Trends In Data Research Articles

Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease

Artificial Neural Networks are excellent at identifying patterns or trends in data, which makes them perfect for forecasting or prediction. Thus, neural networks have extensive application in biological systems. The application of neural networks to kidney stone diagnosis is emphasized in this article. Kidney stone issues can be diagnosed with neural networks by applying technological concepts such as MLP, SVM, RBF, and BPA. The purpose of this research is to use three different neural network algorithms—each with its own specific design and set of properties to identify kidney stone disease. The performance of the three neural networks is compared in this research with respect to training data set size, model creation time, and accuracy. Kidney stone sickness will be diagnosed using radial basis function (RBF) networks, two layers feed forward perceptrons trained with the back propagation training algorithm, and learning vector quantization (LVQ). However, determining the best approach for any particular diagnostic had never been an easy task. Like many other illnesses, kidney stones have already been diagnosed using neural network algorithms. The main objective of this work is to recommend the best medical diagnostic instrument, such as kidney stone detection, to reduce diagnosis times and improve accuracy and efficiency.

Long-term trends and risk factors of tracheostomy and decannulation in patients with cervical spinal cord Injury.

Retrospective study. To investigate the risk factors of tracheostomy and decannulation after cervical spinal cord injury (CSCI) and their epidemiological changes over the past 8 years in Beijing Bo'ai Hospital, China Rehabilitation Research Center (CRRC), China. Beijing Bo'ai Hospital, CRRC. We reviewed 8 years of patient data (2013.1.1 to 2020.12.31) at CRRC, focusing on those hospitalized and diagnosed with CSCI. We analyzed changes in demographic and clinical data's trends. Logistic regression analysis was used to determine factors impacting tracheostomy and decannulation. Finally, 1641 CSCI patients met the inclusion criteria. Over the past 8 years, the proportion of tracheostomized patients with CSCI was 16.3%, and the proportion of successfully decannulated of tracheostomized patients with TCSCI was 77.9%. We found that Traumatic (OR = 1.8, 95% CI = 1.06, 3.22; p = 0.046), Motor level of injury (C5-C8) (OR = 0.32, 95% CI = -1.91,-0.34; p = 0.005), AIS = A/B/C (OR = 22.7/11.1/4.2, 95% CI = 12.16,42.26/5.74,21.56/2.23,7.89; p < 0.001/p < 0.001/p < 0.001), age > 56 (OR = 1.6, 95% CI = 1.04, 2.32; p = 0.031) were the risk factors for tracheostomy. By analyzing the risk factors of decannulation failure in tracheostomized patients with TCSCI through multivariable logistic regression, statistically significant differences were found in age > 45 (OR = 4.1, 95% CI = 1.44, 11.81; p = 0.008), complete injury (OR = 2.7, 95% CI = 1.26, 5.95; p = 0.011), facet dislocation (OR = 2.8, 95% CI = 1.13,7.07; p = 0.027). Recent years have witnessed shifts in the epidemiological characteristics of CSCI. Identifying the factors influencing tracheostomy and decannulation in CSCI can aid in improving patient prognosis.

A COMPARATIVE ANALYSIS OF INJURIES AND DEATHS CAUSED BY ROAD TRAFFIC ACCIDENTS IN POLAND AND SELECTED EU COUNTRIES

The article examines the relationship between three dependent variables: the quantity of accidents on roads in Poland and the quantities of those hurt and killed as a result. Data were obtained from police websites between 2016 and 2021. Multidimensional comparative analyses were used for the research, which, by grouping data and compiling them dynamically in various scales depending on the needs, allowed us to observe trends such as seasonality on a monthly basis (data in a month - Fig. 2) and a downward trend (data in years Fig. 3). This became a premise for building a multiple regression model, which allowed us to dynamically observe the correlation between the quantity of accidents in Poland and the quantity of people hurt and killed as a result. The above-mentioned regularity was described by the function: Y = ‒67,9212 + 1,3341*v(2) + 0,7332*v(3) + +28,4305*v(4) and the built model was analyzed and evaluated. Then, information on the quantity of road accidents and quantities of people killed and hurt in the capital city of Warsaw from January 2021 to December 2022 were used for the research and compared with the data observed throughout Poland. To observe the impact of the random factor, which was a strong increase in the scale of fines in 2022, especially for exceeding the limit of speed by approximately more than 31 km/h. Dynamic indices on a constant base were calculated for the research. It was found that in 2022, compared to 2021, there was a visible decline in the quantity of people killed in road accidents in Warsaw from 42 to 30 people. A similar regularity can be observed in the data on fatalities in road accidents throughout Poland in the identical time period [11]. Moreover, there was no correlation between the time series of fatalities in road accidents in Warsaw and the quantity of accidents and injuries in 2022.

Analisis Trend (Indeks Number Trend Series Analysis) PT. Adaro Energi Indonesia Tbk

PT Adaro Energy Indonesia (ADRO) is one of the leading coal companies in Indonesia. Trend analysis of the company's number trend series index is important to understand the pattern and direction of its performance movement over a certain period of time. This research aims to analyze the trend of the ADRO number trend series index using linear and exponential regression methods. The data used in this research is ADRO Financial Report data for the period 2020 to 2023. The results of the analysis show that the overall ADRO number trend series index trend has increased. Linear and exponential regression models show quite good results in explaining data trends. Trend analysis (Index Number Trend Series Analysis) is carried out by comparing financial reports for the last 5 years. In the index-number trend series analysis approach, comparisons are made by calculating the relative change (percentage) of each element of the financial report over several years based on a certain base year. Usually what is designated as the base year is the earliest year (period) of a series of financial report periods being analyzed. . However, the main consideration in determining the base year is the financial reporting period that shows the most normal conditions.

Experiences with wildfire are associated with private landowners’ management decisions, relationships, and perceptions of risk

As human populations grow and anthropogenic change increases, costly wildland fires increasingly affect rural, public–private interface landscapes. Climate change, a history of fire suppression, and biological invasions are increasing fire risk in systems worldwide. Fires that ignite in one jurisdiction can spread across ownership boundaries, with the result that landscape-scale fire management and planning requires understanding differential fire response and management across jurisdictions. To illuminate relationships between landowner attitudes and fire management and their experience with past wildfires, we combined geospatial wildfire data from the Monitoring Trends in Burn Severity (MTBS) database and survey response data from 586 private landowners across three national park-centered ecosystems in the American West. We aimed to examine whether landowners’ attitudes toward their land, neighbors, public agencies, and fire itself were related to (a) whether they had experienced fire on their parcels, and (b) the extent and severity of the most recent fire event and the time elapsed since that fire. We found that the degree to which respondents reported that they manage for fire is related to whether a fire had burned on their parcels during their land tenure or, if prior to their tenure, within two years of their acquisition of the land. For those respondents that had thus experienced fire, their level of concern about wildfire, interactions with neighbors and public agencies, and perception that their land contributed to a broader economic or natural system were all related to the time that had elapsed since the fire. Furthermore, total burned area and the percent of the burn that was high severity interacted with time since fire to predict respondents’ attitudes and perceptions. Our findings illustrate the importance of personal past experiences in shaping private land managers’ relationships and viewpoints, as well as the likelihood that they invest in current fire management.

Predicting the Value of Agricultural GDP in Iraq for the Period 2019—2030 by Applying the Markov Transition Matrix

This research aims to predict the values of agricultural GDP in Iraq for the period 2019–2030 using the Markov Transition Matrix, whereby the fifth state was chosen due to the convergence of the predicted values with the most recent real values in the original time series. In addition, the predictive error or predictive accuracy of the selected state was better compared to other states. The reason for choosing this methodology in forecasting is that it is based on probabilities derived from old historical data, but this methodology does not need old historical data for the purpose of extracting predictive values, even if the time series includes long-time series data. The predicted values follow the same path as the original time series and are not affected by the general trend of the original data, which gives us an indication that there is a problem of stagnation of agricultural GDP. Therefore, a recommendation that can be given to economic policymakers, in particular the agricultural ones, is the existent need to address the problems that the agricultural sector has always suffered from in order to break this stagnation.

Rapid resurgence of syphilis in Japan after the COVID-19 pandemic: A descriptive study.

Some countries have reported a post-pandemic resurgence in syphilis prevalence, but trend data in the World Health Organization Western Pacific Region (WHO-WPRO), including Japan, are severely lacking. Thus, the present study compares the number of syphilis cases before and after the COVID-19 pandemic in some WHO-WPRO countries. In addition, temporal trends in the number of syphilis cases in Japan pre- and post-pandemic are described. Annual numbers of syphilis cases during the study periods from China, New Zealand, Australia and Japan were compared. Annual trends of the numbers of syphilis cases during the same study periods were examined in Japan. In 2020, the number of syphilis-positive cases decreased in all four countries. In 2021, though, China, Australia and Japan all showed an increase in the numbers of syphilis cases. However, the rate of increase in China (+2.8%) and Australia (+4.8%) was low compared to Japan (+36.0%). The number of syphilis cases in New Zealand in 2021 was 12.6% lower than in 2020. In 2022, the number of cases of syphilis in China was 7.4% lower than in 2021. The increase of syphilis-positive cases was approximately 6.3-fold higher in Japan compared to Australia (+66.2% vs. +10.5%) in 2022. In conclusion, post-pandemic resurgence of syphilis occurred in Australia and Japan, but not in China and New Zealand. The reason for the substantial increase in syphilis-positive cases in Japan remains unclear. Post-pandemic, prevention and control of sexually transmitted infections still require attention.

Ammonia/hydrogen laminar flame speed measurements at elevated temperatures

Laminar flame speeds of ammonia/hydrogen blends diluted in a mixture of 79% Ar and 21% O2 (referred to as “airgon”) were measured for the first time at high temperatures exceeding 500 K and atmospheric pressure using the shock tube flame speed method. Lean (ϕ = 0.8), stoichiometric (ϕ = 1) and rich (ϕ = 1.2) mixtures, with hydrogen fuel fractions xH2 = 0.2, 0.4, and 0.6 were studied in the temperature range of 500–820 K behind reflected shock waves. A significant enhancement in flame speeds and improved flame morphology was observed with increasing concentrations of hydrogen. Equivalent fuel/air data were computed from fuel/airgon measurements using a mixture-scaling approach and compared against available literature data. Although current data do not overlap with the temperatures of data in the literature, the full 300–800 temperature range of experimental data remain consistent with a non-Arrhenius correlation, while a power-law fit does not capture the full data trend. 1-D flame speed simulations were conducted using five recent chemical kinetic models of ammonia combustion available in the literature. Large disagreement was found among the models, with the model by Zhou et al. agreeing well with experiments at all conditions studied in this work. Sensitivity analyses were performed, and the most relevant reactions involved in the computation of flame speed, according to the models, were identified. Overall, the experimental data and modeling demonstrate the value of using blends of ammonia and hydrogen to improve the combustion characteristics of ammonia as a carbon-free fuel.

Tailored wastewater surveillance framework uncovered the epidemics of key pathogens in a Northwestern city of China

Wastewater surveillance enables rapid pathogen monitoring and community prevalence estimation. However, how to design an integrated and tailored wastewater surveillance framework to monitor major health threats in metropolises remains a major challenge. In this study, we first analyzed the historical clinical data of Xi'an city and designed a wastewater surveillance framework covering five key endemic viruses, namely, SARS-CoV-2, norovirus, influenza A virus (IAV), influenza B virus (IBV), respiratory syncytial virus (RSV), and hantavirus. Amplicon sequencing of SARS-CoV-2, norovirus and hantavirus was conducted biweekly to determine the prevalent community genotypes circulating in this region. The results showed that from April 2023 to August 2023, Xi'an experienced two waves of SARS-CoV-2 infection, which peaked in the middle of May-2023 and late August-2023. The sewage concentrations of IAV and RSV peaked in early March and early May 2023, respectively, while the sewage concentrations of norovirus fluctuated throughout the study period and peaked in late August. The dynamics of the sewage concentrations of SARS-CoV-2, norovirus, IAV, RSV, and hantavirus were in line with the trends in the sentinel hospital percent positivity data, indicating the role of wastewater surveillance in enhancing the understanding of epidemic trends. Amplicon sequencing of SARS-CoV-2 revealed a transition in the predominant genotype, which changed from DY.1 and FR.1.4 to the XBB and EG.5 subvariants. Amplicon sequencing also revealed that there was only one predominant hantavirus genotype in the local population, while highly diverse genotypes of norovirus GI and GII were found in the wastewater. In conclusion, this study provided valuable insights into the dynamics of infection trends and predominant genotypes of key pathogens in a city without sufficient clinical surveillance, highlighting the role of a tailored wastewater surveillance framework in addressing public health priorities. More importantly, our study provides the first evidence demonstrating the applicability of wastewater surveillance for hantavirus, which is a major health threat locally.

Case study on long-term deformation monitoring and numerical simulation of layered rock slopes on both sides of Wudongde dam reservoir area

Layered rock slope exists widely. Because of its special slope structure, it is prone to bending deformation and toppling failure, which is a serious threat to engineering construction and safety operation. At present, the research of layered rock slope still has great innovation potential. During the construction of Wudongde Hydropower Station on Jinsha River, safety and stability problems such as slope geological structure development, face rock unloading and relaxation, and even slip and large deformation were encountered. Through field exploration, it is found that the rock and soil stratification of the slope on both sides of Wudongde Hydropower Station is highly obvious. At present, there is a lack of research on-site long-term displacement monitoring of layered rock high-steep slope, especially for layered slope in complex hydrogeology and construction environment. In order to strengthen the research on the deformation and stability of layered rock slope, this paper analyzes the measured displacement data of Wudongde hydropower station slope, and establishes three-dimensional geological finite element model with the help of numerical simulation software. The stability of the slope is calculated by combining the finite difference method and the strength reduction method. Finally, the evolution mechanism of the deformation of the layered rock slope is explained according to the geological structure characteristics. The main conclusions of this paper are as follows: the layered slope in the dam reservoir area is prone to deformation under the combined action of long-term construction disturbance and fissure water seepage, and the construction disturbance has a strong influence on the artificial excavation area below 1070 m, and the maximum rock mass deformation and surface displacement in the artificial excavation area of the slope reach 92.2 mm and 312.5 mm, respectively. However, the influence of construction disturbance on the natural mountain above 1070 m is limited, the valley deformation of the natural mountain on the left bank of the reservoir area is higher than that on the right bank, and the cumulative deformation is still less than 20 mm. The influence of seepage on the displacement of the area with higher elevation at the top of the slope is more obvious, and the influence of excavation and other disturbances on the displacement of the artificial excavation area with lower elevation is more obvious. The deformation of the river valley in the water cushion pond behind the dam increases slowly, and the change trend of the field deformation data is mostly consistent with that of the numerical calculation. The horizontal shrinkage of the mountains on both sides shows a contraction trend on the whole, and the maximum horizontal shrinkage calculated by numerical simulation is close to 20 mm, which is located at the elevation of 990 m.

STOCK PRICE PREDICTION OF NIFTY 50

In this project a forecasting system is presented which can predict the change in stock market value of Nifty Fifty index using the historical data of various factors influencing the stock value. Traditional stock market prediction models only factor in the trends of the historical data of the targeted stock value which is then fed to a time series algorithm to get a forecast. Since the real-world stock market is constantly being influenced by various factors, it is not possible to take all those factors into account and make the forecast, let alone using the target stock by itself. So, the objective of this project is to analyse and use the trends in the factors that majorly influence Nifty Fifty index like international stock indices and commodities that affect the economy addon Recurrent Neural Networks (RNN) which has the architecture that can retain the past information and use it in the prediction along with the observations at the current time step. This will allow the predictions to be more accurate and make it possible to make a better decision when investing in the Nifty Fifty index.

AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition

The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a special emphasis on pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) and long short-term memory (LSTM) neural networks have shown promise in predicting air quality trends in time series data. However, challenges persist due to the unpredictability of air quality data and the scarcity of long-term historical data for training. To address these challenges, this study introduces the AIoT-enhanced EEMD-CEEMDAN-GCN model. This innovative approach involves decomposing the input signal using EEMD (Ensemble Empirical Mode Decomposition) and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to extract intrinsic mode functions. These functions are then processed through a GCN (Graph Convolutional Network) model, enabling precise prediction of air quality trends. The model’s effectiveness is validated using air pollution datasets from four provinces in China, demonstrating its superiority over various deep learning models (GCN, EMD-GCN) and series decomposition models (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy and better data fitting, outperforming other models in key metrics such as MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and R2 (Coefficient of Determination). The implementation of this AIoT-enhanced model in air pollution prediction allows decision-makers to more accurately anticipate changes in air quality, particularly concerning carbon emissions. This facilitates more effective planning of mitigation measures, improvement of public health, and optimization of resource allocation. Moreover, the model adeptly addresses the complexities of air quality data, contributing significantly to enhanced monitoring and management strategies in the context of sustainable urban development and environmental conservation.

Digital economy and institutional dynamics: striving for equitable public service in a digitally transformed era.

The rapid emergence of China's digital economy has sparked profound interest in the complex interplay between digitalization and the provision of public services. This study aims to delve deeper into how the development of the digital economy impacts the level of equalization in public service delivery and evaluates whether institutional factors can moderate this transformation. Against the backdrop of pursuing "common prosperity," this research provides valuable guidance for policymaking and strategic planning. It ensures that the ascent of the digital economy not only elevates the standards of public services but also fosters their equitable distribution, thereby advancing the cause of social equity. The study utilized the System Generalized Method of Moments (GMM) model along with longitudinal trend data spanning from 2009 to 2018. This approach facilitated an in-depth analysis of the relationship between the digital economy and the level of equalization in public service delivery. The application of this model provided deeper insights into the impact of the digital economy on public service equalization and the identification of underlying mechanisms. This study reveals a complex paradox that the digital economy is exacerbating regional disparities in the provision of basic public services. Furthermore, the research underscores the pivotal role of institutional environments in mitigating the adverse effects of the digital economy on public service provision. By examining the interplay between digital economy growth and institutional frameworks, the study suggests that adaptable and robust institutions are essential for harnessing the digital economy's benefits while minimizing its potential drawbacks. In conclusion, the findings from this study offer substantial insights into the dual impact of the digital economy on public service provision, enriching the ongoing discourse on digital transformation and social equity. The research underscores the significance of strategic policy reforms and institutional adjustments to harness the transformative power of the digital economy, promoting equitable access to public services and advancing the goal of "common prosperity" in the digital age.

Addressing Health Disparities using Social Determinants of Health: A Descriptive Analytics Approach

Purpose: This paper explores Social Determinants of Health (SDOH) data and its implications using descriptive data analytics. The analysis aims to help healthcare organizations understand the trends and impact on the industry and develop strategies to resolve disparities and eventually reduce healthcare costs.&#x0D; Methodology: This paper uses descriptive data analytics to analyze Social Determinants of Health (SDOH) data and its implications. The study focuses on the Centers for Disease Control and Prevention's (CDC) five critical areas of SDOH: healthcare quality and access, education quality and access, economic stability, social and economic context, and built and neighborhood environment.&#x0D; Findings: Health equity remains a significant challenge in the world and the United States, with persistent inequalities and injustice among different populations. Failure to address health equity issues could lead to social problems, including violent crimes, alcohol, and substance abuse, and potentially a global pandemic, which could significantly impact healthcare systems and increase costs. According to a report published by Deloitte, inequities in the United States cost more than 300 billion dollars in 2022. If not addressed, they could account for more than a trillion dollars in annual spending by 2040. Social Determinants of Health (SDOH) are critical in providing health equity and reducing disparities.&#x0D; Unique Contribution to Theory, Practice, and Policy: Understanding the implications and trends of SDOH data is crucial in developing targeted strategies to achieve health equity for all. Healthcare organizations can use this analysis's findings to establish policies and practices that address social determinants of health and reduce disparities. This, in turn, will help improve overall health outcomes and reduce healthcare costs.

An Expository Examination of Temporally Evolving Graph-Based Approaches for the Visual Investigation of Autonomous Driving

With the continuous advancement of autonomous driving technology, visual analysis techniques have emerged as a prominent research topic. The data generated by autonomous driving is large-scale and time-varying, yet more than existing visual analytics methods are required to deal with such complex data effectively. Time-varying diagrams can be used to model and visualize the dynamic relationships in various complex systems and can visually describe the data trends in autonomous driving systems. To this end, this paper introduces a time-varying graph-based method for visual analysis in autonomous driving. The proposed method employs a graph structure to represent the relative positional relationships between the target and obstacle interferences. By incorporating the time dimension, a time-varying graph model is constructed. The method explores the characteristic changes of nodes in the graph at different time instances, establishing feature expressions that differentiate target and obstacle motion patterns. The analysis demonstrates that the feature vector centrality in the time-varying graph effectively captures the distinctions in motion patterns between targets and obstacles. These features can be utilized for accurate target and obstacle recognition, achieving high recognition accuracy. To evaluate the proposed time-varying graph-based visual analytic autopilot method, a comparative study is conducted against traditional visual analytic methods such as the frame differencing method and advanced visual analytic methods like visual lidar odometry and mapping. Robustness, accuracy, and resource consumption experiments are performed using the publicly available KITTI dataset to analyze and compare the three methods. The experimental results show that the proposed time-varying graph-based method exhibits superior accuracy and robustness. This study offers valuable insights and solution ideas for developing deep integration between intelligent networked vehicles and intelligent transportation. It provides a reference for advancing intelligent transportation systems and their integration with autonomous driving technologies.

Refining Sugar Production Forecasts: Analyzing Forecasting Techniques for Operational Precision

This study compares the efficacy of moving averages and double exponential smoothing (Holt's method) in predicting sugar production, using historical data. Results reveal that double exponential smoothing outperforms moving averages, offering more accurate forecasts, particularly in contexts requiring responsiveness to data trends. These findings hold significant implications for enhancing operational efficiency and readiness in the sugar industry, guiding maintenance scheduling and production target achievement. Highlights: Double exponential smoothing (Holt's method) surpasses moving averages in accuracy. Importance of forecasting in operational efficiency and maintenance scheduling. Applicability of forecasting techniques in the sugar industry context. Keywords: Forecasting, Moving Average, Sugar Production

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis.

The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.

Mononuclear η6-arene ruthenium(II) complexes with pyrazolyl-pyridazine ligands: synthesis, CT-DNA binding, reactivity towards glutathione, and cytotoxicity.

Organometallic η6-arene ruthenium(II) complexes with 3-chloro-6-(1H-pyrazol-1-yl)pyridazine (Ru1, Ru2, and Ru5) and 3-chloro-6-(3,5-dimethyl-1H-pyrazol-1-yl)pyridazine (Ru3-4) N,N' heterocyclic and η6-arene (cymene (Ru1-4) or toluene (Ru 5)) have been synthesized. The ruthenium(II) complexes have common "three-legged piano-stool" pseudo-octahedral structures known for half-sandwich complexes. Evolution of their UV-Visible absorption spectra in PBS buffer or DMSO over 24h confirmed their good solvolysis stability. Titrations of the complexes with the calf thymus DNA (CT-DNA) were monitored using UV-Visible absorption and fluorescence spectroscopies. The complexes interact moderately with CT-DNA and their binding constants are in the order of 104M-1. Competitive binding of the complexes to a DNA-Hoechst 33,258 depicted competitive displacement of Hoechst from DNA's minor grooves. These complexes bind to glutathione forming GSH-adducts through S coordination by replacement of a halide, with the iodo-analogues having higher binding constants than the chloro-complexes. Cyclic voltammograms of the complexes exhibited one electron-transfer quasi-reversible process. Trends in the molecular docking data of Ru1-5/DNA were similar to those for DNA binding constants. Of the five, only Ru1, Ru3 and Ru5 showed some activity (moderate) against the MCF-7 breast cancer cells with IC50 values in the range of 59.2-39.9 for which Ru5 was the most active. However, the more difficult-to-treat cell line, MDA-MB 231 cell was recalcitrant to the treatment by these complexes.

Conductive magnetic nanowires accelerated electron transfer between C1020 carbon steel and Desulfovibrio vulgaris biofilm

Microbial biofilms are behind microbiologically influenced corrosion (MIC). Sessile cells in biofilms are many times more concentrated volumetrically than planktonic cells in the bulk fluids, thus providing locally high concentrations of chemicals. More importantly, “electroactive” sessile cells in biofilms are capable of utilizing extracellularly supplied electrons (e.g., from elemental Fe) for intracellular reduction of an oxidant such as sulfate in energy metabolism. MIC directly caused by anaerobic biofilms is classified into two main types based on their mechanisms: extracellular electron transfer MIC (EET-MIC) and metabolite MIC (M-MIC). Sulfate-reducing bacteria (SRB) are notorious for their corrosivity. They can cause EET-MIC in carbon steel, but they can also secrete biogenic H2S to corrode other metals such as Cu directly via M-MIC. This study investigated the use of conductive magnetic nanowires as electron mediators to accelerate and thus identify EET-MIC of C1020 by Desulfovibrio vulgaris. The presence of 40 ppm (w/w) nanowires in ATCC 1249 culture medium at 37 °C resulted in 45 % higher weight loss and 57 % deeper corrosion pits after 7-day incubation. Electrochemical tests using linear polarization resistance and potentiodynamic polarization supported the weight loss data trend. These findings suggest that conductive magnetic nanowires can be employed to identify EET-MIC. The use of insoluble 2 μm long nanowires proved that the extracellular section of the electron transfer process is a bottleneck in SRB MIC of carbon steel.

Analysis of Rainfall Trend in North Interior Karnataka (NIK) Meteorological Subdivision of Karnataka by Non-Parametric Methodologies

The present study analyzed the trend in the month-wise, seasonal and annual rainfall employing three non-parametric tests viz. Mann-Kendall (M-K), Modified Mann-Kendall (MM-K) tests and Sen’s slope estimator for North Interior Karnataka (NIK) meteorological subdivision of Karnataka using 60 years (1960-2019) rainfall data. The M-K test results revealed that there is no monotonic trend in all data sets which indicates that there may exist the influence of serial correlation in the rainfall data. The MM-K test was applied to rainfall data of the NIK subdivision, the result of the test revealed that January, February, March and August months have a monotonic increasing trend whereas April, May, July and September months have a monotonic decreasing trend whereas June, October, November and December have no monotonic trend in monthly rainfall data. The winter season has a monotonic increasing trend, monsoon and post-monsoon seasons have a monotonic decreasing trend, and the pre-monsoon season has no monotonic trend in seasonal rainfall data. The MM-K test statistic (Tau) value for the NIK (-0.12) subdivision was found to be significant and negative indicating that there is a monotonic decreasing trend. Larger negative Sen’s slope values for NIK (-1.40) subdivision indicate the high decreasing rate of change of rainfall for annual rainfall data. Therefore, to know the trend in rainfall data most accurately in the presence of outliers and serial correlation, the Modified Mann-Kendall (MM-K) test and Sen’s slope estimator are recommended instead of Mann-Kendall (M-K) and Sen’s slope estimator.