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This study analyzes 209 dog skeletons from two sites in Wolin (9th–mid-13th century AD) using 100 standard metric variables covering cranial, mandibular, and postcranial elements. Estimated withers height, body mass, age at death, and sex were derived using established methods. The results indicate the presence of at least two to three morphotypes: small spitz-like dogs (40–50 cm, 4–6 kg), medium brachycephalic forms (50–60 cm, 10–15 kg), and larger mesocephalic individuals (up to 65 cm, 20–40 kg). Dogs lived 3–10 years, with both sexes represented. Signs of cranial trauma and dental wear suggest utilitarian roles such as guarding. The size range and morphological diversity point to intentional breeding and trade-based importation. Small dogs likely served as companions or city guards, while medium and large types were used for herding, hunting, or transport. These findings highlight Wolin’s role as a dynamic cultural and trade center, where human–dog relationships were shaped by anthropogenic selection and regional exchange.

Background Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%).

The Middle East is an area characterized by strong gradients in meteorological conditions and varied land cover. As a result, some of the hottest places in the world as well as very cold areas can be found in this region. The objective of this paper is to quantitatively assess land surface temperature (LST) variations in the countries of the Middle East to estimate the trends and determine whether relationships suggesting the impact of climate change can be observed. Spatiotemporal variability of LST was investigated through the analysis of MODIS Terra LST images (MOD11A2) from 2001 to 2018. The LST differences in both daytime series and seasonal and international means were assessed. The analyses showed that in spring (MAM), about 20,000 km2 of the study area has an LST higher than 50 °C, whereas 3700 km2 has LST lower than 0 °C. In summer (JJA), about 1 million km2 has LST higher than 50 °C. In the fall (SON), about 5 million km2 of the study area has a land surface temperature higher than 30 °C, with about 11 km2 hotter than 50 °C, whereas 320 km2 has LST lower than 0 °C. In winter (DJF), the hottest countries are Yemen, Oman, and Saudi Arabia, with 44.7, 41.8, and 39.8 °C, respectively, whereas the lowest LST values were recorded in Turkey, Iran, and Iraq, with −19.5, −18.1, and −10.5 °C, respectively. An upward trend in the minimum and a downward trend in the maximum value of the winter LST suggest that winters in the Middle East countries such as Iran, Israel, and Jordan have become milder during the considered period. Negative trends in the spring LST in Bahrain and Oman and the summer LST in Bahrain and Qatar suggest that these seasons in those countries became colder during the study period.

A pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five different classification algorithms were used to discriminate between nitrogen concentrations in plants at different levels of water availability. Several statistical parameters, including kappa and overall classification accuracy for calibration and prediction, were used to determine the efficiency and accuracy of the models. The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery. Additionally, characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants could be recorded. It was also noted that the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region.

The accurate recognition of atmospheric circulation patterns is vital for understanding the intricate relationships among various climatic elements. Therefore, the main goal of this study is to comprehensively identify circulation patterns during the occurrence of the summertime Extended Area Precipitation Event (EAPE) in southeastern Iran. The data used in this study encompass precipitation rates from synoptic and rain gauge stations, Geopotential Height (GPH), omega (upward motion speed), u-wind (east-west), and v-wind (north-south) components at different atmospheric levels, along with satellite images from the visible spectrum. In this research, both subjective and objective clustering methods have been utilized to identify synoptic circulation patterns based on 500-hPa GPH data. Summer precipitation was chosen for analysis because its characteristics and relationships with large-scale circulation patterns are less understood compared to those of winter precipitation. Examination of the 500-hPa GPH data for sixty-two identified cases of EAPE over southeast Iran revealed that the causative factors for these events are comprised of five recurring patterns (referred to here for convenience as AP, BP, CP, DP, and EP). Three of these patterns (AP, BP, and DP) significantly contributed to 71% of all EAPE cases. It was evident that the five patterns responsible for creating the EAPE in southeastern Iran had distinct directions.

The spatiotemporal variability of vegetation in the Middle East was investigated for the period 2001–2019 using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day/500 m composites of the Normalized Difference Vegetation Index (NDVI; MOD13A1). The results reveal a strong increase in NDVI coverage in the Middle East during the study period ( = 0.75, -value = 0.05). In Egypt, the annual coverage exhibits the strongest positive trend ( = 0.99, -value = 0.05). In Turkey, both the vegetation coverage and density increased from 2001 to 2019, which can be attributed to the construction of some of the biggest dams in the Middle East, such as the Atatürk and Ilisu dams. Significant increases in the annual coverage and maximum and average NDVI in Saudi Arabia are due to farming in the northern part of the country for which groundwater and desalinated seawater are used. The results of this study suggest that one of the main factors affecting vegetation coverage in the Middle East are governmental policies. These policies could lead to an increase in vegetation coverage in some countries such as Egypt, Saudi Arabia, Qatar, Kuwait, Iran, and Turkey.

Jet streams are atmospheric phenomena that operate on a synoptic scale and can intensify the descending/ascending conditions of the air at the lower levels of the atmosphere. This study aimed to identify the patterns and location of the jet stream in southwest Asia during the days of widespread rainfall in Iran based on two criteria: “highest frequency of stations involved” and “maximum cumulative amount on the day of peak rainfall”. For this purpose, the daily precipitation data for 42 synoptic stations in Iran during the period 2006–2019 from the Meteorological Organization of Iran, the daily data at 500 hPa Geopotential Height (HGT), and U and V wind components at 500 and 300 hPa from NCEP/NCAR were gathered. Synoptic patterns were obtained based on daily precipitation data, daily maps at HGT 500 hPa, and U and V wind components at 500 and 300 hPa. The analysis of patterns showed that the position of precipitation cores is associated with the position and extension of jet stream centers at 300 hPa in winter, spring, and autumn. The main position of jet stream cores during flood-causing rainfall at 300 hPa was over the northern part of Saudi Arabia, the Mesopotamia basin, and southern Iran. This position seems to have provided the conditions for the convergence of the earth’s surface and the divergence of the atmosphere for the easy passage of moisture from the Red Sea, Aden Sea, and the Persian Gulf, and in the second rank, the Mediterranean Sea and the Arabian Sea.

Severe thunderstorms are often accompanied by strong vertical air currents, temporary wind gusts, and heavy rainfall. The development of this atmospheric phenomenon over tropical shallow water zones, such as bays, can lead to intensification of atmospheric disturbances and produce a small-scale storm surge. Here, the storm surge that occurred on 19 March 2017 in the Persian Gulf coastal area has been investigated. Air temperature, precipitation, mean sea level pressure, wave height, wind direction, wind speed, geopotential height, zonal components, meridional winds, vertical velocity, relative humidity, and specific humidity obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Forecast System (FNL) were used to implement the Weather Research and Forecasting (WRF) model. The results showed that the main cause of the storm surge was the occurrence of a supercell thunderstorm over the Persian Gulf. The formation of this destructive phenomenon resulted from a downburst under Cumulonimbus cloud and high-velocity air subsidence, after collision with the sea surface coinciding with the high tide. This caused a severe, yet temporary, gust, which in turn caused the creation of the four waves of 3.1 m height along the coast of Bandar Dayyer.

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.