Early Alarm Research Articles

Dental Students And Dentists’ Awareness In Handling Pediatric Patients Having Systematic Diseases In Riyadh

It is a well-known fact that maintenance of oral health is a particularly important aspect to have a good general health and having a better quality of life. The oral cavity can be responsible for showing the symptoms of the systemic disease and maybe affected by the same, thus it can help in raising an early alarm. This is a cross-sectional study performed among the dental students and dentists in Riyadh using an online survey. Dental universities, hospitals and clinics in Riyadh were contacted

Evaluation of viral concentration methods for SARS-CoV-2 recovery from wastewaters

Wastewater-based epidemiology (WBE) is a useful tool that has the potential to act as a complementary approach to monitor the presence of SARS-CoV-2 in the community and as an early alarm system for COVID-19 outbreak. Many studies reported low concentrations of SARS-CoV-2 in sewage and also revealed the need for methodological validation for enveloped viruses concentration in wastewater. The aim of this study was to evaluate different methodologies for the concentration of viruses in wastewaters and to select and improve an option that maximizes the recovery of SARS-CoV-2. A total of 11 concentration techniques based on different principles were evaluated: adsorption-elution protocols with negatively charged membranes followed by polyethylene glycol (PEG) precipitation (Methods 1–2), PEG precipitation (Methods 3–7), aluminum polychloride (PAC) flocculation (Method 8), ultrafiltration (Method 9), skim milk flocculation (Method 10) and adsorption-elution with negatively charged membrane followed by ultrafiltration (Method 11). To evaluate the performance of these concentration techniques, feline calicivirus (FCV) was used as a process control in order to avoid the risk associated with handling SARS-CoV-2. Two protocols, one based on PEG precipitation and the other on PAC flocculation, showed high efficiency for FCV recovery from wastewater (62.2% and 45.0%, respectively). These two methods were then tested for the specific recovery of SARS-CoV-2. Both techniques could recover SARS-CoV-2 from wastewater, PAC flocculation showed a lower limit of detection (4.3 × 102 GC/mL) than PEG precipitation (4.3 × 103 GC/mL). This work provides a critical overview of current methods used for virus concentration in wastewaters and the analysis of sensitivity for the specific recovery of SARS-CoV-2 in sewage. The data obtained here highlights the viability of WBE for the surveillance of COVID-19 infections in the community.

Letter: May Cooler Heads Prevail During a Pandemic: Stroke in COVID-19 Patients or COVID-19 in Stroke Patients?

To the Editor: Along with the novel coronavirus (COVID-19) pandemic comes a growing public fear of its severe complications and possible central nervous system involvement.1 Neurointerventionalists in COVID-19 prevalent regions raised an alarm toward an increased risk of acute large-vessel-occlusion stroke in COVID-19 patients. It has been proposed that large-vessel stroke could be the presenting symptom of COVID-19 in younger patients.2 This has brought upon heightened fear and anxiety among younger adults. Yet, the scientific literature lacks evidence supporting a higher risk of stroke with COVID-19 in mildly symptomatic patients, or providing a causal link between COVID-19 and large-vessel stroke in younger adults. Systemic inflammation that may occur after severe COVID-19 infection promotes a prothrombotic state that may predispose to thrombotic events, including stroke. However, stroke as the presenting complaint of COVID-19 patients with otherwise mild or no systemic disease raises concerns over COVID-19 being a bystander given its high prevalence in reporting regions.2 In a report of 5 patients younger than 50 yr, 2 of whom were underscored for having no stroke comorbidities, stroke was described as a presenting symptom of young COVID-19 patients.2 This led to a robust news coverage to alarm young healthy adults of stroke secondary to COVID-19. We find this alarm unfounded. Stroke is less common, but not rare, in young adults. The findings presented remain anecdotal and lack the methodological and statistical rigor to claim that COVID-19 infection increases stroke risk in the youth or that stroke could be the presenting symptom of COVID-19. In fact, our Stroke Thrombectomy and Aneurysm Registry reports that 12% of 5000 patients undergoing thrombectomy are younger than 50 yr.3,4 Of young adults, 47% had no stroke comorbidity. Similarly, 8% of patients from the stroke thrombectomy trials were under 50 yr.5 Therefore, anecdotal reports of small case series of stroke in younger adults with COVID-19 do not reach the threshold, to date, to raise public fear among young adults. Although sounding early alarms during outbreaks may prevent delayed responses to novel threats, sufficient evidence is required to limit unnecessary burdens of the alarm itself on the healthcare system and public. Efforts to increase stroke awareness are always commended to avoid delay in patient presentation; however, in times of uncertainty and anxiety, it is critical to minimize misinformation that can have profound ramifications, even if it were well intended. Physicians are encouraged to seek sound medical evidence during care for patients and news appearances. Disclosures The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.

Hospital and Population-Based Evidence for COVID-19 Early Circulation in the East of France.

Background: Understanding SARS-CoV-2 dynamics and transmission is a serious issue. Its propagation needs to be modeled and controlled. The Alsace region in the East of France has been among the first French COVID-19 clusters in 2020. Methods: We confront evidence from three independent and retrospective sources: a population-based survey through internet, an analysis of the medical records from hospital emergency care services, and a review of medical biology laboratory data. We also check the role played in virus propagation by a large religious meeting that gathered over 2000 participants from all over France mid-February in Mulhouse. Results: Our results suggest that SARS-CoV-2 was circulating several weeks before the first officially recognized case in Alsace on 26 February 2020 and the sanitary alert on 3 March 2020. The religious gathering seems to have played a role for secondary dissemination of the epidemic in France, but not in creating the local outbreak. Conclusions: Our results illustrate how the integration of data coming from multiple sources could help trigger an early alarm in the context of an emerging disease. Good information data systems, able to produce earlier alerts, could have avoided a general lockdown in France.

False Fire Alarm Detection Using Data Mining Techniques

In the era of smart home technology, early warning systems and emergency services are inevitable. To make smart homes safer, early fire alarm systems can play a significant role. Smart homes usually utilize communication, sensors, actuators, and other technologies to provide a safe and smart environment. This research work introduced a model for the fire alarm system and designed a fire alarm detection (FAD) simulator to produce a synthetic dataset. The designed simulator utilizes a variety of sensors (temperature, gas, and humidity) to simulate fire alarm scenarios based on real-world data. The produced data is investigated and analyzed to classify the possible fire behaviors based on key assumptions taken from real-world scenarios. Different classification models are used to determine an optimal classifier for fire detection. The proposed technique can identify the false alarms based on parameters like temperature, smoke, and gas values of different sensors embedded in a fire alarm detection simulator.

Autonomous System for Wildfire and Forest Fire Early Detection and Control

Recurring and increasing large-scale wildfires across the globe (e.g., Southern Europe, California, Australia), as a result of worsening climate conditions with record temperatures, drought, and strong winds, present a challenge to mankind. Early fire detection is crucial for a quick reaction and effective firefighting operations, minimizing the risk to human lives as well as the destruction of assets, infrastructures, forests, and wildlife. Usually, ground firefighting relies on human intervention and dangerous exposition to high temperatures and radiation levels, proving the need for mechanisms and techniques to remotely or autonomously detect and combat fire. This paper proposes an autonomous firefighting system built with a motorized water turret, narrow beam far infrared (FIR) sensors, and a micro-controller running novel algorithms and techniques. Experimental field results validated the technical approach, indicating that when a small fire front is within the field of view of the FIR sensor and within the range of the water jet, it is possible to provide an early alarm and even autonomously extinguish or delay the approaching fire front, increasing the chance for evacuation.

Nacre-like graphene oxide paper bonded with boric acid for fire early-warning sensor

Boric acid-modified graphene oxide (GO-BA) paper is prepared by a green and facile water evaporation-induced self-assembly method, and its application as an early fire-alarm sensor is investigated. The nacre structure is constructed by assembling graphene oxide (GO) and boric acid (BA) as brick and mortar, respectively. Compared with pure GO paper, improved thermal-oxidative stability is obtained for GO-BA. GO nanosheets are bonded with BA molecules by forming hydrogen bonds between hydroxyl in BA and the rich oxygen-containing functional groups on GO. Notably, the insulating GO-BA paper can be rapidly thermally reduced to conductive reduced graphene oxide under flame exposure, thus providing an ideal fire-alarm response with a quick flame-detection time of ∼0.8 s. In addition, boron oxide formed under flame attack covers the surface of GO, inhibiting further oxidation of GO paper, and effectively extending the duration time of GO-BA under combustion. These results indicate that the GO-BA paper prepared has a broad prospect in the field of fire early-alarm.

Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique

Unexpected failures commonly occur in industrial equipment, and condition monitoring could significantly improve the efficiency of maintenance and failure of early alarm. A condition monitoring method using multi-variables state estimate technique (MSET) is proposed, and an improved multi-variables memory matrix construction method is employed, furthermore, an analysis of comprehensive similarity index that considering variable weights is accomplished, and incipient failure alarm thresholds are determined, which lead to effective early detection of failure. The method proposed in this paper is validated using actual data for blower fan in a thermal power plant, and the simulation and comparison results are discussed. The verification results reveal that the proposed method is effective for failure monitoring modeling and achieve a superior accuracy, and incipient failure could be accurately detected.

Advantages of “Life Curve” Mobile Application: An Easier Alternate of Paper Partograph

Background: The WHO approved paper Partograph is a time tested tool for monitoring the progression of labour to reduce life-threatening complications during labour as well as maternal mortality and morbidity. However, inept manpower for recording and interpreting data, chance of retrograde plotting and limited scope of distance monitoring have become a significant barriers to use this tool. The ‘Life Curve’, an Android Apps, is put forward as an easier alternate of the paper partograph.
 Objective: To see the advantages of the ‘life curve mobile application’: an Android apps which support any Android operating system. It is a new tool based on WHO partograph to monitor the progression of labor with automatically generated interpretation. Early warning and alarm system is included here for creating an opportunity of early referral and intervention.
 Method: A cross sectional study was conducted in Department of Gynaecology and Obstetrics, Sir Salimullah Medical College & Mirford Hospital from December 2016 to May 2017 on 30 mothers admitted with labour pain. Labor and all other parameters were recorded and monitored by using “life curve” mobile application along with paper partograph. The research protocol including the ethical clearance was approved by Bangladesh Medical Research Council. It is part of the feasibility study of the “life Curve” mobile application.
 Results: Mean age of the subjects was 25.9±5.7 yrs, mean gestational age 39.6±.9 wks, and 83% had vaginal delivery. The apps is easy to fill up, automatically generates graph, interesting to work with, provides timely automated reminder to evaluate the parturient, generates digital color coded warning figures, sends automated text message to the supervisor in abnormal situation. The performance parameters were scored in a 1-5 scale. For comparing the advantages of using these two partographs, nine features were compared with a total score of 45. Total score for live curve has 42.7 and that for paper postograph was 19.52 the difference is highly significant (p=.001). The mean±SD score of life curve (4.74±.52) was statistically significantly higher than that of paper partograph (2.17±1.18): p<.0002.
 Conclusions: The life curve mobile application is found to be a easier tool for monitoring of labour with many advantages; and can be used as an alternative of paper partograph.
 Bangladesh J Obstet Gynaecol, 2017; Vol. 32(2) : 84-89

Moving mean-based algorithm for dairy cow’s oestrus detection from uniaxial-accelerometer data acquired in a free-stall barn

In precision livestock farming, accelerometers have been adopted to recognise cow’s behavioural activities with the final aim of generating early alarm of illness or specific physiological statuses. Accelerometers are widely used for their low cost and easy integration with other ICT devices and, when they are adopted to acquire data for cow’s behavioural activities recognition, models based on acceleration threshold values make it possible to obtain low computational classifiers.In this study, a model based on an acceleration threshold algorithm is proposed in order to detect cow’s oestrus activity. In detail, a threshold to predict oestrus onset was determined based on experimental data acquired during specific experimental tests carried out in a free-stall barn for dairy cows. Oestrus onset was validated by using images from a video-recording system. Novelty in the results achieved in this study regards the model features that are suitable for the production of a plug-and-play cow’s behaviour monitoring system based on specific firmware-equipped devices.

SCADA-data-based wind turbine fault detection: A dynamic model sensor method

Fault detection based on data from the supervisory control and data acquisition (SCADA) system, which has been installed in most MW-scale wind turbines, has brought significant benefits for wind farm operators. However, the changes in the features of hardware sensor measurements, which are used in current SCADA systems, often cannot provide reliable early alarms. In order to resolve this problem, in this paper, a novel dynamic model sensor method is proposed for the SCADA data based wind turbine fault detection. A dynamic model representing the relationship between the generator temperature, wind speed, and ambient temperature is derived following the first principles and used as the basic structure of the model sensor. When the model sensor is applied for fault detection, its parameters are updated regularly using the generator temperature, wind speed, and ambient temperature data from the SCADA system. Then, from the updated model, the fault sensitive features of wind turbine system are extracted via performing system frequency analysis and used for the turbine fault detection. This novel model sensor method is applied to the SCADA data of a wind farm of 3 wind turbines currently operating in Spain. The results show that the proposed method can not only detect the turbine generator fault but also reveal the trend of ageing with the wind turbine generator, demonstrating its capability of failure prognosis for wind turbine system and components.

Early versus Late Modality Fusion of Deep Wearable Sensor Features for Personalized Prediction of Tomorrow's Mood, Health, and Stress.

Predicting mood, health, and stress can sound an early alarm against mental illness. Multi-modal data from wearable sensors provide rigorous and rich insights into one's internal states. Recently, deep learning-based features on continuous high-resolution sensor data have outperformed statistical features in several ubiquitous and affective computing applications including sleep detection and depression diagnosis. Motivated by this, we investigate multi-modal data fusion strategies featuring deep representation learning of skin conductance, skin temperature, and acceleration data to predict self-reported mood, health, and stress scores (0 - 100) of college students (N = 239). Our cross-validated results from the early fusion framework exhibit a significantly higher (p <; 0.05) prediction precision over the late fusion for unseen users. Therefore, our findings call attention to the benefits of fusing physiological data modalities at a low level and corroborate the predictive efficacy of the deeply learned features.

Multi hours ahead prediction of surface ozone gas concentration: Robust artificial intelligence approach

Forecasting the Ozone concentration is a substantial process in many important environmental issues such air pollution management, risk assessment, public health, and global warming. Early and accurate prediction of Ozone is very significant for efficient monitoring of pollution towards constituting advanced early alarm systems. In this study, wavelet transform (WT) approach was applied to handle input variables prior to introduce them to artificial neural network (ANN). This approach attempts to remove the noise impacts which decreases the accuracy of simulation processes. Additionally, to improve ANN model performance, the selection of suitable type of transfer function with effective input combinations was thoroughly investigated before introducing the WT approach. The hybrid model (W-ANN) was also compared to the classical ANN in the prediction of 1 h ahead Ozone concentrations to insure the significance of this study. Based on statistical measures during all phases (i.e. training, validation, and testing), the W-ANN outperformed ANN model. The W-ANN model yielded fewer values of root mean square error (0.9313 ppm) and mean absolute error (0.6531 ppm) while, ANN model produced higher values of root mean square error (2.902 ppm) and mean absolute error (1.991 ppm) during the testing set. The quantity of predicted values that have absolute relative error between 1 and 3% represented 18% and 36.16% of all data points for each approach, ANN and W-ANN respectively. Additionally, the hybrid W-ANN managed successfully to forecast multiple time scales (2-, 3-, 4 and 5- ahead hour Ozone concentrations) and reported very fewer errors in comparison with modeling of one hour ahead Ozone gas concentration.

A hybrid drought index for drought assessment in Wadi Shueib catchment area in Jordan

Abstract Wadi Shueib catchment in Jordan is a water stress area and climate change is creating a further deficiency in precipitation, streamflow, and soil moisture; which are a deterrent to agriculture production in the area. In order to analyze the drought-like situation in the area, a hybrid drought index (HDI) has been developed considering the combined effect of these three variables. Fuzzy analytical hierarchy process (F-AHP) and entropy weight methods were carried out to develop a hybrid drought index (HDI) which combines meteorological, hydrological, and agricultural drought indices based on precipitation, streamflow, and soil moisture data in the area. The wavelet transform (WT) with cross wavelet (XCT) and wavelet coherence (WTC) were applied to investigate the interaction and the relations between the HDI index, drought indices, and large-scale sunspot activity Niño3.4 index. The results show that HDI can easily capture the trend of the drought-like conditions in the area based on the available data. The trend analysis of HDI revealed an increasing trend in the drought incidences in the near future. The study can be used as an early alarm for drought in the area, which can be helpful in the decision-making process towards water resources planning and management in the future.

DECISION MAKING PRINCIPLES SUPPORTING EARLY RESPONSE TO PANDEMIC: LESSONS IDENTIFIED FROM COVID 19

ABSTRACT The Covid19 pandemic in early 2020 found most of public and private institutions unprepared. Even after the World Health Organization (WHO) declared the pandemic, many public and private decision makers failed to take rapid mitigation measures and responses to the virus. Reasons that determined late response in public and private sectors might be different and may include: the risk of being accused of fearmongering in over-warning the public reluctance to impose heavy restrictions that may interfere with personal rights to liberty, assembly, and movement; fear of constraining economic growth; concerns about losing business opportunities due to adoption of traditional risk assessment principles too often based on probabilistic evaluation or on cost-benefit or cost-effectiveness analyses. The decision-making process required to face an outbreak that could eventually turn into a pandemic is characterized by ambiguity and uncertainty: the situation is what the modern decision theory typically refers to as “decisions under ignorance” or ambiguity. Nevertheless, events such as COVID-19 clearly illustrate the value of early response, which essentially involves listening to early alarm signals, undertaking proactive mitigation strategies and ensuring a safe running of the business. The aim of this paper is to focus on possible decision-making principles supporting early response to be applied at the first alarm sign to take precautionary and preemptive actions in order to mitigate future outbreaks of pandemic flu. Keywords pandemic response; decision making process; precautionary principle.

Self-Powered and Green Ionic-Type Thermoelectric Paper Chips for Early Fire Alarming.

Early fire alarming is of vital importance to lower the damages led by forest fires. Thus far, methods to monitor the forest fires at their early stage are mainly focused on artificial ground patrol, unmanned aerial vehicle cruise monitoring, observation by watchtower, or satellite inspection, whereas these methods are practically encountered with the problems of untimely feedback before the forest fires are out of control. This work proposes a particular kind of self-powered, low-cost, and green thermoelectric paper chips based on the principle of self-assembly and disassembly of ionic liquids on the surface of gold electrodes. By adjustment of the species of ionic liquids, both "n- and p-type" thermoelectric behaviors have been exploited that correspond to the opposite open-circuit voltages. Owing to the fluidic nature of ionic liquids, those "n- and p-type" thermoelectric units can be readily connected in series on one paper chip, leading to remarkable voltage signals in the presence of the temperature difference of 35 K. Followed by signal acquisition and transmission, such a thermoelectric paper chip successfully affords immediate electrical alarming at the early stage of an afire circumstance.

Domestic Violence and Safe Storage of Firearms in the COVID-19 Era.

Domestic Violence and Safe Storage of Firearms in the COVID-19 Era.

DESIGN AND ANALYSIS OF AN INTELLIGENT FIRE DETECTION SYSTEM FOR AIRCRAFT

Fire detection system and fire warning are design features of an aircraft. Fire detection system protects the aircraft and passengers both in case of actual fire during flight. But spurious fire warning during flight creates a panic situation in flight crews and passengers. The conventional fire alarm system of an aircraft is triggered by false signal. ANN based fire detection system provides real observation of deployed zones. An intelligent fire detection system is developed based on artificial neural network using three detection information such as heat (temperature), smoke density and CO gas. This Information helps in determining the probability of three representative of Fire condition which is Fire, smoke and no fire. The simulated MATLAB results Show that the errors in identification are very less. The neural network based fire detection system integrates different types of sensor data and improves the ability of system to correct prediction of fires. It gives early alarm when any kind of fire broke out and helps to decrease in spurious warning.

Templates for handling multi-agency collaboration activities and priorities in mining injury incidents: a Delphi study

PurposeThe purpose of this study was from a Swedish perspective to map experts' opinions on theoretical statements of essential collaboration activities for management of mining injury incidents.Design/methodology/approachA Delphi technique was performed, asking opinions from experts in iterative rounds to generate understanding and form consensus on group opinion around multi-agency management. The experts were personnel from emergency medical service, rescue service and mine industry, all with operative command positions.FindingsThree iterative rounds were performed. The first round was conducted as a workshop to collect opinions about the most important multi-agency collaboration activities to optimize victim's outcome from an injury incident in an underground mine. This resulted in 63 statements and additional three were added during the second round. The statements were divided into one trajectory and seventh time phases and comprised, e.g. early alarm routines, support of early life-saving interventions, relevant resources and equipment for the assignment and command and control center and functions with predefined action plans for response. It also comprised shared and communicated decisions about each agency's responsibility and safety. All statements reached consensus among the experts in Round 3.Research limitations/implicationsThe experts included in this study seem to be adequate but there could be other experts and different statements that other researchers might consider.Practical implicationsThese statements could be used to evaluate collaboration in major incidents exercises. The statements can also be quality indicators for reporting results from multi-agency management.Originality/valueThis paper contributes to the research field of collaboration and joint practices between and among personnel involved in rescue operations.

Analysis and Examination of Heart distress from ECG signal using Artificial Intelligence.

Health data analysis is usually based on a comparison of derived health measures to predefined thresholds. Symptoms can be observed if a value is above or below a threshold. Early detection of signs of heart failure allows the prediction of strokes of heart failure and can therefore prevent these. So identifying “accurate” criteria is the most important task. The accuracy of an experiment depends strongly on the accuracy of the criteria used. Congestive heart failure (CHF) occurs when the heart can not pump sufficient blood for a stable physiological condition. CHF usually occurs when the coronary artery blockage causes the heart tissue to become acidic. The data used to analyze data such as Linear Regression, Missing Enrollment Data, Search Signal, Clinical Data Protection Programs, and Early Adaptive Alarm. The proposed system involves models including server and data warehouse processing, pre-processing, extraction classification characterization in this paper. Classification of heart defects and prediction of heart failure by using applied classifier for hybridization, guideline for treating patients as a gym, level of stress management. In this article, the program tracks the heart disease patients, predicts atrial fibrillation and ventricular fibrillation, and alerts patients when the critical condition occurs.