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• Enhanced feature combinational optimization for power system dynamic early warning. • Maximal information coefficient-enabled enhancement for evolutionary computation. • Feature selection framework embedded with multivariate time series learning models. • Multivariate time series classification-based transmission congestion early warning. Multivariate time series (MTS) learning-based dynamic early warning (DEW) shows promising potential in preventing power systems from underlying risks. However, as a fundamental and essential step for constructing DEW models, performing efficient feature selection (FS) within high-dimensional candidate features (HDCF) of power grids is challenging, especially considering the MTS format and security-dominated characteristics of electric utilities. This paper proposes an enhanced feature combinational optimization (FCO) method to select beneficial variables for MTS-based DEW models within the HDCF of power grids. First, to identify features that contain valuable temporal patterns for DEW, a wrapper FCO framework combined MTS learning and evolutionary computation is designed. Then, to improve the FCO efficiency and solution quality when facing HDCF, a maximal information coefficient (MIC) based enhancement scheme is developed, semi-guiding the FCO initialization and evolution. Finally, by integrating MIC and iterative search, hybrid end-to-end FS is achieved. Experiments on the synthetic data from the IEEE 39-bus system and the real-world data acquired from a provincial power grid in China show that, via the MIC semi-guidance, the proposed method possesses higher FCO efficiency and better results within HDCF compared to wrapper benchmarks. Compared to filter benchmarks, it is more compatible with supervised MTS and can consider the practical performance of features by interacting with the embedded learning models. Results also verify that, with the features from the proposed method, more accurate DEW models can be obtained to predict the target event in early stages. [ABSTRACT FROM AUTHOR]

Continuous and centrifugal cast manganese bronze parts (C86300) experienced intergranular cracking in as few as 24 h after being stored outside. C-ring tests according to ASTM G38 were conducted to assess the material’s susceptibility to stress corrosion cracking (SCC) susceptibility and found that water was the most likely agent. The failure mechanism has been identified as intergranular SCC due to a combination of residual stress from a rough machining operation and water from rain or dew. To prevent cracking, a stress relief heat treatment immediately after the rough machining operation is recommended. [ABSTRACT FROM AUTHOR]

The current subclassifications of dry eye disease (DED) are aqueous deficient (ADDE) and evaporative (EDE) forms, but there lacks consistency in the clinical characteristics used to define each of these. This study used clinical data to inform cut-off values for the subclassification of ADDE and EDE, to allow more consistent study of the epidemiology of both DED subtypes. The study enrolled 261 residents from the UK, extracted from a cohort with demographics representing the population (mean 42.4 ± 18.7 years, 56 % females). The TFOS DEWS II diagnostic criteria were used to identify those with DED. Meibomian gland loss/drop-out (from meibography), lipid layer thickness (LLT − from interferometry graded on the Guillon-Keeler scale), and tear meniscus height (TMH − Keratograph 5M) along with tear evaporation (Delfin Vapometer) were used to characterise the subclassification. The Dry Eye Risk Factor Survey was used to assess risk factors associated with each DED subtype. Compared to individuals who were not diagnosed with DED, EDE was characterized by signs of meibomian gland loss of > 28 %, LLT grade < 3 and tear evaporation > 46 g/m2/h. In contrast, ADDE was best characterized by a reduced TMH < 0.2 mm. Based on these criteria, the prevalence of ADDE was 6.2 %, EDE was 64.2 %, and 11.1 % exhibited features of both ADDE and EDE, with 18.5 % unclassified despite having a DED diagnosis. Contact lens wear and computer use were risk factors for ADDE (p < 0.05), whereas age was a positive risk factor for EDE (p < 0.01). Meibomian gland loss (occurring in 27.9 %) was the most commonly observed sign in EDE. Data driven-classification of DED confirms that the evaporative form is most prevalent and identified that in a generalisable UK population, ADDE alone occurs only in approximately 1 in 16 cases of DED. [ABSTRACT FROM AUTHOR]

Carbonate terrains exhibit diverse weathering features shaped by physico-chemical processes, which lend them distinctive characteristics. In Israel, limestone strata hosting cavernous weathering are distributed across various climate zones, providing an ideal setting for investigating rock decay processes. This study focuses on the interplay of moisture and tensile strength in geomorphic processes in hard lime and dolomitic rocks that host cavernous weathering forms: inland notches (elongated C-shaped caverns) and tafoni (pseudoregular meter-sized caverns). In the inland notches of the Mediterranean climate zone, the evaporation front (capillary water presence) was observed at the rock surface, with no detectable salts. Conversely, in arid and hyper-arid regions characterized by prevalent tafoni features, capillary water subsides to greater depths as precipitation decreases. Predominant salts identified within the arid region include halite and gypsum. The transition from inland notches to tafone-rich landscapes transpires at an annual precipitation threshold of approximately 250–300 mm, marking the shift from karstification to salt weathering regimes. This study thus indicates that the climate, and hence the water source abundance (e.g., rain, dew) in the environment, affects not just rock decay processes but also the specific forms of cavernous weathering observed in the exposed strata. [Display omitted] • Climate and water abundance affect rock decay and the formation of cavernous weathering. • The evaporation front depth shapes cavernous weathering patterns in carbonate lithologies. • The transition from karst to salt weathering occurs at precipitation of ∼250–300 mm/y. • The depth of the evaporation front is positively correlated to the climatic zone. [ABSTRACT FROM AUTHOR]

The accumulation of dust particles on solar collectors can gradually degrade optical performance in solar systems — a phenomenon known as soiling. Additionally, the formation of dew on collectors during early mornings introduces new mechanisms that can impact the soiling process. This study investigates the influence of dew on soiling for uncoated solar mirrors using an artificial soiling station. Several experiments were performed changing testing parameters (e.g. sample tilt, dust amount, dew quantity) and monitoring the outcomes with a range of devices including reflectometers, microscopy, scales, and a luminance camera. The outcomes indicated that condensation becomes relevant for condensation loads above 60 g m−2 and its impact is significantly affected by the tilt angle of the samples. When samples are flat or with low tilt, condensation cycles cause particle dispersion, reducing cleanliness by up to 2 %. On the contrary, for high tilt angles, a honeycomb-like soiling patterns with highly reflective regions appear, enhancing cleanliness restoration by 2.3 %. These findings suggest tilting dewy collectors as a passive self-cleaning strategy. [Display omitted] • Evaluated dew impact on solar collectors through artificial soiling experiments. • Correlation among specular reflectance, microscopy, mass, and luminance records. • High condensation loads can agglomerate particles; improving optical performance. • Dew cycling at low tilt angles can disperse particles; reducing optical performance. • High tilt angle dew cycling improves optics due to honeycomb soiling pattern sagging. [ABSTRACT FROM AUTHOR]

The purpose of the study was to illustrate the roles of three primary indexes, namely sunlight, ventilation and stirring, in the 'bask in sunlight and dewed at night' technique on the quality of shrimp paste, through a laboratory-scale design. The results showed that changes in the post-ripening fermentation conditions, especially sunlight, was instrumental in the physicochemical properties of the shrimp paste. E -nose and SPME-GC–MS were employed to assess the volatile flavor of post-ripening fermentation. A total of 29 key volatile aroma components played a crucial role in the development of post-ripening flavor in shrimp paste with or without sunlight. Lipidomic analysis revealed that sunlight promoted the oxidative degradation of FA, resulting in the production of a diverse range of flavor compounds that imparted the unique aroma of shrimp paste. The findings of this study will establish a theoretical basic for better control of the post-ripening fermentation of traditional shrimp paste. [Display omitted] • Physicochemical properties of shrimp paste was influenced by sunlight. • 29 key flavor compounds contributed to post-ripening flavor in shrimp paste. • Sunlight promoted unique aroma due to the oxidative degradation of fatty acids. • High core temperature and sunlight exposure were the key factors in post fermentation. [ABSTRACT FROM AUTHOR]

This study investigated using environmental covariates to model correlated herd effects and genotype-by-environment interaction (GE), as well as their impact on the prediction accuracy of genomic evaluations in pigs. We used air, dew/frost, wet-bulb, and earth-skin temperatures, relative humidity, rainfall, wind speed and direction. Average daily gain (ADG) and backfat thickness (BFT) from a terminal pig line were analyzed by 6 different models: MG, which included herd as an uncorrelated random effect; ME30, which considered 30 d of daily weather information to correlate herd effects; and ME100, which used 100 d of daily climate covariates; MGE30, same as ME30 but also considering the GE as the Hadamard product of the genomic relationship matrix and the environmental covariates matrix; MGE100, same as ME100 but including the GE; and MTM, a traditional multiple-trait model with each herd as a different trait. All animals were genotyped and phenotyped; therefore, the statistical model of choice was GBLUP. Validation was based on LR method, and focal animals were those born in the last year. Accuracy, bias, and dispersion did not change among models, except for MTM. Comparing all models against MTM, the accuracy increased by 38% for BFT, whereas only slightly increased for ADG. Among all 11 herds, only 4 showed a genetic correlation stronger than 0.80 for BFT, while this number was much greater for ADG. When the model has a variable accounting for environmental effects like herd in the case of MG, using weather information to correlate environments or adding the GE term has little benefit for genomic predictions. [ABSTRACT FROM AUTHOR]

The spinal structures found on Copiapoa cinerea var. haseltoniana, an efficient dew-harvesting cactus, were fabricated and evaluated both in a climate chamber and outdoors in dewy conditions. A mix of aluminium and steel was used to fabricate these surfaces, with aluminium being used for everything but the replicated spine features, which were constructed from steel. Each surface was entirely coated with a highly emissive paint containing an alumina–silicate OPUR additive. Three replica versions (stem only, spine only, and stem & spine) were compared to a flat planar reference surface. Experimental results demonstrated that all three biomimetic macro-structured surfaces significantly enhanced dew harvesting compared to the reference surface. It was established that the stem & spine replica, spine replica, and stem replica all demonstrated significantly more dew harvesting, with mean efficiency ratios in respect of the reference surface of 1.08 ± 0.03, 1.08 ± 0.02, and 1.02 ± 0.01, respectively. Furthermore, the method of surface water collection was found to influence the water collection rate. The diagonal run-off flow across a flat planar surface was 34% more efficient than the parallel run-off flow on the same surface. These findings provide valuable insights for the construction and installation of biomimetic-inspired dew-harvesting devices, particularly in regions that are most challenged by decreasing dew yields as a result of climate change.

Water is a significant primary resource on the Earth's surface. Fresh water is essential for human beings and for the stability and sustainable development of any nation. Many regions in the interior of Syria have an insufficient water balance. This has caused severe shortages of freshwater as a result of climate change. Syria's main source of fresh water, rivers and groundwater, suffers from low levels due to the lack of rainfall amounts. The war in Syria exacerbated water stress, in particular the effects of the war on water sources such as the Euphrates River and the ‘Feijah’ source that feeds the capital Damascus. All of this has prompted us to seek other non-traditional sources such as atmospheric water, which is a renewable and relatively clean source. This paper is considered to be the first of its kind in Syria. Experimental results from semi-arid and arid regions have shown good potential for harvesting dew and fog water as a supplementary and complementary source to the existing freshwater supply. HIGHLIGHTS The main purpose of the research is to test the possibility of obtaining water from the air in a traditional and simple manner.; Research is new over the 2019–2020 timeframe.; Syria suffers from water scarcity and research is the first of its kind in Syria, particularly in its arid areas.; Syria is a country plagued by fierce war, and the population needs every drop of water possible.;

Dew has been considered a supplementary water resource as it constitutes an important water supply in many ecosystems, especially in arid and semiarid areas. Remote sensing allows large-scale surface observations, offering the possibility to estimate dew in such arid and semiarid regions. In this study, by screening and combining different remote sensing variables, we obtained a well-performing monthly scale dew yield estimation model based on the support vector machine (SVM) learning method. Using daytime and nighttime land surface temperatures (LST), the normalized difference vegetation index (NDVI), and three emissivity bands (3.929–3.989 µm, 10.780–11.280 µm, and 11.770–12.270 µm) as the model inputs, the simulated site-scale monthly dew yield achieved a correlation coefficient (CC) of 0.89 and a root mean square error (RMSE) of 0.30 (mm) for the training set, and CC = 0.59 and RMSE = 0.55 (mm) for the test set. Applying the model to the Heihe River Basin (HRB), the results showed that the annual dew yield ranged from 8.83 to 20.28 mm/year, accounting for 2.12 to 66.88% of the total precipitation, with 74.81% of the area having an annual dew amount of 16 to 19 mm/year. We expanded the model application to Northwest China and obtained a dew yield of 5~30 mm/year from 2011 to 2020, indicating that dew is a non-negligible part of the water balance in this arid area. As a non-negligible part of the water cycle, the use of remote sensing to estimate dew can provide better support for future water resource assessment and analysis. [ABSTRACT FROM AUTHOR]

The Bromeliaceae family has been used as a model to study adaptive radiation due to its terrestrial, epilithic, and epiphytic habits with wide morpho-physiological variation. Functional groups described by Pittendrigh in 1948 have been an integral part of ecophysiological studies. In the current study, we revisited the functional groups of epiphytic bromeliads using a 204 species trait database sampled throughout the Americas. Our objective was to define epiphytic functional groups within bromeliads based on unsupervised classification, including species from the dry to the wet end of the Neotropics. We performed a hierarchical cluster analysis with 16 functional traits and a discriminant analysis, to test for the separation between these groups. Herbarium records were used to map species distributions and to analyze the climate and ecosystems inhabited. The clustering supported five groups, C3 tank and CAM tank bromeliads with deep tanks, while the atmospheric group (according to Pittendrigh) was divided into nebulophytes, bromeliads with shallow tanks, and bromeliads with pseudobulbs. The two former groups showed distinct traits related to resource (water) acquisition, such as fog (nebulophytes) and dew (shallow tanks). We discuss how the functional traits relate to the ecosystems inhabited and the relevance of acknowledging the new functional groups. [ABSTRACT FROM AUTHOR]

Diarrhea is one of the most common infectious diseases that affect people of all ages and is a serious public health concern around the world. The main causes of diarrhea include food quality, water, indoor meteorological, and outdoor meteorological conditions. In this study, a dew computing-assisted smart monitoring framework is developed to evaluate the relationship among the health, indoor meteorological, and food factors of an individual to predict the cause of diarrhea with the scale of severity. Smart sensors are utilized at the physical layer to collect the targeted parameters of health, indoor meteorological, and food of the individual. The captured events are classified at the cyber layer by utilizing the Probabilistic Weighted-Naïve Bayes (PW-NB) classification approach for quantifying abnormal health events. Furthermore, a Multi-scale Gated Recurrent Unit (M-GRU) is suggested to obtain the scale of severity by analyzing the correlation between irregular health, food, and environmental events. In this manner, the proposed model M-GRU has achieved a high precision value of ( 93.26 % ), whereas, LSTM, RNN, SVM achieved the precision value of ( 89.13 % ), ( 90.43 % ), ( 88.23 % ), respectively. In addition, the precision value of the PW-NB is ( 97.15 % ), which is also higher as compared to KNN ( 93.25 % ) and DT ( 96.91 % ). The outcome of the proposed solutions is shown the higher Precision values on dew computing and cloud computing. Moreover, a comparative analysis defines the prediction effectiveness of the proposed solution over several other decision-making solutions with regards to event classification, severity determination, monitoring stability, and prediction efficiency. [ABSTRACT FROM AUTHOR]

Dew formation and its influence on soil water content are an important basis for next-level quantification of water cycling and eco-hydrological processes. To reveal the dew formation process, over a consecutive 3-year period, we monitored meteorological parameters and soil water content in the top layer at 30-minute recording intervals in the Central Ningxia Province arid belt. The dew amount was measured using a leaf wetness sensor. Annual dew yield in this region reached 56.41 mm, 75% of which occurred from June to October. Maximum monthly and maximum daily dew were 13.40 mm and 0.80 mm, respectively, and mainly occurred in the early morning from 03:00 to 08:00 am. Meteorological parameters responsible for dew formation were: air temperature between 0—24.4 ℃, relative humidity between 50%—100%, wind speed between 0.16—1.74 m/s, and wind direction between 30—60 degrees and 270—330 degrees. There was a very significant and highly correlated linear and inverse relationship between relative humidity and the difference between air temperature and dewpoint. When the difference was < 3 ℃ and the corresponding humidity > 80%, dew formation was much more likely. When daily dew reached 0.6 mm, 0.7 mm and 0.8 mm, soil water storage in the top 5cm soil increased by 0.10 mm, 0.20 mm and 0.25 mm, respectively. Annual dewfall was 13.5% of the rainfall, and the annual frequency of dew occurrence was 126 days (35%), indicating that dew is a significant water source for local water and ecosystem cycling. [ABSTRACT FROM AUTHOR]

Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage (HV) insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. However, the electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. Therefore, the purpose of this work is to review the different methods adopted to identify the contamination level on high-voltage insulators. Several methods have been developed to observe and measure the contamination level on HV insulators, such as leakage current, partial disgorgement, and images with the help of different techniques. Various techniques have been discussed alongside their advantages and disadvantages on the basis of the published research work in the last decade. The major high-voltage insulator contamination level classification techniques discussed include machine learning, fuzzy logic, neuro–fuzzy interface, detrended fluctuation analysis (DFA), and other methods. The contamination level data will aid the scheduling of the extensive and costly substation insulator, and live line washing performed using high-pressured water. As a result, considerable benefits in terms of improved power system reliability and maintenance cost savings will be realized. This paper provides an overview of the different signal processing and machine-learning methods adopted to identify the contamination level on high-voltage insulators. Various methods are studied, and the advantages and disadvantages of each method are discussed. The comprehensive review of the islanding methods will provide power utilities and researchers with a reference and guideline to select the best method to be used for contamination level identification based on their effectiveness and economic feasibility. [ABSTRACT FROM AUTHOR]

Dew computing differs from the classical cloud and edge computing by bringing devices closer to the end-users and adding autonomous processing independent from the Internet, but it is still able to collaborate with other devices to exchange information on the Internet. The difference is expressed also on scalability, since edge and cloud providers can provide (almost endless) resources, and in the case of dew computing the scalability needs to be realized on the level of devices, instead of servers. In this paper, we introduce an approach to provide deviceless and thingless computing and ensure scalable dew computing. The deviceless approach allows functions to be executed on nearby devices found closer to the user, and the thingless approach goes even further, providing scalability on a low-level infrastructure that consists of multiple things, such as IoT devices. These approaches introduce the distribution of computing to other smart devices or things on a lower architectural level. Such an approach enhances the existing dew computing architectural model as a sophisticated platform for future generation IoT systems. [ABSTRACT FROM AUTHOR]

Building a widely distributed hotspot network is a very tedious task due to its complexity. Providing security, fully distributed network services, and a cost-conscious impact are the major challenges behind this goal. To overcome these issues, we have presented a novel distributed hotspot network architecture with five layers that can provide large-scale hotspot coverage as an assimilated result. Our contributions to this new architecture highlight important aspects. First, scalability can be increased by including many Internet of Things (IoT) devices with sensors and Wi-Fi and/or LoraWAN connectivity modules. Second, hotspot owners can rent out their hotspots to create a distributed hotspot network in which the hotspots can act as an ordinary data gateway, a full-fledged hotspot miner, and a light-weight hotspot miner to earn crypto tokens as rewards for certain activities. Third, the advantages of Wi-Fi and LoraWAN can be seamlessly leveraged to achieve optimal coverage, higher network security, and suitable data transmission rate for transferring sensor data from IoT devices to remote application servers and users. Fourth, blockchain is used to enhance the decentralized behavior of the architecture that is presented here by providing immutability and independence from a centralized regulator and making the network architecture more reliable and transparent. The main feature of our paper is the use of the dew-computing paradigm along with hotspots to improve availability, Internet backhaul-agnostic network coverage, and synchronous update capability, and dew-aware leasing to strengthen and improve coverage. We also discuss the key challenges and future roadmap that require further investment and deployment. [ABSTRACT FROM AUTHOR]

Cloud representation is one of the largest uncertainties in the current weather and climate models. In this article, the observations and modeling of the radiative effect on (cloud) microphysics (REM) from the Arctic to the tropics are overviewed, providing a new direction to meet the challenge of cloud representation. REM deals with the radiation-induced temperature difference between cloud particles and air. It leads to two common phenomena observed at the surface—dew and frost—and impacts clouds aloft significantly, which is noticed via the wide occurrence of horizontally oriented ice crystals (HOICs). However, REM has been overlooked by all of the operational weather and climate models. Based on the bin model of REM and the global distribution of radiative cooling/warming, the observations of REM from several platforms (e.g., aircrafts, field campaigns, and satellites) are coordinated in this article, yielding a global picture on REM. As a result, the picture is compatible with the global distribution of HOICs and other ice crystal characteristics obtained from various clouds on the globe, such as diamond dust (or clear-sky precipitation) in the Arctic, subvisual cirrus clouds in the tropical tropopause layer, and other cirrus clouds from the low to high latitudes. In addition, ice crystals possess relatively strong REM compared to liquid drops because their aspect ratio is usually not one. The global picture on REM can be used by the weather and climate modelers to diagnose their cloud representation biases. It can also be used to improve the atmospheric ice retrieval algorithm from satellite observations. [ABSTRACT FROM AUTHOR]

Dew commonly condenses on plant leaf and can be directly taken up by leaves. The factors affecting dew absorption on leaves are complex. Indoor pot experiments on Zygocactus truncatus, Chlorophytum comosum, and Juniperus formosana were conducted under different air temperatures and wind speeds by using the deuterium (D) stable isotope tracer method. The proportion of dew uptake by leaf (Fu) of different plants was revealed, and the influence of different meteorological conditions and leaf microstructure on dew absorption was identified. Results showed that much of the dew returns to the atmosphere during evaporation, and only 6%-35% can be absorbed by the plant leaves. The capacity of dew uptake by leaves showed considerable differences between thress plants, and the amount of dew absorbed by Zygocactus truncatus leaves (25.96%±2.69% - 34.81%±4.61%) was significantly higher than that by Chlorophytum comosum (20.50%±1.89% - 23.39%±4.35%) (P = 2.19E-10) and Juniperus formosana (6.26%±0.69% - 11.95%±1.35%) (P = 1.06E-30). Fu varied according to different plants with the increase in air temperature and decreased gradually with the increase in wind speed. Leaves of compound or sickle-leaved plants with dense stomatal and tomentum are more able to absorb dew than those of coniferous plants with high wax content. In relation to external meteorological factors, the amount of absorbed dew depends on the plant type and leaf microscopic structure. This study is helpful in comprehensively evaluating the effect of dew evaporation on ecological environment and has importance for the application of dew resources. [ABSTRACT FROM AUTHOR]

Each year, 500 to 700 million dew worms are sold from thousands of gas stations, convenience stores, big box retailers, making this particular species, Lumbricus terrestris, the most popular live bait for recreational freshwater fisherman across North America. In fishing parlance, these effective bait worms are referred to as "Canadian Nightcrawlers," not because they are native to the region (they are not) or because they cannot be found elsewhere (they can), but because the entire global supply comes from a stretch of rural land between Toronto and Windsor in Ontario, Canada. How did this simple garden worm become a valuable commodity, and why is production relegated to one single region in the world? In this paper, I rely on newspaper archives and key informant interviews to construct a "capitalist commodification story" that explain why demand for Canadian Nightcrawlers emerged in North America, why the production of nightcrawlers is relegated to southwestern Ontario, and how this lowly living, breathing, squirming creature beneath our feet became a capitalist commodity producing value through competitive markets. In sum, I argue L. terrestris became a capitalist commodity when burgeoning demand from a new class of recreational fishermen increased competition between nightcrawler suppliers. The inability of capital to take hold of the ecological conditions of nightcrawler production relegated the most productive producers to southwestern Ontario who had the contingent privileges of useful soils combined and cheap and efficient labor. The materiality of L. terrestris physiology is thus constitutive of how capital seeks to accumulate surplus value in a peculiar industry. At the same time, this research shows that no matter how peculiar or banal a commodity may be capitalist logics are constantly experimenting with heterogenous peoples and environments to find a way to increase productivity and accumulate surplus value through market exchange. [ABSTRACT FROM AUTHOR]

Fog computing provides an effective solution to various problems by extending the cloud's functionality to typically more limited computing units closer to user devices. Fog computing can provide a higher level of user experience due to its geographic and network topology location and distribution. IoT services also need to be managed seamlessly to ensure adequate QoS (due to the mobility of devices or the temporary periods without an internet connection). Such domains are combined under the auspices of Dew computing, as in critical cases, extending an IoT service to the end user's device is a feasible task. Such scenarios can hardly be investigated at a large scale due to the lack of dedicated simulation environments. In this paper, we present an extension of the DISSECT-CF-Fog simulator with a Dew computing model, to enable the simulation of IoT-Dew-Fog systems in a cost-effective manner. In particular, we focus on service migration options for mobile devices and cases with temporary internet access limitations. Finally, we performed measurements of real-world use cases with the extended simulator as an evaluation. Our simulation results show that the proposed proactive strategy reduces the processing time of IoT data, exploiting an IoT-Dew-Fog environment. [ABSTRACT FROM AUTHOR]

Jordan is the fourth country in the world suffering from freshwater shortages. Therefore, this research aims to investigate the dew formation potential as an alternative source of water in Jordan. We performed gridded model simulations to estimate the dew yield during 1979-2018. We also utilized cluster analysis to identify the dew formation zones in Jordan. Our investigation revealed that dew can occur almost everywhere in Jordan during the winter (~75 days). As expected, summer is the driest season with the least number of dew days (~37 days). According to the cluster analysis, we distinguished three dew formation zones, which were closely related to the climate zones: Dew zone A (eastern desert), Dew zone B (Jordan Valley), and Dew Zone C (central heights Plateau). Zone A receives the least dew formation potential (on average 0.05 L/m2/day), which mainly occurs during the winter, and Zone B receives the highest dew formation potential (on average 0.15 L/m2/day), which occurs throughout the year. The average yearly dew yield in dew zone A, B, and C was about 18, 55, and 28 L/m2. The outcomes of this study are ought to be useful for managing and planning local feasibility studies for dew harvesting and a better understanding of the feedback processes between the water cycle and climate change in Jordan. [ABSTRACT FROM AUTHOR]

Historically, ecosystem models have treated rainfall as the primary moisture source driving litter decomposition. In many arid and semi-arid lands, however, non-rainfall moisture (fog, dew, and water vapor) plays a more important role in supporting microbial activity and carbon turnover. To date though, we lack a robust approach for modeling the role of non-rainfall moisture in litter decomposition. We developed a series of simple litter decay models with different moisture sensitivity and temperature sensitivity functions to explicitly represent the role of non-rainfall moisture in the litter decay process. To evaluate model performance, we conducted a 30-month litter decomposition study at 6 sites along a fog and dew gradient in the Namib desert, spanning almost an eightfold difference in non-rainfall moisture frequency. Litter decay rates in the field correlated with fog and dew frequencies but not with rainfall. Including either temperature or non-rainfall moisture sensitivity functions improved model performance, but the combination of temperature and moisture sensitivity together provided more realistic estimates of litter decomposition than relying on either alone. Model performance was similar regardless of whether we used continuous moisture sensitivity functions based on relative humidity or a simple binary function based on the presence of moisture, although a Gaussian temperature sensitivity outperformed a monotonically increasing Q10 temperature function. We demonstrate that explicitly modeling non-rainfall moisture and temperature together is necessary to accurately capture litter decay dynamics in a fog-affected dryland system and provide suggestions for how to incorporate non-rainfall moisture into existing Earth system models. [ABSTRACT FROM AUTHOR]

Dew formation takes place when atmospheric water vapor condenses on the surface which cools down due to radiation deficit. As it is a local phenomenon, its study gives information about the pollutants present in the atmosphere. Also, since dew formation is a natural phenomenon, and involves passive cooling, this can be a potential method for atmospheric water harvesting. The main intention of this present research work was the collection of dew samples and their analysis for yield and chemical composition. Dew samples were collected on a self-fabricated dew collection set up during the winter season. In total 30 dew samples were collected and analyzed for ionic constituents. The results were then compared to those of rain samples. The pH of dew was found to be in the range of 7-8.7, showing the neutral to alkaline nature of dew. Ionic compositions were higher in dew than in rain. While sulfate largely contributed to the ion composition of dew, nitrate was the least contributing ion. The average concentration for sulfate and nitrate in dew were found to be 0.55 and 0.03 meq/L, respectively. The average yield of dew was found to be 0.13L/m 2 . This yield value from the simplest of collection set-up ensures that dew can be considered as the potential water source with advanced condensing material and passive/active cooling in arid and semi-arid regions.

The dew condensation frequency is high, and the dew amount is heavy in urban ecosystems. During the condensation process, particulate matter acts as a condensation core, playing an important role in purifying the air. At night, dew mainly condenses on plant leaf surfaces, the plant leaves settle the particles in the dew, and some of the particles are resuspended into the atmosphere in the process of dew evaporation after sunrise. This paper monitored the condensation and evaporation processes of dew on four common plants in Changchun city from June to September 2020. By analyzing the mass and size of particles on different leaves after dew condensation and evaporation, the ability of different plants to retain particles in dew was analyzed. The results showed that there was no significant difference in the TSP capture ability during dew condensation between Buxus sinica (Rehd. et Wils.) Cheng subsp. sinica var. parvifolia M. Cheng, Syringa oblata Lindl., Hemiptelea davidii (Hance) Planch., and Pinus tabuliformis Carrière, with a TSP content of 0.21 ± 0.06 μg/cm2. Coarse particulate matter is the main type of deposit in the dew condensation stage. Particulate deposition varied according to species, leaf shape, and microstructure. The proportion of TSP remaining on leaves after dew evaporation from Pinus tabuliformis Carrière, Hemiptelea davidii (Hance) Planch., Buxus sinica (Rehd. et Wils.) Cheng subsp. sinica var. parvifolia M. Cheng, and Syringa oblata Lindl. tree was 89.7 ± 3.9%, 80.6 ± 3.6%, 75.9 ± 4.5%, and 71.4 ± 3.7%, respectively. The ability of the leaves to trap fine particles was significantly higher than that for coarse particles (P < 0.05) after dew evaporation. The highest amount of particle captured by Syringa oblata Lindl. individual was 15.17 g/y during dew condensation, and the amount of remaining particles after dew evaporation was 10.83 g/y. This paper provides a theoretical basis for the selection of tree species for urban greening. [ABSTRACT FROM AUTHOR]

For a good interpretation of radar backscatter sensitivity to vegetation water dynamics, we need to know which parts of the vegetation layer control that backscatter. However, backscatter sensitivity to different depths in the canopy is poorly understood. This is partly caused by a lack of observational data to describe the vertical moisture distribution. In this study, we aimed to understand the sensitivity of L-band backscatter to water at different heights in a corn canopy. We studied changes in the contribution of different vertical layers to total backscatter throughout the season and during the day. Using detailed field measurements, we first determined the vertical distribution of moisture in the plants, and its seasonal and sub-daily variation. Then, these measurements were used to define different sublayers in a multi-layer water cloud model (WCM). To calibrate and validate the WCM, we used hyper-temporal tower-based polarimetric L-band scatterometer data. WCM simulations showed a shift in dominant scattering from the lowest 50 cm to 50–100 cm during the season in all polarizations, mainly due to leaf and ear growth and corresponding scattering and attenuation. Dew and rainfall interception raised sensitivity to upper parts of the canopy and lowered sensitivity to lower parts. The methodology and results presented in this study demonstrate the importance of the vertical moisture distribution on scattering from vegetation. These insights are essential to avoid misinterpretation and spurious artefacts during retrieval of soil moisture and vegetation parameters. [ABSTRACT FROM AUTHOR]

Climatic conditions in temperate montane forests vary over multiple temporal scales. This study examines the effects of inter-annual climatic variability on the long-term water balance of a small headwater catchment with gradually changing vegetation characteristics. The research is based on direct observations of basic hydrological variables (precipitation, stream discharge), and process-based modelling of difficult-to-measure variables (forest transpiration, wet canopy evaporation, snow sublimation). The catchment water balance is evaluated in terms of the catchment water storage. The uncertainty of the estimated storage changes is expressed as a convolution of partial uncertainties associated with the individual components of catchment water balance. The results suggest a significant decrease of catchment water storage over the study period. The decrease is assumed to be caused by increasing winter temperatures, resulting in a more frequent occurrence of liquid precipitation and less prominent spring snowmelt. Considering vegetation's conservative water-balance management proved essential for the studied environment. [ABSTRACT FROM AUTHOR]

Occult precipitation events (fog, dew and light rain) can alter plant water and nutritional status, both directly through the aerial uptake of surface water and nutrients, and indirectly via redistribution of atmospheric resources to the soil. However, current frameworks that explain niche segregation, species interactions and coexistence still consider that ground‐rooted plants obtain resources almost exclusively via root absorption from soil.Here, we expand the plant hydrological niches model to incorporate both soil and atmospheric resource‐axes, thus providing a more complete picture of how ground‐rooted terrestrial plants obtain, remobilise, share and compete for water and soluble nutrients.First, we describe how plants with different water acquisition strategies access directly or indirectly atmospheric resources. Then, we discuss how the use of such resources may promote spatiotemporal niche segregation, contributing to shape species distribution and abundance within plant communities. We illustrate this argument with examples from arid, mesic and wet vegetation types. Finally, we examine how climate and land‐use changes may influence plant hydrological niches, potentially altering community structure.Synthesis. Understanding how available atmospheric resources influences niche segregation in plant communities is a crucial step towards better predictions of species responses (e.g. changes in distribution, abundance and interactions) to climate change. [ABSTRACT FROM AUTHOR]

Preserved eggs have been reported to possess anti‐inflammatory and antioxidant bioactivities, which are enhanced after protein glycation. However, it is still unknown which proteins are glycated during pickling process of preserved eggs and whether the glycated proteins are related to the bioactivity of preserved eggs. In this study, UPLC‐ESI‐MS/MS was used to identify glycation of proteins in preserved egg white (PEW) and duck egg white (DEW). Altogether, 142 sites on 53 glycated proteins were detected in PEW while 9 sites on 2 glycated proteins were found in DEW. Bioinformatic analysis suggested that the glycation of PEW proteins (e.g. Ovalbumin, Ovotransferrin, Albumin) were supposed to be connected with allergenicity reduction, and the enhancement of antibacterial and antioxidant bioactivities. These findings provide some indications for the future studies on the detailed bioactivities of glycated proteins in PEW, and pave the way for applied research on glycation and the development of functional foods. [ABSTRACT FROM AUTHOR]

Dew is an important water input and promotes plant growth. Dew condenses on plant leaves at night, and a portion of this dew returns to the atmosphere through evaporation. The amount of dew that evaporates is not equal to the amount of condensation; however, the dew evaporation process has not received enough attention. By monitoring the dew condensation and evaporation processes associated with four typical shrubs (Syringa, Hemiptelea, Buxus, and Cornus) in northeast China, we found that dew condensation started approximately 30 min after sunset, finished approximately 30 min before sunrise, and then turned to the evaporation phase. Dew had completely depleted approximately 4 h after sunrise. The dew evaporation period was negatively correlated with the wind speed (p < 0.01) and positively correlated with temperature, solar radiation, and relative humidity (RH) (p < 0.01). The average evaporation periods of Syringa, Buxus, Cornus, and Hemiptelea were 282 ± 21 min, 255 ± 26 min, 242 ± 22 min, and 229 ± 17 min, respectively. The daily evaporation amounts in May and September reached the minimum and maximum values, respectively, and the evaporation intensity of dew was positively correlated with RH (p < 0.01). There was no significant difference in the daily evaporation amounts of Syringa, Hemiptelea, Buxus, or Cornus (p > 0.05), and the annual evaporation amounts of these four plants were 17.05 mm/y, 16.38 mm/y, 21.94 mm/y, and 16.15 mm/y, respectively. The microstructure of leaves affected both the rate and amount of evaporation. Dew evaporated faster on hydrophilic leaves, and leaves with high trichome and stomatal densities had lower proportions of the dew evaporation amount to the condensation amount. The proportions of the dew evaporation amount to the condensation amount derived for Syringa, Hemiptelea, Buxus, and Cornus were 60.38%, 46.07%, 57.24%, and 52.81%, respectively. This study supplements our understanding of dew evaporation amounts, providing information that was missing in the near-surface hydrological cycle and aiding in the assessment of the ecological significance of dew to plants. [ABSTRACT FROM AUTHOR]

Water temperature (WT) is a critical control for various physical and biochemical processes in riverine systems. Although the prediction of river water temperature has been the subject of extensive research, very few studies have examined the relative importance of elements affecting WT and how to accurately estimate WT under the effects of cascaded dams. In this study, a series of potential influencing variables, such as air temperature, dew temperature, river discharge, day of year, wind speed and precipitation, were used to forecast daily river water temperature downstream of cascaded dams. First, the permutation importance of the influencing variables was ranked in six different machine learning models, including decision tree (DT), random forest (RF), gradient boosting (GB), adaptive boosting (AB), support vector regression (SVR) and multilayer perceptron neural network (MLPNN) models. The results showed that day of year (DOY) plays the most important role in each model for the prediction of WT, followed by flow and temperature, which are two commonly important factors in unregulated rivers. Then, combinations of the three most important inputs were used to develop the most parsimonious model based on the six machine learning models, where their performance was compared according to statistical metrics. The results demonstrated that GB3 and RF3 gave the most accurate forecasts for the training dataset and the test dataset, respectively. Overall, the results showed that the machine learning model could be effectively applied to predict river water temperature under the regulation of cascaded dams. [ABSTRACT FROM AUTHOR]

Due to mobile and IoT devices' ubiquity and their ever-growing processing potential, Dew computing environments have been emerging topics for researchers. These environments allow resource-constrained devices to contribute computing power to others in a local network. One major challenge in these environments is task scheduling: that is, how to distribute jobs across devices available in the network. In this paper, we propose to distribute jobs in Dew environments using artificial intelligence (AI). Specifically, we show that an AI agent, known as Proximal Policy Optimization (PPO), can learn to distribute jobs in a simulated Dew environment better than existing methods—even when tested over job sequences that are five times longer than the sequences used during the training. We found that using our technique, we can gain up to 77% in performance compared with using human-designed heuristics. [ABSTRACT FROM AUTHOR]

Dew computing is complementing fog and cloud computing by offering the first layer of connection for any IoT device in the field. Typically, data are stored locally in the dew servers in cases when for instance Internet is not available. Therefore, dedicated authentication and key agreement protocols need to be developed in order to guarantee secure communication without the online presence of a trusted third party. First, a complete and clear presentation on the attack model and the required security features for dew computing scenarios are provided. Next, the relation with client-server security schemes is explained and two particular criteria are identified that need to be addressed in these schemes in order to serve as security scheme for dew computing. It is shown how a recently published client-server authentication scheme, satisfying these two criteria, can be extended with a key agreement feature, resulting in a very efficient authentication and key agreement scheme for a dew computing scenario. The obtained scheme outperforms from a security point of view the currently available alternatives and behaves in a similar line with respect to computational and communication efforts. More in particular, severe security vulnerabilities are demonstrated for a recently proposed dedicated dew computing authentication and key agreement protocol. [ABSTRACT FROM AUTHOR]

Litter decomposition plays a central role in carbon cycling in terrestrial ecosystems worldwide. In drylands, which cover 40% of the Earth's land surface, photodegradation and biotic decomposition driven by non‐rainfall moisture are important mechanisms of litter decay, though studies have only recently begun examining interactions between these two processes. We describe a novel priming mechanism in which photodegradation and biotic decay of the cuticle of plant litter increase litter absorption of non‐rainfall moisture (fog, dew and water vapor), supporting greater microbial decomposition.We used several field experiments in a coastal fog desert and a series of in situ observations to demonstrate a relationship between solar radiation, cuticle integrity, water absorption rates and mass loss.Experimentally attenuating solar radiation for 36 months slowed mass loss, reduced cuticle degradation and decreased litter moisture uptake relative to litter under ambient sunlight controls. In a separate field experiment, removing the cuticle of recently senesced grass tillers increased mass loss fourfold over 6 months relative to controls. Tillers with degraded cuticles also absorbed 3.8 times more water following an overnight dew event than did those with intact cuticles. Finally, fungal growth was consistently greater on the sun‐facing side of in situ tillers than on the shaded side, coincident with greater cuticle degradation.We present a conceptual model where the cuticle of plant litter acts as a water‐resistant barrier that is first degraded by solar radiation and surficial microbes, increasing litter's ability to absorb enough water during non‐rainfall moisture events to support substantial biotic decomposition inside the tissue. Considering how photodegradation and non‐rainfall moisture are both substantial drivers of litter decomposition in drylands, understanding how they interact under realistic field conditions will help us better predict how these systems are responding to changing climate regimes. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

The integration of directed energy weapons (DEWs) into modern military platforms is of considerable interest to those examining the impact of emerging technology on the future fighting force. Hence the performance prediction of DEWs is of importance. The purpose of this study is to develop a simple framework where the minimum number of DEWs deployed in an operational setting can be determined, to achieve a desired level of performance. [ABSTRACT FROM AUTHOR]

Real-time and high-efficient communication becomes a vital property for IoT-enabled equipment, since the application range of the Internet of Things has extended widely. At the same time, the centralized characterization of the cloud computing is gradually unable to meet the demand for both low latency and high computing efficiency. To resolve these issues, new computing paradigms have been introduced, such as edge, dew, and fog computing. Recently, Saurabh et al. introduced a mutual authentication protocol, which was claimed to resist various attacks without the requirement of a trusted server, for dew-assisted IoT devices. However, this paper will show that Saurabh et al.'s scheme lacks forward security and user anonymity. Then, a new authenticated key agreement (AKA) protocol, named e-SMDAS, will be put forward and formally proven secure under the eCK security model. Further, the analysis results of BAN logic and Scyther tool will also confirm the security of e-SMDAS. Finally, the comparative analysis of security features and computation efficiency between e-SMDAS and several recent schemes will be demonstrated at the end of this paper. [ABSTRACT FROM AUTHOR]

This paper introduces a semi-analytical procedure to study fractional flow (fw) and performance of dimethyl ether improved water flooding (DEW). To this end, the fw-curve of DME/water-oil is discussed and the effect of the K-value on the fractional flow and the oil recovery curve is studied. To achieve proper fw-curves, governing equations are solved using a finite difference approach, and saturation and concentrations are obtained in every time step. Therefore, numerous fw-curves are found; we select a DME-free and a DME-rich fw-curve. Then, an initial point, which is determined using the partition coefficient value (K-value), is chosen to plot a tangential line on the fw-curve. The intercept of the tangential line and the horizontal axis is named pseudo-adsorption point. Upon increase of K-value, the intercept moves toward the origin of the plot. Moreover, results show that with the increase of K-value, the DME-rich fw-curve moves to the right side, which makes a larger horizontal distance between DME-free and DME-rich fw-curves. In addition results indicate that at small K-values, the increase of K-value significantly improves oil recovery, but the further increase does not lead to significant recovery improvements. This workflow can be considered useful to analyze DME-improved water flooding in different conditions. [ABSTRACT FROM AUTHOR]

The project arises as a response to the business challenge presented to the University - Company - State Committee (CUUES) and the Research Results Transfer Office (OTRI), which expresses the need to have a technology-based system with the capacity to monitor and manage the cocoa traceability process during the storage and manufacture of chocolate. In view of the above, the objective of this article is to propose the use of the Holt-Winters model as a strategy to predict the behavior of environmental variables that could be present in the cocoa bean storage process, incorporating the use of techniques analysis supported in time series. According to the results obtained, the proposed model allowed predicting the behavior of temperature, relative humidity and dew temperature, as a strategy for the control of fungi and mold that could emerge in the grain during its storage. Additionally, the proposed model can be considered as a very important prediction tool during the cocoa traceability process, reaching adjustment levels higher than 0.9, accompanied by a very low standard error of estimation and with a confidence level of 95%. [ABSTRACT FROM AUTHOR]

Acute bovine liver disease (ABLD) is a hepatic disease affecting cattle sporadically in southern Australia, characterised histologically by striking periportal hepatocellular necrosis. The cause of ABLD is unknown; however, the seasonality and acute presentation of outbreaks suggest mycotoxin involvement. We described the geographical and seasonal occurrence of ABLD reports from 2010 to 2020 in Victoria, Australia, and explored potential weather triggers preceding 26 outbreaks occurring across 23 properties using a case-crossover design. Outbreaks occurred most frequently in autumn/early winter and in herds located along the southern coastal plain of Victoria, and occasionally within the low-lying regions of the Great Dividing Range. Lactating adult dairy cattle represented the most reported cases. We observed a significant association between an increase in average daily dewpoint in the 15 days preceding an ABLD outbreak, suggesting that dew formation may be a key determinant for this disease. Our findings support the etiology of a potent hepatotoxic agent that requires moisture for proliferation and/or toxin production. [ABSTRACT FROM AUTHOR]

Where Turner figures his great work as indicated by a series of natural signs, Gray figures the "insurrection" as inscribed only in Turner's mind. Acknowledging Turner's rebel semiotics results in a new reading of the I Confessions i , redirecting critical attention away from Turner as normative linguistic subject and towards the semiotic activity of the landscape. Google Scholar OpenURL Placeholder Text WorldCat 4 Greenberg Kenneth S., Turner Nat. "And by signs in the heavens ... it would make known to me when I should commence the great work" -Nat Turner In his 1996 introduction to I The Confessions of Nat Turner and Related Documents i , Kenneth S. Greenberg makes a claim that has since received little attention. [Extracted from the article]