A comparative study of temperature extremes analysis by regression methods and by an artificial neural network

<p>Predicting the pattern of soil organic matter (SOM) decomposition as a feedback to climate change, via release of CO<sub>2</sub>, is extremely complex and has received much attention. However, investigations often do not differentiate between the extracellular and intracellular processes involved and work is needed to identify their relative temperature sensitivities. Samples were collected from a grassland soil at Sonning, UK with average daily maximum and minimum soil temperature of 15 °C and 5 °C. We measured potential activities of β-glucosidase (BG) and chitinase (NAG) (extracellular enzymes) and glucose-induced CO<sub>2 </sub>respiration (intracellular enzymes) at a range of assay temperatures (5 °C, 15 °C, 26 °C, 37<sup>  </sup>°C, and 45 °C). The temperature coefficient Q<sub>10</sub> (the increase in enzyme activity that occurs after a 10 °C increase in soil temperature) was calculated to assess the temperature sensitivity of intracellular and extracellular enzymes activities. Between 5 °C and 15 °C intracellular and extracellular enzyme activities had equal temperature sensitivity, but between 15 °C and 26°C intracellular enzyme activity was more temperature sensitive than extracellular enzyme activity and between 26 °C and 37 °C extracellular enzyme activity was more temperature sensitive than intracellular enzyme activity. This result implies that extracellular depolymerisation of higher molecular weight organic compounds is more sensitive to temperature changes at higher temperatures (e.g. changes to daily maximum summer temperature) but the intracellular respiration of the generated monomers is more sensitive to temperature changes at moderate temperatures (e.g. changes to daily mean summer temperature). We therefore conclude that the extracellular and intracellular steps of SOM mineralisation are not equally sensitive to changes in soil temperature. The finding is important because we have observed greater increases in average daily minimum temperatures than average daily mean or maximum temperatures due to increased cloud cover and sulphate aerosol emission. Accounting for this asymmetrical global warming may reduce the importance of extracellular depolymerisation and increase the importance of intracellular catalytic activities as the rate limiting step of SOM decomposition.</p>

Architects and engineers use solar resource information to help design passive solar and daylighting features for buildings. Solar resource information includes data on how much solar radiation and illuminance are available for different window orientations, and how they vary. This manual provides solar radiation and illuminance values for a horizontal window and four vertical windows (facing north, east, south, and west) for 239 stations in the United States and its territories. The solar radiation values are monthly and yearly averages for the period of 1961--1990. Included are values showing the solar radiation incident on the window and the amount transmitted into the living space, with and without exterior shading of the window. Illuminance values are presented r average dismal profiles for 4 months of the year. In addition to the solar radiation and illuminance data, this manual contains tables listing climatic condition such as average temperature, average daily minimum and maximum temperature, record minimum and maxi mum temperature, average heating and cooling degree days, average humidity ratio, average wind speed, an average clearness index. The solar radiation, illuminance, and climatic data a presented in tables. Data for each station are presented on a single page, and the pages are arranged alphabetically by the state or territory two-letter abbreviation. Within a state or territory, the pages are arranged alp betically by city or island.

BackgroundL-citrulline (L-Cit), a non-protein amino acid, has been implicated in several physiological functions including anti-inflammatory, anti-oxidative, and hypothermic roles, however, there is a paucity of information with regards to its potential in poultry production. MethodsThis study was designed to investigate the effects of dietary L-Cit supplementation on the production performance, nitric oxide production, and antioxidant status of laying hens during summer period. Hy-Line Brown laying hens (n = 288, 34 weeks old) were allotted to four treatment, 6 replicates of 12 chickens each. Dietary treatments of control (basal diets), 0.25%, 0.50% and 1.00% L-Cit supplementation were fed to chickens for eight (8) weeks. Production performance, free amino acid profiles, nitric oxide production, and antioxidant properties were measured. Blood samples were collected at the 4th and 8th weeks of the experiment.ResultsAir temperature monitoring indicated an average daily minimum and maximum temperatures of 25.02 °C and 31.01 °C respectively. Dietary supplementation with L-Cit did not influence (P > 0.05) the production performance, and rectal temperature of laying hens. Egg shape index was increased (P < 0.05) with increasing levels of L-Cit. Serum-free content of arginine, citrulline, ornithine, tryptophan, histidine, GABA, and cystathionine were elevated, but taurine declined with L-Cit diets. Plasma nitric oxide (NOx) concentration was highest at 1% L-Cit. Likewise, nitric oxide synthase (NOS) activity for total NOS (tNOS) and inducible NOS (iNOS) were upregulated with increasing L-Cit levels, although, tNOS was not affected at the 4th week. Anti-oxidant enzymes including catalase and superoxide dismutase (SOD) were increased with L-Cit supplementation, however, SOD activity was unchanged at 4th week, while total anti-oxidant capacity increased at the 8th week. L-Cit supplementation attenuated the extent of lipid peroxidation, and also inhibited glutathione peroxidase activity.ConclusionDietary L-Cit supplementation modulated systemic arginine metabolism, nitric oxide synthesis, antioxidant defense system, and increased the egg shape index of laying hens during the summer season. 1% L-Cit supplementation proved most effective in potentiating these effects and may be adopted for feed formulation strategies.

We use regression analysis on data from 208 districts over the period 1981–2009 to examine the impact of temperature and solar radiation (affected by pollution from aerosols) on wheat yields in India. We find that a 1 °C increase in average daily maximum and minimum temperatures tends to lower yields by 2–4% each. A 1% increase in solar radiation increases yields by nearly 1%. Yields are estimated to be about 5.2% lower than they would have been if temperatures had not increased during the study period. We combine the estimated impacts of weather on yield with the estimated impacts of aerosol pollution (measured by moderate resolution imaging spectroradiometer sensor in terms of aerosol optical depth, aerosol optical depth (AOD) in 2001–2013) on weather to compute the net impact of reducing aerosol pollution on wheat yields. A one-standard-deviation decrease in AOD is estimated to increase yields by about 4.8%. Our results imply reducing regional pollution and curbing global warming in the coming decades can counter wheat yield losses.

Research was conducted to examine the marketable yield and fruit weight of six strawberry cultivars (Fragaria × ananassa Duch. ‘Festival’, ‘Fortuna’, ‘Brilliance’, ‘Red Rhapsody’, ‘Sundrench’ and ‘Suzie’) over two years in subtropical Queensland, Australia. In the first year, the transplants were planted on 30 March, while in the second year, they were planted on 22 April. The average daily minimum temperature was 3 °C higher than the long-term average for the area from 1965 to 1990, while the average daily maximum temperature was 1 °C higher. Temperatures and solar radiation were similar in the two years of the study. In contrast, it was wetter in the second year (478 mm) than in the first year (332 mm). Average yield was lower in the second year (142 ± 10 g/plant) than in the first year (330 ± 9 g/plant) (p &lt; 0.001), possibly due to a later planting. Higher rainfall in the second year may have also contributed to a higher incidence of rain damage and fruit rots. Yield was lower in ‘Sundrench’ (176 g/plant) than in the other cultivars (235 to 252 g/plant) (p = 0.003). Fruit weight was lower in the second year (18.2 g) than in the first year (23.8 g) (p &lt; 0.001), and lower in ‘Festival’ and ‘Fortuna’ (18.2 and 19.4 g), intermediate in ‘Brilliance’, ‘Red Rhapsody’ and ‘Sundrench’ (21.0, 21.3 and 21.8 g) and higher in ‘Suzie’ (24.3 g) (p &lt; 0.001). These results demonstrate that yield and fruit size vary in cultivars in Queensland. ‘Suzie’ had the largest fruit, favoring marketing. The low productivity of ‘Sundrench’ suggests that this cultivar is not commercially viable in Queensland. Yields are expected to decline in the future under global warming, in the absence of better-adapted cultivars and other mitigating strategies. Further evaluations of cultivars in Queensland are required under different growing practices, including earlier times of planting, higher plant densities, light shade and protected cropping.

Forthcoming International Mycological Meetings

در این تحقیق ابتدا داده های هواشناسی در بین سال های 1993 تا 2018 مورد بررسی قرار گرفته اند و سپس با استفاده از مدل سازی تغییر اقلیم توسط نرم افزار LARS-WG، با داده های خروجی حاصل از مدل سازی در بازه زمانی سال های 2046 تا 2065 مورد مقایسه قرار گرفته اند. با توجه به بررسی های صورت گرفته، میانگین دمای کمینه و بیشینه روزانه در بازه زمانی سال های 2046 تا 2065 می توانند روند افزایشی داشته باشند و میانگین میزان بارش روزانه نیز می تواند روند کاهشی داشته باشد. اگرچه میانگین میزان بارش های روزانه با کاهش همراه بوده است، اما تعداد نقاط حدی در میزان بارش های روزانه در دوره مدل سازی شده می تواند با افزایش همراه باشد. پس از استخراج داده های بیشینه روزانه بارش در دو دوره سال‌های 1993 تا 2018 و 2046 تا 2065، بارش های متناطر با سیلاب های با دوره بازگشت های مختلف محاسبه گردیده اند. در نهایت با مقایسه میزان بارش های متناظر با سیلاب ها در این دو دوره نتیجه گیری شد که به طور میانگین میزان این بارش‌های حدی در بازه زمانی سال های 2046 تا 2065 می تواند حدود 15 درصد افزایش یابد.

Developing a growing degree day model for North Dakota and Northern Minnesota soybean

ABSTRACTThe relationship between climate and visitation to managed natural areas has been analyzed at a variety of different spatial scales. We expand upon our existing knowledge on this topic by: (1) determining how a wide range of climate variables affect visitation across a regional tourism system; and (2) identifying which variables affect visitation system-wide and which variables only affect visitation at specific parks. Our analysis focuses on five national parks located in southern Utah (USA) commonly referred to as ‘the Mighty 5’. We found monthly average daily maximum temperatures were the best predictor of system-wide visitation, suggesting average daily maximum temperatures play a more direct role in tourists’ travel decisions relative to other climate variables, including other derivations of temperature. We also found declines in monthly park visitation for three parks (Arches, Canyonlands, and Capitol Reef) once average daily maximum temperatures exceed 25 C. For Bryce Canyon and Zion however, monthly visitation continued to increase well above this threshold. The geophysical characteristics of these parks appear to mediate the relationship between average daily maximum temperature and visitation. The commonly found ‘inverted U-shape’ relationship between temperature and visitation should not be seen as a universal maxim. We also found precipitation to be a poor predictor of system-wide visitation, but a significant factor shaping the travel decisions of visitors to Bryce Canyon, the only park to offer snow-based outdoor recreation opportunities. Future research should not disregard the possibility of precipitation being a significant factor shaping visitors’ travel decisions. By conducting our analyses at two distinct scales, we have found there is a difference between the individual climate variables that are regionally-significant drivers of visitation and those that are locally-significant drivers of visitation. Scale matters in analyses of the relationship between climate and visitation.

Güneybatı Anadolu’da yer alan ve 13.338 km2 yüzölçüme sahip olan Muğla ili, genelde dağlık ve engebeli bir araziye sahiptir. Bu çalışmada Muğla ilinin aylık ve yıllık maksimum ve minimum ortalama sıcaklık değerlerinin Ters Mesafe Ağırlıklı (IDW) enterpolasyon yöntemiyle dağılışlarının haritalanıp analiz edilmesi amaçlanmıştır. Bunun için Meteoroloji Genel Müdürlüğü’nün Muğla ilinden 13 istasyonu ve ilin sınırlarına yakın olan Gölhisar, Bozdoğan, Kaş, Elmalı istasyonlarının meteoroloji verileri kullanılmıştır. Bu istasyonların yıllık ve aylık ortalama maksimum ve minimum sıcaklıkları değerleri, Coğrafi Bilgi Sistemleri (CBS) yardımıyla ArcGIS ortamına aktarılıp IDW yöntemiyle dağılışları haritalanmıştır. Bu haritalara göre Muğla’da yıllık maksimum ortalama sıcaklıkların en yüksek olduğu yer Milas ve çevresi iken en düşük olduğu yerler Muğla merkez ve Seydikemer ilçesinin kuzeydoğusundaki il sınırlarıdır. Bu iki yeri, uzun bir yarımadada yer alan Datça ilçesi takip eder. Yıllık minimum ortalama sıcaklıkların en yüksek olduğu yerler ise Datça ve Marmaris ilçeleri çevresi iken en düşük olduğu yerler Kavaklıdere ve Seydikemer’in kuzeydoğusundaki Burdur il sınırıdır. İlde maksimum ortalama sıcaklığın en yükseğe çıktığı ay temmuz (Milas ve Köyceğiz’de 41,6-41,1°C arasında), en düşük değerlere indiği ay ocak (Muğla merkez ve Kavaklıdere’de 14-17°C arasında) ayıdır. İlde minimum ortalama sıcaklıkların en yükseğe çıktığı ay haziran (Marmaris’te 37,7-34,6°C arasında), en düşük değerlere indiği ay ise ocak (Kavaklıdere’de -7,9 ile -6,6°C arasında) ayıdır. Yükselti koşulları, denizellik etki derecesi ve bakı koşulları ile özellikle yazın etkisini hissettiren termik basınç alanlarının buralara kadar sokulması ilde maksimum ve minimum ortalama sıcaklık değerlerinin dağılışında etkili olmuştur. Bu değerler, yıl içinde zamana bağlı olarak da değişkenlik gösterebilmektedir.

Climate variability presents novel challenges to the conservation of maritime cultural and natural heritage. In the south Kenya coast, responses to extreme temperatures and sudden onset high rainfall worsen the condition of historic buildings and sacred landscapes, putting maritime communities at risk. To provide a baseline for prioritizing conservation planning initiatives, the paper sought to establish climate variability for the adaptation of maritime heritage to climate change. Between 1994 and 2024, average maximum and minimum temperatures rose by +1.6°C and +1.4°C, respectively. Linear regressions of average maximum and minimum temperatures showed high extremes of 32.10°C to 32.40°C and low extremes of 19.8°C to 20°C, respectively. The leptokurtic distribution showed a substantial chance of severe outcomes in average minimum and maximum temperatures in September and November, respectively. Over 30 years, the average maximum rainfall increased by 190mm with linear regressions indicating high extremes between 1500mm and 1600mm. The leptokurtic distribution revealed a substantial chance of extreme outcomes in average maximum rainfall in November. Average maximum and minimum temperatures for the final two years were considerably higher than the first two years; t (-5.2086, 1.0000) = 6.3138, P=0.0604) and t (--3.7226, 1.0000) = 6.3138, P=0.0835). However, average mean rainfall over the first two years was significantly lower than that over the latter two years t (-0.525, 1.000) = 6.3138, P =0.3460). Overall, unpredictability of short and long rainy seasons has increased during the last ten years compared to the previous twenty. Conservation planning is to consider physical stabilizations, shoreline consolidation, mangrove restoration, land use control, adjustments of setbacks and buffer zones to adapt maritime heritage to climate change.

In this paper, we study a new model that represents the symmetric connection between capacitance–voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature.

Several researchers have investigated the relationships among different physical attributes of the fruits. For proper design and operation of grading systems, important relationships among the mass and other properties of fruits such as length, width, thickness, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, projected area, shape index, aspect ratio and elongations must be known. Recent researches have focused on artificial neural network (ANN) approaches to predict hard-to-find attributes of the fruits from easily-determined and readily available values. In this study, Modular Neural Network (MNN) and Radial Basis Neural Network (RBNN) structures of Artificial Neural Network (ANN) were employed to predict walnut mass from the physical attributes of the walnuts. Root mean square errors (RMSE) of MNN structure ranged from 0.60 to 0.89, while RMSE of RBNN structure were found to be very low (0.0002) in all of walnut varieties. These results showed that RBNN structures of Artificial Neural Network could potentially be used to estimate mass of walnuts and various physical attributes of walnuts were sufficient to predict the mass characteristics of a walnut.

Thanks to their convex formulation, kernel regressions have shown an improved accuracy with respect to artificial neural network (ANN) structures in regression problems where a reduced set of training samples are available. However, despite the above interesting features, kernel regressions are inherently less flexible than ANN structures since their implementations are usually limited to scalar-output regression problems. This article presents a vector-valued (multioutput) formulation of the kernel ridge regression (KRR) aimed at bridging the gap between multioutput ANN structures and scalar kernel-based approaches. The proposed vector-valued KRR relies on a generalized definition of the reproducing kernel Hilbert space (RKHS) and on a new multioutput kernel structure. The mathematical background of the proposed vector-valued formulation is extensively discussed together with different matrix kernel functions and training schemes. Moreover, a compression strategy based on the Nystrom approximation is presented to reduce the computational complexity of the model training. The effectiveness and the performance of the proposed vector-valued KRR are discussed on an illustrative example consisting of a high-speed link and on the optimization of a Doherty amplifier.

Evapotranspiration (ET) is a key component in the hydrological cycle, responsible for removing almost two thirds of all precipitation over land. Globally modeling ET is of great importance in determining near surface energy and water fluxes, especially in remote areas with limited available data. In the present study, 'actual' daily ET is directly modeled globally based on the Penman-Monteith equation using only remotely sensed data, eliminating the need for a hydrological model to determine the available moisture. Model estimates are validated against 39 Eddy Covariance Flux towers mostly in North America. It is shown that here, air temperature (Ta) and relative humidity (RH) are the main factors in estimating ET. In order to serve the global application of the model, daily Ta is estimated using remotely sensed Land Surface Temperature (LST) from the MODerate resolution Imaging Spectroradiometer (MODIS) product on the Aqua and Terra satellites. Other physical variables such as LAI, albedo, solar elevation angle, latitude, elevation, etc. are also used as auxiliary predictors in determining Ta. Several linear and non-linear regression methods are used, and it is shown that Artificial Neural Networks (ANN) can learn the most variabilities in the training data set and predict the targets most accurately. Similar to Ta, RH is estimated using remotely sensed data. Soil moisture from Soil Moisture Active Passive (SMAP) product is also used as a predictor in estimating RH. Again, several linear and non-linear regression methods are used, and it is shown that the most accurate method to estimate RH is Support Vector Regression (SVR). ET estimates will finally be modified using the improved air temperature and relative humidity.