Total ankle replacement (TAR) implants are an effective option to restore the range of motion of the ankle joint for arthritic patients. An effective tool for analyzing these implants’ mechanical performance and longevity in-silico is finite element analysis (FEA). ABAQUS FEA was used to statically analyze the von Mises stress and contact pressure on the articulating surface of the bearing component in two newly installed fixed-bearing total ankle replacement implants (the Wright Medical INBONE II and the Exactech Vantage). This ultra-high molecular weight polyethylene (UHMWPE) bearing commonly fails and is the cause for surgeon revisions. Six different FEA models for various gait percentages during stance (10%-60%) were created. They varied in magnitude of the compressive load and the ankle dorsiflexion/plantarflexion angle. The results indicated that the von Mises stress and contact pressure distributions increased as compressive load increased, and the largest magnitudes occurred at high dorsiflexion angles (15-30°). High magnitudes occurred in regions where the thickness of the bearing was the least. Additionally, high contact pressures were examined in areas near the talar component's edge or at the bearing's edges. To the author’s knowledge, this is the first study available to the research community that analyzes the Vantage implant with FEA. This study lays an essential foundation for future researchers in presenting a thorough literature review of TAR and methods for a simple FEA model setup. This study provides valuable information that can be beneficial to medical company designers and orthopedic surgeons in understanding the stress response and contact pressure of TAR implants during use.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Wearable sensors have become increasingly popular for assessing athletic performance, but the optimal methods for processing and analysing the data remain unclear. This study investigates the efficacy of discrete and continuous feature extraction methods, separately and in combination, for modelling countermovement jump performance using wearable sensor data. We demonstrate that continuous features, particularly those derived from Functional Principal Component Analysis, outperform discrete features in terms of model performance, robustness to variations in data distribution and volume, and consistency across different datasets. Our findings underscore the importance of methodological choices, such as signal type, alignment methods, and model selection, in developing accurate and generalisable predictive models. We also highlight the potential pitfalls of relying solely on domain knowledge for feature selection and the benefits of data-driven approaches. Furthermore, we discuss the implications of our findings for the broader field of sports biomechanics and provide practical recommendations for researchers and practitioners aiming to leverage wearable sensor data for athletic performance assessment. Our results contribute to the development of more reliable and widely applicable predictive models, facilitating the use of wearable technology for optimising training and enhancing athletic outcomes across various sports disciplines.

The possibility of applying Mo and Cr coatings by CVD method to the inner surfaces of tubular products Ø ˂ 60 mm has been studied.The process of applying Mo and Cr coatings by the method of gas-phase deposition was developed.The areas of parameters for obtaining coatings with a uniform structure, speed, hardness, as well as patterns of change in these characteristics when changing the main parameters of the process of obtaining such coatings are determined.The proven technological processes of applying Mo and Cr CVD coatings can be the basis for the development of pilot technologies for applying functional coatings to precision parts with small-diameter inner tubular surfaces in the mechanical engineering and aviation industries.A controlled process of applying two-component chromium-aluminum coatings by gas-phase deposition using organic and organometallic substances was developed. Optimization of the processes of applying high-quality two-component chromium-aluminum coatings on prototype models of the internal cavities of cooled turbine blades was carried out.The studies revealed the characteristics of two-component chromium-aluminum coatings comparable to those of coatings used in production, while ensuring good quality coatings in terms of adhesion strength to the turbine blade material. The reproducibility of the coatings obtained makes it possible to develop pilot technologies for production in the future.The lower toxicity of the precursors used makes it possible to create an environmentally friendly technology and significantly reduce environmental problems in production when applying two-component chromium-aluminum coatings to the internal cavities of cooled turbine blades.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

Soybean is one of the world’s most economically significant crops and is widely utilized as an essential source of vegetable protein and edible oil. Cultivated soybean is domesticated from its annual counterpart, wild soybean, which is considered valuable germplasm for soybean breeding. However, wild soybean accessions generally produce seeds with impermeable coats, a trait known as hard seededness (HS), which is beneficial for long-term seed survival but is undesirable for the uniform water absorption and germination of seeds, thus limiting the utilization of wild soybeans in breeding. However, moderate HS can isolate the embryo from the surrounding environment and is thus beneficial for long-term seed storage and germplasm preservation. The HS trait is primarily associated with the structure and chemical composition of the seed coat. However, its development is also influenced by various environmental conditions, such as water and temperature. Genetic analysis has revealed that HS of soybean is a complex quantitative trait controlled by multiple genes or minor quantitative trait loci (QTL), with many QTLs and several causal genes currently identified. Investigating the physiological and molecular mechanisms underlying this trait is crucial for soybean breeding, production, and food processing. For this article, the literature was reviewed and condensed to create a well-rounded picture of the current understanding of internal and external factors, QTLs, causal genes, and the regulatory mechanisms related to the HS of soybean, with the aim of providing reference for future research and utilization of this trait.

In this article, we discuss the concept of infinity (∞), infinitesimals □ , and a new mathematical operation that expands calculations into the realm of the infinite. This breakthrough allows for the computation of infinite numbers, leading to new possibilities for mathematical research and practical applications. By introducing infinite numbers, numerous advantages arise, such as accurately representing limits, divergences in integrals and infinite series, and extending calculations to infinity. This mathematical advancement opens doors to exploring uncharted territories, offering valuable insights and breakthroughs.

Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.

The penetrability and directivity of ultrasound in different media is of interest in engineering and medical applications (imaging, nondestructive testing, sonochemistry, among others). The nonlinearity of a liquid can be used in the parametric antenna framework to generate low-frequency components with particular features from several ultrasonic signals at the source. Bubbly liquids are dispersive liquids in which a small amount of tiny gas bubbles leads to the increase of the nonlinear parameter of the media over certain frequency ranges. Parametric antenna applied to this huge nonlinear media gives rise to low-frequency components with relatively small intensity at the source. The evolution of a low-frequency component (difference-frequency component obtained from two primary signals) during its propagation in a bubbly liquid is somehow unknown. It is thus interesting to analyse its characteristics to establish whether this component can benefit from the quality of its own frequency and from the primary frequencies in terms of directivity and penetrability into the medium. It must be noted that no such study in bubbly liquids exists in the litterature, but only for homogeneous media. To this end, several numerical models developed previously are used here to analyse the difference-frequency component obtained from a parametric antenna emitting from two ultrasonic signals at the source in one and two-dimensional domains. These models allow us to observe the behavior of this frequency component. An angle that measures the directivity of a beam is also defined. Our results show a point hardly found in the literature: the high directivity and the huge penetrability of the secondary beam associated to the difference-frequency component into the bubbly liquid, compared to the same frequency signal excited directly from the source in the bubbly liquid and to the parametric acoustic array in homogeneous fluids.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

This study innovatively assesses the Mun River flow components in Thailand, integrating Artificial Neural Networks (ANN) and Isotopic (18O) End-Member Mixing Analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. ANN predicts the UMR and CR contribute approximately 70.1% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings underscore the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning.

Cervical cancer is one of the ailments that endangers the health of women all over the world and causes infertility. Fortunately, this disease can be avoided. Both the results and participation rates of the current preventative strategy remain poor. Hence, preventative or early detection methods remain challenging and open. In gynecology and computer science fields, few studies on identifying cervical cancer are based on behavior risk factor and machine learning. Moreover, various social and behavioral aspects might often make it more difficult to detect cervical cancer especially in low‑ and middle‑income countries (LMICs). Accordingly, focus on predicting the presence of cervical cancer through behavioral risk factors is the basic structure of this research. We proposed an approach to classify cervical cancer from a social-behavioral perspective using logistic regression to predict cervical cancer. We got significantly improved outcomes than the existing methods. Moreover, we used feature engineering to formulate less dimensionality as a pre-processing step and optimized parameters to be prepared for classification phase. The results proved that using the logistic regression with regularization type L1 had a promising performance. The accuracy of Logistic Regression (LR) L1 was 97.2%. Such results support the proposed work as a reliable classification predictive tool.

In light of the prevalent pest and disease issues faced by greenhouse cucumbers, a staple vegetable during winter, this study introduces a detection method based on the enhanced YOLOv8s model. This method aims to provide technical support for detecting and classifying pests and diseases in cucumber agricultural production. The model integrates the 'MultiCat' module for multiscale feature fusion and employs the 'C2fe' and 'ADC2f'modules to strengthen spatial and channel attention. The 'Block2d' function also facilitates the choice between average pooling and attention-based spatial pooling. Channel fusion is achieved through additive and multiplicative operations, allowing the model to delve deeper into feature learning. Experimental results confirm that our approach outperforms the original YOLOv8s model in pest detection, particularly excelling in the identification of small-scale and overlapping afflictions.

A compositional data vector is a special type of multivariate observation in which the elements of the vector are non-negative and sum to a constant, usually taken to be unity. A compositional data does not have zero and only retains relative information. Furthermore, comparisons can only be made between compositional data of the same component. The relevant sample space is the standard simplex. A simplex space is a space that is a generalized form of a triangle. For compositional data, many of the operations defined in Euclidean space are meaningless. Microbiome analyzes have become popular in recent years. Operational Taxonomic Units (OTU) and Amplicon Sequence Variants (ASVs) used in microbiome analysis are one type of compositional data. The microbiome data are counts of different species within a sample and it is compositional. Although there is an order of bacterial abundance within a sample, there is no order of bacterial abundance between samples. Firmicutes and Bacteroidetes, the major phyla in the colon, have been observed in humans worldwide. Gut microbiome analyses often use the Firmicutes/Bacteroidetes ratio and principal coordinates analysis (PCoA). Alpha and beta diversities are used as indicators of bacterial flora diversity. However, misinformation is pervasive in the human microbiome literature and analysis. There is a lot of misunderstanding regarding compositional data and its analysis. An attempt was made to demonstrate how to analyze using the “National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN JMD) (Public Data).” The results showed that PCoA did not work, and principal component analysis (PCA) was useful for analyzing the gut microbiome relative diversity.

Use of probiotic lactobacilli has been proposed as a strategy to mitigate damage associated to exposure to toxic metals. Their protective effect against cationic metal ions, such as those of mercury or lead, is believed to stem from their chelating and accumulating potential. However, their retention of anionic toxic metalloids, such as inorganic arsenic, is generally lower. Through the construction of mutants in phosphate transporter genes (pst) in Lactiplantibacillus plantarum and Lacticaseibacillus paracasei strains, coupled with arsenate [As(V)] uptake and toxicity assays, we determined that the incorporation of As(V), which structurally resembles phosphate, is likely facilitated by phosphate transporters. Surprisingly, inactivation in Lc. paracasei of PhoP, the tran-scriptional regulator of the two-component system PhoPR, a signal transducer involved in phosphate sensing, led to an increased resistance to arsenite [As(III)]. In comparison to the wild type, the phoP strain exhibited no differences in the ability to retain As(III), and there were no observed changes in the oxidation of As(III) to the less toxic As(V). These results reinforce the idea that specific transport, and not unspecific cell retention plays a role in As(V) biosorption by lactobacilli, while they reveal an unexpected phenotype for the lack of the pleiotropic regulator PhoP.

This study employs Principal Component Analysis to examine cranial measurements obtained from both extant and fossil specimens spanning 38 species and comprising over 300 individuals within the subfamily Cervinae. Our findings elucidate craniometric distinctions among cervids characterized by varying body sizes and certain evolutionary adaptations. While our results generally corroborate previous assertions regarding the limited craniometric variability among plesiometacarpal deer, they also unveil specific cranial adaptations within certain genera and species. The Principal Component Analysis of craniometric data revealed that giant and large-sized deer display significantly broader ecomorphological diversity in cranial shape compared to small-sized deer. Secondly, small-sized deer exhibit greater uniformity in their cranial shape, appearing densely clustered on the factorial map. Thus, we conclude that body size imposes ecological constraints, limiting the available niches due to eco-physiological factors. The study demonstrates that endemic insular deer do not evolve consistent craniometric features attributable to insular isolation, while the cranial proportions of medium-to-small sized deer delineate a ubiquitous morphological archetype characteristic of numerous cervid taxa spanning diverse phylogenetic lineages and sharing comparable body sizes. This group of "Dama-like" deer, characterized by similar body size, metabolic rates, ecological needs, and cranial morphometry, is referred to here as the fundamental eco-physiological type, typical of warm regions within the Palearctic and Oriental biogeographic realms..

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley, one in the west/northwest, the other in the southeast. Wavelet analysis revealed the annual cycle to be most persistent temporal mode of PM10 variability in both subregions, with intermittent signals observed at time scales of around 120, 30~90 and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

Retrospective information about the climate system and plant ecophysiology are key inputs in climate and Earth system modelling. Dendrochronology provides such information with large spatiotemporal coverage, and stable carbon isotope (¹³C/¹²C) analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules, and, to this day, ¹³C/¹²C variation is attributed to ¹³C discrimination during CO₂ diffusion into leaves and assimilation by rubisco. By contrast, ¹³C discrimination by post-rubisco processes is presumed constant. Here, recently reported results on the first dataset of intramolecular ¹³C discrimination in tree rings were synthesised by variance component analysis. The emerging picture is not consistent with the classical (DR-discrimination-centred) concepts and practices of carbon-isotope dendrochronology. Specifically, leaf and stem post-rubisco discrimination each account for more variation in the data than diffusion-rubisco discrimination, i.e., post-rubisco discrimination is not constant. Furthermore, diffusion-rubisco discrimination is used widely as proxy of leaf intrinsic water-use efficiency (iWUE), a key determinant in the responses of global biogeochemical cycles to climate change. However, since diffusion-rubisco discrimination is a small component of the total data variance, whole-molecule analysis yields confounded iWUE estimates, yet intramolecular analysis will likely offer solutions. Lastly, all currently observed ¹³C/¹²C-climate relationships are attributed to diffusion-rubisco discrimination. However, here, relationships with temperature and radiation derive from leaf-level post-rubisco discrimination while relationships with precipitation derive from stem-level post-rubisco discrimination. That said, advances in mass spectrometry may soon make intramolecular ¹³C/¹²C analysis broadly available taking carbon-isotope dendrochronology to the next level.

Since 2019, the SARS-CoV-2 virus has caused a global pandemic, resulting in widespread infections and ongoing mutations. Analysing these mutations is essential for predicting future impacts. Unlike influenza mutations, SARS-CoV-2 mutations displayed distinct selective patterns that were concentrated in the spike protein and small ORFs. In contrast to the gradual accumulation seen in influenza mutations, SARS-CoV-2 mutations lead to the abrupt emergence of new variants and subsequent outbreaks. This phenomenon may be attributed to their targeted cellular substances; unlike the influenza virus, which has mutated to evade acquired immunity, SARS-CoV-2 appeared to mutate to target individuals who have not been previously infected. The Omicron variant, which emerged in late 2021, demonstrates significant mutations that set it apart from previous variants. The rapid mutation rate of SARS-CoV-2 has now reached a level comparable to 30 years of influenza variation. The most recent variant, JN.1, exhibits a discernible trajectory of change distinct from previous Omicron variants.

A regional modern river-sand geochemical mapping study in the majority of the Timor-Leste territory, including the research area, has not been previously reported. This study demonstrated the potential of modern river sand geochemical mapping as an effective method for lithological mapping, particularly in areas that face challenges in conducting efficient geological fieldwork and have limited geological information. A total of 53 modern river sand samples were collected and analyzed. Before examining the samples using wavelength-dispersive X-ray fluorescence for ten major elements, the loss-on-ignition method was employed. In the Clerec drainage basin, SiO2, CaO, and K2O showed positive correlations, and CaO was positively correlated with MnO. Similarly, in the Sahe River catchment, a positive relationship was observed between CaO, MnO, and TiO2. These findings suggested the presence of quartz, manganese, secondary K-bearing minerals, and secondary Ti-bearing minerals in carbonate rocks that have undergone silicification and other alteration processes. The principal component analysis results corroborated the SiO2/Al2O3 and K2O/Na2O ratios and the ICV findings. PC1 was related to the variations in Ca-poor and Ca-rich rocks; PC2 indicated notable inputs from SiO2-rich and SiO2-poor rocks; and PC3 revealed the possibility of contributions from secondary mineral assemblages related to silicification and low-grade metamorphic processes.

Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.

Since the outbreak of the COVID-19 pandemic, patients infected with COVID-19 may experience abnormal heart rates, posing potential health risks. This study proposes a non-contact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in more accurate diagnosis and treatment. In this study, except for the different image acquisition method, other research processes and methods are similar to the research methods in [15]. The image acquisition method is changed from forehead image to palm image. The new non-contact measurement performance of the proposed method can effectively not only avoid infection concerns but also obtain heart rate and HRV quickly and conveniently, providing higher accuracy for physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to those in recently published papers.

To mitigate the interference of waves on offshore operation vessels, heave compensation systems find widespread application. The performance of heave compensation systems significantly influences the efficiency and safety of maritime operations. This paper establishes a mathematical model for winch-based active heave compensation system. It introduces a three-loop active disturbance rejection control (ADRC) strategy encompassing piston position control, winch speed control, and load displacement control, enabling real-time estimation and compensation of system disturbances, thereby enhancing the performance of the heave compensation system. To assess the effectiveness of this control strategy, the study employs Matlab/Simulink and AME Sim to construct a co-simulation model and conducts a comparative analysis with traditional PID control systems. The research findings indicate that the three-loop ADRC position control strategy consistently delivers superior compensation performance across various operational scenarios.

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring Volatile Organic Compounds (VOCs) using a Photoionization Detector (PID). This paper analyzes the reasons for the low accuracy of the traditional Support Vector Machine (SVM) regression method. To address the issue, the PID signal is subjected to feature extraction and Principal Component Analysis (PCA) to reduce the data dimensionality. Moreover, the optimal SVM parameters are selected using a Genetic Algorithm (GA), and a combined approach of SVM regression with PCA and GA is utilized for PID signal regression analysis. And the effectiveness of the method is validated through extensive experiments and simulations. Furthermore, the influence of the sample quantity on the regression accuracy is analyzed, enabling accurate monitoring of VOCs concentration in a moist environment.

Soil water infiltration is an important component of the hydrological cycle and it is best evaluated when the raindrop is impacted on the ground surface. For this reason, it is affected by changes in land use and occupation, and by the characteristics and physical-hydric properties of the soil. This study aimed to evaluate soil water infiltration in areas occupied with annual crops (soybean and corn) and pastures, in two watersheds of the Teles Pires River-MT, using simulated rainfall, physical models and principal component analysis. Infiltration rates were evaluated from simulated rainfall with an average intensity of 75 mm h-1, with 4 repetitions per region (upper, middle and lower) of the hydrographic sub-basins of the Caiabi and Renato rivers, and soil use with cover, without cover and disturbed. Soil tillage provided higher water infiltration rates into the soil, especially in pasture areas in the two hydrographic sub-basins. There were significant adjustments of the mathematical models to the infiltration rate data for all land use and land cover conditions. The soil attributes that most interfered with the infiltration rate were microporosity, soil density and total porosity in the crop areas of the middle Caiabi, and microporosity, clay content, total porosity and silt content in the areas farming at the source of the Renato River. The Horton and Philip models presented the best adjustments in the hydrographic sub-basins of the Renato and Caiabi and Renato rivers, being recommended for estimating the rate of water infiltration into the soil in different uses, coverages, and regions.

a Special Issue of Microorganisms devoted to ‘Microbial Biocatalysis and Biodegradation’ would be incomplete without some form of acknowledgement of the many important roles that dioxygen-dependent enzymes (principally mono- and dioxygenases) play in relevant aspects of biooxygenation. This is reflected by the multiple strategic roles that dioxygen -dependent microbial enzymes play both in generating valuable synthons for chemoenzymatic synthesis, and in facilitating key reactions that drive the global geochemical Carbon Cycle. A useful insight into this relationship can be gained by reviewing the evolution of the current status of 2,5-diketocamphane 1,2-monooxygenase (EC 1.14.14.108) from camphor-grown Pseudomonas putida ATCC 17453. Over the last sixty years, the perceived nature of this monooxygenase has transmogrified significantly. Commencing in the 1960s, extensive initial studies consistently reported that the enzyme was a monomeric true flavoprotein dependent on both FMNH₂ and nonheme iron as bound cofactors. However, over the last decade all those criteria have changed absolutely, and the enzyme is currently acknowledged to be a metal ion-independent homodimeric flavin-dependent two-component monooxygenase deploying FMNH₂ as a cosubstrate. That transition is a paradigm of the ever evolving nature of scientific knowledge.

To accurately predict the matching relationships between the various components and the engine performance in the whole aero-engine environment, this study introduces a two-dimensional throughflow simulation method for the whole aero-engine. This method is based on individual throughflow solvers for the turbo-machinery and the combustor. It establishes a throughflow simulation model for the whole engine by integrating with the compressor-turbine co-operating equations and boundary conditions. The turbo-machinery throughflow solver employs a circumferentially averaged form of the time-dependent Navier-Stokes equations (N-S) as the governing equation. The combustor solver uses the Reynolds Average Navier-Stokes (RANS) method to solve flow and chemical reaction processes by constructing turbulence, combustion, and radiation models. The accuracy of the component solver is validated using Pratt & Whitney’s three-stage axial compressor (P&W3S1) and General Electric’s high-pressure turbine (GE-EEE HPT), and the predicted results are consistent with the experimental data. Finally, the developed throughflow method is applied to simulate the throttling characteristics of the WZ-X turboshaft engine. The results predicted by the throughflow program are consistent with the GasTurb calculations, including the trends of shaft power delivered, specific fuel consumption (SFC), inlet airflow, and total pressure ratio of the compressor. The developed method to perform throughflow simulation of the whole aero-engine eliminates the dependence on a general component map. It can quickly obtain the meridian flow field parameters and overall engine characteristics, which is expected to guide the design and modification of the engine in the future.

Immunohistochemistry is a powerful technique, widely used in biomedical research and clinics, that allows to determine the expression levels of some proteins of interest in tissue samples by means of the intensity of color due to the expression of biomarkers using specific antibodies. As such, immunohistochemical images are complex and problematic to be quantified. Recently we proposed a novel method including a first separation stage based on nonnegative matrix factorization (NMF) that achieved good results. However, that method was highly dependent on sparseness and non-negativity parameter choice and on algorithm initialization. Furthermore, the previously proposed method needed a reference image as starting point for the NMF algorithm. In the present work, we propose a new, simpler and robust method for the automated unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest). The new proposed method represents a simpler approach that, in one side, avoids the use of NMF for the separation stage, and in the other side, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest by means of principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing the finding of color vectors by considering the peaks of density of points. The method also develops a new scoring stage by avoiding, again, reference images, making the procedure more robust and less dependent on the parameters. Experiments for the semi-quantitative scoring of images in five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.

The valve controlled cylinder drive system is the most common type among hydraulic applications. The nonlinear behaviour in such system is inevitable when the valve spool around its null position [1] , Leaney [2] utilised component linking method to investigate the nonlinearities in a Moog valve controlled asymmetric cylinder drive system by simulation in Fortran, in which a generalised concept is introduced and validated by comparing to the experimental results. An factor is proposed in the generalised concepts to describe the asymmetric cylinder state, which is a constant when the cylinder in extending or retracting, but numerically calculated when the valve spool in the underlap region [2]. This paper utilises the component linking method to simulate the Moog valve controlled asymmetric cylinder drive system in Matlab Simulink, and proposed an analytical solution for factor when the valve spool in the underlap region.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

The susceptible-infected-recovered/removed-vaccinated (SIRV) epidemics model is an important generalization of the SIR epidemics model as it accounts quantitatively for the effects of vaccination campaigns on the temporal evolution of epidemics outbreaks. Additional to the time-dependent infection ($a(t)$) and recovery ($\mu (t)$) rates, regulating the transitions between the compartments $S\to I$ and $I\to R$, respectively, the time-dependent vaccination rate $v(t)$ accounts for the transition between the compartments $S\to V$ of susceptible to vaccinated fractions. An accurate analytical approximation is derived for arbitrary and different temporal dependencies of the rates which is valid for all times after the start of the epidemics for which the cumulative fraction of new infections $J(t)\ll 1$. As vaccination campaigns automatically reduce the rate of new infections by transferring susceptible to vaccinated persons, the limit $J(t)\ll 1$ is even better fulfilled than in the SIR-epidemics model. The comparison of the analytical approximation for the temporal dependence of the rate of new infections $\jt(t)=a(t)S(t)I(t)$, the corresponding cumulative fraction $J(t)$, and $V(t)$, respectively, with the exact numerical solution of the SIRV-equations for different illustrative examples proves the accuracy of our approach. The considered illustrative examples include the cases of stationary ratios with a delayed start of vaccinations, and an oscillating ratio of recovery to infection rate with a delayed vaccination at constant rate. The proposed analytical approximation is self-regulating as the final analytical expression for the cumulative fraction $\jinf $ after infinite time allows us to check the validity of the original assumption $J(t)\le \jinf \ll 1$.

Prime numbers are the atoms of mathematics and mathematics is needed to make sense of the real world. Finding the Prime number structure and eventually being able to crack their code is the ultimate goal in what is called Number Theory. From the evolution of species to cryptography, Nature finds help in Prime numbers. One of the most important advances in the study of Prime numbers was the paper by Bernhard Riemann in November 1859 called “Ueber die Anzahl der Primzahlen unter einer gegebenen Grösse” (On the number of primes less than a given quantity). In that paper, Riemann gave a formula for the number of primes less than x in terms the integral of 1/log(x) and the roots (zeros) of the zeta function defined by: Where ζ(z) is a function of a complex variable z that analytically continues the Dirichlet series. Riemann also formulated a conjecture about the location of the zeros of RZF, which fall into two classes: the "trivial zeros" -2, -4, -6, etc., and those whose real part lies between 0 and 1. Riemann's conjecture Riemann hypothesis [RH] was formulated as this: [RH] The real part of every nontrivial zero z* of the RZF is 1/2. Proving the RH is, as of today, one of the most important problems in mathematics. In this paper we will provide a proof of the RH. The proof of the RH will be built following these five parts: PART 1: Description of the RZF PART 2: The C-transformation PART 3: Application of the C-transformation to in Re(z) 0 to obtain ζ(z)=X(z)-Y(z) PART 4: Analysis of the values of z such that X(z)=Y(z), and |X(z)|=|Y(z)|, that equates to ζ(z)=0 Proof that |X(z)|=|Y(z)| only if Re(z)=1/2 Conclude that ζ(z)=0 only if Re(z)=1/2 for Re(z) 0 PART 5: We will also prove that all non-trivial zeros of ζ(z) in the critical line of the form are not distributed randomly. There is a relationship between the values of those zeros and the Harmonic function that leads to an algebraic relationship between any two zeros.

Dirac's Large Number Hypothesis (LNH), proposed in 1937, has captivated the scientific community with its exploration of profound correlations between cosmic and atomic scales. This hypothesis delves into the intricate interplay between the infinitesimally small and the vastly large, offering potential insights into the fundamental nature of the universe. As we continue to uncover the mysteries of the cosmos, the LNH oscillates on the precipice of scientific acceptance—neither fully validated nor entirely dismissed. In this review paper, we embark on a comprehensive journey through Dirac's LNH, shedding light on its theoretical underpinnings, its implications for universe models, and its resonance with the Anthropic Principle. We delve into the possibilities of variable gravitational constants and continuous mass creation, inviting further exploration into the intricacies of our cosmic symphony. By embracing the ongoing quest for understanding, we endeavor to unravel the profound harmonies that connect the infinitesimal with the infinite, contributing to the symphony of knowledge in theoretical physics and cosmology.

With the deterioration of the bridge performance and ever-increasing amount of traffic, the bridge safety is becoming a concern for engineering community. A method that can assess the bridge's condition in real-time is urgently needed. The main factors that hinder the bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA) based method is developed for structural damage detection of bridges in operational environments in this paper. Two main operational environmental factors: the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate to assess the bridge condition in operational environments.

: Alluvial soils naturally possess abundant soil nutrients. Nevertheless, excessive nitrogen addition and inappropriate use of nitrogen fertilizers lead to conditions where soil nutrients become immo-bilized, ultimately reducing productivity. This study investigates the potential of natural nano-material zeolite which is important land resources to improve nutrient utilization efficiency and mitigate the harmful effects of excessive fertilizer application. Specifically, the research ex-plores the incorporation of clinoptilolite zeolite and processed sewage sludge blends, alongside reduced recommended nitrogen fertilizer doses, to evaluate their impact on soil quality and plant growth. A greenhouse experiment was conducted (three-year study), comprising eight treatments with varying proportions of zeolite and sewage sludge. The efficacy of these treatments in en-hancing soil carbon pools, nutrient indices, and plant growth responses was assessed. Results in-dicate that a combination of 50% recommended fertilizer dose, 4-6% zeolite, and 5-10% sewage sludge significantly improved multiple aspects of plant growth, including leaves, tillers, culms, and panicles, leading to increased straw and grain yields. The addition of 6% zeolite in conjunc-tion with sewage sludge positively influenced plant tillering capacity. Certain treatment combina-tions exhibited a greater impact on plant height at different growth stages. For example, the treatment with 75% recommended fertilizer dose, 4% zeolite, and 5% sewage sludge significantly affected plant height at 40 and 55 days after treatment. Additionally, the combination of 50% recommended fertilizer dose, 6% zeolite, and 10% sewage sludge promoted plant height, carbon levels, and various yield parameters in later stages. Principal component analysis effectively dis-tinguished treatments incorporating zeolite and sewage sludge based on plant responses. The combination of 6% zeolite and 10% sewage sludge, along with a 50% recommended fertilizer dose, positively influenced soil carbon fractions, nutrient levels, and the growth response of rice plants.

Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.

Abstract: Extensive research shows nurses’ work environment to be particularly stressful. This study develops, explores, and psychometrically tests a new profession specific ques-tionnaire identifying generalized and specific resistance resources, that make it possible to measure resources to manage work-related stress. An exploratory study design was em-ployed. The questionnaire was developed inspired by the MEASURE approach and the salutogenic theory of health. Building on the results from a literature review of nursing re-search and salutogenesis, supplemented by twelve interviews with hospital nurses, an item pool was generated. A first version was pilot tested in a group of nurses who were studying to become specialist nurses. A second version of the questionnaire was psycho-metrically tested on nurses in close patient care (n = 475), analysed using Principal Com-ponent Analysis as the extracting technique to identify the underlying structure of the questionnaire. The Principal Component Analysis revealed a four-component model of 19 items. “Manageability as a resource for handling the workload” was the strongest compo-nent, accounting for 21 % of the explained variance. “Contacts with patients as resources for nurses’ job satisfaction” (17.8 %). “Professional attitudes are labelled as resources to nursing” (15 %). “Colleagues are important resources for remaining in the nursing profes-sion” (11 %). The structure of the questionnaire indicates its usefulness in clinical practice for measuring resistance resources.

To ensure sustainability of the future power grid, the rate of expansion of distributed renewable energy resources (DRER) has introduced operational challenges. These includes managing transmission constraints with DRER power injection, dispatching DRER efficiently, managing system frequency and ensuring sufficient reactive power for voltage support. Coupled with the intensification of wildfires, power infrastructure across the United States faces challenges to minimize the impact to maintain system reliability and resiliency. This research embarks on a comprehensive evaluation, beginning with an in-depth historical analysis to delineate regions most susceptible to wildfires. Utilizing a multidimensional approach, the study assesses wildfire-induced risks to power grids, by integrating historical wildfire occurrences, real-time wildfire proximities, Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation metrics, and system parameters, by employing Principal Component Analysis (PCA) based optimal weights, leading to the formulation of a novel risk factor model. This risk factor has the potential to be the key to ensuring the resilience of a renewable-rich smart grid when faced with a severe weather event. Our model’s applicability was further verified through an empirical assessment, selecting representative networks from diverse regions, providing insights into the geographical variability of risk factors. Ultimately, this study offers stakeholders and policymakers a comprehensive tool-set, empowering decisions regarding infrastructure investments, grid reinforcements, and strategic power rerouting to ensure consistent energy delivery using risk assessment against wildfire.

In large-scale parallel computing and communication systems, an interconnect network structure is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. In a graph, the vertices and edges are likely to fail, so we must think about the fault tolerance of a graph. Connectivity is an important parameter in the study of faulty tolerance for a graph. In this paper, we study a special class of connectivity: $m$-component connectivity, this is a natural generalization of the classical connectivity of graphs defined in terms of the minimum vertex-cut. Let $F$ is a vertex set of $G$ $(i.e, F\subseteq V(G))$, if the following conditions are satisfied, we say $F$ is a $m$-component cut: $(1)$ $G-F$ is disconnected; $(2)$ the number of components in $G-F$ is greater than or equal to $m$. In another word, the $m$-component connectivity $c\kappa_{m}(G)$ is defined as $min\{|F|~|~F\subseteq V(G)$ and $F$ is a $m$-component cut$\}$. Determining $m$-component connectivity is still unsolved in most interconnection networks even for small $m'$s. Leaf-sort graph is a special Cayley graph, it has many special properties that are different from other Cayley graphs. So we need to pay attention to some of it's special properties when we study it. In this paper, we can get the values: $c\kappa_{3}(CF_{n})=3n-6$ $(n$ is odd$)$ and $c\kappa_{3}(CF_{n})=3n-7$ $(n$ is even$)$ for $n\geq3$; $c\kappa_{4}(CF_{n})=\frac{9n-21}{2}$ $(n$ is odd$)$ and $c\kappa_{4}(CF_{n})=\frac{9n-24}{2}$ $(n$ is even$)$ for $n\geq4$; $c\kappa_{5}(CF_{n})=6n-16$ $(n$ is odd$)$ and $c\kappa_{5}(CF_{n})=6n-18$ $(n$ is even$)$ for $n\geq5$.

Background The Delta Neutrophil Index (DNI), a prodiagnostic marker for sepsis, utilizes leukocyte count. Platelet activation, similar to leukocytes, plays a crucial role in host defence against pathogens and may serve as a predictor of sepsis outcome. However, the combined evaluation of Mean Platelet Component (MPC) and DNI in assessing sepsis has been rarely used. Methods To assess the prodiagnostic validity of simultaneous utilization of DNI and MPC in cases of human febrile sepsis, we conducted measurements of cellular indices, including DNI and MPC, as well as molecular biomarkers such as procalcitonin (PCT) and C-reactive protein (CRP). This study was carried out on patients admitted to the emergency department with suspected sepsis. Results Using a cutoff value of 2.65%, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DNI in sepsis were found to be 69%, 73.9%, 77.9%, and 64.1%, respectively. Furthermore, significant differences in DNI and MPC levels were observed between the sepsis and non-sepsis groups (6.7±7.8% versus 2.1±2.2%, P=0.000 and 26.0±1.9 g/dl versus 26.8±1.4 g/dl, P=0.002), respectively. Notably, there was a negative correlation between DNI and MPC, with the strength of the correlation varying based on the cause of sepsis. By setting the cutoff value for DNI at 6.2%, the sensitivity, specificity, and NPV improved to 100%, 80.3%, and 100%, respectively, although the PPV remained at 10.6%. Importantly, the DNI≥6.2 group exhibited a higher incidence of death (10.9%, 5/46) and multiorgan failure (19.6%, 9/46) compared to the DNI<6.2 group (0.0%, 0/46 and 0.0%, 0/167, respectively) (P=0.000 and P=0.000, respectively). Conclusions In our study, DNI demonstrates superior effectiveness compared to other molecular biomarkers such as CRP and procalcitonin in distinguishing febrile septic patients from febrile nonseptic patients and predicting prognosis. Additionally, there exists a negative correlation between MPC and DNI, making MPC a valuable marker for differentiating the etiology of sepsis. These findings hold significant clinical implications as DNI/MPC is a cost-effective and readily applicable approach in various impending sepsis scenarios. Notably, this study represents the first examination of the prodiagnostic validity of simultaneously employing DNI and MPC in human cases of febrile sepsis.

Intracranial compliance (ICC) plays a pivotal role in understanding the underlying mechanisms of various brain disorders and is of great clinical importance. Therefore, addressing the challenges in practical application of ICC is crucial for neurosurgeons. This study explored the significance of ICC assessment considering the time-dependency of specific brain disorders through two distinct approaches: short and large time elapsed (TE) in measuring volume or intracranial pressure (ICP) changes in the ICC equation (∆Volume/∆ICP). Variations in ICC values were observed across various ICC assessment methods and different TE values. Notably, the compensatory response of the brain exhibited non-monotonic and variable changes in a large TE for certain brain disorders, diverging from patterns observed in short TE assessments. Furthermore, the recovery behavior of the brain changed under different brain disorders when exposed to short and long TE conditions. These findings emphasized the dynamic nature of ICC and provided valuable insights into the correct practical assessment of ICC by selecting the appropriate TE, as well as considering the differences in strain rates and loading durations on the brain in different brain disorders. These insights also shed light on the reasons why, despite its clinical significance, ICC monitoring has not yet become a standard component of clinical care, unlike ICP monitoring.

We assume that the current mathematical knowledge K is a finite set of statements from both formal and constructive mathematics, which is time-dependent and publicly available. Any theorem of any mathematician from past or present belongs to K. The set K exists only theoretically. Ignoring K and its subsets, sets exist formally in ZFC theory although their properties can be time-dependent (when they depend on K) or informal. In every branch of mathematics, the set of all knowable truths is the set of all theorems. This set exists independently of K. Algorithms always terminate. We explain the distinction between algorithms whose existence is provable in ZFC and constructively defined algorithms which are currently known. By using this distinction, we obtain non-trivial statements on decidable sets X⊆N that belong to constructive and informal mathematics and refer to the current mathematical knowledge on X. This and the next sentence justify the article title. For any empirical science, we can identify the current knowledge with that science because truths from the empirical sciences are not necessary truths but working models of truth from a particular context. The feature of mathematics from the article title is not quite new. Church's thesis, which is known from the beginning of computability theory, is based on the observation that the currently known computable functions are recursive, where the notion of a computable function is informal. It proves that the predicate of the current mathematical knowledge slightly increases the intuitive mathematics. For a set X⊆N whose infiniteness is false or unproven, we define which elements of X are classified as known. No known set X⊆N satisfies Conditions (1)-(4) and is widely known in number theory or naturally defined, where this term has only informal meaning. (1) A known algorithm with no input returns an integer n satisfying card(X)<ω ⇒ X⊆(-∞,n]. (2) A known algorithm for every k∈N decides whether or not k∈X. (3) No known algorithm with no input returns the logical value of the statement card(X)=ω. (4) There are many elements of X and it is conjectured, though so far unproven, that X is infinite. (5) X is naturally defined. The infiniteness of X is false or unproven. X has the simplest definition among known sets Y⊆N with the same set of known elements. We prove that the set X={n∈N: the interval [-1,n] contains more than 29.5+(11!/(3n+1))∙sin(n) primes of the form k!+1} satisfies Conditions (1)-(5) except the requirement that X is naturally defined. 501893∈X. Condition (1) holds with n=501893. card(X∩[0,501893])=159827. X∩[501894,∞)={n∈N: the interval [-1,n] contains at least 30 primes of the form k!+1}. If we add to X some set W satisfying 14≤card(W)≤23, then the following statements hold: X does not satisfy Condition (1), 159827+14≤card(X), the above lower bound is currently the best known, card(X)<ω ⇒ card(X)≤159827+23, the above upper bound is currently the best known, X satisfies Conditions (2)-(5) except the requirement that X is naturally defined. Analogical statements hold, if we add to X the set ⋃_{x∈N, x divides 99!} {k∈N: k-501894 is the x-th element of P(n^2+1)}, where P(n^2+1) denote the set of primes of the form n^2+1. For a non-negative integer n, let θ(n) denote the largest integer divisor of 10^{10^{10}} smaller than n. Let κ:N→N be defined by setting κ(n) to be the exponent of 2 in the prime factorization of n+1. The set X={n∈N: (θ(n)+κ(n))^2+1 is prime} satisfies Conditions (1)-(5) except the requirement that X is naturally defined. Condition (1) holds with n=0^{10^{10}}. We present a table that shows satisfiable conjunctions of the form #(Condition1)∧ (Condition 2)∧#(Condition 3)∧(Condition 4)∧#(Condition 5), where # denotes the negation ¬ or the absence of any symbol. No set X⊆N will satisfy Conditions (1)-(4) forever, if for every algorithm with no input, at some future day, a computer will be able to execute this algorithm in 1 second or less. The physical limits of computation disprove this assumption.

The flower of Syringa pubescens Turcz. is a Chinese folk medicine and also used as flower tea for healthcare. The effects of five drying methods on the active compound contents, the antioxidant abilities, anti-inflammatory properties and enzyme inhibitory activities were evaluated. The plant materials were treated using shade-drying, microwave drying, sun-drying, infrared drying and oven drying. The seven active compounds were simultaneously determined by an HPLC method. Furthermore, the chemicals differences were assessed using Scanning electron microscopy, UV spectroscopy and infrared spectroscopy. The antioxidant capacities and protective effects on L02 cells induced by hydrogen peroxide were measured. The anti-inflammatory effects on lipopolysaccharide-induced RAW264.7 cells were investigated. The enzyme inhibitory activities were determined against α-amylase, α-glucosidase cholinesterases and tyrosinase. The results indicated that drying methods had significantly influences on the active compound contents and biological properties. In consideration of the contents of bioactive compound, the suitable drying methods were SHD, SD, OD and MD. The SHD sample exhibited stronger radical scavenging ability on DPPH, ABTS, hydroxyl and superoxide anions and higher reducing power compared with other samples prepared by SD, OD, MD and IRD. Moreover, SHD sample had strong protective effects against L02 cells damage induced by H2O2. SPF samples, especially SHD sample, significantly inhibited inflammatory factor secretion in LPS-stimulated RAW 264.7 cell. All studied SPF sample exhibited evidently inhibitory effects on the four enzymes. SHD sample possessed the highest inhibitory activity on α-glucosidase and α-amylase. While SHD and SD samples had the potential inhibitory effects on cholinesterases and tyrosinase. In consideration of active compound contents and biological activities, it was recommended that SHD and SD were applied for drying SPF at an industrial scale.

This study aims to develop an auditory experience evaluation questionnaire to improve the presence of metaverse environments, and to derive evaluation components considering auditory presence and auditory user experience (AUX) through a survey. After conducting a survey with a total of 232 participants, five evaluation components were extracted from auditory presence and AUX evaluation factors through principal component analysis (PCA) and reliability analysis (RA): 'realistic auditory background', 'acoustic aesthetics', 'consideration of acoustic control and accessibility', 'auditory utility and minimalist design', and 'auditory consistency'. In particular, although AUX evaluation factors such as 'ease of access to sound control' have limitations in improving the sense of presence, negative factors of presence such as 'distraction due to sound' can be improved by utilizing AUX evaluation factors, so it is judged that the sense of presence in the metaverse environments can be improved by enhancing the auditory sense of presence and AUX evaluation factors according to the composition of the five evaluation components derived in this study. This study can be used as a basis for developing an auditory experience evaluation questionnaire for the metaverse platform, creating sound design guidelines, and identifying sound development priorities.

Objectives: The purposes of this study were to 1) analyze the disruption of intracardiac flow by flow components and kinetic energy (KE) parameters in single ventricle physiology patients and compare with controls; 2) investigate left ventricular diastolic dysfunction in terms of 4D flow parameters in repaired Fontan (rFontan) group; 3) compare intracardiac flow parameters in morphologic LV and RV rFontan patient groups. Methods: Twenty-five rFontan patients (age: 10±3, M/F: 15/10) and fourteen controls (age: 10±2, M/F: 8/6) were prospectively recruited. All underwent cine and 4D flow cardiovascular magnetic resonance (CMR) on 3.0T scanner. Cardiac function and inter-ventricular mechanical dyssynchrony were analyzed using cine images. From 4D flow CMR, ventricular flow components were assessed: direct flow, retained inflow, delayed ejection flow and residual volume. Global and regional blood flow KE parameters, normalized to end-diastolic volume (EDV) were analyzed for functional single ventricle (FSV). Left ventricular (LV) diastolic dysfunction was assessed on echocardiography guidelines. Results: In comparison of rFontan vs. controls, median FSV residual volume (28% vs. 23%, P= 0.034) were higher in rFontan; median FSV direct flow (32% vs. 40%, P=0.005) and delayed ejection flow (17% vs. 24%, P=0.024) were lower in rFontan. FSV KEiEDV parameters were all lower in rFontan (all P<0.05). No significant differences were observed for flow components and KE parameters between patients with and without inter-ventricular dyssynchrony. FSV direct flow (AUC=0.76, Sensitivity=86%, Specificity=70%) was an independent predictor of LV diastolic dysfunction. Residual volume and E-wave KEiEDV were significantly different between morphologic RV and morphologic LV patient group. Conclusions: The changed flow pattern and decreased KE may be hemodynamic disturbances and impaired ventricular filling in rFontan patients. Reduced direct flow is associated with LV diastolic dysfunction. Morphologic right ventricular subgroup is worse than morphologic left ventricular group in terms of the intracardiac hemodynamics.

Red color conversion materials have often been used in conventional white LEDs (light-emitting diodes) to enhance the insufficient deep red component and thus improve the color rendering property. Quantum dots (QDs) are one of the candidates due to their flexibility in controlling the emission wavelength, which is attributed to the quantum confinement effect. Two types of remote QD components, i.e., QD films and QD caps, were prepared and applied to conventional white LED illumination to improve the color rendering properties. Thanks to the red component near 630 nm caused by the QD components, the color rendering indices(CRIs) of both Ra and R9 could be increased to over 95. It was found that both the diffusing nature of the reflector and the light recycling process in the vertical cavity between the bottom reflector and the top optical films play important roles in improving the color conversion efficiency of remote QD components. The present study showed that the proper application of remote QDs combined with a suitable optical cavity can control the correlated color temperature of the illumination over a wide range, thus realizing different color appearances of white LED illumination. In addition, a high CRI of over 95 could be achieved due to sufficient excitation from fewer QDs due to the strong optical cavity effect.

Software testing is an integral yet time-consuming component of software development, and even with capable software testing resources (technology and people), it is still regarded as problematic. Test automation is widely being utilised within the software industry to provide increased testing capacity to ensure high product quality and reliability. In this paper, we are specifically addressing automated testing whereby test cases are manually written. Test automation has its benefits, drawbacks, and impacts on different stages of development. Furthermore, there is often a disconnect between non-technical and technical roles, where non-technical roles (e.g., management) predominantly strive to reduce costs and delivery time whereas technical roles are often driven by quality and completeness. Although it is widely understood that there are challenges with adopting and using automated testing, there is a lack of evidence to understand the different attitudes toward automated testing (AtAT), focusing specifically on why it is not adopted. In this paper, the authors have surveyed practitioners within the software industry from different roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principle Component Analysis. In total, 81 participants were provided with a series of 22 questions and their responses are compared against job role types and experience levels. In summary, 6 key findings are presented covering expertise, time, cost, tools and techniques, utilisation, organisation, and capacity

Drug-induced pruritus triggers a desire to scratch, thereby diminishing one’s quality of life. Certain instances of this phenomenon follow complex mechanisms of action that diverge from histamine-mediated pathways, known contributors to pruritus. However, investigations into the relationship between drugs and pruritus are limited. In this study, data mining techniques were employed to comprehensively analyze the characteristics of drugs linked to pruritus, using the FDA Adverse Event Reporting System(FAERS) Data maintained in the United States. Reports linked to pruritus demonstrated noteworthy differences in gender, age, and weight when compared with non-pruritus cases. Among the leading candidates for drugs prompting pruritus were ophthalmic drugs, systemic antibacterials, contrast media, dermatological antifungals, and dermatological preparations. Principal component analysis showed that the second principal component served as an indicator for distinguishing between onsets at mucous membranes or the skin surface. Additionally, the third principal component functioned as an indicator for categorizing administration methods as either invasive or noninvasive. Furthermore, a hierarchical cluster analysis conducted on these obtained principal components revealed the potential for classifying drugs based on the site of pruritus onset and the method of drug administration. These findings contribute to the development of targeted prevention and treatment strategies for avoiding pruritus in clinical practice.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common one has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

This research work aims to develop a mathematical modeling for production-inventory systems. Two mathematical models are present: An infinite dimensional, partial differential equation (PDE), production level modeling is present and a finite dimensional system for a coupled dynamic pricing, production rate and inventory level ordinary differential equations (ODE), integrating a proper Lyapunov stability analysis of the dynamical system and simulations.

The object of this paper is the analysis of isotropic rectangular thin plates supported (simply supported or clamped) along two opposite edges whereby the other edges have arbitrary support conditions; the plates are subjected to external bending moments, concentrated or distributed, and perpendicular to the supported edges. The standard approach to this problem is to replace the bending moment with a couple of forces infinitely close and to use the known expressions of efforts and deformations for the plate subjected to concentrated forces; the results are then related to the first derivatives of those efforts and deformations with respect to the position of application of the load. In this study, the external bending moment was expanded into a Fourier series, leading to a distributed external bending moment, and the boundary conditions and continuity equations were applied. Various types of rectangular plates were analyzed, as well as plates of infinite length whose results were identical to those in the literature. In addition, results for cantilever plates of infinite length were presented.

Analyzing big data poses a great challenge for numerous researchers to explore the data structure. Dimension reduction methods can be used to reduce data dimensionality, taking it from occupying a high-dimensional space to existing in a lower-dimensional space while retaining as much information as possible. Principal Component Analysis is one of the most popular used to reduce the dimensional space. The bootstrap sample is obtained by randomly sampling n times with replacement from the original sample, the method provides easy tool to understand the interactive component and develop the process. There are not enough researches discussed the stability of PCA method using bootstrap method. In this paper, the bootstrap method is used to analyze the stability of PCA results and to estimate the number of PCA in efficient way. The method is used to estimate the number of PCA that needed to classify the data set, and the effectiveness of the discussed techniques is demonstrated through real data sets.

Overhead power transmission line icing (PTLI) disasters are one of the most severe dangers to power grid safety. Automatic iced transmission line identification is critical in various fields. However, existing methods primarily focus on the linear characteristics of transmission lines, employing a two-step process involving edge and line detection for PTLI identification. Nonetheless, these traditional methods are often difficult when confronted with challenges such as background noise or variations in illumination, leading to incomplete identification of the target area, missed target regions, or misclassification of background pixels as foreground. We propose a new iced transmission line identification scheme to overcome this limitation. In the initial stage, we integrate the image restoration method with image filter enhancement to restore the image's color information. This combined approach effectively retains valuable information and preserves the original image quality, thereby mitigating noise presented during the image acquisition. Subsequently, in the second stage, we introduce an enhanced multi-threshold algorithm to separate background and target pixels. After image segmentation, we enhance the image and obtain the region of interest (ROI) through connected component labeling modification and mathematical morphology operations, eliminating background regions. Our proposed scheme achieves an accuracy value of 97.72%, a precision value of 96.24%, a recall value of 86.22%, and the specificity value of 99.48% based on the average value of test images. Through object segmentation and location, the proposed method can avoid background interference, effectively solve the problem of transmission line icing identification, and achieve 90% measurement accuracy compared to manual measurement on the collected PTLI dataset.

The Internet of Things technology opens the horizon for a broader scope of intelligent applications in smart cities. However, the massive amount of traffic exchanged among devices may cause security risks, significantly when devices are compromised or vulnerable to cyber-attack. An intrusion detection system is the most powerful tool to detect unauthorized attempts to access smart systems. It identifies malicious and benign traffic by analyzing network traffic. In most cases, only a fraction of network traffic can be considered malicious. As a result, it is difficult for an intrusion detection system to detect attacks at high detection rates while maintaining a low false alarm rate. This work proposes an integrated framework to detect suspicious traffic to address secure data communication in smart cities. This paper presents an approach to developing an intrusion detection system to detect various attack types. It can be done by implementing a Principal Component Analysis method that eliminates redundancy and reduces system dimensionality. Furthermore, the proposed model shows how to improve intrusion detection system performance by implementing an ensemble model.

This study aims to develop novel automated computer vision algorithms and systems for component replacement inspection for Printed Circuit Boards (PCBs). The proposed algorithms are able to identify locations as well as sizes of different components. They are object detection algorithms based on keypoints of the target components. The algorithms can be implemented as neural networks consisting of two portions: frontend networks and backend networks. The frontend networks are used for the feature extractions of input images. The backend networks are adopted for producing component inspection results. Each component class can has its own frontend and backend networks. In this way, the neural model for the component class can be effectively reused for different PCBs. To reduce the computation time for the inference of the networks, different component classes can share the same frontend networks. A two-stage training process is proposed for effectively exploring features of different components for accurate component inspection. The proposed algorithm has the advantages of the simplicity for training data collection, high accuracy for defect detection, and high reusability and flexibility for online inspection. The algorithm is an effective alternative for the automated inspection in smart factory with growing demand for product quality and diversification.

In this study, our primary objective was to analyze the tradeoff between accuracy and complexity in machine learning models, with a specific focus on the impact of reducing complexity and entropy on the production of landslide susceptibility maps. We aimed to investigate how simplifying the model and reducing entropy can affect the capture of complex patterns in the susceptibility maps. To achieve this, we conducted a comprehensive evaluation of various machine-learning algorithms for classification tasks. We compared the performance of these algorithms in terms of accuracy and complexity, considering both "before" and "after" scenarios of dimensionality reduction using Principal Component Analysis (PCA). Our findings revealed that reducing complexity and lowering entropy can lead to an increase in model accuracy. However, we also observed that this reduction in complexity comes at the cost of losing important complex patterns in the produced landslide susceptibility maps. By simplifying the model and reducing entropy, certain intricate relationships and uncertain patterns may be overlooked, resulting in a loss of information and potentially compromising the accuracy of the susceptibility maps. The analysis encompassed a diverse range of machine learning algorithms, including Random Forest (RF), Extra Trees (EXT), XGboost, LightGBM, Catboost, Naive Bayes (NB), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), and Decision Trees (DT). Each algorithm was evaluated for its strengths and limitations, considering the tradeoff between accuracy and complexity. Before dimensionality reduction, the algorithms demonstrated promising results, with RF exhibiting excellent AUC/ROC scores and average accuracy. However, computational costs were noted as a potential drawback for RF, especially when dealing with large datasets. EXT showcased robust performance and good accuracy, while XGboost demonstrated its ability to handle complex relationships within large datasets, albeit requiring careful hyperparameter tuning. The efficiency and scalability of LightGBM made it a suitable choice for large datasets, although it displayed sensitivity to class imbalance. Catboost excelled in handling categorical features, but longer training times were observed for larger datasets. NB showcased simplicity and computational efficiency but assumed independence among features. KNN, known for its capability to capture local patterns and spatial relationships, was found to be sensitive to the choice of distance metric. GBM, while capturing complex relationships effectively, was prone to overfitting without proper regularization. DT, with its interpretability and ease of understanding, faced limitations in terms of overfitting and limited generalization. After dimensionality reduction, certain algorithms exhibited improvements in their AUC/ROC scores and average accuracy, including RF, EXT, XGboost, and LightGBM. However, for a few algorithms, such as NB and DT, a decrease in performance was observed. This study provides valuable insights into the performance characteristics, strengths, and limitations of various machine learning algorithms in classification tasks. Researchers and practitioners can utilize these findings to make informed decisions when selecting algorithms for their specific datasets and requirements. We also aim to identify the potential factors contributing to the high accuracy rates obtained from these ensembled algorithms and explore possible shortcomings of non-ensembled algorithms that may result in lower accuracy rates. By conducting a comprehensive analysis of these algorithms, we seek to provide valuable insights into the benefits and limitations of ensembled approaches for landslide susceptibility mapping. Our study sheds light on the challenges faced when balancing accuracy and complexity in machine learning models for landslide susceptibility mapping. It emphasizes the importance of carefully considering the level of complexity and entropy reduction in relation to the specific patterns and uncertainties present in the data. By providing insights into this tradeoff, our research aims to assist researchers and practitioners in making informed decisions regarding model complexity and entropy reduction, ultimately improving the quality and interpretability of landslide susceptibility maps.

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other there models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

The seasonal variation in fatty acids and minerals concentrations was investigated through the analysis of Pecorino Romano cheese samples collected in January, April, and June. A fraction of samples contained missing values in their fatty acid profile. Probabilistic Principal component analysis coupled with Linear Discriminant Analysis was employed to classify cheese samples on a production season basis while accounting for missing data and quantifying the missing Fatty acids concentration for the sample in which they were absent. The levels of rumenic acid, vac-cenic acid and omega-3 compounds were positively correlated with the spring season, while the length of the saturated fatty acids increased throughout the production seasons. Concerning the classification performances, the optimal number of principal components (i.e., 5) achieved an ac-curacy in cross-validation equal to 98 %. Then, when the model was tasked to impute the lacking Fatty acid concentration values, the optimal number of principal components resulted in an R2 value in cross-validation of 99.53%

Ultra-high temperature oxidation-resistant alloys (UTORAs) have received a lot of attention due to the increased research demand for deep space exploration around the world. But UTORAs have the disadvantages of easy oxidation and chalking. So, in this study, a UTORA was prepared by hot-press sintering on VZrHfNbTa (HEA) substrate coated with hafnium. The bonding mechanism, resistance to high-temperature oxidation, and hardness of the sample tests were carried out. The results show that zirconium in the matrix will diffuse into the hafnium coating during the high-temperature sintering process and form the HfZr alloy transition layer, the coating thickness of the composite is about 120 μm, and the diffusion distance of zirconium in the hafnium coating is about 60 μm, this transition layer chemically combines the hafnium coating and the HEA substrate into a monolithic alloy composite. The results of high-temperature oxidation experiments show that the oxidation degree of the hafnium-coated VZrHfNbTa composite material was significantly lower than that of the VZrHfNbTa HEA after oxidation in air at 1600 °C for 5 h. The weight gain of the coated sample after oxidation is 56.56 mg/cm2, which was only 57.7 % compared to the weight gain of the uncoated sample (98.09 mg/cm2 for uncoated), and the surface of the uncoated HEA showed obvious dents, oxidation, and pulverization occurred on the surface and interior of the sample. In contrast, the coated composite alloy sample mainly underwent surface oxidation sintering to form a dense HfO2 protective layer, and the internal oxidation of the hafnium-coated VZrHfNbTa composite alloy was significantly lower than that of the uncoated VZrHfNbTa HEA.

Panax ginseng C. A. Meyer (P. ginseng), has been widely used in traditional Chinese medicine (TCM). It contains a number of chemical components and possesses a variety of pharmacological activities. Alzheimer's disease (AD) is neurodegenerative disease that creates a huge burden on the lives and health of individuals. In recent years, studies have indicated that the chemical components of P. ginseng, especially ginsenosides, play a pronounced positive role in the prevention and treatment of neurological diseases. This review aimed to summarize currently studies on chemical components and the mechanisms action in AD intervention treatment of P. ginseng, especially ginsenosides. In this review, the components of P. ginseng and their respective active effects were first introduced. Then the key molecular mechanisms and signaling pathways of P. ginseng were introduced, and its different active ingredients in the prevention and treatment of Alzheimer's disease-related pathogenesis were also summarized from the pathogenesis of AD Aβ generation and aggregation, hyperphosphorylation of tau protein，oxidant stress，neuroinflammation，mitochondrial damage，disorder of neurotransmitter and gut microbiota. Signaling pathway networks related to the action of ginseng active ingredients was constructed, which could serve as a therapeutic target for Alzheimer's disease. In addition to a detailed report on P. ginseng in improving Alzheimer's disease-related pathogenesis, the application of the current technology, spatial metabonomics in AD therapeutics and diagnostics were later discussed. Spatial metabonomics could be applied to investigate the multi-target intervention of P. ginseng on Alzheimer's disease. Research perspectives for the study of P. ginseng in the treatment of Alzheimer's disease were provided for further studies.

This study evaluated eight herbal and fruit teas to produce kombucha beverages. Besides, the effect of initial sucrose concentration (10, 40, and 70% w/v) on kombucha fermentation from tea extracts was investigated. Kinetic characterization and modeling of kombucha fermentation were also performed. Results showed that kombucha beverages' characteristics and chemical content changed according to tea type and initial sucrose concentration. It was determined that as initial sucrose concentration increased, the consumed substrate concentration increased. The highest total biomass values were determined when green tea and pomegranate tea were supplemented with 70 g/L sucrose. The highest total phenolic content and antioxidant activity were achieved with green tea samples. The total acidity and phenolic substance concentration increased as fermentation progressed in general. The kinetic parameters including maximum consumption rate, sugar utilization, yield, biomass yield, maximum biomass, total acidity, phenolic production rate, and the phenolic result were computed depending on changing substrate concentration. Concerning kinetic modeling, proposed models (Logistic and Luedeking-Piret models) estimated satisfactorily experimental substrate consumption and total acidity values. Besides, total acidity was associated with the substrate consumption.

This article presents a study conducted within a wind tunnel to enhance understanding of the excitation mechanism of stay cable vibration under arid conditions. Numerous wind tunnel tests were meticulously analyzed. Initially, the vibration of the stay cable was measured under steady flow conditions at a flow angle of 45 degrees and an inclination of 25 degrees, while varying wind velocities were applied. Additionally, an investigation into the flow field surrounding the stay cable was conducted in both vertical and horizontal directions. By utilizing two hot wire anemometers in the cable wake, an extensive database of flow field measurements was obtained. The experimental results revealed that the vibration characteristics of the stay cable under the arid conditions considered in this study aligned with findings reported in existing literature. Notably, a deeper comprehension of the excitation mechanism of a stay cable in a dry state was attained. This mechanism is closely associated with the inhibition of Karman vortices and the development of low-frequency vortices. At low wind speeds, Karman vortices predominated, resulting in small-amplitude vibrations. However, as the wind speed increased, the influence of Karman vortices diminished progressively, while the low-frequency vortices grew stronger. These low-frequency vortices exhibited high energy and a significant correlation with shedding along the stay cable, thereby inducing cable vibration in a dry environment.

It is common knowledge that estimating the height component of GNSS stations in general is much more problematic than estimating the horizontal position. Many different effects, such as tectonic signals, non-tectonic signals, atmospheric delay, noise, etc., are known to affect the height component of GNSS stations more than the horizontal component. However, the height component of GNSS stations is still poorly estimated. In this study, the height time series of 37 continuous GNSS stations covering the 2014–2019 date range is used from the Turkish National Permanent GNSS Network-Active (TUSAGA-Active). Since it is easier to interpret the effects of the height component due to its topographic features and seasonal changes being more effective than in the rest of the country, stations were chosen in the Eastern Anatolia region of Turkey. The daily coordinates of the GNSS stations were obtained as a result of the GAMIT/GLOBK software solution. By applying time series analysis to the daily coordinate values of the stations, statistically significant trends, periodic and stochastic components of the stations were determined. As a result of the analysis, the vertical velocities of the GNSS stations and the standard deviations of the vertical velocities were determined. Furthermore, when the height components of continuous GNSS stations were examined, it was seen that there were seasonal effects, and it was investigated whether the height components were related to meteorological parameters. For that, simple linear regression analysis was performed to determine how dependent the height components of the continuous GNSS stations were on meteorological parameters. As a result of the analysis, the height components of the continuous GNSS stations are dependent on meteorological parameters such as temperature, pressure, relative humidity, wind speed, and precipitation. In addition, height component time series analysis of continuous GNSS stations was performed by using Autoregressive Moving Average (ARMA) models from linear time series methods. As a result of the study, the performance of the ARMA modeling results again indicated the dependence of the height component of the continuous GNSS stations on the meteorological parameters.

Lithium-ion batteries serve as the primary sources of power for electric vehicles (EVs) and hybrid electric vehicles (HEVs). For vehicle applications, battery management systems (BMSs) are nec-essary to protect lithium-ion batteries from overheating and to ensure optimum vehicle perfor-mance. Our approach to developing a BMS was based on recent advances in the application of phase field models for lithium-ion batteries. In particular, our reduced-order model (ROM) uti-lized a dataset generated from the COMSOL® Multiphysics simulation of the Cahn–Hilliard equation for a single particle of a lithium iron phosphate (LiFePO4) cathode: an example of using a reduced-order model (ROM) based on a single-particle model (SPM). The main innovation of our ROM is that the SPM is fully coupled to a heat transfer model at the battery cell level. We utilized principal component analysis to identify a lower-order model that could reproduce the battery’s voltage and temperature response for ambient temperatures ranging from 253 to 298 K and for discharge rates ranging from 1 C to 20.5 C. The reduced-order dataset was then fitted to the ex-perimental data for an A123 Systems 26650 2.3 Ah cylindrical battery using deep neural network (DNN) regression.

: Glaucoma is the most common cause of blindness, which significantly reduces quality of life. Most glaucoma cases are primary glaucoma; nevertheless, many patients suffer from glaucoma caused by drugs, such as corticosteroids. A comprehensive review of the risks associated with corticosteroid-induced glaucoma is limited. Therefore, we used the Japanese Adverse Drug Event Reporting Database (JADER) published by the Pharmaceuticals and Medical Devices Agency (PMDA) to analyze the risk factors associated with glaucoma and the trends and characteristics of corticosteroid-induced glaucoma. We did not find sex or age differences associated with the onset of glaucoma. Hierarchical clustering and principal component analysis revealed that triamcinolone acetonide and betamethasone sodium phosphate, which are used around the eyes in Japan, are more likely to induce intraocular pressure (IOP) elevation compared with other corticosteroids. Increased IOP is a direct cause of glaucoma. Based on these findings, it may be necessary to limit or avoid the use of these corticosteroids.

The digital economy is a fundamental feature of sustainability. The economic, social, and environmental dimensions of sustainability stand up for digitalization and digital transformation projects. In order to understand and practice the digital economy, the goal of our paper is to evaluate accurate variables regarding fintech scope for discussing the efficient implication of these projects. The principal component analysis is used to investigate and evaluate the sustainable digital economy indicators. An empirical case from health care services in Africa from World Bank projects discusses this challenge. Results help produce an effective understanding of how the digital economy enables facing fintech challenges in Africa.

This paper presents a novel high-power rotary waveguide phase shifter based on circular polarizers specifically engineered for high-power microwave (HPM) applications. The phase shifter is capable of performing a precise 360° linear phase shift through rotation and consists of three parts: a linear polarized to left-handed circular polarized (LP-LHCP) mode converter, a left-handed to right-handed circular polarized (LH-RHCP) mode converter, and a linear polarized to right-handed circular polarized (LP-RHCP) mode converter. The paper analyzes the phase shifting principle, optimizes the three parts of the X-band rotary waveguide phase shifter, and conducts simulation studies on the entire phase shifter made of aluminum. The results show that the reflection is less than -20dB and the transmission efficiency is over 95% within 9.5GHz to 10.2GHz. The phase shift has a linear relationship with the rotation angle by a factor of two within this frequency range. Specifically, the phase shifter can achieve a linear phase shift of 360° when rotated from 0° to 180°, with a maximum deviation of less than 1.2°. Moreover, the power-handling capacity of the phase shifter in vacuum exceeds 242mW.

Considering rotation-induced centrifugal stiffening, spin softening, and Coriolis effects, the three-dimensional finite element model of a rotating blade with a dovetail structure is built through the contact dynamics theory. The fixed-interface component mode synthesis method is then adopted to reduce the model for high computational efficiency and adequate model accuracy. The effects of the number of normal modes on the first-six-order modal characteristics of the model varying with the rotating speed are studied between the reduced and full models. Next, the influence of rotating speed and friction factor on the nonlinear dynamic characteristics of the model under the action of aerodynamic force are elaborately discussed. The results show that: (1) tenon-mortise joint contact-induced nonlinearity under low rotating speeds results in the intermittent interference of contact surfaces and frequency multiplications of aerodynamic excitation frequency in the spectrum cascades, while that under high rotating speeds causes the continuous interface contact and asynchronous excitation at the tenon-mortise joint; (2) the increase of friction factor results in a lower contact pressure distribution and the right shift of the resonance peak (i.e., hard nonlinearity).

Rapeseed meal (RM) is an important agroindustrial by-product produced in large quantities by oil extraction from seeds. However, the efficient utilization of RM as animal feed is limited by its low metabolizable energy, poor palatability, and high levels of fiber and anti-nutritional components. Here, we investigate the potential of enriching RM with single-cell protein by fermentation with conventional and unconventional yeasts, to make a nutritionally improved feed component. The process of simultaneous saccharification and fermentation improved the parameters of the waste biomass, especially the protein content, the amount of crude fiber, and the degree of biotransformation of isoflavone compounds present in the waste material. Fermentation yielded the highest protein gain for the Saccharomyces cerevisiae Ethanol Red strain (ΔN=2.38%) at a biomass load of 12.5 g and for Scheffersomyces stipitis (ΔN=2.34%) at an enzyme dose of 0.125ml/10g DM. Crude fiber content (CF) was reduced by 2.55–7.18%. The SSF process resulted in the conversion of isoflavones to forms with fewer adverse effects and lower estrogenic activity.

Soil quality is related to food security and human survival and development. In recent years, due to the acceleration of urbanization and the increase of abandoned land, land degradation occurs, poor topsoil quality. In this study, the minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics and influencing reasons of topsoil quality in Tieling County. The re-sults showed that MDS included total potassium (TK), Clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu). The MDS indicators can well replace all indicators to evaluate the topsoil quality in the study area. The overall soil quality of Tieling County showed a trend of low in the east and high in the west, and gradually increased from the hilly area to the plain area. The evaluation results are consistent with field research, which can provide reference for other topsoil quality evaluation, and it also provide a basis for the formulation of soil quality improvement measures.

The splitting method developed in a previous study for linear, multi – component, convection-diffusion mass transfer equations is extended to non-linear, multi-component, convection-diffusion mass transfer equations. The diffusion term is non-linear (the diffusion coefficients depend on chemical species concentrations). Theoretical results about the splitting error are presented, as well as numerical simulations for mixtures with n ≤ 5. The numerical experiments show second-order accuracy and stability for the present splitting algorithm.

The aim of the presents study is to further expand the model of translational cross-validation by investigating the brain circuits or networks which underpin the same tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesized that there will be neural network patterns of activation and deactivation, which correspond to real time performance on clinical self-evaluation scales. We recruited 42 subjects: 20 healthy controls and 22 patients with major depressive episode. All subjects underwent functional MRI scanning using paradigm comprised of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for major depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with healthy controls. It is for the first time when potential neuroimaging state dependent biomarker has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the ability of both methods to concordantly distinguish groups by means of incremental translational cross-validation.

The scientific work posted on the Internet, which its author constantly keeps up to date, will be called an alive publication. The genre of alive publishing has many attractive features. However, it requires a certain expansion of the composition of the meta-attributes of the publication: along with the traditional attributes, the date of the appearance of the new, fresh revision is brought to the fore here. Such date is placed in a prominent place in the text of the publication. Along with this, it becomes highly desirable to include such a dynamically ("on the fly") generated date in a bibliographic reference to an alive publication. The currently used methods of dynamic extraction of this date are considered for a simple online publication, for a publication that has received a DOI through Crossref, for a publication posted in arXiv and in Preprints. Thanks to adding this meta-attribute, references to alive publications will beautify any bibliographic list.

It is advisable to avoid and, even better, demystify such grandiose terms as "infinity" or "singularity" in the description of the cosmos. Its proliferation does not positively contribute to the understanding of key concepts that are essential for an updated account of its origin and evolutionary history. It will be here argued that, as a matter of fact, there are no infinities in physics, in the real world: all which appear, in any given formulation of nature by means of mathematical equations, actually arise from extrapolations, which are made beyond the bounds of validity of the equations themselves. Such crucial point is rather well-known, but too often forgotten, as discussed in this paper with several examples; namely, the famous Big Bang singularity and others, which appeared before in classical mechanics and electrodynamics, and notably in the quantization of field theories. A brief description of the Universe’s history and evolution follows. Special emphasis is put on what is presently known, from detailed observations of the cosmos and, complementarily, from advanced experiments of very-high-energy physics. To conclude, a future perspective on how this knowledge might soon improve is given.

This study introduces a new procedure for three-component spectral matching of seismic ground acceleration records. The procedure involves preserving the phase angle and varying the Fourier amplitudes to match the spectra of the recorded records with a provided target spectrum that represents the spectral values of ground motions in three orthogonal directions. Several three-component recorded records were matched against two target response spectra to test the method. An accurate convergence was achieved in the velocity and displacement records. In addition, the scales among the components’ magnitude were maintained, creating realistic three-component ground motions. Such ground motion inputs are important to qualify critical structures sensitive to three-component motions. Our proposed procedure is novel because the present practices are mostly created based on one-component ground motions.

Longitudinal image registration of pulmonary computed tomography (PCT) images may serve as an essential tool for investigating the relationship between radiation dose distribution and the occurrence and phenotype of radiation-induced lung disease (RILD). Although numerous longitudinal registration algorithms have been developed for PCT, most similarity-based approaches are not suitable for PCT involving RILD due to the complex tissue variation between two PCT images. Moreover, conventional feature-based approaches might fail to find a sufficient number of matched pairs of feature points due to the disparate lung deformation caused by breathing and RILD. To overcome the challenges resulting from RILD, component structure coherence point drift (CSCPD) was proposed to establish a deformation model by decomposing the chest into several components and matching them with individual parameters based on coherence point drift (CPD). Moreover, a regional vascular point matching (RVPM) algorithm was proposed to generate a vascular subtree and to substantially increase the number of corresponding pairs between two images. Eventually, the components were recomposed and aligned by a thin plate spline algorithm. A performance assessment on 15 pairs of PCT images of patients with RILD yielded recall and precision values of 0.85 and 0.89 for RVPM, respectively. Moreover, the target registration error of CSCPD with RVPM (2.3 ± 1.79) was significantly better than that of conventional CPD with RVPM (2.95 ± 1.89) and conventional CPD (5.04 ± 2.87). Therefore, the proposed registration system is robust enough to address the disparate deformation of lungs with RILD, and it improves registration accuracy within the parenchyma.

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant, and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

Drought and high temperature often occur simultaneously will eventually affect rice (Oryza sativa L.) yield and seed quality. The objective of the study was to compare the effects of high temperature (HT) and water limitation (WL) during seed development and maturation on yield component, seed quality, and amylose content in aerobic rice cv. MRIA. Two experiments were conducted concurrently in greenhouse with ⁓35/26℃ subjected to elevated HT and/or WL; 5 days 38/28°C at 10, 15, 29 DAA and/or HT; 7 days at 40/28℃ at 8 and 21 DAA with 12h photoperiod in control environment growth chamber. Both experiments were respectively harvested at physiological maturity of 31 and 35 DAA. Grain yielded from the irrigated plant declined from over 27% to below 18% between 15 and 29 DAA. Combination stresses resulted in greater reduction of grain yield than single stress. Late seed development (29 DAA) had no effect either on yield component or seed quality as well as produce a high concentration in amylase content (16.7%) with greater germination capacity (91%). The non-stress plant harvested at 21 DAA resulted the highest percentage of germinated seeds (97%) whereas the vigour index (VI) was reduced to 70.3% and 58.9% at 8 and 21 DAA respectively. Extended plant stress to 7 consecutive days incurred the highest VI with low concentration of amylose content. There was a linear relationship incurred between seed amylase content with germination capacity and VI, however, there was an inverse relationship (R2 = 0.19) between amylase content with VI from plant treated with (HT and/or WL) for 7 days. The present results confirmed that the avoidance HT and WL during early (10 DAA) and mid (15 DAA) seed development with short duration of stress (5 days) obtained better in yield component, enhanced the seed quality and amylose concentration. The combined HT and WL damaged seeds’ quality were more than each stress alone. Therefore, avoiding HT and WL can be practically applied by farmers by irrigating throughout the rice plant during histodifferentiation to save water usage during seed maturation.

Taking a DNA-sequence, a word with letters/bases A, T, G and C, as the relation between the generators of an infinite group $\pi$, one can discriminate two important families: (i) the cardinality structure for conjugacy classes of subgroups of $\pi$ is that of a free group on $1$ to $4$ bases and the DNA word, viewed as a substitution sequence, is aperiodic. (ii) The cardinality structure for conjugacy classes of subgroups of $\pi$ is not that of a free group, the sequence is generally not aperiodic and topological properties of $\pi$ have to be determined differently. The two cases rely on DNA conformations such as A-DNA, B-DNA, Z-DNA, G-quadruplexes, etc. We found a few salient results: Z-DNA, when involved in transcription, replication and regulation in a healthy situation implies (i). The sequence of telomeric repeats comprising $3$ distinct bases, most of the time, satisfies (i). For $2$-base sequences in the free case (i) or non free case (ii), the topology of $\pi$ may be found in terms of the $SL(2,\mathbb{C})$ character variety of $\pi$ and the attached algebraic surfaces. The linking of two unknotted curves --the Hopf link-- may occur in the topology of $\pi$ in cases of biological importance, in telomeres, G-quadruplexes, hairpins and junctions, a feature we already found in the context of models of topological quantum computing. For $3$- and $4$ base sequences, other knotting configurations are noticed and a building block of the topology is the $4$-punctured sphere. Our methods have the potential to discriminate potential diseases associated to the sequences.

(1) Background: In order to study the effects of different electric cookers on the nutritional components of millet porridge, five different electric cookers were selected to cook millet porridge, and sensory and nutritional components in millet porridge, millet soup, and millet grains were analyzed. (2) Methods: Using principal component and cluster analysis, a variety of nutritional components were comprehensively compared. (3) Results: The results showed that among the different cooked samples, the content of amylose and reducing sugar was the highest in the samples cooked by electric cooker no. 3. The electric cooker no. 4 samples had the highest sensory evaluation score, crude fat, and protein content. The contents of ash, fatty acids, bound amino acids, and minerals were the highest in the electric cooker no. 5 samples. The sensory evaluation score and content of crude fat, ash, reducing sugar, direct starch, and Cu were higher in millet grains than in millet soup or porridge. The content of fatty acid, protein, amino acid, Zn, Fe, Mg, Mn, and Ca was highest in millet soup. Different electric cookers produced millet porridge with varying nutritional levels. (4) Conclusions: This study provides a reference for the further development of new electric cookers.

Digital twins are the foundation of autonomous off-road vehicles. Dynamic reduction methods are one of several ways to develop digital twins for off-road vehicles. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results of digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built control, electrical, and control systems.

The following article presents an analysis of the determinants of diabetes using a dataset containing the surveys of 2000 patients from the Frankfurt Hospital in Germany. The data were analyzed using the following models, namely: Tobit, Probit, Logit, Multinomial Logit, OLS, WLS with heteroskedasticity. The results show that the presence of diabetes is positively associated with "Pregnancies", "Glucose", "BMI", "Diabetes Pedigree Function", "Age" and negatively associated with "Blood Pressure". A cluster analysis is realized using the fuzzy c-Means algorithm optimized with the Elbow method and three clusters were found. Finally a confrontation among eight different machine learning algorithms is realized to select the best performing algorithm to predict the probability of patients to develop diabetes.

Principal component analysis (PCA) is used to reduce the dimensionalities of high dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods, and their applications for investigating protein dynamics.

The authors of the article performed computer simulation of buffer amplifiers (BA), which have medium and extremely small values of the offset voltage's systematic component (V_off), for different technological processes (Si, GaAs and GaN). The proposed control units are distinguished by a small number of elements and allow operation in the range of low and high temperatures. The variants of circuitry implementation of control units based on GaAs, GaN depletion-mode CMOS and JFET technological processes are considered. The results of the comparative modeling showed that the basic circuit of the BA on two field-effect transistors, when implemented on various modifications of GaN MOS and depletion-mode MOS transistors, provides sufficiently low values of the offset voltage's systematic component (less than 2 μV). The proposed BAs are designed for use in the structure of the Sallen-Key low-pass filter (LPF) when they are implemented both on mid-frequency Si CJFET and on GaAs microwave transistors. Low values of the LPF V_off have a positive effect on the effective capacity of the ADC. An example of switching on a BA in the JFET OpAmp structure based on the depletion-mode MOS input stage and a “folded” cascode, which, with 100% negative feedback, can be used in the Sallen-Key LPF, is considered. Computer simulation of the JFET/MOS OpAmp showed that the OpAmp has an open-loop voltage gain of 76-85dB, and its V_off is within 7µV in the temperature range from -60°C to +120°C. The presented circuitry of buffer amplifiers is intended, first of all, for the tasks of designing precision Sallen-Key low-pass filter (low-pass filter, high-pass filter, PF, RF).

Forest fires though part of a natural forest renewal process, when frequent and assuming large-scale proportions have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air and water quality, infrastructures, and economy. Portugal (PT) endures extreme forest fires, with a record of burned area in 2017. These extreme wildfire events (EWE) concentrated in few days but with high burned areas, are among other factors, linked to severe fire weather conditions. In this study a comparison between several fire danger indices is performed for a reference period 2001‒2021 and 2017 (May‒October) for the Fire Weather Index (FWI), Continuous Haines Index (CHI), Keetch-Byram drought index (KBDI), Burning Index (BI) and Fire Danger index (FDI). A daily analysis for the so-called Pedrogão Grande wildfire (June 17th) and the October major fires (October 15th) included the Spread Component (SP) and Ignition Component (IC). Results revealed high above average values for all indices for 2017 in comparison with 2001‒2021 particularly, for October. High statistically significant monthly correlations between FWI, FDI and BI were found, along with lower between FWI and CHI. These correlations are depicted in the spatial patterns between FWI and FDI for the two EWE. The spatial distribution of FDI, SC and IC had the best performance in capturing the locations of the occurrence of the two EWEs’. The outcomes allowed to conclude, that since fire danger depends on several factors a multi-index’s diagnosis is highly relevant, though calibration and scale adjustment are needed for PT. The implementation of a Multi-index’s Prediction System should be able to further enhance the ability of tracking and forecast unique EWE, since the shortcomings of some indices are compensated by the information retrieved by others as shown in this study. Overall, a new forecast system can help ensuring the development of appropriate spatial preparedness plans, proactive responses by the civil protection regarding firefighter’s management, suppression efforts to minimize the detrimental impacts of wildfires in Portugal.

The zero offset voltage in anti-aliasing low-pass filters (LPF) included at the ADC input has a significant effect on the effective bit rate of the ADC. The article discusses methods for minimizing the systematic component of the zero offset voltage (V_OS) of operational amplifiers (Op-Amp) in the structure of the LPF, due to the degradation of the current gain of the base (b) of bipolar transistors for an extremely common subclass of Op-Amp with one high-impedance node. The methods of matching a high-impedance Op-Amp node and a buffer amplifier with the help of special correcting SCM^p and SCMⁿ multipolars are proposed. Methods of description and formation of the given coefficients of weak current asymmetry of typical Op-Amp functional units (current mirrors, input DS, buffer amplifiers, SRC, etc.) are presented. As an example, the results of computer simulation of GaAs Op-Amp with small V_OS performed on JFET field and p-n-p bipolar transistors are given.

Background: The fulminant course of COVID-19, triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents with a high mortality rate and still lacks a causative treatment. C-reactive protein (CRP) has been shown to increase dramatically during the disease progression and correlates with deleterious outcomes. Selective CRP apheresis can reduce circulating CRP levels fast and effective. Methods: Seven hospitalized patients with documented severe COVID-19 progression, elevated CRP plasma levels (> 100 mg/L) and signs of respiratory failure were treated with CRP apheresis. 2-12 CRP apheresis sessions were performed generally in 24 h time intervals and depending on CRP plasma levels. Results: CRP apheresis reduced CRP plasma levels by up to 84% within a few hours, without exhibiting side effects in any patient. Despite signs of severe lung infiltration in all patients, only one patient died. The other patients showed improvements within the chest x-ray after CRP apheresis and were able to recover regardless of intubation and/or ECMO (4 patients). All remaining six patients were discharged from the hospital in good clinical condition. All patients had comorbidities. Conclusions: This case series presents a mortality rate of only 14%, which is dramatically lower than expected from the presented CRP levels as well as comorbidities and ventilation require-ments. Our clinical observations regarding the here presented seven patients support the hypoth-esis that CRP is a candidate to be therapeutically targeted in the early stage of severe COVID-19.

This paper focuses on the application of principal component analysis (PCA) to conduct a source apportionment of atmospheric aerosols from 8 sampling locations along the Marilao-Meycauayan-Obando River System (MMORS). Aerosols were collected on May 2016 during the same time that water samples were collected. Elemental analysis was conducted using a scanning electron microscope coupled with energy dispersive x-ray (SEM-EDX). Carbon (C), nitrogen (N), oxygen (O), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), bromine (Br), niobium (Nb), barium (Ba), mercury (Hg), and lead (Pb) concentrations were measured and used as inputs in Principal Component Analysis (PCA). The aerosol samples showed the presence of heavy metals Pb and Hg, elements that were also detected in trace amounts in the water measurements. Concentrations of heavy metals Fe, Pb, Hg in the aerosols were attributed to industrial sources. However, it was determined that the primary source of aerosols in the area were traffic and crustal emissions (C, N, O, Si, Al, Ca). Thus, control of traffic emissions would be more beneficial in reducing aerosol emissions in Meycauayan.

Next generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever growing number of life science research projects. Typically, well established software pipelines provide quantitative data informing about gene expression or concentrations of metabolites from the raw data. This data needs to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some tools exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNA-Seq and Metabolomics data visualization and analysis tool entirely running in the user’s web browsers, thus not needing any custom installation, manual programming or upload of confidential data to third party servers. GXP enables the user to generate interactive plots, visually summarize genetic or metabolic responses in scientific sketches (Mapman), carry out cluster and principal component analysis, and conduct overrepresentation analyses. GXP can be used to publish research data along with interactive plots and results of analyses carried out with it. GXP is freely available on GitHub: https://github.com/usadellab/GeneExpressionPlots

Demographic change is leading to the aging of German society. As long as the baby boom co-horts are still of working age, the working population will also age - and decline as soon as this baby boom generation gradually reaches retirement age. At the same time, there has been a trend towards increasing absenteeism (times of inability to work) in companies since the zero years, with the number of days of absence increasing with age. We present a novel stochastic forecast approach that combines population forecasting with forecasts of labor force participation trends, considering epidemiological aspects. For this, we combine a stochastic Monte Carlo-based cohort-component forecast of the population with projections of labor force participation rates and morbidity rates. This article examines the purely demographic effect on the economic costs associated with such absenteeism due to the inability to work. Under expected future employment patterns and constant morbidity patterns, absenteeism is expected by close to 5 percent by 2050 relative to 2020, associated with increasing economic costs of almost 3 percent. Our results illustrate how strongly the pronounced baby boom/ baby bust phenomenon determines demographic development in Germany in the midterm.

Immunoglobulin G (IgG) adopts a modular multidomain structure that mediates antigen recognition and effector functions, such as complement-dependent cytotoxicity. IgG molecules are self-assembled into a hexameric ring on antigen-containing membranes, recruiting the complement component, C1q. To provide deeper insights into the initial step of the complement pathway, we report a high-speed atomic force microscopy study for quantitative visualization of the interaction between IgG and the C1 complex composed of C1q, C1r, and C1s. Results showed that C1q in the C1 complex is restricted regarding internal motion and has a stronger binding affinity for on-membrane IgG assemblages than C1q alone, presumably because of smaller conformational entropy loss upon binding. Furthermore, we visualized a 1:1 stoichiometric interaction between C1/C1q and an IgG variant that lacks the entire CH1 domain in the absence of antigen. In addition to the canonical C1q-binding site on Fc, their interactions are mediated through a secondary site on the CL domain that is cryptic in the presence of the CH1 domain. Our findings offer clues for novel-modality therapeutic antibodies.

Let $f(t)$ be a characteristic function. The question on infinite divisibility of $g_{2k}(t)=f^{(2k)}(t)/f^{(2k)}(0)$ is considered. There are given the condition for that function not to be infinite divisible. Some examples of infinite divisibility of $g_{2k}(t)$ are given.

This paper considers the solution of the equations for ruin probabilities in infinite continuous time. Using the Fourier Transform and certain results from the theory of complex functions, these solutions are obtained as com- plex integrals in a form which may be evaluated numerically by means of the inverse Fourier Transform. In addition the relationship between the re- sults obtained for the continuous time cases, and those in the literature, are compared. Closed form ruin probabilities for the heavy tailed distributions: mixed exponential; Gamma (including Erlang); Lognormal; Weillbull; and Pareto, are derived as a result (or computed to any degree of accuracy, and without the use of simulations).

Microturbines can be used not only in models and education but also to propel UAVs. However, their wider adoption is limited by their relatively low efficiency and durability. Validated simulation models are required to monitor their performance, improve their lifetime, and design engine control systems. This study aims at developing a numerical model of a micro gas turbine for prediction and prognostics of engine performance. To build a reliable zero-dimensional model, the available compressor and turbine maps were scaled to the available test bench data with the least squares method, to meet the performance of the engine achieved during bench and flight tests. A steady-state aeroengine model was implemented in GSP and compared with experimental operating points. The selected flight data was then used as input for the transient engine model. The exhaust gas temperature (EGT) and fuel flow were chosen as the two key parameters to validate the model, comparing the numerical predicted values with the experimental ones. The observed difference between the model and the flight data was lower than 3% for both EGT and fuel flow.

The larvae of some species of the subgenus Orthocladius s. str. (Diptera, Chironomidae) are here described for the first time with corrections and additions to the descriptions of adult males and pupal exuviae. The identification of larvae is generally not possible without association with their pupal exuviae and/or adult males, so the descriptions here are based only on reared material or on pupae with the associated larval exuviae. Usually, Chironomidae larvae can be separated on the basis of morphometric characters, and the most discriminant characters ones are: 1- the ratio between the width of median tooth of mentum (Dm) and the width of the first lateral tooth (Dl) = mental ratio (DmDl), 2- the ratio between the length of the first antennal segment (A1) and the combined length of segments 2-5 (A(2-5)2-5) = antennal ratio (AR). The shape of mandible, maxilla, and other body parts are almost identical in all the species considered in this study. The larva of Orthocladius (Symposiocladius) lignicola is very characteristic and can be separated by the shape of mentum and the larvae of all the known species of Symposiocladius are characterized by the presence of large Lauterborn organs on antennae and of tufts of setae on abdominal segments. The larvae of Orthocladius (Orthocladius) oblidens and Orthocladius (Orthocladius) rhyacobius can be distinguished from other species basing on their large Dm and to each other by AR. A principal component analysis was carried out using 5 characters: 1- Dm, 2- Dl, 3- length of A1, 4- width of A1 (A1W), 5- combined length of segments 2-5 (A2-5). The most discriminant characters were Dm and A1, confirming that DmDl and AR can be used to separate species at larval stage, but the large superposition of morphometric characters in different species confirms that association with pupal exuviae is in any case needed to identify larvae. In future perspective, the development of reference DNA barcodes from specimens identified by specialists is recommended since possibly the best tool for larvae identification, but association of barcodes with morphotypes is in any case fundamental.

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from 4-dimensional cone-beam CT (4D-CBCT) images is developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is done in two steps: 1) deriving motion models and 2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior-inferior (SI) direction and the 95th percentile in two patient datasets were (2.29 mm and 5.79 mm) for patient 1 and (1.89 mm and 4.82 mm) for patient 2. This study has demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.