants and speakers, barriers faced during organizing, and recommendations for creating truly inclusive practices in science communication spaces.
Efficient ALE mesh management for 3D quasi‐Eulerian problems

In computational solid mechanics, the ALE formalism can be very useful to reduce the size of finite element models of continuous forming operations such as roll forming. The mesh of these ALE models is said to be quasi‐Eulerian because the nodes remain almost fixed—or almost Eulerian—in the main process direction, although they are required to move in the orthogonal plane in order to follow the lateral displacements of the solid. This paper extensively presents a complete node relocation procedure dedicated to such ALE models. The discussion focusses on quadrangular and hexahedral meshes with local refinements. The main concern of this work is the preservation of the geometrical features and the shape of the free boundaries of the mesh. With this aim in view, each type of nodes (corner, edge, surface and volume) is treated sequentially with dedicated algorithms. A special care is given to highly curved 3D surfaces for which a CPU‐efficient smoothing technique is proposed. This new method relies on a spline surface reconstruction, on a very fast weighted Laplacian smoother with original weights and on a robust reprojection algorithm. The overall consistency of this mesh management procedure is finally demonstrated in two numerical applications. The first one is a 2D ALE simulation of a drawbead, which provides similar results to an equivalent Lagrangian model yet is much faster. The second application is a 3D industrial ALE model of a 16‐stand roll forming line. In this case, all attempts to perform the same simulation by using the Lagrangian formalism have been unsuccessful. Copyright © 2012 John Wiley & Sons, Ltd.
InGaN semiconductor laser diodes for the visible spectral range: design and process optimization of single emitters and bars for applications from mW to kW output power

InGaN lasers in the blue and green wavelength range have opened a wide variety of applications in the past years, which all require unique properties of the employed laser chips. In this paper we will show design and process developments for various InGaN laser designs, each optimized for its specific application. For applications which are very sensitive to energy consumption, like mobile AR/VR devices, we investigated InGaN laser chips with resonator lengths as short as 50 μm. To achieve this, we developed an etched facets technology to overcome the challenges of scribing and breaking for facet generation for such short resonator lengths. The etched facets of these devices are coated on-wafer with a dielectric mirror to achieve the desired reflectivity. Depending on the reflectivity chosen, these devices show ultra-low threshold currents below 3mA and output powers above 50 mW. Combined with a flip-chip design with both contacts on one side, such chips can be integrated into silicon wafer-based beam combiners to generate RBG PIC chips for VR/AR laser projection. For high power applications, we will present data of laser bars. Bars emitting at 430 nm achieved 100 W of continuouswave output power per bar and conversion efficiencies of 50%. Together with bars emitting at 450 nm, that were shown in previous publications, wavelength-multiplexing for materials-processing systems can be realized yielding blue laser light sources with multiple kilowatts of output powers.
Stochastic representation of model uncertainties in the ECMWF ensemble prediction system

A stochastic representation of random error associated with parametrized physical processes (‘stochastic physics’) is described, and its impact in the European Centre for Medium‐Range Weather Forecasts Ensemble Prediction System (ECMWF EPS) is discussed. Model random errors associated with physical parametrizations are simulated by multiplying the total parametrized tendencies by a random number sampled from a uniform distribution between 0.5 and 1.5. A number of diagnostics are described and a choice of parameters is made. It is shown how the scheme increases the spread of the ensemble, and improves the skill of the probabilistic prediction of weather parameters such as precipitation. A choice of stochastic parameters is made for operational implementation. the scheme was implemented successfully in the operational ECMWF EPS on 21 October 1998.