Trends In Data Research Articles

Mixed mode I/II and I/III fracture toughness investigation for preplaced aggregate concrete mixtures: Experimental study and theoretical prediction

AbstractIn this paper, mixed mode I/II and I/III fracture toughness of preplaced aggregate concrete was studied using an edge‐notched disc bend specimen. The results showed the dependency of the fracture toughness value on the mode mixity. The values of modes I, II, and III fracture toughness were obtained to be equal to , , and , respectively. Several theoretical fracture criteria were examined for predicting the trends of experimental data in the tested preplaced aggregate concrete material. The 3D averaged strain energy density criteria provided the best predictions for the KIIIc/KIc ratio. Mixed mode I/III fracture toughness data were between the predicted envelopes of the maximum tensile stress and 3D maximum tangential strain theories. The generalized maximum tangential stress criterion provided accurate predictions for pure mode II and mixed mode I/II fracture data among the investigated mixed mode fracture criteria. By comparing the preplaced aggregate concrete fracture results with those of other types of concrete, it is observed that preplaced aggregate concrete has acceptable and in‐the‐range fracture resistance.

Surface roughness influence on extracellular electron microbiologically influenced corrosion of C1018 carbon steel by Desulfovibrio ferrophilus IS5 biofilm

Carbon steel microbiologically influenced corrosion (MIC) by sulfate reducing bacteria (SRB) is known to occur via extracellular electron transfer (EET). A higher biofilm sessile cell count leads to more electrons being harvested for sulfate reduction by SRB in energy production. Metal surface roughness can impact the severity of MIC by SRB because of varied biofilm attachment. C1018 carbon steel coupons (1.2 cm2 top working surface) polished to 36 grit (4.06 μm roughness which is relatively rough) and 600 grit (0.13 μm) were incubated in enriched artificial seawater inoculated with highly corrosive Desulfovibrio ferrophilus IS5 at 28 ℃ for 7 d and 30 d. It was found that after 7 d of SRB incubation, 36 grit coupons had a 11% higher sessile cell count at (2.0 ± 0.17) × 108 cells/cm2, 52% higher weight loss at 22.4 ± 5.9 mg/cm2 (1.48 ± 0.39 mm/a uniform corrosion rate), and 18% higher maximum pit depth at 53 μm compared with 600 grit coupons. However, after 30 d, the differences diminished. Electrochemical tests with transient information supported the weight loss data trends. This work suggests that a rougher surface facilitates initial biofilm establishment but provides no long-term advantage for increased biofilm growth.

FOOD DELIVERY TIME PREDICTION USING CNN- LSTM

To tackle the challenge of predicting food delivery times, the study introduces a novel approach crucial for streamlining food delivery service operations. The proposed model combines CNN (Convolution Neural Network)s and (LSTM) Long Short-Term Memory networks, leveraging the unique strengths of each design. The CNN component analyzes delivery data trends on a daily and weekly basis, identifying geographical patterns such as rush hours and seasonal variations. Meanwhile, the LSTM component focuses on recognizing spatiotemporal relationships and retaining data over time to effectively model sequential data. Compared to standard procedure, the model's simultaneous incorporation of spatial and temporal information leads to more precise predictions. This enhanced accuracy holds proven strategy for food delivery services, enabling them to more accurately estimate delivery times, reduce delays, and ultimately improve overall customer satisfaction. Keywords: (LSTM) Long short term memory, Time prediction, CNN (Convolution Neural Networks).

Evolutionary Trends in Data Warehousing: Progress, Challenges and Future Directions

Evolutionary Trends in Data Warehousing: Progress, Challenges and Future Directions

Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains a significant challenge. This review explores the effectiveness and challenges of using machine learning (ML) for forecasting HIV spread in high-risk areas. ML models have shown promise in identifying patterns and trends in HIV data, enabling more accurate predictions and targeted interventions. ML insights into HIV outbreak predictions leverage various data sources, including demographic, epidemiological, and behavioural data. By analysing these data, ML algorithms can identify high-risk populations and geographical areas susceptible to HIV transmission. This information is crucial for public health authorities to allocate resources efficiently and implement preventive measures effectively. Despite the potential benefits, several challenges exist in using ML for HIV outbreak predictions. These include data quality issues, such as incomplete or inaccurate data, which can affect the reliability of predictions. Additionally, the complexity of HIV transmission dynamics and the need for real-time data pose challenges for ML models. To address these challenges, researchers and practitioners are exploring innovative approaches, such as integrating multiple data sources and using advanced ML techniques. Collaborations between researchers, public health officials, and technology experts are also crucial for developing robust ML models for HIV outbreak predictions. In conclusion, while ML offers valuable insights into HIV outbreak predictions in Sub-Saharan Africa, addressing challenges such as data quality and model complexity is essential for its effective use. By overcoming these challenges, ML has the potential to significantly improve HIV prevention efforts and ultimately reduce the burden of the epidemic in the region. Keywords: Machine Learning, AI, HIV Outbreaks: Predictions, Insights.

Recent and projected changes in climate patterns in the Middle East and North Africa (MENA) region

Observational and reanalysis datasets reveal a northward shift of the convective regions over northern Africa in summer and an eastward shift in winter in the last four decades, with the changes in the location and intensity of the thermal lows and subtropical highs also modulating the dust loading and cloud cover over the Middle East and North Africa region. A multi-model ensemble from ten models of the Coupled Model Intercomparison Project—sixth phase gives skillful simulations when compared to in-situ measurements and generally captures the trends in the ERA-5 data over the historical period. For the most extreme climate change scenario and towards the end of the twenty-first century, the subtropical highs are projected to migrate poleward by 1.5°, consistent with the projected expansion of the Hadley Cells, with a weakening of the tropical easterly jet in the summer by up to a third and a strengthening of the subtropical jet in winter typically by 10% except over the eastern Mediterranean where the storm track is projected to shift polewards. The length of the seasons is projected to remain about the same, suggesting the warming is likely to be felt uniformly throughout the year.

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.

Development and Impact Analysis of a Multi-Pandemic Real-Time Data Dashboard: A Comparative Analytical Approach

This research presents the development and implementation of a Multi Pandemic Real-Time Data Dashboard, designed to facilitate enhanced comparative analysis across various global pandemics. Utilizing a combination of real-time data acquisition, statistical modeling, and predictive analytics, the dashboard provides a dynamic platform for analyzing pandemic trends and outcomes. Our methodology involved integrating diverse data sources, employing mathematical models such as the SIR (Susceptible, Infected, Recovered) model and machine learning algorithms for data processing and trend prediction. Key findings indicate that the dashboard effectively identifies patterns in pandemic spread and impact, offering valuable insights for public health decision-making. The research underscores the significance of advanced data analytics in managing public health crises, highlighting the dashboard’s potential in aiding proactive pandemic response and policy formulation.

Mechanistic modeling of in vitro transcription incorporating effects of magnesium pyrophosphate crystallization.

The in vitro transcription (IVT) reaction used in the production of messenger RNA vaccines and therapies remains poorly quantitatively understood. Mechanistic modeling of IVT could inform reaction design, scale-up, and control. In this work, we develop a mechanistic model of IVT to include nucleation and growth of magnesium pyrophosphate crystals and subsequent agglomeration of crystals and DNA. To help generalize this model to different constructs, a novel quantitative description is included for the rate of transcription as a function of target sequence length, DNA concentration, and T7 RNA polymerase concentration. The model explains previously unexplained trends in IVT data and quantitatively predicts the effect of adding the pyrophosphatase enzyme to the reaction system. The model is validated on additional literature data showing an ability to predict transcription rates as a function of RNA sequence length.

Climate change adaptation strategies among smallholder farmers in Senegal’s semi-arid zone: role of socio-economic factors and institutional supports

In dryland agricultural systems, developing appropriate climate-smart technology (CST) options is important to adapt agriculture to climate change and transition toward sustainability, as well as increasing productivity and incomes. This study examines the impact of socio-economic and institutional support on community responses to climate change and the impact of changes in three selected regions of Senegal (Meouane, Thiel, and Daga Birame), which fall within different rainfall gradients. It captures community perceptions of climate change, compares them to long-term meteorological data, and identifies site-specific response strategies. Communities are randomly selected from a list of communities within the target sites. We used a two-stage stratified sampling method to select sample households. First, purposive sampling was conducted to select at least six (6) villages as a cluster within each rainfall gradient. Likewise, the selection of households in each cluster was based on the main value chains of crops grown in the study area, namely groundnut, millet, black pea, and livestock. A total of 145 households participated in this study. Data from surveys conducted during the 2022 post-harvest season were analyzed using descriptive statistics and logit models. The analysis found that smallholders have a comprehensive understanding of climate indicators, including annual rainfall, shortened crop seasons, and rising temperatures, compared to historical data trends. Additionally, the results highlight how farmers view the negative impacts of seasonal rainfall deficiencies (72%), delayed start of the growing season (88%), frequent dry spells (68%), and longer dry spells (76%), which ultimately lead to decreased grain and fodder yields. The logit model also highlights the importance of socio-economic and institutional factors such as access to credit, extension services, agricultural experience, frequency of interaction with extension workers, and access to government subsidies. These factors play a crucial role in farmers’ decision to adopt CST. Given the specificity of community contexts, these insights have important implications for guiding policymakers and making it easier to reduce climate risk among smallholder farmers.

Tunable Solid‐State Properties and Anisotropic Charge Mobility in Hydrogen‐Bonded Diketopyrolopyrrole Polymers via Automated Device Fabrication and Characterization

AbstractThe optoelectronic properties of semiconducting polymers and device performance rely on a delicate interplay of design and processing conditions. However, screening and optimizing the relationships between these parameters for reliably fabricating organic electronics can be an arduous task requiring significant time and resources. To overcome this challenge, Polybot is developed—a robotic platform within a self‐driving lab that can efficiently produce organic field‐effect transistors (OFETs) from various semiconducting polymers via high‐throughput blade coating deposition. Polybot not only handles the fabrication process but also can conduct characterization tests on the devices and autonomously analyze the data gathered, thus facilitating the rapid acquisition of data on a large scale. This work leverages the capabilities of this platform to investigate the fabrication of OFETs using hydrogen bonding‐containing semiconducting polymers. Through high‐throughput fabrication and characterization, various data trends are analyzed, and large extents of anisotropic charge mobility are observed in devices. The materials are thoroughly characterized to understand the role of processing conditions in solid state and electronic properties of these organic semiconductors. The findings demonstrate the effectiveness of automated fabrication and characterization platforms in uncovering novel structure–property relationships, facilitating refinement of rational chemical design, and processing conditions, ultimately leading to new semiconducting materials.

An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods.

Analysing Spatial and Temporal Rainfall Variability of Southern Rajasthan using GIS Approach

This study focuses on analysing the behavioral patterns of rainfall in southern Rajasthan, particularly in reference to its amount and variation over time. The study made use of standard meteorological weeks (SMW) and historic weekly rainfall records. IDW (Inverse Distance Weighting) interpolation technique within the ArcGIS platform was used for spatial analysis, creating spatial variation maps. It was found that significant rainfall occurred from Standard Meteorological Week (SMW)-22 to SMW-42. The weekly mean rainfall across the study area during the monsoon period ranged from 2.1 to 68.4 mm, with corresponding standard deviations ranging from 9.3 to 79.7 mm. Notably, the standard deviation was often higher than the mean, indicating substantial variability in weekly rainfall. Trend analysis using the Mann-Kendall test revealed that most stations in the study area exhibited no significant trend in annual rainfall data. Similarly, weekly rainfall data showed a trend in SMW-30 for 44 stations, while all other weekly data series did not exhibit significant trends, except for SMW-30. Stationarity was generally accepted for most weekly data series, except for SMW-30 and SMW-32, which showed non-stationary behavior. However, homogeneity tests indicated homogeneity in weekly rainfall data series for almost all selected stations. Overall, the study highlighted the significant variability in rainfall patterns in southern Rajasthan, with no significant trend in annual rainfall, except during the peak monsoon period (SMW-30). These findings contribute to a better understanding of regional rainfall behavior, which is crucial for water resource management and agricultural planning in the region.

Assessment of climate change induced rainfall trend and variability with non-parametric and linear approach for Sirajganj district, Bangladesh

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.

The Role of Human Mobility Flow in the Region-to-Region Spread of Respiratory Syncytial Virus Infection among Infants: An Infographic Analysis.

The region-to-region spread of human infectious diseases is considered to be dependent on the human mobility flow (HMF). However, it has been hard to obtain the evidence for this. Since the onset of the coronavirus disease 2019 (COVID-19) pandemic in Japan 2020, the government has enforced countermeasures against COVID-19 nationwide, namely the restriction of personal travelling, universal masking, and hand hygiene. As a result, the spread of acute respiratory infections had been effectively controlled. However, COVID-19 as well as pediatric respiratory syncytial virus (RSV) infections were not well-controlled. The region-to-region spread of pediatric RSV infections in 2020-2021 was recognizable unlike those in 2018 and 2019. In this study, we investigated the correlation between the trend of regional reports of the pediatric RSV infections and the HMF based on cellular phone signal data. Upon closer examination of both epidemiological trend and HMF data, the spread of pediatric RSV infection from one region to another was logically explained by HMF, which would serve as the evidence of the dependence of regional transmission on HMF. This is the first solid evidence where this correlation has been clearly observed for the common respiratory infections. While social implementation of infection control measures has successfully suppressed the droplet-mediated respiratory infections, such as influenza, but not the airborne infections, it was suggested that the aerosol transmission and adult asymptomatic carrier were involved in the transmission of RSV akin to COVID-19.

Exploring a pathway to optimise the carbon tax policy in terms of the economy, the environment and health: A scenario-based system dynamics approach

The carbon tax, a pivotal policy instrument in tackling climate change, holds the potential to significantly influence the development of society. To comprehensively analyse the effectiveness of the policy on carbon taxation and explore its possible optimisation path, this study utilizes system dynamics theory to establish a simulation model. A detailed analysis and evaluation of this policy is then conducted from the perspectives of the economy, the environment, and health. To guarantee the precision of the simulation model, a new, comprehensive evaluation method is proposed, which can test the degree of fit of the relative trend and absolute data of the simulation model with reality. The findings reveal that, despite its negative economic implications, a carbon tax policy has positive ramifications for the environment, energy, health, industrial structure, and carbon intensity targets. Furthermore, the synergistic reinforcement effect of R&D and new energy support policies on carbon taxation surpasses the impact of any individual policy alone. Notably, the influence of auxiliary policies has a temporal difference on this policy. Based on these insights, the study concludes with practical policy recommendations.

Macroeconomic impact of environmental policy uncertainty and monetary policy implications

This paper evaluates the macroeconomic implications of uncertainty arising from delays or unpredictability regarding the timing and extent of environmental policy implementation. An environmental dynamic stochastic general equilibrium (E-DSGE) model shows that increasing uncertainty about environmental policies is associated with a subsequent decline in investment, as firms prefer to postpone investment decisions. The decline in physical investment within polluting sectors subsequently induces a fall in employment, consumption and output. Using a novel climate policy uncertainty index developed by Gavriilidis (2021), alongside Google Trend data index, the paper shows that uncertainty about climate regulations has both recessionary and inflationary effects, corroborating the theoretical findings. Further analysis suggests that appropriate monetary policy tools could moderate the welfare cost arising from the environmental policy uncertainty, emphasizing the need for Central Banks to adopt a more aggressive stance in stabilizing GDP and inflation in the face of environmental policy uncertainty shocks.

Data-driven decision making: Shaping the future of business efficiency and customer engagement

Data-driven decision-making (DDDM) is a critical approach that organizations are adopting to enhance their operational efficiency and customer engagement. This abstract explores the significance of DDDM in shaping the future of businesses. Data-driven decision-making (DDDM) is revolutionizing the way businesses operate and engage with customers. By leveraging data analytics, organizations can extract valuable insights to inform strategic decisions, improve operational efficiency, and enhance customer experiences. This abstract examines the role of DDDM in shaping the future of business efficiency and customer engagement. DDDM enables organizations to optimize their processes and resources through data analysis. By identifying patterns and trends in data, businesses can streamline operations, reduce costs, and improve productivity. For example, predictive analytics can help forecast demand, allowing businesses to adjust their inventory levels accordingly and avoid stockouts or overstock situations. Additionally, data-driven insights can inform resource allocation decisions, ensuring that resources are allocated efficiently to maximize returns. DDDM also plays a crucial role in enhancing customer engagement. By analyzing customer data, businesses can gain a deeper understanding of customer behavior, preferences, and needs. This enables businesses to tailor their products and services to better meet customer expectations, leading to higher customer satisfaction and loyalty. For example, personalized marketing campaigns based on customer data can significantly improve engagement and conversion rates. As the volume and complexity of data continue to grow, DDDM will become even more essential for businesses. Advancements in technology, such as artificial intelligence and machine learning, will further enhance the capabilities of DDDM, allowing businesses to derive even more value from their data. However, organizations must also address challenges such as data privacy and security to ensure that DDDM is implemented ethically and responsibly. In conclusion, DDDM is reshaping the future of business efficiency and customer engagement. By embracing DDDM, organizations can unlock the full potential of their data and gain a competitive edge in today's data-driven world.

행사성 사업의 효과 분석 방안

Recently, local governments have been actively organizing various event-based projects with the aim of strengthening community ties and revitalizing local economies. Consequently, there is a growing need for objective methodologies to evaluate the efficiency and validity of these public events projects. However, despite the increasing number of studies in this area, quantitative research regarding the analysis and prediction of the effectiveness of event-based projects remains scarce. In this study, we propose a methodology using the “Seoul Living Population Data” provided by the Seoul Metropolitan Government to quantitatively analyze and predict the effects of event-based projects. Specifically, by applying the TBATS model, which can consider various trends in time-series data (annual, weekly, daily, hourly), we aim to objectively analyze the effects of event-based projects and present a methodological procedure for quantifying the actual impact of these events. As a result, we confirm that this methodology enables the statistical analysis of the actual effects of event-based projects. Therefore, the proposed methodology in this study is expected to be utilized in assessing the demand for future event-based projects and is anticipated to provide more direct data compared to conventional qualitative assessments.

Track Irregularity Identification Method of High-Speed Railway Based on CNN-Bi-LSTM.

Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train.