Human Life Research Articles

Water, sanitation, and hygiene (WASH) experience during flooding: the Nsukwao community of Ghana

ABSTRACT This research examines the water, sanitation, and hygiene experiences during flooding. Floods are among the disasters that cause widespread destructions to human lives, properties and the environment. Flooding occurs every year at different places with varied scales across the globe. The focus of the study is the Nsukwao community, located in the Eastern Region of Ghana. The Nsukwao community is prone to seasonal flooding, and in the year 2017, the government of Ghana through the Nsukwao Basin Project tried to solve the seasonal flooding. However, the issues persisted. This research utilised a mixed method approach where semi-structured interviews were undertaken and a survey questionnaire was also randomly administered. The survey questionnaire yielded a response rate of 52%. After the data analysis, it was found that the Nsukwao community has acceptable water and sanitation facilities, however, during the rainy season, the community members are negatively impacted. Also, the community members do not take part in water, sanitation, and hygiene programmes because they are never consulted. To improve water, sanitation, and hygiene experiences, it is important for the government through the municipal and district councils to extensively engage the local community.

Catalysts for C-N coupling in urea electrosynthesis under ambient conditions from carbon dioxide and nitrogenous species.

Urea is an indispensable nitrogen-containing organic compound in modern human life. However, the current industrial synthesis of urea involves ammonia, which is produced through the Haber-Bosch process under harsh reaction conditions, causing huge energy consumption and heavy environmental pollution. Electrochemical reduction of carbon dioxide (CO2) and nitrogenous species (N2, NOx- and NO) have achieved significant progress, offering a promising approach for the electrochemical C-N coupling to produce urea under ambient conditions. Urea synthesis driven by renewable electricity represents a suitable alternative to the traditional process, contributing to the goal of carbon neutrality and nitrogen cycles. However, challenges such as low yield rate, poor selectivity and unveiled reaction mechanisms still need to be addressed. This review provides a summary of the latest catalysts utilized in urea electrosynthesis, aiming to provide guidance and prospects for the development of high-performance catalysts.

Predictions & perceptions: A social-ecological analysis of human-carnivore conflict in Botswana

Human-carnivore conflict in agrarian landscapes poses a significant threat to both large carnivore populations and human livelihoods. Conservation strategies aimed at mitigating conflict often rely primarily on ecological, landscape-scale factors. However, assessing the influence of microhabitat, as well as local variation in human perceptions, can offer valuable insights into the spatial patterns of human-carnivore conflict. The objectives of this study were to identify landscape predictors of livestock depredation risk, characterize microhabitat vegetation patterns at kill sites, and assess the spatial alignment between ecologically-predicted and human-perceived depredation risk. Through use of predation risk modeling, microhabitat pattern analysis, and participatory mapping exercises, we provide insight into the spatial patterns of livestock depredation risk from a multi-carnivore guild in an agrarian landscape located in the Makgadikgadi region of Botswana. We identified 170 depredation events and conducted 63 participatory mapping exercises across community lands situated between two wildlife protected areas. Results indicated anthropogenic landscape variables as the primary drivers of general depredation risk. Notably, distinct spatial patterns emerged between African lion (Panthera leo) and African wild dog (Lycaon pictus) depredation, underscoring the need for species-specific conservation strategies. Microhabitat analysis indicated a preference for lower vegetation cover within the multi-predator guild, suggesting livestock microhabitat risk-mitigation behaviors outweigh predator hunting strategies. While human perceptions generally aligned with ecologically predicted risk, identified divergences emphasize the importance of tailored conflict mitigation strategies to accommodate varying influences. Overall, the findings advocate for species-specific depredation risk analyses and the inclusion of human perceptions for a comprehensive spatial understanding of livestock depredation risk.

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.

Land surface temperature and transboundary air pollution: a case of Bangkok Metropolitan Region

In a rapidly urbanizing world, heavy air pollution and increasing surface temperature pose significant threats to human health and lives, especially in densely populated cities. In this study, we took an information theory perspective to investigate the causal relationship between diel land surface temperature (LST) and transboundary air pollution (TAP) from 2003 to 2020 in the Bangkok Metropolitan Region (BMR), which includes Bangkok Metropolis and its five adjacent provinces. We found an overall increasing trend of LST over the study region, with the mean daytime LST rising faster than nighttime LST. Evident seasonal variations showed high aerosol optical depth (AOD) loadings during the dry period and low loadings at the beginning of the rainy season. Our study revealed that TAP affected diel surface temperature in Bangkok Metropolis significantly. Causality tests show that air pollutants of two adjacent provinces west of Bangkok, i.e., Nakhon Pathom and Samut Sakhon, have a greater influence on the LST of Bangkok than other provinces. Also, the bidirectional relationship indicates that air pollution has a greater impact on daytime LST than nighttime LST. While LST has an insignificant influence on AOD during the daytime, it influences AOD significantly at night. Our study offers a new approach to understanding the causal impact of TAP and can help policymakers to identify the most relevant locations that cause pollution, leading to appropriate planning and management.

Performance Evaluation of Carbon-Neutral Cities Based on Fuzzy AHP and HFS-VIKOR

Climate change threatens human survival and development. Cities, as the main gathering places for human production and life, serve as the focal points for the implementation of the policies related to energy efficiency, energy transition, and environmental protection. This study constructs an index system for the evaluation of carbon-neutral cities from the perspectives of carbon sources and carbon sinks. The system includes thirteen indicators under six dimensions. It combines objective and subjective data (i.e., statistical data and expert evaluations) by integrating two approaches: the fuzzy analytic hierarchy process (fuzzy AHP) and vise kriterijumska optimizacija i kompromisno resenje with hesitant fuzzy sets (HFS-VIKOR). We verify the efficacy of the proposed approach through a case study of thirteen low-carbon pilot cities in China. The results indicate that among these cities, Shenzhen performs the best, followed by Guangzhou and Hangzhou, while Kunming, Baoding, and Tianjin show poor performance in terms of carbon neutrality. Kunming and Baoding exhibit shortcomings mainly in carbon sources, while Tianjin faces deficiencies in both carbon sources and carbon sinks. Sensitivity analysis and comparative analysis show the availability and effectiveness of the proposed method. The proposed radar chart further highlights the improvement directions for each city to achieve carbon neutrality.

Sign Language Recognition Using Machine Learning

Communication is very important in human daily life and the most widely used type of communication is verbal communication. But there are people with hearing and speech impairment who cannot communicate verbally and the language which they use for communication is sign language. Many other languages, tools are being developed for inter-language translation from sign language to text. There has been a lot of research done in the field of American Sign Language but the work is limited in the case of Indian Sign Language. This is due to lack of standards and the variation in the language. The proposed system aims to recognize Indian Sign Language digit gestures and convert it into text. By using Machine Learning Techniques, sign language recognition leads to the development of a more accurate and robust system. As Deep learning techniques, ResNet100 and ensemble models continue to evolve, sign language recognition system plays a transformative role in bridging the communication gap between deaf and hearing individuals. It helps the user to recognize the sign language by using this proposed system.

The relationship between conctere composition and structural stability in case of fire

A series of disasters caused by fire have made engineers aware that understanding the consequences of fire is essential, as human lives depend on it, and it can also cause significant material damage. The structural integrity of structures during and after a fire, including their stability, can be better ensured if we accurately understand the effects of fire. Of course, the effects of fire do not always lead to significant damage, but in many cases they do. Fire protection is intended, on the one hand, to ensure that buildings remain stable under fire for a certain period, which is necessary to enable occupants to escape safely and, on the other hand, to minimize damage to building structures. The concrete composition, i.e., the structure's microstructure, significantly influences the stability of reinforced concrete structures in the event of fire or heat. In the case of concrete, in addition to changes in the internal material structure in the event of fire, spalling has also appeared recently, largely due to the denser concrete structure. In summary, the higher strength of concrete and the concomitant change in the test strength will lead to spalling, which will have a major impact on the stability of structures under fire. Both small-element (fiber efficiency, aggregate efficiency) and large-element tests have been carried out to verify this. The small-element tests were carried out in an electric furnace to monitor possible concrete surface delamination and strength loss. The small-element tests can be used to select concrete compositions for the large-element tests. Based on their experimental results, an increase in the fire resistance limit of a structural element can be achieved by reducing the delamination of the concrete surface when plastic fibers are added to the concrete. However, if a further increase in the fire resistance limit is required, a change in the type of cement or a modification of the supplementary material may be necessary to increase the residual strength value. Overall, the microstructure of concrete has a significant impact on the stability of structures in the event of a fire.

Marathi to Indian Sign Language Machine Translation

Machine translation has been a prominent field of research, contributing significantly to human life enhancement. Sign language machine translation, a subfield, focuses on translating spoken language content into sign language and vice versa, thereby facilitating communication between the normal hearing and hard-of-hearing communities, promoting inclusivity. This study presents the development of a ‘sign language machine translation system’ converting simple Marathi sentences into Indian Sign Language (ISL) glosses and animation. Given the low-resource nature of both languages, a phrase-level rule-based approach was employed for the translation. Initial encoding of translation rules relied on basic linguistic knowledge of Marathi and ISL, with subsequent incorporation of rules to address 'simultaneous morphological' features in ISL. These rules were applied during the ‘generation phase’ of translation to dynamically adjust phonological sign parameters, resulting in improved target sentence fluency. The paper provides a detailed description of the system architecture, translation rules, and comprehensive experimentation. Rigorous evaluation efforts were undertaken, encompassing various linguistic features, and the findings are discussed herein. The web-based version of the system serves as an interpreter for brief communications and can support the teaching and learning of sign language and its grammar in schools for hard-of-hearing students.

Planning Policies and Public Health in Kurdistan Region of Iraq (KRI) and Its Policies to Combat COVID-19 in Duhok City

Purpose: The Kurdistan region of Iraq had been affect by the covid pandemic like the rest of the part in the world and Duhok city specifically faced several problems as the city and health policy were not ready to ace the crises. It had a negative impact on the sustainability of health sector specifically the economic side. This study tries to identify the problems to achieve tan appropriate standard of good health among Kurdistan's general population, especially in the city of Duhok, to ensure more equitable access to health services throughout the country's social and geographic expansion, take into account poverty and to strive to get better treatment, understand the condition of the city of Duhok via COVID-19, and how the government deals with the medical system in this condition, protect and encourage the right to health care for individuals and create equality in the health care system for both genders, beliefs and positions. Methodology: Through conducting the qualitative research approach by using two types for collecting data ( Data Collection Methods) which are primary data and secondary data, primary data for collecting direct information from patients and IDPs and refugees to know their health conditions and whether they are satisfied or not for public health system, physicians as well to know their treatment to patients, and secondary data to gather information from health directorate in Duhok city, camp health administration and humanitarian health organizations to understand better the situation. Findings: The findings of the study indicates that the pandemic struck at a time when public faith in the government was low, the economy was damaged by lower oil sales income, protests were common across the country, and violent extremist acts were on the rise. COVID-19 has worsened long-standing and sometimes deep-seated political, economic, social, and security issues. The COVID-19 pandemic has resulted in a dramatic loss of human life around the world, posing an unprecedented challenge to public health, food systems, and the workplace. The pandemic's economic and social impact is devastating: tens of millions of people are at risk of falling into extreme poverty, while the number of undernourished people is currently estimated to be nearly 690 million, By the end of the year, the number could have risen to 132 million. Unique Contribution to Theory, Practice and Policy: Food security, public health, and employment and labor issues, particularly worker health and safety, all intersect during the COVID-19 crisis. However, in Azadi hospital we cannot talk to people because of virus, the risk of contracting COVID-19 via an infected person's feces appears to be low. The COVID-19 virus may cause intestinal infection and be found in feces, according to some data.

Higher Education in the Age of Artificial Intelligence

The breakneck expansion of artificial intelligence (AI) technologies in recent years has attracted the attention of higher education researchers. However, their curiosity often comes down to using specific AI tools, such as text and image generators, translators, and personal assistants in the educational process. This paper considers a broader question: what fundamental problems and challenges does the penetration of AI technologies into human lives originate for higher education? The authors offer a working definition of what it means to give/receive higher education in the age of AI. This definition identifies five critical characteristics of higher education: 1) the professor and the student continue to be in a subject-subject relationship and learn from each other; 2) higher education should prepare for life in conditions of “human-machine” interdependence; 3) these conditions imply choice in situations of uncertainty; 4) the spread of AI technologies brings enormous opportunities and 5) almost unpredictable dangers, risks and threats to humans. The authors consider general principles and specific problems associated with entering AI tools into society and higher education. They discuss the inevitability of the evolvement of “machine-machine interdependence” related to the development of autonomous agents. In conclusion, several theses and counter-theses summarize the article’s argumentation.

The effect of Qi‐Shao‐Tong‐Mai‐An‐Shen herbal paste on coronary heart disease patients with depression and/or anxiety: Study protocol for a randomized controlled trial

AbstractCoronary heart disease (CHD) poses a significant threat to human life and well‐being. The presence of psycho‐cardiology diseases not only exacerbates the progression of CHD but also imposes a substantial financial burden. Consequently, there is a pressing need to prioritize the prevention and management of psycho‐cardiology conditions. Given the complex adverse effects associated with antidepressants, it is imperative to investigate alternative, safer, and more efficacious early interventions for psycho‐cardiology diseases. The trial will randomly divide 99 patients who meet the inclusion criteria into two groups in a ratio of 1:2. Both groups will be administered standard western medicine treatment for CHD. The Qi‐Shao‐Tong‐Mai‐An‐Shen (QSTMAS) group will be treated with an additional 30 mL QSTMAS herbal paste orally twice daily for up to 12 weeks. The primary endpoints are the changes of Generalized Anxiety Disorder‐7 and Patient Health Questionnaire‐9 scores after the intervention for 12 weeks. The secondary endpoints include the scores of Traditional Chinese medicine syndromes, 6‐min walking test, Insomnia severity index sleep scale, laboratory tests, and other examinations of both groups after the intervention for 12 weeks. The QSTMAS herbal paste exhibits potential and promise as a treatment modality for mild‐to‐moderate psychological disorders within the department of cardiology. The implementation of a rigorous trial design will facilitate an objective and scientific assessment of the clinical effectiveness and safety of combining QSTMAS herbal paste with standard western medicine for CHD patients with depression and/or anxiety. Trial Registration: China Clinical Trial Registry, ChiCTR2200065179. Registered 31 October 2022.

Advancements in Fire Safety Technology: A Automatic Fire Extinguishing Robot

Fire security which is a lasting and persistent issue requires the continuous development of technology to reinforce protection and inhibition of the fires which are regular event that often leads to emergencies in many sectors. This research endeavors to delve deeply into the realm of fire safety technology, focusing keenly on two innovative approaches: the Chai roof closers security policy, after house-closers protection process, and a matted fire extinguisher robot that notifies about the fire. Through scrupulously examining their technical detail, experiments, operational patterns, and potential outcomes and impact, the paper in this order to fulfills its objective of giving the reader a comprehensive and all-encompassing understanding of the extent and the take of these types of fire systems in the total field of fire safety. Relying on a broad review of journal articles, experiments, and also a proactive approach to these technological advancements that might contribute to averting fire disasters and on preserving both human lives and valuable properties, this research undertakes a mission to explore the emergence of these technological advancements and how much they instill change in the conduct of disaster response and risk mitigation.

General characteristics of technologies, innovations in intellectual property law

In this work, we consider technologies, innovations and intellectual property rights. In the context of modern development of all spheres of human life, the concepts of «technology», «innovation» and «innovative technologies» are an integral aspect of the present. They play an important and key role in the competitiveness of not only individual enterprises and organizations, but also in law and economics in general, since innovative technologies are the basis for the positive development of society and the legal relations that arise in this regard, including legal relations in the field of intellectual property rights. Intellectual property is the result of intellectual and creative activity, which is formalized and grants the author or a person who is granted the right of ownership of the result by law, which is acquired, exercised and protected in accordance with the established procedure, rules and legally defined norms. It is the objects of intellectual property rights that are in clear interaction with technology and innovation. Detailed attention is paid to the essence and content of innovations and technologies in the field of intellectual activity, in particular in the production and change of characteristics of the relevant objects of intellectual property rights. The author focuses on the conceptual apparatus of such concepts as «innovation», «technology», «intellectual property», «intellectual property rights», and technology transfer. The modern national and foreign literature is analyzed with a view to defining the basic concepts of the study and their legislative consolidation. The author analyzes the features of technology transfer, in particular, it is established that innovative technologies are endowed with novelty, revolutionary nature, irreplaceability, uniqueness, and inevitability. The author establishes the interconnectedness of the content of «innovations» and «technologies» in relation to intellectual property rights, namely, directly to intellectual property objects, which are manifested by applying know-how or an invention to a specific result of intellectual activity in the production process.

Factorial experimental design optimisation of adsorption process for removing chrome ion using crude biomass

ABSTRACT The pollution of water and the environment by heavy metals has recently increased dramatically, creating a severe environmental problem due to their non-biodegradability and high toxicity. Among these metals, we are interested in chromium (Cr), essential for human life but can be toxic in large quantities. It is vital to develop a simple, effective, and inexpensive method for its removal from aqueous media. This work aims to combine two aspects: environmental protection and waste recovery. We studied the adsorption capacity of Cr(VI) by biomass consisting of a mixture of walnut shells, pecan nuts, and peanuts. The ion was quantified using high-resolution magnetic sector inductively coupled plasma mass spectrometry, and several techniques characterised the raw adsorbent. To optimise the removal of Cr(VI), four factors were optimised using a 24-full factorial design, enabling us to determine the optimum adsorption conditions: a contact time of 360 min, an adsorbent mass of 30 mg, an initial ion concentration of 50 mg/L, and a pH of 6. The response efficiency Y (Qe) achieved its optimum at 58.47976 ± 4.168804 (mg/g), resulting in a maximum desirability of 0.957419. The adsorption phenomenon was studied kinetically, and it was found to follow the pseudo-first-order reaction (R 2 = 0.9894); concerning adsorption isotherms, the Freundlich isotherm proved to be the best (R 2 = 1). Overall, the present biosorbent demonstrates excellent capacity to remove Cr (VI) from water and can be used on a large scale.

Energy Performance in Residential Buildings as a Property Market Efficiency Driver

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis.

The Design of Machine Learning-Based Computer-Aided System with LabVIEW For Abnormalities in Mammogram Images

Mammogram is the best way of breast cancer detection nowadays, as breast cancer is the most common form of cancer in the female gender and this form of cancer usually causes death. Many scientists, doctors, and engineers are working together to deal with such serious issues in human life. This paper, it is aimed to develop a new computer-aided system with a graphical coded language to detect abnormalities in mammogram images by using machine learning technics such as ANN and SVM. The developed algorithm has a graphical user interface (GUI) and all results are shown in there. The algorithm was created using three different stages. These are image processing and mass segmentation, feature selection and extraction, and classification. To test the accuracy of the system as the sensitivity, specificity, and accuracy, mammogram images with forty benign and forty malignant masses were used. The obtained results for measuring the sensitivity, specificity, and accuracy are 95%, 97.5%, and 96.25% for ANN and 97.5%, 97.5, and 97.5 for SVM, respectively. As can be said that the algorithm, user-friendly due to its user interface, can be preferred because it can detect many cancerous cells such as breast cancer with high accuracy.

Impact of COVID-19 on the primary level teaching-learning process in rural India

PurposeCOVID-19 outbreak impacts people globally. It is not simply a global health matter; it has a terrible influence on the social life of humans, counting the worldwide education structure and other phases of present social configurations. Therefore, the present study's objective is to identify the impact of COVID-19 on the online education approach in teachers' perceptions.MethodologyThe study performs a mixed research approach for evaluating the teachers' perspectives in the online education system. The quantitative data are collected with the support of a questionnaire from 175 respondents (Public School teachers), and in-depth telephonic interviews are conducted among 25 schoolteachers using a semi-structured questionnaire for qualitative data. The quantitative analysis is performed using Statistical Package for Social Sciences (SPSS), and the qualitative analysis is coded and analyzed thematically.FindingsThe quantitative results show that the COVID-19 pandemic negatively impacted the online teaching method; teachers' adaptation here seems low. In contrast, communication with school leaders is provided a clear view of online teaching methods. The key findings from the qualitative analysis show that the adaptation online education system is complex owing to various struggles. Some institutions facilitate training sessions to teach in a range of adopting the online education system. Finally, the teachers need help making students actively participate in online classes.DiscussionThe study conducts original survey through both interview and questionnaire. The results are determined through thematic and data analysis.

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images.

The Dolan Fire of Central Coastal California: Burn Severity Estimates from Remote Sensing and Associations with Environmental Factors

In 2020, wildfires scarred over 4,000,000 hectares in the western United States, devastating urban populations and ecosystems alike. The significant impact that wildfires have on plants, animals, and human environments makes wildfire adaptation, management, and mitigation strategies a critical task. This study uses satellite imagery from Landsat to calculate burn severity and map the fire progression for the Dolan Fire of central Coastal California which occurred in August 2020. Several environmental factors, such as temperature, humidity, fuel type, topography, surface conditions, and wind velocity, are known to affect wildfire spread and burn severity. The aim of this study is the investigation of the relationship between these environmental factors, estimates of burn severity, and fire spread patterns. Burn severity is calculated and classified using the Difference in Normalized Burn Ratio (dNBR) before being displayed as a time series of maps. The Dolan Fire had a moderate severity burn with an average dNBR of 0.292. The ignition site location, when paired with the patterns of fire spread, is consistent with wind speed and direction data, suggesting fire movement to the southeast of the fire ignition site. Patterns of increased burn severity are compared with both topography (slope and aspect) and fuel type. Locations that were found to be more susceptible to high burn severity featured Long Needle Timber Litter and Mature Timber fuels, intermediate slope angles between 15 and 35°, and north- and east-facing slopes. This study has implications for the future predictive modeling of wildfires that may serve to develop wildfire mitigation strategies, manage climate change impacts, and protect human lives.