Month: April 2024

Evaluating the performance of Grid IMD, NASA POWER, and MarkSim timeseries weather dataset for Uttarakhand Climatic Condition

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Abstract

Increasing weather variability and corresponding increased threat to the sustainability of the system and to the food security of any nation raises the importance of weather analysis in a range of studies. Meteorological data, hence, is used as a key component while developing a weather-based risk assessment and impact assessment models. However, despite of the availability of global meteorological data in real time and several state-of the art dynamic prediction system, such models demand downscaling of these datasets to the regions of interest. The present scientific fraternity has been able to provide a range of datasets at needed spatial resolution, which are generated through interpolation, weather generation methods, satellite-based remote sensing methods, and others. Each of the datasets has their own advantages and limitations. They are not universal, because of which their robustness and reproducibility varies with location. Therefore, the present study is basically evaluation of the freely available data sources (Grid IMD, NASA POWER and MarkSim) to know which one fits best to the study area. Statistical techniques such as error statistics, correlation analysis, anomaly, and percent deviation have been used for weather dataset at three timescales (daily, weekly, and monthly). Results for maximum and minimum temperature indicated that NASA POWER datasets are more reliable than IMD data for Ranichauri (at all the three timescales) and Roorkee (only at daily and weekly timescale), unlike Udham Singh Nagar for which IMD gives better results for daily data; and MarkSim at weekly and monthly scale. It was also observed that for Udham Singh Nagar and Roorkee, MarkSim results are found to be better for RCP 2.6 as well as RCP 4.5 at higher timescales. Better performance of Tmax under RCP 4.5 indicates that the emission activities have increased in the districts, which can be attributed directly to the increased industrial establishments in the region.

Analysis of drought intensity, frequency and trends using the spei in Turkey

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Abstract

This study addresses into the critical issue of drought as a natural disaster, especially in regions characterized by arid and semi-arid climates like Turkey. The primary aim of this study is to investigate the historical occurrences of meteorological drought events in Turkey, focusing on their past frequency, intensity, and spatial distribution. The study employs the Standardized Precipitation Evapotranspiration Index (SPEI) method and utilizes 50 years of monthly temperature and precipitation data collected from 222 meteorological stations across the country. Drought severity is assessed using the run theory method, and trends in drought patterns are analyzed through the Mann–Kendall trend test. Additionally, the text explores the connection between elevation and the geographical distribution of drought events.

The study’s findings reveal a noticeable increase in the occurrence of drought periods over time. Among the selected periods, the most widespread drought event was observed in the year 2001. The Bozcaada meteorology station exhibited the highest frequency of drought with a value of 223, while the Ispir meteorology station recorded the lowest frequency with a value of 151. Over the course of the 50-year analysis, no significant correlation was found between drought and elevation, although a gradual increase was noted in the last 10 years. The results also indicate a gradual north-to-south increase in drought intensity in Turkey. The study identifies four distinct drought hotspots in the country: the Western Anatolia Region, Central and Southern Anatolia Region, Southeastern Anatolia Region, and Eastern Anatolia Region.

Evaluation of precipitation reanalysis products for regional hydrological modelling in the Yellow River Basin

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Abstract

This study evaluates six precipitation reanalysis products for the Yellow River Basin using gridded rain gauge data, runoff data and the Atmospheric and Hydrological Modelling System (AHMS) simulations. The assessment begins with comparing the annual, seasonal, monthly and daily precipitation of the products with gridded rain gauge data. The AHMS is then run with each of the precipitation reanalysis products under two scenarios: one with calibrated rainfall-runoff and the other without. The simulated streamflow is then compared with the corresponding observations. It is found that non-gauge-corrected products tend to overestimate precipitation, especially for mountainous regions. Amongst the six products evaluated, the China Meteorological Forcing Dataset (CMFD) and WATCH Forcing Data methodology applied to ERA5 (WFDE5/CRU+GPCC) are identified as the most accurate products, supported by both statistical and hydrological comparisons. This consistency in statistical and hydrological comparisons suggests the potential applicability of the hydrological comparison method using the AHMS in ungagged catchments, even in the presence of significant anthropogenic impacts. Furthermore, the calibration of the hydrological model significantly impacts the model’s response to precipitation, effectively compensating for deficiencies in rainfall data within certain limits. This study highlights accurate representation of extreme rainfall events in precipitation products has a significant impact on calibrated soil parameters and is particularly important in hydrological modelling. It enhances our understanding of the reliability of hydrological simulations and provides valuable insights for the assessment of precipitation reanalysis products in large arid and semiarid basins affected by human activities.

Cerebellum Lecture: the Cerebellar Nuclei—Core of the Cerebellum

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Abstract

The cerebellum is a key player in many brain functions and a major topic of neuroscience research. However, the cerebellar nuclei (CN), the main output structures of the cerebellum, are often overlooked. This neglect is because research on the cerebellum typically focuses on the cortex and tends to treat the CN as relatively simple output nuclei conveying an inverted signal from the cerebellar cortex to the rest of the brain. In this review, by adopting a nucleocentric perspective we aim to rectify this impression. First, we describe CN anatomy and modularity and comprehensively integrate CN architecture with its highly organized but complex afferent and efferent connectivity. This is followed by a novel classification of the specific neuronal classes the CN comprise and speculate on the implications of CN structure and physiology for our understanding of adult cerebellar function. Based on this thorough review of the adult literature we provide a comprehensive overview of CN embryonic development and, by comparing cerebellar structures in various chordate clades, propose an interpretation of CN evolution. Despite their critical importance in cerebellar function, from a clinical perspective intriguingly few, if any, neurological disorders appear to primarily affect the CN. To highlight this curious anomaly, and encourage future nucleocentric interpretations, we build on our review to provide a brief overview of the various syndromes in which the CN are currently implicated. Finally, we summarize the specific perspectives that a nucleocentric view of the cerebellum brings, move major outstanding issues in CN biology to the limelight, and provide a roadmap to the key questions that need to be answered in order to create a comprehensive integrated model of CN structure, function, development, and evolution.

Phase unwrapping via hierarchical and balanced residue partitioning

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Abstract

Branch cut placement is an important issue in 2D phase unwrapping. The main requirement is to get the shortest branch cut placement with a minimum isolated area. We present a branch cut placement method that is based on hierarchical balanced partitioning (HBP) of the residue map. The method has two stages. In the first stage, the balanced residues which are one pixel or two pixels apart are connected. In the second stage, the remaining residues are processed via hierarchical and balanced partitioning of the residue map and subsequent serial connection. The method considers both local and global positioning of the residues and in this respect produces better results compared to the well-known conventional Goldstein method where the residue groups are connected based on local considerations only with respect to branch-cut length, and isolated area. The proposed method is also compared with the combined and extended methods based on residue searching, phase unwrapping max-flow method, exchange algorithm, minimum-cost matching theory, and hybrid genetic algorithm by using different types and sizes of wrapped input images. The implementation of the proposed method and the RMSE values are also comparable.

Spatial and temporal distribution of emerging airborne viral infectious diseases outbreaks on a global scale

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Abstract

Aim

This study aimed to explore the spatial and temporal characteristics of emerging airborne viral infectious diseases outbreaks worldwide.

Subject and methods

We conducted a systematic literature review on outbreaks of emerging airborne viral infectious diseases and calculated outbreak number and intensity at the country level. Fisher’s exact test was used to compare the viral infectious diseases outbreaks in different income-level regions. To identify the major airborne viral infectious diseases outbreaks, we ranked and extracted the leading viral infectious diseases in outbreak number and intensity in each country by year.

Results

A total of 2505 outbreaks were reported from 1873 to 2021 across 2010 studies. There were 47 countries (47/130, 36.15%) with more frequent emerging airborne viral infectious disease outbreaks (more than nine outbreaks), and these countries mainly distributed in high-income regions (22/47 countries, 46.81%, p < 0.05), especially in Western Europe (14/47 countries, 29.79%, p < 0.05). The number of overall outbreaks was more in the United States and China than in other countries in different years. Outbreaks of measles and influenza are always frequent and intense. Highly pathogenic human coronaviruses infection caused short-term pandemics during which their outbreak number and intensity exceeded other viruses. Rift valley fever outbreaks in the human population are spreading outside of Africa through the flow of goods and travelers.

Conclusion

Countries in high-income regions reported more emerging airborne viral infectious diseases outbreaks, especially in the Western European region, the United States, and China. It is urgent to strengthen collaborative surveillance of emerging airborne viruses, cross-border flow of goods and travelers, and ecological environment to avoid the spread of viral infectious diseases outbreaks worldwide.

Breast lymphedema following breast-conserving treatment for breast cancer: current status and future directions

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Abstract

Purpose

To examine the current evidence on breast lymphedema (BL) diagnosis and treatment after breast-conserving surgery, identify gaps in the literature, and propose future research directions.

Methods

A comprehensive literature review was conducted using Ovid, PubMed, and Cochrane, including studies published between 2000 and 2023. References were reviewed manually for eligible studies. Inclusion criteria were as follows: patients who underwent breast conserving treatment (surgery ± radiation) for breast cancer, goals of the paper included analyzing or reviewing BL measurement with ultrasound or tissue dielectric constant, or BL treatment. Twenty-seven manuscripts were included in the review.

Results

There is variation in incidence, time course, and risk factors for BL. Risk factors for BL included breast size, primary and axillary surgery extent, radiation, and chemotherapy but require further investigation. Diagnostic methods for BL currently rely on patient report and lack standardized criteria. Tissue dielectric constant (TDC) and ultrasound (US) emerged as promising ambulatory BL assessment tools; however, diagnostic thresholds and validation studies with ICG lymphography are needed to establish clinical utility. The evidence base for treatment of BL is weak, lacking high-quality studies.

Conclusion

The natural history of BL is not well defined. TDC and US show promise as ambulatory assessment tools for BL; however, further validation with lymphatic imaging is required. BL treatment is not established in the literature. Longitudinal, prospective studies including pre-radiation measurements and validating with lymphatic imaging are required. These data will inform screening, diagnostic criteria, and evidence-based treatment parameters for patients with BL after breast-conserving surgery and radiation.

A review of intelligent music generation systems

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Abstract

With the introduction of ChatGPT, the public’s perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field’s development prospects.

Generic optimization approach of soil hydraulic parameters for site-specific model applications

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Abstract

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

PCA-VGG16 model for classification of rock types

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Abstract

Conventional convolutional neural networks (CNN) are deficient in the rock type recognition due to large convolutional kernels and numerous network parameters necessitated for recognition of complex images. The advanced convolutional neural network, Visual Geometry Group-16 (VGG16) model, which is based on multiple small convolutional kernels and fully connected layers, attains higher classification accuracy, yet is limited by low computational efficiency. In this paper, we propose a novel approach that integrates the advanced VGG16 with the Principal Component Analysis (PCA), a dimensionality reduction technique. This integration, referred to as the PCA-VGG16 model, aims to enhance the computational efficiency of automatic rock type identification. A dataset comprising 3000 images of six rock types: limestone, shale, dolomite, quartzite, marble, and granite, is assembled for training and testing of the PCA-VGG16 model. The feasibility of the PCA-VGG16 model for classification prediction is demonstrated through evaluation metrics including accuracy, loss value, and F1-score. A comparative analysis with the CNN and VGG16 models reveals that the proposed PCA-VGG16 model exhibits superior classification accuracy and reduced training durations, making a very promising advancement in the field. Furthermore, an in-depth analysis is conducted to understand the impact of dataset size and key hyperparameters (such as epochs and batch size) on the classification accuracy of the PCA-VGG16 model. The findings indicate that a minimum dataset of 1500 sample images is necessary to achieve a classification accuracy above 90%. For optimal model performance, a division of training, validation, and test sets approximately at 6:2:2, along with two epochs and a batch size of 128, is recommended in this study.