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Sensors (Basel, Switzerland) Jun 2024The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by...
The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged, and a growing body of empirical evidence suggests that the Earth's electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals, allowing for more detailed studies on weather and earthquakes. In this paper, we present an approach designed to detect anomalies in electromagnetic field data from Swarm satellites. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements. We develop an enhanced form of a probabilistic model based on the Martingale theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detection overall. Secondly, we use three case studies of seismic activity (namely, earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data.
PubMed: 38894445
DOI: 10.3390/s24113654 -
Sensors (Basel, Switzerland) Jun 2024Reliable testing of aviation components depends on the quality and configuration flexibility of measurement systems. In a typical approach to test instrumentation, there...
Reliable testing of aviation components depends on the quality and configuration flexibility of measurement systems. In a typical approach to test instrumentation, there are tens or hundreds of sensors on the test head and test facility, which are connected by wires to measurement cards in control cabinets. The preparation of wiring and the setup of measurement systems are laborious tasks requiring diligence. The use of smart wireless transducers allows for a new approach to test preparation by reducing the number of wires. Moreover, additional functionalities like data processing, alarm-level monitoring, compensation, or self-diagnosis could improve the functionality and accuracy of measurement systems. A combination of low power consumption, wireless communication, and wireless power transfer could speed up the test-rig instrumentation process and bring new test possibilities, e.g., long-term testing of moving or rotating components. This paper presents the design of a wireless smart transducer dedicated for use with sensors typical of aviation laboratories such as thermocouples, RTDs (Resistance Temperature Detectors), strain gauges, and voltage output integrated sensors. The following sections present various design requirements, proposed technical solutions, a study of battery and wireless power supply possibilities, assembly, and test results. All presented tests were carried out in the Components Test Laboratory located at the Łukasiewicz Research Network-Institute of Aviation.
PubMed: 38894377
DOI: 10.3390/s24113585 -
Sensors (Basel, Switzerland) May 2024In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and...
In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number () of different process states. The is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.
PubMed: 38894302
DOI: 10.3390/s24113511 -
Diagnostics (Basel, Switzerland) Jun 2024The increased prevalence of obesity worldwide has been implicated in the alarming rise of the incidence of gestational diabetes and preeclampsia, which are both...
IL-6 Polymorphism as a Predisposing Genetic Factor for Gestational Diabetes or Preeclampsia Development in Pregnancy with Obesity in Relation to VEGF and VEGFF Receptor Gene Expression Modalities.
The increased prevalence of obesity worldwide has been implicated in the alarming rise of the incidence of gestational diabetes and preeclampsia, which are both considered threatening conditions for both mother and fetus. We studied gene polymorphisms of the proinflammatory cytokine Interleukin 6 (IL-6) and the gene expression levels of VEGF (vascular endothelial growth factor) and VEGF-R (endothelial growth factor receptor), all known to be involved in pregnancy complications, aiming to identify possible predisposing risk factors in pregnancies with obesity. The G allele of IL-6 was found to correspond with an increased risk for gestational diabetes and preeclampsia occurrence. Furthermore, in obese pregnant mothers with either gestational diabetes or pre-existing type 2 diabetes and those who developed preeclampsia, it was confirmed that gene expression levels of VEGF were reduced while they were increased for VEGF receptors. We conclude that the genetic profile of an obese pregnant woman shares a common background with that of a patient with pre-existing type 2 diabetes mellitus, and therefore predisposes them to complications in pregnancy.
PubMed: 38893732
DOI: 10.3390/diagnostics14111206 -
Diagnostics (Basel, Switzerland) May 2024The study aimed to assess the prevalence of COVID-19 and spp. coinfection across continents. Conducted following PRISMA guidelines, a systematic review utilized PubMed,... (Review)
Review
The study aimed to assess the prevalence of COVID-19 and spp. coinfection across continents. Conducted following PRISMA guidelines, a systematic review utilized PubMed, Embase, SCOPUS, ScienceDirect, and Web of Science databases, searching for literature in English published from December 2019 to December 2022, using specific Health Sciences descriptors. A total of 408 records were identified, but only 50 were eligible, and of these, only 33 were included. Thirty-three references were analyzed to evaluate the correlation between COVID-19 and spp. infections. The tabulated data represented a sample group of 8741 coinfected patients. The findings revealed notable disparities in co-infection rates across continents. In Asia, 23% of individuals were infected with , while in Europe, the proportion of co-infected patients stood at 15%. Strikingly, on the African continent, 43% were found to be infected with , highlighting significant regional variations. Overall, the proportion of co-infections among COVID-positive individuals were determined to be 19%. Particularly concerning was the observation that 1 in 6 ICU coinfections was attributed to , indicating its substantial impact on patient outcomes and healthcare burden. The study underscores the alarming prevalence of co-infection between COVID-19 and , potentially exacerbating the clinical severity of patients and posing challenges to treatment strategies. These findings emphasize the importance of vigilant surveillance and targeted interventions to mitigate the adverse effects of bacterial coinfections in the context of the COVID-19 pandemic.
PubMed: 38893674
DOI: 10.3390/diagnostics14111149 -
Animals : An Open Access Journal From... May 2024Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus...
Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings-8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature-humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.
PubMed: 38891614
DOI: 10.3390/ani14111567 -
Foods (Basel, Switzerland) May 2024Non-alcoholic fatty liver disease (NAFLD) is the most common chronic hepatic manifestation of metabolic dysfunction for which effective interventions are lacking. The...
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic hepatic manifestation of metabolic dysfunction for which effective interventions are lacking. The burden of NAFLD is increasing at an alarming rate. NAFLD is frequently associated with morbidities such as dyslipidemia, type 2 diabetes mellitus and obesity, etc. The current study explored the potential role of in treating mice with NAFLD induced by a high-fat diet (HFD). The results indicated the critical role of BPIS in treating NAFLD by effectively restoring the gut microbiota in C57BL/6 mice that received a high-fat diet (HFD) for 12 weeks. At the same time, 16S rRNA analysis demonstrated that BPIS remodeled the overall structure of the gut microbiota from fatty liver diseases towards that of normal counterparts, including ten phylum and twenty genus levels. Further study found that the expression of tight junction proteins was upregulated in the BPIS-treated group. This study provides new insights into the potential NAFLD protective effects induced by polyphenols of foxtail millet.
PubMed: 38890912
DOI: 10.3390/foods13111683 -
JMIR Human Factors Jun 2024The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and...
BACKGROUND
The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential.
OBJECTIVE
This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions.
METHODS
This explorative qualitative study with an ethnographic, multimethods approach was conducted in an ICU of a German university hospital. Using triangulation in data collection, 102 hours of field observations, 12 semistructured interviews with ICU staff members, and the results of a participatory task were analyzed. The data analysis followed an inductive, grounded theory approach.
RESULTS
Nurses and physicians reported interacting with the continuous vital sign monitoring system for most of their work time and tasks. There were no established standards for alarm management; instead, nurses and physicians stated that alarms were addressed through ad hoc reactions, a practice they viewed as problematic. Staff members' perceptions of intelligent alarm management varied, but they highlighted the importance of understandable and traceable suggestions to increase trust and cognitive ease.
CONCLUSIONS
Staff members' interactions with the omnipresent patient monitoring system and its alarms are essential parts of ICU workflows and clinical decision-making. Alarm management standards and workflows have been shown to be deficient. Our observations, as well as staff feedback, suggest that changes are warranted. Solutions for alarm management should be designed and implemented with users, workflows, and real-world data at the core.
Topics: Humans; Clinical Alarms; Qualitative Research; Intensive Care Units; Germany; Male; Female; Adult; Attitude of Health Personnel; Monitoring, Physiologic; Middle Aged; Critical Care
PubMed: 38888941
DOI: 10.2196/55571 -
Frontiers in Public Health 2024The maternal mortality indicator serves as a crucial reflection of a nation's overall healthcare, economic, and social standing. It is necessary to identify the...
INTRODUCTION
The maternal mortality indicator serves as a crucial reflection of a nation's overall healthcare, economic, and social standing. It is necessary to identify the variations in its impacts across diverse populations, especially those at higher risk, to effectively reduce maternal mortality and enhance maternal health. The global healthcare landscape has been significantly reshaped by the COVID-19 pandemic, pressing disparities and stalling progress toward achieving Sustainable Development Goals, particularly in maternal mortality reduction.
METHODS
This study investigates the determinants of maternal mortality in Kazakhstan from 2019 to 2020 and maternal mortality trends in 17 regions from 2000 to 2020, employing data extracted from national statistical reports. Stepwise linear regression analysis is utilized to explore trends in maternal mortality ratios in relation to socioeconomic factors and healthcare service indicators.
RESULTS
The national maternal mortality ratio in Kazakhstan nearly tripled from 13.7 in 2019 to 36.5 per 100,000 live births in 2020. A remarkable decrease was observed from 2000 until around 2015 with rates spiked by 2020. Significant factors associated with maternal mortality include antenatal care coverage and the number of primary healthcare units. Additionally, socioeconomic factors such as secondary education enrollment and cases of domestic violence against women emerged as predictors of MMR. Moreover, the impact of the pandemic was evident in the shift of coefficients for certain predictors, such as antenatal care coverage in our case. In 2020, predictors of MMR continued to include secondary education enrollment and reported cases of domestic violence.
CONCLUSION
Despite Kazakhstan's efforts and commitment toward achieving Sustainable Development Goals, particularly in maternal mortality reduction, the impact of the COVID-19 pandemic poses alarming challenges. Addressing these challenges and strengthening efforts to mitigate maternal mortality remains imperative for advancing maternal health outcomes in Kazakhstan.
Topics: Humans; Kazakhstan; Maternal Mortality; COVID-19; Female; Pregnancy; Adult; Socioeconomic Factors; SARS-CoV-2; Pandemics
PubMed: 38887251
DOI: 10.3389/fpubh.2024.1337564 -
Malaria Journal Jun 2024The recently released 2023 World Malaria Report sheds light on an alarming reality: despite preventive measures, malaria remains a severe issue in Burkina Faso. As...
The recently released 2023 World Malaria Report sheds light on an alarming reality: despite preventive measures, malaria remains a severe issue in Burkina Faso. As researchers in the field working on malaria in Burkina Faso, the assessment suggests significant underreporting, especially in remote areas with limited healthcare access. In addition, the confusion arising from similar diseases, such as dengue, further complicates the situation. Aligning with the 2023 World Health Organization recommendations, it is time to advocate for tailored strategies in high-burden areas by emphasizing community involvement in data collection awareness campaigns for effective disease management to combat the invisible crisis lurking within communities.
Topics: Burkina Faso; Malaria; Humans
PubMed: 38886766
DOI: 10.1186/s12936-024-05016-8