Potential Fatalities Research Articles

Electroencephalographic patterns and their association with functional prognosis in anti-NMDA autoimmune encephalitis

Autoimmune encephalitis is a rare disease with low incidence and prevalence but carries high rates of disability and potential fatality. To determine whether the presence of abnormal electroencephalographic patterns in patients with anti-NMDAR autoimmune encephalitis affects functional prognosis at 3 months. A retrospective, observational, analytical, longitudinal-cross-sectional study was conducted with a retrospective cohort. Out of a total of 44 patients with anti-NMDAR autoimmune encephalitis, concerning electroencephalographic findings, 19 patients had a normal EEG result (43.2%), 18 had cortical dysfunction (40.9%), 7 had epileptiform reports (15.9%), and only 1 had a Delta Brush report (2.3%). The functional outcome of patients at 3 months measured by the modified Rankin Scale (mRS) was as follows: 3 patients without disability (6.8%), 6 patients with mild disability (13.6%), 17 patients with mild to moderate disability (38.6%), 9 patients with moderate to severe disability (20.5%), 1 patient bedridden (2.3%), and 2 deceased (5.6%). In our study, no association was found between an abnormal electroencephalographic pattern and a worse functional prognosis at 3 months in patients with autoimmune encephalitis. Although the electroencephalogram analyzed is the initial one, it would be necessary to assess later whether the persistence of abnormalities in the electroencephalogram is related to a greater number of sequelae and disabilities.

Drought assessment in Kabul River basin using machine learnings

Droughts significantly impact water resources and agriculture, leading to economic losses and potential human fatalities. This study aims to predict droughts by analysing changes in the Standardised Precipitation Evapotranspiration Index (SPEI) for the Kabul River basin using data from 1981 to 2022. The research is divided into three phases: calculating SPEI, splitting the dataset into training (80%) and testing (20%) subsets, and evaluating model performance. Various machine learning algorithms, including XGBoost, Decision tree, AdaBoost, and KNN, were employed alongside different climatic variables. The models were assessed using statistical metrics such as R², RMSE, MAE, MSE for regression, and confusion matrix, accuracy, precision, recall, F1 score, ROC AUC, and Log loss for classification. Results showed strong performance, with R² values of 0.97, 0.86, 0.92, and 0.96 for XGBoost, KNN, Decision tree, and AdaBoost, respectively. SPEI demonstrated significant potential for drought forecasting, and spatial distribution mapping revealed persistent moderate drought occurrences.

Wasp sting-induced rhabdomyolysis and acute liver injury in a young, healthy patient: An uncommon case.

Wasp stings represent a significant environmental hazard in Sri Lanka. While the majority of individuals experience only localized symptoms, a subset may exhibit allergic reactions of varying severity. In rare instances, these reactions can escalate to life-threatening complications. Rhabdomyolysis, a severe consequence of muscle injury induced by wasp venom, necessitates early diagnosis and prompt intervention to mitigate potential fatalities. We present the case of a 32-year-old male with no prior health issues who was admitted following multiple wasp stings. On the second day of admission, he developed rhabdomyolysis and hepatic injury. With appropriate fluid management, the patient made a full recovery. This case underscores the importance of timely intervention in managing the severe complications of wasp stings.

In vitro assessment of Ethanolic extracts from specific herbs on controlling aquatic leech (Tyrannobdella rex) and land leech (Philobdella sp.) infestation in Mithun (Bos frontalis)

Leech infestation poses a significant threat to Mithun (Bos frontalis) in the north-eastern region of India, leading to various health issues and potential fatality. To address this pressing concern, we conducted a comprehensive research study aimed at assessing the efficacy of herbal plant extracts against aquatic leeches, particularly Tyrannobdella rex, and land leeches of the Philobdella sp. Our investigation involved the evaluation of six distinct plant extracts, with a focus on their ability to combat leech infestation. The results of our study revealed that among the various plant extracts tested, only the ethanolic extracts of soapnut (Sapindus mukorossi) and tobacco (Nicotiana tabacum) exhibited notable effectiveness in combating aquatic leeches. At a concentration of 5%, these extracts displayed significant lethality, with soapnut extract achieving a remarkable kill time of 6.0 ± 0.40 min, while tobacco extract showed a kill time of 31.5 ± 1.32 min. In the case of land leeches, tobacco extract proved to be highly efficient, with an average kill time of 1.5 ± 0.28 min at a 5% concentration. Soapnut extract also exhibited effectiveness against land leeches, albeit with a slightly longer kill time of 14.25 ± 1.10 min at the same concentration. Additionally, Litsea grass oil (Litsea cubeba) demonstrated promising efficacy against both aquatic and land leeches, suggesting its potential as a versatile leech control agent. These compelling findings have significant implications for the management and control of leech infestation among Mithun populations. By identifying and harnessing the leech-repelling properties of soapnut, tobacco, and Litsea grass oil, this research offers practical and environmentally friendly solutions for mitigating the adverse effects of leech infestation. Furthermore, the insights gained from this study pave the way for the development of innovative strategies to safeguard the health and well-being of Mithun in the future.

Breast tumor classification using adam and optuna model optimization based on CNN architecture

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.

Cholera: A Latent Threat to Human Health

: Cholera outbreaks caused by the bacterium Vibrio cholerae present a significant global health challenge, with a notable increase in cases recently reported. The disease is characterized by severe watery diarrhea, leading to dehydration and potential fatalities if not promptly addressed. Transmission occurs through contaminated food and water sources, underscoring the importance of water sanitation and hygiene measures to prevent outbreaks. Limited healthcare access and inadequate reporting systems make estimating cholera cases and deaths challenging. Antibiotic resistance is also a concerning issue, necessitating the development of new treatment options. Prompt laboratory diagnosis is essential, with rapid diagnostic tests and PCR showing promise for pathogen detection. Treatment involves fluid replacement and appropriate antibiotic use to reduce disease severity and transmission. Oral cholera vaccines offer preventive measures for high-risk individuals during outbreaks. To combat the escalating cholera epidemic and save lives, a comprehensive approach, including improved water sanitation, early detection, and timely treatment, is crucial.

Automatic identification of incidents involving potential serious injuries and fatalities (PSIF)

Safety incidents have always been a crucial risk in work spaces, especially industrial sites. In the last few decades, significant efforts have been dedicated to incident control measures to reduce the rate of safety incidents. Despite all these efforts, the rate of decline in serious injuries and fatalities (SIFs) has been considerably lower than the rate of decline for non-critical incidents. This observation has led to a change of risk reduction paradigm for safety incidents. Under the new paradigm, more focus has been allocated to reducing the rate of critical/SIF incidents, as opposed to reducing the count of all incidents. One of the challenges in reducing the number of SIF incidents is the proper identification of the risk prior to materialization. One of the reasons for risk identification being a challenge is that companies usually only focus on incidents where SIF did occur reactively, and incidents that did not cause SIF but had the potential to do so go unnoticed. Identifying these potentially significant incidents, referred to as potential serious injuries and fatalities (PSIF), would enable companies to work on identifying critical risk and taking steps to prevent them preemptively. However, flagging PSIF incidents requires all incident reports to be analyzed individually by experts and hence significant investment, which is often not affordable, especially for small and medium sized companies. This study is aimed at addressing this problem through machine learning powered automation. We propose a novel approach based on binary classification for the identification of such incidents involving PSIF (potential serious injuries and fatalities). This is the first work towards automatic risk identification from incident reports. Our approach combines a pre-trained transformer model with XGBoost. We utilize advanced natural language processing techniques to encode an incident record comprising heterogeneous fields into a vector representation fed to XGBoost for classification. Moreover, given the scarcity of manually labeled incident records available for training, we leverage weak labeling to augment the label coverage of the training data. We utilize the F2 metric for hyperparameter tuning using Tree-structured Parzen Estimator to prioritize the detection of PSIF records over the avoidance of non-PSIF records being mis-classified as PSIF. The proposed methods outperform several baselines from other studies on a significantly large test dataset.

Development of fentanyl-specific monoclonal antibody (mAb) to antagonize the pharmacological effects of fentanyl

Fentanyl, a critical component of opioid analgesics, poses a severe threat to public health, exacerbating the drug problem due to its potential fatality. Herein, we present two novel haptens designed with different attachment sites conjugated to keyhole limpet hemocyanin (KLH), aiming to develop an efficacious vaccine against fentanyl. KLH-Fent-1 demonstrated superior performance over KLH-Fent-2 in antibody titer, blood-brain distribution, and antinociceptive tests. Consequently, we immunized mice with KLH-Fent-1 to generate fentanyl-specific monoclonal antibodies (mAbs) using the hybridoma technique to compensate for the defects of active immunization in the treatment of opioid overdose and addiction. The mAb produced by hybridoma 9D5 exhibited the ability to recognize fentanyl and its analogs with a binding affinity of 10−10 M. Subsequently, we developed a human IgG1 chimeric mAb to improve the degree of humanization. Pre-treatment with murine and chimeric mAb significantly reduced the analgesic effect of fentanyl and altered its blood-brain biodistribution in vivo. Furthermore, in a mouse model of fentanyl-induced respiratory depression, the chimeric mAb effectively reversed respiratory depression promptly and maintained a certain level during the week. The development of high-affinity chimeric mAb gives support to combat the challenges of fentanyl misuse and its detrimental consequences. In conclusion, mAb passive immunization represents a viable strategy for addressing fentanyl addiction and overdose.

Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS

Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.

Analysis of local density during football stadium access: Integrating pedestrian flow simulations and empirical data

This study analyzes numerically the access of football fans to a typical football stadium through pedestrian flow simulations. With this aim, we introduce a novel framework to address the difficulty of simulating pedestrian dynamics in highly complex geometries with multiple accesses. The framework consists of a combination of the Social Force Model (SFM) and Computational Fluid Dynamics tools to calculate multiple desired velocity maps. The introduction of this framework allows the predefinition of the desired velocity fields for the complex geometries in both the interior and surrounding area of the stadium. We validate the results of the proposed framework using actual entry-rate data from the turnstiles of three gates for 15 matches provided by the football club. Remarkably, the validation process allows us to describe the temporal evolution of the spatial distribution of pedestrians during their arrival with a single set of parameters valid for all the analyzed matches. Finally, we assess the potential fatality risk based on the local pedestrian density varying the total number of attendees and their arrival rate. The outcome evidences permanent clogs emerge at the entrance in the most extreme cases with high attendance or low standard deviation of the arrival rate, indicating the possibility of fatality occurrence.

Synthesis of FePP@MoS2@Ni‐MOF composites for improving thermal and flame retardant safety properties of polyurea

AbstractPolyurea (PUA) is widely used as a coating in construction, tunnels, bridges, and other fields because of its excellent performance. However, its combustion emits toxic gases and smoke, hindering escape and posing potential fatality risks. Enhancing the flame retardancy of PUA is crucial for safety and expanding its applications. The synthesis of two‐dimensional FePP nanosheets was reported in this paper, employing the solvothermal method, followed by the sequential growth of MoS2 and Ni‐MOF to establish a multilayer composite structure. The elemental composition and morphology of the synthesized FePP@MoS2@Ni‐MOF flame retardants were characterized and analyzed. The flame retardant properties of polyurea composites with varying amounts of FePP@MoS2@Ni‐MOF were investigated using Cone calorimeter tests. The results showed that the prepared flame retardant had good thermal stability and significantly improved fire safety properties. The PUA/FePP@MoS2@Ni‐MOF 3.0 composite exhibited notable improvements compared to pure PUA. Specifically, the peak heat release rate, total heat release rate and peak smoke production rate were reduced by 39.76%, 29.33% and 17.86%, respectively, while total smoke production and total CO production (COP) were reduced by 21.30% and 54.47%. This study provides new insights and experimental basis for the technological development of novel flame retardant coating materials.

Impact of duck astrovirus on susceptibility to infection across duck ages

An outbreak of duck astrovirus (DAstV) has occurred in duck farming regions of China, causing substantial economic setbacks in the duck industry. This investigation aimed to examine the variations in DAstV pathogenicity among ducks at different age intervals. Infections were induced in ducks at distinct age groups (1, 7, 14, 21, and 28 d) utilizing the DAstv-1-GDB-2022 strain. The results indicate increased pathogenicity of the DAstv-1-GDB-2022 strain in ducklings aged 21 to 28 d, manifesting as liver and kidney enlargement, severe bleeding, and potential fatalities. Conversely, ducklings aged 1 and 14 d displayed milder symptoms postinfection. Notably, viral shedding continued in ducks of diverse age groups even 21 d postinfection (Dpi). Moreover, DAstV replicates in various tissues, predominantly affecting the liver. Immunohistochemical tests using rabbit anti-DAstV antibodies revealed robust positive signals in both the liver and kidneys, which correlated with the clinical symptom severity observed through macroscopic and microscopic examinations. Serum biochemical assays and indirect ELISA demonstrated a consistent response to DAstV infection across different age groups, with older ducklings exhibiting increased sensitivity. In conclusion, this study successfully replicated clinical symptoms similar to those of natural DAstV infection using the DAstv-1-GDB-2022 strain. Importantly, we systematically delineated the differences in susceptibility to DAstV among ducks at various ages, laying the foundation for further research into the pathogenic mechanisms of DAstV and potential vaccine development.

The success of empowering cadres in the prevention of acute hepatitis among children in agronursing areas

Acute hepatitis in children with an unknown cause is defined as an extraordinary event. Children experiencing acute hepatitis may progress to severe acute hepatitis and acute liver failure, leading to potential fatality. This study aimed to identify the empowerment of cadres in preventing acute hepatitis with unknown causes in agro-nursing areas. Employing a quasi-experimental research design, the study involved 21 integrated healthcare center cadres in an agro-nursing area. The questionnaire served as the measuring tool, and the data were analyzed using a paired sample test. The results revealed a significant p-value of 0.000, indicating differences in the cadres' abilities before and after empowerment. Empowering cadres through discussions provides them with opportunities to address problems collaboratively, fostering commitment to the prevention and early detection of acute hepatitis in children. An acute hepatitis is relatively new and life-threatening, and the prompt action of cadres, communities, and health workers, supporting government programs, contribute to the success of cadre empowerment efforts in preventing and detecting acute hepatitis in children. Empowering cadres through additional information and discussion yields positive outcomes, emphasizing the importance of efforts to prevent and detect acute hepatitis in children in agro-nursing areas.

Risk factors and prognosis of perioperative acute heart failure in elderly patients with hip fracture: case-control studies and cohort study

BackgroundElderly patients with hip fracture who develop perioperative acute heart failure (AHF) have a poor prognosis. The aim of the present study is to investigate the potential risks of AHF in elderly hip-fracture patients in the postoperative period and to evaluate the prognostic significance of AHF.MethodsA retrospective analysis was conducted on hip fracture patients at the Third Hospital of Hebei Medical University, who were continuously in hospital from September 2018 to August 2020. To identify independent risk factors for AHF in elderly patients with hip fracture, univariate and multivariate Logistic regression analysis was employed. The Kaplan-Meier survival curve illustrated the relationship between all-cause mortality in the AHF and non-AHF groups. An assessment of the correlation between baseline factors and all-cause mortality was conducted by means of univariable and multivariable Cox proportional hazards analysis.ResultsWe eventually recruited 492 patients,318 of whom were in the AHF group. Statistical significance was found between the two groups for age group, concomitant coronary heart disease, COPD, haemoglobin level below 100 g/L on admission, albumin level below 40 g/L on admission, and increased intraoperative blood loss. Age over 75, concomitant coronary artery disease, hemoglobin level below 100 g/L and albumin level below 40 g/L on admission were independent risk factors for AHF in older hip fracture patients. The AHF group exhibited a higher incidence of perioperative complications, such as anemia, cardiovascular issues, and stress hyperglycemia, as well as all-cause mortality. Based on our COX regression analysis, we have identified that the main risk factors for all-cause mortality in AHF patients are concomitant coronary heart disease, absence of pulmonary infection, absence of diabetes, absence of cancer, and absence of urinary tract infection.ConclusionEnhancing hip fracture prevention for AHF is particularly important. It is crucial to make informed decisions to avoid poor prognoses. Patients whose age over 75 years old, concomitant coronary heart disease, hemoglobin < 100 g/L and album< 40 g/L on admission are more likely to develop perioperative AHF. To avert complications and potential fatalities, patients with AHF must receive appropriate care during the perioperative period.

Green Synthesis of Euphorbia tirucalli-Mediated Titanium Dioxide Nanoparticles Against Wound Pathogens.

Background Wound infections caused by pathogens present a considerable global health challenge, resulting in extended healing durations, elevated healthcare expenses, and potential fatalities. Conventional approaches to managing wound pathogens have limitations such as antibiotic resistance, toxicity and allergic reactions. Consequently, there is a rising interest in exploring alternative strategies for preventing and treating wound infections. Titanium dioxide nanoparticles (TiO2NPs) have gained attention for their potential in wound healing, attributed to their distinctive properties, including antimicrobial and anti-inflammatory capabilities. Methods TiO2NPssynthesized through Euphorbia tirucalli were examined for their antibacterial potential against wound pathogens, using the Kirby-Bauer agar-well diffusion method and time-kill curve assay. Furthermore, the cytotoxic effect of the synthesized nanoparticles was evaluated through a brine shrimp lethality assay. Results Green-synthesized TiO2NPs demonstrated potent antimicrobial activity against tested wound pathogens, displaying a zone of inhibition against Pseudomonas aeruginosa (11 mm) and Escherichia coli (10 mm) at the highest concentration of 100 μg/mL. In the time-kill curve assay, the prepared TiO2NPs showed significant bactericidal activity against Pseudomonas aeruginosa followed by Escherichia coli.In the brine shrimp lethality assay, at the lowest concentration of 5 μg/mL of the prepared nanoparticles, 100% of the nauplii remained alive after 48 hours. Conclusion The results indicate that TiO2NPs synthesized using Euphorbia tirucalli extract exhibit potent antimicrobial activity against the tested wound pathogens. Moreover, the prepared nanoparticles exhibit lower toxicity, suggesting their potential use as an alternative to commercially available synthetic drugs.

IoT-controlled explosion

This paper delves into the study and implementation of IoT-controlled explosions, exploring their relevance in various industrial sectors such as quarrying, mining, and building demolition. Additionally, the paper examines the application of controlled explosions in domestic settings, such as the ignition of firecrackers and the use of small explosions to deter animals from farmlands and fields. The term "controlled explosion" in this context refers to the ability to manage detonations remotely, mitigating the risks associated with potential injuries or fatalities.

Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework

A rolling bearing is a crucial element within rotating machinery, and its smooth operation profoundly influences the overall well-being of the equipment. Consequently, analyzing its operational condition is crucial to prevent production losses or, in extreme cases, potential fatalities due to catastrophic failures. Accurate estimates of the Remaining Useful Life (RUL) of rolling bearings ensure manufacturing safety while also leading to cost savings.•This paper proposes an intelligent deep learning-based framework for remaining useful life estimation of bearings on the basis of informed detection of anomalies.•The paper demonstrates the setup of an experimental bearing test rig and the collection of bearing condition monitoring data such as vibration data.•Advanced hybrid models of Encoder-Decoder LSTM demonstrate high forecasting accuracy in RUL estimation.

Efficacy of Repetitive Transcranial Magnetic Stimulation (r-TMS) Therapy on Improving Motor Strength in Storke Patients at Dr. Moewardi Surakarta

Background: Stroke, an acute neurological dysfunction, poses a health challenge with potential fatality, emerging as a leading cause of long-term disability. This study explores the efficacy of repetitive Transcranial Magnetic Stimulation (r-TMS) with medical rehabilitation in enhancing stroke patients' motor strength, comparing it with standard therapy, focusing on the Medical Research Council score. Objective: Stroke is an acute manifestation resulting from neurological dysfunction, persisting for ≥24 hours or leading to death due to blood vessel disorders. The study aims to determine the effectiveness of r-TMS therapy in improving stroke patients' motor strength, comparing it with standard therapy based on the Medical Research Council score. Methods: A quasi-experimental study with a pre-post control group design involved 30 respondents selected through consecutive sampling in stroke units and outpatient clinics. Upper extremity muscle strength, measured with the Medical Research Council score, was analyzed using the Mann-Whitney U test, with a significance level set at p &lt; 0.05. Result: The post-test ranking value of the r-TMS group (20.56) exceeded that of the Non r-TMS group (10.46). The Mann-Whitney U test indicated a significant difference in the average ranking of the Medical Research Council scores between the two groups (p &lt; 0.05, 0.001), highlighting the efficacy of r-TMS therapy in enhancing motor strength. Conclusion: This study shows that stroke patients receiving standard therapy and transcranial magnetic stimulation exhibit improvements in extremity motor strength compared to those undergoing medical rehabilitation alone. The r-TMS group notably showed a significant increase in extremity motor strength.

Electronic performance monitoring framework to quantify unhealthy and unsafe on-site behaviours

Health and Safety (H&S) should be a significant concern in construction projects. The quantification of the work injury accidents started with work A. W. Heinrich (1931), passing per Frank E. Bird Jr. (1969) to a study by ConocoPhillips Marine (2003). These theories, also known as the Accident triangle, Heinrich's triangle, or Bird's triangle, are a base for industrial accident prevention applied in practice by the Dupont STOPTM methodology. Over this methodology, the number of unhealthy and unsafe behaviours is classified and accounted for to diagnose the potential workers' recordable injuries, lost workdays, and fatalities. The At-risk behaviours and the Near Misses are the most challenging safety measurement approach as they depend on the human-observation notes usually conducted in a safety audit. Electronic Performance Monitoring (EPM) concerns the use of innovative hardware and software to assess on-site safety and productivity. The electronic monitoring of construction workers can contribute to evaluating unsafe behaviours. At-risk behaviours could be measured based on location, trajectory, and motions. Also, with the monitoring of the equipment and vehicles, it is possible to measure Near Misses. The Information Technologies integration allows for collecting information about Recordable Injuries, Lost workdays, and Fatalities. This work discusses and presents a framework connecting EPM deployment to data acquisition of unhealthy and unsafe on-site behaviours. The contributions in the theoretical and practical fields concern the opportunities of using new technological assumptions to increase data acquisition and improve the accuracy and effectiveness of quantitative safety analysis.

Feasibility Test on Storage Tank At PT ABC Using Asme/FFS-1 Method

In the operation of oil and gas exploration and exploitation, safety is crucial as it is related to asset safety, environmental safety, and human resource safety. Storage tanks play a crucial role in the process of exploring and exploiting crude oil, serving as storage facilities for liquids in large volumes. These storage tanks are susceptible to corrosion as the materials used in their construction are typically made of steel. Uncontrolled corrosion can weaken or destroy parts of the tank system, leading to holes or structural failures that may release stored products into the environment, resulting in material losses and potential fatalities. One method for assessing the viability of pressure equipment such as pressure vessels, storage tanks, and piping systems is Fitness for Service (FFS). FFS is a quantitative engineering evaluation conducted to demonstrate the structural integrity of a component in operation, even if it has experienced damage, defects, or cracks. Guidelines in FFS procedure manuals can be used to make decisions regarding "continued/repair/replacement" to ensure that components experiencing damage or defects can continue to operate for a specified period. The thickness measurements on the storage tank show that the lowest thickness is found in course 4, with a value of 4.32 mm, while the highest thickness is in the roof at 5.60 mm. The highest corrosion rate is detected in the roof with a value of 0.100 mm/year, and from this corrosion rate value, an estimated remaining life of 20 years for storage tank T-10 is obtained. In assessing the feasibility or Fitness for Services of the storage tank, it still meets the criteria specified by API 579.