Analysis Of Historical Trends Research Articles

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.

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.

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.

Thirty years of research in tropical medicine: historical trends for the world and for the Revista de Biología Tropical (1990-2020)

Introduction: The importance of tropical medicine cannot be overstated, since by the end of the 2030s, most humans will live in the tropics and will need protection from tropical diseases. Nevertheless, until now, there are no bibliometric studies about tropical medicine as a whole; all previous studies are limited to diseases, countries or particular journals, and are often limited to brief periods of time. Here we present an analysis of historical trends for the world and for the Revista de Biología Tropical in celebration of the journal’s 70th anniversary. 
 Objective: To assess geographic and historical trends in the study of tropical medicine included in a general database, the Web of Science and from a detailed analysis of one journal of particular importance for the field in the neotropical region. 
 Methods: We limited our data to journals covered by the Web of Science category “tropical medicine” in the Science Citation Index Expanded (data updated 19 January 2022) and the Journal Citation Report on 30 June 2021. 
 Results: We retrieved 69 480 articles, of which 44 % resulted from international collaboration with 194 participating countries; articles may reach 17–21 citations each, and the countries with outstanding results in the database include USA, Brazil, UK, Kenya and Switzerland. Research focus changes as diseases such as HIV, Dengue and tuberculosis become more or less important over time, but overall there was much research on Plasmodium, leishmaniasis and schistosomiasis. The journal Revista de Biología Tropical has significantly increased the number of topics covered, but still publishes studies about tropical biomedical subjects.
 Conclusions: While countries with large research budgets have a large presence in this database, lower income tropical countries like Brazil and Kenya have done a remarkable contribution in the field of tropical medicine. The Revista de Biología Tropical has been publishing quality research about tropical medicine for the last seven decades.

A comprehensive assessment of transportation emissions in Nigeria: Trends, drivers, and impacts

This study presents an in-depth investigation into the complex dynamics surrounding transportation emissions in Nigeria against the backdrop of rapid urbanization, population growth, and economic development. Through an extensive analysis of historical trends and recent developments, the article elucidates the escalating challenges posed by emissions. Examining the drivers of transportation emissions, including population growth, economic development, and fuel choices, the study underscores the significant correlation between economic growth and emissions while scrutinizing their environmental and public health ramifications. It also explores the prevalent use of fuels in Nigeria's transportation sector, highlighting the imperative of transitioning to cleaner energy sources and leveraging technological innovations for emission reduction and sustainability. Evaluation of existing policy frameworks and regulatory mechanisms offers insights into their efficacy in emission mitigation, along with emphasizing Nigeria's commitments under international agreements like the Paris Agreement and Sustainable Development Goals. The socioeconomic impacts of transportation emissions, encompassing public health costs, economic losses from traffic congestion, and broader economic implications, are scrutinized. The study concludes with a compelling call to action for immediate policy reforms, sustainable transportation solutions, and international collaboration. As the world grapples with climate change and environmental degradation, this comprehensive assessment aims to inform policymakers, researchers, and stakeholders, serving as a crucial resource for charting a sustainable course for Nigeria's transportation sector.

Streamlining Data Archiving for Enhanced Historical Insight and Trend Analysis

Efficient data archiving is crucial for organizations aiming to derive valuable insights from historical data for trend analysis and decision-making. This paper explores various approaches and strategies for optimizing data archiving processes to facilitate historical analysis. It delves into the challenges associated with managing large volumes of historical data and proposes solutions to address these challenges. Additionally, the paper highlights the importance of efficient data retrieval techniques and discusses emerging trends and technologies shaping the future of data archiving for historical analysis n 2023, Throughout my tenure up to 2023 as a Senior Big Data Engineer at Wells Fargo, I have been at the forefront of navigating and resolving complex data challenges. This paper reflects the culmination of insights and experiences gained during this period, focusing on optimizing data archiving processes for historical analysis. The financial sector, known for its stringent regulatory requirements and the critical importance of data integrity and security, offered a unique set of challenges, particularly in the realm of data archiving and historical data analysis.

Sales Forecast of New Energy Vehicles in China Based on LSTM Model

With the increasingly serious environmental protection problems and the rapid increase of global energy consumption, new energy vehicles, as an alternative to traditional fossil fuel vehicles, show a strong development trend in China and the global automobile market. Therefore, whether we can accurately predict the future sales of new energy vehicles is of great significance for government decision-making, enterprise investment strategy and other aspects. Through the analysis of historical sales data, market trends and related factors, we use the long-term and short-term memory (LSTM) model to predict the sales volume of new energy vehicles in China. The experimental results indicate that the model has high accuracy and reliability in predicting the sales of new energy vehicles.

Evaluating the long-term geomorphic process in-relation to hydrodynamics in the central coastal zone of Bangladesh

The central coast of Bangladesh is dynamic for its geographical location, hydrodynamic characteristics and residual flow. The research employed the Digital Shoreline Analysis System (DSAS), an ArcGIS extension tool, to conduct a historical trend analysis of shoreline. The study demonstrates that the central coast is eroding to the north and accreting to the south. The highest accretion value was found as 195.42 m/year, whereas the maximum value of erosion was estimated as −185.83 m/year, according to End Point Rate (EPR). The Linear Regression Rate (LRR) indicates that the average rate of erosion and accretion are −17.77 m/year and 17.88 m/year, respectively. Meanwhile, using Weighted Linear Regression (WLR), 0.48% of all transects demonstrated statistically significant erosion, while 0.43% showed statistically significant accretion. During the wet season, heavy river discharge leads to a low salt level in the ocean. Ocean currents hit central coast of Bangladesh from east to west, affecting the majority of the islands in the Meghna Estuary in the dry season. Changes in current directions can be seen during the wet seasons. Southern central coast areas are hit by south-east currents that split in two directions. The Sandwip Channel has a flow of 10,000 to 15,000 m³ s−1 northward. The Tetulia River, Shahbazpur Channel, and Hatia-Sandwip flow southward at rates ranging from 3000 to 17,000 m³ s−1, 14,000 to 60,000 m³ s−1, and 7000 to 39,000 m³ s−1, respectively. In the Meghna Estuary, the combined forces result in a counter-clockwise residual circulation, with the northward flow in the Sandwip channel and southbound flow in the Hatia and Shahbazpur channels. As a result of hydrodynamic, ocean currents, and residual flow, the Central Coast of Bangladesh is continually changing in appearance.

A universal optimization framework for commercial building loads using DERs from utility tariff perspective with tariff change impacts analysis

To accommodate the changes in the nature and the pattern of electricity consumption with the available resources, utilities have introduced variety of tariffs over the years. This paper develops a comprehensive optimization framework that addresses the challenges associated with the diversity of utility tariffs for commercial loads. Optimization of commercial building net load through Battery Energy Storage System (BESS) and renewable energy resources is modeled and explored for minimizing billing cost for different tariffs. Cost functions are formulated for each possible commercial utility tariff type, engendering the universal cost function format. An algorithm is developed to apply the optimization model and generate the desired optimal outputs (e.g., optimal net load and BESS power, costs and savings, etc.). The results for several building loads and all tariff types are compared and analyzed to present the findings. An impacts analysis is performed to observe how different changes in tariffs affect the optimizations results. Results exhibit that for same building load and renewable generation, the tariff type can have an overwhelming impact on costs and savings. Tariffs without Time of Use (TOU) charges or peak demand charge components offer lower savings opportunity from BESS optimization. Price change sensitivities showed the complicated dynamics among TOU price components on savings and historical trend analysis as an effective tool to predict future savings. Tradeoff between energy and demand charge heavy tariff options are also discussed for user benefit. The recent change in TOU time periods presents limited desired benefits while the modified tariff proposed in this paper exhibit significant improvement in terms of utility operation.

Historical and Projected Trends of the Mean Surface Temperature in South-Southeast Mexico Using ERA5 and CMIP6

This study aimed to determine the mean temperature trends in the south-southeast region of Mexico during the historical period of 1980–2014, as well as during the future periods of 2021–2040, 2041–2060, and 2081–2100, as recommended by the IPCC. Additionally, the study sought to identify the climate change scenario that is most closely aligned with the socio-environmental conditions of the south-southeast zone of Mexico and that has the greatest impact on the region’s average temperature. The downscaling method of bias correction was conducted at a spatial resolution of 0.25° × 0.25°, and an analysis of historical trends was performed for the period 1980–2014 with ERA5 and four CMIP6 models (CNRM-ESM2-1, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0). This process was extended to future projections. The models indicated temperature differences of less than 0.5 °C with respect to ERA5, in agreement with other studies. Additionally, the current study calculated future trends for the south-southeast region using three of the CMIP6 scenarios (SSP2-4.5, SSP4-6.0, and SSP5-8.5). The z-eq proposal was used to compare the slopes, enabling us to determine which of the three scenarios corresponded to the historical trend, assuming identical socio-environmental conditions. The SSP4-6.0 scenario was found to correspond to the historical trend.