This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

Accurate and efficient traffic incident detection methods can effectively alleviate traffic congestion caused by traffic incidents, prevent secondary accidents and improve the safety of urban road traffic.Aiming at the problems that the traditional machine learning event detection method cannot fully extract the parameter characteristics of traffic flow and is not suitable formulti-dimensional and non-linear massive data, we propose a new traffic event detection method(CNN-XGBoost).This method combines the respective advantages of Convolution Neural Network(CNN) and Extreme Gradation Boosting (XGBoost). Firstly, we preprocessed the original freeway traffic incident detection data set by constructing initial variable set, data normalization, data balance processing and dimension reorganization. Secondly,we use CNN network to automatically extract the deep features of event detection data, and use XGBoost as a classifier to classify the extracted features for expressway traffic event detection.Finally, we use the data set of Hangzhou expressway microwave detector in China to carry out simulation experiments on CNN-XGBoost. The experimental results show that compared with XGBoost, CNN, Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) and other methods, CNN-XGBoost method can effectively improve the accuracy of expressway traffic event detection and has better generalization ability.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity to appraise water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that random forest and gradient boosting eclipsed other algorithms, achieving an accuracy rate of 99%. In close pursuit were SVM and XGBoost, registering accuracy scores of approximately 95% and 93%, respectively, while KNN and Decision Trees garnered accuracy rates of 88% and 87%, respectively. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization

Automatic recognition methods for non-stationary EEG data collected from EEG sensors play an essential role in neurological detection. The integrative approaches proposed in this study consists of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-level classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets were adopted to decompose the EEG data into five time-frequency sub-bands, whose statistical features were computed and used as classification features. The grid search optimizer was used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a support vector machine and a random forest classifier constructed according to previous descriptions. Multiple-index were used to evaluate the Symlet wavelet transform-gradient boosting machine-grid search optimizer classification scheme, which provided better classification accuracy and detection effectiveness than has recently reported in other work on three-level classification of EEG data.

Soybean with insignificant differences in appearance have large differences in their internal physical and chemical components, therefore follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybean for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in 4 categories is collected; Ten features is selected by extreme gradient boosting algorithm from 203 hyperspectral bands in range 400 to 1000 nm; A Gaussian radial basis kernel function support vector machine with optimization by the Tree-structured Parzen Estimator algorithm is built as TPE-RBF-SVM model for pattern recognition of soybean categories. The metrics of TPE-RBF-SVM are significantly improved compared with other machine learning algorithms. The accuracy is 0.9165 in the independent test dataset which is 9.786% higher for vanilla RBF-SVM model and 10.02% higher than the extreme gradient boosting model.

Based on the theory of behavioral economics, this study deeply analyzes the principles of behavioral economics involved in the development of mass sports, including psychological expectation, selection deviation and incentive mechanism, rationalize the current situation of group sports development, discusses how to effectively promote the development of mass sports, and puts forward a series of concrete countermeasures. In view of the problem of behavioral expectations, it is suggested to establish a comprehensive publicity and education system to improve the public's awareness and understanding of sports, and to stimulate individuals' active participation through self-setting goals and planning methods. In view of the selection bias, it is suggested that the government and relevant institutions provide diversified and attractive sports activity options, and make full use of technological means to increase the visibility and participation of activities.In addition, in view of the incentive mechanism problem, it is advocated to adopt differentiated incentive measures to meet the needs of different groups. The government can provide some preferential policies and establish incentive mechanisms to provide support for the organization of mass sports activities. It is expected that this research can stimulate the enthusiasm of the masses to participate in sports and promote the healthy development of mass sports.

A dynamic impact test was performed on as-rolled AZ31 alloy with gradient microstructure under various strains. The microstructural evolution and mechanical properties were systematically investigated. As the strain rate gradually increases, an increasing number of twins are formed, facilitating dynamic recrystallization (DRX), and the mechanical properties are also gradually improved as well. The microstructure becomes heterogeneous at higher strain rates, but the peak stress decreases. The impact process results in significantly higher performance due to microstructural refinement, work hardening by dislocations and precipitates. In addition, both the adiabatic shear band and the adjacent crack experience a temperature rise that exceeds the recrystallization temperature of the alloy. This observation also explains the presence of ultrafine recrystallized grains within the adiabatic shear band and the appearance of molten metal around the crack.

The gradient descent algorithm has become the standard algorithm for computing the extreme values of functions, but for multivariate functions this algorithm is mostly ineffective. This is because the convergence rate of each element is inconsistent in most cases. However, in this paper, we found that the gradient sign is a self-optimizing operator, ensuring that the convergence rate is consistent across all elements. This also explains, from an optimization perspective, the success of the Fast Gradient Sign Method (FGSM) in generating adversarial samples that are indistinguishable from the normal input, but can easily fool neural networks. We also found that the fractional order gradient is also self-optimizing, and that the convergence speed of this algorithm can be controlled by adjusting the order of the gradient. Experiments suggest that this algorithm not only generates adversarial samples faster than other algorithms, but that a single source image can generate many such samples. This algorithm is also more effective than others at generating adversarial samples from simple images.

In the field of quality analysis of food and flavoring products, gas chromatography – quadrupole mass spectrometry – ion mobility spectrometry (GC-QMS-IMS) is a powerful technique for the simultaneous detection of volatile organic compounds (VOC) by both QMS and IMS. GC is an established technique for the separation of complex VOC-rich foods such as beverages, dairy and fruit products as well as flavorings in general. While the subsequent detection by IMS features soft ionization of fragile compounds (e.g., terpenes) with characteristic drift times, MS provides the analytes’ m/z-values for database substance identification. A limitation of the prominently used static headspace-based GC-QMS-IMS systems is the substantially higher sensitivity of IMS in comparison to full scan QMS, which results in the detection of relevant VOC only in IMS, but not in the QMS analyzer. The present study describes a new prototypic trapped-headspace (THS)-GC-QMS-IMS setup, with optimized active flow regulation, able to gain valuable data of both detectors. This ultimately allows the combination of a soft ionization with m/z values obtained from database-searchable electron ionization (EI) spectra and as such, offers a substantial advantage in fields of quality control. The new setup features aligned retention times for IMS and MS and sufficient signal intensities for QMS and IMS. The analytical setup was evaluated with a set of mango puree samples from various provenances and the data was evaluated by suitable machine learning approaches. The results demonstrate that THS-GC-QMS-IMS allows for the classification of mango purees from different cultivars and can be a promising alternative method for authenticity control of food, flavors, and beverages.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

This study investigates the impact of plasma gradient modification and gradient illumination on the optoelectronic properties of buckypaper (BP), a flexible and large-scale material composed of multi-walled carbon nanotubes (MWCNTs). The BP samples were subjected to argon ion plasma treatment at varying power levels and durations, thereby creating different carrier concentration gradients on the surface. The photovoltage and photocurrent responses of the samples were then measured under uniform full illumination and gradient illumination conditions. The findings revealed that both plasma gradient modification and gradient illumination significantly enhanced the optoelectronic performance of BP. Notably, the combined application of these two methods yielded superior results compared to the application of either method alone. Specifically, the optimal plasma power for improving BP was found to be 20W. Under conditions of plasma gradient modification and gradient illumination, a photovoltage of 267.76 μV was generated, which represents a 21.44 times increase, and a photocurrent of 15.69 μA, reflecting a 32.69 times enhancement. The mechanism underlying this optoelectronic effect can be attributed to the presence of π-bonds in the carbon atoms. These π-bonds are excited by photons, resulting in the generation of small voltages and currents. This study underscores the potential of BP as an optoelectronic material and introduces a novel approach to enhance its optoelectronic properties through plasma gradient modification and gradient illumination.

In this project the EEG – electroencephalogram - channel(s) will be characterized to diagnose PTSD – Post-traumatic stress disorder – cases. For this aim, we applied boosting methods including a combination of K-mean and Support Vector Machine (SVM) models to find the feature weights to detect the PTSD cases. We classified 32 channels of 12 subjects including 6 PTSD and 6 healthy controls using a 6-mean classifier. The linear SVM found the weights of distinguished channels within each subject for each cluster. It was found that the significant SVM weights of F4, F8, and Pz are smaller in PTSD than in healthy subjects. This new method can be used as a tool to better understand the interaction of EEG signals and diagnosis.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

For many engineering applications it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches length scale parameter have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modelling as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

Cancer risk is known to increase tremendously when the immune system is suppressed, e.g., as observed in young organ-transplant recipients and AIDS patients. Based on such data, it may be hypothesized that the main reason for the development of clinical cancer is the weakening or suppression of the immune system, and that uncontrolled multiplication of cancer cells occurs when some aspects of the immune system fall below certain critical levels. Therefore, cancer may be prevented and treated by boosting these critical aspects of the immune system so that they are maintained above the critical levels. If multiple immune system boosting interventions are utilized, more aspects of the immune system would be boosted, increasing the likelihood of enhancing the critical aspects of the immune system and generating a cancer preventive and/or therapeutic effect. Clinical trials are needed to validate this approach for cancer prevention and treatment. If validated, the proposed approach could result in a major reduction of the death and suffering caused by cancer in the world.

Questions：SAP in mountainous cities of China serve as crucial carriers for species and wild food, constituting potential green infrastructure. These patches should be recognized as locally specific resources. This study investigates the transformation characteristics of the landscape patterns of SAP, along with their elements, across urban-rural gradients, providing foundational support for the planning of conservation and utilization strategies. Location: Yongchuan area of Chongqing in southwest China. Methods: The study investigated urban-rural gradient-based transformation of SAP. A four-direction transect space was selected and divided into uniform sample squares. Fragstats 4.3 calculated the landscape index of SAP, and Spearman correlation coefficients in SPSS were used to evaluate the influence of landform and artificial factors on agroforestry patch transformation. Results: 1) Patch area (PA), Plaque density (PD), and CLUMPY of SAP systematically transform, showing increased SAP areas and higher connectivity with greater distance from the city, resulting in reduced patch fragmentation. However, Edge density (ED) and Largest plaque index (LPI) exhibit irregular transformation patterns. 2) With increased distance from the city, woodland patch area and connectivity increase, leading to decreased fragmentation, dispersal, and lower fractal dimensions. Conversely, Cultivated land follows the opposite trend. garden land patch area, fractal dimension, fragmentation, and dispersal all increase, while connectivity decreases. 3) Landscape pattern transformations of SPA are significantly influenced by artificial factors. Woodland patches are influenced similarly by landform and artificial factors. In contrast, cultivated land and garden land patches are more significantly impacted by artificial factors. Conclusions: SAP exhibit differentiated conservation and utilization measures across urban-rural gradients. Small patches located far from urban centers may benefit from systematic conservation measures to prevent further fragmentation and erosion in peri-urban areas.

(1) Background: Depressional wetlands are highly vulnerable to changes in temperature and rainfall, affecting littoral vegetation, thus HydroGeoMorphic (HGM) wetland types. This study aimed to assess the utility of Sentinel-2A vegetation indices in enhancing delineation and sub-classify depressional HGM types. (2) Methods: We sampled 84 pairs of four vegetation indices from 10x10m plots along 14 belt transects in eight depressional wetlands in Mpumalanga, South Africa. We evaluated vegetation index differences between and within wetlands, focusing on lit-toral gradients. (3) Results: Significant differences (Bonferroni p

The movement of liquid droplets on the energy gradient surface has attracted extensive attention. Inspired by biological features in nature, such as the periodic spindle-shaped nodes in spider silks and conical-like barbs of cacti, the structure-property-function relationship of multifunctional gradient surfaces. In this study, a series of specific patterns are fabricated by 3D printing technology, followed by modification via the atmospheric pressure plasma treatment and liquid phase chemical deposition, resulting in enhancing the ability of water droplets of 5μL to travel 18.47mm on a horizontal plane and 22.75mm against gravity up to 20° tilting angle. Additionally, analysis techniques have been employed including contact angle analyzer, ESCA and laser confocal microscope to evaluate the sample performance. This work could further be applied to many applications related to microfluidic devices, drug delivery and water/fog collection.

Iron-based materials are widely applied in Fenton chemistry and they have promising prospects in the processing of wastewater. The composition complexity and rich chemistry of iron and/or oxides, however, hamper the precise understanding on the active sites and the working mechanism which still remain highly controversial. Herein, iron oxides of four different model systems are designed through a conventional precipitation method plus H2 reduction treatment. These systems feature Fe@Fe3O4 with abundant oxygen vacancy, Fe0 and Fe3O4 particles with interface structures, and Fe3O4-dominated nanoparticles of different sizes. These materials are applied in the decomposition of methyl orange as a model reaction to assess the Fenton chemistry. The Fe@Fe3O4 with core-shell structures exhibited significantly higher decomposition activity than the other Fe3O4-rich nanoparticles. A thin Fe3O4 layer formed by auto-oxidation of iron particles when exposing to air can boost the activity as compared with the Fe0 and Fe3O4 particles with interface structures but poor oxygen vacancy. The unique hetero-structure with the co-existence of both metallic iron and oxygen vacancy displayed excellent redox propensity which might account for the superior Fenton activity. This finding provides a new perspective to understand and design highly efficient iron-based Fenton catalysts.

The potential of generalized machine learning models developed for crop water estimation was examined in the current study. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) are three ensembled machine learning models that were developed using all of the data from a single location from 1976 to 2017 and then immediately applied at eleven different locations without the need for any local calibration. For the test period of January 2018 to June 2020, the model's capacity to estimate the numerical values of crop water requirement (Pen-man-Monteith (PM) ETo values) was assessed. In comparison to the GBM and RF models, the XGBoost model outperformed them both marginally and significantly. The estimate's weighted standard error was smaller than 0.85 mm/day, and the model's effectiveness varied from 96% to 99% across various locations. The model's strong performance was indicated by the decreased noise-to-signal ratio. A real-time water management system at the regional level can be seamlessly linked with this type of model due to its accuracy in estimating crop water requirements and its capacity to generalize.

Tomato quality is intricately regulated by a combination of factors, including the presence of bioactive compounds referred to as secondary metabolites and various organoleptic characteristics. These attributes are notably influenced and harmonized by the specific growing conditions, with a particular emphasis on the type of fertilization employed. Traditionally, chemical fertilizers have been favoured in crop cultivation due to their cost-effectiveness and ability to accelerate crop growth. However, in pursuit of sustainable and intelligent agricultural practices, there is a growing need for alternative fertilizers. In this context, the present study aimed to assess the impact of fertilizers derived from waste materials, specifically sulphur bentonite and orange residue (referred to as SB), on tomato quality. This assessment extended to examining qualitative and quantitative alterations in aroma-related volatile compounds and the antioxidant systems of tomatoes, in comparison to the conventional use of fertilizers such as horse manure and NPK (nitrogen, phosphorus, and potassium). The results obtained revealed distinct effects of different fertilizers on tomato quality. Notably, parameters such as TPRO (total protein), TCARB (total carbohydrate), LIC (lycopene content), TCAR (total carotenoid content), total phenols, total flavonoids, and aroma profiling exhibited significantly superior values in the group treated with SB fertilizer. These findings strongly suggest that the novel fertilizer functioned as a bio-stimulant, enhancing the nutraceutical and sensory attributes of tomatoes, with a pronounced impact on the synthesis of secondary metabolites and the aroma profile of the fruits.

Starting from a very simple economic scenario, we build on it a game and then we introduce a general strategy able to reduce a regression problem to an equivalent binary classification problem. This reduction scheme (that we call adaptive reduction or also dynamic reduction) can be also used to derive a new boosting algorithm for regression problems named bOOst^d. The bOOst^d algorithm is very simple to implement, and it can use any learning algorithm with no priori assumptions. We present a conjecture for bOOst^d performances, which ensures a little error on training set. More important we can also provide a very good theoretical upper bound for the generalization error. We give a set of preliminary experimental results that seems to confirm our conjecture for bOOst^d performances on training set and the theoretical assumptions for the generalization error. We also provide a possible justification of why boosting often does not overfit. Finally, we leave some open problems and argue that in the future an adaptive single boosting (with an unique code) algorithm for binary, multi class and regression problems can be derived.

As a new type of high-performance material, gradient structural steel is widely used in engineering fields due to its unique microstructure and excellent mechanical properties. For the prevalent fatigue failure problem, the rate of change of the local grain size gradients along the structure (referred to as the gradient rate) is a key parameter in the design of gradient structures, which significantly affects the fatigue performance of gradient structural steel. In this work, a method of ‘Voronoi primary + secondary modeling’ is adopted to successfully establish three typical high-strength steel models corresponding to the convex, linear, and concave type gradient rates for gradient structures, focusing on the stress-strain response and crack propagation in structural steel with different gradient rates under cyclic loading. It is found that the concave gradient rate structural model is dominated by finer grains with larger volume fraction, which is conducive to hindering fatigue crack propagation and has the longest fatigue life; the linear structure is the second one, and the convex structure is the shortest one. The simulation results in this work are consistent with the relevant experimental phenomena. Therefore, when regulating the gradient rate, priority should be given to increasing the volume fraction of fine grains and designing a gradient rate structure dominated by fine grains to improve the fatigue life of the material.

Quantifying the landscape pattern change can effectively demonstrate the ecological progresses and the consequences of urbanization. Based on remotely sensed land cover data in 1994, 2000, 2006 and a gradient analysis with landscape metrics at landscape- and class- level, we attempted to characterize the individual and entire landscape patterns of Shanghai metropolitan during the rapid urbanization. We highlighted that a roadscape transect approach that combined the buffer zone method and the transect-based approach was introduced to describe the urban-rural patterns of agricultural, residential, green, industrial, and public facilities land along the railway route. Our results of landscape metrics showed significant spatiotemporal patterns and gradient variations along the transect. The urban growth pattern in two time spans conform to the hypothesis for diffusion–coalescence processes, implying that the railway is adaptive as a gradient element to analyze the landscape patterns with urbanization. As the natural landscape was replaced by urban landscape gradually, the urban fringe expanded radically. The results also showed that the desakota region expanded its extent widely. Satellite towns witnessed the continual transformation from the predominantly rural landscape to peri-urban landscape. Furthermore, the gap between urban and rural areas remained large especially in public service. More reasonable urban plans and land use policies should push to make more of an effort to transition from the urban-rural separation to coordinated urban-rural development. This study is a meaningful trial in demonstrating a new form of urban–rural transects to study the landscape change of large cities from a strategic viewpoint. By combining gradient analysis with landscape metrics, we addressed the process of urbanization both spatially and temporally, and provided a more quantitative approach to urban studies.

The current global pandemic of coronavirus disease 2019 (COVID-19) caused by the coronavirus SARS-CoV-2 has already had a major adverse impact on the world due to the exponentially increasing deaths due to the disease and the extreme actions taken by the world community to prevent its spread. It is important to explore novel methods of reducing the illnesses and fatality rates of the coronavirus-infected patients. Since the weakness of the immune system is one of the major contributing factors for the illnesses caused by such viruses, and since inflammation is a major contributing factor for the mortality of COVID-19 patients, interventions that boost the immune system and/or are anti-inflammatory may reduce the COVID-19 incidence and the mortality due to the disease. A large variety of interventions are known to improve the immune response and/or reduce inflammation. However, all the interventions would not be applicable or acceptable to everyone and so the interventions would need to be individualized based on individual circumstances and preferences. This approach, known as “Individualized Interventions to Improve the Immune Response”, or the I⁴R approach, should be studied in pilot clinical trials urgently, in order to potentially reduce the harm caused by the current coronavirus pandemic.

Increased anthropogenic pressure on forest resources leads to deforestation and forest degradation, significantly limiting the regeneration capacity of native woody species and consequently the restoration of miombo woodlands in anthropized habitats within the rural area of Lubumbashi. This study assessed the diversity and natural regeneration capacity of miombo species through floristic inventories in three different habitats (unexploited forests, degraded forests, and post-cultivation fallows). The results reveal that for the adult’s stratum unexploited and degraded forests exhibit higher dendrometric (density, mean square diameter, basal area) and floristic parameter (taxa, genera, families) values compared to post-cultivation fallows. Furthermore, the regeneration of miombo woody species is higher in degraded forests (21 taxa; 105 juveniles/plot). However, regarding the sapling’s stratum (1 cm ≤ dbh < 10 cm), the three habitats display nearly similar situations. Additionally, the floristic composition and diversity of unexploited and degraded forests show a significantly higher similarity (76.50%) among them compared to these habitats and the post-cultivation fallows (56.00%). These findings indicate that miombo woodlands have the potential to regenerate and maintain floristic diversity. To sustain this natural regeneration capacity of miombo woody species and promote the restoration of forest mass and its floristic diversity, it is imperative to determine the rotation period after habitat exploitation and regulate anthropogenic activities and late bush fires, particularly in anthropized habitats at the village level.

In vertical coordinates, (x,y,z), the gradient Richardson number Ri=N^2⁄S^2 is the square of the ratio between buoyancy frequency N and vertical-shear amplitude S, where N^2=-(g⁄ρ) ∂ρ⁄∂z and S^2=(∂u⁄∂z)^2+ (∂v⁄∂z)^2, with ρ potential density, (u,v) the horizontal velocity components and g gravity acceleration, so that a decrease in stratification brings a decrease in dynamic stability and vice versa. In the isopycnic coordinate system, (x,y,ρ), however, Ri=M^2⁄〖S_ρ〗^2 , is the ratio between 〖M^2≡N〗^(-2) and the squared diapycnal shear 〖S_ρ〗^2=(ρ⁄g)^2 [(∂u⁄∂ρ)^2+ (∂v⁄∂ρ)^2 ], so a decrease in stratification leads to an increase in dynamic stability and vice versa. The apparently different role of stratification arises because S and S_ρ are related through the stratification itself, S_ρ=S⁄N^2 or, equivalently, τ=〖t_o〗^2⁄t_d , which is interpreted as a natural oscillation time τ≡S_ρ that equals the buoyancy or oscillation time t_o=N^(-1) normalized by the ratio t_o⁄t_d , where the deformation time is t_d=S^(-1). Here we follow simple arguments and use field data from three different situations (island shelf-break, Gulf Stream and Mediterranean outflow) to endorse the usefulness of the isopycnal approach. In particular, we define the reduced squared vertical σ^2= S^2-N^2 and reduced squared diapycnal shear 〖σ_ρ〗^2= 〖S_ρ〗^2-M^2, which are positive for unstable conditions and negative for stable conditions. It turns out that Ri and σ^2 remain highly variable for all stratification conditions; in contrast, the mean 〖σ_ρ〗^2 values display a univocal tendency, its magnitude decreasing with stratification. We propose 〖σ_ρ〗^2 and 〖S_ρ〗^2 to be good indexes for the occurrence of effective mixing under high stratification conditions.

Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most oftenly by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad-hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and non-linear local contrast enhancement and colour image daltonisation illustrate the behaviour of the method.

Due to slow progression and susceptibility to radical forms of treatment low-grade PC is associ-ated with high overall survival (OS). With the clinical progression of PC the therapy is getting more complex. The immunosuppressive tumor microenvironment (TME) makes PC a difficult target for most immunotherapeutics. Its general immune resistance is established by i.e. immune evasion through Treg cells, synthesis of immunosuppressive mediators, and defective expression of surface neoantigens. The success of sipuleucel-T in clinical trials initiated several other clinical studies that specifically target the immune escape of the tumor and eliminate the immunosuppres-sive properties of TME. In the settings of PC treatment, this can be commonly achieved with radi-ation therapy (RT). Also, focal therapies usually applied for localized PC, such as high-intensity focused ultrasound (HIFU) therapy, cryotherapy, photodynamic therapy (PDT), or irreversible electroporation (IRE) were shown to boost anti-cancer response. Nevertheless, the present guide-lines restrict their application to localized and low-grade PC. This review explains how RT and focal therapies enhance the immune response. We also provide data supporting the combination of RT and focal treatments with immune therapies.

The World Health Organization (WHO) has released reports indicating that heart disorders hold the unfortunate distinction of being the primary cause of death worldwide. Shockingly, an astonishing estimated 17.9 million lives are claimed by heart diseases annually, accounting for an alarming 31% of all global deaths. With all the flaws in the clinical situation, it is frequently challenging to assess the severity of cardiac disease and forecast its course of progression due to heterogeneity and complex interplay of factors. Therefore, early heart disease detection is essential for effective therapy. To address these challenges, Machine Learning (ML) boosting algorithms play a pivotal role as the main components of predictive analytics required to do this. The main objective of this study is to develop a comprehensive comparative framework to predict heart diseases using state-of-the-art machine learning with boosting techniques such as Decision Tree, Random Forest, Gradient Boosting, Catboost, XGboost, Light GBM, and Adaboost. To evaluate the performance of the models, a large heart disorders dataset is used from the UCI machine learning repository, comprised of 26 feature-based numerical and categorical attributes with 8763 samples from all over the globe. The Experimental results reveal that AdaBoost attained the highest accuracy of 95% and outperforms other algorithms concerning various performance measures like precision= 0.98, recall= 0.95, specificity= 0.95, f1-score=0.01, Negative predicted value= 0.83, False positive rate= 0.04, False negative rate= 0.04 and False Development rate= 0.01.

Alcoholism is a condition associated with psychiatric and psychiatric problems, where the respiratory system is damaged through the mucociliary ladder mechanism and alveolar macrophage dysfunction. In the time COVID-19 has been observed a dramatic increase in alcohol consumption mediated by levels of anxiety and situations of confinement. In this work we analyze the relationship between alcoholism and SARS, especially with SARS-CoV-2, explained by a degradation of the host defenses of the respiratory epithelium by changing the barrier function, the discharge of cytokines and the functions of the cilia. All of them involved in the defense mechanism. of the lungs. This leads to a worse prognosis for patients precisely because of alcohol consumption. Based on this approach, alcoholism will exacerbate the consequences of COVID-19.

Antimicrobial resistance (AMR) is a growing public health concern worldwide, and it poses a significant threat to human, animal, and environmental health. The overuse and misuse of antibiotics have contributed significantly to the emergence and spread of antimicrobial resistance, which may lead to significant economic consequences like reduced productivity and increased healthcare costs. Nanotechnology offers a promising platform for addressing this challenge. Nanoparticles have unique properties that make them highly effective in combating bacterial infections by inhibiting the growth and survival of multidrug-resistant bacteria in three areas of health: human, animal, and environmental. To conduct an economic evaluation of surveillance in this context, it is crucial to have a comprehension of the connections to be addressed by several nations by implementing national action policies based on the One Health strategy. This review provides an overview of the progress made thus far and presents potential future directions to optimize the impact of nanobiotics on AMR.

The Ca Mau Peninsula situated in the Mekong Delta of Vietnam, features a low-lying terrain. Over recent years, the region has encountered the adverse impacts of climate change, leading to both land subsidence and rising sea levels. In this study, we assessed the land subsidence susceptibility in the Ca Mau peninsula utilizing three Boosting machine learning models: AdaBoost, Gradient Boosting, and Extremely Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), Groundwater levels, distance to roads, Digital Terrain Model (DTM), normalized vegetation index (NDVI), geology, soil composition, and proximity to rivers and streams. A dataset comprising 1950 subsidence sample points was employed for the model, with 1910 points obtained from the PSInSAR Radar method, and the remaining points derived from leveling measurements. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the ROC curve, Area under the curve (AUC), Specificity, Sensitivity, and Overall Accuracy. The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC above 0.9 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau peninsula.

Following ischemic stroke, the degradation of myelin and other cellular membranes surpasses the lipid-processing capabilities of resident microglia and infiltrating macrophages. This imbalance leads to foam cell formation in the infarct and areas of secondary neurodegeneration, instigating sustained inflammation and furthering neurological damage. Given that mitochondria are the primary sites of fatty acid metabolism, augmenting mitochondrial biogenesis (MB) may enhance lipid processing, curtailing foam cell formation and post-stroke chronic inflammation. Previous studies have shown that the pharmacological activation of the β2-adrenergic receptor (β2-AR) stimulates MB. Consequently, our study sought to discern the effects of intensified β2-AR signal-ing on MB, the processing of brain lipid debris, and neurological outcome using a mouse stroke model. To achieve this goal, aged mice were treated with formoterol, a long-acting β2-AR ago-nist, daily for two and eight weeks following stroke. Formoterol increased MB in the infarct re-gion, modified fatty acid metabolism, and reduced foam cell formation. However, it did not re-duce markers of post-stroke neurodegeneration or improve recovery. Our findings suggest that boosting MB in myeloid cells is one component of a therapeutic avenue for improving brain lipid debris processing after stroke.

There is a need for alternatives or complements to synthetic fertilizers to enhance agricultural sustainability. Applying organic amendments can play a significant role in this. Insect droppings show high potential, though studies evaluating their agronomic value have only recently begun to emerge. This study compared black soldier fly (Hermetia illucens) and mealworm (Tenebrio molitor) larvae frass with another organic amendment (Nutrimais) derived from composting forestry, agro-industrial, and domestic waste. The experiment also included ammonium nitrate at two rates [the same as the organic amendments, 50 kg ha–1 nitrogen (N) (FullR), and half that rate (HalfR)] and an unfertilized control. The study spanned two growth cycles of lettuce (Lactuca sativa L.) grown in pots, followed by unfertilized oats (Avena sativa L.) to assess the residual effects of the fertilizing treatments. Mealworm larvae frass mineralized rapidly, with an apparent N recovery of 37.4% over the two lettuce growth cycles, indicating its high availability to soil heterotrophic microorganisms. The average dry matter yield (DMY) of lettuce was the highest among all treatments (12.8 and 9.8 g plant–1 in the first and second lettuce cycles), even compared to the FullR treatment (12.2 and 7.8 g plant–1), though without significant differences. Although mealworm larvae frass exhibited a high mineralization rate, the DMY cannot be attributed solely to N supply, as plants in the FullR treatment showed better N nutritional status. Mealworm larvae frass provided strong evidence of a plant biostimulant effect, not explained by the variables measured in this study. Black soldier fly larvae frass exhibited typical behavior of a moderately reactive organic amendment, while Nutrimais showed low reactivity, with a near-neutral mineralization/immobilization balance. The results suggest mealworm larvae frass is recommended for early maturing vegetable crops, whereas Nutrimais appears more suitable for perennial crops with low short-term nutrient requirements.

Applications of gradient method for nonlinear optimization in development of Gradient Neural Network (GNN) and Zhang Neural Network (ZNN) are investigated. Particularly, the solution of the matrix equation AXB=D which changes over time is studied using the novel GNN model, termed as GGNN(A,B,D). The GGNN model is developed applying GNN dynamics on the gradient of the error matrix used in the development of the GNN model. The convergence analysis shows that the neural state matrix of the GGNN(A,B,D) design converges asymptotically to the solution of the matrix equation AXB=D, for any initial state matrix. It is also shown that the convergence result is the least square solution which is defined depending on the selected initial matrix. A hybridization of GGNN with analogous modification GZNN of the ZNN dynamics is considered. The Simulink implementation of presented GGNN models is carried out on the set of real matrices.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known.

Artificial Intelligence is dominated by Artificial Neural Networks (ANNs). Currently, the Batch Gradient Descent (BGD) is the only solution to train ANN weights when dealing with large datasets. In this article, a modification to the BGD is proposed which significantly reduces the training time and improves the convergence. The modification, called Instance Eliminating Back Propagation (IEBP), eliminates correctly-predicted-instances from the Back Propagation. The speedup is due to the elimination of unnecessary matrix multiplication operations from the Back Propagation. The proposed modification does not add any training hyperparameter to the existing ones and reduces the memory consumption during the training.

Video quality assessment (VQA) plays an important role in video applications for quality evaluation and resource allocation. It aims to evaluate the video quality consistent with the human perception. In this letter, a hierarchical gradient similarity based VQA metric is proposed inspired by the structure of the primate visual cortex, in which visual information is processed through sequential visual areas. These areas are modeled with the corresponding measures to evaluate the overall perceptual quality. Experimental results on the LIVE database show that the proposed VQA metric significantly outperforms the state-of-the-art VQA metrics.

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

Using the 2011-2020 digital inclusive finance data released by Peking University and the index system constructed by the Blue Book of China Rural Revitalization and Development Index (2018), this paper analyzes the dynamic evolution of the digital inclusive finance and the rural revitaliza-tion from time and space dimensions with the help of kernel density estimation, Markov chain and Moran index. Using The Gini coefficient decomposition method to analyze the source of differ-ences in digital inclusive finance, and then using the spatial autoregressive (SAR) model and ge-ographically weighted regression (GWR) model to study the boosting effect of digital inclusive finance on rural revitalization from the perspective of spatial aggregation and spatial differentia-tion, respectively. The results show that: (1) The growth rate of China's digital inclusive finance slows down year by year, and the inter-provincial differences increase year by year and show gradient characteristics, indicating that there may be a trend of multipolar differentiation. The overall level of Rural Revitalization shows an increasing trend, and the gap between provinces is still apparent. (2) The evolution of digital inclusive finance and rural revitalization is a slow ad-justment process, and there has been no cross-level jump in the past ten years. In digital inclusive finance, the liquidity from the highest and lowest levels to the medium level is high. However, the liquidity in the states of rural revitalization development level is not high. (3) Rural revitalization has a positive spatial spillover effect. The level of rural revitalization in the western area is signif-icantly lower than in the eastern area. At the same time, there is no significant difference between the east and central areas. The depth of the use of digital inclusive finance has a significant positive impact on the revitalization and development of rural areas, indicating that the further promotion of digital inclusive finance business in rural areas can substantially boost the revitalization of rural areas. (4) The boosting effect of digital inclusive finance on rural revitalization shows prominent spatial differentiation characteristics. The depth of use and the degree of digitization generally show a positive impact. The central and eastern coastal cities have the highest impact, decreasing toward the southwest and northeast. The areas with the lowest usage depth impact are clustered in the northeast, and the areas with the lowest digitization impact are clustered in the southwest.

Glypicans are a family of heparan sulfate proteoglycans that are attached to the outer plasma membrane leaflet of the producing cell by a glycosylphosphatidylinositol anchor. Glypicans are involved in the regulation of many signalling pathways, including those that regulate the activities of Wnts, Hedgehog (Hh), Fibroblast Growth Factors (FGFs) and Bone Morphogenetic Proteins (BMPs), among others. In the Hh signalling pathway, glypicans have been shown to be essential for ligand transport and the formation of Hh gradients over long distances, for the maintenance of Hh levels in the extracellular matrix and for its unimpaired reception in distant recipient cells. Recently, two mechanistic models have been proposed to explain how Hh can form the signalling gradient and how glypicans may contribute to it. In this review, we describe the structure, biochemistry and metabolism of glypicans and their interactions with different components of the Hh signalling pathway that are important for the release, transport and reception of Hh.

The functioning of forest ecosystems depends on the taxonomic and ecological diversity of soil fungi. Urbanization is increasing worldwide and is regarded a major driver of environmental change altering local species assemblages in urban forest. We investigated whether the degree of urbanization and local forest characteristics affect the soil fungal community in 20 beech forests located along a rural–urban gradient in the city of Basel and its suburbs (Switzerland). We analyzed their soil fungal communities by DNA metabarcoding of the rDNA ITS2 region and related these data to local forest vegetation characteristics and soil properties. The number of fungal OTUs in the 20 forests examined ranged from 170 to 303. Richness, diversity and evenness of fungal OTUs were all significantly affected by the degree of urbanization, but in different ways. OTU richness was highest in forests in areas with a low degree of urbanization and lowest in forests in rural areas. In contrast, the diversity of OTUs increased with increasing degree of urbanization. Different fungal phyla and fungal guilds showed distinct patterns in their relative abundance along the rural-urban gradient. The degree of urbanization reduced the relative abundance of symbiotrophic fungi, but increased that of saprotrophic and pathotrophic fungi. Our results show that urbanization alters soil fungal community, which in turn can lead to changes in forest ecosystems.

Salinity acts as a critical environmental filter on microbial communities in natural systems, negatively affecting microbial diversity. However, how salinity affects the community assembly remains unclear. This study used Wendeng multi-pond saltern as a model to evaluate the prokaryotic community composition and diversity and quantify the relative importance of ecological processes across salinity gradients. Results showed that low saline salterns (45-80 g/L) exhibited higher bacterial diversity than those in high saline salterns (175-265 g/L). The relative abundance of taxa assigned to Halanaerobiaceae, Haloferacaceae, Desulfohalobiaceae, Phormidiaceae, Rohodobacteraceae, and Nitrococcaceae was higher with increasing salinity. Salinity and pH were the primary environmental factors that directly or indirectly determined the composition and diversity of prokaryotic communities. Microbial co-occurrence network dynamics were more complex in the sediment than in water of salterns. An infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis (iCAMP) showed that microbial community assembly in sediment and water differed. Our findings provide more information about microbial community structure and the importance of various ecological processes in controlling microbial community diversity and succession along salinity gradients in water and sediment environments.

Modern fighter aircraft increasingly need to conjugate aerodynamic performance and low observability. In this paper we showcase a methodology for a gradient-based bidisciplinary aero-stealth optimization. The shape of the aircraft is parametrized with the help of a CAD modeler, and we optimize with the SLSQP algorithm. The drag, computed with the help of a RANS method, is used as the aerodynamic criterion. The stealth criterion, a function is derived from the radar cross section in a given cone of directions, weighed with a function whose goal is to cancel the electromagnetic intensity in a given direction. Stealth is achieved passively by scattering back the electromagnetic energy away from the radar antenna and no energy is absorbed by the aircraft, which is considered as a perfect conductor. A Pareto front is identified by varying the weights of the aerodynamic and stealth criteria. The Pareto front allows for an easy identification of the CAD model corresponding to a chosen aero-stealth trade-off.

Fluid flow and chemokine gradients play a large part in not only regulating homeostatic processes in the brain, but also in pathologic conditions by directing cell migration. Tumor cells in particular are superior at invading into the brain resulting in tumor recurrence. One mechanism that governs cellular invasion is autologous chemotaxis, whereby pericellular chemokine gradients form due to interstitial fluid flow (IFF) leading cells to migrate up the gradient. Glioma cells have been shown to specifically use CXCL12 to increase their invasion under heightened interstitial flow. Computational modeling of this gradient offers better insight into the extent of its development around single cells, yet very few conditions have been modelled. In this paper, a computational model is developed to investigate how a CXCL12 gradient may form around a tumor cell and what conditions are necessary to affect its formation. Through finite element analysis using COMSOL and coupled convection-diffusion/mass transport equations, we show that velocity (IFF magnitude) has the largest parametric effect on gradient formation, multidirectional fluid flow causes gradient formation in the direction of the resultant which is governed by IFF magnitude, common treatments and flow patterns have a spatiotemporal effect on pericellular gradients, exogenous background concentrations can abrogate the autologous effect depending on how close the cell is to the source, that there is a minimal distance away from the tumor border required for a single cell to establish an autologous gradient, and finally that the development of a gradient formation is highly dependent on specific cell morphology.

Plants are exposed to a variety of abiotic and biotic stresses leading to increased formation of reactive oxygen species (ROS) in plant cells. ROS are capable of oxidizing proteins, pigments, lipids, nucleic acids, and other cell molecules, disrupting their functional activity. During the process of evolution, numerous antioxidant systems were formed in plants, including antioxidant enzymes and low-molecular-weight non-enzymatic antioxidants. Antioxidant systems perform neutralization of ROS and therefore prevent oxidative damage of cell components. In the present review, we focus on the biosynthesis of non-enzymatic antioxidants in higher plant cells such as ascorbic acid (vitamin C), glutathione, flavonoids, isoprenoids, including carotenoids, tocopherol (vitamin E), ubiquinone and plastoquinone. Their functioning and their reactivity with respect to individual ROS are described. The review is also devoted to the modern genetic engineering methods, which are widely used to change the quantitative and qualitative content of the non-enzymatic antioxidants in cultivated plants. These methods allow to obtain various plant lines with given properties in a rather short time. The most successful approaches for plant transgenesis and plant genome editing for the enhancement of the biosynthesis and the content of these antioxidants are discussed.

The magnetic navigation system (MNS) with gradient and uniform saddle coils is one of the effective systems to manipulate various medical magnetic robot due to its compact structure and uniformity of the magnetic field and field gradient. However, it has limited operating time and large inductance effect due to the high current density and numerous turns of coils, respectively. In this paper, we propose an optimization methodology of the MNS considering the current density and inductance of the coils. We introduce objective functions, constraints, and design variables of the MNS. Then, the MNS was designed by an optimization algorithm. The constructed MNS was compared with the conventional MNS, and it could generate 22 % stronger magnetic field or field gradient while the maximum three-dimensional rotating magnetic field was improved by 42 %. We also demonstrated an unclogging performance of a helical robot was improved by 54 % with the constructed MNS.

Zero-dimensional (0D) tin halide perovskites feature extraordinary properties, such as broadband emission, high photoluminescence quantum yield, and self-absorption-free characteristics. The innovation of synthesis approaches for high-quality 0D tin halide perovskites has facilitated the flourishing development of perovskite-based optoelectronic devices in recent years. However, discovering an effective strategy to further enhance their emission efficiency remains a considerable challenge. Herein, we report a unique strategy employing rapid heat treatment to attain efficient self-trapped exciton (STE) emission in Cs4SnBr6 zero-dimensional perovskite. Compared to the pristine Cs4SnBr6, rapid thermal treatment (RTT) at 200°C for a duration of 120 seconds results in an augmented STE emission with photoluminescence (PL) quantum yield rising from an initial 50.1% to a substantial 64.7%. Temperature-dependent PL spectra analysis, Raman spectra, and PL decay traces reveal that the PL improvement is attributed to the appropriate electron-phonon coupling as well as the increased binding energies of STEs induced by the RTT. Our findings open up a new avenue for efficient luminescent 0D tin-halide perovskites toward the development of efficient optoelectronic devices based on 0D perovskites.

The impact of nutrition on immunity is an intense area of research. Malnutrition is linked to a higher risk of microbial infections, while severe infections usually lead to a nutritional imbalance in affected patients. The nutritional status has an impact on the severity of the ongoing SARS-CoV-2 infection. Studies conducted on small numbers of patients have shown the benefits of maintaining optimal vitamin (B12 and D) and mineral (zinc and magnesium) balance in reducing the intensity of COVID-19. Although consuming a balanced diet with a healthy lifestyle is always desirable, the importance of such practice is even more meaningful during the COVID-19 pandemic. Keeping an optimal balance of vitamins and minerals through healthy dietary habits helps to maintain a robust immune system that is essential to combat invading microbial pathogens, including SARS-CoV-2.

Unmanned aerial vehicles (UAV) thermal imagery offers several advantages in environmental monitoring, as it can provide a low-cost, high-resolution, and flexible solution to measure the temperature of the surface of the land. Limitations related to the maximum load of the drone lead to use of lightweight uncooled thermal cameras whose internal components are not stabilized to a constant temperature. Such cameras suffer from several unwanted effects that contribute to the increase in temperature measurement error from ±0.5 °C in laboratory conditions, to ±5 °C in unstable flight conditions. This article describes a post processing procedure, that reduces the above unwanted effects. It consists of following steps: i) devignetting using single image vignette correction algorithm, ii) georeferencing of images using EXIF data, scale-invariant feature transform (SIFT) stitching, and gradient descent optimisation, and iii) temperature calibration by minimisation of bias between overlapping thermal images using gradient descent optimisation. The solution was tested in several case studies of river areas, where natural water bodies were used as a reference temperature benchmark. In all tests, the precision of the measurements was increased. The root of the mean of the Square of Errors RMSE on average was reduced by 39.0% and Mean of the absolute value of Errors MAE by 40.5%. The proposed algorithm can be called self-calibrating, as in contrast to other known solutions is fully automatic, uses only field data and does not require any calibration equipment or additional operator effort. A Python implementation of the solution is available on GitHub.

Polynomial recurrence bounds for a class of stochastic differential equations with a gradient type drift and an additive Wiener process are studied without Lyapunov functions.

The mechanical properties and stability of hydrophobic surface structures prepared by traditional methods are still the main technical bottlenecks restricting the broad application of hydrophobic systems on workpiece surfaces. In this contribution, we propose a technique called selective laser shock peening (SLSP) to enable large-scale high efficient, low-cost manufacturing of hydrophobic metal surfaces with enhanced mechanical properties for durable applications. Using the method of experimental investigation combined with numerical calculation, the hydrophobic properties, mechanical properties, and tribological properties of the samples prepared under SLSP, all-laser shock peening (ALSP), and non-laser shock peening (NLSP) are studied. The SLSP process could prepare a 3D gradient structure material with surface structures, a two-phase (strong phase, soft phase) distribution on the surface, and a multi-level gradient distribution in the thickness direction. Compared with the 2D gradient structure prepared by the traditional process, 3D gradient structures by SLSP have more significant advantages in improving the wetting behavior and the mechanical properties of the material, which proves SLSP to be a novel method to fabricate functional metal surface structures, with highly high engineering application value.

The feasibility of using full ion kinetics, instead of gyrokinetics, in simulating low-frequency Ion-Temperature-Gradient (ITG) instabilities in tokamaks has recently been demonstrated by Sturdevant et al. [Physics of Plasmas 24, 081207 (2017)]. In that work, a variational integrator was developed to integrate the full orbits of ions in toroidal geometry, which proved to be accurate in capturing both the short-time scale cyclotron motion and long time scale drift motion. The present work extends that work in three aspects. First, we implement a relatively simple full orbit integrator, the Boris integrator, and demonstrate that the accuracy of this integrator is also sufficient for simulation of ITG instabilities. Second, the equilibrium magnetic configuration is extended to general toroidal configuration specified numerically, enabling simulation of realistic equilibria reconstructed from tokamak experiments. Third, we extend that work to the nonlinear regime and investigate the nonlinear saturation of ITG instabilities. To verify the new numerical implementation of the orbit integrator and magnetic configuration, the linear electrostatic ITG frequency and growth rate are compared with those given in Sturdevant's work and good agreement is found.

Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's Disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and Magnetic Resonance Imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and Mini-Mental State Exam (MMSE). A Residual Network with 50 layers (ResNet-50) predicted CDR presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4,139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine Learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Traditional lithium-ion batteries cannot meet the high flexibility and bendability requirements of modern flexible electronic devices due to the limitations of their electrode materials. Therefore, the development of high-performance flexible energy storage devices is of great significance for promoting flexible electronics. In recent years, one-dimensional flexible fiber lithium-ion batteries have been rapidly developed due to the advantages of high flexibility and bendability. However, it is still a big challenge to realize 1D flexible fiber lithium-ion batteries with excellent electro-chemical properties and good mechanical performance. In this work, a reduced graphene ox-ide-based printing ink is proposed to fabricate flexible Li4Ti5O12/graphene fiber electrodes through a 3D printing assembly strategy. It is noteworthy that the green reducing agent vitamin C was used to reduce graphene oxide in one step, which improved the conductivity of the fiber elec-trode. Furthermore, a 3D conductive network is constructed inside the fiber electrodes due to the high specific surface area of reduced graphene oxide, which enhances the electronic conductivity and ion mobility. The fiber electrode not only has good mechanical performance, but also has ex-cellent electrochemical properties. Equally important, the method is simple and efficient, and the working environment is flexible. It can precisely control the shape, size and structure of the one-dimensional fiber flexible electrode, which is of great significance for the development of fu-ture flexible electronic devices.

The thermal conditions of transitional (ranging from warm to cold) coldwater streams impact the ranges and resource availabilities for biota inhabiting these lotic systems. With ongoing climate change and increasing land modifications, thermal boundaries may shift, altering thermal transition zones and their biotic communities. The objective of this study was to investigate the condition of trout across three forks of the Whitewater River catchment and to investigate factors influencing fish community composition and distribution. Each fork was characterized into three separate sections: headwater (coolwater), middle (warmwater), and lower (coldwater). Springs were identified throughout each fork, with greatest concentrations in the lower sections of each fork. Using single-pass electrofishing, we sampled 61 sites across the three forks in the Whitewater River system (North = 21 sites, Middle = 19, South = 21), and catch statistics were used to calculate diversity, trout abundance, and trout condition. In general, diversity increased, and trout were healthier but less abundant in middle and headwater sections, whereas diversity decreased slightly, trout condition decreased, and trout abundance increased in lower reaches, with changes differing somewhat among forks. Canonical correlation analysis had strong significant correlations showing simpson diversity and condition increase going upstream with high non trout abundance and catch rates while trout catch rates and reach width decrease. The Whitewater River is a catchment exhibiting transitional temperature-pattern characteristics with generally low fish community diversity and trout conditions that range from thin, normal, and robust. Dominated by a changing landscape (agriculture) and intensifying climate change, we may begin to see stream temperatures increase along with species diversity. Understanding how spring temperature influences species composition and distribution can bring potential stressors to light increasing our understanding of thermal conditions and to help mitigate the negative impacts from land use and climate change.

Magnetorheological (MR) fluids have been known to react to magnetic fields of sufficient magnitudes. While in the presence of the field the material develops a yield stress. The tunable property has made it attractive in e.g. semi-active damper applications in vibration control domain in particular. Within the context of a given application the fluids can be exploited in at least one of the fundamental operating modes of which the so-called gradient pinch mode has been least explored. Contrary to the other operating modes, the MR fluid volume in the flow channel is exposed to a non-uniform magnetic field in such a way that the Venturi-like contraction is developed in a flow channel solely by means of a solidified material in the regions near the walls rather than mechanically driven changes of the channel’s geometry. The pinch mode rheology of the material has made it a potential candidate in developing on a new category of MR valves. By convention, a pinch mode valve features a single flow channel with poles over which non-uniform magnetic field is induced. In this study the authors examine ways of extending the dynamic range of pinch mode valves by employing a number of such arrangements (stages) in series. To accomplish this, the authors developed a prototype of a multi-stage (three-stage) valve, and then compared its performance against that of a single-stage valve across a wide range of hydraulic and magnetic stimuli. To summarize, improvements in the pinch mode valve dynamic range are evident, however, at the same time it is hampered by the presence of serial air gaps in the flow channel.

Radiation is the major driver of snowmelt, and hence its estimation is critically important. Net radiation reaching the forest floor is influenced by vegetation density. Previous studies in mid-latitude conifer forests have confirmed that net radiation decreases and then subsequently increases with increasing vegetation density, for clear sky conditions. This leads to existence of a net radiation minimum at an intermediate vegetation density. With increasing cloud cover, the minimum radiation shifts toward lower densities, sometimes resulting in a monotonically increasing radiation with vegetation density. The net radiation trend, however, is expected to change across sites, affecting the magnitude and timing of individual radiation components. This research explores the variability of net radiation on snow-covered forest floor for different vegetation densities along a latitudinal gradient. We especially investigate how the magnitude of minimum/maximum radiation and the corresponding vegetation density change with the site geographical location. To evaluate these, the net radiation is evaluated using the Forest Radiation Model at six different locations in predominantly white spruce (Picea glauca) canopy cover across North America, ranging from 45 to 66°N latitudes. Results show that the variation of net radiation with vegetation density considerably varies with latitude. In higher latitude forests, the magnitude of net radiation is generally smaller, and the minimum radiation is exhibited at relatively sparser vegetation densities, under clear sky conditions. For interspersed cloudy sky conditions, net radiation non-monotonically varies with latitude across the sites, depending on the seasonal sky cloudiness and air temperature. Latitudinal sensitivity of net radiation is lower on north-facing hillslopes than on south-facing sites.

Synthetic polymer-based gradient foams have considered as promising category of functionally graded materials with unique properties. In this study, the carbon dioxide (CO₂) foaming technology has used for PET-PEN (Polyethylene Terephthalate - Polyethylene Naphthalate) copolymer towards porous functional materials with thermal insulation with reasonable mechanical strength. Through scanning electron microscope based morphological characterization, a potential to fabricate gradient foam structures with micro-pores has identified. It has shown that variation of post-foaming temperature can tune the pore size distribution although the very high post-foaming temperature tends to cause structural instability. Thermal measurement data set the limits of operation, confirmed by simultaneous differential scanning calorimeter and thermo-gravimetric analysis. Mechanical stress and thermal conductivity also has measured to find rationale of thermal insulation with reasonable mechanical strength and to elucidate the actual 3D grid foam of copolymer.

Pollution of urban surface dust with hazardous elements (HEs) is a serious environmental issue due to its toxicity and potential hazardous effects. Surface dust samples were collected from core urban, urban, and suburban gradients in the Urumqi city of the arid NW China, and the concentrations of six HEs, such as arsenic (As), cadmium (Cd), nickel (Ni), lead (Pb), mercury (Hg), and chromium (Cr), were determined. The pollution load index (PLI) and the US EPA health risk assessment model were applied to analyze and compare the pollution levels and potential health risk of HEs in surface dusts in different urbanization gradients. The obtained results indicate that the average concentrations of Hg, Cd, and Ni in surface dust decrease in the order of core urban > urban > suburban, whereas the average concentrations of As, Cr, and Pb decrease in the order of urban > core urban > suburban. The PLI of HEs in surface dust decreased in the order of core urban > urban > suburban. The concentrations of HEs in the core urban and urban gradients are relatively higher than in the suburban gradient. Furthermore, the total non-carcinogenic and carcinogenic risk index of investigated HEs in surface dust decrease in order of urban > core urban > suburban, for both adults and children. In addition, the pollution of surface dust with HEs in all urbanization gradients is more harmful to the children's health than to the adults. Overall, the potential non-carcinogenic and carcinogenic health risks of the investigated HEs, instigated primarily by oral ingestion of surface dust, are found to be within the acceptable range. However, urbanization can effects the accumulation of HEs in surface dust, and Cr is the main non-carcinogenic risk factor, whereas Cd is the main carcinogenic risk factor among the analyzed HEs in surface dust in all urbanization gradients.

Oxygen evolution reaction (OER) is a key half-reaction in electrocatalytic water splitting. Large-scale water electrolysis is hampered by the commercial precious metal-based OER electrocatalysts owing to their high cost and slow kinetics. To address these issues, we present a facile one-pot room-temperature coprecipitation method to quickly synthesize carbon nanotubes-interconnected amorphous NiFe-layered double hydroxides (NiFe-LDH@CNT) as low-cost, efficient and stable OER electrocatalyst. NiFe-LDH@CNT hybrid catalyst delivers outstanding OER performance with a low onset overpotential of 255 mV and a small Tafel slope of 51.2 mV dec-1, as well as outstanding long-term stability. The terrific catalytic capability of NiFe-LDH@CNT can be associated with the synergistic effects of its room-temperature synthesized amorphous structure, bi-metallic modulation, and conductive CNT skeleton. The room-temperature synthesis can not only offer economic feasibility, but also obtain amorphous NiFe-LDH without crystalline boundaries, facilitating long-term stability during OER process. The bi-metallic nature of NiFe-LDH guarantees a modified electronic structure, providing additional catalytic sites. Simultaneously, the highly conductive CNT network fosters a nanoporous structure, facilitating electron transfer, O2 release and enriching catalytic sites. This study presents a strategy to purposefully design nanoarchitecture and facilely synthesize amorphous transition metal-based OER catalysts, ensuring the cost effectiveness, production efficiency, and long-term stability.

Thermally-induced moisture flow in unsaturated soils is a complex coupled thermal-hydro process involving moisture flow both in vapor and liquid phases. Thermally-induced moisture flow in unsaturated sands is usually less pronounced than fine soils due to their low field capacity. It is affected by many factors, including soil density, particle size, moisture content, and gravity-induced flow. Besides, soil moisture sensors need to be highly accurate and calibrated for temperature effects in order to monitor the thermally induced moisture flow accurately. A silty sand with 35% non-plastic fines was selected to study the coupled thermo-hydro processes in a closed heat cell. The heat cell consists of two temperature-controlled heat exchanger plates at the top and bottom, heat flux sensors, moisture sensors, thermocouples, and heat conductivity sensors. The soil was prepared at various initial moisture content and tested under a constant 1-D temperature gradient. The soil moisture sensors were first calibrated in the test soil at room temperature and then at elevated temperatures. The soil moisture redistribution, temperature and thermal conductivity profiles were determined. The critical moisture content, corresponding to maximum moisture movement, was also determined. The results offer new insights into coupled moisture-heat flow in unsaturated sands.

We hypothesize that the first ancestral "ante-cell" molecular structures, i.e., first RNAs and peptides which gradually transformed into real cells once the Earth had cooled sufficiently for organic molecules to appear there, have leaved traces in RNAs and genes of present cells. We propose a circular RNA which could have been one of these ancestral structures whose vestigial pentameric subsequences would mark the evolution from this key moment when the ante-cells have begun to join the living organisms. In particular, we propose that in present RNAs (ribosomal or messenger) playing an important role in the metabolism of current cells, we look for traces of the proposed primitive structure in the form of pentamers (or longer fragments) belonging to their nucleotide sequence. The result obtained can be summarized in the existence of a gradient of occurrence of such pentamers, with a high frequency for the most vital functions (protein synthesis, nucleic synthesis, cell respiration, etc.). This gradient is also visible between organisms, from the oldest (Archaea) to the most recent (Eukaryotes) in evolution of species.

Urbanization affects all elements of the pre-urban environment, including soils, hydrology, vegetation, and microclimate. Recently, Saudi Arabia has experienced rapid urbanization and growth. Thus, the country's biodiversity has been threatened. In Riyadh, beetle assemblages were assessed along a rural-suburban-urban gradient. A total of 2,791 individuals from 94 species belonging to seven families were collected at 15 sites along three different gradients of urbanization in Wadi Hanifa. Tenebrionidae dominated abundance (60.1%) and richness (38%). Beetle abundance, evenness, and diversity were not different among habitats; however, species richness was higher in rural habitats. DCA and CCA analyses showed distinct differences among sites along gradients. Urbanization intensity, soil variables, and land cover were significantly correlated with DCA axis 1, while elevation and flora were significantly correlated with DCA axis 2. The most critical operating environmental variables in Wadi Hanifa were buildings, elevation, soil organic carbon, litter cover, and litter depth, as well as plants such as Launaea capitata, Lycium shawii, Alhagi graecorum, and Heliotropium currasavicum. Ten species in our study are associated with urban habitats, six with suburban habitats, and seven with rural habitats. Consequently, expanding urban areas may negatively affect the richness and composition of beetles and may result in the loss of some native species.

The requirements for efficient replacement of fossil fuel, combined with the growing energy crisis, put hydrogen production into focus. Efficient and cost-effective electrocatalysts are needed for H2 production, and novel strategies for their discovery must be developed. Here we utilized Kinetic Monte Carlo (KMC) simulations to demonstrate that hydrogen evolution reaction (HER) can be boosted via hydrogen spillover to the support when the catalyst surface is largely covered by adsorbed hydrogen under operating conditions. Based on the insights from KMC, we synthesized a series of reduced graphene oxide-supported catalysts and compared their activities towards HER in alkaline media with that of corresponding pure metals. For Ag, Au, and Zn, the support effect is negative, but for Pt, Pd, Fe, Co, and Ni, the presence of the support enhances HER activity. The HER volcano, constructed using calculated hydrogen binding energies and measured HER activities, shows a positive shift of the strong binding branch. This work demonstrates the possibilities of metal|support interface engineering for producing effective HER catalysts and provides general guidelines for choosing novel catalyst-support combinations for electrocatalytic hydrogen production.

Airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units(IMUs), data-processing computer and Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing, such as array SAR, multinode imaging loads and so on. ADPOS can provide multinode high accuracy spatio-temporal reference information to realize multinode motion compensation with the various state estimation methods such as Central Difference Kalman Filtering (CDKF), modifed CDKF. However, these methods focus on noise optimization based on known nonlinear models and its linear minimum variance estimation criterion, which cannot gain better estimation performance for high-dimensional nonlinear systems. For this reason, the variational optimization process based on conjugate gradient algorithm is proposed for the estimation of filtering update step, which is more efficient than linear minimum variance estimation criterion. Therefore, aiming at ADPOS with high-dimensional and nonlinearity, Central Difference Variational Filtering (CDVF) using variational optimization based on conjugate gradient algorithm for mean correction in the filtering update stage is devoted to advancing the estimation accuracy of multinode motion parameters. A real ADPOS flight test has been conducted, the experimental results show that the accuracy of the slave motion parameters has a distinct progress than the up to date CDKF, and the compensation performance is distinct.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

Little is known about how differences in water availability within the “super humid” tropics can influence the distribution of understory plant species and the composition of understory plant communities. We investigated variation in the physiological drought tolerances of understory plants and plant communities across a large elevation and precipitation gradient. We established 58 understory plots along a gradient of 400 – 3600 m asl elevation and 1000 – 6000 mm yr-1 rainfall in and around Manu National Park in southeastern Peru. Within the plots, we sampled all understory woody plants and measured three metrics of physiological leaf drought tolerance - turgor loss point (TLP), cuticular conductance (Gmin), and solute leakage (SL) - and assessed how the community-level means of these traits related to mean annual precipitation (MAP) and elevation (in the study gradient temperature decreases linearly and vapor pressure deficit increases monotonically with elevation). We did not find any correlations between the three metrics of leaf drought tolerance, suggesting that they represent independent strategies for coping with low water availability. Despite being widely used metrics of leaf drought tolerance, neither TLP nor Gmin showed any significant relationships with elevation or MAP. In contrast, SL, which has only recently been developed for use in ecological field studies, increased significantly at higher precipitations and at lower elevations (i.e., plants in colder and drier habitats have lower SL, indicating greater drought tolerances). Our results illustrate that differences in water availability may play a strong role in structuring plant communities even in in the wet tropics and highlight the potential for SL assays to be an efficient and effective tool for measuring drought tolerances in the field.

Soil erosion is the major factor that has contributed to the reduction of soil fertility in the highlands of Ethiopia. This factor was reduced by soil and water conservation (SWC) practices. Hence, the study evaluated the effects of SWC practices and slope gradient on soil physicochemical properties in the Agemi watershed of northwestern Ethiopia. Farmland treated by SWC such as stone-faced soil bund (SFSB) and soil bund (SB), untreated farmland and slope gradient were identified in the study area. About 27 soil samples were collected from the treated farmland and untreated farmland with three slope classes, including lower (5-10%), middle (10-15%) and upper slopes (>15%). The soil physicochemical properties were analyzed by two ways analysis of variance (ANOVA) using SAS 9.2. The results of the study showed that the content of sand, clay, total porosity, pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), exchangeable cations such as Ca²⁺, Mg²⁺, K⁺ and cation exchange capacity (CEC) were significantly affected (p<0.01) by SWC practices and untreated farmland with slope gradients. However, the content of silt, percent of base saturation (PBS) and exchangeable Na were statistically insignificant (p>0.05). Farmland treated with SFSB confirmed higher mean values of soil physicochemical properties than SB and untreated farmland. Moreover, the lower total porosity (TP) and higher bulk density (BD) were recorded on untreated farmland and upper slopes. This study concludes that farmland treated with SFSB improved soil physicochemical properties than farmland treated with SB followed by untreated farmland. Therefore, SFSB should be implemented on farmland to improve soil fertility in the study area.

The Hertzian contact theory, as well as most of the other classical theories of normal and tangential contact, provides displacements and the distribution of normal and tangential stress components directly in the contact surface. However, other components of the full stress tensor in the material may essentially influence the material behaviour in contact. Of particular interest are principal stresses and the equivalent von Mises stress, as well as the gradient of the hydrostatic pressure. For many engineering and biomechanical problems, it would be important to find these stress characteristics at least in the contact plane. In the present paper, we show that the complete stress state in the contact plane can be easily found for axially symmetric contacts under very general assumptions. We provide simple explicit equations for all stress components and the normal component of the gradient of hydrostatic pressure in the form of one-dimensional integrals.

In southern South America, zonal flora, defined by macroclimatic conditions, is strongly influenced by an aridity filter near 29°S. Cushion bogs, high-altitude wetlands along the Andes, display homogeneous flora with cushion species dominance, contrasting with zonal vegetation. Despite being influenced by microclimatic conditions, these ecosystems experience varied environmental effects. This study identifies environmental filters affecting bog communities along a broad-scale latitudinal gradient from 15°S to 42°S. We analyzed 420 bogs and 284 species across three macroclimatic regions with distinct summer, winter, and transitional arid rainfall regimes. Using variance partitioning and membership-based regionalization models, we examined the impacts of climatic, edaphic, and spatial variables on beta diversity. We also assessed species' niche overlap and the influence of environmental filters on the communities' phylogenetic diversity. Results show that species turnover and niche overlap vary with macroclimatic differences, delineating three distinct regions. Notably, phylogenetic clustering in the driest part of the gradient (23°S —24°S) highlights the impact of the environmental filter. Aridity and temperature variations at a broad scale serve as environmental filters shaping the composition of bog communities across southern South America.

In this work, we explore the extreme searching of multidimensional functions by natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing multidimensional surface with probability distributions, which allows us to reduce changing the accuracy of gradient and step-size. In this article, we propose an algorithm of natural gradient descent based on Dirichlet and generalized Dirichlet distributions. We demonstrate that the natural gradient descent with step-size adaptation with Dirichlet and generalized Dirichlet distributions has higher accuracy and does not take a large number of iterations for minimizing test functions than gradient descent and Adam.

Gradient based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving highly nonlinear problems. The BFGS quasi-Newton method is the most commonly studied second order method for neural network training. Recent methods have shown to speed up the convergence of the BFGS method using the Nesterov’s acclerated gradient and momentum terms. The SR1 quasi-Newton method though less commonly used in training neural networks, are known to have interesting properties and provide good Hessian approximations when used with a trust-region approach. Thus, this paper aims to investigate accelerating the Symmetric Rank-1 (SR1) quasi-Newton method with the Nesterov’s gradient for training neural networks and briefly discuss its convergence. The performance of the proposed method is evaluated on a function approximation and image classification problem.

Buckypaper (BP), a flexible and porous material, exhibits photovoltaic properties when exposed to light. In this study, we have employed Radio Frequency (RF) Sputtering of Zinc Oxide (ZnO) followed by rapid thermal annealing to enhance the photovoltaic response of BP. We investigated the impact of various sputtering parameters, such as the gas flow ratio of argon to oxygen and deposition time, on the morphology, composition, resistivity, and photovoltaic characteristics of ZnO-modified BP. Additionally, the photovoltaic performance of the samples under different illumination modes and wavelengths was compared. It was found that optimal sputtering conditions—argon to oxygen flow ratio of 1:2, deposition time of 20 minutes, and power of 100 watts—resulted in a ZnO film thickness of approximately 45 nanometers. After annealing at 400°C for 10 minutes, the ZnO-modified BP demonstrated a significant increase in photocurrent and photovoltage, along with a reduction in resistivity, compared to unmodified BP. Moreover, under gradient illumination, the ZnO-modified BP exhibited a photovoltage enhancement of 14.70 times and a photocurrent increase of 13.86 times, compared to uniform illumination. Under blue light, it showed a higher photovoltaic response than under other colors. The enhancement in photovoltaic response is attributed to the formation of a Schottky junction between ZnO and BP, an increased carrier concentration gradient, and an expanded light absorption spectrum. Our results validate that ZnO sputtering followed by annealing is an effective method for modifying BP for photovoltaic applications such as solar cells and photodetectors.

In order to investigate the vertical distribution pattern of soil fauna community in low-altitude mountain areas. On July 8th, 2022, a low hill was selected as the study area to collect soil arthropods were collected through trap. Leaf litter, vegetation type and distribution quantity were investigated at each sampling site while soil fauna were collected. Also, the soil physical and chemical parameters were measured simultaneously. The results of one-way ANOVA showed that there were significant differences (P

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

In this paper, a novel nonlocal strain gradient anisotropic elastic shell model is developed to analyse the vibrations of simply supported single‒walled carbon nanotubes (SWCNTs). Sanders‒Koiter shell theory is used to obtain the strain‒displacement relationships. Eringen nonlocal elasticity theory and Mindlin strain gradient theory are adopted to derive the constitutive equations, where the anisotropic elastic constants are expressed via Chang molecular mechanics model. The complex variable method is used to analytically solve the equations of motion and to obtain the natural frequencies of SWCNTs. First, the anisotropic elastic shell model is validated via comparisons with the results of molecular dynamics simulations reported in the literature. Then, the effect of nonlocal and material parameters on the natural frequencies of SWCNTs with different geometries and wavenumbers is analysed. From the numerical simulations it is obtained that the natural frequencies decrease with increasing nonlocal parameter, while they increase with increasing material parameter. Moreover, the decrease of natural frequencies with increasing SWCNT radius is exponential as the material parameter increases, while it is linear as the nonlocal parameter increases. Finally, as the number of waves increases, the natural frequencies linearly vary with increasing nonlocal parameter, while they exponentially increase with increasing material parameter.

Intelligent transportation systems (ITS) have recently evolved rapidly, which requires development of highly trustworthy and effective communication technologies for uses, including vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication; where Orthogonal Frequency Division Multiplexing (OFDM) is regarded as a strong candidate and highly popular option technique among these methods. However, the movement of vehicles introduces Doppler frequencies which produce inter-carrier interference (ICI), that is frequently occurs in V2X channels. This interference has the potential to compromise the integrity of subcarrier orthogonality within OFDM, leading to lower-quality communication and an increased likelihood of data transmission errors. When employing channels with doubly dispersive fading, OFDM necessitates the usage of a complex equalization based on the minimum mean-square error (MMSE) equalizer, which requires channel matrix inversion. Several low-complexity equalizers for OFDM have been developed and are based on band factorization, time domain LSQR (Least-Square QR) iterative computing, and banded minimum mean squared error (BMMSE). This paper proposes Conjugate Gradient Least Squares (CGLS), which is a novel iterative computation algorithm integrated with nonlinear equalizers. The suggested nonlinear equalization technique determines the trade-off between computations and performance. According to simulation data, the suggested nonlinear equalizer performs better than the current BMMSE and linear CGLS algorithms across doubly dispersive fading channels.

Purpose: Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI image quality is directly related to low gradient ampli-tudes, since weak gradients must be compensated with long echo times. Methods: We propose a new type of MRl accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients. Results: Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC. Conclusions: Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

Producing complex shaped tungsten carbide cobalt (WC-Co) tools by classical technologies is difficult and often impossible due to their high hardness and brittle fracture behavior. Additive Manufacturing (AM) is a suitable technology for creating complex structures and simultaneously shortening expensive machining processes. Binder Jetting (BJ) is an innovative AM technology that offers several advantages over laser-based processes, for example low manufacturing costs and high build-up rates. Binders with nanoparticle additives have already proven to be effective in in- creasing the packing density of the powder bed and improving the sintering properties. Addition- ally, they offer the possibility of selectively changing the material composition in the part. This paper presents a concept for the use of nanoparticles to generate gradients in the green compact, which leads to a cobalt gradient in the part after sintering. The possibility of introducing particles locally into complex structures allows local modification of the material properties.

Temperature is one of the most important range-limiting factors for many seaweeds. Driven by the recent climatic changes, rapid northward shifts of species’ distribution ranges can potentially modify the phylogeographic signature of Last Glacial Maximum such as increased genetic diversity at lower latitudes. To explore this question in detail in the kelp Saccharina latissima, we used microsatellites and double digest RAD-sequencing derived SNPs on S. latissima sporophytes sampled within 11 sites spanning the entire European Atlantic latitudinal range of this species. In addition, we checked for statistical correlation between genetic marker allele frequencies and three environmental proxies (sea surface temperature, salinity, and water turbidity). Our findings revealed that genetic diversity was significantly higher for the northern localities compared to the southern ones in contrast to the expected phylogeographic pattern. This suggests that the southernmost S. latissima populations are negatively affected by the recent climatic changes but also that the recolonization of S. latissima range following the LGM may have occurred from northerly refugia. Seven SNPs and 12 microsatellite alleles were found to be significantly associated with at least one of the three environmental variables. We discuss the potential adaptive functions of the genes associated with the outlier markers and the importance of these markers for successful conservation and aquaculture strategies for S. latissima in this age of rapid global change.

Over the past 150 million years, the Chilean Atacama Desert has been transformed into one of the most inhospitable landscapes by geophysical changes, which makes it an ideal Mars analog that has been explored for decades. However, two heavy rainfalls that occurred in the Atacama in 2015 and 2017 provide a unique opportunity to study the response of resident extremophiles to rapid environmental change associated with excessive water and salt shock. Here we combine mineral/ salt composition measurements, amendment cell culture experiments, and next-generation sequencing analyses to study the variations in salts and microbial communities along a latitudinal aridity gradient of the Atacama Desert. In addition, we examine the reshuffling of Atacama microbiomes after the two rainfall events by comparing with previous researches. Analysis of microbial community composition revealed that soils within the southern arid desert were consistently dominated by Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, Chloroflexi, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia. Intriguingly, the hyperarid microbial consortia exhibited a similar pattern to the more southern desert. Salts at the shallow subsurface were dissolved and leached down to a deeper layer, challenging indigenous microorganisms with the increasing osmotic stress. Microbial viability was found to change with aridity and rainfall events. This study sheds light on the structure of xerophilic, halophilic, and radioresistant microbiomes from the hyperarid northern desert to the less arid southern transition region, as well as their response to changes in water availability. Our findings may infer similar events that happened on the wetter early Mars.

This study assessed the periodic fluctuations among microbial biomass Carbon (C), Nitrogen (N) and Phosphorus (P), and the consequences of variations in altitude and abiotic factors on the soil microbial biomass (C, N and P) in a temperate mixed-oak pine forest of Central Himalaya. This research was directed at three forest stands along an altitudinal gradient. Samples were collected in triplicates, seasonally from each selected site and microbial C, N and P were determined through the fumigation extraction method. Microbial biomass C, N and P decreased significantly (P<0.01, correlation coefficient -0.985, -0.963, -0.948, respectively) with increasing altitude, while the rainy season showed the highest values, and winter season revealed the least values. Microbial biomass C, N and P showed positive correlation with silt particles, water holding capacity, bulk density, porosity, soil moisture, organic C, total N and P, and negative correlations with sand particles and soil pH. The microbial biomass C showed strong associations with soil microbial N (r=0.80, P<0.01) and P (r=0.89, P<0.01) contents, while the soil microbial biomass N and P also showed strong positive correlation (r=0.92, P< 0.01). Soil microbial biomass was greatly influenced by the altitude and abiotic variables whereas, weakly by temporal variation. The microbial C: N ratio indicated that fertility of soil is influenced by the species assemblage. Our findings suggest that high microbial biomass and low C: N ratio during rainy season could be considered as a strategy to conserve nutrients by temperate mixed-oak pine forest ecosystem.

Oil spillage over a sea or ocean’s surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data has been used efficiently for the detection of oil spills due to its operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, look-alikes, ships and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification; and introduce to use a new loss function, namely ‘gradient profile’ (GP) loss, which is in fact the constituent of the more generic Spatial Profile loss proposed for image translation problems. For the purpose of training, testing and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, look-alikes, ships and land class is 96.00%, 60.87%, 74.61% and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. Experimental results demonstrate that our affine motion estimation algorithm reduces encoding time by approximately 15%-35% while maintaining video bitrate and quality.

Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an image gradient regularized multi-modal multi-discrimination sparse-attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. Methods: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural-similarity-index (SSIM), and mean-squared-error (MSE). A Turing test and experts’ contours evaluated the image synthesis quality. Results: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values <0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, close to random guessing, supporting the model's effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90, compared to the inter-operator DICE of 0.91. Conclusion: We demonstrated the function of a novel multi-modal MR image synthesis neural network, GRMM-GAN, for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.

The response of a single tin oxide nanowire was collected at different temperatures to create a virtual array of sensors working as a nano-electronic nose. The single nanowire, acting as a chemiresistor, was first tested with pure ammonia and then used to determine the freshness status of trout fish (Oncorhynchus mykiss) in a rapid and non-invasive way. The gas sensor reacts to total volatile basic nitrogen, detecting the freshness status of the fish samples in less than 30 seconds. The sensor response at different temperatures correlates well with the total viable count (TVC), demonstrating that it is a good (albeit indirect) way of measuring the bacterial population in the sample. The nano-electronic nose is able to classify the samples according to their degree of freshness, but also to quantitatively estimate the concentration of microorganisms present. The system was tested with samples stored at different temperatures, managing to classify them perfectly (100%) and estimating their log(TVC) with an error lower than 5%.

Salinity is a known factor which shapes population dynamics and community structure through direct and indirect effects towards aquatic ecosystems. The responses of Moina macrocopa (Cladocera) collected from Setiu Wetland lagoon (Terengganu) were evaluated through manipulative laboratory experiments to understand the ability of M. macrocopa to tolerate under high salinity stress. Specifically, the fatty acid composition, growth, survival and reproduction of this cladocerans species were examined. Sodium chloride (NaCl) were used in the treatments water with the concentration 0, 4, 6, 8, 12, and 15psu. Fatty acid levels were determined using Gas Chromatography and Mass Spectrophotometry (GC-MS). Results clearly indicate that normal conditions give the highest content of fatty acid, especially the polyunsaturated fatty acid content like EPA (eicosapentaenoic acid), ALA (alpha-linoleic acid), ARA (arachidonic acid) and DHA (docosahexaenoic acid). Furthermore, M. macrocopa survival also was best at 0psu, with percentage reached 98%, whereas the opposite occurred at 15psu, with approximately 9% of viable animals survived. On other aspects, M. macrocopa also showed the highest reproduction rate at 0psu (e.g. average initial age of reproduction, 4.33±0.58 days) compared with other salinities level. Interestingly, the difference in terms of growth at different salinities was unapparent, an unexpected outcome when adverse effects such as osmoregulation pressure on the organism are considered. Based on the results, we conclude that M. macrocopa can only tolerate salinity below 8psu and, is unable to withstand stressful environmental condition engendered with salinities above 8psu.

Towards a better understanding of vegetation, permafrost, climate, landscape and lithology control on major and trace element (including macro and micro-nutrients and toxicants) transport in riverine systems, we studied two medium size (100-150 thousand km² watershed area) pristine rivers (Taz and Ket) of boreal and subarctic zone, western Siberia. Choosing the river basins of very low population density (< 1 people km-²) in the absence of any industrial or agricultural activity allowed testing the sole effect of natural factors and long-range atmospheric transfer on hydrochemistry of riverine solutes during the open water period. In the permafrost-bearing Taz River (main stem and 17 tributaries), sizable control of vegetation on element concentration was revealed. In particular, light coniferous and broadleaf mixed forest controlled DOC, and some nutrients (N, Mn, Fe, Mo, Cd, Ba); deciduous needleleaf forest positively correlated with macronutrients (P, Si, Mg, P, Ca) and Sr, and dark needle-leaf forest impacted Ntot, Al and Rb. Organic C stock in the upper 30-100 cm soil positively correlated with Be, Mn, Co, Mo, Cd, Sb, and Bi. The lithological control was generally poorly pronounced, due to abundant peat deposits overlaying the mineral strata. However, cretaceous carbonate mineral-bearing sedimentary deposits positively impacted the pH and concentration of Si, Mg, Ca and Cs. In the Ket River basin (large right tributary of the Ob River), we revealed the correlations between the phytomass stock at the watershed and alkaline-earth metals and U concentration in the river water. This control was weakly pronounced during high-water period (spring flood) and mostly evidenced during summer low water period.

Background Few data exist on comparative long-term outcomes of severe aortic stenosis (AS) patients with different flow-gradient patterns undergoing transcatheter aortic valve implantation (TAVI). This study sought to evaluate the impact of pre-TAVI flow-gradient pattern on long-term clinical outcomes after TAVI and assess changes in left ventricular ejection fraction (LVEF) of different subtypes of AS patients following TAVI. Methods Consecutive patients with severe AS undergoing TAVI in our institution were screened and prospectively enrolled. Patients were divided in four subgroups according to pre-TAVI flow/gradient pattern: (i) low flow - low gradient (LF-LG): stroke volume indexed (SVi) ≤ 35ml/m2 and mean gradient (MG) < 40mmHg), (ii) normal flow - low gradient (NF-LG): SVi > 35ml/m2 and MG < 40mmHg, (iii) low flow - high gradient (LF-HG) : SVi ≤ 35ml/m2 and MG ≥ 40mmHg and (iv) normal flow - high gradient (NF-HG): SVi > 35 ml/m2 and MG ≥ 40mmHg. Transthoracic echocardiography was repeated at 1-year follow-up. Clinical follow-up was obtained at 12 months, and yearly thereafter until 5-year follow up was complete for all patients. Results A total of 272 patients with complete echocardiographic and clinical follow-up were included in our analysis. Their mean age was 80±7 years and the majority of patients (N=138, 50.2%) were women. 62 patients (22.8% of the study population) were distributed in LF-LG group, 98 patients (36%) were LF-HG patients, 95 patients (34.9%) were NF-HG, while 17 patients (6.3%) were NF-LG. There was a greater prevalence of comorbidities among LF-LG AS patients. One-year all-cause mortality differed significantly between the four subgroups of AS patients (log rank p: 0.022) and was more prevalent among LF-LG patients (25.8%) compared to LF-HG (11.3%), NF-HG (6.3%) and NF-LG patients (18.8%). At 5-year follow-up, global mortality remained persistently higher among LF-LG patients (64.5%) compared to LF-HG (47.9%), NF-HG (42.9%) and NF-LG patients (58.8%) (log rank p: 0.029). At multivariable Cox hazard regression analysis, baseline SVi (HR: 0.951, 95% C.I.; 0.918 – 0.984), the presence of at least moderate tricuspid regurgitation at baseline (HR: 3.091, 95% C.I: 1.645 - 5.809) and at least moderate paravalvular leak (PVL) post-TAVI (HR: 1.456 , 95% C.I.: 1.106 - 1.792 ) were significant independent predictors of late global mortality. LF-LG patients and LF-HG patients exhibited a significant increase in LVEF at 1-year follow-up. A lower LVEF (p<0.001) and a lower Svi (p<0.001) at baseline were associated with LVEF improvement at 1 year. Conclusions Patients with LF-LG AS have acceptable 1-year outcomes with significant improvement in LVEF at 1-year follow-up, but exhibit an exceedingly high 5-year mortality following TAVI. The presence of low transvalvular flow and at least moderate tricuspid regurgitation at baseline and significant paravalvular leak post-TAVI were associated with poorer long-term outcomes in the entire cohort of AS patients. The presence of a low LVEF or a low SVi predicts LVEF improvement at 1-year.

Sympatric distribution and temporal overlap of cryptic zooplankton species pose a challenge to the framework of the niche differentiation theory and the mechanisms allowing competitor coexistence. We applied the methods of phylogenetic analysis, DNA taxonomy and statistical analysis to study the temporal distribution patterns of cryptic B. calyciflorus species, an excellent model, in three lakes, and to explore the putative mechanisms for their seasonal succession and temporal overlap. The results showed that in the warm-temperate Lake Yunlong, B. fernandoi and B. calyciflorus s.s. underwent a seasonal succession, which was largely attributed to their differential adaptation to water temperature. In the subtropical Lake Jinghu, B. fernandoi, B. calyciflorus s.s. and B. dorcas exhibited both seasonal succession and temporal overlap. Seasonal successions were largely attributed to their differential adaptation to temperature, and temporal overlap resulted from their differential responses to algal food concentration. In the tropical Lake Jinniu, B. calyciflorus s.s. persisted throughout the year, and overlapped with B. dorcas for 5 months. Temporal overlap resulted from their differential responses to copepod predation. These results indicated that the temporal distribution pattern of cryptic B. calyciforus species and the mechanism allowing competitor coexistence vary with different climate zones.

This paper presents three types of tensor Conjugate-Gradient methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatical determination strategy of a suitable regularization parameter is proposed for the tensor conjugate gradient (tCG) method. A truncated version and a preprocessed verion of the tCG method are further presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples are given to show the effectiveness of the proposed tCG methods in image and video restoration.

Quantum computing is an emerging field that have given a significant impact on optimization. Among the diverse quantum algorithm, the quantum gradient descent has become a prominent technique for solving unconstrained optimization problems. In this paper, we propose a quantum-spectral Polak-Ribi{\'e}re-Polyak (PRP) conjugate gradient approach. The technique is considered as a generalization of the spectral PRP method which employs a $q$-gradient that approximate the classical gradient with quadratically better dependence on the quantum variable $q$. Additionally, the proposed method reduces to the classical variant as quantum variable $q$ gets closer to $1$. The quantum search direction always satisfies the sufficient descent condition and does not depend on any line search. This approach is globally convergent with the standard Wolfe line search without any convexity assumption. Numerical experiments are conducted and compared with the existing approach to demonstrate the improvement of the proposed strategy.

A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to wake effect. The main engine of the algorithm is a gradient-descent method, in which an analogy is established between power deficit and repulsive electrostatic potential energy. To achieve a maximum dispersion in the initial solution, throughout the optimization process, new turbines are included in the centre of the concession area, and quickly expand outward looking for areas with less potential, in turn pushing the previous ones. When the optimization process ends, to avoid local maximums, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a potential map of the entire area is created and a greedy algorithm includes new turbines to complete the layout with the final number of turbines. To speed up the search process and the creation of the potential map, two simplifications have been validated and added: the spatial distribution of velocities has been converted into its equivalent with point symmetry; and, for turbines affected by multiple wakes, the power deficit has been calculated by using a linear aggregation of powers, instead of the usual linear (or quadratic) aggregation of speeds. Owing to this type of aggregation, the process is accelerated in a factor of 90.

The object of this paper is the bending analysis of isotropic rectangular Kirchhoff plates subjected to a thermal gradient (TG) using the Fourier transform method. The bottom and top surfaces of the plate are assumed to have different changes in temperature, whereas the change in temperature of the mid-surface is zero. According to classical plate theory, the governing equation of the plate contains second derivatives of the TG; these derivatives are zero by constant value of the TG, which leads to the absence of the TG in the governing equation. This paper presented an approach by which Fourier sine transform was utilized to describe the TG, while the double trigonometric series of Navier and the simple trigonometric series of Lévy were utilized to describe the deflection. Thus, the TG appeared in the governing equation, which simplified the analysis. Rectangular plates simply supported along all edges were analyzed, bending moments, twisting moments, and deflections being determined. In addition, rectangular plates simply supported along two opposite edges were analyzed, the other edges having various support conditions (free, simply supported, and fixed).

Three-dimensional (3D) cell culture is considered more clinically relevant in mimicking the structural and physiological conditions of tumors in vivo compared to two-dimensional cell cultures. In recent years, high-throughput screening (HTS) in 3D cell arrays has been extensively used for drug discovery because of its usability and applicability. Herein, we developed a microfluidic spheroid culture device (μFSCD) with a concentration gradient generator (CGG) that enabled cells to form spheroids and grow in the presence of cancer drug gradients. The device is composed of concave microwells with several serpentine micro-channels which generate a concentration gradient. Once the colon cancer cells (HCT116) formed a single spheroid (approximately 120 μm in diameter) in each microwell, spheroids were perfused in the presence of the cancer drug gradient irinotecan for 3 days. The number of spheroids, roundness, and cell viability, were inversely proportional to the drug concentration. These results suggest that the μFSCD with a CGG has the potential to become an HTS platform for screening the efficacy of cancer drugs.