Manual Testing Research Articles

Crop Suggestive System through Soil Property Characterization using IoT

The emergence of Internet of Things (IoT) technology has brought about a significant transformation in multiple sectors, including agriculture, by facilitating data- driven decision-making and real-time monitoring. In this work, we present a novel Internet of Things (IoT) agricultural suggestion system that uses soil property characterization to enable accurate crop management and selection. Our technology seeks to help farmers optimize agricultural yields while minimizing resource usage and environmental impact by combining IoT sensors with modern data analytics. Our system's primary feature is the installation of Internet of Things (IoT) sensors throughout fields to continuously collect data on important soil attributes. These characteristics include salinity, pH values, temperature, moisture content, nutrient concentrations, and organic matter content. This data is wirelessly transmitted by the sensors to a cloud platform or centralized database, where it is thoroughly analyzed. One of the many difficulties facing modern agriculture is choosing crops optimally to maximize yields while minimizing environmental effect. Conventional crop selection techniques sometimes rely on manual soil testing or broad suggestions, which produces less than ideal results and inefficiencies. A novel method using the Internet of Things (IoT) is suggested to address this: a Crop Suggestive System using Soil Property Characterization (CSS-SPC). Using Internet of Things sensors, this system continuously monitors and analyzes soil parameters in real-time, giving farmers customized crop recommendations depending on the unique conditions of their property. Crop rotations, soil amendments, and irrigation schedule optimization are made possible for farmers through smooth planning and decision-making that ismade possible by integration with current farm management systems. Farmers' ongoing input on the effectiveness ofsuggested crops guarantees the system's accuracy and continued applicability

DYNAMIC STABILITY DURING REPETITIVE AXIAL GLENOHUMERAL ROTATION USING CINE MRI IN HEALTHY ADULTS

Manual testing for dynamic shoulder instability includes noting and shifting of the humeral head, but the corresponding shift of the axis of rotation has been given little attention. The purpose of this study is to clarify translation of the humeral head during active glenohumeral rotation. Magnetic resonance images were taken at 2[Formula: see text]images/s on 22 shoulders of 11 healthy men. At [Formula: see text] shoulder elevation, the supine subject internally and externally rotated the humerus repeatedly over 20[Formula: see text]s. The location over the glenoid of the center of the humeral head (CENHED) projected was studied. During external rotation, projection of CENHED on the glenoid began posterior to the center of the glenoid and migrated to anterior. During internal rotation, this projection was initially positioned above the glenoid center and midway through the motion, shifted inferiorly to nearly the glenoid center. The projection of CENHED did not significantly differ in location over the glenoid at any position along the range of motion during shoulder rotation. Initial values are now available for judging excessive movements of the humeral head on the glenoid fossa during rotation at [Formula: see text] of abduction in the scapular plane. The method used in this study is amenable to clinical use.

Detection method for structural defects of U-shaped hanging ring for power fittings based on magnetic leakage principle

The U-shaped hanging ring of electric power fittings is usually installed outdoors and operates in harsh environments. Under the combined effect of the weight of the conductor and wind load, the bending part at the bottom of the U-shaped hanging ring often experiences wear and cracks due to friction contact, which can easily lead to accidents such as the detachment of transmission lines. Traditional methods for detecting structural defects in ferromagnetic materials, such as ultrasonic testing, eddy current testing, and manual testing, are affected by environmental factors, have a shallow measurement depth, and are difficult to quantify. In order to solve the above problems, this paper proposes a measurement method for detecting structural defects in the U-shaped hanging ring of electric power fittings based on the principle of magnetic leakage. The magnetic leakage signal characteristics of U-shaped hanging ring defects under different excitation structural parameters and different defect types are analyzed. The method of arranging magnetic leakage signal measurement points is studied. The detection of defect parameters is achieved based on the characteristic quantity of the magnetic leakage signal under different defect conditions. Finally, the method is analyzed and verified through examples. The results show that this method can achieve quantitative detection of defect parameters in the U-shaped hanging ring under uniform structural defect and crack structural defect, and has the advantages of high measurement accuracy and small error. The research results of this paper can provide a scientific theoretical basis for the inspection and replacement of transmission line fittings in engineering, which is of great engineering significance for ensuring the safe and stable operation of the power grid.

Mobile-UI-Repair: a deep learning based UI smell detection technique for mobile user interface

The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.

Crop Monitoring System Using IOT

Currently, the low agricultural output is causing farmers to suffer. Low crop growth is primarily caused by knowledge of soil fertility and crop selection, even though proper crop selection is the primary booster key for enhancing agricultural yield by doing soil analysis and taking metrological elements into consideration. Farmers with limited experience in conventional farming are finding it difficult to make wise crop selection choices in the current environment. The low soil fertility is caused by the same crop being chosen for each seasonal cycle. The goal of this research is to use IoT devices to create an accurate and efficient system. Compared to the outdated manual laboratory testing procedures, which are subject to human errors, such a system is predictable. A crop's proper selection is mostly important in the agricultural field. As a contribution, we offer Smart Crop Selection (SCS), an IOT-based approach that is based on soil and metrological factor data. These variables include soil moisture content, temperature, and precipitation. Because these issues are not given as much care as they should, existing IoT-based systems are less efficient than our proposed model. Real-time sensory data is transmitted to Firebase cloud for analysis in the suggested model

Machine Learning Prediction and Evaluation for Structural Damage Comfort of Suspension Footbridge

To investigate the impact of structural damages on the comfort level of suspension footbridges under human-induced vibrations, this study addresses the limitations of traditional manual testing, which often entails significant manpower and material resources. The aim is to achieve rapid estimation and health monitoring of comfort levels during bridge operation. To accomplish this, the study combines finite-element simulation results to establish a data-driven library and introduces three distinct machine learning algorithms. Through comparative analysis, a machine learning-based method is proposed for quick evaluation of bridge comfort levels. Focusing on the Yangjiadong Suspension Bridge, the study evaluates and researches the comfort level of the structure under the influence of human-induced vibrations. The findings revealed a relatively low base frequency and high flexibility. Additionally, when considering the mass of individuals, peak acceleration decreased. The predictive performance of the Artificial Neural Network (ANN) model was found to be superior when accounting for multi-parameter damages, yielding root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2) values of 0.03, 0.02, and 0.98, respectively. Moreover, the error ratio of the generalization performance analysis was below 5%. Furthermore, the study identified a damage coefficient of 0.13 for the bridge’s main cable, hanger, and steel longitudinal beam. Under a crowd density of 0.5 people per square meter, the predicted peak acceleration was 1.098 m/s2, with a model error of less than 10% compared to the observed value of 1.004 m/s2. These results underscore the model’s effectiveness in swiftly evaluating bridge comfort levels, thereby offering valuable insights for the health monitoring of bridge comfort levels.

IoT-Enabled Water Quality Monitoring and Alum Dose Determining System

The water treatment process involves a series of physical, chemical, and biological techniques and methods designed to remove contaminants, impurities, and undesirable substances from raw or untreated water to make it safe, clean, and usable. Adverse weather conditions, such as rainy seasons and floods, present significant challenges, and difficulties to the water treatment process. Alum is mainly used for reducing the turbidity of the water in the treatment process. In Sri Lanka, all water treatment plants use the Jar Test to determine the Alum dosage manually. They conduct Jar Tests to measure raw water quality in their laboratories. The Alum addition valve is set to a pre-determined quantity and the value is decided based on the experience of the technicians. Since there is no specific formula, the presence of an experienced technician is required to determine alum dosage using the Jar test. Thus, this study intends to propose an automated system for determining the optimum Alum quantity to be added to a given raw water sample. This is achieved by designing an Internet of Things (IoT)-enabled sensing device for measuring the water quality, which feeds data to a Machine Learning Model trained to predict the Alum quantity to be added. The task of the Machine learning model is to identify the pattern of raw water parameters and Alum dosage. By doing this, a could be provided a guideline for determining alum dosage instead of a manual Jar test and removed the necessity of experienced technician support always. A user-friendly web interface will allow the operators to customize the parameters according to the requirement and display a detailed analysis report. The predetermined quantity of Alum is transmitted to the output Alum adding value, to enhance its performance as an automated control rather than doing the addition process manually. This is a conceptual study on how to determine the Alum dosage using Raw water parameters and past experiment results.

An alternative method for inferring Pandy's test using cerebrospinal fluid total protein

BackgroundPandy's test is used to assess the globulin level in cerebrospinal fluid (CSF). As a semi-quantitative manual method, the practicality and clinical value of Pandy's test has been challenged. ObjectiveWe tend to summarize the relationship between CSF total protein (CSF-TP) quantification and Pandy's results, providing a formula to estimate Pandy's results merely by CSF-TP value. MethodsThis retrospective study involved 1090 cases hospitalized in Huashan Hospital during 1/1/2023 to 20/4/2023. All samples were divided into six group based on their Pandy's results. Their corresponding CSF-TP quantitative results were subsequently analyzed and summarized. Another 364 patients were also gathered for verification. ResultsThe turbidity of samples won't affect examiners'ocular inspection and interpretation of Pandy's tests in positive groups. The results of Pandy's tests can be deduced based on CSF-TP quantitative results according to following rules: CSF-TP quantitative results 0–614 mg/L for Pandy negative (−), 615–1322 mg/L for extremely weak positive (±), 1323–2953 mg/L for weak positive (1+), 2954–6561 mg/L for medium positive results (2+), 6562–13007 mg/L for strong positive results (3+) and CSF-TP results >13007 for strongest positive (4+). The quantitative range above was experimentally verified as effective and correct by calculating the agreement rate through another 364 samples and the R ratio of each Pandy group was greater than 90 %. ConclusionThere is an excellent correlation between CSF-TP and Pandy's test. Therefore, CSF-TP quantification test through PROT Slides can be used to infer the results of Pandy's test to accelerate the abolish of this traditional manual test.

An AI-Based Approach for Automating Penetration Testing

Abstract: Cyber penetration testing (pen-testing) is important in revealing possible weaknesses and breaches in network systems that can ultimately help in curbing cybercrimes. Nevertheless, even with the current drive to mechanize pen- testing, there are still a number of challenges which include incomplete frameworks and low precision in automation methods. This paper aims at addressing them by suggesting hybrid AI-based automation framework specifically for Pen- Testing through integration of smart algorithms and automated tools. As indicated by recent studies, it goes further into proposing a holistic approach towards maximizing the efficiency as well as effectiveness of Pen-Testing processes. Furthermore, it also identifies the need for machine learning techniques such as reinforcement learning and deep reinforcement learning for automating Pen-Testing activities. MITRE ATT&CK Framework being utilized within the proposed model imitates real-life cyber-attacks and exploits hence facilitating automated testing across diverse target networks. Based on comparison with manual penetration testing reports, this study reviews how effective the new automated method is when compared to old ways used in manual penetration tests while providing some direction for future developments along with suggestions in the field of self- governing intrusion detection systems (IDS)

CIDER : ASSISTED AUTOMATION TOOL FOR C++ LIBRARIES TESTING

Software testing is one of the most important parts of a product development lifecycle (PDL). Companies face significant risks associated with program failures, including financial and reputational ones, having an interest in investing time and money in new testing and quality assurance practices. The programs written in C++ are known as high performant, but complex due to the language specifics. At the same time automated tools that are available mostly for GUI programs can't help with native modules regression testing. Thus, even partial automation of such processes can have a positive effect on PDL in terms of regression testing effectiveness. The article considers a testing technique of recording and playback of program execution on the component level. The proposed tool generates testing scenarios during the execution of a program, giving the ability to perform manual testing and extend the automated component tests execution database. The experiments showed that such scenarios' code coverage results are at a high level and tend to improve when applying optimization algorithms.

Sarcopenia em pessoas idosas: concordância entre métodos preditores

Sarcopenia is a relevant clinical condition worldwide. Its prevalence ranges from 5% to 13% in the elderly aged 65 years and over. Over 80 years, this value can reach 50%, demonstrating the influence of age on the development of the disease. Its diagnosis is based on reduced muscle mass, associated with decreased strength and/or low muscle function. The gold standard for diagnosing the disease is Dual-energy X-ray absorptiometry, but it is inaccessible due to its high cost and diagnostic complexity. Thus, alternative methods for screening for sarcopenia, such as: sit and stand test (TSL), calf circumference (CP) and manual strength test (FPM), SARC-F questionnaire are applied. Defining methods suitable for the outpatient setting is essential for both screening and diagnosis of the disease. The present study aimed to analyze whether there is agreement between predictive methods used to estimate the existence of sarcopenia in the elderly. This is a cross-sectional study carried out with 27 healthy elderly people (23 women and 4 men). The predictive methods were measuring CP, FPM, TSL and Ishii Probability Score (ISHII). The agreement between the classifications was used the Kappa statistic. All predictive methods identified sarcopenic and non-sarcopenic individuals. There was no agreement in the combinations: CTS x FPM; TSL x FPM and; TSL x ISHII. As for the association between the CTS and TSL methods, there was a moderate agreement between the predicted values (k=0.43; p=0.006). Conclusion: Different predictive methods promote different sarcopenia diagnoses. A moderate agreement was observed for the combination between CTS and TSL predictors. It should be noted that the association between different tests is an option to be evaluated.

A new alternative to the Ober test for evaluating the difference between right and left iliotibial band stiffness: A reliability study

IntroductionA dysfunctional stiffness of the iliotibial band can be related to pathologies of the pelvis/lumbar spine and the knee. The classic and modified Ober tests are the gold standard for assessing iliotibial band stiffness. However, to the authors’ knowledge, this test lacks adequate validation, and its specificity is questionable. A more reliable test is needed to better correlate iliotibial band stiffness to pathologies and to support treatment choices. MethodTwo examiners assessed the difference in stiffness (right vs. left limb) of the iliotibial band by direct fingertip palpation in 40 subjects (without clinically evident painful symptoms), before and after a specific 20-h training session. The difference in stiffness was evaluated with both a 3-level and a 7-level graduation. The intra- and inter-examiner (intra-day) reproducibility was calculated, and a validation of the manual assessment was performed with a myotonometric measurement of frequency and stiffness. ResultsThe test achieved post-training agreement consistently greater than 0.88 (weighted Cohen's K test) in intra-examiner assessment and 0.72 in inter-examiner assessment. Before training the agreement was less than 0.48 and 0.12, respectively. Manual versus instrumental agreement was fair to moderate (frequency 0.347; stiffness 0.470). ConclusionThe newly proposed manual test to assess ITB stiffness by direct palpation showed almost perfect intra-examiner reproducibility and good inter-examiner (intra-day) reproducibility. The specific training was fundamental. Comparison of manual vs instrumental stiffness assessment of the iliotibial band, as proposed in this study, is debatable.

External quality assessments for SARS-CoV-2 genome detection in Austria : Acomparison of the first postpandemic round with results from the pandemic era.

External quality assessment (EQA) schemes provide objective feedback to participating laboratories about the performance of their analytical systems and information about overall regional analytical performance. The EQAs are particularly important during pandemics as they also assess the reliability of individual test results and show opportunities to improve test strategies. With the end of the COVID-19 pandemic, the testing frequency significantly decreased in Austria. Here, we analyzed whether this decrease had an effect on participation and/or performance in SARS-CoV‑2 virus detection EQAs, as compared to the pandemic era. Identical samples were sent to all participating laboratories, and the EQA provider evaluated the agreement of the reported results with defined targets. The EQA was operated under two schemes with identical samples and therefore we analyzed it as asingle EQA round. The performance of testing was reported as true positive ratios, comparing the post-pandemic data to previous rounds. Furthermore, subgroups of participants were analyzed stratified by laboratory type (medical or nonmedical) and the test system format (fully automated or requiring manual steps). While the frequency of false negative results per sample did not change during the 3 years of the pandemic (5.7%, 95% confidence interval [CI] 3.1-8.4%), an average per sample false negative ratio of 4.3% was observed in the first post-pandemic EQA (0%, 1.8%, and 11% for the 3 positive samples included in the test panel, n = 109 test results per sample). In this first post-pandemic EQA medical laboratories (average 0.4% false negative across 3samples, n = 90) and automated test systems (average 1.2% false negative, n = 261) had lower false negative ratios than nonmedical laboratories (22.8%, n = 19) and manual test systems (16.7%, n = 22). These lower average ratios were due to alow concentration sample, where nonmedical laboratories reported 36.8% and manual test systems 54.5% true positive results. Overall ratios of true positive results were below the mean of all results during the pandemic but were similar to the first round of the pandemic. Alower post-pandemic true positive ratio was associated with specific laboratory types and assay formats, particularly for samples with low concentration. The EQAs will continue to monitor the laboratory performance to ensure the same quality of epidemiological data after the pandemic, even if vigilance has decreased.

Enhancing Data Trustworthiness in Explorative Analysis: An Interactive Approach for Data Quality Monitoring

The volume of data to be analyzed has increased tremendously in recent years. In order to extract knowledge from this data, domain experts gain new insights with the help of graphical analysis tools for explorative analyses. Here, the reliability and trustworthiness of an exploratory analysis is determined by the quality of the underlying data. Existing approaches require manual testing to ensure data quality which is often neglected. This research aims to introduce a novel interactive approach for seamlessly integrating data quality considerations into the process of explorative data analysis conducted by domain experts. We derive requirements, conduct an extensive literature review, and develop an approach that efficiently combines stakeholders’ strengths, allowing unobtrusive data quality integration in interactive analysis. Our approach enhances trustworthiness due to unobtrusive monitoring of data quality within the context of explorative data analysis. Domain experts gain insights more reliably, bridging the gap between technical requirements and domain expertise. In conclusion, our research presents a promising solution for improving the reliability and trustworthiness of explorative data analysis, especially for domain experts who may lack technical knowledge. By seamlessly integrating data quality into the analytical process, we empower domain experts to extract valuable insights from the ever-increasing volume of data, thereby advancing the field of data-driven decision-making.

Prevalence of Colistin Pan Resistance among Multidrug-Resistant and Extensively Drug-Resistant Escherichia Coli O157:H7

Antimicrobial resistance is an important problem threatening human health. With the appearance of colistin-resistant bacteria, the World Health Organization and Centers for Disease Control and Prevention are declaring a global emergency and an alarming disaster that goes back to the time before antibiotics. The usage of colistin rises as a result of the global growth of Enterobacteriaceae, which produces carbapenemase and certainly causes the emergence of resistance to investigate the prevalence of colistin resistance among multidrug-resistant and extensively drug-resistant E. coli. The cross-sectional study included 140 swab samples and 200 urine samples that were collected from patients attending Al Imam Al Hussein Medical City in Karbala. The identification of bacterial isolates and the pattern of antibiotic resistance were determined using the fully automated VITEK 2 compact system in addition to the manual antibiotic resistance testing confirmation. The isolates were highly resistant to Ticarcillin (94.4%), Trimethoprim/ Sulfamethoxazole (91.1%) and Piperacillin (87.3%). In contrast, colistin had the lowest (4.2%) out of the total multi-drug resistant (MDR) strains that formed (46.4%) and the extensively-drug resistant (XDR) strains (25.4). Antimicrobial resistance is one of the biggest health problems facing people today. In Iraq, the appearance of colistin resistance (2.8%) among extensively drug-resistant Escherichia coli O157:H7 may lead to failure of treatment, especially among burn and UTI patients. It is urgently recommended to lower the occurrence of antibiotic resistance through cautious antibiotic usage and stringent infection control protocols, which are priorities.

Photosvaltable Characteristics Monitoring System Using the Internet of Things (IOT)


 
 
 
 Continuous monitoring of solar panel performance is essential to ensure effective and efficient generation of electrical energy. Developing a monitoring system such as an I-V characteristic curve tracker enables real-time monitoring and identification of potential problems or efficiency improvements. The system utilizes a circuit switching system for accurate and continuous measurement, providing insight into how the solar panel responds to changing environmental and load conditions. The data recorded by temperature, light intensity, current, and voltage sensors on the solar panel provides valuable information about its condition and the environmental factors affecting it. Utilizing IoT technology allows real-time monitoring of the data, accessible via a web page and presented graphically. The data is also stored in Excel format for further analysis, so users can access and monitor continuously as long as they are connected to the internet. From the experimental results, the Voc of the solar panel ranged from 18.7 V to 20.42 V, and Isc between 0.0727 A to 0.2145 A, which were tested under solar irradiation ranging from 100 to 500 W/m². This research demonstrates the importance of continuous monitoring and the effectiveness of the developed monitoring system in providing real-time data for analysis and optimization of solar panel performance. The system has been tested to meet the IEC No. 13B-23 standard for accuracy classes 0.2 and 0.5. A comparison between manual testing and sensor testing shows that despite the difference in accuracy levels, the sensor measurements have a relatively low average error (1.04% for voltage and 0.64% for current) compared to manual measurements, indicating that the sensor is reliable in providing accurate data. This demonstrates the reliability and effectiveness of using the sensor in the solar panel characteristics monitoring system.
 
 
 

COMPARATIVE ANALYSIS OF AUTOMATION FUNCTIONAL TESTING TOOLS PERFORMANCE FOR PLAYSTORE APPS WITH DIA METHOD

The complexity of smartphone applications presents challenges for developers, who must ensure flawless functionality despite limitations such as budget and time constraints. Manual testing is time-consuming, prompting a shift towards automated testing methods to ensure efficiency and reliability. In this context, researchers are evaluating the efficacy of three leading test automation frameworks—Robot Framework, Katalon Studio, and UI Path—against key performance parameters. Using the Distance to the Ideal Alternative (DIA) method on playstore apps. The main performance parameters used as a reference are automated testing progress and tools usability. Katalon Studio emerges as the top performer, securing the top rank with a remarkably close to the alternative ideal positive distance (Ri) value of 0.00001. UI Path occupies the second position with a Ri value of 0.00135, while Robot Framework trails behind with a Ri value of 0.00295. This research contributes to the understanding of the performance of different automation frameworks in the context of functional testing, providing valuable insights for developers and organizations seeking to optimize their testing processes. The findings underscore the significance of Katalon Studio's exceptional performance and highlight opportunities for improvement in UI Path and Robot Framework. Additionally, implementing a robust monitoring and evaluation framework is crucial for tracking the ongoing performance and optimizing the efficiency of these automation frameworks.

Optimal Intermittent Administration Interval of Abaloparatide for Bone Morphogenetic Protein-Induced Bone Formation in a Rat Spinal Fusion Model.

Both bone morphogenetic protein 2 (BMP-2) and abaloparatide are used to promote bone formation. However, there is no consensus about their optimal administration. We investigated the optimal administration theory for the pairing of BMP-2 and abaloparatide in a rat spinal fusion model. Group I was only implanted in carriers and saline. Carriers with 3 µg of recombinant human BMP-2 (rhBMP-2) were implanted in other groups. Abaloparatide injections were administered three times a week for group III (for a total amount of 120 µg/kg in a week) and six times a week for group IV (for a total amount of 120 µg/kg in a week) after surgery. They were euthanized 8 weeks after the surgery, and we explanted their spines at that time. We assessed them using manual palpation tests, radiography, high-resolution micro-computed tomography (micro-CT), and histological analysis. We also analyzed serum bone metabolism markers. The fusion rate in Groups III and IV was higher than in Group I, referring to the manual palpation tests. Groups III and IV recorded greater radiographic scores than those in Groups I and II, too. Micro-CT analysis showed that Tbs. Sp in Groups III and IV was significantly lower than in Group I. Tb. N in Group IV was significantly higher than in Group I. Serum marker analysis showed that bone formation markers were higher in Groups III and IV than in Group I. On the other hand, bone resorption markers were lower in Group IV than in Group I. A histological analysis showed enhanced trabecular bone osteogenesis in Group IV. Frequent administration of abaloparatide may be suitable for the thickening of trabecular bone structure and the enhancement of osteogenesis in a rat spinal fusion model using BMP-2 in insufficient doses.

Digital transformation in public health: a software tool for efficient health record management and improved healthcare delivery

Background In today’s world, the abundance of data in many areas of society has increased the need to use efficient technological tools to analyse, accurately and clearly present information. The objective was to develop a virtual platform to increase efficiency in the control of medical records (MR) and reduce errors in the recording of clinical data. To improve the quality of medical care in public health centres. Method The research used a methodology that follows a four-stage process for the development of the medical records management software. It started with planning where the user stories and their priority were defined. In the design phase, the system architecture and code structure were developed. Subsequently, in the coding phase, pair programming with incremental implementation was emphasised. Finally, in the testing phase, automatic and manual integration tests were carried out, documented by means of acceptance tests to ensure the quality of the developed software. Results The web-based platform has led to a significant increase in the number of patients seen, resulting in significant improvements in resource management at the health centres. In addition, the reduction in the incidence of errors highlights the superior accuracy in recording and tracking medical information, promoting a safer and more efficient care environment for patients. Conclusions The main focus of this study is the management of medical records, with the aim of reducing the time spent on registration, ensuring secure access to information, reducing documentation errors and facilitating instant data retrieval through a web-based platform. The results show the effectiveness of this methodology and its positive impact on the management of medical records.

Test-retest reliability and concurrent validity assessment of a novel high-frequency sensor device for anterior tibial translation measurement in loaded and unloaded condition: an exploratory cross-sectional study

BackgroundMagnetic resonance imaging (MRI) and manual tests remain the standard for diagnosing anterior cruciate ligament (ACL) rupture. Furthermore, the passive knee displacement, also described as anterior tibial translation (ATT), is used in order to make decisions about surgery or to assess rehabilitation outcomes. Unfortunately, these manual tests are limited to passive situations, and their application to assess knee stability in loaded, weight-bearing positions are missing. Therefore, a new device with high-performance sensors and a new sensor setting was developed. The aim of this exploratory cross-sectional study was to assess the test-retest reliability of this new device in a first step and the concurrent validity in a second step.MethodsA total of 20 healthy volunteers were measured. Measurement consistency of the new device was assessed on the basis of reliability during Lachman test setting and in loaded position by artificial knee perturbation in a test-retest procedure. In a second step, the concurrent validity was evaluated with the Lachmeter® as a reference instrument. Intraclass correlation coefficient (ICC), standard error of measurement (SEM), the minimal detectable change (MDC) and Bland-Altman analysis were evaluated to assess the quality criteria.ResultsThe measurements with the new device during the Lachman test provided a mean ATT of 5.46±2.22mm. The SEM ranged from 0.60 to 0.69mm resulting in an MDC between 1.67 and 1.93mm for the new device. In the loaded test situation, the mean ATT was 2.11±1.20mm, with test-retest reliability also showing good correlation (r>0.83). The comparison of the two measurement methods with an ICC of (r>0.89) showed good correlation, which also underlines the reasonable agreement of the Bland-Altman analysis.ConclusionsThe evaluation of the test-retest reliability of the new device during the knee stability testing in passive situation as well as in a functional, loaded situation presented good reliability. In addition, the new device demonstrated good agreement with the reference device and therefore good validity. Furthermore, the quality criteria demonstrated the ability of the new device to detect the cut-off value (3-5mm) described in the literature for the diagnosis of ACL-deficient knees, which underlines the clinical relevance of this new device as a reliable and valid tool.