Search Results (27)

In arid and semi-arid climates, where water scarcity is a persistent challenge, accurately estimating reference evapotranspiration (ET) becomes essential for sustainable water management and agricultural planning. The objectives of this study are to compare hourly ET among P–M ASCE, P–M FAO, and P–M KSA mathematical models. In addition to the accuracy assessment of daily ET derived from hourly timestep calculations for the P–M ASCE, P–M FAO, and P–M KSA. To achieve these goals, a total of 525,600-min data points from the Riyadh region, KSA, were used to compute the reference ET at multiple temporal resolutions: hourly, daily, hourly averaged over 24 h, and daily as the sum of 24 h values, across all selected Penman–Monteith (P–M) models. For hourly investigation, the comparison between reference ET computed as average hourly values and as daily/24 h values revealed statistically and practically significant differences. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001) with R² of 94.75% for ASCE, 94.87% for KSA at h_plt = 50 cm, 92.41% for FAO, and 92.44% for KSA at h_plt = 12 cm. For daily investigation, comparing the sum of 24 h ET computations to daily ET measurements revealed an underestimation of daily ET values. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001), with R² exceeding 90% for all studied reference ET models. This comprehensive approach enabled a rigorous evaluation of reference ET dynamics under hyper-arid climatic conditions, which are characteristic of central Saudi Arabia. The findings contribute to the growing body of literature emphasizing the importance of high-frequency meteorological data for improving ET estimation accuracy in arid and semi-arid regions. Full article

Reactive oxygen species (ROS) are toxic by-products of aerobic cellular metabolism. However, ROS conduct multiple functions, and specific ROS sources can have beneficial or detrimental effects on plant health. This review explores the complex dynamics of ROS in plant defense mechanisms, focusing on their involvement in basal resistance, hypersensitive response (HR), and systemic acquired resistance (SAR). ROS, including superoxide anion (O²⁻), singlet oxygen (¹O₂), hydroxyl radicals (OH), and hydrogen peroxide (H₂O₂), are generated through various enzymatic pathways. They may serve to inhibit pathogen growth while also activating defense-related gene expression as signaling molecules. Oxidative damage in cells is mainly attributed to excess ROS production. ROS produce metabolic intermediates that are involved in various signaling pathways. The oxidative burst triggered by pathogen recognition initiates hyper-resistance (HR), a localized programmed cell death restricting pathogen spread. Additionally, ROS facilitate the establishment of SAR by inducing systemic signaling networks that enhance resistance across the plant. The interplay between ROS and phytohormones such as jasmonic acid (JA), salicylic acid (SA), and ethylene (ET) further complicates this regulatory framework, underscoring the importance of ROS in orchestrating both local and systemic defense responses. Grasping these mechanisms is essential for creating strategies that enhance plant resilience to biotic stresses. Full article

This paper presents numerical works on estimating some logistic models using particle swarm optimization (PSO). The considered models are the Verhulst model, Pearl and Reed generalization model, von Bertalanffy model, Richards model, Gompertz model, hyper-Gompertz model, Blumberg model, Turner et al. model, and Tsoularis model. We employ data on commercial and rural banking assets in Indonesia due to their tendency to correspond with logistic growth. Most banking asset forecasting uses statistical methods concentrating solely on short-term data forecasting. In banking asset forecasting, deterministic models are seldom employed, despite their capacity to predict data behavior for an extended time. Consequently, this paper employs logistic model forecasting. To improve the speed of the algorithm execution, we use the Cauchy criterion as one of the stopping criteria. For choosing the best model out of the nine models, we analyze several considerations such as the mean absolute percentage error, the root mean squared error, and the value of the carrying capacity in determining which models can be unselected. Consequently, we obtain the best-fitted model for each commercial and rural bank. We evaluate the performance of PSO against another metaheuristic algorithm known as spiral optimization for benchmarking purposes. We assess the robustness of the algorithm employing the Taguchi method. Ultimately, we present a novel logistic model which is a generalization of the existence model. We evaluate its parameters and compare the result with the best-obtained model. Full article

The analysis of complex and time-evolving interactions, such as those within social dynamics, represents a current challenge in the science of complex systems. Temporal networks stand as a suitable tool for schematizing such systems, encoding all the interactions appearing between pairs of individuals in discrete time. Over the years, network science has developed many measures to analyze and compare temporal networks. Some of them imply a decomposition of the network into small pieces of interactions; i.e., only involving a few nodes for a short time range. Along this line, a possible way to decompose a network is to assume an egocentric perspective; i.e., to consider for each node the time evolution of its neighborhood. This was proposed by Longa et al. by defining the “egocentric temporal neighborhood”, which has proven to be a useful tool for characterizing temporal networks relative to social interactions. However, this definition neglects group interactions (quite common in social domains), as they are always decomposed into pairwise connections. A more general framework that also allows considering larger interactions is represented by higher-order networks. Here, we generalize the description of social interactions to hypergraphs. Consequently, we generalize their decomposition into “hyper egocentric temporal neighborhoods”. This enables the analysis of social interactions, facilitating comparisons between different datasets or nodes within a dataset, while considering the intrinsic complexity presented by higher-order interactions. Even if we limit the order of interactions to the second order (triplets of nodes), our results reveal the importance of a higher-order representation.In fact, our analyses show that second-order structures are responsible for the majority of the variability at all scales: between datasets, amongst nodes, and over time. Full article

Hong Kong oysters (Crassostrea hongkongensis) are an important marine bivalve with nutritional and commercial value. The expanded off-bottom farming scale in recent years makes the oysters more susceptible to exposure to abiotic stresses, such as salinity stress, an important environmental factor that has been proven to have significant effects on oyster growth and development. However, the molecular mechanism is still unclear. Cyclin E is an important protein in the process of cell cycle regulation that is indispensable for propelling G1/S phase transition in a dose-dependent manner. In order to investigate whether the salinity stress affects cyclin E expression in oysters, the cDNA sequence of C. hongkongensis cyclin E (Ch-CCNE) was isolated from a gill cDNA library, and the 2.8 kbp length cDNA fragment contained a complete open reading frame (ORF) encoding 440 amino acid residues. Ch-CCNE mRNA was highly expressed in the gonad and low in the adductor mussel, mantle, gill, labial palp, and digestive gland. The recombinant CCNE protein was expressed and purified in a pET32a(+)-CCNE/Escherichia coli BL21(DE3) system via IPTG induction and was used for generating mice anti-Ch-CCNE antiserums. Western blot analysis showed that the CCNE protein in the gill was maintained at low expression levels under either hypo- (5 ppt) or hyper- (35 ppt) salinity, and could be produced at high levels under appropriate salinity during a 10-day exposure period. The immuno-localization indicated that the Ch-CCNE protein was distributed in the nucleus. These results suggested that either hypo- or hyper-salinity stress could inhibit the CCNE expression of Hong Kong oysters and their negative impact on cell division and proliferation. Full article

During the COVID-19 (coronavirus disease 2019) outbreak, many people were infected, and the symptoms may persist for several weeks or months for recovering patients. This is also known as “long COVID” and includes symptoms such as fatigue, joint pain, muscle pain, et cetera. The COVID-19 virus may trigger hyper-inflammation associated with cytokine levels in the body. COVID-19 can trigger inflammation in the joints, which can lead to osteoarthritis (OA), while long-term COVID-19 symptoms may lead to joint damage and other inflammation problems. According to several studies, sesame has potent anti-inflammatory properties due to its major constituent, sesamin. This study examined sesamin’s anti-inflammatory, anti-osteoarthritis, and anti-COVID-19 effects. Moreover, in vivo and in vitro assays were used to determine sesamin’s anti-inflammatory activity against the RAW264.7 and SW1353 cell lines. Sesamin had a dose-dependent effect (20 mg/kg) in a monoiodoacetic acid (MIA)-induced osteoarthritis rat model. Sesamin reduced paw swelling and joint discomfort. In addition, the findings indicated that sesamin suppressed the expression of iNOS (inducible nitric oxide synthase) and COX-2 (cyclooxygenase-2) in the RAW264.7 cell line within the concentration range of 6.25–50 μM. Furthermore, sesamin also had a suppressive effect on MMP (matrix metalloproteinase) expression in chondrocytes and the SW1353 cell line within the same concentration range of 6.25–50 μM. To examine the anti-viral activity, an in silico analysis was performed to evaluate sesamin’s binding affinity with SARS-CoV-2 RdRp (severe acute respiratory syndrome coronavirus 2 RNA-dependent RNA polymerase) and human ACE2 (angiotensin-converting enzyme 2). Compared to the controls, sesamin exhibited strong binding affinities towards SARS-CoV-2 RdRp and human ACE2. Furthermore, sesamin had a higher binding affinity for the ACE2 target protein. This study suggests that sesamin shows potential anti-SARS-CoV-2 activity for drug development. Full article

Improving the prediction of blood glucose concentration may improve the quality of life of people living with type 1 diabetes by enabling them to better manage their care. Given the anticipated benefits of such a prediction, numerous methods have been proposed. Rather than attempting to predict glucose concentration, a deep learning framework for prediction is proposed in which prediction is performed using a scale for hypo- and hyper-glycemia risk. Using the blood glucose risk score formula proposed by Kovatchev et al., models with different architectures were trained, including, a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short-term memory (LSTM) network, and an encoder-like convolutional neural network (CNN). The models were trained using the OpenAPS Data Commons data set, comprising 139 individuals, each with tens of thousands of continuous glucose monitor (CGM) data points. The training set was composed of 7% of the data set, while the remaining was used for testing. Performance comparisons between the different architectures are presented and discussed. To evaluate these predictions, performance results are compared with the last measurement (LM) prediction, through a sample-and-hold approach continuing the last known measurement forward. The results obtained are competitive when compared to other deep learning methods. A root mean squared error (RMSE) of 16 mg/dL, 24 mg/dL, and 37 mg/dL were obtained for CNN prediction horizons of 15, 30, and 60 min, respectively. However, no significant improvements were found for the deep learning models compared to LM prediction. Performance was found to be highly dependent on architecture and the prediction horizon. Lastly, a metric to assess model performance by weighing each prediction point error with the corresponding blood glucose risk score is proposed. Two main conclusions are drawn. Firstly, going forward, there is a need to benchmark model performance using LM prediction to enable the comparison between results obtained from different data sets. Secondly, model-agnostic data-driven deep learning models may only be meaningful when combined with mechanistic physiological models; here, it is argued that neural ordinary differential equations may combine the best of both approaches. These findings are based on the OpenAPS Data Commons data set and are to be validated in other independent data sets. Full article

In hyper-arid and arid zones, management of crop water requirements is considered a vital component for sustaining crop production. The efficiency of the irrigation method and the application of many kinds of organic matter are practices that should be followed in Egypt to maximize the use of irrigation water. Two field experiments were conducted during the two successive winter seasons of 2020/2021 and 2021/2022 to study the effect of drip irrigation systems and of several types of compost on yield and yield attributes of four cultivars of wheat in newly reclaimed sandy soils. Studied factors were irrigation levels based on the amount of water evapotranspiration (ET) (I₁, I₂, I₃) and the application of compost types (Com₁, Com₂ and Com₃) on four bread wheat cultivars. The parameters measured at each irrigation level were: heading date (day), plant height (cm²), number of spikes/m², number of grains/spike, 1000-grain weight (g), grain yield (t/fed.), Biological yield (kg/fed.) and harvest index (%). The farmyard manure (Com₃) gave the maximum values under irrigation shortages, reflected in producing the maximum values for traits measured in the 2020/2021 season as compared to (Com₁) or (Com₂) applications, which scored lower values for the traits for the different cultivars for wheat. The interaction (I₁, I₂) × Com₃ × (Mis₁, Mis₂) led to a significant increase during both seasons for all the yield and yield components studied. A drip irrigation system at the level of 80% of ET and application of Com₃ is recommended to optimize wheat productivity from the unit area. The savings in water irrigation would allow expansion of the cultivated area to decrease the gap between local crop production and local requirements. Full article

Deep generative models, such as deep Boltzmann machines, focused on models that provided parametric specification of probability distribution functions. Such models are trained by maximizing intractable likelihood functions, and therefore require numerous approximations to the likelihood gradient. This underlying difficulty led to the development of generative machines such as generative stochastic networks, which do not represent the likelihood functions explicitly, like the earlier models, but are trained with exact backpropagation rather than the numerous approximations. These models use piecewise linear units that are having well behaved gradients. Generative machines were further extended with the introduction of an associative adversarial network leading to the generative adversarial nets (GANs) model by Goodfellow in 2014. The estimations in GANs process two multilayer perceptrons, called the generative model and the discriminative model. These are learned jointly by alternating the training of the two models, using game theory principles. However, GAN has many difficulties, including: the difficulty of training the models; criticality in the selection of hyper-parameters; difficulty in the control of generated samples; balancing the convergence of the discriminator and generator; and the problem of modal collapse. Since its inception, efforts have been made to tackle these issues one at a time or in multiples at several stages by many researchers. However, most of these have been handled efficiently in the boundary equilibrium generative adversarial networks (BEGAN) model introduced by Berthelot et al. in 2017. In this work we presented the advent of adversarial networks, starting with the history behind the models and c developments done on GANs until the BEGAN model was introduced. Since some time has elapsed since the proposal of BEGAN, we provided an up-to-date study, as well as future directions for various aspects of adversarial learning. Full article

Understanding the impacts and extent of both climate change and human activities on ecosystems is crucial to sustainable development. With low anti-interference ability, arid and semi-arid ecosystems are particularly sensitive to disturbances from both climate change and human activities. We investigated how and to what extent climate variation and human activities influenced major indicators that are related to ecosystem functions and conditions in the past decades in Xinjiang, a typical arid and semi-arid region in China. We analyzed the changing trends of evapotranspiration (ET), gross primary productivity (GPP) and leaf area index (LAI) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite product and the Breathing Earth System Simulator (BESS) model in Xinjiang for different climate zones. We separated and quantified the contributions of climate forcing and human activities on the trends of the studied ecosystem indicators using the residual analysis method for different climate zones in Xinjiang. The results show that GPP and LAI increased and ET decreased from 2001 to 2015 in Xinjiang. Factors that dominate the changes in ecosystem indicators vary considerably across different climate zones. Precipitation plays a positive role in impacting vegetation indicators in arid and hyper-arid zones and temperature has a negative correlation with both GPP and LAI in hyper-arid zones in Xinjiang. Results based on residual analysis indicate that human activities could account for over 72% of variation in the changes in each ecosystem indicator. Human activities have large impacts on each vegetation indicator change in hyper-arid and arid zones and their relative contribution has a mean value of 79%. This study quantifies the roles of climate forcing and human activities in the changes in ecosystem indicators across different climate zones, suggesting that human activities largely influence ecosystem processes in the arid and semi-arid regions of Xinjiang in China. Full article

Brazilian tropical ecosystems in the state of Mato Grosso have experienced significant land use and cover changes during the past few decades due to deforestation and wildfire. These changes can directly affect the mass and energy exchange near the surface and, consequently, evapotranspiration (ET). Characterization of the seasonal patterns of ET can help in understanding how these tropical ecosystems function with a changing climate. The goal of this study was to characterize temporal (seasonal-to-decadal) and spatial patterns in ET over Mato Grosso using remotely sensed products. Ecosystems over areas with limited to no flux towers can be performed using remote sensing products such as NASA’s MOD16A2 ET (MOD16 ET). As the accuracy of this product in tropical ecosystems is unknown, a secondary objective of this study was to evaluate the ability of the MOD16 ET (ET_MODIS) to appropriately represent the spatial and seasonal ET patterns in Mato Grosso, Brazil. Actual ET was measured (ET_Measured) using eight flux towers, three in the Amazon, three in the Cerrado, and two in the Pantanal of Mato Grosso. In general, the ET_MODIS of all sites had no significant difference from ET_Measured during all analyzed periods, and ET_MODIS had a significant moderate to strong correlation with the ET_Measured. The spatial variation of ET had some similarity to the climatology of Mato Grosso, with higher ET in the mid to southern parts of Mato Grosso (Cerrado and Pantanal) during the wet period compared to the dry period. The ET in the Amazon had three seasonal patterns, a higher and lower ET in the wet season compared to the dry season, and minimal to insignificant variation in ET during the wet and dry seasons. The wet season ET in Amazon decreased from the first and second decades, but the ET during the wet and dry season increased in Cerrado and Pantanal in the same period. This study highlights the importance of deepening the study of ET in the state of Mato Grosso due to the land cover and climate change. Full article

Estimating evapotranspiration (ET), the main water output flux within basins, is an important step in assessing hydrological changes and water availability. However, direct measurements of ET are challenging, especially for large regions. Global products now provide gridded estimates of ET at different temporal resolution, each with its own method of estimating ET based on various data sources. This study investigates the differences between ERA5, GLEAM, and GLDAS datasets of estimated ET at gridded points across Iran, and their accuracy in comparison with reference ET. The spatial and temporal discrepancies between datasets are identified, as well as their co-variation with forcing variables. The ET reference values used to check the accuracy of the datasets were based on the water balance (ET_wb) from Iran’s main basins, and co-variation of estimated errors for each product with forcing drivers of ET. The results indicate that ET_ERA5 provides higher base average values and lower maximum annual average values than ET_GLEAM. Temporal changes at the annual scale are similar for GLEAM, ERA5, and GLDAS datasets, but differences at seasonal and monthly time scales are identified. Some discrepancies are also recorded in ET spatial distribution, but generally, all datasets provide similarities, e.g., for humid regions basins. ET_ERA5 has a higher correlation with available energy than available water, while ET_GLEAM has higher correlation with available water, and ET_GLDAS does not correlate with none of these drivers. Based on the comparison of ET_ERA5 and ET_GLEAM with ET_wb, both have similar errors in spatial distribution, while ET_GLDAS provided over and under estimations in northern and southern basins, respectively, compared to them (ET_ERA5 and ET_GLEAM). All three datasets provide better ET estimates (values closer to ET_WB) in hyper-arid and arid regions from central to eastern Iran than in the humid areas. Thus, the GLEAM, ERA5, and GLDAS datasets are more suitable for estimating ET for arid rather than humid basins in Iran. Full article

This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%. Full article

Given the estrogen-dependence associated with endometriosis, hyper-stimulation associated with assisted reproduction treatment may exacerbate the disease process and adversely affect endometrial receptivity and subsequent implantation. In this way, a freeze-all deferred embryo transfer (ET) approach may benefit patients with endometriosis, although controversy exists regarding the mechanism of endometriosis-associated infertility and benefits of deferred ET on endometrial receptivity. Hence, the purpose of this study was to compare in vitro fertilization (IVF) outcomes in women with endometriosis, diagnosed by histology, undergoing fresh versus deferred-ET after elective cryopreservation. Of the 728 women included, no significant differences were observed in baseline patient characteristics and response to gonadotrophin stimulation between fresh and deferred ET groups. Furthermore, no significant differences in implantation rate (49.7 vs. 49.9%, p = 0.73), clinical pregnancy rate (40.9 vs. 39.9%, p = 0.49), and miscarriage rate (9.4 vs. 9.9%, p = 0.63) were observed between fresh and deferred ET groups, respectively. Hence, contrary to previous studies, our results suggest that a deferred ET “freeze-all” IVF strategy does not improve early pregnancy outcomes among women with endometriosis. However, prospective studies are required to validate these findings and further insight into the etiology and pathogenesis of endometriosis-associated infertility are necessary to optimize IVF protocols in this population. Full article