Identification Process Research Articles

On the limits of communication: The liminal positioning of older adults and processes of self-ageism and ageism.

Our qualitative study, consisting of in-depth semi-structured interviews with recent retirees in the Dutch city of Leiden, set out to investigate how communication, through processes of self-identification and the negotiation of social identities, relates to (self-)ageism. A letter from the city administration was used to make age identification salient in our research and prompted stories of various liminal spaces and phases that our participants experienced. Whilst liminal phases are usually considered uncertain and ambiguous, in our study we found that for older people liminality can offer a desired ambiguity that allowed them to adopt a more positive identity than ‘being old’, which was rejected as undesirable. Our findings provide insight into the intertwinement of societal, institutional and individual levels of ageism and highlights the necessity of finding and emphasising positive values in being old, since only then positive interpretations of communication can enable processes of positive societal identification.

Probabilistic modeling of energy transfer in disordered organic semiconductors

The non-radiative energy transfer process governs the transport of excitons in organic semiconductors, directly affecting the performance of organic optoelectronic devices. Successful models describe this transfer in terms of energy donor–acceptor pair sites, in contrast to experimental photophysical properties, which reflect the average behavior of the molecular ensemble. In this study, an energetic and spatial probability density function is proposed to determine the average non-radiative energy rate for homotransfer processes. This approach considers the energetic-spatial distribution typical of disordered semiconducting polymers. The average homotransfer rate is significantly dependent on the energy of the donor site, allowing the identification of the photophysical process most likely to occur. Values of the order of 1011 s−1 were predicted and are consistent with experimental results. This approach was used to evaluate how the energy transfer efficiency in heterostructures is affected by the energy and position of the energy donor site. The model presented in this study can be explored in other organic systems to investigate exciton transport mechanisms in new organic optoelectronic device architectures.

Greenhouse Gas Mitigation Potential Through Clean Energy for Cement Production in India

Abstract A preliminary approach has been made to assess the concentrated solar energy applications in cement production as well as greenhouse gas mitigation potential. The work starts with identification of processes that utilize thermal energy in cement production. Then, a cluster of cement plants located at different locations in the country has been made. Also, the availability of solar radiation and wind speed at each plant location have been identified. Subsequently, the solar industrial process heating systems have been developed for different locations in the country. Further, solar reactor output, number of heliostats, total land area and mirror surface have been estimated. All these estimations are done by considering three types of thermal losses in solar reactors, i.e., 15, 30 and 45%, respectively. Solar energy can provide a total of 738.11 PJ of thermal energy, which is needed to fulfill the process heating requirement of the calcination process for the manufacturing of cement in India. Solar industrial process heating systems for cement production in India can reduce yearly CO2 emissions by 2457–7648 thousand tons.

Can Social Identities Improve Working Students’ Academic and Social Outcomes? Lessons from Three Studies

Previous research has linked working for pay while attending university with negative academic and health outcomes; yet, working students are often resilient when experiencing such adverse circumstances. This makes it crucial to explore potential psychological mechanisms that transform adverse experiences into sources of motivation and persistence for working students. We explore one mechanism—social identification—and its different foci—identifying as a student, employee, working student, or a student of one’s discipline of study—as potential predictors of important academic (academic self-efficacy, approaches to learning, and academic achievement) and social (status in society) outcomes in three cross-sectional studies. In Study 1, part-time working hours (but not identification processes) were associated with academic self-efficacy. In Study 2, discipline identification and part-time working hours were associated with using deep approaches to learning. In Study 3, student identification was associated with increased status in society. Overall, discipline identification may be solely linked to academic outcomes, but student identification should be explored further as a potential enhancer of social and graduate outcomes. We discuss additional mechanisms that can help to transform working students’ experiences through their social identities and suggest boundary conditions that can affect the link between these identities and important outcomes.

Principal Component Approach and Relationship between Nomination Scale for Identification of Football Talent and Physical Fitness in Young Soccer Players

The present study aimed to investigate the relationship between the physical capabilities of young soccer players and their performance in game-related variables as assessed through the Nomination Scale for Identifying Football Talent (NSIFT) questionnaire. A total of 80 young soccer players, with an average age of 10.70 ± 1.02 years, participated in the research. Each player underwent a comprehensive assessment session that included the 5-0-5 Change of Direction (COD) test, the Illinois Agility Test, and the Countermovement Jump (CMJ) test. These assessments were selected to evaluate critical physical attributes essential for soccer performance such as agility, explosive strength, and the ability to change direction rapidly. To analyze the data, Principal Component Analysis (PCA), a statistical technique that reduces the dimensionality of large datasets while retaining as much variance as possible, was employed. The PCA results indicated strong sample validity as confirmed by the Kaiser–Meyer–Olkin (KMO) measurement index, which assesses the adequacy of the sample size for factor analysis. The analysis revealed two principal components: development and disposition, which together accounted for 73% of the total variance in the data. The development component encompasses various physical attributes that contribute to a player’s growth and improvement, including strength, speed, and agility. Conversely, the disposition component reflects innate qualities and cognitive skills that predispose players to excel in soccer such as decision making and game awareness. This research highlights the importance of incorporating physical assessments into talent identification processes, providing objective measures that complement subjective evaluations. This study contributed to the literature on talent identification in soccer, emphasizing the need for a multidisciplinary approach to nurture young athletes effectively. Future research should continue to explore the interplay between physical and cognitive skills in soccer to enhance player development and success in competitive environments.

Structural damage identification based on Bayesian updating

Due to factors such as model accuracy, sensor arrangement, noise influence, eigenvalue selection and the influence of local damage on the overall response, the structural damage identification process inevitably has uncertainties, so it is necessary to introduce probabilistic statistical methods to improve its accuracy and robustness. In this paper, structural damage identification is carried out based on Bayesian updating. Bayesian updating is combined with real-time monitoring data (e.g. displacement, acceleration), and the real-time data reflect the current state of the structure and contain damage information. The experimental results show that the update results of the Bayesian method correlate well with the actual data, which verifies the feasibility of Bayesian updating in structural damage identification. The structural prior probability can be effectively updated by this method, which shows high accuracy and robustness in structural damage identification and is able to maintain stable performance under different working conditions and environments. Based on Bayesian updating, this structural damage identification method provides a new technical means for structural health monitoring and damage assessment, with broad application prospect and development space.

أثر تطبيق معايير إدارة المخاطر ISO 31000 على فعالية إدارة المخاطر في الشركات السعودية البتروكيماوية

This study investigates the impact of implementing ISO 31000 risk management standards on the effectiveness of risk management at Saudi petrochemical companies, a leading company in the petrochemical industry. ISO 31000 is a globally recognized framework that provides principles and guidelines for managing risks systematically and effectively. The study begins with an overview of the importance of risk management in large organizations, highlighting the challenges and risks faced by companies in a complex and dynamic environment. It then reviews ISO 31000 standards, including its core principles and framework aimed at improving the risk identification, assessment, and treatment processes. Additionally, the study examines the current risk management practices at Saudi petrochemical companies and evaluates how the application of ISO 31000 standards has influenced these practices. The research employs a methodology that includes collecting data through surveys and interviews with employees and risk management officials at the company, as well as analyzing relevant documents related to processes and policies. The findings indicate that the implementation of ISO 31000 has significantly enhanced the effectiveness of risk management at Saudi petrochemical companies. Notable benefits include improved risk identification and assessment accuracy, as well as the development of more effective risk treatment strategies. The standards also contributed to raising risk awareness within the company and fostering a more adaptive organizational culture capable of addressing new challenges. The study also identifies some challenges encountered during the application of the standards, such as the need for ongoing training and the development of advanced analytical tools. However, the benefits achieved outweigh these challenges, suggesting that investing in ISO 31000 implementation can be highly valuable for leading companies. Based on the results, the study recommends continuing to enhance the application of ISO 31000 across all departments within the company, developing regular training programs to ensure employee adherence to the standards, and further improving the tools and methods used in risk management to achieve optimal effectiveness. This study provides valuable insights into improving risk management in large organizations and offers practical models that other companies across various sectors can benefit from.

An evaluation of the role of social identity processes for enhancing health outcomes within UK‐based social prescribing initiatives designed to increase social connection and reduce loneliness: A systematic review

AbstractThe UK's National Health Service has introduced Social Prescribing initiatives to tackle loneliness and ill‐health, yet it lacks a theoretical foundation and evidence base for Social Prescribing's effectiveness. Recent research applies the Social Identity Approach to Health (SIAH) to explain Social Prescribing's health benefits, emphasising how social connection unlocks health‐enhancing psychological mechanisms. This systematic review therefore aims to assess UK‐based Social Prescribing programmes designed to boost social connection and alleviate loneliness, examining programme efficacy and the role of SIAH processes in health outcomes. Following PRISMA guidelines, a narrative synthesis of articles published from May 5, 2006 (when social prescribing was first introduced in the NHS), to April 8, 2024, was conducted, and their quality assessed using CONSORT‐SPI (2018). Of these programmes, 10 employed a mixed‐methods design, 8 qualitative and 1 quantitative service evaluation, totalling 3,298 participants. Results indicate that Social Prescribing's psychological value lies in quality rather than quantity of social connections, with meaningful connections fostering shared identity, perceived support and self‐efficacy, the latter of which sustains social engagement post‐programme. The SIAH was a useful tool for mapping mixed‐methods findings onto a common theoretical framework to highlight these key proponents. Overall, this review underscores the importance of SIAH‐informed Social Prescribing interventions in enhancing social connectedness, reducing loneliness, and promoting overall health. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

Increased climate change skepticism among farmers? The roles of motivated cognition and social identity processes

Increased climate change skepticism among farmers? The roles of motivated cognition and social identity processes

Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images

Glaucoma is one of the leading causes of permanent blindness and remains a current challenge in the field of ophthalmology. This research aims to present a comprehensive investigation into the development and evaluation of new technology for glaucoma detection in retinal fundus images. The development and evaluation are presented on a customized architecture, using the Convolutional Neural Network (CNN) method. The proposed CNN architecture is designed to address the complex characteristics of glaucoma changes in the identification process. The research dataset consists of 506 retinal images categorized into 117 glaucoma images, 19 suspected glaucoma images, and 370 healthy images. Through our in-depth exploration, we conducted a careful analysis to uncover patterns and fundamental trends related to glaucoma-related features. During the training phase, the proposed CNN achieved outstanding average accuracy, sensitivity, and specificity values of 92.88%, 94.66%, and 89.31%, respectively. In the unseen test dataset, the model demonstrated competitive performance with an accuracy of 80.87%, sensitivity of 85.65%, and specificity of 71.26%. These findings emphasize the potential of the model as a reliable tool for glaucoma detection. The results indicate that the proposed method utilizing a customized CNN architecture is designed for glaucoma detection in retinal fundus images. The presented output results also hold promise for clinical relevance and can be considered an improvement in the care of retinal fundus patients.

The application of vibrational spectroscopy in forensic analysis of biological evidence.

Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.

Research on Detection Algorithm of Green Walnut in Complex Environment

The growth environment of green walnuts is complex. In the actual picking and identification process, interference from near-background colors, occlusion by branches and leaves, and excessive model complexity pose higher demands on the performance of walnut detection algorithms. Therefore, a lightweight walnut detection algorithm suitable for complex environments is proposed based on YOLOv5s. First, the backbone network is reconstructed using the lightweight GhostNet network, laying the foundation for a lightweight model architecture. Next, the C3 structure in the feature fusion layer is optimized by proposing a lightweight C3 structure to enhance the model’s focus on important walnut features. Finally, the loss function is improved to address the problems of target loss and gradient adaptability during training. To further reduce model complexity, the improved algorithm undergoes pruning and knowledge distillation operations, and is then deployed and tested on small edge devices. Experimental results show that compared to the original YOLOv5s model, the improved algorithm reduces the number of parameters by 72.9% and the amount of computation by 84.1%. The mAP0.5 increased by 1.1%, the precision increased by 0.7%, the recall increased by 0.3%, and the FPS is 179.6% of the original model, meeting the real-time detection needs for walnut recognition and providing a reference for walnut harvesting identification.

Unified theory of acceptance and use of technology (UTAUT) applied in higher education research: A systematic literature review and bibliometric analysis

Higher education must evolve and adapt to technological changes in developing skilled human capital. Nevertheless, each technology has features that may eventually lead to user acceptance. This paper reviews factors that led to technology acceptance in the higher education context based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model since 2009. The related articles were extracted from the Scopus database. A screening process was performed to filter the related articles guided by the PRISMA approach. Specific keywords were used for searching, and 50 articles were identified. Only 44 related articles were reviewed after the identification and screening processes. Bibliometric analysis was used to understand further and highlight the impact of past studies and certain trends of their works that would benefit future research. The review revealed that performance expectancy is the most critical determinant that could affect users' intention to accept technology used in higher education institutions when the original UTAUT and its extended framework were used. However, all variables revealed some inconsistencies. Future studies should further investigate the reason behind this occurrence. Most of the past studies used covariance-based structural equation modeling (CB-SEM) as a data analysis tool when dealing with a complex model. This paper provides insights to policymakers in the higher education sector that are useful for their capital investment decision-making in future technology developments. It triggers that any technology that will be put into practice needs to be efficient and have features that could improve individual performance in performing tasks.

Are contemporary facial recognition algorithms making human facial comparison performance worse?

Facial recognition plays a vital role in several security and law enforcement workflows, such as passport control and criminal investigations. The identification process typically involves a facial recognition system comparing an image against a large database of faces to return a list of probable matches, called a candidate list, for review. A human then looks at the returned images to determine whether there is a match. Most evaluations of these systems tend to examine the performance of the algorithm or human in isolation, not accounting for the interaction that occurs in operational contexts. To ensure optimal whole system performance, it is important to understand how the output produced by an algorithm can impact human performance. Anecdotal claims have been made by users of facial recognition systems that the images being returned by new algorithms in these systems have become more similar in appearance compared to old algorithms, making their job of determining the presence of a match more difficult. This paper explores whether these claims are true and whether the latest facial recognition algorithms decrease human performance compared to an old algorithm from the same company. We examined the performance of 40 novice participants on 120 face matching trials. Each trial required the participant to compare a face image against a candidate list containing eight possible matches returned by either a new or old algorithm (60 trials of each). Overall, participants were more likely to make errors when presented with a candidate list from a new algorithm. Specifically, they were more likely to misidentify an incorrect identity as a match. Participants were more accurate, confident, and faster on candidate lists from the older algorithm. These findings suggest that new algorithms are generating more plausible matches, making the task of determining a match harder for humans. We propose strategies to potentially improve performance and recommendations for future research.

Production Optimization of Premium Food Can with Distortion Printing under Waving Requirement

This research aims to propose a novel approach for evaluating and minimizing scraps in an industrial production of premium food cans with distortion printing. Beyond conventional formability criteria, a waving requirement is introduced to ensure aesthetic quality of the printed graphics. The research focuses on real production conditions, specifically involving double-cold-reduced (DR) low-carbon steel sheets and chromium-coated tin-free steel with a thickness of 0.16 mm. The sheets are laminated on both sides with a plastic film prior to undergoing distortion printing on the exterior. Subsequently, a blank is subjected to a drawing-redrawing process to form a food can. To address challenges associated with characterizing these thin sheets, a material parameter identification method is proposed and demonstrated. The thickness profile and flange length are identified as key criteria for this identification process. Measurements of thickness distribution and flange length are obtained using digital image correlation (DIC) and microscopy techniques. Within the manufacturing system, uncertainties related to material properties and forming processes can result in scraps or defects. To analyze these processes, finite element analysis (FEA) is employed and validated through experiments. For the evaluation of scrap rates, uncertainty propagation is conducted using a metamodeling technique, specifically employing radial basis function (RBF) neural networks. The study concludes by offering process optimization recommendations aimed at reducing the scrap rate.

Smart structural health monitoring using computer vision and edge computing

Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising alternative to conventional contact-type sensors for structural displacement measurement and SHM. Many of the currently reported vision-based structural displacement measurement systems typically temporarily set up a video camera from a distance to the structure. The collected images or videos are usually stored locally and post-processed offline to obtain structural displacement responses, which is cumbersome and limited to short-term SHM applications. The recent development of technologies empowered by the Internet of Things (IoT) and edge computing has enabled real-time video processing and analysis at the source, minimizing latency, reducing bandwidth requirements, and enabling prompt decision-making, thereby enhancing efficiency and responsiveness compared to traditional offline video recording and processing systems. In this paper, an edge computing vision-based displacement measurement system (EdgeCVDMS) is developed. Video recording, processing, and displacement response identification are entirely performed on an edge device integrated with the vision-based displacement tracking algorithm, thereby greatly reducing the amount of data transmitted to the cloud server. The feasibility and applicability of the developed sensing system are experimentally validated on a laboratory-scaled transmission tower structure. The proposed EdgeCVDMS is cost-effective, easily deployable, and of great potential to be applied for the condition assessment of a larger population of aging civil infrastructure.

Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography.

Circular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies.

Automatically Generated Texts for Fauna and Flora from Structured Data Based on a Controlled Vocabulary

Information systems like Xper3 and Fulgoromorpha Lists On the Web (FLOW) play a crucial role in managing and using biological data. These platforms store extensive collections of normalized data and structured taxonomic descriptions. By using controlled terminologies, they standardize the vocabulary, significantly enhancing the processes of identification, description, and comparison of various taxa. The massive assemblages of data hosted in these repositories could be reused to generate texts in natural languages automatically. The most immediate goal is to produce, from these information systems, more accessible and user-friendly displays in the form of taxon summary pages. This automated production of textual outputs is a great addition that can be continuously updated as the databases evolve. Can structured data be reused to provide better species pages and to ensure updating if the data evolves? Will AI assist in this process, or will specific computing be needed? To address these questions, multiple possible approaches have been identified. The most basic level of this process involves converting a single line from a taxon-by-character matrix into text that resembles natural language, similar to the descriptions found in botanical or zoological guides. The primary goal is to move beyond the rigid format of matrix lines or lists of characteristics (such as characters and states) of a species, and instead generate a coherent, easy-to-read paragraph intended for human eyes. To achieve this objective, a first solution is given by Descrxp, a tool currently developed in conjunction with Xper. The user specifies the desired key outline for the output, and Descrxp fills this canvas using the database contents. While this approach is highly reliable and adaptable to various contexts, its drawback is being labor-intensive, and requiring significant human input and oversight (Fig. 1). The second solution uses AI. Data on well-known taxa, and on more obscure taxa have been tested with ChatGPT 3.5 or 4.0. ChatGPT succeeds well at generating natural language descriptions for well-known taxa (Fig. 2), providing text that appears accurate and coherent due to the extensive information available online for these groups. However, when it comes to more obscure taxa, less represented on the internet, the results can be more inconsistent and unpredictable (Fig. 3). This variability can be explained by the quality of the data the model has been trained on, which is significantly richer for well-known species. Consequently, for lesser-known groups, the generated descriptions may lack the precision and reliability seen with more familiar taxa. However, if more careful attention is given to the output of chatGPT (Fig. 4), it is possible to notice that the AI filled part of the description with inaccurate characters. Due to its inability to recognize specific scientific terminology, and to process hierarchical information, chatGPT ends up producing an over-generalized and redundant description. To go further, it would be possible to use a comparison between taxa to produce a text that highlights the remarkable features of a taxon. Moreover, it could be interesting to try and synthesize several lines of the initial matrix to extract generalized descriptions of groups of taxa, such as the description of a genus based on its included species. Using data from FLOW, Fishbase, and Xper3, the French ACDC (Counterfactual Learning for Controlled Data-to-text) project is creating a variety of text outputs. These range from "fill-in-the-blank" canvas to fully AI-generated content, utilizing data matrices derived from these pilot databases.

An Analysis of Sport Employee Identification of NCAA Division I Compliance Employees

The current study examined NCAA Division I compliance employees utilizing Sport Employee Identification (SEI; Oja et al., 2020). Little academic inquiry has focused on the various ways sport employees, like NCAA compliance employees, identify with their organization. The current study attempted to establish SEI as an applicable construct in sport scholarship and examine potential outcomes of SEI. Thus, the purpose of this study was to examine if SEI contributes to organizational behavior outcomes of job satisfaction, lower turnover intentions, and organizational citizenship behaviors with NCAA Division I compliance employees. The researchers utilized a demographic form and a 24-item survey including the SEI instrument, job satisfaction, turnover intentions, and the relationship between SEI and organizational citizenship behaviors. Data analysis consisted of confirmatory factor analysis, multiple one-way analysis of variance to analyze the connection between demographic variables and SEI, and regression analyses to examine the relationship between SEI and potential organizational behavior outcomes. A total of 217 responses highlighted job satisfaction and lower turnover intentions as outcomes of SEI, while organizational citizenship behaviors lacked enough support as an outcome of SEI. The findings have the potential to assist intercollegiate executives in better managing their compliance personnel and understanding compliance identification processes.

Isolation and identification of Candida species from catheter-associated urinary tract infection in Thamar city hospitals, Yemen.

The incidence of urinary tract infections associated with Candida is increasing in Yemeni public hospitals. The primary objective of this research was to isolate specific Candida species responsible for catheter-associated urinary tract infections (UTIs) and to examine the antifungal sensitivity of these Candida isolates. A total of 200 samples were collected from patients with catheters admitted to multiple hospitals of Thamar city (Yemen). There were 50 positive samples with Candida out of 200 samples. We conducted the primary identification process using the established protocols. Before isolation and identification, all yeast isolates underwent sub-culturing on Sabouraud dextrose agar. We employed the standard microbiological procedures such as Gram staining, colonial morphology analysis, lactophenol cotton blue assay, germ tube formation assessment, colony staining on chrom agar Candida medium, and incubation at 37°C for 48h. The assessment of cultures was conducted by evaluating their predominant species. All Candida isolates were tested for antifungal susceptibility using the disk diffusion technique, as indicated by the Clinical and Laboratory Standards Institute (CLSI) M44-A document recommendations. In this study, the prevalence of Candida species obtained from catheter-associated UTIs was shown to be the highest among individuals aged 51-60years (28.0%) and the lowest was among those aged 10-20years (8.0%). Males exhibit higher rates than females, with males accounting for 56.0 and females for 44.0%, respectively. The predominant strain from catheters linked to urinary tract infections was Candida albicans. The Candida isolates had the highest susceptibility to itraconazole, with fluconazole and nystatin at sensitivity rates of 64, 60, and 50%, respectively. Amphotericin B and ketoconazole exhibited the most elevated concentrations. The p value of duration of catheterization < 5 was significant (p = 0.01), as well as significant in anti-fungal susceptibility testing of itraconazole, ketoconazole, and nystatin which are (p = 0.03), (p = 0.04), and (p = 0.03) respectively. Urinary tract infection due to candiduria was more common in patients with indwelling urinary catheter. The catheter-associated urinary tract infection caused by Candida species occurred mainly in old male patients. Candia albicans was the predominant Candida species isolated from urinary tract infection associated with urinary catheter at Thamar city hospitals. This study determined that diabetes and antibiotic use are significant predisposing factors associated with isolation of Candida in specimens submitted by patients at Thamar city hospitals.