Monitoring Procedures Research Articles

Reducing the Invalid Alarm Rate for Fetal Heartbeat Monitoring in High-Risk Pregnancies in the Delivery Room

Fetal heartbeat (FHB) monitoring is used to monitor the condition of a fetus in the uterus and estimate the expected time of delivery. It is crucial to accurately and comprehensively monitor fetal heart beats in high-risk pregnancies. However, invalid alarms frequently occur in clinical environments, easily promoting alarm fatigue among nurses and causing anxiety and distrust in the laboring women and their families. Health professionals may ignore valid alarms due to alarm fatigue and miss the opportunity to save a fetus' life. A survey in our hospital identified invalid alarm rates for fetal heart beats as high as 65.1%, with causes including a lack of standard operating procedures for FHB monitoring in high-risk pregnancies and incomplete health education. This project was developed to reduce the rate of invalid alarms in FHB monitoring in high-risk pregnancies to less than 42.8%. According to patient safety references for medical equipment management, our team established FHB monitoring standards and procedures for high-risk pregnancies. We edited FHB monitoring nursing guidance leaflets and multimedia health education videos. Nurses distributed the health education leaflets and explained the purpose and precautions during labor to expectant mothers at hospital admission. We invited nurses to share the dilemmas of caring for high-risk pregnancy experiences with nurses and obstetricians. Obstetricians and nurses proposed joint solutions for monitoring to improve the feasibility of comprehensive FHB monitoring and reduce the rate of invalid alarms. The invalid alarm rate associated with FHB monitoring in high-risk pregnancies was reduced from 65.1% to 21.3%. In addition, the integrity of FHB monitoring was increased from 41.7% to 100%. This project effectively reduced the invalid alarm rate for delivery room nurse FHB monitoring in high-risk pregnancies and improved the integrity of this monitoring procedure. Adopting this approach has the potential of reducing anxiety and distrust in women in labor and their families, thereby enhancing the quality of care provided to these women experiencing high-risk pregnancies.

Improving pediatric magnetic resonance imaging safety by enhanced non-technical skills and team collaboration.

Safe pediatric magnetic resonance imaging (MRI) ideally relies on non-sedative techniques, as avoiding risky sedation is inherently safer. However, in practice, sedation often becomes unavoidable, particularly for younger children or those with anxiety, to ensure motion-free, high-quality imaging. This narrative review explores the current practices and proposes strategies to enhance safety in pediatric MRI examinations. We identified and analyzed 247 studies addressing various aspects of pediatric MRI safety, including sedation protocols, patient monitoring, and team-based management approaches. Safe sedation requires careful drug selection tailored to individual needs, continuous monitoring, and robust emergency preparedness. While efforts are underway to minimize sedation, safer drug protocols and improved monitoring technologies remain essential. Assembling dedicated MRI teams trained in both technical and non-technical skills-such as situational awareness, communication, and teamwork-supports these strategies. Structured team briefings covering monitoring procedures, emergency scenarios, response protocols, and specific resuscitation roles are also critical. Developing a strong organizational culture that promotes patient safety and continuous learning from incident reports helps ensure ongoing improvements. Achieving safe pediatric MRI examinations requires balancing the need for sedation with the goal of minimizing its use. Strengthening collaboration, refining sedation protocols, and implementing advanced safety monitoring systems are essential steps. Further advancements in imaging technologies are also necessary to reliably obtain high-quality scans without sedation, reducing risks and improving patient outcomes.

Bridges monitoring and assessment using an integrated bim methodology

Risk assessment of long-existing infrastructure has become one of the main challenges in civil engineering. Major efforts have been made in recent years to develop new techniques for rapid damage identification and ensure proper management of these structures. This paper presents a data management approach utilizing BIM methodology to create a digital database for bridge monitoring procedures. Initially, two BIM methodologies for creating a damage database are introduced, focusing on beams from a dismantled urban viaduct. Subsequently, the most suitable methodology is applied to an existing bridge in Turin, Italy. Through the chosen methodology a damage identification and classification process based on a triangular mesh is performed, assisted by a convolutional neural network (CNN) for automatic damage detection. Additionally, the paper outlines a digitalization process within a BIM environment, integrating official guidelines for bridge risk evaluation, classification, and monitoring in Italy. By employing programming tools, all data required by the guidelines is efficiently incorporated into the database. The outcomes demonstrate the effectiveness of remote sensing applications for bridge inspection and the possibility of merging BIM methodology into the inspection process to enhance the damage assessment of existing structures.

Building Academic Integrity: Evaluating the Effectiveness of a New Framework to Address and Prevent Contract Cheating

Academic integrity is a cornerstone of education systems, yet the rise of contract cheating poses significant challenges for higher education institutions. Current approaches to managing contract cheating often lack the comprehensive structure needed to address the complexities of modern cheating methods. The primary objective of this study is to investigate the effectiveness of the proposed Three-Tier Framework (TTF), designed in our previous study to combat contract cheating. The proposed framework comprises three tiers: awareness, monitoring, and evaluation. It engages stakeholders within the system and encourages a proactive and collaborative stance against contract cheating while reinforcing a culture of academic honesty. The evaluation focuses on three key aspects: the clarity of the framework’s functions and objectives, the potential challenges in implementing the proposed monitoring process, and the perceived limitations in detecting and mitigating contract cheating through this framework. Supervised and unsupervised assignments are considered, excluding the option of e-proctoring, as some students encountered difficulties setting up necessary tools and software for online exams. Survey results reveal a broad consensus among respondents, who expressed strong confidence in the clarity and effectiveness of the framework and its monitoring procedures. These positive perceptions were consistent across respondents, regardless of their prior experience or familiarity with contract cheating. Although the overall feedback was positive, concerns were raised regarding implementing the framework in current educational settings. Specific challenges cited include tight timelines and the increased workload associated with the new procedures, emphasising a need for additional guidance, training, and institutional support to ensure effective adoption. The proposed framework incorporates an instructor dashboard designed to streamline academic workflow and simplify the monitoring process introduced in this framework. The survey results confirm that the framework can be adopted to address the unique needs of academics and diverse educational environments; however further research is needed to explore its applicability across the broader higher education community.

Ensuring Data Accuracy and Compliance in Remote Healthcare Monitoring

Remote healthcare delivery and clinical trials have undergone a dramatic transformation, necessitating robust frameworks for ensuring data accuracy and regulatory compliance in virtual settings. This comprehensive article explores the intricate challenges healthcare organizations face when implementing remote monitoring systems while maintaining data integrity and adhering to stringent regulatory requirements. Through examination of current technological infrastructure, quality control methodologies, and regulatory frameworks, this article presents a structured approach to achieving reliable data collection in remote care environments. The article addresses critical aspects, including device validation protocols, HIPAA compliance in virtual settings, and standardized procedures for remote patient monitoring, while offering practical solutions for common challenges in technology adoption and data discrepancy management. This article indicates that the successful implementation of remote healthcare data systems requires a three-pronged approach: robust technical infrastructure, comprehensive staff training programs, and patient-centric education initiatives. Furthermore, this article identifies emerging trends in artificial intelligence and predictive analytics that promise to enhance data validation processes and compliance monitoring in remote healthcare settings. This article provides healthcare professionals and organizations with actionable insights for establishing and maintaining reliable remote data collection systems while ensuring regulatory compliance and optimal patient care outcomes.

Assessment and management of existing bridges following the innovative Italian guidelines: A pilot study

In 2022, the Italian Government introduced innovative regulations for evaluating and monitoring existing bridges and viaducts, tackling issues related to risk classification, safety assessment, and monitoring. These regulations employ a multilevel approach for bridge inspections, safety assessments and monitoring procedures. The guidelines have garnered international acclaim for their forward-thinking approach. This paper proposes a comprehensive study concerning the whole process proposed in the new Italian guidelines, using a case study constituted by two prestressed RC box-girder half-joint span bridges that exhibited signs of ageing several years ago. The paper in deeply addresses all phases of the existing bridge safety assessment and management discussed in the guidelines from both a scientific and practical standpoint, in collaboration with the national agency for road administration. It starts from visual inspections, conducted for supporting the bridge safety assessments, and concludes with the experimental testing and structural monitoring of bridges. For the first time in the scientific literature, this work provides a pilot study for a proper and aware use of the innovative Italian guidelines, with focus on prestressed concrete bridges. The paper objective is to present advanced procedures for the classification, evaluation, and management of existing bridges to be adopted by road administrator, which may inspire international readers in upgrading codes of their own countries.

Excessive Supraventricular Ectopic Activity in Patients With Acute Ischemic Stroke Is Associated With Atrial Fibrillation Detection Within 24 Months After Stroke: A Predefined Analysis of the MonDAFIS Study.

Excessive supraventricular ectopic activity (ESVEA) is regarded as a risk marker for later atrial fibrillation (AF) detection. The investigator-initiated, prospective, open, multicenter MonDAFIS (Impact of Standardized Monitoring for Detection of Atrial Fibrillation in Ischemic Stroke) study randomized 3465 patients with acute ischemic stroke without known AF 1:1 to usual diagnostic procedures for AF detection or additive Holter monitoring in hospital for up to 7 days, analyzed in a core laboratory. Secondary study objectives include the comparison of recurrent stroke, myocardial infarction, major bleeding, and all-cause death within 24 months in patients with ESVEA (defined as ectopic supraventricular beats ≥480/day or atrial runs of 10-29 seconds or both) versus patients with newly diagnosed AF versus patients without ESVEA or AF (non-ESVEA/AF), randomized to the intervention group. Overall, 1435 (84.8%) of 1714 patients randomized to the intervention group had analyzable study ECG monitoring of at least 48 hours' duration within the first 72 hours of monitoring. ESVEA was detected in 363 (25.3%) patients, while AF was first detected in 48 (3.3%) patients. Within 24 months, AF was newly detected in 67 (18.5%) patients with ESVEA versus 60 (5.9%) patients without ESVEA/AF- (P<0.001). The composite outcome at 24 months was not different between patients with ESVEA and patients without ESVEA/AF (15.2% versus 12.6%; P=0.242). All-cause death was numerically higher in patients with ESVEA (6.6% versus 3.2%), but failed statistical significance (P=0.433) in multivariate analysis (including age, heart failure, stroke severity, and creatinine at baseline). ESVEA in the acute phase of ischemic stroke or transient ischemic attack is associated with AF detection during follow-up and therefore may be used to select patients for prolonged ECG monitoring. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02204267.

Remote Follow-Up/Monitoring of Cardiac Implantable Electronic Devices.

Cardiac implantable electronic device (CIED) implantation is a diagnostic and therapeutic method that is being employed on a growing number of patients globally. These devices require long-term follow-up and monitoring, and after implantation, regular follow-ups are conducted at specific intervals. These follow-ups provide crucial information about both the device and the patient, aiding in diagnosis and guiding treatment. These monitoring procedures, which are usually performed in a clinical setting, place a substantial burden on the healthcare system and its personnel. Remote follow-up and monitoring, as discussed in this article, has the potential to replace many in-clinic follow-up/monitoring procedures, meeting the device and patient monitoring needs without compromising safety. Thus, it can alleviate the burden on the healthcare system and its personnel in a cost-effective manner. This article aims to provide a comprehensive exploration of remote follow-up and monitoring for CIEDs.

Real-time monitoring procedures for early detection of bubbles

Real-time monitoring procedures for early detection of bubbles

Sequential monitoring of time-to-event safety endpoints in clinical trials

Background/aims Safety monitoring is a crucial requirement for Phase II and III clinical trials. To protect patients from toxicity risk, stopping rules may be implemented that will halt the study if an unexpectedly high number of events occur. These rules are constructed using statistical procedures that typically treat the toxicity data as binary occurrences. Because the exact dates of toxicities are often available, a strategy that handles these as time-to-event data may offer higher power and require less calendar time to identify excess risk. This work investigates several statistical methods for monitoring safety events as time-to-event endpoints and illustrates our R software package for designing and evaluating these procedures. Methods The performance metrics of safety stopping rules derived from Wang-Tsiatis tests, Bayesian Gamma–Poisson models, and sequential probability ratio tests are evaluated and contrasted in Phase II and III trial scenarios. We developed a publicly available R package “stoppingrule” for designing and assessing these stopping rules whose utility is illustrated through the design of a stopping rule for Blood and Marrow Transplant Clinical Trials Network 1204 (National Clinical Trial number NCT01998633), a multicenter, Phase II, single-arm trial that assessed the efficacy and safety of bone marrow transplant for the treatment of hemophagocytic lymphohistiocytosis and primary immune deficiencies. Results As seen previously in group sequential testing settings, rules with strict stopping criteria early in a study tend to have more lenient stopping criteria late in the trial. Consequently, methods with aggressive early monitoring, such as Gamma–Poisson models with weak priors and certain choices of truncated sequential probability ratio tests, usually yield a smaller number of toxicities and lower power than ones that are more permissive at early stages, such as Gamma–Poisson models with strong priors and the O’Brien–Fleming test. The Pocock test and maximized sequential probability ratio test performed contrary to these trends, however, exhibiting both diminished power and higher numbers of toxicities than other methods due to their extremely aggressive early stopping criteria, failing to reserve adequate power to identify safety issues beyond the start of the study. In contrast to binary toxicity approaches, our time-to-event methods offer meaningful reductions in expected toxicities of up to 20% across scenarios considered. Conclusion Safety monitoring procedures aim to guard study participants from being exposed to and suffering toxicity from unsafe treatments. Toward this end, we recommend considering the time-to-event-oriented Gamma–Poisson model—weak prior model or truncated sequential probability ratio test for constructing safety stopping rules, as they performed the best in minimizing the number of toxicities in our investigations. Our R package “stoppingrule” offers procedures for creating and assessing stopping rules to aid trial design.

A hybrid approach based on critical chain for project buffer consumption prediction and monitoring

PurposeThe critical chain project buffer monitor process addresses uncertainty and variability in project duration. However, classical buffer monitor methods only consider buffer consumption, while the dynamic allocation of buffer zones and the buffer consumption trend of activities are ignored. This paper presents the innovative framework for dynamic monitoring of project buffer which covers the dynamic buffer allocation, predictive analytics of buffer utilization and a new monitoring technique based on control chart graph.Design/methodology/approachFirst, a dynamically buffer allocation model is framed, and buffer zones are given to the activities considering risks. Then, a predictive model amalgamating Bayesian Optimization, Convolutional Neural Networks, and Long Short-Term Memory networks (BO-CNN-LSTM) is framed. Finally, a new buffer monitor framework is constructed that takes into account historical information about buffer usage and utilizes two thresholds derived from control chart theory.FindingsThis approach is empirically tested on a representative agricultural website project in China. The results show that, first, the dynamic buffer allocation makes better use of the project buffer, reduces buffer waste and increases the possibility of timely completion of the project. Second, the BO-CNN-LSTM model predicts better than Long Short-Term Memory (LSTM) and Grey Neural Network Model (GNNM), providing project managers with new management insights and perspectives. Third, the novel monitoring procedure makes the leveraging of historical data possible in the control of the schedule deviations, allowing for more timely interventions in the course of the implementation of the project.Originality/valueA new project buffer monitoring method suitable for uncertain project environments is proposed.

Intra-Articular Injection of Stem Cells for the Regeneration of Knee Joint Cartilage: a Therapeutic Option for Knee Osteoarthritis - a Narrative Review.

Current approaches to regulating osteoarthritis primarily focus on symptom management; however, these methods often have significant side effects and may not be suitable for long-term care. As an alternative to conventional treatments, injecting stem cells into knee joint cartilage is a promising option for repairing damaged cartilage. In this review, we outline the general procedure for stem cell treatment of knee joint cartilage regeneration, emphasizing the potential of intra-articular stem cell injections as a therapeutic option for osteoarthritis. We examined and summarized patient evaluation and preparation for knee joint stem cell therapy, stem cell harvesting, stem cell preparation, injection procedures for stem cell therapy, post-injection care and monitoring, potential outcomes of stem cell therapy, and considerations and risks associated with stem cell therapy. Overall, stem cell injections for knee joint cartilage damage represent a promising frontier in orthopedic care. They offer potential benefits such as pain and inflammation reduction, promotion of cartilage repair and regeneration, and the possibility of avoiding more invasive treatments such as knee surgery. Ongoing collaboration among researchers, clinicians, and regulatory organizations is crucial for advancing this field and translating scientific discoveries into effective clinical applications.

Assessment of Quality Assurance Practices on Performance of Graduates of Selected Private Universities in Western Uganda

This study investigated the influence of quality assurance systems, practices and regulatory standards on the performance of graduates of selected universities in Western Uganda. The core question of this research was, is the teaching and learning provided in higher institutions of learning fit for purpose, does it prepare graduates with appropriate skills that employers need to meet the needs of society? To address this largely unresolved topic of discussion, this research aligned with a transformative research approach with qualitative methodologies. The study targeted 16 members of the university management team through purposive sampling; 19 employers and 17 graduates through snowball sampling. Data was collected through in-depth interviews and document reviews analysed thematically. Findings revealed that universities tried to meet stipulated regulatory standards for the minimum entry requirements, study programs always defined student learning outcomes, and monitoring and evaluation procedures were in place. Withal, it was reported that some accreditation requirements were not adhered to and some gaps in monitoring and evaluation practices were earmarked. It’s recommended that universities enhance quality assurance procedures and standards to improve the efficiency and effectiveness of service delivery and the teaching-learning environment to cater for the labour market needs

KNOWLEDGE OF BLOOD TRANSFUSION AMONG NURSES AND MEDICAL TECHNICIANS

Introduction: In blood transfusion, nurses play a key role in ensuring patient safety by performing identification, compatibility and vital parameter monitoring procedures as well as providing support and education to patients. Their contribution is crucial for achieving high standards of medical care and improving medical treatment outcomes.Objective: To assess the level of knowledge amongst nurses with regards to safe and proper application of transfusion therapy. Participants and Methods: This research was conducted as a cross-section descriptive-analytical study with the aim of assessing knowledge of routine blood transfusion among three groups of nurses and analyzing differences in their results. The sample consisted of three target groups of nurses studying at the Faculty of Health Studies (of the University Clinical Hospital Mostar, the Cantonal Hospital Dr. "Safet Mujić" Mostar and the County Hospital "Dr. fra Mihovil Sučić" Livno). A modified version of the RBTKQ questionnaire was used for the research. The questionnaire consists of 6 sections and 49 items. Descriptive and inferential statistical measures were used, including frequencies and percentages, normality distribution tests, as well as the Mann Whitney U test and Kruskal-Wallis test, with results evaluated at both levels of significance.Results: The analysis of nurses' knowledge on blood transfusion indicates an average level of 77.4%, with notable variation among participants. These findings underscored the need for more intensive education particularly in recognizing symptoms and responding to serious complications. Additionally, statistically significant differences between male and female participants, as well as participants from different institutions and qualifications, highlight specific areas where overall improvement of nurses' knowledge in these domains require focus.Conclusion:Lack of knowledge in recognizing the causes of fatal transfusion reactions, especially patient identification errors, compromises patient safety during transfusions, emphasizing the urgent need to improve education of nurses in this area. Keywords: blood transfusion, nurses, knowledge, patient safety, education

Detecting irregularities in randomized controlled trials using machine learning.

Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the trial are analyzed and disseminated. Machine learning algorithms have shown promise for identifying center-level irregularities in multi-center clinical trials with minimal human intervention. We aimed to characterize the form-level data irregularities in several historical clinical trials and evaluate the ability of a machine learning-based outlier detection algorithm to identify them. Data irregularities previously identified by humans in historical clinical trials were ascertained by comparing preliminary snapshots of the trial databases to the final, locked databases. We measured the ability of a machine learning based outlier detection algorithm to identify form-level irregularities using concordance (area under the receiver operator characteristic), positive predictive value (precision), and sensitivity (recall). We examined preliminary snapshots of seven historical clinical trials which randomized a total of 77,001 participants. We extracted a total of 1,267,484 completed entries from 358 case report forms containing irregularities from all snapshots across all trials, containing a total of 24,850 form-wide irregularities (median per-form form-level irregularity rate: 1.81%). Our proposed machine learning algorithm detects form-level irregularities with a median concordance of 0.74 (interquartile range = 0.57-0.89), slightly exceeding the performance of a previously proposed machine learning approach with a median area under the receiver operator characteristic of 0.73 (interquartile range = 0.54-0.88). Data irregularities in historical clinical trials were ascertained by comparing preliminary snapshots of the trial database to the final database. These irregularities can be categorized according to their scope. Irregularities can be successfully detected by a machine learning algorithm as early or earlier than a human can, without human intervention. Such an approach may complement existing techniques for central statistical monitoring in large multi-center randomized controlled trials and possibly improve the efficiency of costly data verification processes.

Аналіз стану поверхневих вод населених пунктів Закарпатської області протягом 2022-2023 років

Objective. To study the pollution of surface waters of settlements of the Transcarpathian region during 2022-2023. Materials and methods. The objects of the study were the surface waters of settlements of the Transcarpathian region. The statistical data of the Transcarpathian Regional Center for Hydrometeorology on operational and diagnostic monitoring of surface waters of 34 dams of Transcarpathia was analyzed in accordance with the Decree of the Cabinet of Ministers of 19.09.1918, No. 758 "On Approval of the Procedure for State Monitoring of Waters." The assessment of physicochemical indicators of the quality of rivers of settlements of the Transcarpathian region was carried out in accordance with the Order of the Ministry of Ecology and Natural Resources of Ukraine of 14.01.2018, No. 5 "On the approval of the Methodology for classifying the body of surface water, as well as the classification of an artificial or significantly altered body of surface water to one of the classes of ecological potential of an artificial or significantly altered body of surface water". The obtained materials were processed statistically using the Microsoft Excel computer program. Results. In total, during 2022-2023, 3,876 measurements of physico-chemical indicators of surface water pollution in populated areas of the Transcarpathian Regionwere carried out, of which 3,306 measurements of these indicators in 174 samples were measured by operational monitoring, and 570 measurements were made by diagnostic ones in 30 samples. Pollution of the surface waters of settlements in the Transcarpathian region occurred mainly due to the ingress of insufficiently treated sewage, but a small amount of atmospheric precipitation that fell during the studied years also played a role. The research was conducted monthly, and the average values of the content of pollutants were determined quarterly for the years under study. Thus, according to the results of observations in all quarters during 2022-2023, the main pollutants of surface water in settlements of the Transcarpathian region are ammonium compounds, namely: ammonia Nitrogen, nitrite Nitrogen and nitrate Nitrogen. Conclusions. 1. Significant contamination of surface waters of populated areas of the Transcarpathian region by organic and chemical compounds is observed during the 2022-2023 research years. 2. During 2022-2023, water was found to be the most polluted in the Tova river, the village of Baranyntsi, the Verke river in the town of Beregovo and the Kosino-Bovtradsky canal, the village of Koson, in which the concentrations of physical and chemical indicators, such as mineralization, ammonium Nitrogen, nitrite Nitrogen, nitrate Nitrogen, and chloride ion significantly exceeded the MPC. 3. A number of measures have been developed to prevent surface water pollution of the rivers of the Transcarpathian region, namely, sufficient funding for the construction and reconstruction of most sewage and water supply facilities can improve the quality of surface water. 4. In order to prevent pollution of surface water, it is necessary to put into practice modern technologies of wastewater treatment of settlements and industrial enterprises, modernization of existing and construction of new water treatment facilities, as well as to systematically carry out a set of measures for sanitary and hygienic monitoring of the quality of surface water of rivers in the Transcarpathian region. Keywords. Water samples, surface water, water pollution, preventive measures.

Modeling the Risk Factors of Miscarriage Using Censored Quantile Regression in Kenyan Cohort

Background: Numerous researches have been undertaken to delineate the significant risk factors linked to miscarriage. The etiology of miscarriage has presented challenges in its elucidation, necessitating a comprehensive understanding of the elements that are linked to this phenomenon. The examination of these aspects has been conducted utilizing diverse methodological techniques and analytical procedures. The Cox's proportional hazards model and the accelerated failure time models are commonly utilized in the field of survival analysis. While these models provide a certain level of flexibility, they do not allow for the differentiation of predictor indicators between high-risk and low-risk subpopulations. In contrast, quantile regression allows for the assessment of separate effects of covariates at different quantiles of the conditional distribution of miscarriage. Objective: This study assessed the risk factors for miscarriage using the censored quantile regression model and compared the results to that of Cox proportional hazard model. Methodology: A secondary data from 6077 records of pregnant women with recognized pregnancy and enrolled in the pre-natal care in Kakamega County General Teaching and Referral Hospital (KCGTRH) in Kakamega county, western Kenya during the period from 1 January, 2019 up to 31 October, 2020 were recruited into the study. The study used chi-squared tests for descriptive analysis. Cox proportional hazards and censored quantile regression were used for modeling. Analysis was done using the R package and 5% was the level of significance. Results: Censored quantile regressions were conducted to estimate the parameters for the quantiles, \(\tau\) = 0.05, 0.1, 0.15, 0.2. The results showed that ethnicity had a positive effect and was statistically significant at \(\tau\) = 0.1 (p = 0.0023) and number of previous stillbirths had a negative effect and was significant at \(\tau\) = 0.15 (p = 0.032) and at \(\tau\) = 0.2 (p = 0.036). Graphical comparison of censored quantile model effects to that of cox PH model revealed that the variables; prior miscarriages, prior stillbirths, place of residence, and number of ANC visits have a statistically significant adverse effect on the survival time of miscarriages in the initial phases of pregnancy, contrary to cox PH model. Conclusion: No variable had a statistically significant influence across all quantiles. The results depicts that at a certain moment in the monitoring procedure, they will have a significant impact on the length of survival for miscarriages. A quantile regression model have the capacity to incorporate cross-over effects, whereby the influence of a covariate can manifest as either positive or negative. In contrast, Cox estimates do not allow for the potential scenario when a covariate initially increases the hazard for a specific time period and subsequently decreases it.

2024 Thai Rheumatism Association Guideline for the Treatment of Axial Spondyloarthritis with Biologic and Targeted Synthetic Disease-Modifying Anti-Rheumatic Drugs

Objective: To provide rheumatologists with the decisions for the appropriate and standardized treatment of patients with axial spondyloarthritis (axSpA) with biologic disease-modifying antirheumatic drugs (bDMARD) and targeted synthetic DMARDs (tsDMARD), according to current evidence and expert opinion. Materials and Methods: The development process involved 46 rheumatologist representatives from medical schools, government hospitals, and private hospitals nationwide. Relevant clinical questions related to treatment initiation criteria, administration, evaluation, monitoring, and intensive treatment options were selected. The evidence was systematically identified and summarized. Quality of evidence was evaluated and ranked according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scoring methodology. The recommendations were then proposed and voted on using the nominal group process. The input was collected from stakeholders through a public hearing. The final 13 recommendations were provided. Results: The guideline addresses the criteria for starting bDMARD/tsDMARD and details the first-line drugs for specific disease profiles, including predominantly axial involvement, predominantly peripheral joint involvement, and uveitis. It also addresses the management of patients with comorbid conditions such as tuberculosis, chronic hepatitis B, malignancy, and pregnancy. Additionally, it covers monitoring and follow-up procedures, alternative treatments for first-line treatment failures, tapering treatment after remission, and reinstitution of bDMARD/tsDMARD in case of flare-up. Conclusion: Thai Rheumatism Association has proposed a set of recommendations to outline concepts and provide guidance on the treatment of axSpA with bDMARD and tsDMARD for Thai rheumatologists.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Quantile analysis for financial bubble detection and surveillance

Understanding and monitoring financial bubbles is critical, as they can lead to market instability, asset price crashes, and economic downturns with widespread consequences. This article explores the usefulness of quantile regression (QR) technique in detecting and surveilling financial bubbles, encompassing both global testing and real‐time monitoring. We demonstrate that the QR‐based quantile unit root test, coupled with an optimal quantile selection technique, serves as an effective tool for a global bubble test without necessitating additional recursive techniques. Moreover, we propose two QR‐based bubble monitoring techniques. We show that the monitoring statistics follow a random variate under the null hypothesis of no bubbles but diverge to positive infinity in the presence of a mildly explosive bubble, and hence consistently date the origination of a bubble. Monte Carlo simulations suggest that compared with their LS counterparts, in the presence of skewed distributions, the QR‐based global test delivers substantially greater power, while the QR‐based monitoring procedures offer higher bubble detection rate and more accurate dating of the bubble origination. As an illustration, we conduct a pseudo real‐time monitoring exercise with the S&amp;P 500 composite index.