Selecting drought-tolerant and more water-efficient wheat genotypes is a research priority, spe-cifically in regions with irregular rainfall or areas where climate change is expected to result in reduced water availability. The objective of this work was to use high-throughput measurements with morphophysiological traits to characterize wheat genotypes in relation to water stress. Field experiments were conducted from May to September 2018 and 2019, using a sprinkler bar irriga-tion system to control water availability to eighteen wheat genotypes: BRS 254; BRS 264; CPAC 01019; CPAC 01047; CPAC 07258; CPAC 08318; CPAC 9110; BRS 394 (irrigated biotypes) and Aliança; BR 18_Terena; BRS 404; MGS Brilhante; PF 020037; PF 020062; PF 120337; PF 100368; PF 080492; TBIO Sintonia (rainfed biotypes). The water regimes varied from 22 to 100% of the crop evapotranspiration replacement. Water stress negatively affected gas exchange, vegetation indices and grain yield. High throughput variables TCARI, NDVI, OSAVI, SAVI, PRI, NDRE and GNDVI had higher correlation with yield and morphophysiological measurements. The drought re-sistance index indicated that genotypes Aliança BRS 254, BRS 404, CPAC 01019, PF 020062, and PF 080492 were more drought tolerant.

Antibiotics have played a crucial role in the reduction of TB incidence globally as evidenced by the fact that before the mid-20th century, the mortality rate within five years of the onset of the disease was 50%. The use of antibiotics has eliminated TB as a devastating disease, but the challenge of resistance to anti-TB drugs, which had already been described at the time of the introduction of streptomycin, has become a major global issue in disease management. Mismanagement of multidrug-resistant tuberculosis (MDR-TB) cases, resulting from intermittent drug use, prescription errors and non-compliance of patients, has been identified as a critical risk factor for the development of extensively drug-resistant tuberculosis (XDR-TB). Antimicrobial resistance (AMR) in TB is a multi-factorial, complex problem of microbes evolving to escape antibiotics, the gradual decline of antibiotic development, and different economic and social conditions. In this review, we summarise recent advances in our understanding of how Mycobacterium tuberculosis evolves drug resistance. We also highlight the importance of developing shorter regimens that rapidly reach bacteria in diverse host environments, eradicating all mycobacterial populations and preventing the evolution of drug resistance. Lastly, we also emphasise that the current burden of this ancient disease is driven by a combination of complex interactions between mycobacterial and host factors, and that only a holistic approach that effectively addresses all the critical issues associated with drug resistance will limit the further spread of drug-resistant strains throught the community.

Nutrients are the driving force of plant and herbivore interactions. Different elements and fertilization levels in soil, or artificially fertilized substrates can have a direct impact on plant defense responses. Meanwhile, nutrient levels mediated by root associated microbes can indirectly affect plant-herbivore interactions. Nutrient uptake promoted by beneficial mycorrhizal fungi or rhizobacteria interacting with roots can stimulate plant tolerance to herbivore attack, as evidenced by herbivore performance, plant nutrient status, plant biomass allocation and compensatory growth. In return, herbivory affects nutrient fluxes between plants and their associated beneficial microbes. Furthermore, aboveground and belowground herbivory can affect the nutrient exchange between plants and soil microbial communities, mainly for carbon-and nitrogen-based resources supporting herbivores´ life cycles. These ecological interactions are governed by jasmonic acid (JA) and key enzymes on the molecular level. This review summarizes the current scientific literature on the role of nutrients in plant-herbivore interactions. This will support efforts to optimize plant defense strategy via managing nutrient variance.

Abstract: Background: Cow’s milk allergy (CMA) is one of the most common and complex food allergies affecting children worldwide and with few exceptions, presents in the first few months of life. Baked milk containing diets are well tolerated in the majority of milk allergic children and allow dietary restrictions to be relaxed. In addition, the early introduction of tolerated forms of allergenic foods to the infant’s diet in small amounts may enhance the outgrowth of milk allergy through oral tolerance induction. The methods of milk introduction vary widely across the globe. Methods: We convened an expert group to develop a comprehensive milk ladder based on calculated milk protein content using Indian foods. To validate the milk ladder, foods chosen for the ladder were analyzed and the ladder was re-evaluated based on amount cooked milk protein content. Results: Combining expert consensus and validation of milk protein content, we created the world’s first milk ladder containing Indian foods. This is the first ladder that provides information on timing and temperature of cooking, with validated milk protein content. Conclusions: This is the first milk ladder based on unique features of Indian food habits built by consensus of Indian experts along with international collaboration with quantified of milk protein in each step. We believe the “The Indian Milk Ladder” will be a very helpful tool for pediatricians helping manage CMA in children as well as their parents and caregivers, not only in India but in countries world-wide where these foods are commonly consumed.

This study aimed to assess the physical characteristics of elite international soccer referees, compare them with other referee populations in the literature, and establish reference tolerance ellipses for the bioelectrical impedance vector analysis (BIVA) point graph. Forty-one elite international soccer referees (age 38.8 ± 3.6 years) participated in the study. the participants underwent body composition assessments, including dual-energy X-ray absorptiometry, BIVA, and somatotype. The Somatotype Attitudinal Distance (SAD), the two-sample Hotelling’s T2 test, and the Mahalanobis test were used to determine somatotype and bioelectrical vector differences with the literature. The average somatotype of the referees was a balanced mesomorph (2.8, 6.5, 2.8). Elite international referees significantly differed from other samples in the literature (SAD = 2.1, 2.6, 2.9 with respect to Zimbabwean, Brazilian, and South African referees, respectively). The bioelectrical vector was significantly different from the general population (T2  76.6; F = 38.8; D = 1.44; P < 0.001) and athletes (T2  25.3; F = 12.6; D = 0.8; P < 0.001). Somatotype values and tolerance ellipses from this study may be useful as a reference for developing training programs and improving the selection process of referees in soccer.

Soil salinity is one of the most serious problems for achieving better crop growth and yield. The study was conducted to isolate, characterize and screen salt tolerant microbes from spinach grown in saline soils. The efficient salt tolerant microbes were characterized for beneficial traits such as nitrogen fixation, phosphate solubilization, indoleacetic acid (IAA), biofilm production, catalase and antagonistic activities, salt tolerance and optimization of bacterial fermentation. Molecular identification of potential microbes was done by 16S rRNA gene sequencing. The collected soil samples were moderately to strongly saline, alkaline in nature, low in N and P while adequate in K and Ca concentration. A total of 17 salt tolerant bacteria differing in colony and cell morphology were characterized. All isolated rhizobacterial microbes had capability to biosynthesize phytohormone IAA, 14 were able to fix atmospheric N2, 11 microbes solubilized inorganic P, 10 produced biofilm and 15 were positive in catalase activity. The isolated rhizobacteria showed salt tolerance up to 10 % induced salinity, only six microbes tolerated salt concentration of 12.5% however, none of any rhizobacteria remained viable at 15% NaCl amended media plates. The two potential salt tolerant microbes (NIA SP-6 & NIA SP-11) were screened and found for optimum growth curve and fermentation at pH 7.0 after 48 hrs of incubation period. Both potential microbes were identified as Enterobacter cloacae (NIA SP-06) and Bacillus safensis (NIA SP-11). Hence, it can be concluded that isolated salt tolerant rhizobacterial microbes have potential for beneficial traits and have ability for salt tolerance under saline conditions.

Objectives and Background: Gestational diabetes (GDM) is a common pregnancy complication, defined as a glucose intolerance diagnosis during pregnancy. GDM is strongly associated with adverse fetal and maternal outcomes. In Germany, to screen and diagnose GDM we use a 1-hour 50g oGCT (oral glucose challenge test) followed by a 2-hour 75g oGTT if the first was pathological. This analysis examines the correlation of 75g oGTT glucose levels and fetomaternal outcome. Methods: Data of 1664 patients from a gestational diabetes consultation clinic at the Charité University hospital in Berlin, Germany, were analyzed retrospectively from 2015 until 2022. The 75g oGTT blood glucose levels were categorized into isolated fasting hyperglycemia (GDM-IFH), isolated post-load hyperglycemia (GDM-IPH) and combined hyperglycemia (GDM-CH), using the levels of the fasting, 1-hour and 2-hour values after glucose application. These subtypes were compared regarding baseline characteristics as well as fetal and maternal outcome. Results: GDM-IFH and GDM-CH women displayed higher preconceptional BMI and required insulin therapy more frequently (p<0,001). The GDM-IFH group was at higher risk of having a primary cesarean section (p=0,047), while GDM-IPH women were significantly more likely to have an emergent cesarean section (p=0,013). The offspring of GDM-IFH and GDM-CH women were born with a significantly higher mean birthweight (p<0,001) and birth weight percentiles (p<0,001) and were at increased risk of being large for gestational age (LGA) (p=0,004). Women from the GDM-IPH group delivered significantly more neonates who were small for gestational age (p=0,027) or with low fetal weight <30th percentile (p=0,003). Conclusion: This analysis shows a strong association between glucose response pattern in the 75g oGTT and adverse perinatal fetomaternal outcome. The differences among the subgroups, specifically concerning insulin therapy, mode of delivery and fetal growth, suggest an individualized approach in prenatal care after a GDM diagnosis.

This manuscript provides a new integrated view of the development of allergen-specific TH2 and the lack of their associated Treg cells in a unified model, justifying the need for IL-2 to correct allergy conditions.

Increasing attention has been paid to the potential probiotic effects of Latilactobacillus sakei. To explore the genetic diversity of L. sakei, 14 strains isolated from different niches (feces, fermented kimchi and meat products) and 54 published strains were compared and analyzed. The results showed that the average genome size and GC content of L. sakei were 1.98Mb and 41.22%, respectively. Its core genome mainly encodes translation and transcription, amino acid synthesis, glucose metabolism and defense functions. L. sakei has an open pan-genomic characteristics, and its pan-gene curve shows an upward trend. L. sakei has open pan-genome feature, and its pan-genome curve is on the rise. The genetic diversity of L. sakei is mainly reflected in carbohydrate utilization, antibiotic tolerance, and immune/competition-related factors, such as clustering regular interval short palindromic repeat sequence (CRISPR)-Cas. The CRISPR system is mainly IIA type, and a few are IIC types. This work provides a basis for the study of this species.

Elevated salinity is one of the major environmental limitation factors of plant growth and development and salinity stress compromises the production and survival of plantation and urban forests and agricultural crops in the arid, semi-arid, and intertidal zones. Ulmus pumila, a salt- indigenous tree species in Asia and is widely deployed in salt-affected areas in China, and U.pumila is promising for multi-varietal forestry in plantation and urban forests. The comprehensive mechanism of the intraspecific salt tolerance is still not clear yet. Here, we investigated the physiological responses of the salinity stress based on the antioxidant enzyme activities, osmotic adjustments, and gas exchange among salt-tolerant U. pumila genotypes for 100 days under five different NaCl levels (0%, 0.3%, 0.5%, 0.7%, and 0.9% w/v) with natural surroundings and rain shade at age-2. Salt stress decreased height (HR), ground diameter (DR), and dry weight (biomass) were significantly different among genotypes. HR and performance indices were positively correlated with photosynthesis rate (Pn), apparent mesophyll conductance (AMC), and chlorophyll (CHLL) with (r= 0.7 - 0.8 ***), but were negatively related to the free proline, sugar, and protein accumulation (r=-0.5 ~ -0.7***). We found that high accumulation of sugars and more activities of SOD enzyme in leaf tissue contribute to the osmotic adjustment and ROS scavenging system under salinity treatment; the sugar content and SOD activity play key roles in U. pumila’s tolerance to salt stress, and are promising indicators for U. pumila species ex vitro selections. The ex vitro selection results align with the previous in vitro studies [37] and is promising for the MVF development.

Heat stress occurring during the reproductive stage of wheat has a detrimental effect on productivity. A durum wheat core set was exposed to simulated terminal heat stress by applying plastic tunnels at the time of flowering over two seasons. Mean grain yield was reduced by 54% compared to control conditions, and grain number was the most critical trait for tolerance to this stress. The combined use of tolerance indices and grain yield identified five elites: Kunmiki, Berghouata1, Margherita2, IDON37-141, and Ourgh. The core set was also subjected to genome wide association study using 7,652 polymorphic SNPs markers. The most critical genomic regions were identified in association with spike fertility and tolerance indices on chromosome 1A, 5B and 6B. Haplotype analysis on a set of 208 elites confirmed that lines that carried the positive allele at all three QTLs resulted in a yield advantage of 8% when field tested under daily temperatures above 31° C. Two of the QTLs were successfully validated into KASP markers and explained >10% of the phenotypic variation for an independent elite germplasm set. These genomic regions can now be readily deployed via breeding to improve resilience to climate change and increase productivity in heat-stressed areas.

Biostimulants are materials that when applied in small amounts are capable of promoting plant growth. Nanoparticles (NPs) and nanomaterials (NMs) can be considered as biostimulants since, in specific ranges of concentration, generally in small levels, they increase the plant growth. Pristine NPs and NMS have a high density of surface charges capable of unspecific interactions with the surface charges of the cell walls and membranes of plant cells. In the same way, the functionalized NPs and NMS, and the NPs and NMs with a corona formed after the exposition to natural fluids such as water, soil solution, or the interior of organisms, presents a high density of surface charges that interact with specific charged groups in cell surfaces. The magnitude of the interaction will depend on the materials adhered to the corona, but the high-density charges located in a small volume causes an intense interaction capable of disturbing the density of surface charges of cell walls and membranes. The electrostatic disturbance can have an impact on the electrical potentials of the outer and inner surfaces, as well as on the transmembrane electrical potential, modifying the activity of the integral proteins of the membranes. The extension of the cellular response can range from biostimulation to cell death and will depend on the concentration, size, and the characteristics of the corona.

The Eastern Kunlun Fault Zone (EKFZ) is located in a seismic blank area. This study investigates the hydrogeochemical characteristics of varied-temperature springs in the EKFZ to understand the relationship between fractures, fluids and earthquakes. It aids in establishing their induction mechanisms and assists in monitoring the earthquake precursors of the EKFZ. In this study, the main elements, trace elements, hydrogen and oxygen isotopes and strontium isotopes of 23 varied-temperature springs in the EKFZ were analyzed. The results reveal the source of groundwater recharge, hydrochemical types, mineral saturation, and for a select few springs indicative of geothermal activity, collected geothermal reservoir temperatures. The study also explores the relationship between earthquakes and springs, identifying sensitive chemical indicators. Based on continuous monitoring at Maqu Station, we found that before the occurrence of the Maerkang Ms 6.0 earthquake, the concentration of Cl− rose by 2.9 times, and 46 days before the Gande earthquake (Ms = 4.7), the concentrations of Na+ and SO42− significantly increased. Finally, a conceptual model of the hydrogeochemistry of springs is presented to describe the groundwater circulation in the study area. This research is of great significance for the analysis of fluid geochemistry in this area and for assessing the seismic hazards of the EKFZ.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.

Using numerical software to establish a three-dimensional geological model of shield tunneling through fault fracture zones, considering the synchronous grouting effect at the shield tail, the disturbance of various geotechnical structures in the fault fracture zone under deep burial and high water pressure conditions is studied. According to the comparison between the numerical model analysis results and the on-site inspection results of the entire process of Shiziyang B3 section, synchronous grouting at the shield tail is the main reason for the changes in soil and water pressure in the fault fracture zone area. The soil and water pressure changes strongly during shield tunneling, and the maximum excess pore pressure value occurs when the shield tail passes. When the shield tunneling machine is far away, the soil and water stress shows a downward trend. The impact of synchronous grouting decreases with the increase of tunneling distance. Due to the difference in permeability, the variation law of pore pressure in the fault fracture zone is significantly different from the original rock, leading to a faster increase in pore pressure in the fracture zone area and a wider range of influence.

Recently, the triple-network convergence system (TNCS) has emerged from the deep integration of the power grid, transportation network, and information network. Fault recovery research in the TNCS is important since this system's complexity and interactivity can expand the faults scale and increase faults impact. Currently, fault recovery focuses primarily on single power grids and cyber-physical systems, but there are certain shortcomings, such as ignoring uncertainties including generator start-up failures and the occurrence of new faults during recovery, energy supply-demand imbalances leading to system security issues and communication delay caused by network attacks. In this study, we propose a recovery method based on the improved TD3 algorithm, factoring in shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay and an improved Actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefit in power grid failure scenarios and demonstrates a strong resilience against communication delay caused by DoS attacks.

The construction of sustainable road and highway networks in the world, despite numerous feasibility, pre-feasibility and execution studies, are always confronted with the demands and vagaries of foreseeable and unforeseeable natural disasters. Studying cyclones, earthquakes, fracturing and landslide zones along roads is therefore a challenge for the sustainability of these infrastructures. In many countries around the world, the methods generally used for these studies are not only expensive and time-consuming, but the results obtained are not always efficient. This work examines whether Landsat 8 (with a high cloud level) and SRTM data can be used in both equatorial and coastal Central Africa zones to produce relevant mapping, locating fracture and landslide zones, in order to contribute not only to better road layout at lower cost and in a relatively short time, but also to better prevention of geological disasters that may occur on its network. To this end, a map of the main road network was produced and validated with field data, as well as the maps of the main unstable slopes, faults and fractures zones intersecting the road or highway network. These approaches are useful for sustainable planning, management, monitoring and extension of roads worldwide especially, in Central Africa.

Safe, high-quality and economical electric energy transportation is the basic requirements of modern power system operation. Transformer, as one of the core equipment of power system and national grid system, has the characteristics of diverse types, variable models and wide deployment. It is the basic equipment for power system to realize voltage change and electric energy distribution. Because the power system transformer needs to operate with load for a long time, the probability of failure is usually higher than that of other power equipment in general. This paper starts from the transformer fault and fault diagnosis. Firstly, the transformer fault types, traditional transformer fault diagnosis techniques and the advantages of machine learning in transformer fault diagnosis are reviewed. Secondly, the application of information system theory and information entropy in transformer fault diagnosis is introduced. Then it introduces machine learning technology, feature selection and extraction technology in transformer fault diagnosis, and machine learning algorithms such as support vector machine and extreme learning machine for transformer fault diagnosis. Finally, the potential development trend of information system theory, information entropy and machine learning in transformer fault diagnosis is forecasted. The combination of transformer fault prediction and machine learning algorithm is helpful for power system maintenance personnel to accurately predict the running state of power equipment, and also provides a new method and technology for the safe and reliable operation and regular maintenance of power system.

Centralized control algorithm limits the hardware flexibility of a Modular Multilevel Converter (MMC). Therefore, industrial MMC applications have started adopting distributed control structures. Even though distributed controller reduces a single point of failure risk compared to the centralized controller, the failure risk of the entire control systems increases due to the number of local controllers. However, the distributed controller can be programmed in such a way as to bypass the faulty local controller and take care of the power modules with the adjacent local controllers. This paper implements a modular distributed fault-tolerant controller for a scaled laboratory MMC prototype. Experimental results show that an MMC can operate without interruption, even under two local controller failures. Besides, the experimental results are verified with the Opal-RT, a real-time simulator with the same controller in a Control Hardware in Loop (CHIL) environment.

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method called the WeDoWind Ecosystem is developed and demonstrated. The method is centred around specific "challenges", which are defined by "challenge providers" within a topical "space" and made available to participants via a digital platform. The data required in order to solve a particular "challenge" is provided by the "challenge providers" under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the methods perform significantly better than EDP’s existing method in terms of Total Prediction Costs (saving up to €120,000). The WeDoWind Ecosystem is found to be a promising solution for enabling co-innovation in wind energy, providing a number of tangible benefits for both challenge and solution providers.

In the recent years, intelligent data-driven faultdiagnosis methods on gearboxes have been successfully developedand popularly applied in the industries. Currently, most ofthe machine learning techniques require that the training andtesting data are from the same distribution. However, thisassumption is difficult to be met in the real industries, sincethe gearbox operating conditions usually change in practice,which results in significant data distribution gap and diagnosticperformance deteriorations in applying the learned knowledgeon the new conditions. This paper proposes a deep learning-based domain adaptation method to address this issue. Theraw current signals are directly used as the model inputs fordiagnostics, which are easy to collect in the real industries andfacilitate practical applications. The maximum mean discrepancymetric is introduced to the deep neural network, the optimizationof which guarantees the extraction of generalized machineryhealth condition features across different operating conditions.The experiments on a real-world gearbox condition monitoringdataset validate the effectiveness of the proposed method, whichoffers a promising tool for cross-domain diagnosis in the realindustries.

The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.

The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.

Pre-existing fracture and secondary cracks in rock mass are formed by natural power, such as magma condensed to igneous rocks and tectonic movement. The orientation and inclination of these fractures obey certain laws relating to the stress, temperature, minerals, water and so on. Therefore, cracks react differently under the same external loading on the condition of various inclination, fissure apertures, stiffness and joint roughness. To simulate the crack propagation, experiments on hollow cylinder cut by one oblique interface mimicking single fracture accumulated numerous data discovering the failure criterion in accordance with the Mohr-Coulomb criterion. And theory on the Terzaghi’s effective principle take an essential role in controlling the behavior of triggering fault. This paper introduced a series of oblique plane cutting the cylinder regarded as fractures at different inclination to concentrate on how the fracture characteristics effect the stress and strain distribution inside the specimen, especially, the relationship between displacement and water head. The key point of this numerical simulation is coupling the solid phase and the fluid phase, specifically, the mechanic and seepage field. According to the statics, curves referring to deformation and water head could be described as increasing lines. Besides, simulation on coupling solid phase and fluid phase can supply crucial evaluation on activating existing fault, and thus predicting induced seismicity in reservoirs or estimating damage in shale gas exploration.

The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, a crucial consideration for emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication link. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyberattacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control, and modern probabilistic and data-driven approaches for modelling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

Wheat pollen, characterized by its short lifespan, exhibits rapid germination after anthesis. The preservation of wheat pollen is contingent upon environmental factors, including temperature, relative humidity, light, and wind. The objective is to develop a method for preserving wheat pollen effectively, specifically for the purpose of breeding. The short longevity of wheat pollen grains renders it impractical to conduct tests for pollen viability and in vitro germination on a large scale. Herein, the impact of storage temperatures and duration was assessed on pollen viability and in vitro germination to optimize storage conditions for preserving pollen viability. Pollen grains from 50 diverse spring wheat genotypes, each with three replicates, were harvested and stored at temperatures of 22°C, -20°C, and 4°C. Subsequently, pollen viability and in vitro germination rates were determined after storage of 1, 3, and 6 days. The results revealed that storage temperatures, durations, genotypes, and their interactions had a statistically significant impact on both pollen viability and in vitro germination. Notably, when pollen was kept at 22°C, almost all genotypes exhibited a loss of pollen viability and in vitro germination after 1, 3, and 6 days of storage. Likewise, storage at -20°C failed to extend pollen germination. However, at a storage temperature of 4°C, the pollen of 36 wheat genotypes exhibited a range of 6-14% in vitro pollen germination and remained viable even for 6 days. Simple linear regression analysis further revealed a significant negative correlation between pollen viability, in vitro germination, and storage time and temperature. In conclusion, our findings underscore 4°C as the optimal temperature for preserving pollen viability and in vitro pollen germination in spring wheat for up to 6 days. The results of the present study suggests that pollen viability of wheat is genotype de-pendent, storage temperature and storage duration. Thus, the 36 wheat genotypes identified during the present work could be efficiently maintained at 4◦C for short-term storage (6 days) and could be further used for genetics and breeding purposes.

Diseases pose a significant and pressing concern for the sustainable development of the aquaculture sector, particularly as their impact continues to grow due to climatic shifts such as rising water temperatures. While various approaches, ranging from biosecurity measures to vaccines, have been devised to combat infectious diseases, their efficacy is disease- and species-specific and contingent upon a multitude of factors. The field of genetics and genomics offer effective tools to control and prevent disease outbreaks in aquatic animal species. In this study, we present the key findings from our recent research, focusing on the genetic resistance to three specific diseases: White Spot Syndrome Virus WSSV) in white shrimp, Bacterial Necrotic Pancreatitis (BNP) in striped catfish and skin fluke (a parasitic ailment) in yellowtail kingfish. Our investigations reveal that all three species possess substantial heritable genetic components for disease resistant traits, indicating their potential responsiveness to artificial selection in genetic improvement programs tailored to combat these diseases. Also, we observed a high genetic association between disease traits and survival rates. Through selective breeding aimed at enhancing resistance to these pathogens, we achieved substantial genetic gains, averaging 10% per generation. These selection programs also contributed positively to the overall production performance and productivity of these species. Although the effects of selection on immunological traits or immune responses were not significant in white shrimp, they yielded favourable results in striped catfish. Furthermore, our genomic analyses, including shallow genome sequencing of pedigreed populations, enriched our understanding of the genomic architecture underlying disease resistance traits. These traits are primarily governed by a polygenic nature, with numerous genes or genetic variants, each with small effects. Leveraging a range of advanced statistical methods, from mixed models to machine and deep learning, we developed prediction models that demonstrated moderate to high levels of accuracy in forecasting these disease-related traits. In addition to genomics, our RNA-seq experiments identified several genes that undergo upregulation in response to infection or viral loads within the populations. Preliminary microbiome data, while offering limited predictive accuracy for disease traits in one of our studied species, underscore the potential for combining such data with genome sequence information to enhance predictive power for disease traits in our populations. Lastly, this paper briefly discusses the roles of precision agriculture systems, AI algorithms, and outlines the path for future research to expedite the development of disease-resistant genetic lines tailored to our target species. In conclusion, our study underscores the critical role of genetics and genomics in fortifying the aquaculture sector against the threats posed by diseases, paving the way for more sustainable and resilient aquaculture development.

Growing food autonomously on Mars is challenging due to the Martian soil's low nutrient content and high salinity. Understanding how plants adapt and evaluating their nutritional attributes are pivotal for sustained Mars missions. This research delved into the regeneration, stress tolerance, and dietary metrics of sweet potato (Ipomoea batatas) across different Mars Global Simulant (MGS-1) concentrations (0, 25, 50, and 75%). In our greenhouse experiment, 75% MGS-1 concentration most significantly inhibited sweet potato growth, storage root biomass, and chlorophyll content. This concentration also elevated plant tissues' H2O2, proline, and ascorbic acid levels. Higher MGS-1 exposures (50 and 75%) notably boosted vital amino acids and sugar groups in the plant's storage roots. Yet, increased MGS-1 concentrations notably diminished the total C: N ratio and elemental composition in both vines and storage roots. In summary, sweet potato exhibited optimal growth, antioxidant properties, yield, and nutrient profiles at 25% MGS-1 exposure, compared to higher concentrations. This study underscores the need for future interventions, like nutrient enhancement and controlled metal accessibility, to render it a suitable plant for space-based studies.

In recent years, many studies have reported that several distinct families of transcription factor (TF) genes play crucial roles in the response of plants to abiotic stress. Although some of these families got systematically studied in many species, little knowledge exists about these genes in oil palm (Elaeis guineensis Jacq.). In this study, 20 genes differentially expressed in the leaves of oil palm plants subjected to salinity or drought stress and encoding TFs belonging to four families of TFs - MYB, HD-ZIP, NF-Y, and HSFF - got selected for further characterization. The genes underwent a structural and functional annotation, besides having their RNA-Seq expression profile validated by the qPCR technique, and a correlation analysis of their response under both stresses got performed. The annotation analysis of the promoter region showed the presence of seven cis-acting elements, all already reported to be involved in plants' response to abiotic stress. The structural and functional characterization of the proteins encoded by the selected genes showed that some groups present patterns of conserved regions and are orthologs to genes already linked to salinity or drought resistance in other plant species. It was possible to confirm the RNA-Seq expression profile of 19 genes, only gene 4 showed the opposite response when using qPCR. Through correlation analysis, it was possible to observe a high level of similarity in the behavior of the 20 genes in response to drought or salinity stress, a behavior already reported in other species. The results presented here shed light on the role of 20 TFs in the leaves of young oil palm plants under severe levels of soil salinity or water deprivation. The high level of similarity in the molecular response to both stresses allowed the identification of genes that might grant strategies aiming to develop oil palm plants with a tolerance to both salinity and drought at once.

Forest vegetation dynamics (succession and dominance) is an ecological phenomenon that is still difficult to characterize and integrate into management practices. In this study, the understanding of forest dynamics is explored based on Grime’s theoretical triangular model of plant adaptive strategies using the example of Lithuania’s forest ecosystems. The idea behind this is the hypothesis that forest dynamics is linked to natural selection as an evolutionary process that exhibits differential species responses to competition, stress, and disturbance. The aim of this study is to explore the adaptive relationships in hemi-boreal forests. Grime’s and Pierce’s secondary CSR strategies, which describe various equilibria between competitiveness (C), stress tolerance (S), and ruderalism (R), were considered to reflect four establishment and development adaptive specialization characteristics of forest tree species. As a result of the study, four types of tree functional groups were identified: stress-resistant ruderals, competitive stress-sensitive ruderals, ruderal stress-sensitive competitors, and stress-resistant competitors. Based on this, we propose that reforestation move away from single species regeneration by implementing the maintenance of these four types of functional groups. In conclusion, forest management must consider the existence of the established equilibria between plant competitiveness, stress tolerance, and ruderalism. The formal concepts presented in this article can serve as a guide for future relevant research and development of appropriate methods for studying real forests. This study is unique in that no previous work has linked forest dynamics and natural selection in the context of Lithuania’s forest ecosystems.

The article presents research results of the γ-ray and fast neutrons impact on various rice species, in order to obtain mutant forms resistant to salinity (NaCl) and drought factors (sorbitol. They are going to be used as initial forms in synthetic selection when creating varieties, adapted to the stressful conditions of, both, soil and climate in the Kazakhstan Aral Sea. The average lethal doses (LD50) of γ-rays and fast neutrons, as well as the average NaCl and sorbitol lethal concentrations were established. Such environment is best suited for clear manifestation of the mutagenic effect of ionizing radiation and the resistance of rice plants to salinity and drought. A distinct dependence in the effect of ionizing radiation and stress factors on the number of induced resistant mutant forms has been related to the initial rice sort. The largest number of mutant forms was obtained from the local variety Syr Suluy, followed by Leader and AyKerim varieties. From all varieties, the number of mutants obtained from exposure to γ-rays was 43 pieces out of 4500 grains, and from the impact of fast neutrons - 115 pieces out of 2700 grains. M1 mutant plants significantly differ from the initial forms in terms of morphological features – plant height, panicle length, grain size Most plants are characterized by short growth and even dwarfism (˂80 cm). They are lodging tolerant, have short and highly sterile panicles, indicating that they are mutants and resistant to salinity, drought, or both.

The current and projected climate change that is represented by increasing temperatures, humidity levels and irregular rainfall patterns, promotes the occurrence of preharvest sprouting (PHS) in wheat. PHS results in significant economic losses, globally, which necessitates the need for high-yielding cultivars with increased PHS tolerance, hence this study was conducted. The current study evaluated two doubled-haploid (DH) wheat populations of Tugela-Dn × Elands and Elands × Flamink across six environments in the Free State Province of South Africa to select genotypes with increased PHS tolerance and further map the underlying loci. Significant effects of DH lines (194) and environments (6) were observed for PHS tolerance. The results of this study validate previous findings that PHS is only expressed when environmental conditions are conducive. Quantitative trait loci (QTL) mapping using single nucleotide polymorphism (SNP) and silicoDArT markers revealed three additive QTL with major effects on chromosomes 5B and 7B, and these QTL were detected more than once, when conditions were favourable. These QTL explained a phenotypic variation (PVE) varying between 10.08% and 20.30% (LOD = 2.73 – 3.11). About 16.50% of DH lines performed to the level of Elands (the PHS-tolerant parent) and are recommended for further selection in a pre-breeding or breeding programme. The findings of the study are expected to facilitate the on-going breeding efforts for PHS tolerance in winter wheat.

Low temperatures in the autumn induce enhanced expression/relative accumulation of several cold-inducible transcripts/proteins with protective functions from Late-embryogenesis-abundant (LEA) superfamily including dehydrins. Several studies dealing with plants grown under controlled conditions revealed a correlation (significant quantitative relationship) between dehydrin transcript/protein relative accumulation and plant frost tolerance. However, to apply these results in breeding, field experiments are necessary. The aim of the review is to provide a summary of the studies dealing with the relationships between plant acquired frost tolerance and COR/LEA transcripts/proteins relative accumulation in cereals grown in controlled and in the field conditions. The impacts of cold acclimation and vernalization processes on the ability of winter-type Triticeae to accumulate COR/LEA proteins are discussed. The factors determining dehydrin relative accumulation under controlled cold acclimation treatments versus field trials during winter seasons are discussed. In conclusion, it can be stated that dehydrins could be used as suitable indicators of winter surival in field-grown winter cereals but only in plant prior to the fullfilment of vernalization requirement.

Although the failure of antibiotic treatment is normally attributed to resistance, tolerance and persistence display a significant role in the lack of response to antibiotics. Due to the fact that several nosocomial pathogens show a high level of tolerance and/or resistance to chlorhexidine, in this study we analyzed the molecular mechanisms associated with chlorhexidine adaptation in two clinical strains of Klebsiella pneumoniae by phenotypic and transcriptomic studies. These two strains belong to ST258-KPC3 (high-risk clone carrying β-lactamase KPC3) and ST846-OXA48 (low-risk clone carrying β-lactamase OXA48). Our results showed that K. pneumoniae ST258-KPC3CA and ST846-OXA48CA strains exhibited a different behavior under CHLX pressure, adapting to this biocide through resistance and tolerance mechanisms, respectively. Furthermore, the appearance of cross-resistance to colistin was observed in the ST846-OXA48CA strain (tolerant to CHLX), using the broth microdilution method. Interestingly, this ST846-OXA48CA isolate contained a plasmid that encodes a novel type II toxin/antitoxin (TA) system, PemK/PemI. We characterized this PemK/PemI TA system by cloning both genes into the IPTG-inducible pCA24N plasmid, and found their role in persistence and biofilm formation. Accordingly, the ST846-OXA48CA strain showed a persistence biphasic curve in the presence of a chlorhexidine-imipenem combination, and these results were confirmed by the enzymatic assay (WST-1).

Halophytic plants are, by definition, well adapted to saline soils. However, even halophytes can face nutritional imbalance and accumulation of high levels of compounds such as oxalic acid (OA), and nitrate (NO₃^¯). These compounds compromise the potential nutritional health benefits associated with salt tolerant plants such as Portulaca oleracea. Thus, preventing the accumulation of non-nutritional compounds will allow plants to be grown in saline conditions as crops. To this end, two ecotypes (ET and RN) of Portulaca oleracea plants were grown under growth room conditions with two levels of salinity (0, 50 mM NaCl) and three ratios of nitrate: ammonium (0:100%; 33:66%; 25:75% NO₃^¯:NH₄⁺). The results showed that both ecotypes exposed to elevated NO₃^¯, showed severe leaf chlorosis, high levels of OA, citric acid, and malic acid, while plants of ecotype ET exposed to elevated NH₄⁺ concentrations (33% and 75%) and 50 mM NaCl displayed a marked reduction in OA content, increased total chlorophyll and carotenoid contents, crude protein content, total fatty acid (TFA) and α-Linolenic acid (ALA) thus enhancing leaf quality. This opens the potential to grow high biomass, low OA P. oleracae crops. Lastly, our experiments suggest that ecotype ET copes with saline conditions and elevated NH₄⁺ through shifts in leaf metabolites.

Water stress (WS) and heat stress (HS) have a negative effect on soybean plant growth and crop productivity. During WS, soybean plants opt for survival through ion homeostasis and the conformations of proteins are disconcerted as plant cells lose water while HS leads to difficulties in flowering and fruiting. Some of these changes include oxidative stress leading to the destruction of photosynthetic apparatus, macromolecules within cells and the onset of complex signaling cascades. Changes in the physiological characteristics, proteome, and certain metabolites investigated on molecular and cellular functions were studied in two soybean cultivars exposed to different heat and water stress conditions independently and in combination. Leaf protein composition was studied using 2-DE and complemented with MALDI TOF mass spectrometry. While two cultivars displayed genetic variation in response to water and heat stress, thirty-nine proteins were significantly altered in their relative abundance in response to WS, HS and combined WS+HS in both cultivars; a majority of them involved in metabolism, response to heat and photosynthesis showing significant cross-tolerance mechanisms. Functional analysis revealing a majority of heat responsive-proteins were more abundant during HS and combined stress (WS+HS) whereas these proteins were low to WS in cultivar PI 471938 and heat shock proteins were in low abundance to water, heat and combined stresses in cultivar R95-1705. Most protein abundances were not correlated with their expression at mRNA levels in PI cultivar, however, in cultivar R 95, the expression levels of transcript follow their relative abundance in proteins. Our systems bioinformatics analyses revealed that MED37C, a probable mediator of RNA polymerase transcription II protein showed potential interacting partners in Arabidopsis and our studies signifies the marked impact of this protein in PI cultivar. Elevated activities in antioxidant enzymes indicate that the PI-371938 cultivar has the ability to restore the oxidation levels and sustain the plant during the stress. Our study hypothesizes the plant’s development of cross-stress tolerance which will help foster the ongoing ventures in genetic modifications in stress tolerance.

In this study, we evaluated the potential of fungal endophytes to control yellow rust in wheat (Triticum aestivum L.) as endophytes are beneficial microbes and alternate to pesticides for confronting pathogens. The in-vitro efficacy of the fungal endophytes isolated from different desert plants was evaluated and the best four namely Colletotrichum lindemuthianum, Piriformospora indica, Acremonium lolii and Trichoderma viride were selected. Seeds of two susceptible wheat genotypes namely Fareed-06 and Shafaq-06 obtained from screening experiment were inoculated by dipping in four endophytic spore suspensions and were sown using randomized complete block design under factorial arrangement. Data concerning about area under disease progress curve, final disease severity percentage, coefficient of infection,1000- grains weight and grain yield were recorded. Results showed that endophyte P. indica showed significant decrease in final disease severity (FDS) and area under disease progress curve (AUDPC), resultantly 12.2% grain yield gain in rust susceptible wheat genotypes of Fareed-06 and Shafaq-06 followed by the endophytes T. viride, C. lindemuthianum and A. lolii with the grain yield gain of 10.6%, 06.2% and 04.2% respectively. In crux, fungal endophytes are valuable microbes which can be employed to induce tolerance against P. striiformis in yellow rust vulnerable areas for better and sustainable wheat production.

The authors undertook pilot studies to assess the accuracy of the reverse engineering process of worn nonstandard spur gears (non-standardized modulus value, unusual profile/pressure angle, high or low teeth), using conventional techniques and measuring instruments. Eight gears were tested, the module of which ranges from 1.020 to 4.98 mm and the number of teeth from 13 to 42. The key parameter, the basic pitch, was selected to estimate the accuracy of the process. The goal is to determine the value of the profile angle. Eleven models were proposed to estimate the nominal tolerance field, using various types of random data distribution. The tested gears were made in IT grade: 6, 7, 8 and 9 according to DIN 3961. Vernier disc micrometers with various measurement ranges were used for research. It has been shown that the nominal module does not have to be treated as a random variable in the population. EIG (Equation of Identity) was developed, allowing conversion of any gear with specific values of geometric parameters into an identical gear with alternative values of these parameters. The directions and degrees of shifts of the estimated tolerance fields relative to the nominal field were determined. The most effective estimating model was selected in the context of the percentage filling of the nominal tolerance field, taking into account the symmetric Student-Fisher distribution with a confidence level of 60%. Further directions of the research activities were described.

This study addresses the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONF) and Recurrences of Faults (RF). To make the predictions, they used predictive models based on Machine Learning, more specifically on MLP (Multi Layer Perceptron) neural networks. The study revealed that, by analyzing data from past failures and considering factors such as altitude, number of failures on the same street and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes to the field of water infrastructure management by suggesting that the data-driven approach can be applied in other cities or in networks of different types, such as energy or communication networks. The conclusions highlight the importance of data collection and analysis in preventing failures and optimizing water utilities’ resources.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

Energy supply interruptions or blackouts caused by faults in power distribution feeders entail several damages to power utilities and consumer units: financial losses, damage to power distribution reliability, power quality deterioration, etc. Most studies in the specialized literature concerning faults in power distribution systems present methodologies for detecting, classifying, and locating faults after their occurrence. In contrast, this study aims to prevent faults by estimating the city regions whose power grid is most vulnerable to them. This work incorporates the geographic space study to prevent faults in the distribution power grid through a spatial data analysis using local variables that increase the fault risk in feeders in some city regions. Geographically weighted spatial analysis is applied to data aggregated by regions to produce thematic maps with the city regions whose feeders are more vulnerable to failures. Spatial data analysis is developed in QGIS and R programming environments. It is applied in real data of electrical discharges in a medium-sized city with approximately 200,000 inhabitants. In this study, we highlight a moderate positive correlation between electrical discharge density and the percentage of faults in transformers by regions in the central and western areas of the city under study.

Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. In this paper, these typical manifold learning methods are synthesized to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for dimension reduction of the process data. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s T2 and Q statistics are established for the purpose of fault detection. Experiments on an industrial benchmark Tennessee Eastman process and a real blast furnace ironmaking process are carried out to demonstrate the superiority and effectiveness of the proposed method.

In the development of power systems it is indicated very often, that transformation of power systems should be carried out in accordance with the idea of energy democracy. This will develop energy communities, that are trying to meet energy needs by using local renewable generation sources. This may result with a temporary low load on the MV lines connecting the community grid and the power system. Such state may cause incorrect operation of power protection systems. This can cause an extended protection operation time, due to decision algorithms inactivity at low values of measurement currents. Therefore, the detailed MV lines overcurrent digital protection model and a dynamic model of the power network were developed. The simulation results are showing that the settings of the parameters activating the protection decision algorithms affect their operation time in dynamic conditions. The conclusion is that the development of the power protection automatics must be carried out in the same time (preferably in advance) with the change of the power system operation model. This is very important for future power systems with high penetration energy communities and renewable generation sources.

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

Diesel engines, as power equipment, are widely used in the fields of automobile industry, ship and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in time domain and angular domain, on which the current diagnosis methods based, are easily affected by working conditions or hard to extract accurate enough, as the diesel engine keeps running in transient and non-stationary process. This work arms at diagnosing this fault mainly based on frequency band features which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively，a decomposition technique based on improved variational mode decomposition is investigated in this work. As the connection between the features and the fault is fuzzy, the random forest algorithm is used to analyze the correspondence between features and faults. In addition, the feature dimension is reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition show that the method based on variational mode decomposition and random forest is capable to detect valve clearance fault effectively.

This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classifica-tion in Induction Motors (IMs), utilizing the Machinery Fault Prevention Technology (MFPT) Bearing Dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customization. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset's unique characteristics. Phase 1 involves the exploration of load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a wide neural network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in en-hancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

Antler is the fastest growing tissue. Because is based on proto-oncogenes, to avoid the risk of cancer, antlers evolved strong anticancer mechanisms, and thus its extract (DVA) is effective also against the few human tumours studied so far. We assessed whether DVA is a general anticancer compound testing direct effects in cells of different tumours: glioblastoma (GBM), colorectal and breast cancer, and leukaemia. DVA reduced viability of tumour but not healthy cells. Mobility decreased at least at the longest test (72 h). Intraperitoneal/oral 200 mg DVA/kg administration in GBM xenograft mice for 28 d reduced tumour weight 66.3% and 61.4% respectively, and also spleen weight. In addition, tumours treated with DVA showed symptoms of liquefactive necrosis. Serum cytokines showed DVA up-regulated factors related with tumour fighting and down-regulated those related with inducing immune tolerance to the tumour. DVA shows general anticancer effects in the lines tested and, in GBM mice, also strong indirect effects apparently mediated by the immune system.

Background: Bacteria can survive antibiotic exposure by mechanisms like tolerance without a shift in antimicrobial susceptibility. In this study, we investigated the presence of tolerance to three antimicrobials, ceftriaxone (CRO), azithromycin (AZM), and ciprofloxacin (CIP), in clinical isolates and WHO reference panel of N. gonorrhoeae and explored the prevalence between urogenital and anorectal infection sites. Methods: We examined 66 N. gonorrhoeae clinical isolates, evenly divided between anorectal and urogenital infection sites, using a modified tolerance disk (TD test) for three antibiotics. The isolates underwent a three-step incubation process wherein the isolates were exposed to an antibiotic disc for 20 hours of incubation (Step I), followed by the replacement of the antibiotic disc with a nutrient disc for overnight incubation (Step II) and additional overnight incubation with extra nutrients (Step III). Results: Four isolates were lost due to contamination. Of 62 clinical isolates, 7 exhibited tolerance to AZM, with 6 from anorectal and 1 from urogenital sites (p = 0.0496). Tolerance to CRO was observed in 11 isolates, which was significantly associated with the infection site, with 9 from the anorectum, and 2 from the urogenital site being tolerant (p = 0.02). Tolerance to CIP was found in 11 isolates, but was not significantly associated with the infection site. Tolerance was also detected in 8 (K, M, N, O, P, U, V, W) out of the 14 WHO (World Health Organization) reference strains, with varying patterns of tolerance to CRO (n=8), CIP (n=2) and AZM (n=1). Conclusion: This study identified CRO, AZM, and CIP tolerance in clinical Neisseria gonorrhoeae isolates. Significant variations in tolerance proportions between anorectal and urogenital infection sites, particularly for CRO, were observed. Additionally, tolerance was detected in WHO reference strains.

Salt stress is one of the most severe environmental stresses limiting the productivity of crops, including rice. However, there is a lack of information on how salt-stress sensitivity varies across different developmental stages in rice. In view of this, a comparative evaluation of contrasting rice varieties CSR36 (salt tolerant) and Jaya (salt sensitive) was conducted, wherein NaCl stress (50 mM) was independently given either at seedling (S-stage), tillering (T-stage), flowering (F-stage), seed-setting (SS-stage) or throughout the plant growth, from seedling till maturity. Except for S-stage, CSR36 exhibited improved NaCl stress tolerance than Jaya, at all other tested stages. Principal component analysis (PCA) revealed that the improved NaCl stress tolerance in CSR36 coincided with enhanced activities/levels of enzymatic/non-enzymatic antioxidants (root ascorbate peroxidase for T- and S+T stages and root catalase for F-, S+T and S+T+F-stages) and higher accumulation of osmolytes (shoot proline for F-stage and S+T+F-stage), indicating better antioxidant capacitance and osmotic adjustment, respectively. In contrast, higher shoot accumulation of Na+ and consequent increase in Na+/K+, Na+/Ca+2 and Na+/Mg+2 ratio in root and shoot, were identified as major variables associated with NaCl stress induced growth reduction in Jaya. In addition, CSR36 exhibited higher levels of Fe3+, Mn2+ and Co3+ and lower Cl- and SO42-, suggesting its potential to discriminate essential and non-essential nutrients, which might contribute to NaCl stress tolerance. Taken together, the findings provided the frame-work for stage-specific salinity responses in rice, which will facilitate crop-improvement programs for specific ecological niches including the coastal regions. Rice, salt stress, stage-specific salinity, antioxidant defense, ionic homeostasis, salt tolerance

Chum salmon (Oncorhynchus keta) exhibit a remarkable ability to adapt to changes in salinity during their life cycle. However, the fertilized egg stages are sensitive to salinity, affecting on-togeny and hatching. This study investigated the effect of salinity (0, 1, 3, and 5 psu) on the sur-vival of two developmental stages (< 1 day after fertilization and < 1 day after the eyed-egg stage) of fertilized eggs. Based on the experimental results, we assessed the spawning ground environ-ment of the Namdae River, where the largest number of salmon in Korea migrate to spawn. Sur-vival of the < 1 day old fertilized eggs decreased sharply at 3 psu or more, and all eggs died at 5 psu. Hatching rates of the eyed-egg stage varied depending on salinity, but hatching occurred under all environmental conditions. After 2010, the salinity of the layer of water in contact with the sediment in the lower reaches of the river tended to increase (> 6.9 psu) with the frequency of high waves. Overall, the function of the lower river in spawning and hatching is weakening. This study enhances our understanding of the effects of climate change, including increased wave ac-tivity, on salmon spawning grounds

Background: The red swamp crayfish Procambarus clarkii (Girard, 1852), native to the United States and northern Mexico, has been introduced in many countries worldwide. The species has a remarkable invasiveness, due to its high reproductive rate and to its ability to tolerate broad ranges of water parameters like salinity. Nevertheless, no consistent observations of P. clarkii have been reported in the marine environment until now. Methods: Through the observations of a personal acquaintance and of two volunteers who published photos of the species on Facebook marine biology and fishing groups, we collected data showing the presence of the crayfish within marine ecosystems. Results: P. clarkii has been spotted in three different localities, in two of which observations are recurrent. The species can live miles from the nearest freshwater stream, and can colonise the depths up to 20 meters. Conclusions: The multiple observations of P. clarkii collected in our study uncover a persistent presence of the species within some marine areas, rather than its mere sporadic occurrence. As resistance to increasing salinities is influenced by multiple genes, genetic admixture between genetically-differentiated individuals coming from distinct geographical origins could have brought together different salinity-resistance genes, leading to the formation of these resilient phenotypes.

Microemulsions with excellent properties have shown significant potential for enhancing oil recovery from tight formations through spontaneous imbibition. This work present a high-temperature-tolerant lower-phase microemulsion (HTLP-ME) using a microemulsion dilution method. The properties and morphological characteristics of HTLP-ME were evaluated and proposed a enhanced spontaneous imbibition oil recovery mechanism using imbibition tests and CT scanning technology. The results show that the optimum concentration of HTLP-ME is 0.2wt% has a superior thermal stability, small particle size, lower IFT, good wettability alteration ability, and minimum adsorption loss. Imbibition and CT experiments showed that the longer the imbibition distance, the better is the effect on spontaneous imbibition oil recovery. Furthermore, the solubilization coefficient and self-driving force were defined and calculated to quantitatively analyze the imbibition mechanisms. The imbibition mechanisms demonstrate that the reduction of oil/solid adhesion is due to the synergistic effect of IFT reduction and wettability alteration, and the ability to increase the imbibition distance through a larger self-driving force.

Contrary to the long-held belief that the effects of vaccines are specific for the disease they were created; compelling evidence has demonstrated that vaccines can exert positive or deleterious non-specific effects (NSEs). In this review, we compiled research reports from the last 40 years, showing that live vaccines induce positive NSEs, whereas non-live vaccines induce several negative NSEs, including increased female mortality associated with enhanced susceptibility to other infectious diseases, especially in developing countries. These negative NSEs are determined by the vaccination sequence, the antigen concentration in vaccines, the type of vaccine used (live vs. non-live), and also by repeated vaccination. We do not recommend stopping using non-live vaccines, as they have demonstrated to protect against their target disease, so the suggestion is that their detrimental NSEs can be minimized simply by changing the current vaccination sequence. High IgG4 antibody levels generated in response to repeated inoculation with mRNA COVID-19 vaccines could be associated with a higher mortality rate from unrelated diseases and infections by suppressing the immune system. Since most COVID-19 vaccinated countries are reporting high percentages of excess mortality not directly attributable to deaths from such disease, the NSEs of mRNA vaccines on overall mortality should be studied in depth.

Bread wheat (Triticum aestivum L.) is one of the world's most important staple food crop providing 20% global energy and dietary proteins. It is widely grown in sub-tropical and tropical areas and as such exposed to heat-stress especially at grain filling period (GFP). Global warming has further affected its production and productivity in these heat-stressed environments. We examined the effect of heat-stress on 18 morpho-physiological and yield-related traits in 96 bread wheat accessions. Heat susceptibility index (HSI <0.60) and yield stability (i <0.55) used as criteria for selecting the tolerant accessions. Heat-stress, imposed by delayed sowing, decreased crop growth and GFP, and as a result reduced morphological and yield-related traits, namely days to 50% anthesis, plant height, peduncle length, flag-leaf area, spike length, spikelets per spike, grain length and width, thousand grain weight (TGW), harvest index and yield. The reduction in the trait values was severe in susceptible accessions (48.2% yield reduction in IC277741) than the tolerant. Physiological traits like chlorophyll content, canopy temperature depression (CTD), normalized difference vegetation index (NDVI), plant waxiness and leaf rolling showed higher expression in the tolerant accessions under heat-stress. Scanning electron microscopy of matured wheat grains revealed ultrastructural changes in endosperm and aleurone cells caused due to heat-stress. The reduction of size and density of large starch granules is the major cause of yield and TGW decrease in the heat-stress susceptible lines. The most stable and high-yielding accessions namely IC566223, IC128454, IC335792, EC576707, IC535176, IC529207, IC446713 and IC416019 were identified as the climate-smart germplasm lines. Germplasm lines possessing desirable traits were selected as po-tential parents for the development of bi-parental and multi-parental populations.

Accumulation of extra fats in the body results in disturbance in homeostasis causing obesity. The pathophysiology of obesity has been associated with various genetic, epigenetic factors and the disturbed lifestyles of an individual. One of the outcomes is disturbance in the sleep cycle which leads to sleep disorders. The evolutionary significance of sleep lies in the fact that it saves energy and is crucial for adjustment of an individual to ecological and environmental factors, and thus maintains homeostasis. Sleep has an anabolic function and replenishes the wakefulness-associated loss of glycogen stores. However, the modern lifestyle is well known to disrupt sleep behavior and patterns affecting human health negatively. Alteration in homeostasis leads to several acute and chronic pathophysiological conditions like obesity and related metabolic morbidities being one amongst them. Shorter sleep duration and poor sleep quality are important risk factors associated with the development of obesity and have more adverse effects on energy homeostasis. Consequently, it is crucial to comprehend the obesity and sleep relationship and its therapeutic strategies, given that strong crosstalk exists between energy balance that favors obesity and sleep apnea. It is therefore highly recommended to the people with a high risk of obesity to maintain sleep discipline and good sleep hygiene in our daily life. Interventions are required to improve both sleep quality and quantity, and associated obesity. More focused research is needed for uncovering the pieces of evidence that determine whether such interventions can improve obesity-related health consequences. The present review discusses the important findings on the therapeutic and neuroendocrine mechanisms regulating sleep and obesity.

The article presents the results of the γ-ray and fast neutrons impact on various rice species, in order to obtain mutant forms resistant to salinity (NaCl) and drought factors (sorbite). They are going to be used as initial forms in synthetic selection when creating varieties, adapted to the stressful conditions of, both, soil and climate in the Kazakhstan Aral Sea. The average lethal doses (LD50) of γ-rays and fast neutrons, as well as, the average NaCl and sorbite lethal concentrations were established. A distinct dependence on the effect of ionizing radiation and stress factors on the number of induced resistant mutant forms has been related to the initial rice variety. The largest number of mutant forms was obtained from the local variety Syr Suluy, followed by Leader and Aikerim varieties. Out of all varieties, only 1% of the initially irradiated by γ-rays seeds survived, while after fast neutron irradiation – 4,3%. M1 mutant plants significantly differ from the initial forms in terms of morphological features – plant height, panicle length, and grain size. They are tolerant to lodging, and have short and highly sterile panicles, indicating that they are mutants.

In humans and animal models, intermittent fasting (IF) interventions promote body weight loss, improve metabolic health, and are thought to lower cardiovascular disease risk. However, there is a paucity of reports on the relevancy of such nutritional interventions in the context of dyslipidemia and atherosclerotic cardiovascular diseases. The present study assessed the metabolic and atheroprotective effects of intermittent fasting intervention (IF) in atherosclerosis-prone apolipoprotein E-deficient (Apoe-/-) mice. Groups of male and female Apoe-/- mice were fed a regular (chow) or atherogenic (high-fat, high-cholesterol, HFCD) diet for 4 months, either ad libitum or in an alternate-day fasting manner. The results show that IF intervention improved glucose and lipid metabolism independently of sex. However, IF only decreased body weight gain in males fed chow diet and differentially modulated adipose tissue parameters and liver steatosis in a diet composition-dependent manner. Finally, IF prevented spontaneous aortic atherosclerotic lesions formation in mice fed chow diet, irrespective of sex but failed to reduce HFCD-diet-induced atherosclerosis. Overall, the current work indicates that IF interventions can efficiently improve glucose homeostasis and treat atherogenic dyslipidemia, but a degree of caution is warranted with regard to the individual sex and the composition of the dietary regimen.

The stress imposed by ethanol to Saccharomyces cerevisiae cells are one of the most challenging limiting factors in industrial fuel-ethanol production. Consequently, the toxicity and tolerance to high ethanol concentrations has been the subject of extensive research, allowing the identification of several genes important for increasing the tolerance to this stress factor. However, most studies were performed with well characterized laboratory strains, and how the results obtained with these strains work in industrial strains remains unknown. In the present work we have tested three different strategies known to increase ethanol tolerance by laboratory strains in an industrial fuel-ethanol producing strain: overexpression of the TRP1 or MSN2 genes, or overexpression of a truncated version of the MSN2 gene. Our results show that the industrial CAT-1 strain tolerates up to 14% ethanol, and indeed the three strategies increased its tolerance to ethanol. When these strains were subjected to fermentations with high sugar content and cell-recycle, simulating the industrial conditions used in Brazilian distilleries, only the strain with overexpression of the truncated MSN2 gene showed improved fermentation performance, allowing the production of 16% ethanol from 33% of total reducing sugars present in sugarcane molasses. Our results highlight the importance of testing genetic modifications in industrial yeast strains under industrial conditions in order to improve the production of industrial fuel ethanol by S. cerevisiae.

Farmers and seed companies constantly require high-quality seeds with excellent agronomic performance. However, faced with environmental adversity, limited natural resources and increasing food demand around the globe, more attention has turned to improving crop plant production by implementing efficient strategies. Seed priming technology has shown promising biological improvements leading to suitable agronomic performance in crop plants under adverse environmental conditions. Seeds are subjected to controlled conditions that are conducive to complex physiological, biochemical, and molecular changes, conferring specific stress tolerance to subsequent germination and growth conditions. In this review paper, we aimed to study the recent approaches in the efficiency of hydropriming, osmopriming, chemopriming, hormopriming, nanopriming, matrix priming, biopriming, physical priming and hybrid priming procedures in the production of crop plants under environmental adversity, as well as their biological mechanism changes. All priming methods demonstrated relevant changes in the biological mechanism related to crop plant production by mitigating salinity effects, heavy metals, and flooding stress and enhancing chilling, heat, drought and phytopathogen tolerance. We strongly recommend that researchers combine multiple priming methods, known as hybrid priming, in their investigations to provide novel technologies and additional biological approaches to enhance the knowledge of crop plant science. Thus, the findings shed light on the use of seed priming technology as a key strategy to increase crop plant production under environmental adversity by acquiring stress tolerance and enhancing agronomic traits to meet the global food demand.

Desiccation tolerance appeared as the key adaptation feature of photoautotrophic organisms for survival in terrestrial habitats. During the further evolution, vascular plants developed complex anatomy structures and molecular mechanisms to maintain the hydrated state of cell environment, which essentially increased their ability to sustain water deficit and dehydration. However, the role of the genes encoding the mechanisms behind this adaptive feature in the higher vascular plants is restricted to the dehydration protection of spores, seeds and pollen, whereas the mature vegetative stages became sensitive to desiccation. During maturation, orthodox seeds lose up to 95% of their water and successfully enter dormancy. This feature allows seeds maintaining their viability even under strongly fluctuating environmental conditions. The mechanisms behind the desiccation tolerance are activated at the late seed maturation stage and are associated with the accumulation of late embryogenesis abundant proteins (LEA proteins), small heat shock proteins (sHSP), non-reducing oligosaccharides, and antioxidants of different chemical nature. The main regulators of maturation and desiccation tolerance onset are abscisic acid and protein DOG1, which control the network of transcription factors, among which are LEC1, LEC2, FUS3, ABI3, ABI5, AGL67, PLATZ1, PLATZ2. This network is complemented by epigenetic regulation of gene expression by methylation of DNA, post-translational modifications of histones and chromatin remodeling impact on seed desiccation tolerance and longevity. Moreover, orthodox seeds are able to maintain desiccation tolerance during germination up to the stage of radicle protrusion. This time point is critical in the process of seed development, as the seeds lose desiccation tolerance at this moment.

Breakage and damage of fiber optic cable fibers seriously affect the normal operation of fiber optic networks and it is important to quickly and accurately determine the type and location of faults when they occur. The advantages of wavelet transform in singular signal detection and signal filtering are used to analyze Optical Time Domain Reflectometer (OTDR) curve signal and the fault detection method of fiber communication link with no relay and large span in high altitude area is given, which realizes the accurate detection and location of optical fiber communication link fault events under strong noise. The theoretical analysis is basically consistent with the experimental data. The accuracy of optical fiber fault location has been greatly improved in performance. The method has been directly applied to the field detection of ultra-long optical fiber links in high altitude areas, which has certain significance for the future real-time monitoring of optical fiber cables.

Redundancy in ship systems is provided to ensure operational resilience through equipment backups which ensures system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems, hence is provided with good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired black outs are common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensure a more robust maintenance and support platform for efficient ship operations. In this regard hybrid reliability and fault detection analysis were conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of 4 marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate overall low system reliability of less than 70 per cent within the first 24 months of the 78 operational months. Component criticality using reliability importance measures was used to identify all components with more than 40 per cent contribution to sub systems failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Networks classification. The ANN has identified failure patten threshold at about 200kva which can be attributed to overheating, hence establishing link between component failure and generator performance.

Las Loras UNESCO Global Geopark (UGGp) is geologically diverse, particularly in relation to water-derived features: springs, karst springs, travertine buildings, waterfalls, caves. In this work, the interactions between geology, geomorphology, structures and hydrogeology are analyzed. These four components are the fundamentals of geodiversity and their interactions provide a conceptual model of hydrogeological functioning at Las Loras UGGp. The most plausible hypothesis is that the system is formed by two superimposed aquifer systems, separated by an aquitard formed by Lower Cretaceous material. The deep lower aquifer formed by the Jurassic limestones only outcrops on the northern and southern edges of the Geopark and in a small arched band to the south of Aguilar de Campoo. It forms a basement subject to intense deformation. The upper aquifer system, formed by outcropping materials from the Upper Cretaceous, is a free aquifer. It is formed by a multilayered aquifer system that is highly compartmentalized by the very disturbed geomorphology of the landscape, constituting each moorland and each lora, an individualized recharge-discharge system. This model explains the base level of the rivers, the abundant number of existing springs and the permanent nature of some rivers, providing the keys to understand the bases of the geoconservation of a rich geological heritage linked to the active processes of the water cycle.

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

The acquisition of high resolution morpho-bathymetric data on the Calabro Tyrrhenian continental margin (Southern Italy) enabled us to identify several mass-wasting processes, including shallow gullies, shelf-indenting canyons and landslides. In particular, we focus our attention on submarine landslides occurring from the coast down to -1700 m, with mobilized volumes ranging from some hundreds up to tens of millions of cubic meters. These landslides also show a large variability of geomorphic features in the headwall, translational and toe domain. Based on their morphology and distribution, four main classes of coastal/submarine landslides have been recognized: a) rocky coastal/shallow-water failures characterized by large hummocky deposits offshore; b) large-size and isolated scars with associated landslide deposits, mostly occurring on open slope environment and lower part of tectonically-controlled escarpments; c) a linear array of coalescent and nested landslide scars occurring in the upper part of tectonically-controlled escarpments and canyon flanks; d) a cauliflower array of small and coalescent scars occurring in canyon headwall. The latter two classes of landslides are also characterized by a marked retrogressive evolution and their landslide deposits are generally not recognizable on the morpho-bathymetric data. By integrating the morpho-bathymetric dataset with the results of previous studies, we also discuss the main factors controlling the variability in size and morphology of these submarine landslides to provide insights on their failure and post-failure behavior.

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

In this study we used a non-autonomous Chua’s Circuit, and the fractional Lorenz chaos system together with a detection method from Extension theory to analyze the voltage signals. The measured bearing signals by acceleration sensor were introduced into the master and slave systems through a Chua’s Circuit. In a chaotic system minor differences can cause significant changes that generate dynamic errors, and extension matter-element models can be used to judge the bearing conditions. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from Discrete Fourier Transform analysis, Wavelet analysis and an integer order chaos system. The diagnostic ratio showed the fractional order master and slave chaos system calculations. The results show that the method presented in this paper is very suitable for monitoring the operational state of ball bearing system to be superior to the other methods. The diagnosis ratio was better and there were other significant advantages such as low cost and few.

Reactivation of pre-existing weaknesses in the upper crust can be documented using surface features, and has occurred throughout time and space, particularly in regions where the basement material dates from the Precambrian and has undergone successive deformation events. This study aims to use surface features such as fracture patterns to document evidence of such reactivation in the Paleozoic and Cenozoic of Nebraska and Kansas (units separated by an unconformity in the study area). The most prominent basement features in southeast Nebraska and northeast Kansas are oriented NE-SW, likely related to the midcontinent rift, and oriented NW-SE, likely related to fabrics from the Central Plains Orogen. These features are well defined in the potential fields data. Fracture patterns in the study area show an E-W oriented trend, as well as clearly discernable NE-SW and subsidiary N-S and NW-SE trends. The E-W trend is interpreted to be related to far-field stresses from Laramide and Ancestral Rocky Mountain orogenic events, whilst the NE-SW trend is interpreted to be related to subtle reactivation on the Mid-continent rift and related faults, observed in basement data. These movements produced stresses of sufficient magnitude to produce extensional fractures in the overlying rock units, but not sufficient to generate shear. Similarly, the ~N-S and NW-SE fracture trends are taken as evidence of subtle reactivation on the Nemaha Uplift and Central Plains Orogen systems, generating fractures but not shear movement. This contribution therefore provides a convincing case-study of the value of fracture orientations (that is, surface morphodynamics) in discerning buried tectonic trends and subtle reactivation thereon.

Building a new power plant to address the growing demand for power due to population concentration in the metropolitan area is one of the world's major concerns. However, since a large power plant can not be located around the city due to burden of economic cost, building power plant outside metropolitan and cities is necessary. Therefore, new power generation facilities are promoting policies to provide distributed generator(DG) with a small capacity relatively near the metropolitan. When the DG (photovoltaic, wind farm, etc.) is connected with the grid using medium voltage direct current (MVDC) system, voltage sourced converter(VSC) should supply reactive power to the grid, because of fault ride through(FRT) operation in grid fault. If the voltage drop is severe, the converter should be disconnected from the grid immediately without supplying the reactive power, resulting in a considerable economic loss. In general, superconducting fault current limiter(SFCL) is introduced as a measure to enhance FRT capability. In this paper, we use trigger type SFCL which protects superconducting element and reduces low voltage. On the other hand, the active power unbalance of the DC-link and the DC voltage rise due to the reactive power supply of the grid-side converter. The rise of the DC voltage causes the P (active power), Q (reactive power) control of the converter to deviate, causing malfunction and damage of the DC equipment. Therefore, the rise of the DC voltage must be prevented. In this paper, we consider the suppression the DC voltage rising caused by the FRT operation through the active power tracking control (APTC).

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers calculation. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer's risk. If a product doesn't meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended, and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with an opening upwards. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties are determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model's performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

Plant growth and productivity are hindered by abiotic stresses. Crop productivity and the ecosystem are suffering due to climate change and harmful farming practices. We urgently need to use environment-friendly techniques like Arbuscular Mychorriza Fungi (AMF) to improve crop productivity. It is a well-established fact that AMF are effective bio-fertilizers. It is believed that AMF injections can increase plant resilience to stressors like heat,metals, salt, drought, and extreme temperatures. AMF aids in enhancing tolerance mechanisms and safeguarding essential metabolic pathways. AMF, as natural root symbionts, give plants important inorganic nutrients, helping them grow better, produce more, and acclimatize even under stress. AMF acts as a bio-fertilizer, boosting plants’ ability to adjust to different environments. Thus, researchers need to conduct further studies on how AMF promotes crop quality and productivity. This review describes the current understanding of AMF’s impact on host plants at their different growth stage, their benefits, applications, and the significance of plant nutrient relationships with AMF.

When the zoonotic parasite of rodents that can cause human neuroangiostrongyliasis, i.e., Angiostrongylus cantonensis, is found in its natural definitive hosts, it is usually reported in isolation, as if the rat lungworm were the only component of the within-rat parasite community. In this study we report the coinfections found in rats naturally infected by A. cantonensis in urban populations of Rattus norvegicus and Rattus rattus in Valencia, Spain. In addition to the rat lungworms, which were found in 14 of the 125 rats studied (a prevalence of 11.20%), 18 other parasite infrapopulations (intestinal and tissular protists, microsporidia, and helminths) were found; some of them with high burdens. Fourteen of these 19 species found are potential zoonotic parasites, namely Blastocystis, Giardia duodenalis, Cryptosporidium spp., Enterocytozoon bieneusi, Encephalitozoon hellem, Toxoplasma gondii, Brachylaima spp., Hydatigera taeniaeformis larvae, Hymenolepis nana, Hymenolepis diminuta, Angiostrongylus cantonensis, Calodium hepaticum, Gongylonema neoplasticum and Moniliformis moniliformis. Despite the limited number of rats studied, the total predominance of coinfected rats as well as their high parasite loads seem to indicate a clear trend towards parasite tolerance.

Cheese whey, a significant byproduct of the dairy industry, poses a substantial environmental challenge due to its organic pollutant load and large size demands for effective and affordable valorization methods. The abundance of lactose and other organic compounds in whey contributes to its elevated Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD), further straining natural ecosystems. This study aimed to investigate the nutritional potential of whey and its conversion into value-added products: bioethanol and probiotic drinks. To achieve this, Bacillus megaterium and Bacillus subtilis strains, isolated from soil samples in Nepal, were co-cultured with Lactobacillus and Saccharomyces cerevisiae in the whey broth for fermentation. Ethanol distillation was carried out using a Rota-evaporator to maintain the viability of the fermenting organisms within the broth. Moreover, the probiotic criteria of the fermenting strains were extensively examined. Experimental observations revealed a remarkable concentration of 9.2 g/L of ethanol, resulting in an ethanol yield of 0.23 g/L. Extrapolating this rate, it is theoretically possible to produce an annual global bioethanol output of 5 million tons using whey as a sustainable and abundant resource. Furthermore, the study explores novel advancements in the distillation process, demonstrating the successful extraction of ethanol at room temperature. Additionally, the fermentation organisms employed in this research exhibit robust viability, surviving various in-vitro stress-tolerant tests, including exposure to bile salt, pH fluctuations, and gastric juice. These resilient strains also demonstrated long-term stability in fermented whey broth, making them promising candidates for the development of probiotic beverages. This dual approach not only enables the sustainable management of organic waste but also presents an opportunity to create value-added products with positive implications for both the environment and human health.

Due to the health crisis caused by SARS-CoV-2, the creation of a new vaccine platform based on mRNA was implemented. Globally, around 13.32 billion COVID-19 vaccine doses of diverse platforms have been given, and up to this date, 69.7% of the total population received at least one injection of a COVID-19 vaccine. Although these vaccines prevent hospitalization and severe forms of the disease, increasing evidence has shown they do not produce sterilizing immunity, allowing people to suffer frequent re-infections. Recent research has also raised concerns that mRNA vaccines could induce immune tolerance, which, added to that caused by the virus itself, could complicate the clinical course of a COVID-19 infection. Furthermore, recent investigations have found high IgG4 levels in people who were administered two or more injections of mRNA vaccines. It has been suggested that an increase in IgG4 levels could have a protecting role by preventing immune over-activation, similar to that occurring during successful allergen-specific immunotherapy by inhibiting IgE-induced effects. Altogether, evidence suggests that the reported increase in the IgG4 levels detected after repeated vaccination with the mRNA vaccines is not a protective mechanism; rather, it may be a part of the immune tolerance mechanism to the spike protein that could promote unopposed SARS-CoV2 infection and replication by suppressing natural antiviral responses. IgG4-induced suppression of the immune system due to repeated vaccination can also cause autoimmune diseases, promotes cancer growth, and autoimmune myocarditis in susceptible individuals.

Viroids are known the smallest plant pathogens, and although their genome sequences do not encode proteins, they can cause disease in economically important crops. In order to control viroid diseases and mitigate their damage, genetic resources used for breeding of the viroid-resistant crop have been searched, but the practical resistant trait has not been found in almost all viroid-crop combinations, as well as the tolerant trait. Due to the difficulty in exploiting naturally occurring resistance or tolerance, various effective strategies have been devised to control viroid diseases using non-transforming or transforming techniques. Meanwhile, extensive findings related to viroid resistance and tolerance may lead to confer resistance or tolerance to viroid infection by combining with the recently emerged new technologies (e.g., spray-induced gene silencing and genome-editing technologies), which are believed to be more environmentally viable and acceptable to the general public than previously reported approaches. In particular, some genome-modified crops produced by the latter technology are already on the market. In this review, we comprehensively summarize the current status about investigation of naturally occurring genetic traits for viroid resistance and tolerance, accumulating knowledge about host factors involved in viroid pathogenicity, and various basic technologies developed to try to possible viroid disease control strategies. Furthermore, we discuss prospects and challenges for the achievement of more effective, practical, and sustainable disease control of viroid.

Abiotic stresses, including drought, salinity, cold, heat, and heavy metals, extensively reduce global agricultural production. Approaches such as conventional breeding and transgenic breeding have been widely used to cope with these environmental stresses. The clustered regularly interspaced short palindromic repeat- Cas (CRISPR/Cas) based gene-editing tool has revolutionized due to its simplicity, accessibility, adaptability, flexibility, and wide applicability. This system has a great potential to build up crop varieties with enhanced tolerance against abiotic stresses. In this review, we summarize the most recent findings on understanding the mechanism of abiotic stress response in plants and the application of CRISPR/Cas mediated gene-editing system towards enhanced tolerance to drought, salinity, cold, heat, and heavy metals stresses. Furthermore, in this review, we highlighted the recent advancements in prime editing and base editing tools for crop improvement.

Pv11, an insect cell line established from the midge Polypedilum vanderplanki, is capable of ametabolic desiccation tolerance, so-called anhydrobiosis. We previously discovered that heat shock factor 1 (HSF1) contributes to the acquisition of desiccation tolerance by Pv11 cells, but the mechanistic details have yet to be elucidated. Here, by analyzing the gene expression profiles of newly established HSF1-knockout and -rescue cell lines, we show that HSF1 has a genome-wide effect on gene regulation in Pv11. HSF1-knockout cells exhibit a reduced desiccation survival rate, but this is completely restored in HSF1-rescue cells. By comparing mRNA profiles of the two cell lines, we reveal that HSF1 induces anhydrobiosis-related genes, especially genes encoding late embryogenesis abundant proteins and thioredoxins, but represses a group of genes involved in basal cellular processes, thus promoting an ametabolic state in the cell. In addition, HSF1 binding motifs are enriched in the promoters of anhydrobiosis-related genes and we demonstrate binding of HSF1 to these promoters by ChIP-qPCR. Thus, HSF1 directly regulates the transcription of anhydrobiosis-related genes and consequently plays a pivotal role in the induction of anhydrobiotic ability in Pv11 cells.

The ectoparasitic mite Varroa destructor is the most significant pathological threat to the western honey bee, Apis mellifera, leading to the death of most colonies if left untreated. An alternative approach to chemical treatments is to selectively enhance heritable honey bee traits of resistance or tolerance to the mite through breeding programs, or select for naturally surviving untreated colonies. We conducted a literature review of all studies documenting traits of A. mellifera populations either selectively bred or naturally selected for resistance and tolerance to mite parasitism. This allowed us to conduct an analysis of the diversity, distribution and importance of the traits in different honey bee populations that can survive V. destructor throughout the globe. In a second analysis, we investigated the genetic bases of these different phenotypes by comparing ’omics studies (genomics, transcriptomics, and proteomics) of A. mellifera resistance and tolerance to the parasite. Altogether, this review provides a detailed overview of the current state of the research projects and breeding efforts against the most devastating parasite of A. mellifera. By highlighting the most promising traits of varroa-surviving bees and our current knowledge on their genetic bases, this work will help direct future research efforts and selection programs to control this pest. Additionally, by comparing the diverse populations of honey bees that exhibit the traits, this review highlights the consequences of anthropogenic and natural selection on the interactions between hosts and parasites.

Viral infections are a natural part of our existence. They can affect us in many ways that are the result of the interaction between the viral pathogen and our immune system. Most times the resulting immune response is beneficial for the host. The pathogen gets cleared thus protecting our vital organs with no other consequences. Conversely, the reaction of our immune system against the pathogen can cause organ damage (immunopathology) or lead to autoimmune disease. To date, there are several mechanisms for virus-induced autoimmune disease, including molecular mimicry and bystander activation, in support of the “fertile field” hypothesis, terms defined in our review. On the flip side, viral infections have been associated with protection from autoimmunity through mechanisms that include Treg invigoration and immune deviation, in support of the “hygiene hypothesis”, also defined here. Infection with lymphocytic choriomeningitis virus (LCMV) is one of the prototypes showing that the interaction of our immune system with viruses can either accelerate or prevent autoimmunity. Studies using mouse models of LCMV have helped conceive and establish several concepts that we today know and explain how viruses can lead to autoimmune activation or induce tolerance. Some of the most important mechanisms established during the course LCMV are described in this short review.

Fault detection is an important step for subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complicated faulting structures of seismic images, manual seismic interpretation is time taking and need intensive work. To overcome this problem, geoscientists are coming up with productive computer-aided techniques for assisting in interpreter science for many years. However, in this paper, we used a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field of Browse Basin, Australia. Basically, this field is highly structured with complex normal faulting throughout the targeted Plover Formations. So, our motivation for this work in this field is to compare machine learning-based fault prediction to user interpreted faults identification with supported by seismic variance attribute. We found very satisfying result using DL with having an improved fault probability volume that outperform variance technology. Therefore, we propose that this workflow could reduce time and able to predict fault quite accurately in any field area.

Northeastern margin is a natural experimental field for studying crustal extrusion and expansion mechanism. Accurate crustal deformation pattern is a key point in the analysis of regional deformation mechanism and seismic hazard research and judgment. In this paper, the present-day GPS velocity field on the northeastern margin of the Tibetan Plateau was obtained from encrypted GPS observations around the Haiyuan-Liupanshan fault zone, combined with GPS observations on the northeastern margin of the Tibetan Plateau from 2010 to 2020. Firstly, we divided the study area into three relatively independent blocks: ORDOS block, Alxa block and Lanzhou block; secondly, the accurate fault distribution of the Haiyuan-Liupanshan fault zone was taken into account to obtain the optimal inversion model; finally, using the block and fault back-slip dislocation model, the inversion got the slip rate distribution, locking depth and slip deficit rate of each fault. The results indicate that the Laohushan Fault and Haiyuan Fault are dominated by left-lateral strike-slip, while the Liupanshan Fault is dominated by thrust dip-slip, and the Guguan-Baoji Fault has both left-lateral strike-slip and thrust dip-slip components. The maximum locking depths of the Laohushan Fault, Haiyuan Fault, Liupanshan Fault and Guguan-Baoji Fault are 5 km, 13 km, 15 km and 10 km respectively, and the locking of Haiyuan Fault is strong in the middle section and weak in the eastern and western section. The Haiyuan Fault is still in the post-earthquake stress adjustment stage. The slip deficit rate decays from 3.6 mm/yr to 1.8 mm/yr from west to east along the fault zone. Combined with geological and historical seismic data, the results suggest that the mid-long term seismic risk in the Liupanshan Fault is high.

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

Doubly Fed Induction Generator (DFIG) has a stator winding directly coupled with grid. Whereas, rotor winding is connected via a fault-prone back to back power converters. DFIG is known to be vulnerable to the grid faults. In early times, when a fault occurred, these generators were required to disconnect from the grid to secure the generator and power converters. However, due to the increased penetration of wind turbines into the power system, grid operators demanded that the wind turbines remain connected to the grid, as disconnecting them would further disrupt the grid. When a fault at the grid terminal occur, a high stator current is induced which further result in high rotor current. This current will trigger the DC-link voltage to rise. This high currents and DC-link voltage will cause harm to the converters. Thus, in this paper work, the crowbar protection system is employed for protecting the converters against excess energy. Furthermore, the analysis of DFIG is rendered by integrating the crowbar protection with the Battery Energy Storage System (BESS) for a much effective outcome in enhancing machine to drive the fault. MATLAB-Simulink software is used for modeling and simulation. All system parameters are obtained from ADAMA-II Wind Farm.

Our understanding of pull-apart basins and their fault systems has been enhanced by analog experiments and simulations. However, there has been no opportunity to compare the faults that constitute pull-apart basins with surface ruptures during earthquakes. In this study, we investigated the effects of a 2018 earthquake (Mw 7.5) on a pull-apart basin in the Palu-Koro fault system, Sulawesi Island, Indonesia, using geomorphic observations in digital elevation models, optical correlation with pre- and post-earthquake satellite images. A comparison of active fault traces determined by geomorphology with the locations of surface ruptures from the 2018 earthquake shows that some of the boundary faults of the basin are inactive and that active faulting has shifted to basin-shortcut faults and relay ramps. We also report evidence of lateral spreading, in which alluvial fan materials moved around the end of the alluvial fan. These phenomena may provide insights for anticipating the location of future surface ruptures in pull-apart basins.

The paper presents dynamic and transient behaviour of the Doubly Fed Induction Generator (DFIG) in the wind farms. When Voltage sag or any faults occurs in the network, the variables in the doubly Fed Induction Generators are varying severely. If the voltage sag occurs, Active and Reactive power that generated by the DFIG will decrease and will increase respectively, The DC voltage link will be bigger, and the rotor current will increase. The DFIG in the wind farms uses Proportional Integral controller for controlling of electronic devices (Rotor Side Converter and Grid Side Converter). Even though the model of Doubly Fed Induction Generators and electronic device in the paper are linear, The Proportional Integral controllers cannot protect and control the variables. So, the power electronic converters and the DC-link can damage due to over voltages and over currents. Doubly Fed Induction Generator in the wind farm is simulated in MATLAB software. The results of the simulation present over voltage and over current in the DC-link and in the rotor of the Doubly Fed Induction Generator. In addition, it will explore the effect of Proportional Integral controller in Doubly Fed Induction Generator when three-phase short circuit fault occurs

Our study investigated the innate immune response to Toxoplasma gondii infection by assessing microglial phenotypic changes and sickness behavior as inflammatory response markers post-ocular tachyzoite instillation. Disease progression in Swiss albino mice was compared with documented outcomes in BALB/c mice using identical ocular route and parasite burden (2x105 tachyzoites), with saline as control. Contrary to expectations, Swiss albino mice displayed rapid, lethal disease progression, marked by pronounced sickness behaviors and mortality within 11-12 days post-infection, while survivors showed no apparent signs of infection. Comparative analysis revealed T. gondii-infected BALB/c mice exhibited reduced avoidance of feline odors, while in-fected Swiss albino mice showed enhanced avoidance responses. There was a significant increase in microglial cells in the dentate gyrus molecular layer of infected Swiss albino mice compared to BALB/c mice and their respective controls. Hierarchical cluster and discriminant analyses iden-tified three microglial morphological clusters, differentially affected by T. gondii infection across strains. BALB/c mice exhibited increased microglial branching and complexity, while Swiss al-bino mice showed reduced shrunk microglial arbors, diminishing morphological complexity. These findings highlight strain-specific differences in disease progression and inflammatory reg-ulation, indicating lineage-specific mechanisms in inflammatory responses, tolerance, and re-sistance. These elements are critical in devising control measures for toxoplasmosis.

Sublingual vaccines offer the benefits of inducing mucosal immunity to protect against respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and influenza, while also enabling needle-free self-administration. Previously, we developed a sublingual vaccine formulated with the SARS-CoV-2 receptor binding domain antigen and Poly(I:C) adjuvant in nonhuman primates, cynomolgus macaques. This study examined the immune and inflammatory responses elicited by the sublingual influenza vaccine containing hemagglutinin (HA) antigen and Poly(I:C) adjuvant and assessed the safety of this vaccine in nonhuman primates. The Poly(I:C)-adjuvanted sublingual vaccine induced both mucosal and systemic immunities. Specifically, the sublingual vaccineproduced HA-specific secretory IgA antibodies in saliva and nasal washings, and HA-specific IgA and IgG were detected in the blood. This vaccine appeared to be safe, as judged from the results of blood tests and plasma C-reactive protein levels. Notably, sublingual vaccination neither increased the production of inflammation-associated cytokines—IFN-alpha, IFN-gamma, and IL-17—in the blood, nor upregulated the gene expression of proinflammatory cytokines—IL12A, IL12B, IFNA1, IFNB1, CD69, and granzyme B—in white blood cells. Moreover, DNA microarray analyses revealed that sublingual vaccination evoked both enhancing and suppressing expression changes in genes associated with immune-related responses in cynomolgus monkeys. Therefore, the sublingual vaccine with Poly(I:C) adjuvant is safe and creates a balanced state of enhancing and suppressing the immune-related response.

The development of modern genomic tools has sped up the breeding program of food crops. More recently, genomic resources are being developed for tropical forages and will be key resources for developing climate resilient high yielding forage varieties. In this study, we present a genome-wide association study for biomass yield and feed quality traits in buffel grass (Cenchrus ciliaris L. aka Pennisetum ciliare L.,). Genome-wide markers generated using the DArTSeq platform and mapped onto the reference genome of Setaria italica were used for the genome-wide association study. The results revealed several markers associated with biomass yield and feed quality traits. A total of 78 marker trait associations were identified with R2 values ranging from 0.138 to 0.236. The marker trait associations were distributed across different chromosomes. Of these associations, the most marker trait associations (23) were observed on Chr9, followed by Chr5 with 12. The fewest number of marker trait associations were observed on Chr4 with 2. In terms of traits, 17 markers were associated with biomass yield, 24 with crude protein, 26 with TDN, 14 with ADF, 10 with NDF and six with DMI. Twenty of the identified markers were associated with at least two traits. The identified marker trait associations provide a useful genomic resource for future improvement and breeding of buffel grass.

SARS-CoV-2 can trigger autoimmune central nervous system (CNS) diseases in genetically susceptible individuals, a mechanism poorly understood. Molecular mimicry (MM) has been identified in other viral diseases as potential triggers of autoimmune CNS events. This study investigated if MM is the process through which SARS-CoV-2 induce the breakdown of immune tolerance. The frequency of autoimmune CNS disorders was evaluated in a prospective cohort with patients admitted in the COVID-19 Intense Care Unity (ICU) in Rio de Janeiro. Then, an in silico analysis was performed to identify the conserved regions which share high identity between SARS-Cov-2 anti-gens and human proteins. The sequences with significant identity and antigenic properties were then assessed for their binding capacity to HLA subtypes. Of the 112 patients included, 3 were classified as having an autoimmune disorder. A total of eleven combinations had significant linear and three-dimensional overlap. NMDAR1, MOG and MPO were the self-antigens with more significant combinations, followed by GAD65. All sequences presented at least one epitope with strong or intermediate binding capacity to the HLA subtypes selected. This study underscores the possibility that CNS autoimmune attacks observed in COVID-19 patients, including those in our population, could be driven by MM in genetically predisposed individuals.

Long-term, multi-decade research on planted tree survival in urban settings is sparse. One understudied urban environment is highway rights-of-way (ROW), lands adjacent to high-speed, unsignalized roadways. We conducted a re-inventory of tree planting cohort in northern Illinois, U.S. on a 48 km-long highway near Chicago which were 10-, 21-, and 30-years old to evaluate long-term patterns of survival and diversity. Using each randomly selected planting site along the highway as a unit of observation and analysis, we compared the number of trees documented in record drawing to the number of trees currently alive to determine percent survival. We evaluated 224 planting sites which originally contained 2,944 trees and collected data about the planting site location. For the oldest cohort, 26% of trees were still alive in 2018 (median survival by species = 16%, Q1 = 0%, Q3 = 48%), while 31% of the 21-year-old cohort (med. = 6%, Q1 = 0%, Q3 = 47%) and 86% of the 10-year-old cohort were still alive (med. = 85%, Q1 = 74%, Q3 = 96%). The survival of the 21- and 30-year-old cohort matches urban tree survival estimates by other researchers, while the 10-year-old survival is higher than expected. The only planting location characteristic that significantly affected survival was traffic islands (areas between the highway and entrance/exit ramps). Species with low drought tolerance were less likely to be alive for the 10-year-old cohort. Waterlogging tolerant species were more likely to be alive in the 10-year-old cohort. Since some species in the 21- and 30-year-old cohorts had very low survival, the tree species richness and diversity s in study areas declined between the initial record drawings and reinventory. This study demonstrates the challenges of maintaining long-term survival and diversity in the highway ROW and emphasizes the importance of species selection.

Upland cotton is sensitive to 2,4-dichlorophenoxyacetic acid (2,4-D), and the identification of potentially 2,4-D tolerant cotton chromosome substitution (CS) lines and understanding tolerance mechanisms provide a significant step into the development and genetic improvement of upland cotton to reduce yield loss caused by 2,4-D herbicide effects including the drifts. Experiments were conducted to understand the possible mechanism of herbicide tolerance in CS-T04-15, CS-T07, and CS-B15sh, 2,4-D herbicide-tolerant cotton CS lines compared with TM-1, the 2,4-D herbicide susceptible recurrent parent of the CS line as control, using [14C]2,4-D. Percent absorption rate and translocation patterns of the 14C-labeled herbicide application at 5.17 kBq at 6 to 48 hours after treatment (HAT) were determined. The tolerant cotton CS lines showed 15-19% [14C]2,4-D uptake while TM-1 exhibited a reduced uptake of only 1.4% [14C]2,4-D at 24 HAT. Distribution of the absorbed [14C]2,4-D showed that 2-5% was translocated outside the treated leaf. In TM-1, 77% of the herbicide was translocated above and below the treated leaf, contrasting with the reduced translocation of 14C-labeled herbicide observed in the tolerant CS lines. Interestingly, CS-T04-15 showed a restricted movement of 14C below the treated leaf at 6 to 48 HAT, suggesting a novel mechanism of herbicide tolerance. This finding is the first report on upland cotton demonstrating a complex differential uptake and translocation associated with herbicide tolerance for [14C]2,4-D in cotton CS lines.

Drought is one of the major stress factors affecting growth and development of plants. In this context, drought-related losses of crop plant productivity impede sustainable agriculture all over the world. In general, plants responses to water deficit by multiple physiological and metabolic adaptations at the molecular, cellular and organism levels. To understand the underlying mechanisms of drought tolerance, adequate stress models and arrays of reliable stress markers are required. Therefore, in this review we comprehensively address currently available models of drought stress, based on culturing plants in soil, hydroponic or agar culture. These experimental setups give access to different aspects of plant response to drought, like decrease of tissue water potential, reduction of stomata conductance and photosynthesis efficiency, accumulation of low-molecular weight solutes (metabolic adjustment) and drought protective proteins. Till now, this pattern of markers was successfully extended to the methods of enzyme chemistry, molecular biology and omics techniques. Thus, conventional tests can be efficiently complemented by determination of phytohormone and reactive oxygen species (ROS) contents, activities of antioxidant enzymes, as well as comprehensive profiling of transcriptome, proteome and metabolome.

In this review, we show the unique potential of spleen as an optimal site for islet transplantation and a source of mesenchymal stem cells. Islet transplantation is a cellular replacement therapy to treat severe diabetes mellitus, but its clinical outcome is unsatisfactory at present. One factor in clinical success of this therapy is selection of the most appropriate transplantation site. The spleen has been studied for a long time as a candidate site for islet transplantation. Its advantages include physiological insulin drainage and regulation of immunity. Recently it has also been shown that the spleen contributes to the regeneration of transplanted islets. The efficacy of transplantation is not as high as that obtained with intraportal transplantation, which is the current representative method of clinical islet transplantation. Safer and more effective methods of islet transplantation need to be established before the spleen can be effectively used in the clinic. Spleen also has an interesting aspect as a mesenchymal stem cell reservoir. The splenic mesenchymal stem cells contribute to tissue repair in damaged tissue, and thus, the infusion can be a promising therapy for autoimmune diseases, including type 1 diabetes mellitus and Sjogren’s syndrome.

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

A detection system, modeled in a graph, uses "detectors" on a subset of vertices to uniquely identify an "intruder" at any vertex. We consider two types of detection systems: open-locating-dominating (OLD) sets and identifying codes (ICs). An OLD set gives each vertex a unique, non-empty open neighborhood of detectors, while an IC provides a unique, non-empty closed neighborhood of detectors. We explore their fault-tolerant variants: redundant OLD (RED:OLD) sets and redundant ICs (RED:ICs), which ensure that removing/disabling at most one detector guarantees the properties of OLD sets and ICs, respectively. This paper focuses on constructing optimal RED:OLD sets and RED:ICs on the infinite king's grid, and presents the proof for the bounds on their minimum densities; [3/10, 1/3] for RED:OLD sets and [3/11, 1/3] for RED:ICs.