ALBERT

Description: Declines in Ogallala aquifer levels used for irrigation has prompted research to identify methods for optimizing water use efficiency (WUE) of cotton (Gossypium hirsutum L). In this experiment, conducted at Lubbock, TX, USA in 2014, our objective was to test two canopy temperature based stress indices, each at two different irrigation trigger set points: the Stress Time (ST) method with irrigation triggers set at 5.5 (ST_5.5) and 8.5 h (ST_8.5) and the Crop Water Stress Index (CWSI) method with irrigation triggers set at 0.3 (CWSI_0.3) and 0.6 (CWSI_0.6). When these irrigation triggers were exceeded on a given day, the crop was deficit irrigated with 5 mm of water via subsurface drip tape. Also included in the experimental design were a well-watered (WW) control irrigated at 110% of potential evapotranspiration and a dry land (DL) treatment that relied on rainfall only. Seasonal crop water use ranged from 353 to 625 mm across these six treatments. As expected, cotton lint yield increased with increasing crop water use but lint yield WUE displayed asignificant (p ≤ 0.05) peak near 3.6 to 3.7 kg ha−1 mm−1 for the ST_5.5 and CWSI_0.3 treatments, respectively. Our results suggest that WUE may be optimized in cotton with less water than that needed for maximum lint yield.

Description: The irrigation water available for agriculture will be scarce in the future due to increased competition for water with other sectors, and the issue may become more serious due to climate change. In Chile, the table grape is only cultivated under irrigation. A five-year research program (2007–2012) was carried out in the Aconcagua Valley, the central area of grapes in Chile, to evaluate the response of table grape vines (Vitis vinifera L., cv Thompson Seedless) to different volumes of irrigation water. Four irrigation treatments were applied: 60, 88, 120 and 157% of crop evapotranspiration (ETc) during the first four years, and 40, 54, 92 and 108% of ETc in the last year. Irrigation over 90%–100% of ETc did not increase fruit yield, whereas the application of water below 90% ETc decreased exportable yield, berry size and pruning weight. For example, 60% ETc applied water reduced exportable yield by 20%, and only 40% of the berries were in the extra and large category size, while pruning weight was 30% lower in comparison to the treatment receiving more water.

Description: New risk-based solvency requirements for insurance companies across European markets have been introduced by Solvency II and will come in force from 1 January 2016. These requirements, derived by a Standard Formula or an Internal Model, will be by far more risk-sensitive than the required solvency margin provided by the current legislation. In this regard, a Partial Internal Model for Premium Risk is developed here for a multi-line Non-Life insurer. We follow a classical approach based on a Collective Risk Model properly extended in order to consider not only the volatility of aggregate claim amounts but also expense volatility. To measure the effect of risk mitigation, suitable reinsurance strategies are pursued. We analyze how naïve coverage as conventional Quota Share and Excess of Loss reinsurance may modify the exact moments of the distribution of technical results. Furthermore, we investigate how alternative choices of commission rates in proportional treaties may affect the variability of distribution. Numerical results are also figured out in the last part of the paper with evidence of different effects for small and large companies. The main reasons for these differences are pointed out.

Description: In this paper, we study the valuation of stochastic cash flows that exhibit dependence on interest rates. We focus on insurance liability cash flows linked to an index, such as a consumer price index or wage index, where changes in the index value can be partially understood in terms of changes in the term structure of interest rates. Insurance liability cash flows that are not explicitly linked to an index may still be valued in our framework by interpreting index returns as so-called claims inflation, i.e., an increase in claims cost per sold insurance contract. We focus primarily on the case when a deep and liquid market for index-linked contracts is absent or when the market price data are unreliable. Firstly, we present an approach for assigning a monetary value to a stochastic cash flow that does not require full knowledge of the joint dynamics of the cash flow and the term structure of interest rates. Secondly, we investigate in detail model selection, estimation and validation in a Heath–Jarrow–Morton framework. Finally, we analyze the effects of model uncertainty on the valuation of the cash flows and how forecasts of cash flows and interest rates translate into model parameters and affect the valuation.

Description: This paper develops an extended financial stress measure that considers the supervisory objective of identifying risks to the stability of the financial system. The measure provides a continuous and bounded signal of financial stress using daily public market data. Broad coverage of material financial system markets over time is achieved by leveraging dynamic credit weights. We consider how this measure can be used to monitor, analyze, and alert financial system stress.

Description: This paper develops a financial stress measure for the United States, the Cleveland Financial Stress Index (CFSI). The index is based on publicly available data describing a six-market partition of the financial system comprising credit, funding, real estate, securitization, foreign exchange, and equity markets. This paper improves upon existing stress measures by objectively selecting between several index weighting methodologies across a variety of monitoring frequencies through comparison against a volatility-based benchmark series. The resulting measure facilitates the decomposition of stress to identify disruptions in specific markets and provides insight into historical stress regimes.

Description: Guarantees are often seen as the key characteristics of pension saving products, but securing them can become costly and is of central relevance especially in the course of the current low interest rate environment. In this article, we deal with the question of how costly the typical types of guarantees are, in the sense that they reduce a pension saving scheme’s financial performance over time. In this context, we aim to provide a presentation of insights from selected literature studying the impact of point-to-point guarantees and cliquet-style interest rate guarantees on the performance of pension contracts. The comparative analysis emphasizes that, in most cases, guarantee costs are not negligible with regard to a contract’s financial performance, especially compared to benchmarks, and that customers knowingly opt for such guarantees (or not) is, thus, indispensable. To further investigate the willingness-to-pay for guarantees in life insurance is an area for future research, in particular for innovative contract design.

Description: Arsenic (As) in soils causes several detrimental effects, including death. Arsenic toxicity in soybean plants (Glycine max L.) has been little studied. Arbuscular mycorrhiza (AM) increase the tolerance of host plants to abiotic stress, like As. We investigated the effects of AM fungi on soybean grown in As-contaminated soils. A pot experiment was carried out in a glasshouse, at random with five replications. We applied three levels of As (0, 25, and 50 mg As kg−1), inoculated and non-inoculated with the AM fungus Rhizophagus intraradices (N.C. Schenck & G.S. Sm.) C. Walker & A. Schüßler. Plant parameters and mycorrhizal colonization were measured. Arsenic in the substrate, roots, and leaves was quantified. Arsenic negatively affected the AM percentage of spore germination and hyphal length. As also affected soybean plants negatively: an extreme treatment caused a reduction of more than 77.47% in aerial biomass, 68.19% in plant height, 78.35% in number of leaves, and 44.96% reduction in root length, and delayed the phenological evolution. Mycorrhizal inoculation improved all of these parameters, and decreased plant As accumulation (from 7.8 mg As kg−1 to 6.0 mg As kg−1). AM inoculation showed potential to reduce As toxicity in contaminated areas. The AM fungi decreased As concentration in plants following different ways: dilution effect, less As intake by roots, and improving soybean tolerance to As.

Description: Maize (Zea mays L) is the most important food grain in sub-Saharan Africa and is mostly grown by small-scale farmers under rainfed conditions. Aluminum toxicity caused by low pH is one of the abiotic factors limiting maize production among smallholder farmers. Therefore, breeding maize hybrids that are tolerant to aluminum toxicity will sustain and increase maize production in these areas. Hence this study was undertaken to assess the genotypic variation for aluminum toxicity in maize inbred lines. Fourteen maize inbred lines of historical importance that are used in maize hybrid breeding in Zambia were studied for seedling root variation under different aluminum concentrations using hydroponic conditions. The aluminum tolerance membership index based on three traits (actual root length, relative root length and root length response) classified genotypes L3233 and L1214 as highly tolerant, L5527 and ZM421 as tolerant, and L12, L3234, and ZM521 as intermediate. The high PCV, GCV, and heritability observed for the root traits indicate that opportunities for selection and breeding for aluminum tolerance among Zambian inbred lines exist. Furthermore, the study indicated that a higher genetic gain would be expected from net root growth followed by shoot length response as selection traits, thus supporting the use of root traits for aluminum tolerance screening.

Description: The objective of this study is to understand which requirements for cereal cultivars—with regard to climate change adaptation—are in demand by farmers and advisors, and to clarify whether there are any differences in their assessments. A comparative survey was used to collect data from 410 farmers and 114 advisors in Germany. The majority of both farmers and advisors reported perceivable effects of climatic change on plant production. The increase in droughts and hot spells, the increased incidence of torrential rain, and mild winters were mentioned as the main effects of climate change. For climate change adaptation, the farmers and advisors mostly relied on a locally-adapted cultivar selection. It is estimated that eco-stability, grain yield, resistance to lodging and drought tolerance are important cultivar properties. In the study, farmers and advisors equally pointed out the need for additional cultivar evaluation according to eco-stability. Finally, only minor differences regarding farmers’ and advisors’ assessments were found within the study. The outcome of this research points to the need of implementing farmers’ demands in cultivar recommendations. For example, an impartial assessment of cultivars’ eco-stability could help support the choice of cultivars and reduce the growing risks in cereal production with regard to climate change.

Description: Extreme weather and climate change can have a significant impact on all types of infrastructure and assets, regardless of location, with the potential for human casualties, physical damage to assets, disruption of operations, economic and community distress, and environmental degradation. This paper describes a methodology for using extreme weather and climate data to identify climate-related risks and to quantify the potential impact of extreme weather events on certain types of transportation infrastructure as part of a vulnerability screening assessment. This screening assessment can be especially useful when a large number of assets or large geographical areas are being studied, with the results enabling planners and asset managers to undertake a more detailed assessment of vulnerability on a more targeted number of assets or locations. The methodology combines climate, weather, and impact data to identify vulnerabilities to a range of weather and climate related risks over a multi-decadal planning period. The paper applies the methodology to perform an extreme weather and climate change vulnerability screening assessment on transportation infrastructure assets for the State of Tennessee. This paper represents the results of one of the first efforts at spatial vulnerability assessments of transportation infrastructure and provides important insights for any organization considering the impact of climate and weather events on transportation or other critical infrastructure systems.

Description: This paper studies the problem of optimal reinsurance contract design. We let the insurer use dual utility, and the premium is an extended Wang’s premium principle. The novel contribution is that we allow for heterogeneity in the beliefs regarding the underlying probability distribution. We characterize layer-reinsurance as an optimal reinsurance contract. Moreover, we characterize layer-reinsurance as optimal contracts when the insurer faces costs of holding regulatory capital. We illustrate this in cases where both firms use the Value-at-Risk or the conditional Value-at-Risk.

Description: We study risk-minimization for a large class of insurance contracts. Given that the individual progress in time of visiting an insurance policy’s states follows an F -doubly stochastic Markov chain, we describe different state-dependent types of insurance benefits. These cover single payments at maturity, annuity-type payments and payments at the time of a transition. Based on the intensity of the F -doubly stochastic Markov chain, we provide the Galtchouk-Kunita-Watanabe decomposition for a general insurance contract and specify risk-minimizing strategies in a Brownian financial market setting. The results are further illustrated explicitly within an affine structure for the intensity.

Description: The intermarket analysis, in particular the lead–lag relationship, plays an important role within financial markets. Therefore, a mathematical approach to be able to find interrelations between the price development of two different financial instruments is developed in this paper. Computing the differences of the relative positions of relevant local extrema of two charts, i.e., the local phase shifts of these price developments, gives us an empirical distribution on the unit circle. With the aid of directional statistics, such angular distributions are studied for many pairs of markets. It is shown that there are several very strongly correlated financial instruments in the field of foreign exchange, commodities and indexes. In some cases, one of the two markets is significantly ahead with respect to the relevant local extrema, i.e., there is a phase shift unequal to zero between them.

Description: The mobilization of nutrients from fish sludge (i.e., feces and uneaten feed) plays a key role in optimizing the resource utilization and thus in improving the sustainability of aquaponic systems. While several studies have documented the aerobic and anaerobic digestion performance of aquaculture sludge, the impact of the digestate on plant growth has yet to be understood. The present study examines the impact of either an aerobic or an anaerobic digestion effluent on lettuce plant growth, by enriching a mixture of aquaculture and tap water with supernatants from both aerobic and anaerobic batch reactors. The lettuce plants grown in the hydroponic system supplied with supernatant from an anaerobic reactor had significantly better performance with respect to weight gain than both, those in the system where supernatant from the aerobic reactor was added, as well as the control system. It can be hypothesized that this effect was caused by the presence of NH4+ as well as dissolved organic matter, plant growth promoting rhizobacteria and fungi, and humic acid, which are predominantly present in anaerobic effluents. This study should therefore be of value to researchers and practitioners wishing to further develop sludge remineralization in aquaponic systems.

Description: Arbuscular mycorrhizal fungi (AMF) play a major role in the uptake of nutrients by agricultural plants. Nevertheless, some agricultural practices can interrupt fungal-plant signaling and thus impede the establishment of the mycorrhizal symbiosis. A field experiment performed over a 5-year period demonstrated that both the absence of tillage and of nitrogen (N) fertilization improved AMF colonization of wheat roots. Moreover, under no-till conditions, N uptake and aboveground biomass production did not vary significantly between N-fertilized and N-unfertilized plots. In contrast, both N uptake and above ground biomass were much lower when N fertilizer was not added during conventional tillage. This finding strongly suggests that for wheat, no-till farming is a sustainable agricultural system that allows a gradual reduction in N fertilizer use by promoting AMF functionality and at the same time increasing N uptake.

Description: The Turkish Private Pension System is an investment system which aims to generate income for future consumption. This is a volunteer system, and the contributions are held in individual portfolios. Therefore, management of the funds is an important issue for both the participants and the insurance company. In this study, we propose an optimal private pension plan with a guarantee feature that is based on Constant Proportion Portfolio Insurance (CPPI). We derive a closed form formula for the optimal strategy with the help of dynamic programming. Moreover, our model is evaluated with numerical examples, and we compare its performance by implementing a sensitivity analysis.

Description: Foliar fertilization, or the application of nutrient solutions to the foliage of plants, has become a very important tool as a supplement to traditional soil fertilization. So far, knowledge about the real mechanisms of foliar nutrient uptake is still limited. In this study different manganese (Mn) and zinc (Zn) carriers differing in their solubility and chemical characteristics (chelated or non-chelated, with or without the presence of a surfactant-penetrant) were compared with regard to their penetration characteristics through enzymatically-isolated cuticles. The experiments were explicitly conducted under high humidity conditions in order not to penalize compounds with a higher deliquescent point. The results show that Mn penetrates more rapidly through the cuticle than Zn ions for unknown reasons. The addition of a surfactant-penetrant enhances the penetration rate in the case of Mn ions. This trend is much less pronounced for zinc ions. Formulations based on insoluble carriers, such as carbonate or oxide, only poorly penetrate through the cuticle. In order to rapidly control micronutrient deficiency problems, only fully water soluble micronutrient carriers should be used.

Description: Nitrogen (N) losses negatively impact groundwater quality. Spring wheat genotypes varying in N-fertilizer recovery were studied (by using lysimeters) for their potential to minimize NO3-N leaching during spring and summer, over a three-year period. Additionally, we examined to what extent root growth and NO3-N leaching explain the well-known difference found between apparent and isotopic N recovery. The genotypes were grown under low (2 g m−2) and high (27 g m−2) N fertilizer supply. On average, the apparent and isotopic recoveries of N fertilizer by wheat were 43% and 51%, respectively. The three genotypes varied in fertilizer N recovery but not in NO3-N leaching, which only accounted for 15% of the applied N fertilizer. The differences in N uptake, fertilizer N recovery and root growth among the genotypes were not associated with the leached NO3-N because root growth and N uptake were not well synchronized with NO3-N leaching. Already at stem elongation 70% to 98% of the season-long NO3-N leaching had already taken place. Thus, the ability to minimize in-season NO3-N leaching by using spring wheat genotypes with higher fertilizer N recovery was limited because maximum N leaching occurred in the early crop season.

Description: Agronomic N-use efficiency is the basis for economic and environmental efficiency, and an effective agro-ecosystem management practice, improving nutrient use efficiency, is a crucial challenge for a more sustainable production of horticultural, industrial and cereal crops. However, discrepancy between theory and practice still exists, coming from large gaps in knowledge on net-N immobilization/mineralization rates in agro-ecosystems, as well as on the effects of indigenous and applied N to crop response. A more thorough understanding of these topics is essential to improve N management in agricultural systems. To this end, the present Special Issue collects research findings dealing with different aspects of agronomic efficiency of N in different agro-ecosystems, and environmental impact derived from fertilization management practices. In particular, the Special Issue contains selected papers, which concern a wide range of topics, including analyzing tools, options of management, calculation equation and modeling approaches.

Description: A two-year field study was conducted to investigate the performance of a lightweight trash-board moldboard plow (with and without a trash-board), as influenced by stubble height and water content. Both fields were measured for the performance of a trash-board moldboard plow when used during the optimization of the plowing depth, the water content, and the reaction forces. The results showed that in the first year, when a trash-board was required, the results were significantly different. The fields had lower draft and reaction force in the soil with only stubble height, which was greater than that in the soil with dense straw for all water content levels. This was also observed in the second year for the whole depth. This study shows that the moldboard plow with a trash-board provided minimum draft and reaction forces with only straw and heavy straw. The results indicate that straw nearby shear significantly increased displacement for all treatments, with variance of straw nearby moldboard. Hence, the results verify that a trash-board continuously created large soil fragmentation with different water content. Straw labels create a position of straw which also allows for better results. It is important to install trash-boards with the moldboard plow for heavy straw incorporation.

Description: This paper studies the dependence between coupled lives, i.e., the spouses’ dependence, across different generations, and its effects on prices of reversionary annuities in the presence of longevity risk. Longevity risk is represented via a stochastic mortality intensity. We find that a generation-based model is important, since spouses’ dependence decreases when passing from older generations to younger generations. The independence assumption produces quantifiable mispricing of reversionary annuities, with different effects on different generations. The research is conducted using a well-known dataset of double life contracts.

Description: Greater early vigour has potential for increasing biomass and grain yields of wheat crops in Mediterranean-type environments. Embryo size is an important determinant of early vigour in barley and likely to contribute to greater vigour in wheat. Little is known of the underlying genetic control for embryo size, or its genetic association with early vigour in wheat. Over 150 doubled-haploid lines in each of three unrelated wheat populations varying for embryo size and early vigour were phenotyped across multiple controlled environments. The Quantitative Trait Locus (QTL) mapping was then undertaken to understand genetic control and chromosomal location of these characteristics. Genotypic variance was large and repeatable for embryo and leaf size (width and length) but not specific leaf area or coleoptile tiller size. Genetic correlations for embryo size with leaf width and area were moderate to strong in size while repeatabilities for embryo size and early vigour were high on a line-mean basis. Multiple genomic regions were identified of commonly small genetic effect for each trait with many of these regions being common across populations. Further, collocation of regions for many traits inferred a common genetic basis for many of these traits. Chromosomes 1B, 5B, 7A and 7D, and the Rht-B1b and Rht-D1b-containing chromosomes 4B and 4D contained QTL for embryo size and leaf width. These studies indicate that while early vigour is a genetically complex trait, the selection of larger embryo progeny can be readily achieved in a wheat breeding program targeting development of high vigour lines.

Description: In this article, we consider the generalized Erlang risk model and its dual model. By using a conditional measure-preserving correspondence between the two models, we derive an identity for two interesting conditional probabilities. Applications to the discounted joint density of the surplus prior to ruin and the deficit at ruin are also discussed.

Description: In this paper, a dynamic inflation-protected investment strategy is presented, which is based on traditional asset classes and Markov-switching models. Different stock market, as well as inflation regimes are identified, and within those regimes, the inflation hedging potential of stocks, bonds, real estate, commodities and gold are investigated. Within each regime, we determine optimal investment portfolios driven by the investment idea of protection from losses due to changing inflation if inflation is rising or high, but decoupling the performance from inflation if inflation is low. The results clearly indicate that these asset classes behave differently in different stock market and inflation regimes. Whereas in the long-run, we agree with the general opinion in the literature that stocks and bonds are a suitable hedge against inflation, we observe for short time horizons that the hedging potential of each asset class, especially of real estate and commodities, depend strongly on the state of the current market environment. Thus, our approach provides a possible explanation for different statements in the literature regarding the inflation hedging properties of these asset classes. A dynamic inflation-protected investment strategy is developed, which combines inflation protection and upside potential. This strategy outperforms standard buy-and-hold strategies, as well as the well-known 1 N -portfolio.

Description: In order to standardize the quality of agricultural products, it is necessary to control the factors affecting plant development, such as plant nutrition. The best results in terms of homogeneity of the quality of vegetable crops were achieved using inert substrates and application of nutrients; however, production costs are high due to the cost of irrigation systems and substrate management and importation. This work aims to evaluate the effect of the local substrate mix and the amount of organic fertilizer on different quality parameters of coriander. To evaluate the quality of coriander, we considered different parameters such as size, biomass, antioxidant capacity and aroma (evaluated by volatile compounds detection with gas chromatography). The results show that the culture system differentially affects each parameter, and the compounds associated with the aroma of coriander and the diameter of plants are sensitive to the culture system, while the length of plants, number of leaves and antioxidant activity are not affected by the concentration of fertilizer. Moreover, organic farming conditions do not reduce quality parameters of the crops when using adequate fertilization. Additionally, local substrates would be practical substitutes for expensive importations.

Description: In intensive agriculture, N supply often exceeds crop requirements, even in nitrate vulnerable zones (NVZ). In farmland, the N surplus gives rise to NO3− leaching and consequent groundwater pollution. The present study aimed at proposing measures to reduce N leaching and hence improve N efficiency in a buffalo livestock farm located in the NVZ of Latina plain (Central Italy). The farm was cultivated with forage crops in a double annual crop rotation: Italian ryegrass (Lolium multiflorum Lam.) in winter and silage corn (Zea mays L.) in summer. Mineral and organic fertilizers were supplied to both crops. The annual N budget and soil solution NO3-N concentrations were evaluated using a modeling approach. The performance of the WinEPIC model in simulating the response of the NO3-N concentration in percolation to the N application rate was assessed and validated by field measurements of the NO3-N concentration in the soil solution. Three scenarios were proposed to identify the best practice to minimize the environmental impact of N application without significant yield loss. Also, recommendations of best practices in N fertilization and animal manure spreading were given. This study thus provides useful preliminary information for decision-making in agriculture/environmental policies.

Description: Genotypic variation in N efficiency defined as high grain yield under limited nitrogen (N) supply of winter oilseed-rape line-cultivars has been predominantly attributed to N uptake efficiency (NUPT) through maintained N uptake during reproductive growth related to functional stay-green. For investigating the role of stay-green, N retranslocation and N uptake during the reproductive phase for grain yield formation, two line cultivars differing in N starvation-induced leaf senescence were grown in a field experiment without mineral N (N0) and with 160 kg N·ha−1 (N160). Through frequent harvests from full flowering until maturity N uptake, N utilization and apparent N remobilization from vegetative plant parts to the pods could be calculated. NUPT proved being more important than N utilization efficiency (NUE) for grain yield formation under N-limiting (N0) conditions. For cultivar differences in N efficiency, particularly N uptake during flowering (NUPT) and biomass allocation efficiency (HI) to the grains, were decisive. Both crop traits were related to delayed senescence of the older leaves. Remobilization of N particularly from stems and leaves was more important for pod N accumulation than N uptake after full flowering. Pod walls (high N concentrations) and stems (high biomass) mainly contributed to the crop-residue N at maturity. Decreasing the crop-inherent high N budget surplus of winter oilseed-rape requires increasing the low N remobilization efficiency particularly of pod-wall N to the grains. Addressing this conclusion, multi-year and -location field experiments with an extended range of cultivars including hybrids are desirable.

Description: Stripe rust (caused by Puccinia striiformis f. sp. tritici) of wheat (Triticum aestivum) is a devastating disease in temperate regions when susceptible varieties are grown and environmental conditions sustain high disease pressures. With frequent and severe outbreaks, disease resistance is a key tool for controlling stripe rust on wheat. The goal of this research was to identify quantitative trait loci (QTL) involved in stripe rust resistance from the important US Pacific Northwest soft white winter wheat varieties “Eltan” and “Finch”. An F2:5 recombinant inbred line (RIL) mapping population of 151 individuals derived from the Finch × Eltan cross was developed through single seed descent. A linkage map comprising 683 unique single nucleotide polymorphism (SNP) loci and 70 SSR markers were used to develop 22 linkage groups consisting of 16 of the 21 chromosomes. Stripe rust data were collected on the RILs during the summers of 2012 to 2014. QTL analysis identified two genomic regions on chromosomes 4A (QYrel.wak-4A) and 6B (QYrfi.wak-6B) associated with resistance from Eltan and Finch, respectively. The results of the QTL analysis showed that QYrel.wak-4A and QYrfi.wak-6B reduced infection type and disease severity. Based upon both molecular and phenotypic differences, QYrel.wak-4A is a novel QTL for adult plant resistance (APR) to stripe rust.

Description: Ascochyta blight (AB) caused by Ascochyta rabiei (Pass.) Labr. is an important and widespread disease of chickpea (Cicer arietinum L.) worldwide. The disease is particularly severe under cool and humid weather conditions. Breeding for host resistance is an efficient means to combat this disease. In this paper, attempts have been made to summarize the progress made in identifying resistance sources, genetics and breeding for resistance, and genetic variation among the pathogen population. The search for resistance to AB in chickpea germplasm, breeding lines and land races using various screening methods has been updated. Importance of the genotype × environment (GE) interaction in elucidating the aggressiveness among isolates from different locations and the identification of pathotypes and stable sources of resistance have also been discussed. Current and modern breeding programs for AB resistance based on crossing resistant/multiple resistant and high-yielding cultivars, stability of the breeding lines through multi-location testing and molecular marker-assisted selection method have been discussed. Gene pyramiding and the use of resistant genes present in wild relatives can be useful methods in the future. Identification of additional sources of resistance genes, good characterization of the host–pathogen system, and identification of molecular markers linked to resistance genes are suggested as the key areas for future study.

Description: Field experiments at Oakes, ND, USA in 2010 and Carrington, ND, USA in 2011 were conducted to evaluate the potential for cover crops grown in the Northern Great Plains, USA in order to reduce weed emergence and density in irrigated potatoes. Treatments included five cover crop treatments and three cover crop termination treatments. Termination of cover crops was done with glyphosate, disk-till, and roto-till. Cover crop biomass accumulation was greatest for rye/canola and triticale at Oakes, and hairy vetch and hairy vetch/rye at Carrington. Cover crop and termination affected weed control 14, 29, and 51 days after planting (DAP) at Oakes. Weed control at Carrington was at least 90% for all cover crop and termination treatments at all three evaluation timings. Marketable yield at Oakes was greater when roto-till was used to terminate the cover crops compared with disk-till or herbicide, which is beneficial for organic systems where herbicides are not used. Marketable yield at Carrington was not affected by cover crop or termination treatments. Results suggest that cover crops can successfully be integrated into irrigated potato production for weed control with yields equal to no cover crop, and with attention to potential mechanical difficulties.

Description: Soil and water pollution by metals and other toxic chemicals is difficult to measure and control, and, as such, presents an ongoing global threat to sustainable agriculture and human health. Efforts to remove contaminants by plant-mediated pathways, or “phytoremediation”, though widely studied, have failed to yield consistent, predictable removal of biological and chemical contaminants. Emerging research has revealed that one major limitation to using plants to clean up the environment is that plants are programmed to protect themselves: Like white blood cells in animals, border cells released from plant root tips carry out an extracellular trapping process to neutralize threats and prevent injury to the host. Variability in border cell trapping has been found to be correlated with variation in sensitivity of roots to aluminum, and removal of border cell results in increased Al uptake into the root tip. Studies now have implicated border cells in responses of diverse plant roots to a range of heavy metals, including arsenic, copper, cadmium, lead, mercury, iron, and zinc. A better understanding of border cell extracellular traps and their role in preventing toxin uptake may facilitate efforts to use plants as a nondestructive approach to neutralize environmental threats.

Description: A decrease in water resources around the globe in irrigated agriculture has resulted in a steep decline in irrigation water availability. Therefore, management options for efficient use of available irrigation water are inevitable. Deciding the critical time, frequency and amount of irrigation are compulsory to achieve higher crop outputs. Hence, this two-year field study was conducted to assess the role of different row spacings, i.e., 20, 25 and 30 cm, on growth, productivity, and water use efficiency (WUE) of wheat under deficit supplemental irrigation (DSI) at the vegetative and reproductive phase by using surplus supplemental irrigation (SSI) throughout the growing season as the control. DSI at both growth stages, and the reproductive stage in particular, changed the crop allometry, yield and net income of wheat. However, narrow spacing (20 cm) resulted in efficient use of available irrigation water (DSI and SSI) with higher yield, WUE and economic returns. Interestingly, wider spacing resulted in a higher number of grains per spike with higher 1000-grain weight under SSI and DSI, but final yield output remained poor due to a lower number of productive tillers. It was concluded that reducing irrigation during the vegetative stage is less damaging compared with the reproductive phase; therefore, sufficient supplemental irrigation must be added at the reproductive stage, particularly during grain-filling. Further, narrow spacing (20 cm) resulted in efficient utilization of available irrigation water; therefore, wheat must be grown at a narrow spacing to ensure the efficient utilization of available irrigation water.

Description: The time synchronization protocol is indispensable in various applications of wireless sensor networks, such as scheduling, monitoring, and tracking. Numerous protocols and algorithms have been proposed in recent decades, and many of them provide micro-scale resolutions. However, designing and implementing a time synchronization protocol in a practical wireless network is very challenging compared to implementation in a wired network; this is because its performance can be deteriorated significantly by many factors, including hardware quality, message delay jitter, ambient environment, and network topology. In this study, we measure the performance of the Flooding Time Synchronization Protocol (FTSP) and Gradient Time Synchronization Protocol (GTSP) in terms of practical network conditions, such as message delay jitter, synchronization period, network topology, and packet loss. This study provides insights into the operation and optimization of time synchronization protocols. In addition, the performance evaluation identifies that FTSP is highly affected by message delay jitter due to error accumulation over multi-hops. We demonstrate that the proposed extended version of the FTSP (E-FTSP) alleviates the effect of message delay jitter and enhances the overall performance of FTSP in terms of error, time, and other factors.

Description: The Internet of Things (IoT) will feature pervasive sensing and control capabilities via the massive deployment of machine-type communication devices in order to greatly improve daily life. However, machine-type communications can be illegally used (e.g., by criminals or terrorists) which is difficult to monitor, and thus presents new security challenges. The information exchanged in machine-type communications is usually transmitted in short packets. Thus, this paper investigates a legitimate surveillance system via proactive eavesdropping at finite blocklength regime. Under the finite blocklength regime, we analyze the channel coding rate of the eavesdropping link and the suspicious link. We find that the legitimate monitor can still eavesdrop the information sent by the suspicious transmitter as the blocklength decreases, even when the eavesdropping is failed under the Shannon capacity regime. Moreover, we define a metric called the effective eavesdropping rate and study the monotonicity. From the analysis of monotonicity, the existence of a maximum effective eavesdropping rate for a moderate or even high signal-to-noise (SNR) is verified. Finally, numerical results are provided and discussed. In the simulation, we also find that the maximum effective eavesdropping rate slowly increases with the blocklength.

Description: GNSS receiver data crowdsourcing is of interest for multiple applications, e.g., weather monitoring. The bottleneck in this technology is the quality of the GNSS receivers. Therefore, we lay out in an introductory manner the steps to estimate the performance of an arbitrary GNSS receiver via the measurement errors related to its instrumentation. Specifically, we do not need to know the position of the receiver antenna, which allows also for the assessment of smartphone GNSS receivers having integrated antennas. Moreover, the method is independent of atmospheric errors so that no ionospheric or tropospheric correction services provided by base stations are needed. Error models for performance evaluation can be calculated from receiver RINEX (receiver independent exchange format)data using only ephemeris corrections. For the results, we present the quality of different receiver grades through parametrized error models that are likely to be helpful in stochastic modeling, e.g., for Kalman filters, and in assessing GNSS receiver qualities for crowdsourcing applications. Currently, the typical positioning precision for the latest smartphone receivers is around the decimeter level, while for a professional-grade receiver, it is within a few millimeters.

Description: The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Description: The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.

Description: One of the most significant challenges in Internet of Things (IoT) environments is the protection of privacy. Failing to guarantee the privacy of sensitive data collected and shared over IoT infrastructures is a critical barrier that delays the wide penetration of IoT technologies in several user-centric application domains. Location information is the most common dynamic information monitored and lies among the most sensitive ones from a privacy perspective. This article introduces a novel mechanism that aims to protect the privacy of location information across Data Centric Sensor Networks (DCSNs) that monitor the location of mobile objects in IoT systems. The respective data dissemination protocols proposed enhance the security of DCSNs rendering them less vulnerable to intruders interested in obtaining the location information monitored. In this respect, a dynamic clustering algorithm is that clusters the DCSN nodes not only based on the network topology, but also considering the current location of the objects monitored. The proposed techniques do not focus on the prevention of attacks, but on enhancing the privacy of sensitive location information once IoT nodes have been compromised. They have been extensively assessed via series of experiments conducted over the IoT infrastructure of FIT IoT-LAB and the respective evaluation results indicate that the dynamic clustering algorithm proposed significantly outperforms existing solutions focusing on enhancing the privacy of location information in IoT.

Description: There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.

Description: The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

Description: Wireless sensor networks are widely used in many fields. Nodes in the network are typically powered by batteries. Because the energy consumption of wireless communication is related to the transmission distance, the energy consumption of nodes in different locations is different, resulting in uneven energy distribution of nodes. In some special applications, all nodes are required to work at the same time, and the uneven energy distribution makes the effective working time of the system subject to the node with the largest energy consumption. The commonly used clustering protocol can play a role in balancing energy consumption, but it does not achieve optimal energy consumption. This paper proposes to use the power supply line to connect the nodes to fully balance the energy. The connection scheme with the shortest power line length is also proposed. On the basis of energy balance, the method of transmitting data with the best hop count is proposed, which fully reduces the power consumption of the data transmission. The simulation results show that the proposed method can effectively reduce the energy consumption and prolong the lifetime of wireless sensor networks.

Description: High frequency guided-waves offer a trade-off between the high sensitivity of local bulk ultrasonic thickness measurements and the large area scanning of lower frequency guided-waves, so it has been a growing interest for corrosion inspection with the dispersive SH1 mode. However, according to the dispersive curve, it is hard to generate the pure SH1 mode since the non-dispersive SH0 mode will be excited simultaneously. Thus, this paper investigates a transducer design method to generate a pure SH1 guided-wave, where the dual periodic-permanent-magnet electromagnetic acoustic transducers (PPM EMATs) are placed on exactly opposite positions either side of the plate symmetrically. The suppression effect for SH0 and the enhancement effect for SH1 of the dual PPM EMATs are mainly discussed by theoretical analysis and simulation analysis, and the influence of positioning errors of PPM EMATs placed on opposite sides of the plate on its performances are analyzed. Employing the proposed dual PPM EMATs, some experiments are performed to verify the reliability of finite element simulation. The results indicate that the dual PPM EMATs can suppress the SH0 mode and generate the pure SH1 mode effectively. Moreover, the longitudinal and lateral positioning errors can affect the dual PPM EMATs performances significantly.

Description: In this paper, we report a wireless gas sensor based on surface acoustic waves (SAW). For room temperature detection of oxygen gas, a novel nanostructured ZnxFeyO gas-sensitive film was deposited on the surface of a SAW resonator by an oblique magnetron co-sputtering method. The measurements of X-ray diffraction (XRD) and a scanning electron microscope (SEM) showed that the crystal phase composition and the microstructures of ZnxFeyO films were significantly affected by the content of Fe. The experimental results showed that the sensors had a good response to O2 at room temperature. The max frequency shift of the sensors reached 258 kHz as the O2 partial pressure was 20%. Moreover, X-ray photoelectron spectroscopy (XPS) was performed to analyze the role of Fe in the sensitization process of the ZnxFeyO film. In addition, the internal relationship between the Fe content of the film and the sensitivity of the sensor was presented and discussed. The research indicates that the nanostructured ZnxFeyO film has a good potential for room temperature O2 gas detection applications.

Description: According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Description: Smart grids incorporating internet-of-things are emerging solutions to provide a reliable, sustainable and efficient electricity supply, and electric vehicle drivers can access efficient charging services in the smart grid. However, traditional electric vehicle charging systems are vulnerable to distributed denial of service and privileged insider attacks when the central charging server is attacked. The blockchain-based charging systems have been proposed to resolve these problems. In 2018, Huang et al. proposed the electric vehicle charging system using lightning network and smart contract. However, their system has an inefficient charging mechanism and does not guarantee security of key. We propose a secure charging system for electric vehicles based on blockchain to resolve these security flaws. Our charging system ensures the security of key, secure mutual authentication, anonymity, and perfect forward secrecy, and also provides efficient charging. We demonstrate that our proposed system provides secure mutual authentication using Burrows–Abadi–Needham logic and prevents replay and man-in-the-middle attacks using automated validation of internet security protocols and applications simulation tool. Furthermore, we compare computation and communication costs with previous schemes. Therefore, the proposed charging system efficiently applies to practical charging systems for electric vehicles.

Description: This paper presents a mathematical model of measuring blood flow based on electromagnetic induction for predicting the rate of arterial stenosis. Firstly, an electrode sensor was used to collect the induced potential differences from human skin surface in a uniform magnetic field. Then, the inversion matrix was constructed by the weight function theory and finite element method. Next, the blood flow volume inversion model was constructed by combining the induction potential differences and inversion matrix. Finally, the rate of arterial stenosis was predicted based on mathematical relationship between blood flow and the area of arterial stenosis. To verify the accuracy of the model, a uniform magnetic field distribution of Helmholtz coil and a 3D geometric model of the ulnar artery of the forearm with different rates of stenosis were established in COMSOL, a finite element analysis software. Simulation results showed that the inversion model had high accuracy in the measurement of blood flow and the prediction of rate of stenosis, and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

Description: Magnetic Induction Tomography (MIT) is a non-invasive imaging technique that has been widely applied for imaging materials with high electrical conductivity contrasts. Steel production is among an increasing number of applications that require a contactless method for monitoring the casting process due to the high temperature of hot steel. In this paper, an MIT technique is proposed for detecting defects and deformations in the external surfaces of metal, which has the potential to be used to monitor the external surface of hot steel during the continuous casting process. The Total Variation (TV) reconstruction algorithm was developed to image the conductivity distributions. Nonetheless, the reconstructed image of the deformed square metal obtained using the TV algorithm directly does not yield resonable images of the surface deformation. However, differential images obtained by subtracting the image of a perfect square metal with no deformations from the image obtained for a deformed square metal does provide accurate and repeatable deformation information. It is possible to obtain a more precise image of surface deformation by thresholding the differential image. This TV-based threshold-differencing method has been analysed and verified from both simulation and experimental tests. The simulation results reported that 0.92 % of the image region can be detected, and the experimental results indicated a 0.57 % detectability. Use of the proposed method was demonstareted in a MIT device which was used in continuous casting set up. The paper shows results from computer simulation, lab based cold tests, and real life data from continoeus cating demonstating the effectiveness of the proposed method.

Description: Gas mixture quantification is essential for the recording and reproducing odors, because an odor consists of multiple chemical compounds. Gas mixture quantification using field asymmetric ion mobility spectrometry (FAIMS) was studied. Acetone, ethanol, and diethyl ether were selected as components of a ternary gas mixture sample as representatives of the ketone, alcohol, and ether chemical classes, respectively. One hundred and twenty-five points with different concentrations were measured. The results were evaluated by error hypersurface, variance, and the coefficient of variation. The error hypersurface showed that it is possible to reach the target composition by following the error-hypersurface gradient. Successful convergence was achieved with the gradient descent method in a simulation based on the measurement data. This result verified the feasibility of the quantification of a gas mixture using FAIMS.

Description: In this study, a novel strain gauge arrangement and error reduction techniques were proposed to minimize crosstalk reading and simultaneously increase sensitivity on a decoupled six-axis force–moment (F/M) sensor. The calibration process that comprises the least squares method and error reduction techniques was implemented to obtain a robust decoupling matrix. A decoupling matrix is very crucial for minimizing error and crosstalk. A novel strain gauge arrangement that comprised double parallel strain gauges in the decoupled six-axis force–moment sensor was implemented to obtain high sensitivity. The experimental results revealed that the maximum calibration error, F/M sensor measurement error, and crosstalk readings were reduced to 3.91%, 1.78%, and 4.78%, respectively.

Description: Conventional eddy current testing (ECT) using a pickup coil probe is widely employed for the detection of structural cracks. However, the inspection of conventional ECT for steel structures is difficult because of the magnetic noise caused by the nonuniform permeability of steel. To combat this challenge, we have developed a small magnetic sensor probe with a dual-channel tunneling magnetoresistance sensor that is capable of reducing magnetic noise. Applying this probe to a complicated component of steel structures—such as the welds joining a U-shaped rib and deck plate together—requires the reduction of signal fluctuation caused by the distance (liftoff) variations between the sensor probe and the subject. In this study, the fundamental crack signal and the liftoff signal were investigated with the dual-channel sensor. The results showed that the liftoff signals could be reduced and differentiated from the crack signals by the differential parameters of the dual-channel sensor. In addition, we proposed an extraction technique for the crack signal using the Lissajous curve of the differential parameters. The extraction technique could be applied to the inspections not only for flat plates but also for welded angles to detect cracks without the influence of the liftoff signal.

Description: The objective of this paper is to study the ability of polymer optical fiber (POF) to be inserted in a knitted fabric and to measure both pressure and friction when walking. Firstly, POF, marketed and in development, have been compared in terms of the required mechanical properties for the insertion of the fiber directly into a knitted fabric on an industrial scale, i.e. elongation, bending rigidity, and minimum bending radius before plastic deformation. Secondly, the chosen optical fiber was inserted inside several types of knitted fabric and was shown to be sensitive to friction and compression. The knitted structure with the highest sensitivity has been chosen for sock prototype manufacturing. Finally, a feasibility study with an instrumented sock showed that it is possible to detect the different phases of walking in terms of compression and friction.

Description: Dielectric grating-based sensors are usually based on the guided mode resonance (GMR) obtained using a thin planar waveguide layer (PWL) adjacent to a thin subwavelength grating layer. In this work, we present a detailed investigation of thick subwavelength dielectric grating structures that exhibit reflection resonances above a certain thickness without the need for the waveguide layer, showing great potential for applications in biosensing and tunable filtering. Analytic and numerical results are thoroughly discussed, as well as an experimental demonstration of the structure as a chemical sensor in the SWIR (short wave infrared) spectral range (1200–1800 nm). In comparison to the GMR structure with PWL, the thick grating structure has several unique properties: (i) It gives higher sensitivity when the spaces are filled, with the analyte peaking at certain space values due to an increase in the interaction volume between the analyte and the evanescent optical field between the grating lines; (ii) the TM (transverse magnetic) resonance, in certain cases, provides a better figure of merit; (iii) the sensitivity increases as the grating height increases; (iv) the prediction of the resonance locations based on the effective medium approximation does not give satisfactory results when the grating height is larger than a certain value, and the invalidity becomes more severe as the period increases; (v) a sudden increase in the Q-factor of the resonance occurs at a specific height value accompanied by the high local field enhancement (~103) characteristic of a nano-antenna type pattern. Rigorous numerical simulations of the field distribution are presented to explain the different observed phenomena.

Description: In 2016, the IEEE task group ah (TGah) released a new standard called IEEE 802.11ah, and industrial Internet of Things (IoT) is one of its typical use cases. The restricted access window (RAW) is one of the core MAC mechanisms of IEEE 802.11ah, which aims to address the collision problem in the dense wireless networks. However, in each RAW period, stations still need to contend for the channel by Distributed Coordination Function and Enhanced Distributed Channel Access (DCF/EDCA), which cannot meet the real-time requirements of most industrial applications. In this paper, we propose a channel-aware contention window adaption (CA-CWA) algorithm. The algorithm dynamically adapts the contention window based on the channel status with an external interference discrimination ability, and improves the real-time performance of the IEEE 802.11ah. To validate the real-time performance of CA-CWA, we compared CA-CWA with two other backoff algorithms with an NS-3 simulator. The results illustrate that CA-CWA has better performance than the other two algorithms in terms of packet loss rate and average delay. Compared with the other two algorithms, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11ah.

Description: Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

Description: Depth cameras play a vital role in three-dimensional (3D) shape reconstruction, machine vision, augmented/virtual reality and other visual information-related fields. However, a single depth camera cannot obtain complete information about an object by itself due to the limitation of the camera’s field of view. Multiple depth cameras can solve this problem by acquiring depth information from different viewpoints. In order to do so, they need to be calibrated to be able to accurately obtain the complete 3D information. However, traditional chessboard-based planar targets are not well suited for calibrating the relative orientations between multiple depth cameras, because the coordinates of different depth cameras need to be unified into a single coordinate system, and the multiple camera systems with a specific angle have a very small overlapping field of view. In this paper, we propose a 3D target-based multiple depth camera calibration method. Each plane of the 3D target is used to calibrate an independent depth camera. All planes of the 3D target are unified into a single coordinate system, which means the feature points on the calibration plane are also in one unified coordinate system. Using this 3D target, multiple depth cameras can be calibrated simultaneously. In this paper, a method of precise calibration using lidar is proposed. This method is not only applicable to the 3D target designed for the purposes of this paper, but it can also be applied to all 3D calibration objects consisting of planar chessboards. This method can significantly reduce the calibration error compared with traditional camera calibration methods. In addition, in order to reduce the influence of the infrared transmitter of the depth camera and improve its calibration accuracy, the calibration process of the depth camera is optimized. A series of calibration experiments were carried out, and the experimental results demonstrated the reliability and effectiveness of the proposed method.

Description: Two types of urea biosensors were integrated with a wireless measurement system and microfluidic measurement system. The two biosensors used were (i) a magnetic beads (MBs)-urease/graphene oxide (GO)/titanium dioxide (TiO2)-based biosensor and (ii) an MBs-urease/GO/ nickel oxide (NiO)-based biosensor, respectively. The wireless measurement system work exhibited the feasibility for the remote detection of urea, but it will require refinement and modification to improve stability and precision. The microchannel fluidic system showed the measurement reliability. The sensing properties of urea biosensors at different flow rates were investigated. From the measurement results, the decay of average sensitivity may be attributed to the induced vortex-induced vibrations (VIV) at the high flow rate. In the aspect of wireless monitoring, the average sensitivity of the urea biosensor based on MBs-urease/GO/NiO was 4.780 mV/(mg/dl) and with the linearity of 0.938. In the aspect of measurement under dynamic conditions, the average sensitivity of the urea biosensor based on MBs-urease/GO/NiO were 5.582 mV/(mg/dl) and with the linearity of 0.959. Both measurements performed NiO was better than TiO2 according to the comparisons.

Description: Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC.

Description: Complex environments pose great challenges for autonomous mobile robot navigation. In this study, we address the problem of autonomous navigation in 3D environments with staircases and slopes. An integrated system for safe mobile robot navigation in 3D complex environments is presented and both the perception and navigation capabilities are incorporated into the modular and reusable framework. Firstly, to distinguish the slope from the staircase in the environment, the robot builds a 3D OctoMap of the environment with a novel Simultaneously Localization and Mapping (SLAM) framework using the information of wheel odometry, a 2D laser scanner, and an RGB-D camera. Then, we introduce the traversable map, which is generated by the multi-layer 2D maps extracted from the 3D OctoMap. This traversable map serves as the input for autonomous navigation when the robot faces slopes and staircases. Moreover, to enable robust robot navigation in 3D environments, a novel camera re-localization method based on regression forest towards stable 3D localization is incorporated into this framework. In addition, we utilize a variable step size Rapidly-exploring Random Tree (RRT) method which can adjust the exploring step size automatically without tuning this parameter manually according to the environment, so that the navigation efficiency is improved. The experiments are conducted in different kinds of environments and the output results demonstrate that the proposed system enables the robot to navigate efficiently and robustly in complex 3D environments.

Description: In this work, we isolate individual wurtzite InAs nanowires and fabricate electrical contacts at both ends, exploiting the single nanostructures as building blocks to realize two different architectures of conductometric sensors: (a) the nanowire is drop-casted onto—supported by—a SiO2/Si substrate, and (b) the nanowire is suspended at approximately 250 nm from the substrate. We test the source-drain current upon changes in the concentration of humidity, ethanol, and NO2, using synthetic air as a gas carrier, moving a step forward towards mimicking operational environmental conditions. The supported architecture shows higher response in the mid humidity range (50% relative humidity), with shorter response and recovery times and lower detection limit with respect to the suspended nanowire. These experimental pieces of evidence indicate a minor role of the InAs/SiO2 contact area; hence, there is no need for suspended nanostructures to improve the sensing performance. Moreover, the sensing capability of single InAs nanowires for detection of NO2 and ethanol in the ambient atmosphere is reported and discussed.

Description: Displacement is crucial for structural health monitoring, although it is very challenging to measure under field conditions. Most existing displacement measurement methods are costly, labor-intensive, and insufficiently accurate for measuring small dynamic displacements. Computer vision (CV)-based methods incorporate optical devices with advanced image processing algorithms to accurately, cost-effectively, and remotely measure structural displacement with easy installation. However, non-target-based CV methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation. This paper presents a reference frame-based Deepflow algorithm integrated with masking and signal filtering for non-target-based displacement measurements. The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error. The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions. The accuracy of the proposed method is compared with that of a reference laser displacement sensor for validation. The significant advantage of the proposed method is its flexibility in extracting structural displacement in any region on structures that do not have distinct natural features.

Description: Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.

Description: A mixture of red and blue light-emitting diodes (LEDs; at a ratio of 7:3, respectively) were used to analyze the effects of different photosynthetic photon flux densities (PPFDs) (40, 80, and 120 µmol m−2 s−1 hereafter known as LED 40, 80, and 120, respectively) on the micropropagation of Gerbera jamesonii Bolus shoots. The experiment also examined the effect of 6-benzyladenine (BA) in 1, 2.5, and 5 µM concentrations in the media. Biometrical observations and analyses of leaf morphometry and photosynthetic pigment content were conducted. Shoot multiplication increased with an increasing BA concentration. A PPFD of 80 µmol m−2 s−1 and 5 µM BA is suggested as efficient for shoot propagation and economically viable. LED 120 increased the leaf blade area and its width, and circularity and elongation ratios. The intensity of light did not affect the fresh weight, which increased at higher BA concentrations (2.5 and 5 μM). The dry weight content decreased with increasing cytokinin concentration; the greatest content was observed on media with 1 µM BA under PPFD 120 µmol m−2 s−1. LED 80 increased the photosynthetic pigments content in the leaves in comparison to the standard intensity of LED 40. Increased BA concentration raises the content of chlorophyll a.

Description: In this paper, we measure the systemic risk with a novel methodology, based on a “spatial-temporal” approach. We propose a new bank systemic risk measure to consider the two components of systemic risk: cross-sectional and time dimension. The aim is to highlight the “time-space dynamics” of contagion, i.e., if the CDS spread of bank i depends on the CDS spread of other banks. To do this, we use an advanced spatial econometrics design with a time-varying spatial dependence that can be interpreted as an index of the degree of cross-sectional spillovers. The findings highlight that the Eurozone banks have strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present and persistent. Moreover, we analyse the role of the European Central Bank in managing contagion risk. We find that monetary policy has been effective in reducing systemic risk. However, the results show that systemic risk does not imply a policy intervention, highlighting how financial stability policy is not yet an objective.

Description: Soil pH is a key factor affecting the growth of blueberries. Understanding the response mechanism of blueberries to different pH values and selecting suitable evaluation indexes are the basis of breeding new blueberry cultivars with high pH tolerances. The effects of different soil pH treatments for 17 months on the plant growth, fruit yield, photosynthetic characteristics, and leaf microelement concentration of Vaccinium ashei Reade ‘Climax’ and V. corymbosum hybrid ‘Chaoyue No. 1′ were studied. Plant height, main stem diameter, branch number per plant, leaf dry weight, stem dry weight, root dry weight, and total dry weight decreased with increasing soil pH. With an increase in soil pH, the first flowering date, 50% flowering date, first ripening date, and 50% ripening date of the two cultivars were postponed, and the flower bud numbers per plant, the floret numbers per bud, and yield per plant showed a downward trend. Moreover, the fruit quality decreased, which was reflected in the increase in the titratable acid content (TA) and the decrease in the total soluble solids content (TSS) and the TSS:TA ratio in the high pH treatment. With increasing soil pH, the chlorophyll content index (CCI), maximal photochemical efficiency of the PSII (Fv/Fm), quantum photosynthetic yield of the PSII (Y(II)) and net photosynthetic rate (Pn) of the two cultivars showed a downward trend, and some microelement concentrations in the leaves were imbalanced. Under high pH treatment, ‘Chaoyue No. 1′ had a relatively higher plant biomass and fruit yield, so it had a stronger tolerance to high pH than ‘Climax’ did. More strongly acidified rhizosphere soil capacity, as well as higher CCI, Fv/Fm, Y(II), and Pn values were the main reasons for the high pH tolerance of ‘Chaoyue No. 1′. Compared with destructive biomass indicators such as plant weight, nondestructive indicators such as CCI, Fv/Fm, and Y(II) can be more valuable indicators for fast and accurate evaluation of blueberry tolerance to high pH at early stages of treatment.

Description: The Foundation Fighting Blindness leads a collaborative effort among patients and families, scientists, and the commercial sector to drive the development of preventions, treatments, and cures for inherited retinal diseases (IRDs). When the nonprofit was established in 1971, it sought the knowledge and insights of leaders in the retinal research field to guide its research funding decisions. While the Foundation’s early investments focused on gaining a better understanding of the genetic causes of IRDs, its portfolio of projects would come to include some of the most innovative approaches to saving and restoring vision, including gene replacement/augmentation therapies, gene editing, RNA modulation, optogenetics, and gene-based neuroprotection. In recent years, the Foundation invested in resources such as its patient registry, natural history studies, and genetic testing program to bolster clinical development and trials for emerging genetic therapies. Though the number of clinical trials for such therapies has surged over the last decade, the Foundation remains steadfast in its commitment to funding the initiatives that hold the most potential for eradicating the entire spectrum of IRDs.

Description: A conspicuous cell-shape phenotype known as “screwy” was reported to result from mutations at two or three uncharacterized loci in the ciliate Paramecium tetraurelia. Here, we describe a new screwy mutation, Spinning Top, which appeared spontaneously in the cross of an unrelated mutant with reference strain 51. The macronuclear (MAC) genome of the Spinning Top mutant is shown to lack a ~28.5-kb segment containing 18 genes at the end of one chromosome, which appears to result from a collinear deletion in the micronuclear (MIC) genome. We tested several candidate genes from the deleted locus by dsRNA-induced silencing in wild-type cells, and identified a single gene responsible for the phenotype. This gene, named Spade, encodes a 566-aa glutamine-rich protein with a C2HC zinc finger. Its silencing leads to a fast phenotype switch during vegetative growth, but cells recover a wild-type phenotype only 5–6 divisions after silencing is stopped. We analyzed 5 independently-obtained mutant alleles of the Sc1 locus, and concluded that all of them also lack the Spade gene and a number of neighboring genes in the MAC and MIC genomes. Mapping of the MAC deletion breakpoints revealed two different positions among the 5 alleles, both of which differ from the Spinning Top breakpoint. These results suggest that this MIC chromosome region is intrinsically unstable in strain 51.

Description: Extracellular vesicles (EVs) have received increasing attention over the last two decades. Initially, they were considered as just a garbage disposal tool; however, it has progressively become clear that their protein, nucleic acid (namely miRNA and mRNA), and lipid contents have signaling functions. Besides, it has been established that cells release different types of vesicular structures for which characterization is still in its infancy. Many stress conditions, such as hypoxia, senescence, and oncogene activation have been associated with the release of higher levels of EVs. Further, evidence has shown that autophagic–lysosomal pathway abnormalities also affect EV release. In fact, in neurodegenerative diseases characterized by the accumulation of toxic proteins, although it has not become clear to what extent the intracellular storage of undigested materials itself has beneficial/adverse effects, these proteins have also been shown to be released extracellularly via EVs. Lysosomal storage disorders (LSDs) are characterized by accumulation of undigested substrates within the endosomal–lysosomal system, due either to genetic mutations in lysosomal proteins or to treatment with pharmacological agents. Here, we review studies investigating the role of lysosomal and autophagic dysfunction on the release of EVs, with a focus on studies exploring the release of EVs in LSD models of both genetic and pharmacological origin. A better knowledge of EV-releasing pathways activated in lysosomal stress conditions will provide information on the role of EVs in both alleviating intracellular storage of undigested materials and spreading the pathology to the neighboring tissue.

Description: Previous study has demonstrated that the riboflavin treatment promoted the early ripening of the ‘Kyoho’ grape berry. However, the molecular mechanism causing this was unclear. In order to reveal the regulation mechanism of riboflavin treatment on grape berry development and ripening, the different berry developmental stages of the ‘Kyoho’ berry treated with 0.5 mmol/L of riboflavin was sampled for transcriptome profiling. RNA-seq revealed that 1526 and 430 genes were up-regulated and down-regulated, respectively, for the comparisons of the treatment to the control. TCseq analysis showed that the expression patterns of most of the genes were similar between the treatment and the control, except for some genes that were related to the chlorophyll metabolism, photosynthesis–antenna proteins, and photosynthesis, which were revealed by the enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The differentially expressed genes and weighted gene co-expression network analysis (WGCNA) analysis identified some significantly differentially expressed genes and some hub genes, including up-regulation of the photosynthesis-related ELIP1 and growth and development-related GDSL; and down-regulation of the oxidative stress-related ATHSP22 and berry softening-related XTH32 and GH9B15. The results suggested that the riboflavin treatment resulted in the variations of the expression levels of these genes, and then led to the early ripening of the ‘Kyoho’ berry.

Description: Due to its possible utilization in cosmetics, medicine and crop protection, as a valuable alternative to petrochemical-derived products, hemp essential oil is now considered a product with high value added and a promising marketing potential. This experiment was conducted with the aim of evaluating the effect of four different locations of Northern Italy during two years (four environments) and three hemp monoecious varieties on the production and quality of essential oils (EOs) obtained by inflorescences harvested at full flowering of female flowers. The highest inflorescence yield was obtained at Maiano 2017, where a superficial groundwater layer (1.5 m) was present, with values that ranged from 1.69 of Fedora to 2.06 t ha−1 of Futura. EOs production ranged between 3.4 and 4.9 L ha−1, affected mainly by the variety effect. The terpene in EOs, very similar between varieties and environments, was mainly composed of sesquiterpenes (caryophillene and humulene, as the most abundant) rather than monoterpenes (α-pinene, β-myrcene and trans-β-ocimene, in particular). Phytocannabinoids, and in particular cannabidiol (CBD), were not removed from tissues by the steam during hydrodistillation, and if this is confirmed by further experiments, the residual biomass, now considered as waste, could assume significant importance as a source for further utilization.

Description: Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0.75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.

Description: Traditional compensation methods based on temperature-related parameters are not effective for complex total reflection prism laser gyro (TRPLG) bias variation. Because the high frequency oscillator voltage (UHFO) fundamentally affects the TRPLG bias, and the UHFO has a stronger correlation with the TRPLG bias when compared with the temperature, an introduction of UHFO into the TRPLG bias compensation can be evaluated. In consideration of the limitations of least squares (LS) regression and multivariate stepwise regression, we proposed a compensation method for TRPLG bias based on iterative re-weighted least squares support vector machine (IR-LSSVM) and compared with LS regression, stepwise regression, and LSSVM algorithm in large temperature cycling experiments. When temperature, slope of temperature variation, and UHFO were selected as inputs, the IR-LSSVM based on myriad weight function improved the TRPLG bias stability by 61.19% to reach the maximum and eliminated TRPLG bias drift. In addition, the UHFO proved to be the most important parameter in the process of TRPLG bias compensation; accordingly, it can alleviate the shortcomings of traditional compensation based on temperature-related parameters and can greatly improve the TRPLG bias stability.

Description: A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

Description: This paper investigates mobile relaying in wireless powered communication networks (WPCN), where an unmanned aerial vehicle (UAV) is employed to help information delivery from multiple sources to destination with communication channels severely blocked. The sources are low-power without energy supply. To support information transmission, the UAV acts as a hybrid access point (AP) to provide wireless power transfer (WPT) and information reception for sources. We set the issue of system throughput maximization as the optimization problem. On the one hand, the system is subject to the information causality constraint due to the dependent processes of information reception and transmission for the UAV. On the other hand, the sources are constrained by a so-called neutrality constraints due to the dependent processes of energy harvesting and energy consumption. In addition, we take account of the access delay issue of all ground nodes. Specifically, two paradigms of delay-tolerant case and delay-sensitive case are presented. However, the formulated problem including optimizations for time slot scheduling, power allocation and UAV trajectory is non-convex and thus is difficult to obtain its optimal solution. To tackle this problem, we apply the successive convex approximation (SCA) technique and propose an iterative algorithm by which a suboptimal solution can be achieved. Simulation results validate our proposed design, and show that the obtained suboptimal solution is high-quality, as compared to benchmark scheme.

Description: A piezoelectric fiber patch (PFP) is a transducer type that is suitable for guided-wave-based structural health monitoring (SHM) due to its light, thin, and flexible characteristics. In our previous work, a PFP-based transducer design for selective excitation of the zero-order shear horizontal wave mode (SH0) was introduced (shear horizontal PFP (SHPFP)). In this work, two modified SH0 wave PFP transducer designs are proposed: the rounded corner design and the dual design. The degree of improvement is determined by a numerical simulation and the dual design—the design with the most promise—is experimentally realized. Laser Vibrometry measured the generated wave field, confirming the results from the simulation. The new designs can generate an almost pure SH0 wave. The dual design has a very strong directivity that is useful for several guided-wave-based SHM applications. The conclusions on the design’s properties as a transmitter are also valid for its properties as a sensor due to the reciprocity of piezoelectric transducers.

Description: The taxonomical identification merely based on morphology is often difficult for ancient remains. Therefore, universal or specific PCR amplification followed by sequencing and BLAST (basic local alignment search tool) search has become the most frequently used genetic-based method for the species identification of biological samples, including ancient remains. However, it is challenging for these methods to process extremely ancient samples with severe DNA fragmentation and contamination. Here, we applied whole-genome sequencing data from 12 ancient samples with ages ranging from 2.7 to 700 kya to compare different mapping algorithms, and tested different reference databases, mapping similarities and query coverage to explore the best method and mapping parameters that can improve the accuracy of ancient mammal species identification. The selected method and parameters were tested using 152 ancient samples, and 150 of the samples were successfully identified. We further screened the BLAST-based mapping results according to the deamination characteristics of ancient DNA to improve the ability of ancient species identification. Our findings demonstrate a marked improvement to the normal procedures used for ancient species identification, which was achieved through defining the mapping and filtering guidelines to identify true ancient DNA sequences. The guidelines summarized in this study could be valuable in archaeology, paleontology, evolution, and forensic science. For the convenience of the scientific community, we wrote a software script with Perl, called AncSid, which is made available on GitHub.

Description: The role of small bioactive molecules (〈500 Da) in mechanisms improving resource use efficiency in plants under stress conditions draws increasing interest. One such molecule is omeprazole (OMP), a benzimidazole derivative and inhibitor of animal proton pumps shown to improve nitrate uptake and exclusion of toxic ions, especially of chloride from the cytosol of salt-stressed leaves. Currently, OMP was applied as substrate drench at two rates (0 or 10 μM) on hydroponic basil (Ocimum basilicum L. cv. Genovese) grown under decreasing NO3−:Cl− ratio (80:20, 60:40, 40:60, or 20:80). Chloride concentration and stomatal resistance increased while transpiration, net CO2 assimilation rate and beneficial ions (NO3−, PO43−, and SO42−) decreased with reduced NO3−:Cl− ratio under the 0 μM OMP treatment. The negative effects of chloride were not only mitigated by the 10 μM OMP application in all treatments, with the exception of 20:80 NO3−:Cl−, but plant growth at 80:20, 60:40, and 40:60 NO3−:Cl− ratios receiving OMP application showed maximum fresh yield (+13%, 24%, and 22%, respectively), shoot (+10%, 25%, and 21%, respectively) and root (+32%, 76%, and 75%, respectively) biomass compared to the corresponding untreated treatments. OMP was not directly involved in ion homeostasis and compartmentalization of vacuolar or apoplastic chloride. However, it was active in limiting chloride loading into the shoot, as manifested by the lower chloride concentration in the 80:20, 60:40, and 40:60 NO3−:Cl− treatments compared to the respective controls (−41%, −37%, and −24%), favoring instead that of nitrate and potassium while also boosting photosynthetic activity. Despite its unequivocally beneficial effect on plants, the large-scale application of OMP is currently limited by the molecule’s high cost. However, further studies are warranted to unravel the molecular mechanisms of OMP-induced reduction of chloride loading to shoot and improved salt tolerance.

Description: Taenia pisiformis is a tapeworm causing economic losses in the rabbit breeding industry worldwide. Due to the absence of genomic data, our knowledge on the developmental process of T. pisiformis is still inadequate. In this study, to better characterize differential and specific genes and pathways associated with the parasite developments, a comparative transcriptomic analysis of the larval stage (TpM) and the adult stage (TpA) of T. pisiformis was performed by Illumina RNA sequencing (RNA-seq) technology and de novo analysis. In total, 68,588 unigenes were assembled with an average length of 789 nucleotides (nt) and N50 of 1485 nt. Further, we identified 4093 differentially expressed genes (DEGs) in TpA versus TpM, of which 3186 DEGs were upregulated and 907 were downregulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analyses revealed that most DEGs involved in metabolic processes and Wnt signaling pathway were much more active in the TpA stage. Quantitative real-time PCR (qPCR) validated that the expression levels of the selected 10 DEGs were consistent with those in RNA-seq, indicating that the transcriptomic data are reliable. The present study provides comparative transcriptomic data concerning two developmental stages of T. pisiformis, which will be of great value for future functional studies on the regulatory mechanisms behind adult worm pathogenesis and for developing drugs and vaccines against this important parasite.

Description: The monotypic genus Deraiotrema Machida, 1982 has only been reported once, from the orbicular batfish Platax orbicularis (Forsskål) in the waters around Palau in Micronesia (Machida, 1982). It has a body-shape similar to other lepocreadiids from batfishes, such as species of Bianium Stunkard, 1930 and Diploproctodaeum La Rue, 1926, but differs in having multiple testes in ventral and dorsal layers. Here we report Deraiotrema platacis Machida, 1982 for just the second time, infecting the dusky batfish Platax pinnatus (Linnaeus) from the waters off Lizard Island on the northern Great Barrier Reef. We present a molecular phylogenetic analysis of the position of this genus inferred from 28S rDNA sequences. Surprisingly, we find the species most closely related to Echeneidocoelium indicum despite the infection of completely unrelated hosts and the presence of two characters (lateral fold in the forebody and multiple testes) that are found elsewhere in the Lepocreadiidae. We conclude that homoplasy within the Lepocreadiidae is extensive and that morphology-based prediction of relationships has little prospect of success.

Description: Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

Description: Radio frequency identification is one of the key techniques for Internet of Things, which has been widely adopted in many applications for identification. However, there exist various security and privacy issues in radio frequency identification (RFID) systems. Particularly, one of the most serious threats is to clone tags for the goal of counterfeiting goods, which causes great loss and danger to customers. To solve these issues, lots of authentication protocols are proposed based on physical unclonable functions that can ensure an anti-counterfeiting feature. However, most of the existing schemes require secret parameters to be stored in tags, which are vulnerable to physical attacks that can further lead to the breach of forward secrecy. Furthermore, as far as we know, none of the existing schemes are able to solve the security and privacy problems with good scalability. Since many existing schemes rely on exhaustive searches of the backend server to validate a tag and they are not scalable for applications with a large scale database. Hence, in this paper, we propose a lightweight RFID mutual authentication protocol with physically unclonable functions (PUFs). The performance analysis shows that our proposed scheme can ensure security and privacy efficiently in a scalable way.

Description: Sensors provide the foundation of many smart applications and cyber–physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors—and accelerometers and gyroscopes in particular—are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods. We describe in detail the prediction methods that our team used in the OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender (GAG 2019) at the 12th IAPR International Conference on Biometrics. In these two competitions, our team obtained the best solutions amongst all international participants, and this for both the age and gender predictions. Our research shows that it is feasible to predict age and gender with a reasonable accuracy on gait traces of just a few seconds. Furthermore, it illustrates the need to put in place adequate measures in order to mitigate unintended information leakage by abusing sensors as an unanticipated side channel for sensitive information or private traits.

Description: In rehabilitation procedures related to the lower limbs, gait monitoring is an important source of information for the therapist. However, many of the approaches proposed in the literature require the use of uncomfortable and invasive devices. In this work, an instrumented tip is developed and detailed, which can be connected to any crutch. The instrumented tip provides objective data of the crutch motion, which, combined with patient movement data, might be used to monitor the daily activities or assess the recovery status of the patient. For that purpose, the tip integrates a two-axis inclinometer, a tri-axial gyroscope, and a force sensor to measure the force exerted on the crutch. In addition, a novel algorithm to estimate the pitch angle of the crutch is developed. The proposed approach is tested experimentally, obtaining acceptable accuracies and demonstrating the validity of the proposed lightweight, portable solution for gait monitoring.

Description: The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU).

Description: In the last years, several attempts to combine the Internet of Things (IoT) and social networking have been made. In the meantime, things involved in IoT are becoming increasingly sophisticated and intelligent, showing a behavior that tends to look like the one of users in social networks. Therefore, it is not out of place to talk about profiles of things and about information and topics exchanged among them. In such a context, constructing topic-driven virtual communities starting from the real ones operating in a Multi-IoT scenario is an extremely challenging issue. This paper aims at providing some contributions in this setting. First of all, it presents the concept of profile of a thing. Then, it introduces the concept of topic-guided virtual IoT. Finally, it illustrates two approaches (one supervised and one unsupervised) to constructing topic-guided virtual IoTs in a Multi-IoT scenario.

Description: As an important development direction of star sensor technology, the All-Time star sensor technology can expand the application of star sensors to flight platforms inside the atmosphere. Due to intense atmospheric background radiation during the daytime, the commonly used star sensors operating in the visible wavelength range are significantly limited in their ability to detect stars, and hence the All-Time star sensor technology which is based on the shortwave infrared (SWIR) imaging system has become an effective research direction. All-Time star sensor detection capability is significantly affected by observation conditions and, therefore, an optimized selection of optical parameters, which mainly includes the field of view (FOV) and the detection wavelength band, can effectively improve the detection performance of All-Time star sensors under harsh observation conditions. This paper uses the model simulation method to analyze and optimize the optical parameters under various observation conditions in a high-altitude environment. A main parameter among those discussed is the analysis of detection band optimization based on the SWIR band. Due to the huge cost constraints of high-altitude experiments, we conducted experiments near the ground to verify the effectiveness of the detection band selection and the correctness of the SWIR star sensor detection model, which thereby proved that the optimization of the optical parameters for high altitudes was effective and could be used as a reference.

Description: Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.

Description: Local administrations demand real-time and continuous pollution monitoring in sewer networks. Spectroscopy is a non-destructive technique that can be used to continuously monitor quality in sewers. Covering a wide range of wavelengths can be useful for improving pollution characterization in wastewater. Cost-effective and in-sewer spectrophotometers would contribute to accomplishing discharge requirements. Nevertheless, most available spectrometers are based on incandescent lamps, which makes it unfeasible to place them in a sewerage network for real-time monitoring. This research work shows an innovative calibration procedure that allows (Light-Emitting Diode) LED technology to be used as a replacement for traditional incandescent lamps in the development of spectrophotometry equipment. This involves firstly obtaining transmittance values similar to those provided by incandescent lamps, without using any optical components. Secondly, this calibration process enables an increase in the range of wavelengths available (working range) through a better use of the LED’s spectral width, resulting in a significant reduction in the number of LEDs required. Thirdly, this method allows important reductions in costs, dimensions and consumptions to be achieved, making its implementation in a wide variety of environments possible.

Description: Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.

Description: Background: Genomes are non-randomly organized within the interphase nucleus; and spermatozoa are proposed to have a unique hairpin-loop configuration, which has been hypothesized to be critical for the ordered exodus of the paternal genome following fertilization. Recent studies suggest that the hairpin-loop model of sperm chromatin organization is more segmentally organized. The purpose of this study is to examine the 3D organization and hairpin-loop configurations of chromosomes in human spermatozoa. Methods: Three-color sperm-fluorescence in-situ hybridization was utilized against the centromeres, and chromosome p- and q-arms of eight chromosomes from five normozoospermic donors. Wide-field fluorescence microscopy and 3D modelling established the radial organization and hairpin-loop chromosome configurations in spermatozoa. Results: All chromosomes possessed reproducible non-random radial organization (p 〈 0.05) and formed discrete hairpin-loop configurations. However, chromosomes preferentially formed narrow or wide hairpin-loops. We did not find evidence to support the existence of a centralized chromocenter(s) with centromeres being more peripherally localized than one or both of their respective chromosome arms. Conclusion: This provides further evidence to support a more segmental organization of chromatin in the human sperm nucleus. This may be of significance for fertilization and early embryogenesis as specific genomic regions are likely to be exposed, remodeled, and activated first, following fertilization.

Description: The design and operation of a custom-built LIDAR-compatible, four-channel Raman spectrometer integrated to a 532 nm pulsed laser is presented. The multichannel design allowed for simultaneous collection of Raman photons at two spectral regions identified as highly sensitive to changes in water temperature. For each of these spectral bands, the signals having polarization parallel to (∥) and perpendicular to (⟂), the excitation polarization were collected. Four independent temperature markers were calculated from the Raman signals: two-colour(∥), two-colour(⟂), depolarization(A) and depolarization(B). A total of sixteen datasets were analysed for one ultrapure (Milli-Q) and three samples of natural water. Temperature accuracies of ±0.4 °C–±0.8 °C were achieved using the two-colour(∥) marker. When multiple linear regression models were constructed (linear combination) utilizing all simultaneously acquired temperature markers, improved accuracies of ±0.3 °C–±0.7 °C were achieved.

Description: There is a growing body of research that recognizes the potentials of biochar application in agricultural production systems. However, little is known about the effects of biochar, especially hydrochar, on production of containerized seedlings under nursery conditions. This study aimed to test the effects of hydrochar application on growth, quality, nutrient and heavy metal contents, and mycorrhizal association of containerized pine seedlings. The hydrochar used in this study was produced through hydrothermal carbonization of paper mill biosludge at 200 °C. Two forms of hydrochar (powder and pellet) were mixed with peat at ratios of 10% and 20% (v/v) under three levels of applied commercial fertilizer (nil, half and full rates). Application of hydrochar had positive or neutral effects on shoot biomass and stem diameter compared with control seedlings (without hydrochar) under tested fertilizer levels. Analysis of the natural logarithmic response ratios (LnRR) of quality index and nutrient and heavy metal uptake revealed that application of 20% (v/v) hydrochar powder or pellet with 50% fertilizer resulted in same quality pine seedlings with similar heavy metal (Cu, Ni, Pb, Zn and Cr) and nutrient (P, K, Ca and Mg) contents as untreated seedlings supplied with 100% fertilizer. Colonization percentage by ectomycorrhizae significantly increased when either forms of hydrochar were applied at a rate of 20% under unfertilized condition. The results of this study implied that application of proper rates of hydrochar from biosludge with adjusted levels of liquid fertilizer may reduce fertilizer requirements in pine nurseries.

Description: The tank cascade system (TCS) has been used for over 2000 years for water management in Sri Lanka. Since surface water is limited in the dry zone of Sri Lanka, agricultural production, especially of upland crops, relies on groundwater for irrigation. We sampled 29 wells in the Ulagalla cascade, a prominent TCS near Anuradhapura city in the dry zone of Sri Lanka, in Yala (dry) and Maha (wet) seasons, the two main cropping seasons in Sri Lanka. We evaluated the suitability of groundwater for irrigation using the analytic hierarchy process and geographical information system. Water quality did not vary notably between seasons. However, it deteriorated with the onset of high intensity heavy rain, especially during the Maha season. A water quality zoning map indicated that groundwater in 4% and 96% of the study area is suitable and moderately suitable for irrigation, respectively. Irrigation water quality in tank cascade landscapes and similar environments can be assessed using this methodology and our results.

Description: Increasing evidence suggests that overlapping genes are much more common in eukaryotic genomes than previously thought. These different-strand overlapping genes are potential sense–antisense (SAS) pairs, which might have regulatory effects on each other. In the present study, we identified the SAS loci in the equine genome using previously generated stranded, paired-end RNA sequencing data from the equine chorioallantois. We identified a total of 1261 overlapping loci. The ratio of the number of overlapping regions to chromosomal length was numerically higher on chromosome 11 followed by chromosomes 13 and 12. These results show that overlapping transcription is distributed throughout the equine genome, but that distributions differ for each chromosome. Next, we evaluated the expression patterns of SAS pairs during the course of gestation. The sense and antisense genes showed an overall positive correlation between the sense and antisense pairs. We further provide a list of SAS pairs with both positive and negative correlation in their expression patterns throughout gestation. This study characterizes the landscape of sense and antisense gene expression in the placenta for the first time and provides a resource that will enable researchers to elucidate the mechanisms of sense/antisense regulation during pregnancy.

Description: The low energy adaptive clustering hierarchy (LEACH) is the classical hierarchical topology control algorithm and still widely used today in wireless sensor networks. There are some problems in LEACH such us an unreasonable selection and uneven distribution of cluster heads, not considering the residual energy of nodes, the cluster head is overloaded and there is a high energy consumption of data transmission. In this paper, a topology control algorithm with double cluster heads and multi-hop based on affinity propagation clustering (APDC-M) was proposed. In APDC-M, firstly, a cluster head election algorithm with double choices based on the affinity propagation clustering algorithm (AP) and reference node strategy (APDC) was proposed. APDC is responsible to elect the fusion cluster head for node clustering. Secondly, a forwarding cluster head is elected within each cluster. The forwarding cluster head is responsible for the data transmission in order to reduce the energy consumption of a single cluster head. Lastly, the forwarding cluster heads complete the data transmission from a cluster to the base station by the shortest path inter-cluster multi-hop mode. The comparison simulation results show that APDC-M can make the cluster head election more reasonable and the cluster head distribution more uniform, and effectively reduce the energy consumption of the cluster head nodes when transmitting data, thus prolonging the network life.

Description: This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.

Description: Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

Description: Based on the situation that the traditional SINS (strapdown inertial navigation system)/CNS (celestial navigation system) integrated navigation system fails to realize all-day and all-weather navigation, this paper proposes a SINS/Landmark integrated navigation method based on landmark attitude determination to solve this problem. This integrated navigation system takes SINS as the basic scheme and uses landmark navigation to correct the error of SINS. The way of the attitude determination is to use the landmark information photographed by the landmark camera to complete feature matching. The principle of the landmark navigation and the process of attitude determination are discussed, and the feasibility of landmark attitude determination is analyzed, including the orthogonality of the attitude transform matrix, as well as the influences of the factors such as quantity and geometric position of landmarks. On this basis, the paper constructs the equations of the SINS/Landmark integrated navigation system, testifies the effectiveness of landmark attitude determination on the integrated navigation by Kalman filter, and improves the navigation precision of the system.

Description: Ground-based arc-scanning synthetic aperture radar (ArcSAR) is the novel ground-based synthetic aperture radar (GBSAR). It scans 360-degree surrounding scenes by the antenna attached to rotating boom. Therefore, compared with linear scanning GBSAR, ArcSAR has larger field of view. Although the feasibility of ArcSAR has been verified in recent years, its imaging algorithm still presents difficulties. The imaging accuracy of ArcSAR is affected by terrain fluctuation. For rotating scanning ArcSAR, even if targets in scenes have the same range and Doppler with antenna, if the heights of targets are different, their range migration will be different. Traditional ArcSAR imaging algorithms achieve imaging on reference plane. The height difference between reference plane and target in scenes will cause the decrease of imaging quality or even image defocusing because the range migration cannot be compensated correctly. For obtaining high-precision ArcSAR image, we propose interferometric DEM (digital elevation model)-assisted high precision imaging method for ArcSAR. The interferometric ArcSAR is utilized to acquire DEM. With the assist of DEM, target in scenes can be imaged on its actual height. In this paper, we analyze the error caused by ArcSAR imaging on reference plane. The method of extracting DEM on ground range for assisted ArcSAR imaging is also given. Besides, DEM accuracy and deformation monitoring accuracy of proposed method are analyzed. The effectiveness of the proposed method was verified by experiments.

Description: The validation of significant wave height (SWH) data measured by the Sentinel-3A/3B SAR Altimeter (SRAL) is essential for the application of the data in ocean wave monitoring, forecasting and wave climate studies. Sentinel-3A/3B SWH data are validated by comparisons with U. S. National Data Buoy Center (NDBC) buoys, using a spatial scale of 25 km and a temporal scale of 30 min, and with Jason-3 data at their crossovers, using a time difference of less than 30 min. The comparisons with NDBC buoy data show that the root-mean-square error (RMSE) of Sentinel-3A SWH is 0.30 m, and that of Sentinel-3B is no more than 0.31 m. The pseudo-Low-Resolution Mode (PLRM) SWH is slightly better than that of the Synthetic Aperture Radar (SAR) mode. The statistical analysis of Sentinel-3A/3B SWH in the bin of 0.5 m wave height shows that the accuracy of Sentinel-3A/3B SWH data decreases with increasing wave height. The analysis of the monthly biases and RMSEs of Sentinel-3A SWH shows that Sentinel-3A SWH are stable and have a slight upward trend with time. The comparisons with Jason-3 data show that SWH of Sentinel-3A and Jason-3 are consistent in the global ocean. Finally, the piecewise calibration functions are given for the calibration of Sentinel-3A/3B SWH. The results of the study show that Sentinel-3A/3B SWH data have high accuracy and remain stable.