Unavailability Of Data Research Articles

Enhancing Financial Stability in Ghana's Universal Banking Sector: A Governance Perspective and Practical Insights

Purpose: This study investigates the impact of corporate governance structures on financial distress within the universal banking sector in Ghana.
 Methodology: The research systematically gathers data from 19 banks spanning 10-year period. Financial distress indicators were extracted from financial statements, while corporate governance measures were obtained from annual reports. The data was analysed through regression analysis and counterfactual analysis through an endogenous switching regression model.
 Findings: Key findings reveal that profitability, liquidity, and effective risk management are critical factors influencing financial distress in both strong and weak governance environments. The results also showed that improving the strength of Corporate Governance has a positive influence on Altman Z Scores, contributing to a potential reduction in financial distress.
 Unique contributor to theory, policy and practice: The study provides valuable insights for policymakers, regulators, and banking institutions in Ghana, emphasizing the need to prioritize and strengthen governance practices for a resilient banking sector. Limitations include data unavailability, reliance on unbalanced panel data, and a narrow focus on board characteristics.

Comparative approach to the financing of agricultural enterprises in the Republic of Kosovo and EU countries

PurposeThe purpose of this study is to examine the financial challenges faced by agricultural enterprises in the Republic of Kosovo. It aims to compare the various forms of financing used in Kosovo with those in European Union (EU) countries. The study seeks to highlight opportunities and challenges with a focus on how they can learn from the experiences of EU countries.Design/methodology/approachThe study is based on responses from agricultural enterprises in both the EU and Kosovo. Data was gathered through a survey conducted with 50 agricultural enterprises in Kosovo, whereas for EU context, information from the 2020 Survey on the Financial Needs and Access to Finance of EU Agricultural Enterprises (provided by EAFRD). Statistical data were processed using the Stata and SPSS programs.FindingsIn agriculture sector, loan is the primary form of financing to expand their activities and capacities, whereas financing for agricultural enterprises exhibits a negative relationship with the bureaucratic procedures associated with financing.Research limitations/implicationsThe main research’s limitations include the unavailability of official data from the relevant institutions in Kosovo.Practical implicationsA possible implication arising from this research is the reliance of the development of agriculture enterprises on debt.Social implicationsDue to the lack of comprehensive data in this regard, is unable to analyze the specific impact of gender in financing patterns of these enterprises.Originality/valueThis study provides real data on the current situation of agricultural enterprises in Kosovo. Considering Kosovo’s goal to integrate with the EU, this comparative approach adds significant value to the study.

A Holistic Integration of Conventional and Machine Learning Techniques to Enhance the Analysis of Power Transformer Health Index Considering Data Unavailability

A Holistic Integration of Conventional and Machine Learning Techniques to Enhance the Analysis of Power Transformer Health Index Considering Data Unavailability

Ransomware Threats Targeting the Healthcare Sector

Security is essentially a consideration in any computer system which holds sensitive or critical data. Ransomware attacks which are primarily based on encrypting critical data and demanding for a ransom to decrypt them, is one of the best approaches in harvesting money from critical industrial firms rather than damaging the information infrastructure. Even though general ransomware practices include encrypting critical data, sometimes it can come in a form of a threat to leaking of sensitive data to the public. Among the critical infrastructures in the healthcare industry, patient’s Personally Identifiable Information (PII) and also the operational data related to mission critical systems are the top concerns. Leaking of PII can cause a huge damage to the privacy of the patients who obtain the healthcare service for that particular healthcare provider. On the other hand, any system delays, malfunctions of inaccessibility or unavailability of operational data related to mission critical systems can cause hindrances to the usual operations of the healthcare provider and eventually will case a life-threatening situation to the patients as well. Through this research review, a comprehensive understanding to the topics; what is ransomware, why ransomware target health industry, what is the damage caused, new ransomware attack trends, mitigative steps and future research scopes are presented to the audience. At the end of the paper, overall conclusion made by analyzing reported incidents is presented with recommendation to mitigate the effects of future ransomware threats, targeted on healthcare industry.

Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data

Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs. The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach proposed in this research is sonic log prediction based on Extreme Gradient Boosting (XGBoost) machine learning algorithm, using available log data to build a reliable sonic log prediction model. In this research, the predicted DT log type is the Differential Time Shear Slowness (DTSM) log, which is the velocity of shear waves propagating in a formation. Log features used for training include gamma ray (GR), density (RHOB), porosity (NPHI), resistivity (RS and RD) logs with DTSM log as the prediction target. To optimise the performance and generalisation of the XGBoost algorithm in predicting log DTSM, hyperparameter tuning was applied using grid search technique to obtain optimal parameters for the prediction model. Based on the experimental results, this research found that hyperparameter tuning using grid search technique improved the accuracy of sonic log (DTSM) model prediction based on XGBoost algorithm, as proven by the decrease of RMSE and MAPE values to 19.699 and 7.713%. The results also pointed out the need for methods other than listwise deletion to handle missing values as an alternative to improving model accuracy. This research highlighted the need for continuous improvement in data processing methods and algorithm optimization to advance the application of machine learning in geophysical exploration.

Determining the significant input parameters of a forecasting model for material resources of residential construction projects at the investment feasibility assessment stage

Any investor/developer spends considerable time to make a construction decision in analyzing various parameters, such as the building site area, height restrictions, total area, quantity of necessary materials, etc. First of all, this is due to a high level of uncertainty and the complexity of estimating costs for the initial stages of project implementation. The unavailability of detailed design data at the investment justification stage makes it impossible to conduct a sufficiently accurate assessment of the value and period of construction to make highly accurate managerial decisions. However, one of the key issues, namely determining the significant parameters that have the greatest impact on future project-related decisions, has not been studied sufficiently. This article identifies the most significant parameters that impact the future characteristics of residential buildings based on an expert assessment using the a priori ranking method. The reliability of the assessment is confirmed using the coefficient of concordance. A conclusion is provided about their applicability as the input values of a forecasting model that uses machine learning and artificial intelligence to determine various technical and economic characteristics of residential buildings.

Mathematical model analysis of mandatory state health insurance : Revisiting risks and opportunities for private health

This article aims to determine a feasible entrance brink constant for secluded healthiness indemnification corporations to access the newest purchaser excess for Kosovo’s planned compulsory wellbeing indemnification fund, as well as to evaluate the reasoning behind it and measure the surplus. We begin with a succinct overview of the current status of the private insurance sector. Despite experiencing a substantial growth spurt in recent years, its share in the overall financial sector has, in fact, decreased. Following this, we delve into an intuitive approach for calculating the threshold coefficient. Due to the unavailability of similar contingent valuation survey data, we adopt a method based on adjusting prices using the consumer price index. These adjusted prices are then used to estimate the most recent data. Our findings reveal the presence of a consumer surplus, signifying an opportunity for original sequestered guarantors to enter the marketplace or for prevailing companies to offer additional services at an additional cost. However, it’s important to note that the 2021 surplus is notably lower compared to the estimate for the year 2015.

Federated Data Quality Assessment Approach: Robust Learning With Mixed Label Noise.

Federated learning (FL) has been an effective way to train a machine learning model distributedly, holding local data without exchanging them. However, due to the inaccessibility of local data, FL with label noise would be more challenging. Most existing methods assume only open-set or closed-set noise and correspondingly propose filtering or correction solutions, ignoring that label noise can be mixed in real-world scenarios. In this article, we propose a novel FL method to discriminate the type of noise and make the FL mixed noise-robust, named FedMIN. FedMIN employs a composite framework that captures local-global differences in multiparticipant distributions to model generalized noise patterns. By determining adaptive thresholds for identifying mixed label noise in each client and assigning appropriate weights during model aggregation, FedMIN enhances the performance of the global model. Furthermore, FedMIN incorporates a loss alignment mechanism using local and global Gaussian mixture models (GMMs) to mitigate the risk of revealing samplewise loss. Extensive experiments are conducted on several public datasets, which include the simulated FL testbeds, i.e., CIFAR-10, CIFAR-100, and SVHN, and the real-world ones, i.e., Camelyon17 and multiorgan nuclei challenge (MoNuSAC). Compared to FL benchmarks, FedMIN improves model accuracy by up to 9.9% due to its superior noise estimation capabilities.

Continual Learning From a Stream of APIs.

Continual learning (CL) aims to learn new tasks without forgetting previous tasks. However, existing CL methods require a large amount of raw data, which is often unavailable due to copyright considerations and privacy risks. Instead, stakeholders usually release pre-trained machine learning models as a service (MLaaS), which users can access via APIs. This paper considers two practical-yet-novel CL settings: data-efficient CL (DECL-APIs) and data-free CL (DFCL-APIs), which achieve CL from a stream of APIs with partial or no raw data. Performing CL under these two new settings faces several challenges: unavailable full raw data, unknown model parameters, heterogeneous models of arbitrary architecture and scale, and catastrophic forgetting of previous APIs. To overcome these issues, we propose a novel data-free cooperative continual distillation learning framework that distills knowledge from a stream of APIs into a CL model by generating pseudo data, just by querying APIs. Specifically, our framework includes two cooperative generators and one CL model, forming their training as an adversarial game. We first use the CL model and the current API as fixed discriminators to train generators via a derivative-free method. Generators adversarially generate hard and diverse synthetic data to maximize the response gap between the CL model and the API. Next, we train the CL model by minimizing the gap between the responses of the CL model and the black-box API on synthetic data, to transfer the API's knowledge to the CL model. Furthermore, we propose a new regularization term based on network similarity to prevent catastrophic forgetting of previous APIs. Our method performs comparably to classic CL with full raw data on the MNIST and SVHN datasets in the DFCL-APIs setting. In the DECL-APIs setting, our method achieves 0.97×, 0.75× and 0.69× performance of classic CL on the more challenging CIFAR10, CIFAR100, and MiniImageNet, respectively.

Практика використання блокчейн-технології в мультимодальних контейнерних перевезеннях

In this study, the main emphasis is on the role of multimodal container transportation in global logistics and transport. The aim of the article is to consider important aspects and challenges associated with this form of transportation, including issues of efficiency, regulation and prospects for further development of this sphere and its infrastructure. The article discusses different definitions of the concept of «multimodal transportation»; the authors’ vision of the concept of «multimodal container transportation» is presented; the main participants and key factors influencing multimodal transportation are described; the advantages and disadvantages of multimodal container transportation are indicated; significance of these transportations for world trade is determined. Particular attention is paid to opportunities for improvement and innovative approaches, including the use of blockchain technology, to increase the transparency, efficiency and security of multimodal container transportation. Examples of the use of blockchain technology in the practice of world players in the container transportation market and ways to improve their work after its implementation are provided. It is noted that blockchain technology provides an opportunity to create a distributed database that guarantees the stability and inaccessibility of data for manipulation, and this makes it an ideal tool for tracking goods in multimodal transportation. This technology provides transparency, the ability to track every stage of the delivery and automated data verification, which helps to avoid errors and fraud in the logistics process. This approach to the use of blockchain technology in the logistics of multimodal container transportation can be a step towards creating more reliable, secure and efficient transport networks. This is in line with the main goal of improving and perfecting the global supply chain in the context of today’s challenges and technological possibilities.

Satellite Video Remote Sensing for Flood Model Validation

AbstractSatellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics.

Quantitative analysis and development of alopecia areata classification frameworks

Alopecia areata is an autoimmune disorder resulting in rapid and unpredictable hair loss on the scalp or body as the immune system mistakenly attacks human hair follicles. In the United States alone, about 6.7 million people experience a form of Alopecia. Early identification of the condition has shown notable potential in improving treatment outcomes and reducing complications. To diagnose Alopecia, researchers have proposed the use of deep learning (DL) techniques to classify images of hair as healthy or alopecia-affected, which has shown high potential. However, the research implementing relevant DL algorithms in the field of hair loss detection and estimation is limited. This paper presents a comparative analysis of our two newly optimized Convolutional neural networks (CNN) with other existing models. For training, we considered datasets comprised of images of healthy hair and alopecia-affected hair. Due to data unavailability, we gathered images of alopecia-affected hair from two distinct datasets: one from Figaro1k and one independently created dataset. After training the algorithms, we performed a contrastive assessment to determine the most effective one based on relevant criteria. We hypothesized that the initial performance of the base neural network would be closely connected to the subsequent accuracy of the algorithm when training it for a new task. As expected, the modified Inception-Resnet-v2 model achieved the greatest performance, with a validation accuracy and loss of 97.94% and 10.4%, respectively. The experimental results indicated that the proposed algorithm serves as an effective framework for Alopecia Areata classification.

Prevalence of Anatomical Anomalies in Pediatric Patients Hospitalized with UTIs in a Tertiary Care Center's Urology and Nephrology Ward

Background: Urinary tract infections (UTIs) are a significant pediatric health concern, often linked to anatomical anomalies of the urinary tract. Understanding the prevalence and characteristics of these anomalies in children with UTIs is crucial for effective diagnosis and management. Objective: This study aimed to investigate the prevalence and nature of anatomical anomalies in children diagnosed with UTIs, focusing on age and gender distribution, and to identify common anomalies associated with UTIs in pediatric patients. Methods: A retrospective cross-sectional study was conducted at the Children's Medical Center in Tehran, from March to September 2022. The study included pediatric patients admitted with UTIs, excluding those over 18 years of age. Convenience sampling was used to target at least 70 patients with definitive anatomical anomalies. Data collection involved reviewing medical records and laboratory results, focusing on variables such as age, sex, paraclinical tests, and types of anatomical anomalies. Statistical analysis was performed using SPSS software version 25, employing Fisher’s exact or χ2 tests for categorical variables, and the student’s t-test, ANOVA, and Chi-Square tests for continuous variables. Results: The average age of patients was 29.61 months (SD = 35.28), with a median of 12.50 months (IQR = 36). The average number of UTI episodes was 3.43 (SD = 2.31), ranging from 1 to 10 episodes. Bilateral hydronephrosis was observed in 15 patients (21.7%), while neurogenic bladder and Reflux Grade 4 or 5 were identified in 11 (15.9%) and 14 (20.3%) patients, respectively. VCUG was performed in 63 patients (91.3%). Unilateral hydronephrosis was the most common imaging finding, observed in 33 patients (47.8%). Conclusion: The study concludes that children around 29 months of age, particularly females, are more prone to have anatomical anomalies associated with UTIs. Bilateral hydronephrosis was a prevalent anomaly. However, limitations such as potential data unavailability or misinterpretation, errors in laboratory data, and patient comorbidities must be considered when interpreting these findings.

Українські воєнні мігранти – мотивації, очікування, прагнення та інші напрями соціологічного осмислення

The author considers the problem of Ukrainian war migrants in the context of the sociological study of this phenomenon. Almost from the first weeks of crossing the western border, Ukrainian war migrants became the object of surveys, which encountered several methodological problems: insufficient sensitivity of quantitative research, socially desirable responses about returning, the impossibility of long-term planning under stress and uncertainty, etc. In addition, quantitative surveys of migrants are not representative, given the unavailability of data on the general population of such people in the countries of temporary stay. Despite the large array of accumulated empirical data, knowledge about the motivations for migration and plans for return remains somewhat fragmented. The paper examines available empirical data on this topic, conducts a theoretical analysis of modern migration studies, and takes steps to form a sociological framework for further study of the situation of Ukrainian war migrants. Among other things, it is suggested to consider positive and negative motivations and comparisons between Ukraine and the country of current residence. It is also recommended to apply an importance filter and to use the terminology of “delayed return”. The necessity of studying the aspirations of migrants is proved, which will help to shed light on the aspect of agency, which remains in a shadow. The relevance of studying family dynamics of migration is also argued. The article also discusses the need to understand that returning to Ukraine after the war can be another migration into the unknown, not a return to the familiar. As for the object of the research, the importance of studying the motivations of those who returned after a more or less long experience in other countries and those who changed from one host country to another is emphasized. Implementing these frames to the research arsenal will expand the possibilities of sociological understanding of this phenomenon.

Diversification of good governance in European Union countries using the TOPSIS method

ObjectivesThe main goal of this study is to identify and evaluate the level of good governance in European Union countries and to determine whether there is a relationship between good governance and economic growth.Material and methodsEmpirical materials are based on currently available statistical data from Eurostat. In case of unavailability of unit data in the studied year, they were replaced with data from the nearest period, i.e. 2020. Due to the complexity of the analyzed categories and the possibility of ordering objects from best to worst to achieve the goal, we used the TOPSIS method for multidimensional data analysis.ResultsThe study found that the variable with the greatest impact on the ranking achieved by a given country in the GG range was the Corruption Perception Index, followed by the proportion of the population who trust EU institutions. In the presented comparison, three countries, Luxembourg, Germany, and Sweden, occupied the top positions in the GG scale, while Greece and Cyprus were at the bottom. Countries with very high GG accounted for 11% of the total number of EU countries. No countries were identified as belonging to the fourth class, which is characterized by a very low level of GG. The majority of countries, over 67%, were represented by those with medium to low levels of GG.ConclusionsThe analysis allowed for identifying a significant correlation between Good Governance and Economic Growth in European Union countries. It can be assumed that the higher the level of economic development (measured by many SDGs), the lower the level of corruption in state institutions, and the greater the trust of citizens in public institutions, which translates into a higher level of GG.

Localising vulnerability assessment to urban floods: A comparative analysis of top-down and bottom-up geospatial approaches in Patna City, India

A notable increase in research related to geospatial vulnerability assessment of urban floods is observed, primarily driven by the advances in exponential technologies and increasing frequency and severity of such events. Several studies have been attempted in Indian contexts for local-level risk mitigation and preparedness and informed decision-making with a limited scope due to unavailability of granular data at various levels as required. In response to this gap, current study examines the potential of top-down and bottom-up geospatial vulnerability assessment approaches for urban floods in Patna City of Bihar state, India. A qualitative articulation is used to identify and characterise a set of critical indicators and attributes relevant to the local vulnerability assessment of urban floods specific to the study area. Principal Component Analysis (PCA) is applied for dimension reduction and assigning weights to indicators, and Composite Vulnerability Indices (CVIs) at Ward and Household (HH) levels are developed using multicriteria decision analysis. The results show over a fifty percent variation in the vulnerability profiles of households using ward-level (top-down) and HH-level (bottom-up) approaches. Moreover, a narrower range in the z-score of HH-level CVI (−1.26 to 2.60) over ward-level CVI (−3.95 to 2.49) from sensitivity analysis indicates the robustness of the former over the latter. The findings reveal a potential risk of maladaptation when employing a ward-level approach to vulnerability assessment as a policy instrument. The study also analytically validates the significance of localising indicators in regional and global frameworks for achieving efficient decision-making toward sustainability targets.

A person VS the state: studying access to justice

The purpose of this article is to expand the access to justice research agenda by focusing on a person’s identity in dispute with state authority. Keeping in mind that everyone should be able to challenge governmental actions and decisions adverse to their rights or interests, in- depth research on access to justice in “a person vs the state” disputes which takes place in courts of administrative jurisdiction, is needed. The methodology of the research acknowledges the unavailability of statistical data about the litigants, and the finding of the study which shows that different groups defended their rights with varying degrees of success. Thus, an innovative approach to the analysis of the Supreme Court judgements was used. It envisions leaving aside legal issues such as statutory interpretation, and instead examines the judgements through the lens of litigant’s characteristics. The findings show that the judgements of the Administrative Court of Cassation within the Supreme Court reflect the diversity of characteristics of persons who apply to administrative courts, including categories directly named in the Constitution of Ukraine as well as not specifically mentioned in it, such as war veterans, IDPs, etc. The financial status of the litigants varies from low-income to quite wealthy, and a certain category of plaintiff is not limited to one type of specific legal problem. For example, “property owners” challenge the decisions of state agencies in a wide range of areas: taxation, urban planning, social benefits, freedom of movements, etc. However, the vast majority of “a person vs the state” disputes concern the refusal to provide some benefits rather than action against a violation of human rights and fundamental freedoms. This can be seen as a Soviet legacy to perceive the state as a welfare provider. Following the people-centered approach to access to justice is essential to reorient the court statistics from “cases and files” to litigants and their characteristics which allow to relay on data to find out whether there is any connection between some features (characteristics) of litigants and types of lawsuits.

Occurrence and environmental risks of contaminants of emerging concern across the River Athi Basin, Kenya, in dry and wet seasons

Globally, the environmental occurrence of Contaminants of Emerging Concern (CECs) including pharmaceuticals (PhACs), personal care products (PCPs) and modern polar pesticides has raised ecological and human health awareness. However, as the developed world races against time to establish regulatory measures to mitigate their effects, developing nations including Kenya are lagging behind, partly due to unavailability of adequate data. In this work, a multi-residue analysis of 86 CECs was carried out on 198 surface water and 18 effluent samples collected at 24 sites across the River Athi basin area, Kenya, in both dry and rainy seasons. Overall, 57 CECs comprising 31 PhACs (0.4 ng L−1–142 μg L−1), 6 PCPs (0.7–570 ng L−1) and 20 pesticides (0.3 ng L−1–8.3 μg L−1) were detected. The maximum loads varied from 217 g day−1 (PCPs) to 46 kg day−1 (PhACs). Individually, carbamazepine, nevirapine, sulfamethoxazole and DEET were the most ubiquitous CECs, with detection frequencies (DF) higher than 80 %. The highest concentrations were observed at river sites that are heavily impacted by informal settlements, highlighting the critical role of slums in urban rivers pollution. At least 8 CECs including acetamiprid, alachlor, atrazine, diuron, nevirapine and paracetamol show potential risk to algae, Daphnia magna and fish, as exemplified by Risk Quotients (RQ) up to 174. Similarly, potential risk of antibiotic resistant bacteria development is evident (RQ up to 64), being driven by metronidazole, sulfamethoxazole and trimethoprim. Ultimately, further studies on the occurrence and distribution of antibiotic resistant bacteria within the basin and among the communities consuming untreated river water for drinking is merited.

Influence of governance indicators on public debt accumulation in Africa

PurposeThe unsustainable public debt of most African economies adversely affects their economic growth and stability. This study aims to explore the influence of cross-country indicators of governance from African countries on public debt accumulation.Design/methodology/approachThe study deployed a quantitative research design technique. Secondary data was used in this study. The frequency of the data is annual, and it is available from 1996 to 2022 for 48 countries in Africa. The study deployed the system generalized method of moments for the estimation.FindingsThe study finds that countries with high regulatory quality standards, control corruption and ensure effective governance accumulate less government debt while countries that abide by the rule of law instead accumulate more government debt. The study also finds that economic growth and government revenue reduce government gross debt while government expenditure and investments increase public debt.Research limitations/implicationsDue to data unavailability, other factors which are likely to influence government debt accumulation were not included in the study as control variables. This is the limitation of the study.Social implicationsAfrican governments should strive to maintain high regulatory quality standards through the formulation and implementation of sound policies and regulations that permit and promote private sector development, and ensure quality and accountability of public and civil services. Governments are also urged to control corruption and enact good laws so that the enforcement of these laws will not worsen the risk of becoming debt-distressed.Originality/valueRecent studies on governance and public debt were focused on the Arabian Gulf countries, countries of the Middle East and North Africa (MENA) region and a combination of high and low-income countries. This study scrutinizes exclusively the effects of the quality of governance indicators on public debt accumulation, in the context of Africa.

ANALISIS GEN KOMPARATIF KARSINOMA SEL SKUAMOSA PARU-PARU ANTARA INDIVIDU MEROKOK DAN TIDAK MEROKOK [Comparative Gene Analysis of Squamous Cell Lung Carcinoma Between Smoking and Non-smoking Individuals

Squamous cell lung carcinoma (SCC) is a form of non-small cell lung cancer that commonly arises in the primary airway. The development of SCC is closely linked to changes in squamous cells that line the airways, primarily caused by exposure to tobacco smoke. To gain insights into SCC, bioinformatics techniques have been employed to detect biomarkers and analyze gene expression patterns, utilizing data from the Cancer Genome Atlas (TCGA) database, which was preprocessed for analysis. By employing DESeq2, a differential gene expression analysis method, identified genes showed significant variations in expression between smoking and non-smoking groups among the 11,530 genes examined. Notably, five genes, namely CT45A1, GCGR, TPTE, ABCC2, and PI16, were found to play a significant role in tumor development and were susceptible to under- or over-expression due to smoking. The majority of these genes were found to be underexpressed rather than overexpressed. These identified genes hold potential as biomarkers for tumor development and exhibit a strong correlation between smoking history and the development of SCC. However, a limitation encountered during this analysis was the unavailability of data from normal non-tumor patients, which could have facilitated a more comprehensive analysis of differential gene expression. Furthermore, this research gives a deeper implementation regarding the molecular mechanisms and genomics underlying SCC development, identifies differentially expressed genes associated with SCC and smoking history, and highlights potential biomarkers that warrant further investigation.