Background: This work aimed to undertake a bibliometric analysis of the Rubella virus. Medical studies were conducted between 2000 and 2022 to discover trends, dynamics, and research outputs in the industry. Methods:A bibliometric study was performed using R software to determine research characteristics indexed worldwide and published in Rubella research in medical studies. The Rubella virus was chosen as the subject in the PUBMED database, and 374 papers from the previous two decades were reviewed. Results: There was an increase in the number of publications after 2003. The United States was the most essential countryamong all which had the most contributions on Rubella Virus. Conclusion:Rubella research has increased in the medical profession over the previous decade, with the United States leading to publications in this field.

The response of building elements during earthquakes has been an area of concern for different researchers around the world. The NSEs are the first elements to attain failure, the buildings after damages in safety priority are classified as in “Life Safety” mode, but in reality, they are in “Collapse Safety” mode and the occupants inside the buildings are in a state of “Non-Life Safety” as a result of falling of the hazardous NSE’s like cladding's, partitions, hanging objects. In addition to the Life Cycle Cost Assessment (LCCA), the Social Life Cycle Assessment (SLCA) is overlooked by the majority of the researchers, the community must be prepared to respond to an event more efficiently without any major casualties or loss of life. It is expected that by the end of 2050, almost double the construction will take place, the embodied energy during the pre-construction, post-construction and demolition generates a lot of CO2s into the environment, hence sustainability should be given due importance, helping in the achievement of Sustainable Developmental Goals (SDGs).

This article investigates the features of heat and mass transfer for the steady two-dimensional Williamson nanofluid flow across an exponentially stretched surface depending on suction/injection. The boundary conditions incorporate the impacts of the Brownian motion and thermophoresis boundary. The analysis of heat transfer is carried out for the two cases of prescribed exponential order surface temperature (PEST) and prescribed exponential order heat flux (PEHF). The ongoing flow problem is mathematically modeled under the basic laws of motion and heat transfer. The similarity variables are allowed to transmute the governing equations of the problem into a similarity ordinary differential equation (ODEs). The solution of this reduced non-linear system of ODEs is supported by the Homotopy analysis method (HAM). The combination of HAM arrangements is acquired by plotting the h-curve. In order to evaluate the influence of several emergent parameters, the outcomes are presented numerically and are plotted diagrammatically as a consequence of velocity, temperature and concentration proles.

In everyday life, the birth of a number of blind people takes place and millions of people are already available. Visually impaired people struggle to walk safely and in- dependently because they have trouble detecting obstacles. In this work, we suggest a novel approach to detecting obstacles utilizing an Arduino UNO board and an ul- trasonic sensor (HC-SR04). The main goal of this research is to increase the mobility and security of blind people in a variety of settings. The suggested system makes use of the HC-SR04 ultrasonic sensor’s capability to precisely measure distance and identify nearby obstructions. As the main processor component, the Arduino UNO board processes algorithms for real-time obstacle detection while also collecting data from the sensor. The device is intended to give the visually impaired user immedi- ate aural input while informing them of the location and proximity of obstacles. To Extensive testing is done in controlled and real-world settings to assess the system’s performance and dependability. In the studies, the effectiveness of obstacle detec- tion, the system’s response time, and its practicality and usefulness for blind people are all measured. The findings show that the suggested obstacle detection method provides very accurate obstacle identification at a safe distance. Users who are blind or visually handicapped may successfully understand their surroundings thanks to the aural feedback system, which helps them to navigate by making judgments that are well-informed. Additionally, the system responds quickly, guaranteeing that the user receives real-time obstacle updates to increase safety and confidence while mov- ing. The study’s conclusions demonstrate the possibility of using the Arduino UNO board with the HC-SR04 ultrasonic sensor as a practical and affordable approach for detecting obstacles.

More than fifty million people over the age of fifty have osteoporosis issues due to low bone mass, disease contribution, and not exercising continuously. Certain most important risk factors and causes of osteoporosis contain age, gender, hormones, visits to inexperienced doctors, and certain medical conditions. In this manuscript, we diagnosed certain frank operational laws under the availability of complex intuitionistic fuzzy (CIF) information. Furthermore, using the evaluated operational laws, we pioneered the theory of CIF frank weighted averaging (CIFFWA), CIF frank ordered weighted averaging (CIFFOWA), CIF frank hybrid averaging (CIFFHA), CIF frank weighted geometric (CIFFWG), CIF frank ordered weighted geometric (CIFFOWG), CIF frank hybrid geometric (CIFFHG) operators, and described their beneficial and valuable results with certain useful properties. Moreover, we aim to evaluate the most dangerous type of osteoporosis based on their symptoms, causes, and risk factors using diagnosed approaches. Finally, to enhance the worth of the evaluated operators, we compare the diagnosed result with certain prevailing results and illustrated their geometrical representation with the help of MATLAB.

To enhance system reliability and mitigate vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute position and reducing data gaps. To address shortcomings of traditional Kalman Filter (KF) such as sensor error, imperfect nonlinear system model, and KF estimation error, a GRU-aided ESKF architecture is proposed to enhance positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to priories and identify the potential faults in the urban environment facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association error and navigation environment error during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting higher efficiency in complex environments.

Unmanned aerial vehicles (UAVs)-based communication system is a promising solution to meet coverage and capacity requirements of future wireless networks. However, UAV-enabled communications is constrained with its coverage, energy consumption, and flying regulations, and the number of works focusing on the sustainability aspect of UAV-assisted networking has been limited in the literature so far. In this paper, we propose a solution to this limitation; particularly, we design a $Q$-learning-based UAV positioning scheme for sustainable wireless connectivity considering key constraints, that are, altitude regulations, non-flight zones, and transmit power. The objective is to find the optimal position of the UAV base station (BS) and minimize the energy consumption while maximizing the number of users covered. Moreover, a weighting mechanism is developed, where the energy consumption and number of users covered can be prioritized according to network/battery conditions. The proposed Q-learning-based solution is compared to the baseline k-means clustering method, where the UAV BS is positioned at the centroid location that minimizes the cumulative distance between the UAV BS and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

Hemodialysis (HD) patients experience significant psychological stress related HD. Coping strategies are utilized to help these patients cope with this stress. Prevalence of different types of coping strategies in relation with differential levels of HD-related stress scores and by demographics were assessed. HD patients aged 18 years and above who were seeking regular treatment in a specific HD-unit of a hospital in the upper Midwest, were surveyed between March 2020 and July 2020. Descriptive statistics, correlational analyses, and regression analyses were performed. Females could be expected to have emotional coping scores 8 units greater than males, as B = 8.017 for sex in the model, p =.000. Females could be expected to have coping scores 6.4 points higher than males when adjusted for stress levels, as: B = 6.351, p =.001 for sex in the model. For each 1point increase in Stress Score, the Coping Score could be expected to: increase 6.8 points when adjusted for Sex, where B = 6.751 and p =.000. Increased prevalence of emotional pre-occupation was only observed among female patients which increased with the increase in stress. Psychological interventions in female HD patients should be strongly considered in addressing emotional pre-occupation coping.

In this work, the ternary titanium, copper and silver (Ti-Cu-Ag) system is investigated as a potential candidate for the production of mechanically robust biomedical thin films. The coatings are produced by physical vapor deposition-magnetron sputtering (MS-PVD). The composite thin films are deposited on a silicon (100) substrate. The ratio between Ti and Cu was approximately kept one, with the variation of the Ag content between 10 and 35 at.%, while the power on the targets is changed during each deposition to get the desired Ag content. Thin film characterization is performed by x-ray diffraction (XRD), nanoindentation (modulus and hardness) and Atomic force microscopy to determine the surface topography. The residual stresses are measured by focused ion beam and digital image correlation method (FIB-DIC). The produced Ti-Cu-Ag thin films appear to be smooth, uniformly thick and exhibit amorphous structure for the Ag contents lower than 25 at.%, with a transition to partially crystalline structure for higher Ag concentrations. The Ti-Cu control film shows higher values of 124.5 GPa and 7.85 GPa for modulus and hardness respectively. There is a clear trend of continuous decrease in the modulus and hardness with the increase of Ag content, as lowest value of 105.5 GPa and 6 GPa for 35 at.% Ag containing thin films. In particular, a transition from the compressive (-36.5 MPa) to tensile residual stresses between 229 MPa and 288 MPa are observed with an increasing Ag content. The obtained results suggest that the Ag concentration should not exceed 25 at.%, in order to avoid an excessive reduction of the modulus and hardness with maintaining (at the same time) the potential for an increase of the antibacterial properties. In summary, Ti-Cu-Ag thin films shows characteristic mechanical properties that can be used to improve the properties of biomedical implants such as Ti-alloys and stainless steel.

As urban populations and transport demand expand, Pakistani metropolitan centers face traffic congestion and environmental challenges. This research aims to assess carpooling (CP) to alleviate traffic congestion, fuel demand, and pollution. This study was conducted in Islamabad, a fast-growing city with a high percentage of personal cars in Pakistan, to assess the travelers’ perceptions, and public acceptance of CP and determine the effects on traffic congestion, fuel consumption, and CO2 emissions. The investigation of travelers' perceptions towards CP was conducted through a stated preference questionnaire survey. A total of 700 responses from one million population of Islamabad were collected as per Sloven's Formula. SPSS and Smart PLS software were used for data analysis. Various factors influencing peoples’ travel perceptions, mode choice, and tendency to carpool, were categorized to evaluate the relationship between travelers’ perception and the tendency to carpool in the presence of mode choice as a mediating variable. Confirmatory factor analysis was conducted in SMART PLS to refine the number of scale items for each variable. One-way ANOVA, step-wise and mediated multiple regression analysis was conducted in SPSS to check the direct and mediating relationship between different variables. Moreover, the probability of reducing traffic congestion and assessing fuel reduction resulting from CP was also analyzed. The findings imply that travelers’ mode choice acts as a mediator between the relationship between travelers’ perception and tendency to carpool. Furthermore, results predicted that CP might reduce 0.25 million cars on the road, about 33.6% of the total private vehicles. Thereby, saving up to 546,810 barrels per year, about 3.42% of Pakistan's total petrol consumption. The findings of this study could be helpful for transport planners, environmentalists, and policymakers to implement carpool systems. Thus, reducing traffic congestion and harmful CO2 emissions and maintaining a sustainable environment.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

This study endeavors the preparation and evaluation of Phenol Furfural Resin (PFR) from bagasse and its nanocomposites for Electrically Conductive Adhesives (ECAs) application. PFR was prepared with the furfural extracted from bagasse using modified acid digestion method. Three different formulations of PFR nanocomposites with conductive nanoparticles i.e., PFR-silver, PFR-graphite, and PFR-silver + graphite were prepared using 20-60 w/w% of fillers via impregnation method. Resultant products were characterized by FT-IR, SEM, EDS and XRD spectroscopy. Electrical conductivity was measured using four probe technique while band gap was calculated via Tauc plot. Virgin PFR has shown poor conductivity up to 2.6 × 10-4 Scm-1 while the highest conductivity achieved was 8.2 × 10-1 Scm-1 for nanocomposite constituted silver, graphite, and PFR with 40:30:20 ratios respectively. This synergism was exhibited because graphite and Ag NPs supply excellent junctions for building networks. Both tend to coalesce due to van der Waals forces and high surface energies, therefore conductive pathways numbers can be increased, and contact area can be effectively enlarged. This ternary composite has exhibited the lowest bandgap energy value i.e., 3.1 eV. Thermogravimetric temperatures T0 and Tdeg values were increased up to 120 °C and 484 °C respectively, showing significant increase in thermal stability. Therefore, the resultant nanocomposite material has good potential to be employed as ECAs in electronic industry.

N-Acyl-homoserine lactones, the Quorum sensing signaling molecules predominantly found in gram-negative bacteria, which regulate several bacterial genes including virulence and antibiotic resistant genes. The study was aimed to identify and characterize QS and QQ bacteria from different samples. 5 samples with different ecological background were collected from soil and 10 samples from hospital setup. 31 different bacteria were isolated with either QS or QQ activities all together. CV026 and A136 biosensor strains were used for the detection of QS and QQ positive strains. QS activity was observed by cross streaking test of bacteria against CV026, it was affirmed that 13 isolates from the soil and 5 from hospital equipment’s showed positive QS activity. QQ activity of each isolate was tested by well diffusion assay, C6-HSL and C12-HSL were our candidate AHL molecules. The AHL molecule degradation was detected in 4 isolates of soil and none from the samples obtained from hospital setup. The total of 6 strongly positive QS and QQ isolates were identified and selected for 16S rRNA gene sequencing. The phylogenetic analysis revealed that these isolates were closely related to Pseudomonas, Bacillus and Exiguobacterium genera. In contrast, 1 Gram positive bacterial isolate was also purified with QS potential.

We present an Arduino-based automatic robotic system which is used for cutting grass or lawns, mostly healthy grass which needs to cut neatly like in a public park or a private garden. The purpose of this proposed project is to design a programmable automatic pattern design grass cutting robot with solar power which no longer requires time-consuming manual grass-cutting, and that can be operated wirelessly using an Android Smartphone via Bluetooth from a safe distance which is capable of cutting the grass in indeed required shapes and patterns; the cutting blade can also be adjusted to maintain the different length of the grass. The main focus was to design a prototype that can work with a little or no Physical user interaction. The proposed work is accomplished by using an Arduino microcontroller, DC geared Motors, IR obstacle detection sensor, motor shield, relay module, DC battery, solar panel, and Bluetooth module. The grass-cutting robot system can be moved to the location in the lawn remotely where the user wants to cut the grass directly or in desired patterns. The user can press the desired pattern button from the mobile application, and the system will start cutting grass in the similar design such as a circle, spiral, rectangle, and continue pattern. Also, with the assistance of sensors positioned at the front of the vehicle, an automatic barrier detection system is introduced to enhance safety measurements to prevent any risks. IR obstacle detector sensors are used to detect obstacles, if any obstacle is found in front of the robot while traveling; it avoids the barrier by taking a right/right turn or stop automatically appropriately, thereby preventing the collision. Also, the main aim of this project is the formation of a grass cutter that relieves the user from mowing their own grasses and reduces environmental and noise pollution. The proposed system is designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (Simple and Pattern based grass cutting with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We note that the systems proposed can be implemented on a large scale under real conditions in the future, which will be useful in robotics applications and cutting grass in playing grounds such as cricket, football, and hockey, etc.

The present work demonstrates the new nanofiber mats prepared through co-electrospinning of two different polymers i.e. corn protein namely Zein and Nylon-6. The composite nanofiber membrane was used as an effective adsorbent material for the removal of toxic reactive dye i.e. Reactive Blue 19 (RB 19) from water solution. These co-electrospun nanofibers had good mechanical strength compared to zein nanofibers alone. Experimental results suggested that zein/nylon nanofibers have greater potential for total removal of RB19 at room temperature within 10 min of contact time from aqueous solution. The maximum capacity was found to be 70 mg/g of nanofibers. The mechanism of RB19 removal on proposed nanofibers is mainly through hydrogen bond and electrostatic means.

Chronic eosinophilic pneumonia (CEP) is an idiopathic disorder characterized by an abnormal and marked accumulation of eosinophils in the interstitial and alveolar spaces of the lung. Idiopathic chronic eosinophilic pneumonia is reported to comprise anywhere from 0-2.5% of cases within the registries of interstitial lung disease. Diagnosis is based on the clinical constellation of symptoms, characteristic radiographic findings and peripheral blood or BAL eosinophilia, in the absence of infection or drug-induced eosinophilia. There is no consensus on the dose and duration of treatment, but most authors recommend initial doses of prednisone at 0.5–1 mg/kg/day with gradual tapering of the dose for total treatment duration of 6–12 months.

In this study, we evaluated the potential of fungal endophytes to control yellow rust in wheat (Triticum aestivum L.) as endophytes are beneficial microbes and alternate to pesticides for confronting pathogens. The in-vitro efficacy of the fungal endophytes isolated from different desert plants was evaluated and the best four namely Colletotrichum lindemuthianum, Piriformospora indica, Acremonium lolii and Trichoderma viride were selected. Seeds of two susceptible wheat genotypes namely Fareed-06 and Shafaq-06 obtained from screening experiment were inoculated by dipping in four endophytic spore suspensions and were sown using randomized complete block design under factorial arrangement. Data concerning about area under disease progress curve, final disease severity percentage, coefficient of infection,1000- grains weight and grain yield were recorded. Results showed that endophyte P. indica showed significant decrease in final disease severity (FDS) and area under disease progress curve (AUDPC), resultantly 12.2% grain yield gain in rust susceptible wheat genotypes of Fareed-06 and Shafaq-06 followed by the endophytes T. viride, C. lindemuthianum and A. lolii with the grain yield gain of 10.6%, 06.2% and 04.2% respectively. In crux, fungal endophytes are valuable microbes which can be employed to induce tolerance against P. striiformis in yellow rust vulnerable areas for better and sustainable wheat production.

It is not new in medical history to propose a global concern to be classified as a disease. Defining a concern into disease allows to assign ethical responsibilities to develop powerful and effective interventions. It also allow to appropriate distribute the resources uniformly economically and morally. In 2003, World Health Organization report stated that 30-50 % of patients do not take their medications as prescribed associated with morbidity, mortality and health cost. It was considered a global concern, however, irrespective of decades of researches conducted on medication adherence, we are still unable to state that medication adherence issues are being resolved. In this review, we have described few apprehensions in current understandings of medication adherence that have limited its research. We have also proposed medication adherence as disorder and provided its’ definition and classification

3D Printing (3DP) technology has revolutionized the field of the use of bioceramics for maxillofacial and periodontal applications, offering unprecedented control over the shape, size, and structure of bioceramic implants. In addition, bioceramics have become attractive materials for these applications due to their biocompatibility, biostability, and favorable mechanical properties. However, despite their advantages, bioceramic implants are still associated with inferior biological performance issues after implantation, such as slow osseointegration, inadequate tissue response, and increased risk of implant failure. To address these challenges, researchers have been developing strategies to improve the biological performance of 3D printed bioceramic implants. The purpose of this review is to provide an overview of 3DP techniques and strategies for bioceramic materials designed for bone regeneration. The review also addresses the use and incorporation of active biomolecules in 3D printed bioceramic constructs to stimulate bone regeneration. By controlling the surface roughness, and chemical composition of the implant, the construct can be tailored to promote osseointegration and reduce the risk of adverse tissue reactions. Additionally, growth factors, such as bone morphogenic proteins (rhBMP-2) and pharmacologic agent (dipyridamole), can be incorporated to promote the growth of new bone tissue. Incorporating porosity into bioceramic constructs can improve bone tissue formation and the overall biological response of the implant. As such, by employing surface modification, combining with other materials, and incorporation of 3DP workflow can lead to better patient healing outcomes.

The development of antibody therapies against SARS-CoV-2 remains a challenging task during the ongoing COVID-19 pandemic. All approved therapeutic antibodies are directed against the receptor binding domain (RBD) of Spike and lost neutralization efficacy against continuously emerging SARS-CoV-2 variants, which especially mutate in the RBD region. Previously, phage display has been used to identify epitopes of antibody responses against several diseases. Such epitopes have been applied to design vaccines or neutralizing antibodies. Here, we constructed an ORFeome phage display library for the SARS-CoV-2 genome. Open reading frames (ORFs) representing the SARS-CoV-2 genome were displayed on the surface of phage particles in order to identify enriched immunogenic epitopes from COVID-19 patients. Library quality was assessed by both, NGS and epitope mapping of a monoclonal antibody with known binding site. The most prominent epitope captured represented parts of Spike´s fusion peptide (FP). It is associated with the cell entry mechanism of SARS-CoV-2 into the host cell and the serine protease TMPRSS2 cleaves Spike within this sequence. Blocking of this mechanism could be a potential target for non-RBD binding therapeutic anti-SARS-CoV-2 antibodies. As mutations within the FP amino acid sequence were rather rare among SARS-CoV-2 variants so far, this may be an advantage in the fight against future virus variants.

One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation. Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.

The most important plastic resins used for wire coating are Polyvinyl Chloride (PVC), Nylon, Polysulfone and Low-high density polyethylene (LDPE / HDPE). In this article,the coating process is performed using elastic-viscous fluid as a coating material for wire coating in a pressure type coating die. The elastic-viscous fluid is electrically conducted in the presence of an applied magnetic field. The governing non-linear equations are modeled and then solved analytically by utilizing an Adomian decomposition method (ADM). The convergence of the series solution is established. The results are also verified by Optimal Homotopy Asymptotic Method (OHAM). The effect of different emerging parameters such as non-Newtonian parameters α and β, magnetic parameter M and the Brinkman number Br on solutions (velocity and temperature profiles) are discussed through several graphs. Additionally, the current result also compares with the published work already available in the literature.

Background: Dermatophytosis, commonly known as tinea, poses a significant public health concern worldwide, especially in environments with poor hygiene and overcrowding, such as prisons. Despite its prevalence and impact on quality of life, there is a lack of research on the knowledge and hygiene practices regarding dermatophytosis among prisoners, particularly in Nepal. Objective: The study aimed to assess prisoners' knowledge, hygiene practice and infection status regarding dermatophytosis in Central Prison, Nepal. Methods: A descriptive cross-sectional study with a sample size of 184 respondents was designed to collect data using a validated pre-tested questionnaire from September 2023 to January 2024. The collected data was then analysed using IBM SPSS version 21. Knowledge and hygiene practices were measured on an eight and 11-point scale and rated as poor (≤4) and sound (>4), bad (≤6), and good (>6), respectively. Summary data were presented by descriptive, while Chi-square and logistic regression were used for inferential statistics at P < 0.05. Results: The findings revealed moderate knowledge among prisoners regarding dermatophytosis, with significant gaps in understanding its spread and prevention. While most prisoners recognised the importance of treatment, there were misconceptions about the inevitability of contracting dermatophytosis and the role of personal hygiene. Conclusions: Despite good knowledge levels, adherence to recommended hygiene practices was suboptimal, highlighting the need for targeted interventions. The study underscores the importance of addressing knowledge gaps, changing attitudes, and promoting hygienic practices to mitigate the burden of dermatophytosis among prisoners.