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Study region: Oulujoki River basin- Inland sub-Arctic. Study focus: Understanding the impact of projected accelerated warming and increasing precipitation in the Arctic and sub-Arctic is crucial. This study examines how these changes will reshape the hydroclimatic dynamics of the inland sub-Arctic river basin, using cold climate and precipitation indices derived from statistically downscaled General Circulation Models. New hydrological insights for the region: The results show an increase in precipitation indices, with mid-century primarily dominated by scenarios SSP1–2.6 and SSP3–7.0. Under SSP5–8.5, precipitation magnitude and intensity steadily rise throughout the century. The projected increase in precipitation magnitudes, intensity, and extreme events are highly correlated with the increasing spring and summer precipitation, linking intensified and heavy precipitation events during these seasons under all future scenarios. Cold climate indices are projected to decrease with a shortened frost season by 3.3 months, advance in spring freshet by 1.5 months under SSP5–8.5, and a drastic decline in cold spell duration index in all scenarios, indicating a lack of supercooling events. These decreasing indices are strongly correlated with warming temperatures with a Pearson correlation coefficient reaching 0.84 under SSP5–8.5. The findings also highlight the limitations of the CMIP6 ensemble in capturing extreme values, as extreme climate indices were not statistically significant. This study thus contributes to the broader understanding of climate dynamics and their potential impacts on cold regions.

Advanced genome sequencing technologies have provided us with the opportunity to deeply understand the mechanisms underlying conditions associated with the genome. There has been significant interest recently in understanding the characteristics of de novo mutations, which are genetic changes that arise in reproductive cells and are not present in parents, as well as the mechanisms involved in their occurrence. These mutations can be transmitted to subsequent generations and have the potential to influence genetic diversity and susceptibility to diseases, making this topic important. Due to limited studies in this area, the formation mechanisms and characteristic features of such mutations have not yet been fully understood. Background/Aims: In this study, we aimed to conduct a comprehensive analysis of de novo mutations in families undergoing trio clinical exome sequencing analysis. The objectives of the study were to investigate the relationship between parental ages and the frequency of de novo mutations, the distribution, prevalence, relationships, and molecular characteristics of de novo mutations. Methods: A total of 69 families who underwent Trio Clinical Exome Sequencing (CES) analysis at the Department of Medical Genetics, Faculty of Medicine, Selçuk University, between January 1, 2017, and December 31, 2023, were included in the study. DNA samples extracted from peripheral venous blood of individuals were sequenced using the Roche CES kit and DNBSEQ-G400™ sequencing device, and a total of 3892 genes were analyzed using the Seq Platform. Results: After analysis, 407 de novo variants were identified, with the majority being variants of unknown significance (55.28%). When examining the base change profile, the most common changes were found to be C -> G, G -> A, A -> G. The most commonly mutated genes were found to be DSPP, HPS4, VCL, and BMP4 genes. Conclusions: Correlation analysis revealed no significant relationship between parental age and the number of de novo mutations, and regression analysis showed that age was not a significant parameter in determining the number of de novo mutations

Understanding the impact of Climate Variability (CV) on crop yield is crucial for developing sustainable agriculture practices, especially in regions vulnerable to climate change. This study aims to identify the causes of the observed decline in potato yield in Northern Finland by analyzing Remote Sensing data and statistical analysis on climatic variables from 2001 to 2020. First, the exception years with critical production (2004, 2006, 2008, 2012, 2015) were selected based on the anomaly in the annual potato yield. Then a variety of extreme indices based on precipitation and Land Surface Temperature occurrence and intensity during the growing season were calculated to find how much impact they have on potato yield. Principal Component Analysis was used to select the most significant ones among the extreme indices. Finally, through a multiple regression analysis, we found that extreme precipitation events and cumulative temperature explain around 48 % potato yield variations in which extreme precipitation (with 38 %) had much more impact than cumulative temperature (with 10 %). We concluded that one degree increase in cumulative temperature increases potato yield by 19.64 kg/ha and in contrast, heavy precipitation plays a negative role, i.e., one day increase in extreme precipitation events, decreases potato yield by 2085 kg/ha.

Abstract Since spring discharge, especially in arid and semiarid regions, varies considerably in different months of the year, a time series of spring discharge observations is needed to determine the firm yield of the spring and the amount of water allocated to different needs. Because most springs are in mountainous and inaccessible areas, long-term observational data are often unavailable. This study proposes a probabilistic method based on bivariate analysis to estimate the discharge of the Absefid spring in Iran. This method constructed the bivariate distribution of the outflows of Absefid (AS) and Gerdebisheh (GS) springs using Copula functions. For this purpose, the fit of 11 different univariate distributions to the discharge data of each spring was tested. The results revealed that the GEV and log-normal distributions best fit the discharge data of GS and AS springs, respectively. In addition, among eight different copula functions, the Joe copula function was selected to construct the bivariate distribution of the discharge data of AS and GS springs. With the help of the created bivariate distribution and assuming a certain probability level, it is possible to estimate the discharge of Absefid spring based on the discharge of Gerdebisheh spring in a particular month. The estimated values of the discharge of the Absefid spring in the period from March 1993 to August 2022 show that with a probability of 90%, the lowest discharge of this spring is 600 L per second and occurred in June 2001. Therefore, to allocate the water from this spring for drinking purposes, this discharge value can be considered as the firm yield of this source. However, the amount of allocated water from this source should be determined by considering the ecological needs of the river downstream of this spring.

In recent years, the effects of human activities and climate change on river flow patterns have become a major concern worldwide. This is particularly true in the southern Caspian Sea (SCS) region of Iran, where increasing water-intensive socio-economic development and climate change have significantly altered river flow regimes. To better understand these changes, this study employs two nonparametric methods, the modified Mann-Kendall method (MK3) and Innovative Trend Analysis (ITA), to examine spatial and temporal changes in hydrometeorological variables in the SCS. The study also evaluates the impact of human activities and climate change on river flow alteration using elasticity-based methods and the Budyko hypothesis in 40 rivers on the closest gauges to the Caspian Sea. The results indicate an alarming trend of increasing temperature, potential evapotranspiration, and decreasing river flows in the SCS region. In particular, human activities were found to be responsible for around 91.7 % of the change on average, resulting in a significant decline in inflow to the Caspian Sea by about 3216 MCM annually. This declining trend in inflow could potentially exacerbate the eutrophication conditions in the Sea and negatively impact its ecosystem and economics. Therefore, appropriate measures need to be taken to address these environmental and socio-economic issues in the southern Caspian Sea region.

Abstract Introduction Telomeres protect the ends of chromosomes, and shorter leukocyte telomeres are associated with major group diseases. Maternal psychological stress may be related to the shortening of telomeres in infants. This systematic review and meta-analysis set out to consolidate the varying effect sizes found in studies of maternal psychological stress and telomere length (TL) in newborns and identify moderators of the relationship between stress during pregnancy and newborn TL. Methods Our systematic review was registered in Prospero. Six databases (PubMed, Scopus, Embase, PsycINFO, Web of Science, and CINAHL Complete) were searched for records in English from inception to February 10, 2023. Observational studies were included that measured the relationship of psychological stress of the mother during pregnancy on the TL of the newborn. The Newcastle–Ottawa quality assessment scale was used to assess the quality of the included studies. A random-effect model was selected. Statistical analysis performed by Stata software version 17. Results Eight studies were included for qualitative and four for quantitative analysis. There was an inverse statistically significant relationship between maternal stress and newborn TL; A one score increase in maternal psychological stress resulted in a 0.04 decrease in the TL of the newborn (B = -0.04, 95% CI = [-0.08, 0.00], p = 0.05). Selectivity analysis showed that the pooled effect size was sensitive to one study; After removing this study, the pooled effect size remained significant (B = -0.06, 95% CI = [-0. 10, -0.02], p

Climate change and anthropogenic activities have impacts on fish habitat suitability, demanding more accurate modeling of species abundance for effective conservation and management. In this study, we applied Machine Learning techniques to model the habitat-abundance relationship of juvenile Atlantic salmon (Salmo salar) in the Teno catchment in Finland and Norway. To capture the complexity and nonlinearity of the habitat-abundance relationship, we employed Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Classification (SVC) and compared their performances. Among the regression models considered, those incorporating input variables such as substrate, shade, and vegetation demonstrate higher performance. Support Vector Regression yields the highest mean cross-validation score (R2 = 0.58), and Gradient Boosting produces the highest test score (R2 = 0.6) among the regression techniques. The mean cross-validation and test scores obtained for the classification models are notably higher compared to the regression models across all scenarios. A comparison between regression and classification results highlights the challenges of accurately modeling the habitat-abundance relationship. This study provides insights into the challenges and potential of machine learning techniques for juvenile Atlantic salmon habitat-abundance modeling in complex riverine habitat environments. The findings emphasize the importance of considering the limitations of machine learning models, particularly in ecological contexts, and the need for further research to address temporal variations and improve the precision of habitat-abundance modeling.

This research study investigates the impact of air temperature and precipitation on annual potato crop yield in Finland region and a local area in northern Finland. For this, the annual crop yield data of regional and local case study area is processed using Z-score normalization technique. Classification of computed z-score values was carried out into different classes ranging between the most beneficial crop yield year and the most vulnerable crop yield year. Later, the detection of feasible potato cropping season at monthly and daily scale was carried out using different weather parameters. Further, long-term trend analysis using Mann-Kendall's approach at annual, seasonal, and monthly scales was carried out using 60 years of dataset of both case study areas. Then after, the comparative analysis of annual crop yield and the characteristics obtained within climate data using different statistical approaches at annual, seasonal, and monthly scale. Finally, multivariate analysis was carried out to find most influential climate variables obtained using different statistical signatures. The results shows that, over the 100 years of period the annual potato crop yield of regional case study area shows a rising trend with declining area under potato crop. Classification approach found 16 % annual yield scored lowest Z-score while, 18% the highest. Likewise, 20 years of data for the local case study area found that 25 % of the dataset scored the highest Z-score and 15 % of the dataset values scored the lowest. Long-term trend analysis of air temperature shows significant increasing trend annually, seasonally and for monthly during the months between July-September. Whereas, similar procedure with the precipitation data showed increasing trend only at annual scale with the precipitation data analysis. During the comparative analysis between the annual crop yield data based on z-score classification and different statistical results obtained from weather parameters such as variation of precipitation and temperature sum, long-term climate data maximum-minimum totals seasonally as well as monthly during crop growing season; noticeable anomalies were observed in the weather parameters in line with the crop yield years grouped into the lowest and the highest Z-scores. The Multivariate analysis emphasized the impact of air temperature over precipitation on potato crop yield. Present study provides quantitative assessment on usefulness of long-term climate signatures for region specific either event-based or crop-based modelling development efforts to provide efficient decision support for enhancing farm-level crop productivity and sustainability of local and regional agriculture.

Sustainable water management measures are being developed to address the challenges posed by agriculture runoff and leaching on water resources. These measures are based on experts' opinions from various sectors and disciplines, ensuring that all stakeholders' perspectives are considered. For this, establishing a common understanding of 'sustainability' is essential to avoid misunderstandings, conflicts, and operational challenges. In this research, the Delphi survey technique was utilized to develop a definition of ‘sustainability’ in agricultural water management (SAWM) by considering the interdisciplinary group of experts from different parts of the world and those involved in a Horizon 2020 Research and Innovation Action. Twenty-six experts' perspectives on environmental, economic, and social dimensions of sustainability were assessed, and identified key concepts included climate change, water quality, water availability, stakeholder participation, capacity building, subsidies, and incentives. These concepts were used to define sustainability for multi/interdisciplinary project settings. The definition was validated with consortium members of the project in the regular consortium-wide meetings and used in the respective deliverables dealing with sustainability. The results serve as a foundation for communication between the involved actors and the project's definition of 'sustainability.' One recommendation from this work for broader policy formulation for SAWM in Europe is to prioritize farmer needs and focus on environmental sustainability. HIGHLIGHTS The research in agricultural water sustainability is essential to find pathways that optimize food production, limit water resource pollution, and promote sustainability of water management practices.; The absence of a common definition of sustainability can lead to miscommunication among partners due to varying interpretations resulting in ineffective collaboration, confusion, and a lack of coordination.; The Delphi survey technique is a powerful tool to capture diverse opinions from various experts in a complex discipline like "Sustainability".; Climate change, the environmental pillar of sustainability, and farmers as major stakeholders should be taken into prime focus when dealing with sustainable water management in agriculture.;

The COVID-19 pandemic has caused new conditions, problems, and different research platforms for qualitative research. The aim of the present study was to analyse the challenges and opportunities facing qualitative researchers during the COVID-19 pandemic in Iran. This qualitative research was conducted with a conventional content analysis approach with twenty-four Iranian health sciences researchers. The participants were selected by snowball and purposive sampling. Data collection was carried out using semi-structured face-to-face and online interviews until reaching saturation. Data analysis was also carried out using the Graneheim & Lundman approach in MAXQDA-2018 software, and to improve the trustworthiness of the results, Guba and Lincoln’s criteria were used. Data analysis led to the identification of two main categories, 13 subcategories, and 69 primary codes: The challenges were in areas such as data collection methods, access to participants and how to conduct interviews. Opportunities also included formation of new topics for qualitative research, highlighting the importance of qualitative research, strengthening the technological knowledge of researchers, research cost-effectiveness, and presenting further information on sensitive topics. It is also possible to strengthen qualitative research by supporting qualitative researchers at universities and research centres, facilitating the administrative processes, providing communication infrastructure such as suitable Internet in universities, ensuring more diversity in data collection methods, developing an appropriate protocol during the COVID-19 pandemic, and dedicating some university rooms to qualitative researchers to conduct interviews.

Abstract Heatwaves have had adverse affects on human life, ecosystems, and society. Global warming is expected to heighten the characteristics of heatwaves such as severity, duration, and frequency. Therefore, understanding the evolution of heatwaves is a central issue in climate change research with high relevance for society. Establishing a proper threshold for issuing a heatwave can be one of the most important issues in heatwaves studies, however, fewer studies focused on characterizing and modeling a heatwave threshold according to non‐stationary behavior, local geographic and physiographic characteristics. We have developed an applicable probabilistic framework to explore the heatwave events with different durations by applying appropriate thresholds. In this study, the proposed framework was applied to define the nonstationary frequency analysis of heatwaves models to account for the temporal trend caused by climate variability and change across Iran. The results showed that shorter heatwaves odds have been intensified more over Iran while longer heatwaves have not changed substantially. We find a substantial increase in heatwave hazard across Iran with a 40‐year available daily record, collectively from 1977 to 2019. The spatial distribution of the best non‐stationary models confirms the temporal evolution of heatwaves features due to global warming over Iran.

Abstract Extended periods of hydro-climate extremes with excessive or scarce rainfall associated with high or low temperatures have resulted in an imbalanced water cycle and inefficient socio-economic systems in several regions of Iran. However, there is a lack of comprehensive investigations on short-term to long-term variations in timing, duration, and temperature of wet/dry spells. This study bridges the current gap through a comprehensive statistical analysis of historical climatic data (1959–2018). Results indicated that the negative tendency of the accumulated rainfall (− 0.16/ − 0.35 mm/year during the past 60/30 years) in 2- to 6-day wet spells had made significant contributions to the ongoing downward trend in annual rainfall (− 0.5/ − 1.5 mm/year during the past 60/30 years) owing to a warmer climate condition. Warmer wet spells are likely responsible for precipitation patterns changes in snow-dominated stations since their wet spells temperature has more than threefold growth with increasing distance to coasts. The most detected trends in climatic patterns have started in the last two decades and become more severe from 2009 to 2018. Our results confirm the alteration of precipitation features across Iran due to anthropogenic climatic change, and suggest expected increase in air temperature would likely result in further dry and warm conditions over the coming decades.

This article aims to show that virtual water has historically been an adaptation strategy that enabled some arid regions to develop a prosperous economy without putting pressure on their scarce water resources. Virtual water is referred to as the total amount of water that is consumed to produce goods and services. As an example, in arid central Iran, the deficiency in agricultural revenues was offset by more investment in local industries that enjoyed a perennial capacity to employ more workers. The revenues of local industries weaned the population from irrigated agriculture, since most of their raw materials and also food stuff were imported from other regions, bringing a remarkable amount of virtual water. This virtual water not only sustained the region’s inhabitants, but also set the stage for a powerful polity in the face of a rapid population growth between the 13th and 15th centuries AD. The resultant surplus products entailed a vast and safe network of roads, provided by both entrepreneurs and government. Therefore, it became possible to import more feedstock such as cocoons from water-abundant regions and then export silk textiles with considerable added-value. This article concludes that a similar model of virtual water can remedy the ongoing water crisis in central Iran, where groundwater reserves are overexploited, and many rural and urban centers are teetering on the edge of socio-ecological collapse. History holds an urgent lesson on sustainability for our today’s policy that stubbornly peruses agriculture and other high-water-demand sectors in an arid region whose development has always been dependent on virtual water.

This study evaluated the comprehensive Water-Energy-Food-Carbon Nexus (WEFC) by focusing on energy assessment in northwest Iran. The energy evaluation indices for different products were calculated by estimating the total input and output energies. Multi-objective optimization based on five individual objectives and WEFC Nexus policies was used to identify the optimal land-use allocation of wheat, barley, rapeseed, and sugar beet, silage corn, and potato while minimizing water and energy consumption and CO2 emissions, and maximizing food production and profit. The results indicate that the suggested framework provides a practical methodology for determining the optimal land-use allocation considering quantitative WEFC Nexus. To increase economic efficiency and reduce energy consumption, agricultural practices and policy recommendations should be adopted, including promoting renewable energy sources, implementing energy-saving technologies, improving fertilizer management, improving crop rotation practices, conservation tillage, and improving water management and adoption of sustainable farming practices. The results allow policymakers to optimize multiple resources and recommend the best resource allocation under recommendation policy, technology, and constraints to achieve sustainable development in agriculture.

Study region: Once, the Tigris River (with its twin, the Euphrates) was the remarkable river in the west of Asia, making Mesopotamia a cradle of civilization thousands of years ago. Upstream anthropogenic activity has choked the Tigris River, the connecting lifeline across Iraq, and, due to droughts and desertification, caused the country to be plagued by poverty. Study focus: Here, we give a perspective on flow regime alteration in the main corridor of the Tigris River at five crucial points (Cizre, Mosul, Baiji, Baghdad, and Kut) before and after the planned water resources development in Turkey. Turkey’s Tigris River regulation goal is to generate about 7247 GWh of energy and irrigate over 640,000 ha of farmlands. New hydrological insights for the region: We reconstructed the natural flow along the Tigris River. In addition, to evaluate hydrological droughts, we proposed a modified streamflow drought index (MSDI) and compared it with the original streamflow drought index (SDI). The results show that the worst hydrological conditions could be found below the Samarra barrage in Iraq before the Tigris River regulation in Turkey. This negative hydrological condition will be extended to the whole corridor of the Tigris River in Iraq after the implementation of Turkey's goal. As a result, for example, Cizre and Mosul will experience extreme conditions in 37.5–87.5% of the years; this means a considerable reduction in the Mosul reservoire’s inflow (135–326 m3/sec). Consequently, some parts of Mosul's hydropower and reservoir capacity will be useless, and hydrological drought upstream of the Samarra barrage will be dominated.

The accumulation of pollutants in the sediment along surface water may negatively affect riparian zones and increase ecological risk. This article investigates the effects of metal sediments on riparian soil via field monitoring and ICP-OES analysis. To this end, pollution levels, seasonal changes, and potential sources of the pollutants were determined for the Melen River watershed, Turkey. The ecological statuses (contamination factor, enrichment factor, index of geo-accumulation, pollution index, modified pollution index, and potential and modified ecological risk indexes) of the watershed were also analyzed. Although no significant seasonal differences in the metal sediments were observed, their spatial distribution in the sediments and riparian soils varied markedly. Cr (11.4 to 136), Co (7.7 to 21.52), Cu (11.4 to 76.6), and Ni (14.06 to 128.2) recorded as mg/kg significantly increased from the upstream to the downstream. The metals possessing the highest risk in the sediment and riparian soil regarding the river health were Cu, Co, and Ni. The risk values were found to be heavily polluted (PI > 3 and MPI > 10), and the risk indexes were above the “desired environment without the risk”. The risk index was found to be more than 50, and the modified risk indexes exceeded 200 at many points. The transportation of pollutants in surface water became evident in the sediment, resulting in adverse effects on the riparian zone and the ecological system.

The change in the global energy production mix towards variable renewable energy sources requires efficient utilization of regulated rivers to optimise hydropower operations meet the needs of a changing energy market. However, the flexible operation of hydropower plants causes non-natural, sub-daily fluctuating flows in the receiving water bodies, often referred to as ‘hydropeaking’. Drastic changes in sub-daily flow regimes undermine attempts to improve river system health. Environmental decision makers, including permitting authorities and river basin managers facing the intense and increasing pressure on river environments, should consider ecosystem services and biodiversity issues more thoroughly. The need for research innovations in hydropeaking operation design to fulfil both the water and energy security responsibilities of hydropower is highlighted. Our paper outlines optimized hydropeaking design as a future research direction to help researchers, managers, and decision-makers prioritize actions that could enable better integration of river science and energy system planning. The goal of this is to find a balanced hydropower operation strategy.

Abstract Urban development broadly impacts the hydrological cycle, leading to increased peak flow and flooding. Surface water detention ponds are among the most efficient measures for attenuating peak flow and returning it from development to pre‐development conditions. However, the major challenge is identifying optimal locations and cost‐effective designs for these ponds. This paper presents a new framework for identifying the best strategies for using detention ponds to control floods in urban drainage systems (UDS). The framework comprises a portfolio of simulation tools coupled with evolutionary optimisation and multi‐criteria decision analysis models. Hydraulic simulation of UDS is first modelled using SWMM and GIS tools. A multi‐objective optimisation model was used to find the optimal location and design for detention ponds. The compromise programming (CP) multi‐criteria decision‐making method was then used to prioritise potential best management solutions for detention ponds based on several sustainability criteria comprising economic, environmental, physiographic and social factors. The results identified the key features of potential detention ponds appearing in all multi‐objective optimal solutions that are useful for decision‐makers/designers when planning/designing for new detention ponds. The selected optimal pond strategies can significantly improve the UDS performance by decreasing flood damage between 66% and 90% at the cost of between $50,000 and $160,000.

Study region: Iran Study focus: This study evaluated three satellite precipitation products, including PERSIANN-CCS and TRMM-3B42RT V7 (near real-time satellites) and CMOROH (post real-time satellite), on hourly, daily, monthly, and annual time scales in 52 synoptic stations over Iran. This study aims to evaluate the accuracy of satellite- precipitation products over different return periods and durations as well as their performance in different climates and altitudes. The Pearson’s correlation coefficient (Rp), Relative Root Mean Square Error (RRMSE), and Relative Bias (RBias) as well as three probability indices including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) were applied to evaluate the accuracy of these products. In addition, the sub-daily IDF curves (6, 12, 18, and 24 h) were derived to evaluate the studied satellites precipitation for 5, 10-, 20-, 50-, and 100-year return periods. New hydrological insights for the region: The results showed that the modification method could significantly improve all satellites' performance in precipitation estimation. The IDF curves derived from the CMORPH satellite as post real-time satellite precipitation product, showed the highest accuracy compared to synoptic stations. The TRMM and CMORPH underestimated precipitation by 56% and 8%, respectively, while PERSIANN-CCS overestimated precipitation by 143%. However, PERSIANN-CCS performed better in detecting the occurrence and non-occurrence of precipitation than other products. For Northeastern and Southwestern Iran, all products showed the same performance in accurately detecting occurrence and precipitation.

The paper presents prominent Nordic contributions to stochastic methods in hydrology and water resources during the previous 50 years. The development in methods from analysis of stationary and independent hydrological events to include non-stationarity, risk analysis, big data, operational research and climate change impacts is hereby demonstrated. The paper is divided into four main sections covering flood frequency and drought analyses, assessment of rainfall extremes, stochastic approaches to water resources management and approaches to climate change and adaptation efforts. It is intended as a review paper referring to a rich selection of internationally published papers authored by Nordic hydrologists or hydrologists from abroad working in a Nordic country or in cooperation with Nordic hydrologists. Emerging trends in needs and methodologies are highlighted in the conclusions. HIGHLIGHTS Historical development in use of stochastic methods.; From stationarity to climate change impacts.; From univariate to multivariate problems.; From local to regional problems.;

Background: Influenza vaccines based on conserved proteins are being developed persistently. The conserved protein vaccines based on Nucleoprotein (NP) are highly protected vaccines against influenza viruses that can be used as a Universal vaccine. Aluminum hydroxide (Alum) is the most common adjuvant used in vaccine formulation to improve immunization by altering the epitopes’ folds. However, due to its toxic effects on the nervous system, especially in infants and young children exposed to multiple vaccine injections during brain development, it is better to use more desirable options such as carbohydrate-based adjuvants. Sucrose ester (SE) is a carbohydrate and non-ionic surfactant that is compatible with the human body and environmentally friendly. This study evaluated the immunogenicity of recombinant NP molecule prepared in a prokaryotic with the accompaniment of sucrose ester adjuvant against lethal influenza virus challenge in a Balb/c mice model. Methods: The recombinant vector of PET-28a-NP was used to produce NP molecule. The vaccines containing an NP with or without Alum or sucrose ester adjuvants were injected into the mice. The Effectiveness and immunogenicity were examined by evaluating the humeral immunity induction by Immunoglobulin G (IgG), and its subunits production, and cellular immunity induction by Interferon-Gamma (IFN-γ) and Interleukin-4 (IL-4) production by ELISA Method and also animal’s surveillance was documented. The study took part at the Influenza and other respiratory viruses department of Pasteur institute of Iran in November 2018. Results: The animals’ surveillance in the Np group was 57.1%, NP+SE was (71.4%), and NP+SE was 64.28%. Also, IgG and its subunits, IL4, and IFN-γ production in both Alum and SE combined vaccines compared to NP alone were significant. Conclusion: In combination with the carbohydrate adjuvant containing sucrose ester compared to the formulation with alum adjuvant, the NP could provide proper and considerable protection and immunity against the homologous strain (H1N1) of the Influenza A virus. It is recommended that SE usage as an adjuvant results in an adequate immune response and less toxic effect.

Restoration and water table control on peatlands to limit fire risk are national priorities in Indonesia. The present study was initiated at Padang Island, Sumatra, to increase understanding on peatland hydrology in the tropic. At the pilot site, water table and precipitation were monitored at different stations. The results show variation in water table depths (WTDs) over time and space due to spatial and temporal variability in rain intensity and drainage networks. In part of the island, large-scale drainage for plantations led to deep WTD (−1.8 m) and high WTD recession rates (up to 3.5 cm/day). Around villages, farm-scale drainages had a smaller impact with a lower recession rate (up to 1.8 cm/day) and shallow WTD, typically below −0.4 m, the threshold for sustainable peatland management in Indonesia. The recession rates levelled off at 1.0 cm/day near the drained forest/plantation and at 0.5 cm/day near the farm. Deeper layers had much lower specific yield (Sy), 0.1 at −1.5 m depth, compared with top peat soils with Sy up to 0.3. Proximity to drainages extended discharge flow to deeper layers. The results highlighted the severity of peatland drainage impact on most coastal zones of Padang Island, which have intensive drainage networks. HIGHLIGHTS High spatial and temporal variability of water table was observed in Padang Island.; The variability was partially driven by variation in land use and farm drainages.; Recession rate near pulp plantations remained high (0.01 m/day) at −1.5 m depth.;

This study examined the spatiotemporal climate variability over the Ceyhan River basin in Southern Anatolia, Türkiye using historical rainfall and temperature observations recorded at 15 meteorology stations. Various statistical and geostatistical techniques were employed to determine the significance of trends for each climatic variable in the whole basin and its three sub-regions (northern, central, and southern regions). The results revealed that the recent years in the basin were generally warmer compared with previous years, with a temperature increase of approximately 4 °C. The standardized temperature index analysis indicated a shift towards hotter periods after 2005, while the coldest periods were observed in the early 1990s. The spatial distribution of temperature showed non-uniform patterns throughout the basin. The first decade of the study period (1975–1984) was characterized by relatively cold temperatures, followed by a transition period from cold to hot between 1985 and 2004, and a hotter period in the last decade (2005–2014). The rainfall analysis indicated a decreasing trend in annual rainfall, particularly in the northern and central regions of the basin. However, the southern region showed an increasing trend in annual rainfall during the study period. The spatial distribution of rainfall exhibited considerable variability across the basin, with different regions experiencing distinct patterns. The standardized precipitation index analysis revealed the occurrence of multiple drought events throughout the study period. The most severe and prolonged droughts were observed in the years 1992–1996 and 2007–2010. These drought events had significant impacts on water availability and agricultural productivity in the basin.

Abstract Background Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. Methods In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). Results According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. Conclusion The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

Arctic charr is one of the fish species most sensitive to climate change but studies on their freshwater habitat preferences are limited, especially in the fluvial environment. Machine learning methods offer automatic and objective models for ecohydrological processes based on observed data. However, i) the number of ecological records is often much smaller than hydrological observations, and ii) ecological measurements over the long-term are costly. Consequently, ecohydrological datasets are scarce and imbalanced. To address these problems, we propose jittered binary genetic programming (JBGP) to detect the most dominant ecohydrological parameters affecting the occurrence of Arctic charr across tributaries within the large subarctic Teno River catchment, in northernmost Finland and Norway. We quantitatively assessed the accuracy of the proposed model and compared its performance with that of classic genetic programming (GP), decision tree (DT) and state-of-the-art jittered-DT methods. The JBGP achieves the highest total classification accuracy of 90% and a Heidke skill score of 78%, showing its superiority over its counterparts. Our results showed that the dominant factors contributing to the presence of Arctic charr in Teno River tributaries include i) a higher density of macroinvertebrates, ii) a lower percentage of mires in the catchment and iii) a milder stream channel slope.

Introduction Two complicated and difficult problems in the Islamic philosophy are knowledge and intellect. These were of great significance to philosophers from Plato and Aristotle to such Muslim philosophers as Avicenna, Fakhr al-Razi, Suhrawardi, and Nasir al-Din al-Tusi as well as to the Schools of Shiraz and Isfahan. Following them, Mulla Sadra raised the question of knowledge throughout his philosophical work. As the Sadrai thought is still dynamic and can be pursued, examined, critiqued or reconstructed, Muslim thinkers continue to publish their research in the form of books, dissertations, and articles on the issue of knowledge and the intellect. It goes without saying that in order to prevent doing what others already do or have already done the evolution of research on a problem needs to be brought to the researcher’s attention. Therefore, literature plays an important role in the research process, and the present article intends to assist researchers on Mulla Sadra with the literature review. It goes over scientific productions in the area of dissertations and articles from 2001 to 2020 on the subject of knowledge in Sadra’i philosophy. This includes 218 works, out of which 85 articles, 32 master’s degree dissertations and 9 PhD theses are written in the field of the intellect. Master’s degree dissertations and PhD theses on the subject of knowledge are 35 and 9 titles respectively, which does not show a marked difference to the field of the intellect. This is while the articles in the field of knowledge are 45 titles i.e. half as many as the articles written in the field of intellect. The research method As a systematic review, using the search engines and data bases such as Noormags, Comprehensive Portal of Human Sciences, Magiran, and the Treasure System (sāmāni-yi ganj) in the Iranian Research Institute for Information, Science and Technology (IranDoc), and Islamic World Science Citation Center (ISC), this article has searched titles of articles, dissertations, and PhD theses and conducted a statistical study of those Farsi titles written between 2001 and 2020 in Iran in the areas of knowledge and intellect in Mulla Sadra’s transcendent wisdom. A discussion of results More attention has been paid to the question of knowledge than that of the intellect. Within the subject of knowledge, discussions of divine knowledge are of the highest frequency, but less attention has been paid to the levels of the Creator’s knowledge and the status of the Creator’s knowledge in creation, or, to put it the other way, to the levels of knowledge in parallel to the levels of creation. When it comes to theology in the more specific sense, less attention has been paid to the knowledge-led Shia theology versus power-led Sunni theology, and the basics and results of such a discussion are not examined. Within the subject of intellect, discussions of the epistemology of the intellect are of the highest frequency, whereas less attention has been paid to various meanings and usages of the intellect in Islamic sciences including theosophy, philosophy, ethics, and to their distinction from the intellect in common parlance. Conclusion Among the conclusions is that there are methodological limitations: lack of attention to a needs assessment in the process of deciding research topics; lack of proper attention to other researchers’ findings (which leads up to many problems including repetitious work and lack of attention to certain topics, as witnessed by the results of the present study); lack of variety in the articles (for the most part, they are written for academic research journals, while a helpful research activity of high efficiency and influence relies on a whole variety of introductory studies, including reviews, which are visibly missing in the field of Islamic philosophy); and finally the lack of information about research activities in the field in the international arena and even Islamic countries.

In this study, metal concentrations in the water column and surface sediment of the Sabalan Dam Reservoir (SDR) were determined. Moreover, heavy metal pollution index (HPI), contamination index (CI), heavy metal evaluation index (HEI), enrichment factor (EF), geo-accumulation index (Igeo), sediment quality guidelines (SQGs), consensus-based SQGs (C-BSQGs), and mean probable effect concentration quotients (mPECQs) were evaluated for water and sediments of SDR. It was observed that metal concentrations in river entry sediment were lower, but those in river entry water were higher than corresponding values in the vicinity of the dam structure. The HPI values of water samples taken from 10 m depth in the center of SDR exceeded the critical limit, due to high concentrations of arsenic. However, according to CI, the reservoir water was not contaminated. The HEI values indicated contamination of SDR water with metals at 10 m depth. A comparison of water quality indices revealed that HEI was the most reliable index in water quality assessment, while CI and HPI were not sufficiently accurate. For SQGs, As and Cu concentrations in sediments were high, but mPECQ, Igeo, and EF revealed some degree of sediment pollution in SDR. The calculated EF values suggested minor anthropogenic enrichment of sediment with Fe, Co, V, and Ni; moderate anthropogenic enrichment with As and Mn; and moderate to severe anthropogenic enrichment with Cu. A comparison of SQG values revealed that the threshold effect and probable effect levels were the most reliable metrics in the assessment of sediment toxicity. Statistical analysis indicated similarities between metal concentrations in the center of the reservoir and near to the dam structure, as a result of similar sediment deposition behavior at these points, while higher flow velocity at the river entry point limited deposition of fine particles and associated metals.

Abstract Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.

Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations.

In an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve (ROC-AUC) assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city.

Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency.

Abstract High‐latitude lakes are warming faster than the global average with deep implications for life on Earth. Using an approximately six‐decade long in situ data set, we explored the changes in lake surface‐water temperature (LST), lake deep‐water temperature (LDT), lake depth‐weighted mean water temperature (LDMT), and ice‐free days in Lake Kallavesi, a boreal lake in central Finland, when the lake was stratified (June–August). Our results suggest that the LST is warming faster than the local air temperature (AT). As for the LST, fast warming was also observed in the LDT and LDMT, but at rates slower than those in the LST. The number of ice‐free days also shows an upward trend, with a rate of about 7 days per decade during the study period. The corresponding local AT is the main driver of the LST, followed by the ice‐free days and annual mean AT. Air temperature and ice‐free days also mainly contribute to the changes in the LDMT. The LDT is affected more by the North Atlantic Oscillation signals in this freshwater lake. The AT in the prior months does not affect the LDT in Lake Kallavesi although the AT during the prior season, that is, spring, is the main driver of summer LDT. This highlights the local AT impact on the LDT at time scales longer than a month. The warming rates in the lake water are at a minimum in June because the lake is not yet strongly stratified in this month when compared to July and August. These findings improve our knowledge of long‐term changes in the lake water temperature in a high‐latitude lake, a region with severe environmental consequences due to fast changes in the AT and lake ice phenology.

Energy harnessing from renewable energy sources has become more flexible with power electronic technologies. Recent advancements in power electronic technologies achieve converter efficiency higher than 98%. Today, reliable power electronic devices are needed to design a PV-based energy converter (inverter) to reduce the risk of failure and maintenance costs during operation. Wide-bandgap SiC devices are becoming more common in power electronic converters. These devices are designed to reduce switching loss and improve the efficiency of the system. Nevertheless, the cost of SiC devices is a major concern. Hence, to improve the reliability of the PV inverter while considering the economic aspects, this paper develops a highly reliable PV inverter with a hybrid Si/SiC power module that consists of a Si-IGBT with a SiC anti-parallel diode. A test case of a 3 kW PV inverter is considered for reliability analysis. The loading of the PV inverter is done under uncertain environmental conditions by considering the yearly Mission Profile (MP) data related to Ambient Temperature (AT) and Solar Irradiance (SI) at the India and Denmark locations. The effectiveness of the proposed hybrid Si/SiC power module is tested by comparing it with a conventional IGBT power module. The results showcase the marked improvement in PV inverter reliability with the proposed hybrid power module.

Abstract Iran's groundwater hydrochemistry has not been well understood. In this study, Iran's groundwater hydrochemistry is evaluated using a rich, ground‐trusted data sampled from 9,468 wells distributed across the country in 2011. Twelve groundwater quality parameters were analyzed in each sample, resulting in 113,616 parameters over the study period. Examination of anions‐cations shows that concentrations of sodium, calcium, chloride, and sulphate are higher than the acceptable threshold for drinking‐use suggested by the World Health Organization in about 40%, 21%, 25%, and 20% of the samples, respectively. The results of the water quality index reveal that most of the groundwater resources in the central, southern and eastern regions of Iran, which supply the majority of the domestic water for populated cities, do not meet the requirements for drinking‐use. Although the groundwater in northern parts fulfills the requirements for irrigation‐use, it is only suitable for irrigation of salinity‐friendly crops in central, eastern and southern regions. Ionic types and hydrochemistry facies indicate the dominance of mix water type in 13 out of 30 of Iran's sub‐basins, followed by sodium‐chloride water type in nine sub‐basins. Local geology and lithology are mainly attributed to the distribution of groundwater facies in Iran. In general, our findings reveal a distinctive relationship between Iran's geological‐geomorphological features and hydrochemical facies/groundwater quality. The findings can be used in the formulation of new strategies and policies for Iran's groundwater quality management in the future.

In the drastic period of climate change the con- tinuous data monitoring of snow characteristic is required.The immensely impact of snow on hydro production, water resource management and its inhabitants, drive to the need for the importance of snow information such as its extent, dynamics and water it holds at global and local scale. At present, there are various approaches such as traditional ground-based approach, optical satellite imaging and the radio, which are available for snow monitoring at global scale. However, the use of these approaches incurs from large labor and high monitoring cost. Since, the advance in sensor technologies and Internet of Things (Iot), provides an appealing possibility to develop a framework for monitoring snow parameters at enormously low cost. In this study. we implemented two machine learning classifiers model based on the input acquired from the low-cost wearable sensor platform. The results of Random forest classifier showed the accuracy of 88.8%, indicate a promising alternative in snow depth measurements with in-situ validation, when data or wireless sensor network are not available or affordable.

Vegetation height plays a crucial role in various ecological and environmental applications, such as biodiversity assessment and monitoring, landscape characterization, conser- vation planning and disaster management. Its estimation is traditionally based on in situ measurements or airborne Light Detection and Ranging sensors. However, such methods are often proven insufficient in covering large area landscapes due to high demands in cost, labor and time. Since, the emergence of wearable technology, ubiquitous sensors and Internet of Things offers an appealing framework for monitoring environmental parameters at extremely low cost, which, in turn, contributes to the development of affordable real-time vegetation monitoring system. This is especially relevant to rural environments and underdeveloped countries. We proposed a methodology for data acquisition from a ubiquitous sensor wearable platform and developed a machine-learning model to learn vegetation height on the basis attribute associated with pressure sensor. The proposed methods are proven particularly effective in a region where the land has forestry structure. The results of linear regression model (r2 = 0.81 and RSME = 16.73 cm) and multi-regression model (r2= 0.83 and RSME = 15.73 cm), indicate a promising alternative in vegetation height estimation when in situ or Light Detection and Ranging data or wireless sensor network are not available or affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.

Iranian peasants and farmers have long sought to devise and use appropriate methods to provide water for their cultivation by limiting water resources for agriculture. Studying the different traditional methods of water extraction in the central plateau of Iran, which faced with more serious limitations, can help to identify their indigenous knowledge in this regard. The prominence of the emergence of the knowledge of Iranian peasants worldwide indicates the maturity of Iranian civilization in this geography. Although, in some parts of Iran, with a little more detailed exploration and examination, clear examples of this kindcan be observed. Therefore, this study attempts to investigate and introduce one of the manifestations of indigenous knowledge of villagers of central plateau of Iran in water extraction for agriculture. The concept of cow-wells especially reasons, necessities and especially the characteristics of the knowledge contained therein can be a reflection of Iranian culture, civilization and knowledge in this arid region. The cow-wells of Haj Ibrahim in Varzaneh district in Isfahan township due to being in the driest part of the Iran and the constant exposure to drought on the one hand reflects the genius of Iranians in the past and also on the other hand, its revived by a farmer (Haj Ibrahim) indicated of adaptation of innovation for Iranian farmers' to takes advantage of its traditional cultural and economic and social benefits for achieve the sustainable development in this region. The use of entrepreneurial approaches, especially the creation of sustainable jobs in the field of tourism development for themselves and others can be one of the positive aspects of using such an approach in difficult conditions and water shortages in the region. Continuing to pay attention to traditional cultural heritage, and especially to the underlying knowledge base, along with the need to support not only the material but the spiritual revival of these traditions, may be the result and suggestion of the present study.

Abstract Background Fibroblast growth factors (FGFs) are growth factors that have diverse biological activities including broad mitogenic and cell survival activities. They function through the activation of a specific tyrosine kinase receptor that transduces the signal by activating several intracellular signaling pathways. Objective To identify the different signaling pathways involved in the mechanism of action of FGF8 and FGF18 on ovine granulosa cells using mass spectrometry. Materials and Methods Ovine ovarian granulosa cells were harvested from adult sheep independently at the stage of the estrous cycle and were cultured at a density of 500,000 viable cells in 1 ml DMEM/F12 medium for five days. The cells were then treated on day 5 of culture with 10 ng/mL FGF8 and FGF18 for 30 minutes, and total cell protein was collected for mass spectrometry. Results Mass spectrometry showed that both FGF8 and FGF18 significantly induce simultaneous upregulation of several proteins, including ATF1, STAT3, MAPK1, MAPK3, MAPK14, PLCG1, PLCG2, PKCA, PIK3CA, RAF1, GAB1, and BAG2 ( > 1.5-fold; p < 0.01). Conclusion ATF1 and STAT3 are important transcription factors involved in cell growth, proliferation and survival, and consequently can hamper or rescue the normal ovine reproductive system function.

To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.

The study shows that the current network of hydrometeorological observation in the permafrost zone of Russia is insufficient to provide data for the statistical approaches adopted at the state level for engineering surveys and calculations. The alternative to the financially costly and practically impossible expansion of the monitoring network is the development of hydrological research stations and the implementation of new methods for calculating streamflow characteristics based on mathematical modeling. The data of the Kolyma Water-Balance Station, the first research basin in the world in a permafrost environment (1948–1997), and the process-based hydrological model Hydrograph are applied to simulate streamflow hydrographs in remote mountainous permafrost basins. The satisfactory results confirm that mathematical modeling may substitute or replace statistical approaches in the conditions of extreme data insufficiency. The improvement of the models in a changing climate requires the renewal of historical observations at currently abandoned research stations in Russian permafrost regions. The study is important for forming the state policy in climate change adaptation and mitigation measures.

Abstract The anthropogenic impacts of development and frequent droughts have limited Iran's water availability. This has major implications for Iran's agricultural sector which is responsible for about 90% of water consumption at the national scale. This study investigates if declining water availability impacted agriculture in Iran. Using the Mann‐Kendall and Sen's slope estimator methods, we explored the changes in Iran's agricultural production and area during the 1981–2013 period. Despite decreasing water availability during this period, irrigated agricultural production and area continuously increased. This unsustainable agricultural development, which would have been impossible without the overabstraction of surface and ground water resources, has major long‐term water, food, environmental, and human security implications for Iran.

Herein, how robotic and autonomous systems and smart wearables complement and support healthcare delivery and the healthcare staff during the COVID‐19 pandemic are presented. For instance, robotic and telerobotic systems significantly reduce the risk of infectious disease transmission to frontline healthcare workers by making it possible to triage, evaluate, monitor, and treat patients from a safe distance. Various examples of where the medical, engineering, and science communities come together to aid the healthcare system, healthcare workers, and society during the current crisis are presented. The goal is to encourage an interdisciplinary dialog so that ethical, practical, and beneficial technological solutions are found to effectively tackle this and similar crises.

Contrary to conventional fossil fuel-based electricity generation technologies, renewable energy centered technologies, specifically small hydropower, release a lesser amount of anthropogenic greenhouse gases but are normally more expensive. A major segment of the capital investment in the current small hydropower scenario accounts for equipment and construction process costs. The construction and cost administration process are generally limited to analysis of the capital cost of civil constructions, electro-mechanical equipment works, neglecting the costs related to operating and maintaining the plant, replacement or refurbishment, certified emission reductions, among others. Contemporary studies indicate that these costs form a substantial fraction of the total capital investment. Consequently, for cost management and investment decision making, small hydropower plant developers are drawing increased attention in recent years towards conducting life cycle costing studies that take into account the ignored costs. In addition, small hydropower plants in developing nations can become more competitive by trading the emission reductions achieved under the provision of the Clean Development Mechanism, an outcome of the Kyoto Protocol proposed at the United Nations Framework Convention on Climate Change. In this paper, a modest attempt has been made to determine the Levelized cost of electricity generation using life cycle costing methodology, which accounts for all the costs over operating lifetime on a range of small hydropower plants and the results are analyzed.

River flow in cold climates is known to be one of the hydrological systems most affected by climate change, playing a central role in the sustainability of downstream socio-ecological systems. Numerous studies on the temporal and spatial variations of streamflow characteristics have been done, and a comprehensive study on the variation of hydrologic extremes is becoming increasingly important. This study evaluated the long-running changes in the magnitude, time, and inter-annual variability of hydrologic extremes, including high and low flow in 16 major Finnish rivers. We applied four new hydrologic extreme indices for summer–winter low flow ratio, spring-absolute high flow ratio, time-to-peak index, and increasing rate index during the snowmelt period to analyze the spatiotemporal variations of extreme streamflow from 1911 to 2020. The most detected trends in flow regimes have started in the last six decades and become more severe from 1991 to 2020, which is likely to be dominated by anthropogenic global warming. The results also indicated that alteration of low pulses in most rivers was associated with an increase (decrease) in winter (summer) flows, suggesting the annual minimum flow in summer frequently contradicts natural hydrologic regimes in Arctic rivers. Southern Finland has experienced higher variations in extreme hydrology over the last century. A new low flow regime was detected for southern rivers, characterized by frequent annual minimum flow in summer instead of winter. Moreover, the annual maximum flow before/after spring dictated a new high-flow regime characterized by frequent double peak flows in this region.

Objective: Influenza A virus infections are still a major health problem and the choices available for the control and treatment of the disease are limited. This research evaluated in vitro and in vivo antiviral effects of Peganum harmala L. seeds (PHS) extract against influenza A virus. Materials and Methods: In this research, in vitro anti-influenza A virus activity of the extract was assessed in Madin-Darby canine kidney (MDCK) cells. In order to evaluate anti-influenza activity of PHS extract in vivo, BALB/c mice were infected with 5LD50 of mouse-adapted influenza virus (H1N1; PR8) and received 200 mg/kg/day of PHS extract or 20 mg/kg/day oseltamivir. Lungs of seven mice per group were removed on day 3 post-infection and lung virus titers were determined by qRT-PCR. Mice survival, body weights and general conditions were observed for up to 14 days post-infection. Results: The results demonstrated that, the ethanolic extract of PHS possesses high activity against influenza virus with IC50 value of 15.7 (CI95%:11.7-21) μg/ml in MDCK cells. Our results also showed that, oral administration of PHS extract (200 mg/kg/day) or oseltamivir (20 mg/kg/day) to infected mice, increased the survival rate, reduced body weight loss, and decreased lung virus titer. Conclusion: Based on our findings, P. harmala seeds extract can inhibit influenza A virus replication in vitro and in vivo. Therefore, isolation and characterization of the plant’s active compounds and investigation of the underlying mechanisms of its antiviral action are highly suggested.

In terms of fuel resource, hydropower possesses a prominent advantage over any other large power plants which burn fossil fuels to generate electricity. Moreover, due to the abundance in resource availability (as a domestic source in small streams and rivers), small hydropower (SHP) plants are showing prominence all over the world. SHP plants have led to improved access to electricity usage in under-developed and developing nations, thereby contributing to sustainable development goals and social empowerment. SHP, as a technology, is regarded as the largest density renewable resource with high adaptability, and low investment costs. The primary objective of the paper is to study and analyze recent developments in SHP technologies with reporting statistical figures in terms of installed capacity and MW potential in several parts of the world. Methodologies adopted by researchers to conduct techno-economic analysis of SHP projects are reviewed. Various costs involved in conducting pre-feasibility studies—such as constructing, maintaining, and sustainably operating SHP projects—are studied. The results of the study indicate cost and regulatory issues are the major factors affecting the growth of the small hydropower sector in many nations. Major impediments to construction, development and deployment of SHP projects, mutually existing among the nations worldwide, are also reported. Technical hindrances include non-availability of the grid and very limited accessibility to SHP sites, emissions due to storage of water, disruptive technologies with limited manpower and non-technical hindrances include discouragement from local bodies and groups, lack of suitable and precise pathways to accomplish SHP goals of a nation, lack of incentives for encouraging private players to invest in SHP projects, complex approval processes, and many more.

During the past three decades, sea water level (SWL) in the Caspian Sea has declined by about 2 m and sea area has decreased by about 15 000 km ^2 . This has affected coastal communities, the environment and economically important gulfs of the sea (e.g. Dead Kultuk). To assess the effects of coastline change and evaluate zones vulnerable to desiccation, we simulated SWL using total inflow from feeder rivers and precipitation and evaporation over the sea. We determined potential vulnerable areas of the sea over the past 80 years by comparing the minimum and maximum annual water body maps (for 1977 and 1995). We then determined the linear regression between SWL rise and covered potential vulnerable area (CVA), using annual Normalised Difference Water Index (NDWI) maps and SWL data from 1977 to 2018. Combining SWL-CVA regression and SWL simulation model enabled us to determine desiccated areas in different regions of the Caspian Sea due to changes in precipitation, evaporation and total inflow. The results showed that 25 000 km ^2 of the sea is potentially vulnerable to SWL fluctuations in terms of desiccation, with 70% of this vulnerable area located in Kazakhstan. Potential vulnerable area per kilometre coastline was found to be 6 km ^2 in Kazakhstan, 4 km ^2 in Russia and whole of Caspian Sea, 1.5 km ^2 in Iran, 1 km ^2 in Azerbaijan and 0.5 km ^2 in Turkmenistan. The results also indicated that SWL in the Caspian Sea is sensitive to evaporation and that e.g. a 37.5 mm decrease in mean annual net precipitation would lead to a 1875 km ^2 decrease in the sea area, while a 1 km ^3 decrease in mean annual inflow would lead to a 1400 km ^2 decrease in the sea area. Thus the developed framework enabled the spatial consequences of changes in water balance parameters on sea area to be quantified. It can be used to assess future changes in SWL and sea area due to anthropogenic activities and climate change.

Background: The influenza virus is one of the most important factors for higher morbidity and mortality in the world. Recently, researchers have been focused on influenza conserved antigenic proteins such as hemagglutinin stalk domain (HA2) for vaccine production and serological studies. The HA2 plays a major role in the fusion of the virus with host cells membrane. The immunity system enables to produce antibody against HA2. The aim of this study is polyclonal antibody production against influenza HA2. Methods: This study was done in the Influenza Research Lab, Pasteur Institute of Iran, Tehran for one year from September 2013 to October 2014. In the present study, recombinant HA2 protein was produced in prokaryotic system and purified using Nickel affinity chromatography. The purified HA2 was mixed with Freund’s adjuvant (complete and incomplete) and injected into two New Zealand white rabbits by intramuscularly and subcutaneously routes. Immunization was continued for several months with two weeks interval. Before each immunization, blood was drawn by venous puncture from the rabbit ear. Function of rabbit's sera was evaluated using radial immunodiffusion (RID) in both forms, Single RID (SRID) and Double RID (DRID). Finally, antiserum activity against HA2 was evaluated using western blotting as serological assay. Results: Sedimentary line and zone was observed in RID assays (SRID and DRID) represent interaction between HA2 protein and anti- HA2 antibody. As well as, western blotting results was positive for HA2 protein. Therefore, these results showed that polyclonal antibody produced against HA2 protein can identify HA2 protein antigenic sites. Conclusion: These findings show that humoral immune responses have properly been stimulated in rabbits and these antibodies can identify HA2 protein and may be suitable for other serological methods.

مقدمه: ویروس‌های آنفلوانزا عامل عمده‌ی بیماری و مرگ و میر ناشی از بیماری‌های تنفسی در جهان به شمار می‌روند و واکسیناسیون، بهترین راه پیشگیری می‌باشد. به علت تغییرات آنتی ژنیك ویروس آنفلوانزا، بازآرایی هر ساله‌ی واكسن اجتناب ناپذیر است. یکی از روش‌های مقابله با این مشكل، طراحی واکسن با استفاده از آنتی ژن‌های حفاظت شده‌ی ویروس آنفلوانزا است. پروتئین 2M که در پوشش ویروس، كانال یونی را تشكیل می‌دهد و با تغییرات pH باعث ورود ویروس و ایجاد عفونت در سلول‌های میزبان می‌شود، در بین همه‌ی ویروس‌های آنفلوانزای A حفاظت شده است و هدف مناسبی برای تولید واكسن با ایمنی وسیع‌الطیف می‌باشد. در این مطالعه، به منظور تولید یك واكسن كارآمد، بخشی از ژن 70HSP (70Heat shock proteins) لیشمانیا ماژور به ژن 2M ویروس آنفولانزای 1N1H/A متصل شد و پروتئین نوترکیب كایمر در سیستم پروکاریوتی بیان گردید. روش‌ها: برای ساخت سازه‌ی بیانی، ابتدا ژن كد كننده‌ی HSP در پلاسمید a28pET در بین محل اثر آنزیم‌های BamHI و HindIII جای‌سازی گردید. سپس ژن 2M به روش PCR (Polymerase chain reaction) و با استفاده از پرایمرهای اختصاصی، تکثیر یافت و در محل اثر سایت آنزیمی BamHI در وکتور خطی و دفسفریله شده‌ی a28pET و در بالادست ژن 70HSP کلون گردید. پس از تعیین ترادف و تأیید صحت كلونینگ، بیان پروتئین نوتركیب در سلول‌های 21BL مورد بررسی قرار گرفت. یافته‌ها: نتایج کلنی PCR و هضم آنزیمی، صحت سازه‌های نوترکیب را تأیید کردند. نتایج تعیین توالی نشان داد كه ژن 2Mدر وكتور a28pET و در بالادست ژن 70HSP به طور صحیح و در قاب خواندنی دنباله‌ی هیستیدینی كلون شده است. تولید پروتئین‌های نوتركیب به روش وسترن بلات (Western blot) تأیید گردید. نتیجه‌گیری: پروتئین‌های شوک حرارتی توانایی تحریک و ایجاد هر دو نوع ایمنی ذاتی و اکتسابی را دارند و اتصال آن به آنتی ژن هدف باعث افزایش پاسخ‌های ایمنی می‌شود. پروتئین كایمر تهیه شده در این مطالعه، پس از تخلیص می‌تواند به عنوان یك كاندیدای واكسن زیر واحدی مناسب برای پیشگیری از عفونت آنفلوانزا در نظر گرفته شود. برای بررسی اثرات ادجوانتی 70HSP لیشمانیا ماژور بر روی پروتئین 2M، ارزیابی ایمونوژنیسیتی آن در مدل‌های حیوانی در مطالعات آتی انجام خواهد شد.