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To reduce the energy consumption of the hydrogen liquefaction process, a hydrogen liquefaction process based on cascaded mixed refrigerant cycles is proposed in this paper. The two mixed refrigerant cycles are connected in series so that the cooling capacity of refrigerants can be fully utilized. Cascaded processes with different numbers of pre-cooling stages are compared. The processes are optimized using the particle swarm algorithm. And cascaded and non-cascaded processes are compared under the same conditions. The results show that the more the number of pre-cooling stages, the lower the SEC and the higher the exergy efficiency. And the cascaded process has higher energy efficiency than the non-cascaded process. The specific power consumption of the process is 5.664 kWh/kg LH2 , the coefficient of performance is 0.2322, and the exergy efficiency is 52.77%. This research can provide valuable ideas and methods for the improvement of hydrogen liquefaction process design. • A cascaded dual mixed refrigerant hydrogen liquefaction process is put up. • The cascaded process has higher energy efficiency than the non-cascaded process. • The SEC of the process is 5.664 kWh/kg LH2 and the exergy efficiency is 52.77%. • The efficiency of this liquefaction process reaches the highest level currently. [ABSTRACT FROM AUTHOR]

Although the advantage of spike timing-based over rate-based network computation has been recognized, the underlying mechanism remains unclear. Using Tempotron and Perceptron as elementary neural models, we examined the intrinsic difference between spike timing-based and rate-based computations. For more direct comparison, we modified Tempotron computation into rate-based computation with the retention of some temporal information. Previous studies have shown that spike timing-based computation are computationally more powerful than rate-based computation in terms of the number of computational units required and the capability in classifying random patterns. Our study showed that spike timing-based and rate-based Tempotron computations provided similar capability in classifying random spike patterns, as well as in text sentiment classification and spam text detection. However, spike timing-based computation is superior in performing a task involving discriminating forward vs. reverse sequence of events, i.e., information mainly temporal in nature. Further studies revealed that this superiority required the asymmetry in the profile of the postsynaptic potential (PSP), and that temporal sequence information was converted to biased spatial distribution of synaptic weight modifications during learning. Thus, the intrinsic PSP asymmetry is a mechanistic basis for the high efficiency of spike timing-based computation for processing temporal information. [ABSTRACT FROM AUTHOR]

Abstract Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradigm is still limited when facing the need to learn new concepts efficiently from scarce data. In this paper, we present a memory-augmented neural network which is motivated by the process of human concept learning. The training procedure, imitating the concept formation course of human, learns how to distinguish samples from different classes and aggregate samples of the same kind. In order to better utilize the advantages originated from the human behavior, we propose a sequential process, during which the network should decide how to remember each sample at every step. In this sequential process, a stable and interactive memory serves as an important module. We validate our model in some typical one-shot learning tasks and also an exploratory outlier detection problem. In all the experiments, our model gets highly competitive to reach or outperform those strong baselines. [ABSTRACT FROM AUTHOR]

Abstract Distant supervised relation extraction is an important task in the field of natural language processing. There are two main shortcomings for most state-of-the-art methods. One is that they take all sentences of an entity pair as input, which would result in a large computational cost. But in fact, few of most relevant sentences are enough to recognize the relation of an entity pair. To tackle these problems, we propose a novel hierarchical selective attention network for relation extraction under distant supervision. Our model first selects most relevant sentences by taking coarse sentence-level attention on all sentences of an entity pair and then employs word-level attention to construct sentence representations and fine sentence-level attention to aggregate these sentence representations. Experimental results on a widely used dataset demonstrate that our method performs significantly better than most of existing methods. [ABSTRACT FROM AUTHOR]

Abstract Most current textual reasoning models cannotlearn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via deep reinforcement learning. It takes the form of Memory Networks but features a special memory that stores relational tuples, mimicking the “Image Schema” in human cognitive activities. We redefine textual reasoning as a sequential decision-making process modifying or retrieving from the memory, where logic rules serve as state-transition functions. Activating logic rules for reasoning involves two problems: variable binding and relation activating , and this is a first step to solve them jointly. Our model achieves an average error rate of 0.7% on bAbI-20, a widely-used synthetic reasoning benchmark, using less than 1k training samples and no supporting facts. [ABSTRACT FROM AUTHOR]

Graphene nanoplatelets(GNP)/cement composites were prepared using three types of GNP with different structures. In order to investigate the effects of GNP and styrene-acrylate emulsion on properties of GNP/cement composites, GNP with different addition (0-2.0 wt%) and styrene-acrylate emulsion (10 wt%) were mixed into cement through the method of mechanical stirring. Electrical performance and the pressure-sensitive property of GNP/cement composites were studied. The results showed that the addition of GNP to cement would lead to a significant drop of resistivity and make composites manifest pressure sensitivity. In addition, the structure (C/O atomic ratio) of GNP greatly affected the properties of the GNP/cement composites. A distinct enhancement in pressure sensitivity was found when emulsion was added to GNP/cement composites. The gauge factor of emulsion modified GNP/cement composites reached a peak value of 7.783, which was 1 order of magnitude higher than composites without emulsion. This work offered a new opportunity to make use of traditional cement materials combining with GNP. [ABSTRACT FROM AUTHOR]

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC 2 ), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K -means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets. [ABSTRACT FROM AUTHOR]

A bacterial strain capable of degrading di(2-ethylhexyl) phthalate (DEHP) from the artificially contaminated water (with over 90% removal within 5 days of incubation) was isolated from the activated sludge obtained from a regional wastewater treatment plant in Macau Special Administrative Region, China. The isolate was identified as Acinetobacter sp. SN13 by the 16S rRNA gene sequence analysis. Two major experimental parameters, temperature (25, 30, 35 °C) and pH (3–9), were further optimized to enhance the biodegradation efficiency for DEHP. The optimal temperature was 30 °C while there were no significant differences for the pH range tested, 6–9 (p = 0.87). The growth kinetics of this indigenous isolate on DEHP followed the inhibition model, with the maximum degradation rate, the half saturation constant, and the inhibition constant of 124.8 mg l −1 day −1 , 272.3 mg l −1 , and 720.5 mg l −1 , respectively. The inhibition model for the specific growth rate was also simulated using Matlab software, and the respective maximum specific growth rate, half saturation constant, and inhibition constant were 0.1192 day −1 , 137.6 mg l −1 , and 850.3 mg l −1 . The highest degradation rate was achieved when the initial concentration of DEHP was 400 mg l −1 . Ferric ion (Fe 3+ at 100–1000 μg l −1 ) showed the stimulatory effect on the DEHP biodegradation, while Mn 2+ stimulated the biodegradation at lower concentrations (100 μg l −1 ) but inhibited at higher concentrations (500–1000 μg l −1 ). The respective DEHP degradation pathway for the isolate is also proposed by the identification of the following intermediates: mono-(2-ethylhexyl) phthalate (MEHP), phthalic acid (PA), protocatechuate, β-carboxy- cis , cis -muconic acid, and 3-katoadipate. [ABSTRACT FROM AUTHOR]

A novel multifunctional structural supercapacitor based on graphene and geopolymer infused with 2 M KOH electrolyte was fabricated. Metakaolin-based geopolymers, which were first applied as structural separators in this paper, were prepared with metakaolin and different moduli of alkaline activator solution. The widespread pores in geopolymer matrix provide enough channels for ion storage and motion. The effects of alkaline activator solution modulus and curing age on the electrochemical properties were analyzed. The results revealed the ideal capacitive behavior of structural supercapacitor. The samples with modulus of 2.0 exhibited the highest specific capacitance of 36.5 F g −1 at curing age of 28 days. A possible mechanism was proposed to explain the factors that influence the specific capacitance of geopolymer-based structural supercapacitor. The result of multifuncitonality analysis showed that samples with modulus of 1.6 exhibited the best level of multifunctionality, with compressive strength of 33.85 MPa and specific capacitance of 33.4 F g −1 at curing age of 28 days. It achieved a balance between mechanical property and electrochemical performance. [ABSTRACT FROM AUTHOR]

Relation extraction is to identify the relationship of two given entities in the text. It is an important step in the task of knowledge extraction. Most conventional methods for the task of relation extraction focus on designing effective handcrafted features or learning a semantic representation of the whole sentence. Sentences with the same relationship always share the similar expressions. Besides, the semantic properties of given entities can also help to distinguish some confusing relations. Based on the above observations, we propose a neural network based framework for relation classification. It can simultaneously learn the relation pattern’s information and the semantic properties of given entities. In this framework, we explore two specific models: the CNN-based model and LSTM-based model. We conduct experiments on two public datasets: the SemEval-2010 Task8 dataset and the ACE05 dataset. The proposed method achieves the state-of-the-art result without using any external information. Additionally, the experimental results also show that our approach can represent the semantic relationship of the given entities effectively. [ABSTRACT FROM AUTHOR]

A rotating suspension cartridge reactor immobilized with white-rot fungus Phanerochaete chrysosporium was employed to treat synthetic wastewater containing 1000 μg l −1 of the recalcitrant pharmaceutically active compound carbamazepine. The reactor was continuously operated for 160 days under non-sterile conditions. After one month of fungal adaptation, the removal efficiency for carbamazepine exceeded 90% through such strategies as the immobilization of P. chrysosporium on the polyurethane foam cubes (1.0 × 1.0 × 1.0 cm 3 ), the pattern of liquid/air flow inside the cartridge created through the application of intermittent operational mode, and the gradual cut of external carbon source loading in the influent. Both nutrients and oxygen were effectively transferred to fungi, which contributed to the excellent bioreactor treatment performance for the long-term continuous operation. The bacterial contamination occurring in the bioreactor was effectively suppressed, providing a feasible alternative to treat recalcitrant compounds under non-sterile conditions. [ABSTRACT FROM AUTHOR]

Gait is an important biometric which can operate from a distance without subject cooperation. However, it is easily affected by changes in covariate conditions (carrying, clothing, view angle, walking speed, random noise etc.). It is hard for training set to cover all conditions. Bipartite ranking model has achieved success in gait recognition without assumption of subject cooperation. We propose a multi-view hypergraph learning re-ranking (MHLRR) method by integrating multi-view hypergraph learning (MHL) with hypergraph-based re-ranking framework. Sparse coding re-ranking (SCRR) and MHLRR are integrated under the graph-based framework to get a model. We define it as the sparse coding multi-view hypergraph learning re-ranking (SCMHLRR) method, which makes our approach achieve higher recognition accuracy under a genuine uncooperative setting. Extensive experiments demonstrate that our approach drastically outperforms existing ranking based methods, achieving good increase in recognition rate under the most difficult uncooperative settings. [ABSTRACT FROM AUTHOR]

A countercurrent seepage bioreactor immobilized with Phanerochaete chrysosporium was continuously operated under non-sterile conditions to treat a synthetic wastewater spiked with naproxen and carbamazepine (1000 μg/L each) for 165 days. There were no serious bacterial contaminations occurred during the operational period. Naproxen was always removed to the undetectable level regardless of the experimental conditions, while the average removal efficiency for carbamazepine, a well-known recalcitrant pharmaceutically active compound, reached around 80%. The excellent removal performance was mainly attributed to the application of countercurrent seepage mode and the cardhouse fabric of the carriers, which provided the high efficiency in the transfer of oxygen and nutrients inside the bioreactor. From the fungal immobilization combined with the temperature adjustment, the fungal activity including the enzyme production was protected as well as the bacterial contamination inside the reactor was suppressed effectively. [ABSTRACT FROM AUTHOR]

Recently, our proposed speaker extraction model, WASE (learning When to Attend for Speaker Extraction) yielded superior performance over the prior state-of-the-art methods by explicitly modeling onset clue and regarding it as important guidance in speaker extraction tasks. However, it still remains challenging when it comes to the deployments on the resource-constrained devices, where the model must be tiny and fast to perform inference with minimal budget in CPU and memory while keeping the speaker extraction performance. In this work, we utilize model compression techniques to alleviate the problem and propose a lightweight speaker extraction model, TinyWASE, which aims to run on resource-constrained devices. Specifically, we mainly investigate the grouping effects of quantization-aware training and knowledge distillation techniques in the speaker extraction task and propose Distillation-aware Quantization. Experiments on WSJ0-2mix dataset show that our proposed model can achieve comparable performance as the full-precision model while reducing the model size using ultra-low bits (e.g. 3 bits), obtaining 8.97x compression ratio and 2.15 MB model size. We further show that TinyWASE can combine with other model compression techniques, such as parameter sharing, to achieve compression ratio as high as 23.81 with limited performance degradation. Our code is available at https://github.com/aispeech-lab/TinyWASE. [ABSTRACT FROM AUTHOR]

The state-of-the-art (SOTA) crack segmentation methods still face the challenges in low-contrast shallow crack recognition and multi-scene compatibility. Therefore, this paper proposes a solution based on a multi-dimensional structure information fusion-based network (MDSIFNet). This network consists of two branches, one for extracting two-dimensional (2D) spatial feature information and another for acquiring three-dimensional (3D) geometric one. In the former, the designed 2D curve structure constraint module based on prior knowledge combined with a pretrained 2D feature extraction module can reduce learning samples and realize crack segmentation in unseen scenes. In the latter, a one-hot transformation and a 3D geometric structure information extraction module are designed to make the network pay attention to the geometric features of shallow cracks and improve their segmentation accuracy. Finally, a fusion module couples the multi-dimensional structure feature information of these two branches and outputs pixel-wise segmentation results that can be used for crack measurement and quantitative analysis. The experimental results show that the designed model network MDSIFNet performs better than the SOTA methods in terms of performance and visualized results. • A pixel-wise crack segmentation network model for the safety assessment of concrete structures is proposed. • 2D curve structure constraint and 3D geometric structure information extraction modules are designed. • 2D curve structure information can improve the multi-scene compatibility of the network model. • 3D geometric structure features of small receptive fields are beneficial for low-contrast shallow crack recognition. • Experimental results of the network model on three different datasets are analyzed. [ABSTRACT FROM AUTHOR]

Holographic beam modulation is widely applied in optical tweezers, hard-brittle material marking, high-density storage, etc. To generate high-fidelity arbitrary 3-dimensional (3D) parallel multifoci inside the material, the spherical aberration compensated automatic differentiation (SACAD) algorithm is presented. All polarization components are included and the spherical aberration compensation is embedded in the physical model. The technique of automatic differentiation is used in the error backpropagation procedure, ensuring efficient parallel computing of pixel-by-pixel gradients. In several simulation tests, the root mean square errors of the generated 3D multifoci distributions are all less than 0.01 and the diffraction efficiencies are all beyond 90%, outperforming the results of the established algorithms. In the experiments, we have verified the advantage of SACAD algorithm in complicated 3D internal marking with spherical aberration compensation. Since the SACAD algorithm can achieve high fidelity and efficiency phase retrieval with a straightforward procedure, it has the potential to become a well-received solution for internal parallel beam modulation. • Comprehensive algorithm for beam modulation with built-in aberration compensation. • Surpassing the GSW algorithm in diffraction efficiency and fidelity. • Compatible with high efficiency parallel computing using automatic differentiation. • High fidelity and low interlayer crosstalk in internal laser direct writing. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Si-based microchannel heatsink was used to improve the transient response characteristics of IR emitter. • A 3-D FEM was used to analyze the electrical-thermal and heat transfer behaviors of devices. • The devices performance can be improved when the flow velocity of coolant is larger than 0.5 m/s. Structure design and thermal simulation of MEMS-based thermal infrared (TIR) emitter integrated with Si-based microchannel heatsink (MCHS) were presented for gas sensing application. The MCHS attached on the bottom of TIR emitter was used to transfer heat to coolant for improving the transient response characteristics of TIR emitters. A three-dimensional finite element method was used to analyze the electrical heating and heat transfer behaviors of the TIR emitter with MCHS through COMSOL Multiphysics® platform. The surface temperature distribution and transient response were simulated when different flow velocities of coolant passed through the MCHS, and the influence of the flow velocity on the response characteristics was analyzed. The result shows that the temperature in the Si frame of TIR emitters will be significant reduced when the flow velocity of coolant is larger than 0.5 m/s, which will enable an effective improvement in the response characteristics of MEMS TIR emitters. [ABSTRACT FROM AUTHOR]

The Bi 2 O 3 -CaO-Nb 2 O 5 (BCN) materials were prepared by solid-state reaction. The BCN pyrochlores exhibited a layered structure with general formula of CaBi 2 Nb 2 O 9 when the sintering temperature below 1100 °C, and the sample in a nearly pure phase at 1100 °C. The structure of BCN ceramics was gradually transformed into Ca 2 Nb 2 O 7 pyrochlore structure with further increasing the sintering temperature. The dielectric properties varied with structures. The BCN ceramic sintered at 1100 °C exhibited excellent dielectric properties with a dielectric constant of ~ 110 and a dielectric loss of ~ 0.001. The dielectric constants of all the ceramics were independent with the measurement frequency (100 Hz–1 MHz) at room temperature. However, the dielectric loss decreased dramatically at low frequency and then increased slightly with increasing frequency. The temperature dependence of dielectric properties of BCN ceramics was also investigated. The BCN materials with a Ca 2 Nb 2 O 7 pyrochlore structure were more sensitive to the temperature than that with a CaBi 2 Nb 2 O 9 structure. [ABSTRACT FROM AUTHOR]

Text classification can help users to effectively handle and exploit useful information hidden in large-scale documents. However, the sparsity of data and the semantic sensitivity to context often hinder the classification performance of short texts. In order to overcome the weakness, we propose a unified framework to expand short texts based on word embedding clustering and convolutional neural network (CNN). Empirically, the semantically related words are usually close to each other in embedding spaces. Thus, we first discover semantic cliques via fast clustering. Then, by using additive composition over word embeddings from context with variable window width, the representations of multi-scale semantic units 1 1 Semantic units are defined as n -grams which have dominant meaning of text. With n varying, multi-scale contextual information can be exploited. in short texts are computed. In embedding spaces, the restricted nearest word embeddings (NWEs) 2 2 In order to prevent outliers, a Euclidean distance threshold is preset between semantic cliques and semantic units, which is used as restricted condition. of the semantic units are chosen to constitute expanded matrices, where the semantic cliques are used as supervision information. Finally, for a short text, the projected matrix 3 3 The projected matrix is obtained by table looking up, which encodes Unigram level features. and expanded matrices are combined and fed into CNN in parallel. Experimental results on two open benchmarks validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The ortho-to-para hydrogen conversion (OPC) is an integral part of the hydrogen liquefaction process. The optimal stages and conversion temperatures of OPC are important to reduce the specific energy consumption (SEC) of the process. However, a limited number of studies directly discuss this aspect of the effect of the OPC stages on the SEC. Therefore, based on an efficient refrigeration liquefaction cycle, this paper analyzes the effect of different OPC stages on the SEC of the efficient refrigeration cycle. Additionally, the impact of different combinations of conversion temperatures for the same OPC stages on the SEC is investigated. Results are obtained using a process model developed in HYSYS V10 and the particle swarm optimization algorithm in MATLAB. The results show the impact of OPC stages on the SEC, and that the increase in the stages of OPC can reduce the SEC, but the reduction tends to be slow. The SEC of the five-stage OPC process decreases by 16.39% compared to the SEC of the one-stage. Furthermore, the reduction of SEC for the process with the same stages of OPC can be achieved by setting the optimal combinations of conversion temperatures, which can reduce the SEC by up to 10.63%. The results of this study demonstrate that optimizing the OPC stages is important to reduce the SEC and will provide valuable information for the design of OPC for hydrogen liquefaction processes. • The SEC decreases with increasing OPC stages, and the SEC for five-stage OPC is 16.39% lower than that for one-stage. • The SEC under the same OPC stage can be reduced up to 10.63% by setting the conversion temperatures appropriately. • The SEC under the same OPC stage is extremely influenced by the temperature of a particular conversion node. [ABSTRACT FROM AUTHOR]

Di (2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) are the most widely used plasticizers for agricultural mulching films and one of the most common organic pollutants in black soil. However, little is known about the effect of these two contaminants on nitrification in black soil. This study investigated the changes of 20 mg/kg DEHP and DBP on the diversity of nitrification microbial communities, the abundance of ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) related genes, and the activities of key enzymes involved in nitrification. During ammonia oxidation, DEHP and DBP had uncompetitive inhibition of urease, reducing the copy number of amoA gene, and microorganisms (Azoarcus , Streptomyces and Caulobacter) would use inorganic nitrogen as a nitrogen source for physiological growth. During nitrite oxidation, the copy number of nxrA gene also reduced, and the relative abundance of chemoautotrophic nitrifying bacteria (Nitrosomonas and Nitrobacter) decreased. Moreover, the path analysis results showed that DEHP and DBP mainly directly or indirectly affect AOB and NOB through three ways. These results help better understand the ecotoxicological effects of DEHP and DBP on AOB and NOB in black soil. [Display omitted] • DEHP and DBP have a negative competitive impact on the ammonia oxidation. • DEHP and DBP have a greater impact on inorganic nitrogen than enzymes. • Actinobacteria may be affected first and play a major role in microbial functions. [ABSTRACT FROM AUTHOR]

Entity and relation extraction is a task that combines detecting entity mentions and recognizing entities’ semantic relationships from unstructured text. We propose a hybrid neural network model to extract entities and their relationships without any handcrafted features. The hybrid neural network contains a novel bidirectional encoder-decoder LSTM module (BiLSTM-ED) for entity extraction and a CNN module for relation classification. The contextual information of entities obtained in BiLSTM-ED further pass though to CNN module to improve the relation classification. We conduct experiments on the public dataset ACE05 (Automatic Content Extraction program) to verify the effectiveness of our method. The method we proposed achieves the state-of-the-art results on entity and relation extraction task. [ABSTRACT FROM AUTHOR]

[Display omitted] • La/PDA/DNS32 had superior performance in the removal of P and atrazine. • The La/PDA nanoparticles provided a distinct protective effect for strain DNS32. • La/PDA-induced sorption facilitated atrazine transmembrane transport. • Doping with La increased electrophilic sites and reduced adsorption energy. A novel biobased composite was developed for the removal of phosphate (P) and atrazine from agricultural wastewater. A composite with strong P affinity and good biocompatibility, synthesized from La3+ and polydopamine (PDA), was immobilized onto an atrazine-degrading bacterium Acinetobacter lwoffii DNS32 (La/PDA/DNS32). Following Box-Behnken design optimization, the maximum removal rate of P (500 mg L-1) and atrazine (100 mg L-1) by La/PDA/DNS32 reached 28 % and 100 %, respectively. Density functional theory calculations revealed that La/PDA had more negative adsorption energy (−5.90 eV) than PDA alone and exhibited prominent electrophilic sites. Additionally, La/PDA-induced sorption of atrazine improved transmembrane transport and enhanced expression of degradation-associated genes in strain DNS32. La/PDA nanoparticles surrounding strain DNS32 provided a shielding effect and exhibited desirable biostability, thermal stability, and acid-alkaline resistance under contamination stress. This study demonstrates the promising potential of La/PDA/DNS32 in reducing the P and atrazine pollution caused by agricultural production. [ABSTRACT FROM AUTHOR]

Increased model complexity and data quantities have raised the computing power requirement for efficient evapotranspiration (ET) estimation. A cloud-based service is presented to encapsulate and publish the ETWatch modeling algorithms as web application programming interfaces (APIs) in a consistent style to provide extensible model calculation service and data storage service in a cloud platform for water managers and stakeholders. The prototype system, named ETWatch Cloud allows users to rapidly and easily set up an ET generation project for any region of interest by invoking APIs directly to produce ET data using a web browser or local integrated development environment. The case study demonstrates that ETWatch Cloud can provide a highly scalable and interoperable ET generation tool for stakeholders from ET community, helping to facilitate the application of remote sensing-based ET algorithms for water management in hydrology sector. • A new high scalability, easy interoperability, cloud-based for ET generation at reginal scale. • Encapsule and publish ETWatch modeling algorithm to web API in a consistent style. • A general solution for the publishing, running and management of modules in the cloud. • Provides stakeholders in the ET community and hydrology sector with an innovative tool. • Generate ET data without additional computing, storage, or algorithm development. [ABSTRACT FROM AUTHOR]

In this work, Caenorhabditis elegans was employed as an in vivo model to determine the toxic effects of atrazine at different concentrations. After the exposure period from the larval stage L1 to adulthood day 1, atrazine (10 mg/L) significantly decreased the body length and lifespan of nematodes. In addition, exposure to ≥0.01 mg/L atrazine remarkably increased the intestinal reactive oxygen species (ROS) levels and reduced locomotion behavior of nematodes, while exposure to ≥ 1 mg/L atrazine decreased the brood size of nematodes. Moreover, atrazine (0.001–0.1 mg/L) upregulated the expression levels of hsp-6::GFP and hsp-6 / 60 in nematodes, indicating the activation of mitochondrial unfolded protein response (mtUPR). On the contrary, atrazine (1–10 mg/L) downregulated the expression levels of hsp-6::GFP and hsp-6 / 60 in nematodes. Furthermore, mtUPR induction governed by the RNAi knockdown of atfs-1 could increase the vulnerability of nematodes against atrazine toxicity. Overall, our findings highlighted the dynamic responses of nematodes toward different concentrations of atrazine, which could be monitored using different sublethal endpoints as bioindicators. [Display omitted] • Dynamic response to atrazine could be monitored by different sublethal endpoints. • mt UPR was very sensitive to atrazine exposure. • mt UPR could provide a protective response to atrazine. [ABSTRACT FROM AUTHOR]