Topoclimate and wine quality: results of research on the Gewürztraminer grape variety in South Tyrol, northern Italy

The aim of the study was to identify the ecological indicators that facilitate predictive analysis and to search for patterns in local geographical information to identify risks and opportunities in viticulture. The study focused on environmental factors that significantly affect the ripeness of the Gewürztraminer grape variety cultivated near Tramin, a village in northern Italy. In particular, the reliability of the new Solar Radiation Identity (SRI) topoclimate classification method was tested, along with its predictive capability in terms of the biosynthetic activity of the vine and the quality of its grapes. The SRI index characterises each vineyard in a precise and comparable way and helps to understand the way in which the topoclimate acts as an important abiotic stress factor for vines. A direct relationship between grape must sugar content and the SRI topoclimate index was observed. Our findings indicate an increase in sugar content of approximately 0.8 °KMW for every 10 points of the SRI index. Thus, a novel prediction model of grape ripening based on an SRI curve analysis is proposed. The correct application of the SRI index could be useful for discriminating and predicting geographical charactersitics of a given area strongly connected to ecological diversity and wine quality. It could support decision making in viticulture in terms of, for example, correctly matching vineyard and grape varieties, reducing wine vulnerability and production risk and predicting optimal ripeness and harvesting days. The use of the SRI prediction curve could help in adopting a more sustainable approach to agriculture and in finding new methods for adapting to climate change, such as by improving the match between the cultivars’ phenological status, vineyard location and growing season average temperature.

A Study on the Theoretical Model of Port Safety Resilience under Major Emergencies and Its Quantitative Evaluation

With the rapid development of urbanization, China’s security system in ports is facing many problems and challenges, especially after the outbreak of COVID-19 has made a great impact on China. In order to avoid the serious losses caused by safety problems in ports under major emergencies due to the lack of necessary, forward-looking prevention in the increasingly complex system of ports, this paper introduced the theory of safety resilience into the field of ports for the first time and proposed a triangle model of safety resilience in ports, which included three elements: the disaster system, the disaster bearing system and the safety resilience management. Aiming at the quantitative evaluation demands of safety resilience in ports, the system evaluation indexes of safety resilience in ports were established, including 6 secondary indexes and 37 tertiary indexes. The theory of entropy weight based on normalization standard processing was used to calculate and analyze the importance of each index, and the influence weight value of each index on safety resilience and the prediction curve chart of safety resilience in ports were obtained. The research results can provide a reference for the capacity construction and application of safety resilience in ports, which will effectively solve the new challenges faced by port safety at this stage and help to improve the ability of the port not only in disaster prevention and mitigation but also in disaster recovery and continuous improvement.

Risk factors for and predictive nomogram of postoperative hypoxaemia in elderly patients with femoral neck fractures

Objective To investigate the related risk factors and predictive nomogram of postoperative hypoxaemia in elderly patients with femoral neck fractures. Methods This study included patients aged ≥65 years who underwent surgical treatment of acute femoral neck fractures. Univariate and multivariate logistic analyses were performed to determine the incidence of and risk factors for postoperative hypoxaemia. A predictive nomogram was constructed based on the multivariable model. Using the bootstrap method, discrimination was determined by the C-index and calibration plot. Results The logistic regression analysis showed that the anaesthesia type, surgical procedure, American Society of Anesthesiologists (ASA) classification, preoperative hypoxaemia occurrence, and age were independent predictors of development of postoperative hypoxaemia. The predictive formula for hypoxaemia was established as follows: hypoxaemia=−0.8668×spinal anaesthesia (whether)+0.1162×nerve anaesthesia (whether)+1.9555×plate/screw fixation (whether)+1.4950×hip replacement (whether)+0.4883×ASA classification+1.7153×preoperative oxygenation index+0.1608×age. With the bootstrap method, the prediction curve fit well with the ideal curve, suggesting that the prediction curve constructed in this study has good predictive ability. Conclusions Anaesthesia type, surgical procedure, ASA classification, preoperative hypoxaemia occurrence, and age were risk factors for postoperative hypoxaemia in elderly patients with femoral neck fractures. The predictive nomogram was designed for preoperative assessment of the risk of postoperative hypoxaemia by calculating the risk score.

Investigation on the Time Factor of CO-Based Emission Factors for Sustainable Development of Urban Tunnels in China

Environmental pollution problems and energy-saving issues of urban tunnels have increasingly been attracting people’s attention. The paper aims at investigating a reasonable time factor for CO-based emission factors in the ventilation design of urban road tunnels. The study results show that the prediction curve of CO-based emission factor for passenger cars consists of two stages. The average reduction rates of Stage I (2004–2010) and Stage II (2010–2030) are 38% and 14% per year, respectively. The CO emission factor series of passenger cars conforms to exponential function distribution. The time factors of CO-based emission backcalculated from the measured data range from 4.9% to 12.6%, which are 2.4–6.3 times the specified value in the Chinese ventilation specification but slightly smaller than predicted ones. Based on four aspects of predicted results, back analysis results, related references, and engineering applications, it is indicated that the time factor of CO-based emission factor (2%) in the current ventilation specification is conservative. And when defining 2000 and 2010 as base years, respectively, 4% and 3% can be used as the time factors of CO-based emission factors respectively for the ventilation design of urban tunnels. This study can provide a reference for ventilation design of urban tunnels in other regions.

Proposed Constitutive Law of Uniaxial Compression for Concrete under Deterioration Effects

In order to study the ductile deformation characteristics and failure process of plain concrete under uniaxial compression, this paper proposes a new constitutive model. The new model was used to fit and analyze the constitutive curve of concrete under uniaxial compressive under various degradation forms and was compared with the traditional constitutive models. Finally, the new model was used to quantitatively analyze and predict the stress–strain curve of concrete in different degradation periods of a set of freeze–thaw measured data. The results show that, compared with the traditional constitutive model, the new model is simple in form and has few parameters, and the numerical value of the parameter can reflect the ductile deformation capacity of concrete. The fitting curve of the new model has the highest fitting degree with the measured stress–strain curve of concrete, and the goodness of fit (R2) is also the largest. The new model is suitable for fitting the stress–strain curve of concrete under uniaxial compression under various deteriorating forms, and the degree of fit between the constitutive prediction curve and the measured curve is high. It can be seen from the fitting results of the new model parameters that the ductile deformation capacity of concrete decreases first and then increases slightly, which is inconsistent with the law of gradual deterioration of strength. There is a minimum moment of ductility deformation capacity of concrete (MDC). The MDC of O-C40 concrete is about 114 freeze–thaw cycles, and the MDC of O-C50 concrete is about 116 freeze–thaw cycles; the degree of fit between the constitutive prediction curve and the measured curve is high. We hope that the improvement mentioned offers valid reference to the study of ductile deformation characteristics and failure process of compressed concrete under different deterioration forms.

A Mathematical prediction of the time evolution of the Covid-19 pandemic in some countries of the European Union using Monte Carlo simulations

In this paper we study the statistical evolution in time of the Covid-19 pandemic in Spain, Italy, Germany, Belgium, The Netherlands, Austria and Portugal, i.e., the countries of the European Union (EU) that have a number of positive cases higher than 12 thousand at April 7, 2020. France is the third country of the EU for number of cases but a jump in the data on April 3, 2020 does not allow, at least for the moment, to have a reliable prediction curve. The analysis is based on the use of a function of the type of a Gauss Error Function, with four parameters, as a Cumulative Distribution Function (CDF). A Monte Carlo analysis is used to estimate the uncertainty. The approach used in this paper is mathematical and statistical and thus does not explicitly consider a number of relevant issues, including number of nasopharyngeal swabs, mitigation measures, social distancing, virologic, epidemiological and models of contamination diffusion.

Predicting Environmental Contribution to Ship’s Average Fuel Consumption Based on Non-Uniform Time Set

Real data obtained from ship in operation are processed and analyzed in this paper. The intention was to provide software which would predict ship’s fuel consumption in some future time instant. It is showed that it is possible to develop such software based on numeric fitting of known data. In order to check how well the prediction of future fuel consumption is, we used only the first half of data for obtaining prediction curve. The second part of data was used to compare different prediction curves goodness. Hence, the presented research used actually a “real future data” and forecasted future data, which are used to numerically evaluate goodness of prediction. The research is of interest for companies logistics, to provide adequate fuel for fleet when and where actually needed. It is concluded that there are several prediction functions which satisfy used statistical quality measures.

Analysis of Factors Affecting the Rearing of Early-Weaned Lambs of Dairy Breeds for the Meat Market

Sheep farming makes an important socio-economic contribution to Spain’s rural areas by sustaining farming and employment in less favored regions, but it is a low-profit sector because of high operating and regulatory costs. One possible solution would be to raise for slaughter lambs from dairy sheep that are not required as replacements. However, the early weaning of these lambs and their artificial rearing generally lead to reduced growth rates and high mortalities. In the work presented herein, three housing systems (slat + heating plate, slat + infrared lamp, and deep-litter + infrared lamp) were tested on 763 lambs over an eight-month period in a farm located in Ejea de los Caballeros (Zaragoza, Spain), with a view to reducing mortality during the period of artificial lactation. The effect of the environmental parameters of the lamb housing on the development of the lambs was evaluated by monitoring environmental (temperature, relative humidity, CO2 and NH3) and productive parameters (weight, average daily gain, and deaths). The results suggested that the combination of deep litter with straw bedding and infrared lamp heating would be the most favorable option, as it led to the highest average daily gain and the lowest percentage of deaths. From the study of the relationship of the initial weight with deaths, a prediction curve was obtained, defining the maximum prices to be paid per lamb as a function of their initial weight to guarantee the profitability of the raising process. This may be regarded as a valuable guidance for Spanish sheep farmers, as it can reduce their economic uncertainty.

Availability Enhancement of Riak TS Using Resource-Aware Mechanism

The dependability and elasticity of various NoSQL stores in critical application are still worth studying. Currently, the cluster and backup technologies are commonly used for improving NoSQL availability, but these approaches do not consider the availability reduction when NoSQL stores encounter performance bottlenecks. In order to enhance the availability of Riak TS effectively, a resource-aware mechanism is proposed. Firstly, the data table is sampled according to time, the correspondence between time and data is acquired, and the real-time resource consumption is recorded by Prometheus. Based on the sampling results, the polynomial curve fitting algorithm is used to constructing prediction curve. Then the resources required for the upcoming operation are predicted by the time interval in the SQL statement, and the operation is evaluated by comparing with the remaining resources. Using the real hydrological sensor dataset as experimental data, the effectiveness of the mechanism is experimented in two aspects of sensitivity and specificity, respectively. The results show that through the availability enhancement mechanism, the average specificity is 80.55% and the sensitivity is 76.31% which use the initial sampling dataset. As training datasets increase, the specificity increases from 80.55% to 92.42%, and the sensitivity increases from 76.31% to 87.90%. Besides, the availability increases from 40.33% to 89.15% in hydrological application scenarios. Experimental results show that this resource-aware mechanism can effectively prevent potential availability problems and enhance the availability of Riak TS. Moreover, as the number of users and the size of the data collected grow, our method will become more accurate and perfect.