Conventional Analysis Methods Research Articles

Transition experiences of patients with post stroke dysphagia and family caregivers: A longitudinal, qualitative study.

Stroke patients with dysphagia and family caregivers will experience multiple transitions during the whole process of the disease and various nursing needs will be generated. There is a lack of knowledge about their experiences at different transition stages. Thus, we aimed to explore the transition experiences of patients with post stroke dysphagia and family caregivers from admission to discharge home. A semi-structured interview based on Meleis's transition theory was used during hospitalization and telephone follow-up interviews were conducted in the first, third, and sixth month after the diagnosis of dysphagia. Interview transcripts were analyzed using the conventional content analysis method. A total of 17 participants enrolled in the first face-to-face interview, 16 participants took part in the first month's telephone follow-up interview, 14 participants in the third month, and 12 participants in the sixth month. The transition experiences of patients with post stroke dysphagia and family caregivers could be summarized into three themes: (1)transition from onset to admission; (2)transition from discharge to other rehabilitation institutions; and (3)transition from discharge to home. Each theme had identified interrelated subthemes. The experiences of patients with post stroke dysphagia and family caregivers during transition are a dynamic process with enormous challenges in each phase. Collaboration with health care professionals, follow-up support after discharge, and available community and social support should be integrated into transitional nursing to help patients facilitate their transition.

Care Bermuda, families of the patients with chronic mental disorders in Iran surrounded by psychosocial problems and needs: a qualitative study

Background: A mental disorder is characterized by a clinically significant impairment of cognition, emotion regulation, or behavior. As a result of the shift in care from medical centers and hospitals to home care, we now see a change in the treatment of patients with chronic mental disorders. Consequently, families have become the main support system in the progressive care of these patients, leading to psychological and social problems. The current qualitative study investigated the social and psychological health needs and problems of family caregivers of patients with chronic mental disorders in Iran’s cultural and social context. Methods: This was a content analysis qualitative study. The study was conducted in three psychiatric hospitals in Farabi, Noor, Modares. Purposive sampling was done and continued until data saturation. Semi-structured, face-to-face, and individual interviews were conducted with 49 participants (15 family caregivers of patients with CMD and 34 members of the mental health care team). Data analysis was done using the conventional content analysis method. Results: Eight hundred seventy-five primary codes were obtained, which were classified into 10 subcategories and 3 main categories of ʻhealth challenges of caregiverʼ, ʻconfused concept of careʼ and “the need for a coherent support system. Conclusion: In Iranian society, after the patient is discharged from the hospital or care center, family members will change their role to the most important caregivers of these patients. The psychological and social health of these caregivers is destroyed because of the problems they have in providing care for their patients and their needs, which are not met.

Building functional networks for complex response analysis in systems of coupled nonlinear oscillators

Some aspects of engineering dynamics, such as nonlinearities and transient motion of many interconnected parts, remain difficult to handle today. To comply with increasing demands on resilience and safety, the dynamics of large machines need to be better understood. Complex network methods, already present in many scientific disciplines, provide a tool set complementary to conventional methods of system analysis. This work aims at providing a new, function-based view on mechanical systems by generating functional networks. To this end, a network algorithm is applied to sets of cyclically coupled Duffing oscillators as a common example of a complex nonlinear mechanical system. In the functional network, each node represents an oscillator while the direction of the network edges represents a functional coupling. Results show that the network method is capable of identifying dynamical transitions and synchronization between components, as well as determining the number of different states present within a system. Additionally, the time evolution of the component interactions, especially in response to a disturbance, is studied via a sliding-window approach. The results of this analysis might hopefully open new ways for a more efficient system analysis through optional sensor placement, and for effective countermeasures against unwanted dynamics through improved analysis of transient dynamics.

The experiences of patients with COVID-19 and their relatives from receiving professional home care nursing: a qualitative content analysis

BackgroundTo overcome of patients with COVID-19 over the capacity of hospitals and mild to moderate severity of the disease in most cases, the World Health Organization and the Centers for Disease Control and Prevention in the United States, recommend home care for these patients. Receiving care at home will face challenges that can be context-based, especially in crises like the Coronavirus pandemic. The present study aimed to describe the experiences of patients with COVID-19 and their relatives from receiving professional home care nursing.MethodsThis study was conducted using a qualitative content analysis method. Nine participants with COVID-19 who were receiving home care nursing in Semnan participated in this study. The purposive sampling method was used. Sampling continued until no new categories appeared, meaning the category’s theoretical saturation. Deep and semi-structured interviews were used to collect data based on the research question. Data was analyzed using the conventional content analysis method using Graneheim and Lundman’s approach.ResultsAfter analyzing the interviews and comparing codes based on similarities and differences, three main themes, 11 categories, and 30 subcategories were identified. The main themes included “The value of home care” (personalization of care, being economical, providing intellectual security, and reducing the concern of family), “Comprehensive care” (professional commitment, empathy, mastery in care, and patronage), and “Care challenges” (cultural barriers, inadequate services, and lack of information about costs and conditions).ConclusionThe patients with COVID-19 who received professional nursing care at home mentioned some challenges, such as the caregiver not being of the same sex as the patient, delay in receiving the service, the inadequacy of the centers, the limitation of the right to choose the care provider, and insufficient information about the cost of services received before receiving each care.

Ion-selective paper-based chromogenic strip and electrochemical sensor for the detection of ammonium ions

The fraudulent practice of adding ammonium salts to milk in order to enhance the nitrogen content has serious health implications including renal failure. Ammonium ion detection has been targeted through the conventional analytical methods however, lack of portability and complex operation limits their use for field applications. In this scenario, paper-based test strips and electrochemical sensors can provide strategies for point-of-test applications. Here, an ion-selective paper-based chromogenic strip and an electrochemical sensor has been developed for detection and quantification of ammonium ions. The developed paper-based chromogenic strip showed detection limit of 0.008 % (w/v) for ammonium ions, whereas, electrochemical sensor showed an LOD of 0.0062 ± 0.0023 % (w/v) with a sensitivity of 1.6 × 10−5 A.% −1.mm−2. The developed paper-based chromogenic strip and electrochemical sensor can be employed by regulatory bodies and dairy industries to ensure the safety and quality of milk.

Numerical Investigation of Seismic Behaviour for a Flexible Cantilever Retaining Wall with Cohesive Backfill

In the seismic design of flexible cantilever walls retaining cohesive backfill soil, the common practice is to neglect the cohesion effect. Dynamic lateral earth pressure is typically evaluated based on approaches primarily intended for cohesionless soils or through analytical pseudo-static methods. Nevertheless, both experimental and theoretical evidence has demonstrated significant effects due to soil cohesion that are not accounted for by these methods. This study involved finite element modeling (FE) of a flexible cantilever wall with a height of 5.4m, supporting homogeneous cohesive backfill under initial static and seismic loadings. The calculated active earth thrust was then compared with values obtained experimentally and through conventional analytical methods. The obtained results indicate that the presence of soil cohesion significantly reduces seismic demands on flexible cantilever retaining walls, resulting in a substantial reduction of seismic active earth pressures and total seismic thrust by up to 50% and 52%, respectively. It enhances also the overall stability of the system by shifting the point of application of seismic thrust toward the base of the wall, thereby increasing the safety margin. In addition, it significantly decreases the wall displacement at the stem top, with reductions of up to 104% compared with the case involving cohesionless backfill. It was observed that the conventional methods recommended by some seismic regulations largely underestimate seismic active pressure.

Qualitative exploration of sexual dysfunction and associated coping strategies among Iranian prostate cancer survivors.

Our understanding of the experiences of prostate cancer survivors regarding their sexual life and related issues remains limited. Therefore, this study aimed to explore sexual dysfunction and associated coping strategies among Iranian prostate cancer survivors. A qualitative study was undertaken. Participants were 15 Iranian prostate cancer survivors chosen using purposeful sampling. Data collection involved conducting in-depth semi-structured interviews, followed by the utilization of the conventional qualitative content analysis method for data analysis. Three themes were developed: (a) suspension of sexual life-treatment for prostate cancer significantly impacted participants' sexual lives; (b) emotional resilience and psychological coping strategies-various psychological strategies were used to tackle sexual dysfunction; (c) efforts to regain sexual function-several strategies were applied to enhance sexual performance. Our study findings provide insights into the experiences of prostate cancer survivors and subsequent shifts in their sexual dynamics and coping with the disease. Healthcare providers should actively identify patients' concerns and develop culturally tailored care strategies to address sexual challenges and improve coping among prostate cancer survivors.

Magnetic anisotropy in heterogeneous amorphous thin films: insights from thickness- and temperature-driven spin-reorientation

Magnetization orientation in thin films is intricately influenced by multiple anisotropy components, with the dominant anisotropy serving as a key determinant. This complexity becomes particularly intriguing when considering thin films composed of subnanometer-scale heterogeneous amorphous structures. Our investigation builds upon this foundation, specifically focusing on the Fe–Ni–B–Nb alloy system, known for its moderate glass-forming ability and susceptibility to nanocrystallization. In this study, we present thickness- and temperature-driven spin-reorientation (SRT) transition, attributed to competing magnetic anisotropy energies in thin films featuring a heterogeneous amorphous structure. Thermogravimetric investigations unveiled a unique heterogeneous amorphous structure, a revelation unattainable through conventional structural analysis methods. The observed spontaneous perpendicular magnetization in amorphous films, as evidenced by transcritical hysteresis loops and magnetic stripe domains, is ascribed to the pronounced residual stress arising from the substantial magnetostriction of the alloy system. The temperature-driven SRT is correlated to the order-disorder magnetic transition of the heterogeneous amorphous phase, characterized by a Curie temperature of ∼225 K. This transformative magnetic state of the heterogeneous amorphous matrix limits the exchange interaction among the densely distributed α-Fe nuclei regions, ultimately governing the dynamic magnetic responses with varying temperature. This work provides valuable insights into the dynamic magnetic orientation of thin films, especially those with heterogeneous amorphous structures, contributing to the broader understanding of the underlying mechanisms of magnetization reversals.

Antecedents of Spiritual Health Affecting Family Management and Sexual Relationships of Couples: A Conventional Content Analysis

Background: Spiritual Health (SH) is the most meaningful and universal part of health affecting all human dimensions functions. Given that community health depends on the health of the family environment, Islam pays special attention to the basic foundation of the family and its persistence. Therefore, this qualitative study aimed to determine the antecedents of SH in Muslim couples according to Islamic sources. Methods: To collect the required data, some keywords, including “spiritual health”, “spirituality”, “spouses”, “couples”, and “Islam” were searched both in Persian and Arabic in Google Scholar, Noormags, SID, and Magiran databases in 1980-2023. After examining 37 documents, the conventional content analysis method was adopted to determine the antecedents of SH. Results: The 139 extracted codes were then classified into six categories (affecting the family management and their sexual, spiritual, behavioral, emotional, and cognitive relations). This paper elaborates on the antecedents of SH “affecting the family management”, and “sexual relationships” of couples. The first category includes four subcategories, “family boundaries and management”, “mutual and exclusive rights”, “planning”, and “cooperation and division of labor”. The second includes five subcategories, “encouragement to marry with trust in God”, “having offspring”, “preparation and contextualization”, “request and response”, and “infidelity prevention”. Conclusion: Both categories of “affecting the family management” and “sexual relationships” affect the lives of Muslim couples as antecedents and consequences of the concept of SH. It is suggested to perform more research by considering the texts of Sunni scholars.

Composition and influencing factors of professionals' capacity in public health emergency rescues: a qualitative study.

To explore the composition and influencing factors of professionals' capacity in public health emergency rescues. A descriptive qualitative design was used in this study. Medical workers, managers, and members of an emergency rescue team in Hangzhou, Zhejiang, were recruited for participation through a purposive sampling method. The data were collected using semi-structured interviews and analyzed using a conventional content analysis method. A total of 2 themes and 13 sub-themes emerged from the analysis: ability composition (knowledge reserve, early warning assessment, information reporting, emergency response, self-protection, personal ability, coordination and cooperation, health education) and influencing factors (educational background, region, experience, hospital level, human resources, and financial investment). These findings offer a basis for the construction of a related indicator system and provide a reference for relevant departments to further optimize their emergency education and training, strengthen their emergency drills, and improve their emergency rescue abilities. The findings indicate that it is necessary to pay attention to the construction of an emergency rescue team, adjust the ratio of personnel, improve their remuneration, and promote work enthusiasm to improve the emergency rescue ability of an organization.

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

Hazard analysis is a vital step in developing intelligent connected vehicles, aiming to eliminate or control hazards in the initial stages of system development and to provide theoretical support for the system’s safety design. However, conventional hazard analysis methods, such as Fault Tree Analysis and Failure Mode and Effects Analysis, suffer from two shortcomings: they do not account for the impact of cybersecurity factors on system safety and do not provide sufficient quantification of hazard scenarios. To this end, we propose a quantifiable hazard analysis method with security consideration, which integrates System Theoretic Process Analysis for Security (STPA-Sec) and Generalized Stochastic Petri Net (GSPN), supporting the extraction, modeling, and quantification of hazards. Specifically, we employ STPA-Sec for qualitative analysis to identify causal scenarios, safety requirements, security requirements, and the corresponding mitigations. Then, based on the identified causal scenarios, a GSPN model is established to quantify system-level hazards. A case study on a real open-source test vehicle demonstrates that the proposed method not only offers a comprehensive analysis of hazards but also provides a quantitative assessment. Comparative assessments suggest that the proposed method exhibits an advantage in terms of analysis processes (integrating security) and results (quantification).

Effects by geothermal gradient on thermal response tests for boreholes

A thermal response test (TRT) on a borehole provides information needed in the design of ground heat exchangers for ground source heat pump systems. Conventional analysis of TRT data sets uses the late-time period to estimate the ground thermal conductivity and the effective borehole resistance. During this quasi-steady-state period, the transient average fluid temperature has a logarithmic linear trend, which is consistent with conventional heat transfer models that assume a uniform undisturbed ground temperature. As the depth of the borehole increases the undisturbed ground temperature increases with depth following the geothermal gradient. This paper focuses on how the geothermal gradient affects a TRT. As the geothermal gradient increases, the required time to reach the conventional logarithmic linear trend may increase beyond any practical testing duration in coaxial boreholes. Then, conventional analysis methods based on a quasi-steady-state period incorrectly estimate the ground thermal conductivity. The magnitude of this effect is controlled through a dimensionless parameter |qratio|, which includes the geothermal gradient and other test parameters. In place of conventional models, a transient model that includes the geothermal gradient and fluid thermal storage can estimate the ground thermal conductivity and local borehole resistance within the typical TRT testing duration.

USER PERCEPTION TOWARDS STOCK PRICE PREDICTION USING LINEAR REGRESSION MODEL

Stock market prediction is a critical area of research with profound implications for investors, financial analysts, and policymakers. This study explores the efficacy of linear regression analysis within the domain of business analytics for forecasting stock market movements. Leveraging historical stock prices and relevant economic indicators, we employ a rigorous methodology to construct and evaluate predictive models using linear regression techniques. Technological advancement increases the study on stock and share market industry. Decision making is enhanced by various statistical and machine learning algorithms. Enormous research work has been concentrated on the feature prediction of stock prices based on historical prices and volume. Performance measures are analyzed in this work with S&P 500 Index using statistical methods in Python environment. Results obtained in this study are superior than the existing methods. The conventional methods for financial market analysis are based on linear regression. This paper focuses on the best independent variables to predict the closing value of the stock market. This study is used to determine specific factors which are providing the most impact on the prediction of the closing price.

A Review of Fabrication Techniques and Optimization Strategies for Microbial Biosensors

Challenges of stability and specificity associated with early generation sensors necessitate the fabrication and optimization of microbial biosensors. More so, the global biosensors market size currently valued at USD25.5 billion in 2021 is expected to grow at a compound annual growth rate (CAGR) of 7.5% to USD36.7 billion in 2026. Microbial biosensors are bioanalytical systems that integrate microorganisms with a physical transducer to generate signals, thus, aiding the identification of analytes. The biosensors are fabricated through a series of steps comprising microbe selection, immobilization onto a matrix, microfabrication, calibration, and validation. The transducers integrated microorganisms generate quantifiable signals, enabling real-time monitoring of a diversity of analytes within food samples. The optimization strategies are scrutinized, with a particular focus on the integration of sundry nanoparticles, such as magnetic, gold, and quantum-dot nanoparticles, which enhance sensor performance. Distinct advantages offered by microbial biosensors promise to revolutionize food quality assessment via cost-effectiveness, rapid sample testing, and the ability to provide access to real-time data. Literature have highlighted certain limitations including interference from complex matrices, instability of microorganisms, and microbial lifespan. In assessing their economic importance, a comparative analysis is presented against conventional food analytical methods like ELISA, PCR, and HPLC; thus, highlighting the unique strengths of microbial biosensors. The future perspectives focus on the potential of the technology in addressing the need for continuous monitoring challenges, and research for further improvements in the biocompatibility of fabrication processes and long-term reusability.

Image-processing-based ultrasonic velocimetry development with high applicability to flows in microparticle dispersion

This study presents the development of a novel methodology, image-processing-based ultrasonic velocimetry (IPUV). For evaluating flow velocity, we present a new perspective utilizing image processing for spatiotemporal echo images instead of conventional ultrasonic analysis methods such as pulse compression and quadrature detection. In addition to traditional tracer particle size O(100 μm), IPUV has a high applicability for measuring the velocity of microparticle dispersion of the particle size O(1 μm) and dilute concentration O(0.001 wt.%). Velocimetry utilizing microparticles has significant merit: Velocity measurements can be conducted under conditions with less influence on the flow and higher traceability to fluid flow than the size conventionally used as a tracer particle. The accuracy and effectiveness of velocity analysis based on the IPUV principle are verified numerically and experimentally. In the experiments, IPUV was applied to Couette flow and stirring flow. In the former, the validity of the velocity profiling by IPUV was confirmed compared to the theoretical profiles of Couette flow. In the latter, IPUV measured velocity profiles in the stirring flows with different dispersions [spherical particle O(100 μm) and mica O(1 μm)]. Differences in the ability of dispersed particles to follow turbulent fluctuations in stirring water flow appeared in the frequency spectrum of IPUV velocity profiles.

Experimental study of Cryogenic jet injection using centrifugal nozzles at supercritical pressure

The new generation of large-thrust rockets commonly employ cryogenic liquids such as liquid hydrogen (LH2), liquid oxygen (LOX), and methane as propellants. Conventional analytical methods established under subcritical (specifically, low-pressure) conditions are inadequate for addressing the atomization issues under trans-critical and supercritical conditions. Therefore, conducting experimental research on atomization of cryogenic fluids under supercritical conditions holds significant practical value. This paper employs liquid nitrogen (LN2) as the working fluid and focuses on conducting injection atomization experiments on LN2 under supercritical pressure conditions. The research involves the design and construction of an experimental system for supercritical cryogenic fluid injection, with the aim of measuring and studying the spray characteristics of centrifugal nozzles under various conditions. Three major categories of experiments were designed for low-temperature LN2 injection: subcritical nitrogen into subcritical environment (BB), subcritical nitrogen into supercritical environment (BP), and supercritical nitrogen into supercritical environment (PP). The experimental results ultimately reveal that for jets in BB, both the spray cone angle and the breakup length increase with higher injection pressure drop. In the case of BP, the spray cone angle decreases while the penetration distance increases with higher pressure drop. As for the jets in PP, the spray cone angle increases and the penetration distance decreases with higher injection pressure drop. Across different geometric parameters of the centrifugal nozzle, it was observed that with an increase in the geometric parameter, the rate of decrease in the spray cone angle with respect to pressure drop amplifies, and the penetration distance exhibits an increasing trend as the geometric parameter grows.

Simple method to assess foam structure and stability using hydrophobin and BSA as model systems.

The properties and arrangement of surface-active molecules at air-water interfaces influence foam stability and bubble shape. Such multiscale-relationships necessitate a well-conducted analysis of microscopic foam properties. We introduce a novel automated and precise method to characterize bubble growth, size distribution and shape based on image analysis and using the machine learning algorithm Cellpose. Studying the temporal evolution of bubble size and shape facilitates conclusions on foam stability. The addition of two sets of masks, for tiny bubbles and large bubbles, provides for a high precision of analysis. A python script for analysis of the evolution of bubble diameter, circularity and dispersity is provided in the supplementary information. Using foams stabilized by bovine serum albumin (BSA), hydrophobin (HP), and blends thereof, we show how this technique can be used to precisely characterize foam structures. Foams stabilized by HP show a significantly increased foam stability and rounder bubble shape than BSA-stabilized foams. These differences are induced by the different molecular structure of the two proteins. Our study shows that the proposed method provides an efficient way to analyse relevant foam properties in detail and at low cost, with higher precision than conventional methods of image analysis.

Less interference fluorescence analytical strategy: Bridging substance-triggered ratiometric sensor with convenient preparation and application

High interference and narrow application range are key of bottleneck of recent fluorescence analysis methods, which limit their wide application in the sensing field. Therefore, to overcome these disadvantages, a ratiometric fluorescence sensing system utilizing berberine (BER) and silver nanoclusters protected by dihydrolipoic acid (DHLA-AgNCs) was constructed for the first time in this work, to achieve determination of BER and daunorubicin (Dau). BER aqueous solution (non-planar conformation) has no fluorescence emission. When it was mixed with DHLA-AgNCs, the conformation of BER became planar, producing fluorescence emission at 515 nm besides the fluorescence emission peak of DHLA-AgNCs at 653 nm. With the increase of BER concentration added in system, the fluorescence intensity of BER (planar conformation) at 515 nm increased obviously and the fluorescence intensity of DHLA-AgNCs decreased slightly. Therefore, the dual emission fluorescence sensing system was constructed based on a fluorescence substance and non fluorescence substance, to achieve determination of BER. Meanwhile, based on the bridging effect of BER and fluorescence resonance energy transfer effect from Dau, the altering of two peaks intensity was utilized to achieve determination of Dau. Thus, this dual emission sensing system can not only be used for fluorescence analysis of BER and its analogues, but also based on the bridging effect of BER, allowing the determination of Dau and its analogues that could not be directly measured with silver nanoclusters, expanding the application range of traditional dual emission detection systems. Meanwhile, this system has strong anti-interference ability and low toxicity to the human body and less pollution to the sample and environment. This provides a new direction and universal research strategy for the construction of new fluorescence sensing systems in the future for the analysis of target substances that cannot be directly detected with conventional fluorescence analysis methods.

Environmentally safe chemometric evaluation and data analysis for the kinetic investigation of mirabegron stability

The physicochemical characteristics of different pharmaceuticals may change with transportation, storage, and usage which directly affect their efficacy, quality, and other features. It is crucial to investigate the kinetic action of pharmaceutical compounds to produce predictive data that aid in the computation of their shelf lives. Several conventional analytical methods introduce unwanted dangerous risks, like employing unsafe solvents, that harm both the analyst and the environment. This created the motive for analysts to apply green approaches that consider using safe chemicals and producing as little waste as possible. In this study, powerful green assessment methods were investigated to study the kinetic process of Mirabegron (MIR) in different hydrolytic conditions giving its main degradation product. First, the optimal solvent was identified for the given kinetic procedures with the aid of Principal Component Analysis. Then two advanced chemometric methods were applied to estimate the kinetic parameters of the MIR degradation process namely: Multivariate Curve Resolution-Alternating Least Squares and Artificial Neural Networks. By data manipulation, both algorithms could provide an explanation of the MIR kinetic process in different hydrolytic conditions. Moreover, modified National Environmental Methods Index (NEMI), eco-scale, Hexagon algorithm, Complementary Green Analytical Procedure Index (Complex GAPI), and analytical greenness metric for sample preparation (AGREEprep) were adopted to ensure the greenness of the proposed study. Eventually, the presented investigation could successfully present an eco-friendly chemometric approach for monitoring the different kinetic processes of pharmaceutical compounds with minimum effort and solvent consumption and maximum time saving and analyst safety compared to other traditional methods.

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.