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In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash–Sutcliffe Efficiency (NSE), Pearson's r, and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction. HIGHLIGHTS Transformer model surpasses persistence, LSTM, GRU, Seq2Seq in 24-h streamflow prediction.; Employed different data extension methods, zero-padding and persistence, to enhance predictive capabilities.; Evaluated model performance using Nash–Sutcliffe Efficiency, Pearson's r, and normalized root mean square error metrics.; Conducted region-specific analysis, demonstrating the transformer model's adaptability across varied hydrological environments.;

Abstract Flooding is the most frequent type of natural disaster, inducing devastating damage at large and small spatial scales. Flood exposure analysis is a critical part of flood risk assessment. While most studies analyze the exposure elements separately, it is crucial to perform a multi-parameter exposure analysis and consider different types of flood zones to gain a comprehensive understanding of the impact and make informed mitigation decisions. This research analyzes the population, properties, and road networks potentially exposed to the 100, 200, and 500-year flood events at the county level in the State of Iowa using geospatial analytics. We also propose a flood exposure index at the county level using fuzzy overlay analysis to help find the most impacted county. During flooding, results indicate that the county-level percentage of displaced population, impacted properties, and road length can reach up to 46%, 41%, and 40%, respectively. We found that the most exposed buildings and roads are laid in residential areas. Also, 25% of the counties are designated as very high-exposure areas. This study can help many stakeholders identify vulnerable areas and ensure equitable distribution of investments and resources toward flood mitigation projects.

Abstract Green spectrophotometric and HPLC methods have been developed for the quantification of metoclopramide. In the spectrophotometric method, it was determined by direct absorbance measurement at 273 nm wavelength using ultrapure water as solvent. The Extend C18 column was used for the HPLC method. The mobile phase system consisted of a combination of ethanol and formic acid solution (pH 2.0; 30:70 v/v). Isocratic elution was applied and the flow rate was set at 1.0 mL min−1. Metoclopramide was detected at 273 nm. The methods performed were economical, rapid, environmentally friendly, and simple, providing metoclopramide analysis within 5 min. The methods have been successfully applied in pharmaceutical products without matrix interference. The results of the application of the developed methods to pharmaceutical products were statistically compared and no significant difference was observed between the methods. In addition, the greenness assessment of the developed methods was performed using AGREE software. Our developed methods, based on the use of solvents such as ethanol and water, are proposed as a more environmentally and analyst-friendly option for the quantification of metoclopramide in pharmaceutical products than other methods currently in use.

Abstract Background An earthquake with a magnitude of 7.7 occurred in Pazarcık District of Turkey at 04.17 on February 6, 2023 and another earthquake of 7.6 occurred at 13.24 on the same day. This is the second largest earthquake to have occurred in Turkey. The aim of this study is to investigate the earthquake-related level of knowledge, attitudes and behaviours, general health and psychological status of survivors who were affected by the 2023 Kahramanmaraş Earthquake and who were living in Nurdağı District of Gaziantep after the earthquake. Methods Data of 2317 individuals older than 18 years of age who were living in earthquake neighbourhoods, tents and containers in Nurdağı District of Gaziantep were examined. Variables were evaluated to find out the demographic characteristics and general health status of earthquake victims. General Health Questionnaire (GHQ-12) was used to find out psychological states of earthquake victims. Results The rate of injuries was 14.2% and leg and foot injuries were the most common with 44.2%. The relationship between injury status; and age, marital status, and being trapped under debris was revealed (p

Objective: Sex determination has been found interesting in forensic handwriting examinations and has been researched by scientists. The inclusion of the sex parameter as a supporting element in the examination of forensic handwriting while deciding belonging will increase the reliability of the results. In addition, it will help reduce the number of people to be investigated for a large group of suspects, both men and women. In this study, it was aimed to investigate the contribution of the ascender and descender parts of the letters to sex prediction by measuring them. Methods: In line with this purpose, handwriting samples were collected from 50 female and 50 male participants by having them write 11 sentences containing the letters “b, d, f, g, h, k, t, y, p” at initial, medial, and end positions. The ascender and descender parts of these letters were measured in millimeters. Logistics, k-nearest neighbor (KNN), support vector machine (SVM) and artificial neural network (ANN) were selected and applied to these data. Results: The ascender and descender parts of these letters were measured in millimeters and statistically significant differences were found between male and female participants. The ascender parts of the “b, d, h, k, t” were determined to be statistically significantly longer in males. Accuracy rates are 0.65, 0.60, 0.71 and 0.82 for Logistics, KNN, SVM and ANN, respectively. Conclusion: In our opinion, this result is promising. If the studies on this subject are increased, higher success rates can be achieved, and more contributions can be made to forensic handwriting examination.

Fermatean fuzzy soft set is the most powerful and effective extension of Fermatean fuzzy sets which deals with the parametrized values of the alternatives. It is also a generalization that provides better and more precise information in the decision-making process compared to Pythagorean and intuitionistic fuzzy soft sets. In this study, “OR” and “AND” operations and some different algebraic operations will be given. Then, some basic properties have been established for the Fermatean fuzzy soft set employing the improved operations. Furthermore, a decision-making approach has been suggested for the Fermatean fuzzy soft set based on a score matrix. To demonstrate the validity of the proposed approach, a numerical example has been presented.

The significance of improving rainfall prediction methods has escalated due to climate change-induced flash floods and severe flooding. In this study, rainfall nowcasting has been studied utilizing NASA Giovanni satellite-derived precipitation products and the convolutional long short-term memory (ConvLSTM) approach. The goal of the study is to assess the impact of data augmentation on flood nowcasting. Due to data requirements of deep learning-based prediction methods, data augmentation is performed using eight different interpolation techniques. Spatial, temporal, and spatio-temporal interpolated rainfall data are used to conduct a comparative analysis of the results obtained through nowcasting rainfall. This research examines two catastrophic floods that transpired in Türkiye Marmara Region in 2009 and the Central Black Sea Region in 2021, which are selected as the focal case studies. The Marmara and Black Sea regions are prone to frequent flooding, which, due to the dense population, has devastating consequences. Furthermore, these regions exhibit distinct topographical characteristics and precipitation patterns, and the frontal systems that impact them are also dissimilar. The nowcast results for the two regions exhibit a significant difference. Although data augmentation significantly reduced the error values by 59% for one region, it did not yield the same effectiveness for the other region. HIGHLIGHTS A ConvLSTM model was created for rainfall nowcasting.; Satellite data augmentation was conducted by employing eight distinct interpolation approaches.; The investigation examined two flood case studies and their nowcast outcomes using original and augmented data.; Although the use of augmented data reduced nowcast error by 59% in one flood event, its impact on the nowcast result in the other event was found to be negligible.;

A recent trend in hydroinformatics has been the growing number of data, models, and cyber tools, which are web accessible, each aiming to improve common research tasks in hydrology through web technologies. Coupling web-based models and tools holds great promise for an integrated environment that can facilitate community participation, collaboration, and scientific replication. There are many examples of server-side, hydroinformatics resource coupling, where a common standard serves as an interface. Yet, there are few, if any, examples of client-side resource coupling, particularly cases where a common specification is employed. Toward this end, we implemented the basic model interface (BMI) specification in the JavaScript programing language, the most widely used programing language on the web. By using BMI, we coupled two client-side hydrological applications (HydroLang and HLM-Web) to perform rainfall–runoff simulations of historical events with rainfall data and a client-side hydrological model as a case study demonstration. Through this process, we present how a common and often tedious task – the coupling of two independent web resources – can be made easier through the adoption of a common standard. Furthermore, applying the standard has facilitated a step toward the possibility of client-side ‘Model as a Service’ for hydrological models. HIGHLIGHTS We present the basic model interface (BMI) specification for the JavaScript programming language.; We present a comprehensive example of how BMI may be used as a common standard to couple client-side, hydroinformatics web resources.; For developers, BMI for JavaScript simplifies the effort needed to implement an Application Programing Interface for their resource.; For users, BMI for JavaScript accelerates learning and working with a new resource.;

Background and Objectives: Small airway fibrosis plays a critical role in the progression of chronic obstructive pulmonary disease (COPD). Previous research has suggested that Urotensin-II (U-II) and transforming growth factor-β (TGF-β) may contribute to pathological fibrosis in various organs, including the cardiovascular system, lungs, and liver. However, their specific relationship with airway fibrosis in COPD has not yet been thoroughly investigated. This study aims to evaluate the concentrations of U-II and TGF-β in individuals with COPD, as well as in healthy smokers and non-smokers, to explore their potential roles in COPD-related fibrosis. Materials and Methods: The study included three distinct groups: a healthy non-smoker control group (n = 98), a healthy smoker group (n = 78), and a COPD group (n = 80). All participants in the COPD group had a smoking history of at least 10 pack-years. COPD was defined according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines, with only patients classified as GOLD stage 2 or higher being included in the study. Urotensin-II (U-II) and transforming growth factor-β (TGF-β) levels were measured using a commercially available ELISA kit. Results: COPD patients had a significantly lower FEV1 (58 ± 15.4%) compared to smokers (79 ± 4.5%) and non-smokers (92 ± 3.7%) (p < 0.001). Similarly, COPD patients had a lower FEV1/FVC ratio (55 ± 9.4%) compared to smokers (72 ± 4.2%) and non-smokers (85 ± 3.6%) (p < 0.01 and p < 0.05, respectively). SaO2 was significantly lower in COPD patients (87%) compared to smokers (96.5%) and non-smokers (98%) (COPD vs. smokers: p < 0.05 and smokers vs. non-smokers: p > 0.05). U-II levels were significantly higher in COPD patients (175.10 ± 62.40 pg/mL) compared to smokers (118.50 ± 45.51 pg/mL) and non-smokers (85.29 ± 35.87 pg/mL) (p < 0.001 and p < 0.05, respectively). COPD patients also had significantly higher levels of TGF-β (284.60 ± 60.50 pg/mL) compared to smokers (160.00 ± 41.80 pg/mL) and non-smokers (92.00 ± 25.00 pg/mL) (p < 0.001 and p < 0.05, respectively). Conclusions: Our study supports the growing body of evidence that U-II and TGF-β play central roles in the development and progression of fibrosis in COPD. The negative correlation between these markers and lung function parameters such as FEV1 and FEV1/FVC indicates that they may be key drivers of airway remodeling and obstruction. These biomarkers could serve as early indicators of fibrotic changes in smokers, even before the onset of COPD.

This paper presents a novel framework, artificial intelligence-enabled intelligent assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and natural language processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA’s capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled virtual teaching assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with learning management systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education.

In this study, nine different types of essential facilities in the state of Iowa (such as hospitals, fire stations, schools, etc.) were analyzed on a county level in terms of flood depth, functionality and restoration time after flooding, and damage sustained during flooding. These essential facilities were also analyzed on the state level in terms of their location relative to the 100 y and 500 y flood zones. Results show that the number of essential facilities within the flood extent reached up to 39%, and during the 100 y flood scenario all but one of the six chosen counties lost functionality of 100% of their facilities. Most essential facilities were found to have a flood depth of 1 to 4 ft deep and a restoration time of 480 days. The purpose of this study is to bring awareness to decisionmakers regarding the risk that flooding events pose to essential facilities and to highlight the increasing dangers of flooding on a broader scale. This study will be beneficial to improve mitigation strategies, emergency response plans, and ensuring that emergency services and facilities are available in the event of future floods for the affected areas.

Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, with a focus on hydrological applications such as flood management, water level monitoring, agricultural water discharge, and water pollution management. We evaluated these MLLMs on hydrology-specific tasks, testing their response generation and real-time suitability in complex real-world scenarios. Prompts were designed to enhance the models’ visual inference capabilities and contextual comprehension from images. Our findings reveal that GPT-4 Vision demonstrated exceptional proficiency in interpreting visual data, providing accurate assessments of flood severity and water quality. Additionally, MLLMs showed potential in various hydrological applications, including drought prediction, streamflow forecasting, groundwater management, and wetland conservation. These models can optimize water resource management by predicting rainfall, evaporation rates, and soil moisture levels, thereby promoting sustainable agricultural practices. This research provides valuable insights into the potential applications of advanced AI models in addressing complex hydrological challenges and improving real-time decision-making in water resource management

Beyşehir Lake is the largest freshwater lake in the Mediterranean region of Turkey that is used for drinking and irrigation purposes. The aim of this paper is to examine the potential for data-driven methods to predict long-term lake levels. The surface water level variability was forecast using conventional machine learning models, including autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA). Based on the monthly water levels of Beyşehir Lake from 1992 to 2016, future water levels were predicted up to 24 months in advance. Water level predictions were obtained using conventional time series stochastic models, including autoregressive moving average, autoregressive integrated moving average, and seasonal autoregressive integrated moving average. Using historical records from the same period, prediction models for precipitation and evaporation were also developed. In order to assess the model’s accuracy, statistical performance metrics were applied. The results indicated that the seasonal autoregressive integrated moving average model outperformed all other models for lake level, precipitation, and evaporation prediction. The obtained results suggested the importance of incorporating the seasonality component for climate predictions in the region. The findings of this study demonstrated that simple stochastic models are effective in predicting the temporal evolution of hydrometeorological variables and fluctuations in lake water levels.

We introduce HydroLang Markup Language (HL-ML), a programming interface that uses a markup language to perform environmental analyses using the hydrological and environmental framework HydroLang. The software acts as a self-contained interface that uses HTML tags powered by the web component specification to generate simple hydrological computations that enable data analysis, visualization, and manipulation via semantically driven instructions. It enables hydrological researchers and professionals to use markup language to retrieve, analyze, visualize, and map data with basic programming skills. The components' adaptability enables users to run analytical routines that perform simple and complex analyses on the client side. We present the implementation details of the approach, the use of custom elements in web technologies and academia, and share sample usages to demonstrate the simplicity of use of the human-readable and computer-executable framework. HIGHLIGHTS An HTML-based computational framework has been developed to facilitate hydrology research and education.; The software is based on an effortless interface designed for environmental and hydrological analyses.; By employing architecture-agnostic web components, the library ensures reusability throughout web platforms.; The software has a modular design using a powerful core engine with third-party geospatial tools.;

Abstract Miscommunication between instructors and students is a significant obstacle to post-secondary learning. Students may skip office hours due to insecurities or scheduling conflicts, which can lead to missed opportunities for questions. To support self-paced learning and encourage creative thinking skills, academic institutions must redefine their approach to education by offering flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students. Its GPT-3-based knowledge discovery component and the generalized system architecture are presented accompanied by a methodical evaluation of the system’s accuracy and performance.

Healthcare, food, and energy are the basic needs of life in the globalizing world. Humankind's quest to maintain a healthy life and find ways to meet its needs has continued since its existence. The causal relations between the healthcare, food, and energy sectors are explored using data from the pandemic when COVID-19 was a global risk, and human health sustainability underwent a complicated process. It aims to examine the interaction between the healthcare, food, and energy sectors and model the causal relationship within the framework of probabilistic dependencies. For this purpose, the relationships between these sectors during the pandemic are modeled via Bayesian Networks (BNs). This highly successful inference method makes the complex structure of causal relationships graphically understandable. The data consists of stock returns at the end of the business day between March 11, 2020, when the pandemic was declared, and December 26, 2022. Data on 13 stocks actively traded on the Istanbul Stock Exchange (BIST) during the 700 days were obtained from tr.investing.com. Causal modeling uses Gaussian Bayesian Networks (GBNs) for continuous variables. To make the inferences drawn from the data more successful and minimize the loss of information, the GBN model is built with continuous variables. The posterior Probability Density Functions (PDFs) of the stocks in the network are constructed over the structure of the Directed Acyclic Graphs (DAG) of the BNs, and inferences are made by querying possible cases (tips). Markov Chain Monte Carlo (MCMC) simulations are performed with the posterior PDFs, and measures of the central tendency of the stocks are calculated. GBNs are used to generate daily return estimates for ULKER with the lowest MSE (1.06e-03) and RMSE (3.22e-02) values and ULUUN with the highest MSE (3.43e-03) and RMSE (5.83e-02) values.

Bu kitap tanıtımı; giriş, beş bölüm ve sonuçtan oluşan bir yüksek lisans tezi olarak hazırlanıp basılan Dr. Acâc el-Hatîb’in es-Sünne kable’t-tedvîn isimli eserini ele almaktadır. Sünnetin resmî olarak yazıya geçirilmeden önce nasıl muhafaza edildiğini ve aktarıldığını ihtiva eden kitap, sünnetin devlet eliyle yazıya geçirilmeden önce de ferdi olarak yazıldığını savunmakta ve aksi görüşleri çürütmeye çalışmaktadır. Sünnetin tedvinin Ömer b. Abdulazîz’den daha önce babası Abdulazîz b. Mervân tarafından Mısır valisi olduğu dönemde başlatıldığına dair veriye ulaştığını ifade eden yazar, bu konu etrafında kapsamlı çalışmalar yapılmasının önemli olduğunu vurgulamaktadır.

Objective:In this study, it was aimed to compare “incapacity to work rate scale” and “disability rate scale” in isolated eye traumas and to draw attention to differences between the scales.Methods:In our study, 605 files for which incapacity to work or disability rate was calculated only due to “eye defects” were examined by Council of Corensic Medicine 3rd Specialization Board in 2016, 2017 and 2018. Five hundred and ninety-two cases were analyzed retrospectively. The cases were evaluated in terms of parameters such as year of event, gender, age range, type of event, court types, and discretion. SPSS 21.0 statistical program was used to evaluate the data.Results:It is seen that average of “incapacity to work rate” is 29.24, “disability rate” is 21.56, “incapacity to work rate” is higher than “disability rate” in 455 cases (76.9%) and the “disability rate” is higher than “incapacity to work rate” in 38 cases (6.4%). In 99 cases (16.7%), both rates were not calculated. It was determined that there was a significant difference between rates in terms of both genders. In calculation of “incapacity to work rate”, it was determined that 8.28% of cases did not have exact equivalent of ratio and it was used with discretion, and this situation was all in diplopia cases.Conclusion:It was revealed that disability rate scale was more detailed and sensitive than “incapacity to work rate scale”, especially in diplopia, and that there was a difference between both scales in eye diseases in terms of methods and variables.

In this case, we present a condition where the extension of the hamate hook in the Guyon canal can damage the ulnar artery or its branches, leading to the development of an aneurysm or pseudoaneurysm. The patient, a 12-year-old female, presented to our clinic with a complaint of an uncontrolled palm lump that has been growing for several months and began to bleed in a pulsatile manner after trauma. She was an amateur volleyball player who trained twice weekly for two hours. Color Doppler ultrasound examination revealed a 1.1×0.8 cm pseudoaneurysm in a branch of the ulnar artery. Aneurysmectomy and primary repair were performed. Timely diagnosis and treatment planning are crucial for ulnar artery pseudoaneurysms or aneurysms to prevent ischemic events in a later period.

Accurate, vast, and real-time coverage of water level monitoring is crucial for the advancement of environmental research, specifically in the areas of climate change, water distribution, and natural disaster preparedness and management. The current state of the monitoring network requires an immediate solution to produce low-cost and accurate water level measurement sensors. This research presents a novel methodology for intelligent stream stage measurement, creating a distinct opportunity for a low-cost, camera-based embedded system that will measure water levels and share surveys to support environmental monitoring and decision-making. It is implemented as a stand-alone device that utilizes a registry of structures and points of interest (POI) along with the core modules of the application logic: (1) deep-learning powered water segmentation; (2) visual servoing; and (3) POI geolocation computation. The implementation relies on a Raspberry-Pi with a motorized camera for automated measurements and is supported by a Proportional–Integral–Derivative controller and multiprocessing. For future work, the involvement of the camera supports further use cases such as recognizing objects (e.g., debris, trees, humans, and boats) on the water surface. Additionally, the method shown can be made into a Progressive Web Application (PWA) that can be used on smartphones to allow crowdsourced citizen science applications for environmental monitoring. HIGHLIGHTS A methodology is proposed for intelligent stream stage measurements to support widespread environmental monitoring.; It is implemented on a Raspberry-Pi for affordable and continuous water level monitoring.; The system is applicable worldwide with validated functionality for operational viability.;

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep-learning approach that augments rainfall data with increased time resolutions to complement relatively lower-resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs), namely TempNet, to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. While TempNet achieves a mean absolute error of 0.332 mm/h, comparison methods achieve 0.35 and 0.341, respectively. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies. HIGHLIGHTS This paper improves the temporal resolution of radar rainfall products from 10 to 5 min.; A residual Convolutional Neural Network architecture, namely TempNet, is proposed for the temporal downscaling of radar rainfall.; Results show that the TempNet outperforms the optical flow-based temporal interpolation method in the study area.;

Computational hydrological models and simulations are fundamental pieces of the workflow of contemporary hydroscience research, education, and professional engineering activities. In support of hydrological modelling efforts, web-enabled tools for data processing, storage, computation, and visualization have proliferated. Most of these efforts rely on server resources for computation and data tasks and client-side resources for visualization. However, continued advancements of in-browser, client-side compute performance present an opportunity to further leverage client-side resources. Towards this end, we present an operational rainfall-runoff model and simulation engine running entirely on the client side using the JavaScript programming language. To demonstrate potential uses, we also present an easy-to-use in-browser interface designed for hydroscience education. Although the use case presented here is self-contained, the core technologies can extend to leverage multi-core processing on single machines and parallelization capabilities of multiple clients or JavaScript-enabled servers. These possibilities suggest that client-side hydrological simulation can play a central role in a dynamic, interconnected ecosystem of web-ready hydrological tools. HIGHLIGHTS We present the Hillslope Link Model (HLM)-Web, a simulation engine for rainfall-runoff modelling in JavaScript.; The simulation engine runs full-scale, physically based rainfall-runoff models.; All computations are performed on the client side within the browser.; HLM-Web is demonstrated with a use case for hydroscience education.; The use case facilitates hydrological education and research ‘sandboxing’.;

Hydrometeorological disasters, including floods and droughts, have intensified in both frequency and severity in recent years. This trend underscores the critical role of timely monitoring, accurate forecasting, and effective warning systems in facilitating proactive responses. Today’s information systems offer a vast and intricate mesh of data, encompassing satellite imagery, meteorological metrics, and predictive modeling. Easily accessible to the general public, these cyberinfrastructures simulate potential disaster scenarios, serving as invaluable aids to decision-making processes. This review collates key literature on water-related disaster information systems, underscoring the transformative impact of emerging information and Internet technologies. These advancements promise enhanced flood and drought warning timeliness and greater preparedness through improved management, analysis, visualization, and data sharing. Moreover, these systems aid in hydrometeorological predictions, foster the development of web-based educational platforms, and support decision-making frameworks, digital twins, and metaverse applications in disaster contexts. They further bolster scientific research and development, enrich climate change vulnerability frameworks, and strengthen associated cyberinfrastructures. This article delves into prospective developments in the realm of natural disasters, pinpointing primary challenges and gaps in current water-related disaster information systems, and highlighting the potential intersections with future artificial intelligence solutions.

Background: COVID-19 is a new disease that is associated with high morbidity that has spread around the world. Credible estimating is crucial for control and prevention. Nowadays, hybrid models have become popular, and these models have been widely implemented. Better estimation accuracy may be attained using time-series models. Thus, our aim is to forecast the number of COVID-19 cases with time-series models. Objective: Using time-series models to predict deaths due to COVID-19. Design: SARIMA, NNAR, and SARIMA-NNAR hybrid time series models were used using the COVID-19 information of the Republic of Turkey Health Ministry. Participants: We analyzed data on COVID-19 in Turkey from March 11, 2020 to February 22, 2021. Main Measures: Daily numbers of COVID-19 confirmed cases and deaths. Materials and methods: We fitted a seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNAR) hybrid model with COVID-19 monthly cases from March 11, 2020, to February 22, 2021, in Turkey. Additionally, a SARIMA model, an NNAR model, and a SARIMA–NNAR hybrid model were established for comparison and estimation. Results The RMSE, MAE, and MAPE values of the NNAR model were obtained the lowest in the training set and the validation set. Thus, the NNAR model demonstrates excellent performance whether in fitting or forecasting compared with other models. Conclusions The NNAR model that fits this study is the most suitable for estimating the number of deaths due to COVID-19. Hence, it will facilitate the prevention and control of COVID-19.

With this study, it was aimed to scientifically detect the weeds that pose a problem in the meadow-pasture areas of Muş province. For this purpose, field studies were carried out in 37 meadow pasture areas in Muş province. The information such as location, habitat, and altitude of the plant samples collected as a result of the field studies was recorded. For the identification of plant taxa, "Flora of Turkey and the East Aegean Islands" has been used. The determined taxa were evaluated at the species level, and the damage patterns of weed species were categorized according to the relevant literature. As a result of this study, 211 weed species belonging to 22 families were determined throughout the Muş province. The families with the most taxa were Asteraceae (50 species), Lamiaceae (36 species), Cyperaceae (21 species), and Ranunculaceae (18 species). Damage patterns of each weed species were determined in meadow pasture areas. As a result, it has been determined that harmful species are dense in some areas. Among the most important reasons for this is early spring grazing, uncontrolled and over-capacity grazing can be counted.

This study investigated the somatic chromosome numbers and morphometric properties of 11 different taxa belonging to the genus Cuscuta L., which is one of the flowering parasitic plants and causes great economic losses on agricultural products. For this purpose, the species were examined karyologically and compared statistically. Belonging to the genus Cuscuta, C. campestris Yunck., C. hyalina Roth, C. kotschyana Boiss., C. babylonica Aucher ex Choisy, C. europaea L., C. kurdica Engelm., C. brevistyla A.Braun ex A.Rich, C. planiflora Ten., C. approximata Bab., C. lupuliformis Krock. C. palaestina Boiss., the chromosome number and morphology of the species were investigated using karyological techniques. Chromosome numbers of the species; C. kotschyana, C. babylonica, C. europaea, C. kurdica, C.planiflora 2n=14; C. campestris, C. hyalina, C. approximata, C. lupuliformis, and C. palaestina 2n=28 and C. brevistyla 2n=42 is determined. Also, the species' chromosome number, total chromosome length, relative length, arm ratio, centromere index and centromere states, and karyotype asymmetry values were determined. Chromosome numbers of C. kotschyana and C. kurdica taxa were defined for the first time in this study. Thus, new data on the systematics of these species have been revealed.

In recent years, advancements in artificial intelligence (AI) have led to the development of large language models like GPT-4, demonstrating potential applications in various fields, including education. This study investigates the feasibility and effectiveness of using ChatGPT, a GPT-4 based model, in achieving satisfactory performance on the Fundamentals of Engineering (FE) Environmental Exam. This study further shows a significant improvement in the model's accuracy when answering FE exam questions through noninvasive prompt modifications, substantiating the utility of prompt modification as a viable approach to enhance AI performance in educational contexts. Furthermore, the findings reflect remarkable improvements in mathematical capabilities across successive iterations of ChatGPT models, showcasing their potential in solving complex engineering problems. Our paper also explores future research directions, emphasizing the importance of addressing AI challenges in education, enhancing accessibility and inclusion for diverse student populations, and developing AI-resistant exam questions to maintain examination integrity. By evaluating the performance of ChatGPT in the context of the FE Environmental Exam, this study contributes valuable insights into the potential applications and limitations of large language models in educational settings. As AI continues to evolve, these findings offer a foundation for further research into the responsible and effective integration of AI models across various disciplines, ultimately optimizing the learning experience and improving student outcomes.

Asset scores are widely used as the preferred method of measuring socioeconomic wellbeing of households in developing countries. We examine the degree of discrepancies in reporting asset ownership by male and female heads of the same household. Household asset scores were estimated separately for male and female responses, using Principal Component Analysis, the method widely used in the literature, and households were categorized into wealth quintiles. The results indicate that only half of the households belonged to the same quintile groups for both male and female response-based asset scores. In addition, the two estimates of asset scores within the same quintile deviate by more than 20% for 71% of households in the top three quintiles and for 18% in the poorest two quintiles. Inter-individual (male/female) variability in reporting the asset ownership was high enough to raise concerns about the validity and reliability of asset scores as a metric of household socioeconomic status. Although the study did not try to ascertain underlying reasons for differential reporting, possible explanations could be a lack of awareness among household members on asset ownership or differential propensity to demonstrate relatively better social status of the household by male and female respondents. To improve reliability of asset scores, methodology for collecting asset ownership information should define who in the household may or may not be used as a respondent. Visual verification of reported ownership of assets will reduce male-female discrepancies but the verification process is time-consuming and intrusive, thus negating the advantages of collecting asset data. Alternatives to asset-based scoring need to be considered and one approach could be to solicit subjective opinions from male and female heads on the location of households in the social hierarchy.

Objective:According to the International Labor Organization, 153 workers are injured and one worker dies because of occupational accidents or diseases every 15 seconds. Every day, 6,300 people die due to occupational accidents or work-related diseases in the world.Methods:Forensic investigation documents and autopsy reports of patients who were autopsied between 2011 and 2015 at the Forensic Medicine Department of Morgue Specialization in Trabzon Group Presidency and whose causes of death were related to occupational accidents were examined retrospectively.Results:It was determined that 111 (99.1%) were male and 1 (0.9%) were female from a total of 112 worker deaths. Their ages ranged between 18 and 64 years. When the distribution of cases by business types was analyzed, it was determined that 61 (54.5%) cases worked in the construction sector and 12 (10.7%) in the energy sector. It was determined that work accidents occurred due to falling from height in 34 (30.4%) cases and electric shock in 19 (17%) cases. There were skull bone fractures in 44 (39.3%) cases and rib cage fractures in 22 (19.6%) cases during autopsies. In histopathological examinations, there were cardiovascular pathologies in 13 (11.6%) cases, edema and bleeding in the lungs in 20 (17.9%) cases.Conclusion:According to the results of this study, construction sector took place on the top, which is consistent with the national work accident statistics. As a cause of death, general body traumas due to falling from high, electric shocks and work machine accidents come to the fore.

The world is faced with disasters caused by natural or human effects from time to time. The various political, economic, health, and social consequences of these disasters affect people for different periods of time. In natural disasters and especially in epidemic diseases, some measures are taken to protect people from the negative effects of the situation. One of the measures that can be taken is quarantine. The target audience of this study is children aged 5-6 in early childhood. Children of this age group are in the process of gaining skills in expressing their feelings during this period. In addition, the emotional responses of these children can be noticed by a careful observer or even an expert. The aim of the paper is to evaluate the risks of the impacts of quarantine status related to the COVID-19 pandemic on the cognition and behavior of children staying at home. Risks of the quarantine process in children in early childhood were evaluated using the Pythagorean fuzzy AHP method.

Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community. HIGHLIGHTS Water Ethics Web Engine (WE)2 is a web framework to incorporate voting-based ethical preferences into water resources decision support.; We share (WE)2 openly at our project repository: https://github.com/uihilab/waterethicswebengine.; A proof-of-concept use case is presented where decision models are learned and deployed with flooding scenarios.; Results indicate that the framework can capture group ‘wisdom’ in learned models.;

The study aimed to test various vigour tests to correlate them with seedling emergence in two field sowing dates and two different sowing depths in controlled room emergence of ten dill seed lots. Mean germination time, accelerated aging; for 24 hours, 48 hours, and 72 hours, electrical conductivity readings at 16 hours and 24 hours, and EC readings after AA test were used as vigour tests, and results were correlated with seedling emergence percentages. Seedling emergence was tested in two sowing dates in the field and two different sowing depths in controlled room conditions. Controlled room seedling emergence values (80-95% in 2 cm, 76-92% in 4 cm) were higher than those of field emergence values (67-85% in the first sowing and 60-80% in the second sowing). Most of the vigour tests were significantly correlated with emergence potential. The highest correlation values were observed in EC16h and EC24h tests, significance between emergence and these two vigour tests were at P

Multi-objective capacitated multiple allocation hub location problem (MOCMAHLP) is a variation of classic hub location problem, which deals with network design, considering both the number and the location of the hubs and the connections between hubs and spokes, as well as routing of flow on the network. In this study, we offer two meta-heuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and archived multi-objective simulated annealing method (AMOSA) to solve MOCMAHLP. We attuned AMOSA based approach to obtain feasible solutions for the problem and developed five different neighborhood operators in this approach. Moreover, for NSGA-II based approach, we developed two novel problem-specific mutation operators. To statistically analyze the behavior of both algorithms, we conducted experiments on two well-known data sets, namely Turkish and Australian Post (AP). Hypervolume indicator is used as the performance metric to measure the effectiveness of both approaches on the given data sets. In the experimental study, thorough tests are conducted to fine-tune the proposed mutation types for NSGA-II and proposed neighborhood operators for AMOSA. Fine-tuning tests reveal that for NSGA-II, mutation probability does not have a real effect on Turkish data set, whereas lower mutation probabilities are slightly better for AP data set. Moreover, among the AMOSA based neighborhood operators, the one which adds/removes a specific number of links according to temperature (NS-5) performs better than the others for both data sets. After analyzing different operators for both algorithms, a comparison between our NSGA-II based and AMOSA based approaches is performed with the best settings. As a result, we conclude that both of our algorithms are able to find feasible solutions of the problem. Moreover, NSGA-II performs better for larger, whereas AMOSA performs better for smaller size networks.

In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support modeling and analysis in surface water estimation, river meandering, wetland loss, and other hydrological research studies. First, we have created an extensive repository of overhead river images to be used in training. Second, we incorporated the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution to generate high quality (i.e., 1,024 × 1,024) synthetic river imagery. With simpler GAN architectures, difficulties arose in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seemed to significantly reduce. The results presented in this study show great promise in generating high-quality images and capturing the details of river structure and flow to support hydrological modeling and research.

Most deep learning application studies have limited accessibility and reproducibility for researchers and students in many domains, especially in earth and climate sciences. In order to provide a step towards improving the accessibility of deep learning models in such disciplines, this study presents a community-driven framework and repository, EarthAIHub, that is powered by TensorFlow.js, where deep learning models can be tested and run without extensive technical knowledge. In order to achieve this, we present a configuration data specification to form a middleware, an abstraction layer, between the framework and deep learning models. Once an easy-to-create configuration file is generated for a model by the user, EarthAIHub seamlessly makes the model publicly available for testing and access using a web platform. The platform and community-enabled model repository will benefit students and researchers who are new to the deep learning domain by enabling them to access and test existing models in the community with their datasets, and researchers to share their novel deep learning models with the community. The platform will help researchers test models before adapting them to their research and learn about a model’s details and performance.

The study was conducted to test whether radicle emergence (RE) would correlate with the storage potential of ten seed lots of watermelon (Citrullus lanatus (Thunb.) Matsum. and Nakai) hybrid cultivars (>98% normal germination). The RE test was performed by frequent counting radicle emergence (2 mm) percentage between 34 h and 60 h after germination was set up at 25 °C in the dark. Seed longevity was hermetically determined by artificial storage of seeds (air and waterproof) at 17 ± 0.3% of seed moisture and at 35 ± 2 °C over 63 days. Twelve seed samples were taken out during aging, and seed survival curves were constructed based on normal germination percentages conducted at 25 °C for seven days in the dark. The seed longevity criterion was P50 (time for the germination to fall to 50%), which was determined through probit analysis by using survival curves. Correlation analysis showed that RE counts at 42 h during germination were highly correlated (p < 0.01) with initial seed quality, Ki (r = 0.7538), and the half-viability period, P50 (r = 0.7936). Pre-storage normal germination percentages of seed lots were not related to longevity. Results showed that the RE vigor test has the potential to predict longevity in highly germinating hybrid watermelon seed lots.

There is a shifting paradigm in gifted education from person-based approaches (i.e., identifying giftedness) to process-based approaches (i.e., transacting giftedness). This new framework is centered on enriching educational opportunities that will make the process meaningful (i.e., gifted) to everyone in a setting. However, little is known about how this renewed perspective can be applied in teacher professional development. In line with the socio-ecological models, our study aims to identify the best appropriate model to describe teacher self-efficacy (i.e., the dependent variable in the study) as professional development from an ecological perspective and to propose an ecologically intelligent school (EIS) for the advancement of self-efficacy. Structural equation modeling (SEM) was performed to create a model using TALIS 2018 dataset. Afterward, indices of goodness-of-fit criteria were examined for each model. The results indicate that there is a complex ecological background, in that various factors affect the dependent variable. Model 3 was determined as the most suitable model that can be proposed as an ecologically intelligent school (EIS) for the advancement of self-efficacy. The factors within the three layers of the socio-ecological model—communication with teachers, communication with students, school climate, and feeling valued by the national level—altogether created an appropriate model explaining teacher professional development, regarding self-efficacy.

Examining the factors that affect the vaccination rate among young people in an ethical frame can support vaccination promotion. Therefore, this study will elaborate, through an ethical lens, on young people’s hesitation about and decisions regarding getting vaccinated. The cross-sectional study was conducted with 2428 people aged 15–30 in Turkey in June 2022. The questionnaire included the following subtitles: psycho-social situation, health services and health policies, COVID-19 vaccine, and predictions about life and health after the pandemic. The average age was 22.9 years. In the study sample, 80% were vaccinated, while 20% were not. Vaccinated participants acted more cautiously to protect their health. Receiving accurate and sufficient information on proposed vaccines affects vaccination status. The primary reason for getting vaccinated was “to protect their health, families, and relatives”, and the primary reason for not getting vaccinated was “not trusting the vaccine content or the country where the vaccine was produced”. Specifically, those vaccinated felt more relaxed physically, psychologically, and socially. In addition, the expectations for the future of those vaccinated were significantly higher. Accurate and adequate information is essential for reducing vaccine hesitancy. In addition, promoting prosocial behaviors in young people and highlighting related values will support vaccination.

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km2. The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

This work was conducted on the use of the RE (radicle emergence) test to estimate the seedling emergence (SE) and quality (seedling shoot weight (SSW), seedling root weight (SRW) and seedling height (SH)) of twelve hybrid cucumber seed lots with high germination (>95%) at low temperature conditions, high salt conditions and a combination of the two. The percentages of RE, which is a 2 mm radicle protrusion at 25 °C in the dark, were counted between 18 h and 32 h. The seeds were sown in peat moss in trays and kept at 15 °C (low temperature stress), or salty water was applied as 100 mM NaCl at 25 °C (salt stress) for three days, and both were applied in a combined-stress environment. Seedling emergence and quality parameters were reduced by both stress environments and their combination. SE, SSW, SRW and SH values ranged between: 91–100%, 614–844 mg, 102–143 mg, 6.8–8.8 cm at low temperature; 90–98%, 598–904 mg, 101–154 mg, 6.5–7.8 cm at salt stress; and 76–92%, 464–608 mg, 97–133 mg, 5.8–6.9 cm at their combination. The RE values with great differences seen among the lots (20–28 h) were regressed with seedling emergence and quality values. RE 24 h had the highest R2 and significant values in all stress environments as R2 = 0.596–0.858, p < 0.05–0.001 at low temperature; R2 = 0.620–0.827, p < 0.05–0.001 with salt; and R2 = 0.686–0.842, p < 0.05–0.001 with combined stresses. We concluded that RE as a vigour test can be used successfully to estimate the seedling quality of highly germinating hybrid cucumber seed lots. Use of the RE test in hybrid cucumber seeds and its influence on high-quality seedling production were discussed.

A trustworthy assessment of soil moisture content plays a significant role in irrigation planning and in controlling various natural disasters such as floods, landslides, and droughts. Various machine learning models (MLMs) have been used to increase the accuracy of soil moisture content prediction. The present investigation aims to apply MLMs with novel structures for the estimation of daily volumetric soil water content, based on the stacking of the multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR). Two groups of input variables were considered: the first (Model A) consisted of various meteorological variables (i.e., daily precipitation, air temperature, humidity, and wind speed), and the second (Model B) included only daily precipitation. The stacked model (SM) had the best performance (R2 = 0.962) in the prediction of daily volumetric soil water content for both categories of input variables when compared with the MLP (R2 = 0.957), RF (R2 = 0.956) and SVR (R2 = 0.951) models. Overall, the SM, which, in general, allows the weaknesses of the individual basic algorithms to be overcome while still maintaining a limited number of parameters and short calculation times, can lead to more accurate predictions of soil water content than those provided by more commonly employed MLMs.

The study was conducted to test whether a vigor test of a single count of radicle emergence (RE test) would correlate with the storage potential of 10 seed lots of onion (Allium cepa L.) cultivars in artificial storage conditions. The RE test was performed by counting radicle emergence (2 mm) percentage at 80 h after germination was set up at 20 °C. Seed longevity was determined by storage at 75% relative humidity, obtained by saturated NaCl, at 35 °C over 60 days. Twelve seed samples were taken out during ageing, and seed survival curves were constructed based on normal germination percentages conducted at 20 °C for 12 days in the dark. The seed longevity criterion was P50 (time for the viability to fall to 50%), which was determined through probit analysis by using survival curves. Correlation analysis showed that RE counts at 80 h during germination were highly correlated (p < 0.001) with initial seed quality, Ki (R2 = 0.888) and the half-viability period, P50 (R2 = 0.823). Non-aged pre-storage final normal germination percentages of seed lots were also significantly related to longevity (Ki, R2 = 0.808, p < 0.001, P50, R2 = 0.750, p

We introduce an analytical framework for analyzing tweets to (1) identify and categorize fine-grained details about a disaster such as affected individuals, damaged infrastructure and disrupted services; (2) distinguish impact areas and time periods, and relative prominence of each category of disaster-related information across space and time. We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disaster-relatedness. We employ LSTM (Long Short-Term Memory) networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies. Second, we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation (LDA), and identify latent categories of tweets such as affected individuals and disrupted services. Third, we employ spatially-adaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category, respectively. Using Hurricane Irma as a case study, we analyze over 500 million keyword-based and geo-located collection of tweets before, during and after the disaster. Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.

In this paper, we propose a new model for the multi-objective multiple allocation hub network design and routing problem which contains determining the location of hubs, the design of hub network, and the routing of commodities between source-destination pairs in the given network. The selected hubs are not assumed to be fully connected, and each node and arc in the network has capacity constraints. The multiple objectives of the problem are the minimization of total fixed and transportation costs and the minimization of the maximum travel time required for routing. We propose a mathematical formulation for the multi-objective problem and present a meta-heuristic solution based on a well-known multi-objective evolutionary algorithm. Using the proposed formulation, we are able to find the optimal solution for small networks of five nodes and seven nodes. To evaluate the performance of our heuristic approach on real data, the computational experiments are conducted on Turkish postal system data set. The results demonstrate that our heuristic approach can find feasible solutions to the problem in reasonable execution time, which is less than 10 min.

This work tested the effect of after-ripening on seed germination of thirteen commercial seed lots from four open pollinated cultivars (Aydın Siyahı, Kemer, Pala and Topan) of aubergine (Solanum melongena L.). Seeds were stored (after-ripened) at 5 °C with at 9% seed moisture content over 14 days in the dark in hermetic conditions. After-ripened seeds were tested at 25 °C and 20/30 °C (16/8 h) together with a control at 20/30 °C for 14 days in petri dishes. Results indicated that after-ripening significantly (p < 0.05) increased total germination in six lots and normal germination in eight lots out of thirteen when germination was tested at 25 °C. Increases were also observed eleven lots in total and thirteen in normal germination percentages out of thirteen when seeds were tested at 20/30 °C. Cumulative germination showed that after-ripened seeds germinated faster than control either germinated at 25 or 20/30 °C. Results indicated that aubergine seed germination can be increased through after-ripening treatment which shows the presence of seed dormancy in this species.

Merkez Bankaları ülkelerin finansal düzenlemelerinin yapılması ve ülkenin genel ekonomik dengelerinin korunması için çalışmaktadırlar. Bunun için beklenmedik ihtiyaçlar doğduğunda kullanmak üzere rezerv bulundururlar. Ancak belirsizliğin yoğun olduğu finans piyasalarında piyasa hareketlerinin doğru tahmin yöntemleriyle desteklenmemesi beklenmedik sonuçlar doğurabilir. Bu nedenle merkez bankaları, ülkelerinin finansal yapısını göz önüne alarak rezervleri ile ilgili tahmin çalışmaları yürütmektedirler. Piyasada meydana gelen dalgalanmalar verilerin klasik istatistiksel yöntemlerle modellenmesini zorlaştırmaktadır. Birçok çalışmada yapay zekâya dayalı tekniklerin doğrusal olmayan verilerin modellenmesinde klasik tekniklerle göre üstün performans gösterdiği belirtilmektedir. Bu amaçla çalışmamızda Türkiye Cumhuriyet Merkez Bankası (TCMB)’nın aylık altın rezervi Yapay Sinir Ağları (YSA) ve Holt-Winters Üstel Düzleme yöntemleri ile analiz edilmiştir. Çalışmada Aralık 1987– Mayıs 2017 dönemine ait aylık ağırlıklı ortalama rezerv tutarları ($/milyon) kullanılmıştır. Toplamsal Holt-Winters Üstel Düzleme yöntemiyle elde edilen modelin performans sonuçları YSA modeli ile karşılaştırılmıştır. R2, MAPE ve RMSE değerleri bakımından YSA modeli Toplamsal Holt-Winters Üstel Düzleme yönteminden daha başarılı sonuçlar vermiştir.

The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits.

This study was conducted to test the effect of a priming combination on the seed germination percentage and seedling emergence performance of purslane under climate chamber and field conditions. Four purslane seed lots were treated according five different methods, which were T1: Seeds kept at a hundred percent relative humidity for four hours at 20 °C; T2: Seeds kept at a hundred percent relative humidity for four hours at 20 °C, and then soaked in distilled water for 8 hours at 5 °C; T3: Seeds kept at a hundred percent relative humidity for four hours at 20 °C, and then soaked in distilled water for 8 hours at 20 °C; T4: Seeds soaked in distilled water for 8 hours at 5 °C; T5: Seeds soaked in distilled water for 8 hours at 20 °C; and C: Control (untreated). Seed germination was calculated for 14 days at 20 °C, seedling emergence percentages were calculated in the climatically-controlled chamber for 21 days at 22 °C, and in the field for 35 days at 15-25 °C. The highest seed germination (94%) and seedling emergence in the climatically-controlled chamber (87%) and field (82%) were obtained from seeds that had been kept at a hundred percent relative humidity for four hours at 20 °C, then soaked in distilled water for eight hours at 5 °C. Results indicated that farm-priming, can be an efficient priming method in purslane seeds.