Analysis Of Historical Trends Research Articles

Long Short-Term Memory Algorithm for Personalized Tacrolimus Dosing: A Simple and Effective Time Series Forecasting Approach Post-Lung Transplantation.

Management of tacrolimus trough levels influences morbidity and mortality after lung transplantation. Several studies have explored pharmacokinetic and artificial intelligence models to monitor tacrolimus levels. However, many models depend on a wide range of variables, some of which, like genetic polymorphisms, are not commonly tested for in regular clinical practice. This study aimed to verify the efficacy of a novel approach simply utilizing time series data of tacrolimus dosing, with the objective of accurately predicting trough levels in the variety of clinical settings. Data encompassing 36 clinical variables for each patient were gathered, and a multivariate long short-term memory algorithm was applied to forecast subsequent tacrolimus trough levels based on the selected clinical variables. The tool was developed using a dataset of 87,112 data points from 117 patients and its efficacy was confirmed using six additional cases. Shapley Additive exPlanations revealed a significant correlation between trough levels and prior dose-concentration data. By using simple trend learning of dose, administration route, and previous trough levels of tacrolimus, we could predict values within 30% of the actual values for 88.5% of time points, which facilitated the creation of a tool for simulating tacrolimus trough levels in response to dosage adjustments. The tool exhibited the potential for rectifying clinical misjudgments in a simulation cohort. Utilizing our time series forecasting tool, precise prediction of trough levels is attainable independently of other clinical variables, through the analysis of historical tacrolimus dose-concentration trends alone.

Advanced data-driven FBG sensor-based pavement monitoring system using multi-sensor data fusion and an unsupervised learning approach

Pavement health monitoring and assessment using sensor technology provides valuable insights into the condition of roads and helps in effective maintenance and management. This paper presents an advanced fiber Bragg grating (FBG) sensor-based pavement monitoring system that leverages multi-sensor data fusion techniques and an unsupervised learning approach for enhanced structural health assessment. The study commences with the fusion of data from various FBG sensors based on specific considerations and employing a novel function to improve the accuracy of pavement condition assessment. A dedicated data management system is introduced to address the challenges associated with continuous monitoring. This system automates data organization, pre-processing, and storage, facilitating historical trend analysis and post-processing. Then, the research introduces a novel unsupervised learning approach utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This algorithm identifies abnormal patterns in the FBG sensor data, which may indicate structural anomalies or unexpected events, aiding the operator by automatically flagging abnormal data. By incorporating principle component analysis (PCA)-based feature fusion, the system reduces the dimensionality of the data, which enhances the efficiency of the clustering process. Various anomalies were detected in the data collected for four months from the test track. The cause and relevance of these anomalies were assessed to facilitate informed decision-making. This approach enables the operator to achieve early detection of potential structural issues and distinguish abnormal damage data from other anomalous data, thereby contributing to the overall success of the pavement monitoring system. In conclusion, the integration of advanced FBG sensor technology with multi-sensor data fusion and unsupervised learning techniques results in a sophisticated and robust pavement monitoring system. The presented approach enhances accuracy by detecting anomalies at any strain level, computational time using multi-sensor data fusion, and reliability of structural health assessment through anomaly evaluation. It also provides a framework for proactive decision-making, ultimately improving the longevity and structural integrity of critical infrastructure.

Long Term Historical Precipitation and Temperature Trend Analysis for Selected Stations in Bale and East Bale Zones of Southeastern Ethiopia

Trend analysis for the long-term average temperature and precipitation of Bale and East Bale Zones during 1901 to 2016 has been performed for selected 5 stations to understand the pattern of these important meteorological features under climate change. The precipitation characterizations viz. standardized Precipitation Index has been considered for Sinana, Goro, Ginnir, Dallo menna and Agarfa stations. This study was aimed to analysis ‘’long term historical climate analysis selected stations in Bale and East Bale zones’’. The study considered station nearby kebeles was selected purposively from highland, midland and lowland districts. Standardized Precipitation Index drought index method was used to classify drought condition of the area. The findings showed that, the average temperature of the district showed an increased in the past three decades whereas the annual precipitation of the districts showed a decreasing trend with some variation from the mean in the period 1901- 2016 which was validated with 1984 – 2016 observed data. In line with this, representative respondents were considered from lowland, highland and midland agro-ecologies. Long term annual mean temperature showed increase trends by 0.17°C per decade at Sinana, Goro, Ginnir and Agarfa stations. Additionally, it was revealed more increment by 0.19°C per decade at Dallo menna station. From long term climate data analysis, it was indicated that extremely wet recorded in 1903, 1914, 1917, 1926, 1977 and 2013, while extremely dry conditions were recorded in 1919, 1938 and 1943 annual precipitation recorded for the whole period has been calculated and interpreted.

Historical trend analysis of the methodological teaching work management in the medical career

Introduction: The Cuban educational model of professional training in the health sciences involves the merger of the pedagogical model that has been developed with the particular historical conditions of our country, which undoubtedly distinguishes the conception and development of formative process that is very specific to the Cuban medical university. Methods: In order to support the historical trend analysis of the methodological teaching work management in the medical career, the following methods were used Historical-Logical, systematization and documentary analysis. Results: For the historical trend analysis of the methodological teaching work management in the medical career, 5 stages were established; First Stage (1962 to 1980: Consolidation of the University Reform and First Regulation of Methodological Work); Second Stage (1981 to 1987: Implementation of the Methodological Teaching and Methodological Work of the specialties of higher medical education); Third Stage (1988 to 2006: Ministerial Resolutions that recognized the existence of two fundamental directions for the development of methodological work). Fourth Stage (2007 to 2017: Implementation of Resolution 210 /2007); Fifth Stage (2018 - Present: Activity of Resolutions associated to the E curricula). Conclusions: The use of theoretical methods, such as the historical-logical method, systematization and documentary analysis, made possible to reference the historical trend analysis of the methodological teaching work management in the medical career

Part-load analysis and preliminary annual simulation of a constant inventory supercritical CO2 power plant for waste heat recovery in cement industry

The present work investigates the part-load performance of a MW-scale sCO2 power plant designed as waste heat recovery unit for an existing cement plant located in Czech Republic, in the framework of the H2020 funded project CO2OLHEAT. The study first presents the selected power plant configuration and then focuses on the evaluation of its part-load operation due to variation of flue gas mass flow rate and temperature. The range of flue gas conditions at the outlet of the upstream process is retrieved from a preliminary statistical analysis of historical trends obtained through the cement plant monitoring. The numerical model developed for this study aims at providing realistic results thanks to the adoption of turbomachinery performance maps provided by the turbomachinery manufacturer of the project. Moreover, heat exchangers have been modelled through a discretized approach which has been validated against manufacturer data, while piping inventory and pressure losses have been assessed through a preliminary sizing that considers the actual distances to be covered in the cement plant. Performance decay is estimated for the whole range of flue gas conditions, reporting the most significant power cycle parameters, and identifying the main causes of efficiency loss. The part-load analysis is carried out considering a constant CO2 inventory, in order to reduce the system complexity and capital cost and simplify plant operation. Results show that the operation entails minor variation of the compressors operative points in the whole range of operating conditions of the cement plant, avoiding the risk of anti-surge bypass activation. Moreover, the plant is able to work close to the nominal thermodynamic cycle efficiency (20.5 %–23.0 %) for most of the year and benefits from part-load operation in terms of overall performance. In the last part of the work, a preliminary techno-economic analysis of the plant is also presented to highlight the potential advantages of sCO2 technology for waste heat recovery applications. The results of the part-load performance of the plant are combined with the flue gases data obtained from the preliminary statistical analysis and the cement plant historical monitoring. An annual electricity production equal to 13′909.7 MWh is obtained, corresponding to 6560 equivalent hours and a system capacity factor of 74.9 %. The investment cost of each CO2OLHEAT plant component is estimated by means of cost correlations obtained from literature and the non-discounted payback time is computed as a function of the electricity selling price. The results show that, even considering electricity prices before 2022, the payback time of the CO2OLHEAT plant is estimated to be lower than 8 years, justifying the industrial interest in the proposed technology.

Dying to understand how historical trends and influential intermediaries impact the future of sustainable deathcare

PurposeConsumers often prefer sustainable goods and services but fail to follow through with purchases that reflect these espoused values. The green intention–outcome gap is studied in many contexts but has yet to inform deathcare decisions. Industry reports suggest that most Americans prefer sustainable deathcare options, yet unsustainable corpse dispositions dominate the market. The purpose of this paper is to understand how history informs this phenonea.Design/methodology/approachThis study looks to the past – using historical narrative analysis of deathcare trends and influential intermediaries – to understand the future of sustainable deathcare and the prospective role that marketers can play in bridging the gap between decedents’ preferences and survivors’ purchase outcomes.FindingsHistorical ritualization, medicalization and commercialization have resulted in the monopolization of traditional deathcare services. Mortuary professionals remain unresponsive to consumer preferences for sustainable alternatives.Social implicationsSocioeconomic shocks can allow humanity to reflect and transition from consumerism to sustainability. COVID-19 has led to greater awareness of self-mortality, and death has become less taboo. The slow market penetration of sustainable deathcare services suggests a lack of communication between a decedent and their survivors. Marketing scholars need to help marketing practitioners bridge the preference-outcome gap.Originality/valueTo the best of the authors’ knowledge, this study is amongst the first to examine how history informs the sustainable action–outcome gap for deathcare preferences in a post-COVID environment and the role that marketers can play in perpetuating change.

Home Air Quality Monitoring System

Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.

Sustainability Indicators of Foreign Assistance in the Field of Media and Information Literacy: A Case Study on Kyrgyzstan and Uzbekistan

Media and information literacy (MIL) projects are crucial for a country’s development. They provide citizens with access to vital information and resources. This enables them to learn about their rights and responsibilities, as well as gain access to critical resources to improve their quality of life. For the last decade several international organizations such as UNESCO, Internews, DW-Akademie annually have been providing grants for the training courses, and projects at various levels to increase the MIL level of the population in the Central Asian context. The present study will discuss ensuring the sustainability and efficiency of projects in the field of MIL in this region. The paper investigates the sustainability indicators of foreign aid initiatives in the realm of MIL within Central Asia. It aims to assess the long-term viability of foreign aid programs in fostering MIL across Central Asian nations through a comprehensive analysis of historical trends, policy frameworks, and stakeholder perspectives. As part of the research, face-to-face interviews were conducted with prominent trainers and experts from Kyrgyzstan and Uzbekistan. Emphasizing the role of public-private partnerships, technological integration, and cultural considerations, the paper explores avenues for promoting enduring change and enhancing the effectiveness of foreign assistance in this critical field. The findings contribute valuable insights to policymakers, practitioners, and scholars interested in optimizing foreign aid strategies for sustainable development in the ever-evolving landscape of media and information literacy in Central Asia.

Energy Intensity Forecasting Models for Manufacturing Industries of “Catching Up” Economies: Lithuanian Case

The objective of this research was to construct energy intensity forecasting models for key manufacturing industries, with a particular focus on “catching up” European economies. Future energy intensity values serve as the foundation for energy demand forecasts, which are essential inputs for the analysis of countries’ decarbonisation scenarios. The Lithuanian case is analysed in the context of its efforts to reach the economic development level of the most advanced European Union (EU) countries. The scientific literature and energy policy analysis, interdependence (correlation and regression), tendency and case analysis, logical economic reasoning, and graphical representation methods have been applied. The energy intensity forecasts until 2050 were based on historical statistical data of value added and final energy consumption of EU countries from 2000 to 2021. The analysis of historical trends revealed a remarkable decrease in industrial energy intensity in most EU countries, including Lithuania. Given the rapid pace of decline in historical energy intensity, the values observed in individual Lithuanian industries have already reached levels comparable to the most economically advanced EU countries. Four econometric trendlines were employed to construct forecasting models for energy intensity. The results for Lithuania demonstrated that the selected trendlines exhibited a high degree of fit with historical energy intensity data from the EU, as evidenced by their R2 values. Furthermore, the forecasts were shown to be highly accurate, with their MAPEs remaining below 10% in most cases. Nevertheless, the logarithmic trendline was found to be the most accurate for forecasting energy intensity in total manufacturing (MAPE = 4.0%), non-metallic minerals (MAPE = 3.5%), and food, beverages, and tobacco (MAPE = 4.1%) industries, with the exponential trendline in the chemical industry (MAPE = 8.7%) and the moving average in the total manufacturing industry (MAPE = 4.0%), food industries (MAPE = 4.0%), and remaining aggregate industries (MAPE = 14.5%). It is forecasted that energy intensity could decline by 8 to 16% to 1.10–1.20 kWh/EUR in Lithuania’s manufacturing industries by 2050.

Interrelations between bitcoin market sentiment, crude oil, gold, and the stock market with bitcoin prices: Vision from the hedging market

PurposeThe purpose of this study is to examine Bitcoin's price behavior across market conditions, focusing on the influence of Bitcoin's historical prices, news sentiment and market indicators like oil prices, gold and the S&P index. The authors also assess the stability of Bitcoin-inclusive hedging portfolios under different market conditions, for example, bearish, bullish and moderate market states.Design/methodology/approachThis study uses the Quantile Autoregressive Distributed Lag model to explore the effects of different factors on Bitcoin's prices across various market situations. This method allows for a detailed analysis of historical trends, investor expectations and external market influences on Bitcoin's price movements and systematic stability.FindingsKey findings reveal historical prices and investor expectations significantly influence Bitcoin in all market scenarios, with news sentiment exhibiting substantial volatility. This study indicates that oil prices have minimal impacts on Bitcoin, whereas gold is a stabilizing asset in bear markets, with the S&P index influencing short-term fluctuations. At the same time, Bitcoin's volatility varies with market conditions, proving more efficient as a hedging tool in bear and stable markets than in bull ones.Originality/valueThis study highlights the intrinsic correlation between Bitcoin's prices, news sentiment and financial market indicators, enhancing understanding of Bitcoin's market dynamics. The authors demonstrate Bitcoin's weak direct correlation with commodities like oil, the stabilizing role of gold in crypto portfolios and the stock market's indirect effect on Bitcoin prices. By examining these factors' impacts across various market conditions, the findings offer strategies for investors to improve hedging and portfolio management in cryptocurrency markets.

Tax Implications of Real Estate Investments: A Comprehensive Analysis

Real estate investments constitute a significant portion of many investors' portfolios, offering the potential for long-term wealth accumulation and diversification. However, beyond considerations of property location, market trends, and financing options, investors must also navigate the intricate landscape of tax implications associated with real estate investments. Taxes play a critical role in shaping investment decisions, impacting cash flow, profitability, and overall returns. Therefore, understanding and effectively managing tax obligations is paramount for maximizing investment success in the real estate market. This comprehensive analysis aims to delve into the multifaceted realm of tax implications related to real estate investments. By examining various tax strategies, structures, regulations, and their implications, this study seeks to provide investors with valuable insights and tools to optimize their tax position and enhance investment outcomes. From the tax treatment of rental income and capital gains to the utilization of tax-efficient investment structures like REITs and LLCs, every aspect of real estate taxation will be explored in detail. Moreover, with the ever-evolving nature of tax legislation and economic conditions, it is imperative for investors to stay abreast of changes that may impact their tax liabilities and investment strategies. Through a thorough analysis of historical trends and current tax laws, this study aims to equip investors with the knowledge and foresight necessary to adapt and thrive in a dynamic tax environment. In essence, this comprehensive analysis endeavors to empower real estate investors with the tools and understanding needed to navigate the complex terrain of tax implications effectively. By unraveling the intricacies of real estate taxation and providing actionable insights, this study aspires to assist investors in making informed decisions, optimizing their tax position, and ultimately realizing the full potential of their real estate investments.

Sustainable Energy Consumption Analysis through Data Driven Insights

Energy is the backbone of our society, supporting our daily activities and driving progress. It plays a crucial role in shaping our modern way of life. The future of global energy consumption is influenced by many factors, including demographics, economic dynamics, technological developments, political actions, environmental demands and geopolitical considerations. As the world's population continues to grow and urbanize, the demand for energy is increasing. At the same time, rapid technological innovations are shaping the energy landscape and changing production, distribution and consumption patterns. In the midst of this development, it is very important to optimize energy consumption, accurately anticipate needs, curb climate change, limit emissions of greenhouse gasses, fight against pollution and promote sustainability. This study includes an in-depth analysis of historical consumption trends, assessing the multiple benefits of renewable energy integration, estimating carbon emissions, formulating practical policy recommendations and providing empirically informed insights. The work is based on various data obtained from platforms such as Kaggle and using advanced visualization techniques such as Power BI dashboards. The study provides invaluable perspectives on future energy needs, the penetration of renewable sources into the energy mix, and the strategic needs to achieve sustainable energy use.

The Critical Role of Energy Intensity in Decarbonizing ASEAN: Integrating Growth and Emissions Reductions

This research analyzes drivers of CO2 emissions across ASEAN countries from 1971-2017 to inform effective policies for sustainable decarbonized development. The goal is to identify critical factors influencing emissions growth and reductions to guide strategic climate mitigation planning. Data includes 3128 emissions, GDP, population, and energy consumption observations from 9 ASEAN nations. Results using the Kaya Index and Logarithmic Mean Divisia Index decomposition reveal GDP growth and population as primary drivers of increasing emissions, while energy efficiency dampens growth. Indonesia saw the highest emissions growth, driven by population and economic expansion. Thailand's phenomenal GDP growth of 3003.7% led to its emission increases. Singapore and the Philippines achieved notable reductions. Findings suggest integrated policies like clean energy, efficiency gains, infrastructure planning, and low-carbon economic reform are needed. Transitioning towards affordable clean energy systems with proactive leadership can enable ASEAN to sustain growth while mitigating climate risks. Further research should identify optimal policy mixes to maximize socio-economic progress and welfare while meeting urgent emission targets tailored to each nation. This study provides vital insights into key factors influencing ASEAN emissions and highlights pathways towards sustainable decarbonized development. The analysis of historical trends aims to inform strategic policymaking for decarbonization.

Evaluating Trends of Temperature Index in Gia Lai Province Based on IITA and IPTA Approaches under Climate Change

This study performs historical trend analysis for average, maximum, and minimum temperatures using the integration of the Improved visualization for trend analysis (IITA) and Innovative Polygon Trend Analysis (IPTA) of Şen to evaluate the tendency of extreme temperatures on different time scales (annual, monthly and seasonal) in Gia Lai province from 1980 to 2019 at three stations, including Pleiku, AyunPa, and An Khe. Results recorded over the past four decades, the temperature trend in Gia Lai has been gradually warming. The average temperature in each month of the year ranges from 19 to 28°C, December and January usually have the lowest temperature of the year with a variation lower than 20°C. On the contrary, the months of April, May, and June often have the highest temperatures of the year, with a variation greater than 26°C. The outcomes of this research have provided a general picture of temperature characteristics, useful information for the management and planning of land use, irrigation and water supply in Gia Lai province under unpredictable climate change.

Indonesian Women and Terrorism: An Analysis of Historical and Contemporary Trends

From 2016 to 2021, women attempted to or perpetrated suicide attacks in Indonesia. These attacks were committed by them as individuals or in family units, who were affiliated to the Islamic State. These incidents marked the first occurrence of suicide attacks carried out by women in Indonesia. Current scholarship and policy analysis of female terrorism attributed to the Islamic State or proxy groups is still catching up with the implications of trends emerging from women’s actions as suicide attackers in Indonesia and worldwide. Primarily, scholarly and policy analyses of female terrorism focus on the individual woman engaged in violence, whereas women who support terrorist groups—as ideologues, wives, and online activists—are given a secondary analytical focus. This creates conceptual limitations in understanding women’s pathways to violence, which can encompass violent and supportive roles within the social world in which they operate. Using Indonesia as a case study, this article advances a framework to account for the mobilisation of different identities to commit violence across personal and political linkages. In examining historical and current developments in Indonesia, this article illustrates that women as supporters or actors of violence, while largely conforming to traditional gender ideology and roles, are driven by both personal and political considerations. <br /><p> </p>

Automated pricing and replenishment decisions for vegetable products based on evaluation optimization models

Based on the commodity information of the supermarket in the annex, the detailed data of historical sales flow, the wholesale price of vegetable commodities and the recent loss rate of vegetable commodities, and through the data analysis of each category and each single product, the automatic pricing and replenishment decision-making model of commodities is established. Use the optimization evaluation algorithm to formulate the total daily replenishment and pricing strategy of each category and each single product. In order to solve the first problem, firstly, the outliers in the original data of Annexes 2 and 3 are cleaned, normalized, feature selected and dimensionally reduced. Secondly, a quarter is taken as a sales cycle of supermarkets, so as to find the proportion of sales volume of a certain category in the same quarter of three years to the total sales volume, and give the distribution law of sales volume of different categories, the results are shown. Considering different periods again, the daily sales volume distribution law is calculated by taking one day as a sales cycle, and the results are shown. Finally, the Pearson grade correlation coefficient is used to judge the relationship between the processing indicators, and the matrix heat map is obtained. According to the two results, it was concluded that there was a significant positive correlation between the sales volume of mosaic and cauliflower vegetables, and a significant negative correlation between the sales volume of nightshade and aquatic root vegetables. In view of the second problem, firstly, considering the functional relationship between the total sales volume and the cost pricing, the correlation analysis and linear fitting were carried out to obtain the linear relationship between the sales price of each category and the maximum value of the sales volume of each category in July of the previous year can be described as Through further nonlinear fitting and optimization problem solving, the total daily replenishment volume and pricing strategy of each vegetable category in the coming week (July 1-7, 2023) are shown in Table 1 and Table 2, which makes the supermarket have the largest revenue In response to the third question, based on the known data, we can analyze the data requirements for each data: we need to know the sales volume of various vegetables during this period, we need to determine the purchase cost of each vegetable, we need to understand the past pricing strategy and response, and we need to know the inventory of various vegetables on June 30. On this basis, a multi-objective dynamic programming model is established, and the total number of saleable items is 30 by using the greedy algorithm to obtain the replenishment quantity of single items on July 1, and the pricing strategy is further solved by using the linear equation fitted in problem 2. In response to the fourth problem , on the basis of the existing sales, wholesale price and loss rate data, in order to better formulate the replenishment and pricing decisions of vegetable products, supermarkets also need to consider and collect the following 12 aspects of relevant data to assist in planning the pricing and replenishment decisions of vegetable products, such as: customer preference and satisfaction survey, seasonality and availability of vegetables, competitor information, inventory costs and storage conditions, historical sales data and trend analysis, customer flow and purchase period, nutritional value and health benefits of vegetables, Socio-economic factors, external environmental factors, policy and regulatory factors, technological and innovation factors, and supply chain and logistics information to ensure more comprehensive and accurate decision-making. Among them, the analysis of historical sales data and trends is mainly carried out.

Downstream Nutrient Concentrations Depend on Watershed Inputs More Than Reservoir Releases in a Highly Engineered Watershed

AbstractIn this study, we characterized the impact of regulatory water releases relative to watershed inputs on the quality of receiving waters to identify if and how managed releases could be scheduled to mitigate nutrient export and downstream water quality impairment. We specifically investigated freshwater flow partitioning to the Caloosahatchee River and Estuary (CRE) from a large managed lake, Lake Okeechobee, and the CRE's upstream watershed, the C‐43 basin, in southwest Florida (USA). A water balance was developed to identify dominant freshwater inflow sources (i.e., Lake Okeechobee vs. watershed inputs) over time. From the water balance, analyses of historical trends were performed to detect changes in freshwater inflow contributions to the CRE. Further, seasonal and annual concentration variations and long‐term concentration‐discharge (C‐Q) relationships were analyzed to better understand biogeochemical and hydrological processes in the system in relation to freshwater source. Since 1966, we found the duration and magnitude of flows from the C‐43 basin were higher than those from Lake Okeechobee releases. However, recent increases in the annual water volume and proportion of inflow coming from Lake Okeechobee to the CRE were observed. The C‐Q analysis revealed that nitrate and ammonium concentrations in the CRE were responsive to changes in discharge, while total phosphorus and orthophosphate concentrations were chemostatic. While modifications to the Lake Okeechobee operation schedule could potentially mitigate downstream inorganic nitrogen loading, this potential is limited by complex, seasonal C‐Q relationships and confounding effects from surrounding watersheds.

Climate change and hydropower resilience in Nepal: an integrated modeling approach in the Madi River Basin

ABSTRACT Responding to Nepal's hydropower sector's vulnerability to climate change, this study investigates its impact on energy security, focusing on the Madi River Basin's river systems. This study conducted a rigorous analysis of the basin's historical and future hydroclimatic trends, using the linear scaling method to correct inherent biases in 13 GCMs, resulting in the selection of 6 BCMs with above-satisfactory performance. Future projections reveal an increase in annual precipitation with a higher increment in SSP585 by the end of the century, and a temperature rise ranging from 1.8 to 3.5 °C from the baseline in the far future under moderate- to high-emission scenarios. These hydroclimatic projections are then forced into the calibrated soil and water assessment tool (SWAT) model with very good performance (both R2 and NSE greater than 0.8). The streamflow projection demonstrates an overall increasing trend, marked by significant flow reduction in early months and pronounced monsoon peaks. The analysis of three distinct hydropower projects reveals unique challenges and opportunities, underscoring the heterogeneous nature of projects and the need for location-specific planning and strategic management. This study provides crucial insights for sustainable development in renewable energy, laying the foundation for future research and policy in similar hydroclimatic settings.

Predictive analytics in climate finance: Assessing risks and opportunities for investors

Predictive analytics is increasingly recognized as a pivotal tool in climate finance, offering investors invaluable insights into both the risks posed by climate change and the opportunities for sustainable investment. This Review delves into the burgeoning field of predictive analytics within climate finance, emphasizing its significance in aiding investors to navigate the multifaceted landscape of climate-related risks and opportunities. By leveraging advanced data analytics techniques, predictive analytics empowers investors to anticipate and mitigate climate-related risks, ranging from physical risks such as extreme weather events and sea-level rise to transition risks associated with regulatory changes and technological shifts. Moreover, predictive analytics enables investors to identify emerging opportunities in sectors poised for sustainable growth, such as renewable energy, clean technology, and climate resilient infrastructure. This Review also sheds light on the methodologies and data sources utilized in predictive analytics for climate finance, encompassing climate models, satellite imagery, socioeconomic indicators, and financial data. Through the analysis of historical trends and future projections, predictive analytics provides investors with actionable insights to inform their investment decisions and align their portfolios with climate-related goals and mandates. Despite its potential benefits, the adoption of predictive analytics in climate finance is not without challenges. This Review examines the hurdles associated with data quality, model uncertainty, regulatory complexities, and the integration of climate-related factors into financial decision-making processes. Addressing these challenges necessitates interdisciplinary collaboration, robust risk assessment frameworks, and ongoing innovation in predictive analytics methodologies. In conclusion, this Review underscores the critical role of predictive analytics in climate finance and its transformative potential in enhancing the resilience and sustainability of investment portfolios. By harnessing the power of data-driven insights, investors can proactively manage climate-related risks, capitalize on sustainable investment opportunities, and contribute to the transition towards a low-carbon economy. As climate change continues to exert profound impacts on financial markets, the integration of predictive analytics represents a strategic imperative for investors seeking to navigate the evolving landscape of climate finance effectively.

A Century of Reforestation Reduced Anthropogenic Warming in the Eastern United States

AbstractRestoring and preserving the world's forests are promising natural pathways to mitigate some aspects of climate change. In addition to regulating atmospheric carbon dioxide concentrations, forests modify surface and near‐surface air temperatures through biophysical processes. In the eastern United States (EUS), widespread reforestation during the 20th century coincided with an anomalous lack of warming, raising questions about reforestation's contribution to local cooling and climate mitigation. Using new cross‐scale approaches and multiple independent sources of data, we uncovered links between reforestation and the response of both surface and air temperature in the EUS. Ground‐ and satellite‐based observations showed that EUS forests cool the land surface by 1–2°C annually compared to nearby grasslands and croplands, with the strongest cooling effect during midday in the growing season, when cooling is 2–5°C. Young forests (20–40 years) have the strongest cooling effect on surface temperature. Surface cooling extends to the near‐surface air, with forests reducing midday air temperature by up to 1°C compared to nearby non‐forests. Analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations surrounded by reforestation were up to 1°C cooler than neighboring locations that did not undergo land cover change, and areas dominated by regrowing forests were associated with cooling temperature trends in much of the EUS. Our work indicates reforestation contributed to the historically slow pace of warming in the EUS, underscoring reforestation's potential as a local climate adaptation strategy in temperate regions.