Mitigation Issues Research Articles

Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation

With the galloping progress of the changing climates all around the world, Machine Learning (ML) approaches have been prevalently studied in many types of research in this area. ML is a robust tool for acquiring perspectives from data. In this paper, we elaborate on climate change mitigation issues and ML approaches leveraged to solve these issues and aid in the improvement and function of sustainable energy systems. ML has been employed in multiple applications and many scopes of climate subjects such as ecosystems, agriculture, buildings and cities, industry, and transportation. So, a Systematic Literature Review (SLR) is applied to explore and evaluate findings from related research. In this paper, we propose a novel taxonomy of Deep Learning (DL) method applications for climate change mitigation, a comprehensive analysis that has not been conducted before. We evaluated these methods based on critical parameters such as accuracy, scalability, and interpretability and quantitatively compared their results. This analysis provides new insights into the effectiveness and reliability of DL methods in addressing climate change challenges. We classified climate change ML methods into six key customizable groups: ecosystems, industry, buildings and cities, transportation, agriculture, and hybrid applications. Afterward, state-of-the-art research on ML mechanisms and applications for climate change mitigation issues has been highlighted. In addition, many problems and issues related to ML implementation for climate change have been mapped, which are predicted to stimulate more researchers to manage the future disastrous effects of climate change. Based on the findings, most of the papers utilized Python as the most common simulation environment 38.5 % of the time. In addition, most of the methods were analyzed and evaluated in terms of some parameters, namely accuracy, latency, adaptability, and scalability, respectively. Lastly, classification is the most frequent ML task within climate change mitigation, accounting for 40 % of the total. Furthermore, Convolutional Neural Networks (CNNs) are the most widely utilized approach for a variety of applications.

Coupled coordination and pathway analysis of food security and carbon emission efficiency under climate-smart agriculture orientation

Owing to the contradiction between agricultural production and environmental development, the issues of food security and carbon mitigation cannot be isolated, and achieving coupled and coordinated development is the key to agricultural sustainability. This study adopted the coupled coordination model and dynamic qualitative comparative analysis (dynamic QCA) method to measure the coupled coordination degree (CCD) of the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in 31 provinces of China from 2010 to 2021, seeking paths to achieve high coupled coordination from Climate-Smart Agriculture technology, external environment, and incentive dimensions, and simulating path selection differences under various CSA priority scenarios. The results indicated that the CCD of the FSI and ACEE in China significantly increased year-on-year increase, with significant regional differences primarily reflected in the Northeast > East > West > Central regions. Based on the CSA orientation, the “technology-environmental safeguard” linkage path and the “technology-environment-incentive” hybrid path are proposed. There are differences in CSA practices across regions, which require customization based on their unique socioeconomic, ecological, and political landscapes. When priorities favour food security, the “technology-environment-incentive” hybrid pathway supports high CCD, and as priorities increase, the contribution of CSA technologies, centred on water-saving irrigation, increases and the role of the external environment diminishes. When the priority tendency is to mitigate emissions, both paths can achieve high CCD. As the priority tendency for carbon emissions increases, urbanisation and CSA technologies such as water-saving irrigation and straw return become essential factors contributing to higher coupling coordination, and the role of agriculture-related financial expenditures diminishes. These findings provide policy support for safeguarding food security and low-carbon agriculture.

Research on the Effective Sheltering Rates of Public Buildings in Villages in Western Sichuan, China—A Case Study of Ganbao Tibetan Village

The western region of Sichuan Province, located in the Hengduan Mountains, is one of China’s most seismically active zones. Due to limited land resources, many villages in western Sichuan Province are situated in mountainous and valley areas, facing harsh climatic conditions, lagging economic development, and insufficient infrastructure. After experiencing seismic disasters, these villages often encounter challenges, such as cut-off mountain roads, difficulties in delivering relief supplies, and sharply a significant drop in nighttime temperatures due to the high-altitude climate. Consequently, in the case of the economic underdevelopment and limited resources, how to repurpose existing buildings for disaster relief has become a crucial issue for disaster prevention and mitigation in these villages. This paper takes Ganbao Tibetan Village, located in the alpine gorge regions of western Sichuan Province, as a case study. It evaluates the interior space utilization status of different types of existing public buildings during disasters, calculates the effective utilization rate of interior space disaster reduction conversion based on the spatial layout model, and extracts the typical interior space dimensions that are conducive to disaster relief and conversion of public buildings based on the interior space utilization characteristics of disaster relief. This study provides a reference for the design of public service buildings considering the requirements of disaster resilience transformation.

"Issues of mitigation of punishment for legal entities in the criminal legislation of foreign countries "

"Issues of mitigation of punishment for legal entities in the criminal legislation of foreign countries "

Progress in understanding the social dimensions of desalination and future research directions

The piece outlines the contributions of key works in the field of the political ecology of desalination over the past decade. We note that the field is diverse in terms of contributions from geographers, sociologists, anthropologists, and public policy scholars. The research to date has been concerned with the ways in which the deployment of desalting techniques can reflect and reinforce social processes of inequality, political power and economic flows. In this way, desalination has been opened up for intellectual debate beyond technical considerations of the desalting industry and engineers. A critical perspective that complements the recent discussions of environmental harm caused by the desalination industry has emerged as well across a number of global and transboundary contexts. A number of themes emerged that will continue to be of interest to scholars and that need to be addressed in the years ahead. First, desalination intersects transboundary water governance and geopolitics between different water uses and emerges from complex assemblages of local and global actors, including financial actors, water companies, governments, technologies, and natural forces. Second, critical scholarship on desalination needs to continue to pay attention to the interests in and overarching patterns of, the Green New Deal and Blue Economy, each of which intersect with the worlds of academia and policymaking, and involve issues of climate adaptation and mitigation. Third, questions about equity remain with desalination as it is a solution deeply imbricated in the unequal distribution of resources, and questions about representation in decision-making remain. Fourth, research on finance and infrastructure have been at the core of critical desalting research and should remain so. Fifth, there is a growing heterogeneity in terms of research in types of desalting, from reverse osmosis to inland desalting to nuclear and more. This variety will make for rich research for the years ahead. Our hope is that the epistemological, theoretical, and methodological flexibility of this area of research will remain a strong point continuing its rigor, as well as the already robust collegiality among scholars in this interesting, and still nascent field.

Transforming Zeolite Tuff and Cigarette Waste into Eco-Friendly Ceramic Bricks for Sustainable Construction

The use of waste materials has gained attention as a sustainable approach in various industries. Cigarette waste, which is typically discarded as a non-recyclable material, poses a significant environmental challenge due to its toxicity and slow decomposition rate. However, by incorporating this waste into ceramic bricks, new approaches for waste management and resource utilization are explored. This research work provides a detailed evaluation of the possibility of utilizing natural zeolite tuff incorporated with cigarette waste to produce sustainable ceramic bricks. Uniform powders are produced by milling various combinations of zeolitic tuff and cigarette waste using a planetary ball mill. The substitution ratios ranged from 0% to 12% by weight of the zeolitic tuff, with increments of 2%. Ceramic discs were formed by dry pressing and then subjected to sintering at different heat treatment temperatures (950–1250 °C). The impact of the inclusion of cigarette waste on the microstructural and technical features of zeolite tuff-based ceramic bricks has been thoroughly investigated. The results of the experiments demonstrate that incorporating cigarette waste into the development of ceramic bricks leads to improved thermal insulation properties, with thermal conductivity ranging from 0.33 to 0.93 W/m·K. Additionally, these bricks exhibit a lighter weight in a range of 1.45 to 1.96 g/cm3. Although the inclusion of cigarette waste slightly reduces the compressive strength, with values ranging from 6.96 to 58.6 MPa, it still falls within the acceptable range specified by standards. The inclusion of cigarette waste into zeolite tuff is an innovative approach and sustainable practice for reducing energy consumption in buildings while simultaneously addressing the issue of waste disposal and pollution mitigation.

Capitalizing on Structured Experience on Disasters to Build Disaster Mitigation Awareness Among Muhammadiyah Activists

Abstract. This article explains the assistance provided to the Head of the Muhammadiyah Branch (PRM), Kadirojo, in mitigating natural and social disasters. The issue of disaster mitigation is one of the important variables in the process of reducing the number of disaster risks that are likely to occur. This includes the potential megathrust disaster in the southern part of the Java Sea, where the position of Palbapang village is within a radius of 7 km from the southern coast of Java. This step will be managed using an androgy approach, which explores the structured experience of the Kadirojo community related to disaster problems such as fires, earthquakes, infectious diseases, and social disaster problems. Meanwhile, the pedogogic approach is used to socialize the Kadirojo PRM management from a number of disaster mitigation experiences from communities outside Palbapang village and abroad. Focus Group Discussion and training will be carried out systematically in order to change the perspective of the community productively. According to the findings of this article, Muhammadiyah Kadirojo's Islamic teachings have not yet institutionalized disaster literacy, so it is still necessary to spread the message about it in order for it to become ingrained in people's minds.

Survey on Machine Learning Biases and Mitigation Techniques

Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation.

AI SENTRY: REINVENTING CYBERSECURITY THROUGH INTELLIGENT THREAT DETECTION

In recent years, the escalating complexity and frequency of cyber threats have presented a formidable challenge to traditional cybersecurity measures. The emergence of artificial intelligence (AI) technologies has revolutionized the landscape, offering a promising solution to fortify defenses against evolving threats. This paper introduces AI Sentry, an innovative approach to cybersecurity that leverages the power of AI for intelligent threat detection and prevention. AI Sentry embodies a paradigm shift in cybersecurity, integrating machine learning, neural networks, and advanced algorithms to proactively identify, analyze, and mitigate potential threats in real time. By continuously learning from vast datasets and adapting to new attack vectors, AI Sentry enhances its ability to recognize anomalous patterns and behaviors, thereby thwarting sophisticated cyber assaults. The core strength of AI Sentry lies in its capability to detect anomalies and predict threats with a high degree of accuracy, surpassing the limitations of traditional signature-based systems. Through anomaly detection, behavioral analysis, and contextual understanding, AI Sentry not only identifies known threats but also anticipates zero-day attacks and previously unseen malicious activities. This paper delves into the technical underpinnings of AI Sentry, elucidating its architecture, data processing techniques, machine learning models, and the orchestration of various AI components. Furthermore, it explores the ethical considerations and challenges associated with AI-powered cybersecurity, including issues of privacy, bias mitigation, and transparency in decision-making.

Transitional justice mechanisms and processes: international experience for restoring Ukraine’s sovereignty and national security

The mechanisms and processes used in post-conflict countries that contributed to the restoration and ensuring of sustainable peace after the end of armed conflict or active hostilities are analysed. Among the elements of transitional justice, the author identifies those whose implementation is appropriate and possible before the end of the active phase of the conflict in Ukraine: bringing to individual responsibility, recording, documenting and collecting evidence of crimes allegedly committed on the territory of Ukraine (including war crimes, crimes against humanity, genocide and the crime of aggression), cooperation with the International Criminal Court on international crimes, determining the procedure for bringing the top military and political leadership of the russian federation to justice (tribunal); establishing the circumstances of the conflict and recording them, which is especially important in the context of information and psychological operations and other methods of hybrid warfare used by the Russian Federation. With regard to reparations and guarantees of non-recurrence of the conflict, it is already advisable to discuss both issues, develop strategies for compensating Ukraine for losses and act during and after the de-occupation of the territories.
 It has been also noted that the issue of exemption from liability and punishment may be raised during negotiations on the terms of ending the conflict, as well as the issue of mitigation of punishment in case of guilt and cooperation in establishing the truth. In this aspect, the experience of Colombia has been analysed with a reservation regarding the individualisation of the concept of transitional justice, taking into account specific circumstances. In particular, it has been determined that for Ukraine, the experience of transitional justice in Colombia can be taken into account when addressing a number of issues of amnesty, organisation of work of truth commissions and others, with an understanding of the different nature of the conflict in Colombia, which is purely internal (non-international armed conflict).

Анализ выбросов парниковых газов стран BRICS и пути сокращения выбросов на железнодорожном транспорте

Purpose: To analyze greenhouse gas emissions from the BRICS countries, as well as ways to reduce emissions from rail transport. Methods: The contribution of the BRICS countries to global greenhouse gas emissions based on an array of statistical data for the period from 1990 to 2019 has been estimated. The value of CO2 emissions per unit of energy produced in the BRICS countries from 1965 to 2021, as well as the sources of CO2 emissions, have been determined. Results: It is shown that the most effective way to combat climate change is to directly reduce emissions. In general, the emissions of the railway industry depend on the level of electrification. However, when assessing not only direct but also indirect emissions, the energy source and its carbon footprint become of primary importance. The development of intermodal transportation in BRICS countries is significant because these routes have the potential to reduce emissions. Carbon regulation leads to a redistribution in the sphere of international freight transportation among BRICS countries. Competitive advantages are increasingly evident in the railway transportation sector. However, this effect depends on the measures taken to adapt to changes in the transportation market by other modes of transportation. Practical significance: Ways to improve the sustainability of transport infrastructure have been formulated. The lowcarbon transport industry in the BRICS countries can be seen as an important component in reducing gross greenhouse gas emissions given the current high level of reliance on fossil carbon fuels and the inertia driven by the long life of fossil fuel infrastructure. Without the use of negative emission technologies, the required costs for reducing greenhouse gas emissions can be 40 % higher than with the use of negative emission technologies. As part of the reduction of greenhouse gas emissions by railway transport facilities in the BRICS countries, the following measures can be implemented: direct reduction of greenhouse gas emissions; introduction and implementation of technologies for negative emissions at railway transport facilities, taking into account their geospatial distribution and their specific characteristics; attracting investments and ensuring access to more affordable financing for these projects; integration of climate change mitigation issues into the strategies of transportation companies; further electrification in transportation and transitioning to alternative fuel sources; utilization of digital technologies to optimize transportation systems and reduce the environmental impact of the railway industry.

Study on the Legal Regime for Space Debris Mitigation — Taking the Inter-Agency Space Debris Coordination Committee Space Debris Mitigation Guidelines as an Example

As human space activities continue, the problem of space debris which is regarded as the “killer of satellites, spacecraft, and space shuttles”, remains unresolved. 2021 saw the release of the latest version of the IADC Space Debris Mitigation Guidelines (hereinafter referred to as the IADC Guidelines), the only remediation program to date that has had a significant positive impact on addressing the accumulation of space debris. The IADC Guidelines complement the body of outer space law in the area of space debris and provide a good model for addressing the issue of space debris mitigation. The present paper proposes a path for the future development of the IADC Guidelines to contribute to the improvement of the legal regime for space debris mitigation.

Backstepping Based Grey Wolf and DPC for Power Quality Improvement and Active Power Injection in PV Grid-connected System Based on Interleaved Boost Converter

This research offers the backstepping based grey wolf control design for a multifunctional PV grid-connected system (MPGC) based on four phases interleaved boost converter. This work proposes a solution to the issues of harmonic mitigation, reactive power compensation, and PV-generated power injection into the grid-based MPGC. The interleaved boost converter (IBC), controlled using maximum power point tracking (MPPT), is utilized to harvest the photovoltaic (PV) system's peak power and overcome the conventional topology's drawbacks. Direct power control (DPC) based on space-vector pulse width modulation (SVPWM) is used to control the instantaneous power of the MPGC, and the backstepping control (BSC) is applied to the whole system to maintain the robustness and stability of the suggested method. The Grey Wolf Optimizer (GWO) optimized the system's dynamic response by adjusting the BSC parameters. The results were obtained using MATLAB/Simulink software. The suggested work shows excellent performance based on the obtained results, achieving the sinusoidal waveform of the currents and a unity power factor. Total harmonic distortion (THD) has been decreased below 5% in accordance with IEEE 519-2014 standard.

Creating an Organizational Culture and Climate of Meaningful Recognition for Nurse Managers.

To examine the current nurse manager (NM) recognition culture and climate in organizations and to develop a theoretical foundation for meaningful recognition. Nurse managers call for wider recognition, but the theoretical conceptualization and underpinnings of meaningful recognition are sparse in the literature. A qualitative descriptive design was used utilizing individual interviews. The study participants included 30 individuals consisting of NMs, chief nursing officers, and chief nursing executives. Content analysis was conducted. A theoretical model with super-categories and supportive quotes was created to operationalize a culture and climate of meaningful recognition in the organizations represented by the sample. This qualitative descriptive study highlights the importance of recognizing the value of NMs for their contributions to nursing leadership and patient care. Implications of recognizing NMs are counteracting the feeling of being invisible and mitigating issues within the work environment.

The Role and Use of Mathematical Programming in Agricultural, Natural Resource, and Climate Change Analysis

Climate change undeniably impacts agriculture and natural resources, enterprises and markets. For informed decision making, there is a need for information on climate change adaptation possibilities and mitigation alternatives. Mathematical programming has been used to address the economic aspects of such questions and allows analysis as climate change moves the environment into previously unobserved conditions. It allows us to model spatial and dynamic features of the issue and analyze heretofore unobserved adaptation and mitigation possibilities. This review provides an overview of and references for modeling techniques, conceptual issues, and major assumptions involved with using mathematical programming as a climate change economic analyzing engine, along with a brief comparison with other methods. We also review a number of studies applying mathematical programming to examine climate change impacts, adaptation, and mitigation issues in the agricultural and natural resources arena. Finally, we present a very brief discussion on research needs.

Overviewing Global Surface Temperature Changes Regarding CO2 Emission, Population Density, and Energy Consumption in the Industry: Policy Suggestions

The focus of this study is to investigate the causal relationships between global surface temperature changes and various relevant economy-related factors and to provide a clearer regime for authorities. The study reveals that the growth rate of production-based CO2 productivity and energy consumption in industrial, service, and transport sectors positively correlates with global surface temperature changes, aggravating the problem in the long run. However, it is evident that, on the one hand, the energy efficiency of industrial and service sectors needs to be highly scrutinized to address the mitigation issues of global surface temperature change. On the other hand, the contributions of the agricultural and transport sectors are not obvious due to their bidirectional causal relationships with respect to global surface temperature changes. Thus, improving energy efficiency and consumption in these sectors should also be a significant concern. Furthermore, the study highlights the positive causal relationship between population density and the contribution of renewable energy to global surface temperature change. Although population density aggravates the issue, the use of renewable energy confronts it. The contribution from empirical evidence presented in this study emphasizes the need for industries to improve their energy efficiency and consumption in order to mitigate global surface temperature changes.

Definition and forecasting of GGDP in the landscape of climate issues

In this paper, we propose a GGDP assessment system for climate mitigation, analyze its ecological impacts, and discuss the potential advantages and disadvantages of the GDP transformation. Firstly, the definition and traditional calculation of GGDP are elaborated, and eight indicators are divided into direct and indirect factors for climate mitigation. Then, five rep-presentative countries are selected and the data of indicators are substituted. Besides, the weights corresponding to the indicators are calculated by the entropy weighting method and the coefficient of variation method respectively. Next, the comprehensive weighting coefficients of the indicators are figured out by the combined method. The established GGDP system based on the CV-EWM evaluation model is used to obtain the GDP and GGDP change of five countries from 2001 to 2022. Additionally, the BP neural network algorithm is used to predict the trends of CO2 and temperature under the two systems of GDP and GGDP, respectively. Finally, the neural network was optimized by a genetic algorithm to establish the global climate impact assessment model based on GA-BP neural network prediction, and the global effect of the GGDP system on climate mitigation issues was studied.

Emerging Single-Atom Catalysts and Nanomaterials for Photoelectrochemical Reduction of Carbon Dioxide to Value-Added Products: A Review of the Current State-of-the-Art and Future Perspectives

Rapid industrialization has resulted in a drastic increase in the consumption of natural resources, particularly fossil fuels. Fossil fuels in the form of coal, natural gas, oils, etc. have been extensively used to meet ever-increasing energy demands, resulting in adverse environmental effects. Massive fossil fuel consumption has led to enormous carbon dioxide (CO2) emissions, adversely impacting the environment through global warming. Photoelectrochemical (PEC) reduction of CO2 into valuable fuels, for instance, formic acid (HCOOH), methanol (CH3OH), ethanol (C2H5OH), etc., which combines the merits of both photochemical and electrochemical techniques, has been considered as a potential approach to address the issue of CO2 mitigation. Single-atom catalysts (SACs) along with emerging nanomaterials (NMTs) as photoelectrode composite materials present an effective avenue to augment PEC CO2 reduction owing to their well-defined structures and almost complete atom utilization. The present review focuses on the application of various SACs and NMTs as emerging photoelectrode materials that are advantageous for efficient PEC CO2 reduction to valuable fuels, such as HCOOH, CH3OH, C2H5OH, and carbon monoxide. The mechanism pertaining to the effective utilization of incident light energy to overcome the band gap and produce photogenerated charge carriers that contribute to the PEC reduction of CO2 has also been elucidated. At the outset, some challenges and opportunities that lie ahead in boosting SAC’s PEC performance have been discussed.

Community Conceptions and Perceptions of Climate Change Mitigation and Adaptation in Ternate

The community's input on climate change issues in the city of Ternate is urgently needed. The purpose of this study is to document how the people of Ternate City understand and respond to issues of climate change mitigation and conditions. This study used a community-based participatory research method involving 30 people consisting of community members, teachers, lecturers, and students. Focus group discussions (FGD) and photovoices were conducted to obtain data on causes, impacts and solutions to climate change problems in the city of Ternate. The research results confirmed that the people in the city of Ternate feel the negative impacts of climate change in the city, such as reduced water sources, loss of plant vegetation (for example: mangroves, sago and sea grass), reduced marine habitats, floods and landslides at several points, increased sea level. The main causes of climate change in the city of Ternate are the establishment of non-environmentally friendly development policies, the lack of public knowledge and awareness of climate change adaptation, and the lack of regional regulations at the city to village levels related to environmental issues. The solutions offered by the community are very diverse but according to the community, the government and young people need to conduct education, mitigation, and adaptation to climate changes in the city of Ternate.

Study on an air conditioning system with the use of natural cooling

Issues of microclimate mitigation in Uzbekistan during the summer period require urgent resolution. It should be noted that currently there are very few researches, as well as practical recommendations related to the formation of a favorable microclimatic environment in cities with a hot climate. The simplest and most economical way of cooling, in dry and hot climates, is the evaporative cooling of a dry air stream. One of the possible ways to increase the efficiency of the system is the use of night evaporative cooling of water, followed by its accumulation for daytime cooling in heat-insulated batteries. The conducted experiments testify that rather simple and cheap natural sources of cold can be used along with machine cold sources for air conditioning of residential buildings in Uzbekistan.