Alert System Research Articles

Full-frame Data Reduction Method: A Data Mining Tool to Detect the Potential Variations in Optical Photometry

A Synchronous Photometry Data Extraction (SPDE) program, performing indiscriminate monitoring of all stars appearing in the same field of view of an astronomical image, is developed by integrating several Astropy affiliated packages to make full use of time series observed by traditional small/medium aperture ground-based telescopes. The complete full-frame stellar photometry data reductions implemented for the two time series of cataclysmic variables: RX J2102.0+3359 and Paloma J0524+4244 produce 363 and 641 optimal light curves, respectively. A cross-identification with SIMBAD finds 23 known stars, of which 16 are red giant-/horizontal-branch stars, 2 W UMa-type eclipsing variables, 2 program stars, an X-ray source and 2 Asteroid Terrestrial-impact Last Alert System variables. Based on the data products from the SPDE program, a follow-up light curve analysis program identifies 32 potential variable light curves, of which 18 are from the time series of RX J2102.0+3359, and 14 are from that of Paloma J0524+4244. They are preliminarily separated into periodic, transient, and peculiar types. By querying for the 58 VizieR online data catalogs, their physical parameters and multi-band brightness spanning X-ray to radio are compiled for future analysis.

Analysis of Accelerometer Applications

An accelerometer is a digital tool that measures modifications in velocity, acceleration, or vibration of a bodily object. It is typically used in various packages, along with smartphones, vehicles, aerospace, and healthcare. The accelerometer measures the acceleration of the object via detecting modifications within the capacitance, piezoelectric impact, or other phenomena resulting from the motion of the item. Here we have studied and analysed various accelerometer applications to identify their capabilities and usability, based on this we have concluded that accelerometer can be used for the design and development of an Epilepsy Patient Fall Detection and Alert System using Acceleromete

Alert System for New User to Find Safe Area using Blockchain

Alert System for New User to Find Safe Area using Blockchain

Tropical Cyclone Identification and Alert System using AlexNet and Deep CNN

Tropical cyclones (TCs) are catastrophic weather occurrences that can inflict massive damage in coastal regions across the world. Weather nowcasting, which is the weather forecast for a short time period, is one of the most challenging topics in meteorology. Weather radar and satellite are essential technologies for nowcasting, but issuing nowcasting warnings based on radar and satellite data is a difficult process. This proposed approach is of a deep learning approach for TC identification from aerial imagery, which includes a Deep Convolutional Neural Network of Alex-Net architecture as the classifier.

Traffic Sign Board Detection and Voice Alert System

Automated tasks have simplified practically everything we perform in today's world. Drivers often miss signage on the side of the road in an effort to focus on the road, which can be dangerous for them and others. This issue may be avoided if there was a quick way to alert the driver without requiring them to divert their attention. TSDR (Traffic Sign Detection and Recognition) is useful in this situation because it detects and recognizes signs, alerting the motorist to any forthcoming signs. This not only assures road safety, but also puts the driver at ease when driving on unfamiliar or difficult roads. Another issue that arises frequently is the inability to comprehend the sign's meaning. Drivers will no longer struggle to grasp what the sign is saying thanks to this Advanced Driver Assistance Systems (ADAS) application. In this paper, we propose a method for detecting and recognizing traffic signs that uses image processing for detection and an ensemble of Convolutional Neural Networks (LeNet) for recognition. Because LeNet CNN have a high recognition rate, they are ideal for a variety of computer vision tasks. For the CNN implementation, Tensor Flow is employed. And here for object detection we implement yolo frame work On the German data sets, we achieved recognition accuracies of more than 99 percent for circular signs.

Wildlife Detection and Intrusion Alert System

Wildlife Detection and Intrusion Alert System

Phase Curves of Kuiper Belt Objects, Centaurs, and Jupiter-family Comets from the ATLAS Survey

The Kuiper Belt objects (KBOs), the Centaurs, and the Jupiter-family comets (JFCs) form an evolutionary continuum of small outer solar system objects, and their study allows us to gain insight into the history and evolution of the solar system. Broadband photometry can be used to measure their phase curves, allowing a first-order probe into the surface properties of these objects, though limited telescope time makes measuring accurate phase curves difficult. We make use of serendipitous broadband photometry from the long-baseline, high-cadence Asteroid Terrestrial-impact Last Alert System survey to measure the phase curves for a sample of 18 KBOs, Centaurs, and JFCs with unprecedentedly large data sets. We find phase curves with previously reported negative slopes become positive with increased data and are thus due to insufficient sampling of the phase-curve profile, and not a real physical effect. We search for correlations between phase-curve parameters, finding no strong correlations between any parameter pair, consistent with the findings of previous studies. We search for instances of cometary activity in our sample, finding a previously reported outburst by Echeclus and a new epoch of increased activity by Chiron. Applying the main belt asteroid HG 1 G 2 phase-curve model to three JFCs in our sample with large phase angle spans, we find their slope parameters imply surfaces more consistent with those of carbonaceous main belt asteroids than silicaceous ones.

Implementing an Acute Text-Based Wellness Alert System for Pediatric Residents: A Pilot Study.

Residency training presents a unique combination of factors affecting well-being. Studies suggest that mental/emotional distress is pervasive among residents, caused by contextual factors affecting emotional resilience (eg, sleep deprivation) and abrupt, stressful triggers (eg, patient deaths). 1 Moore KA O'Brien BC Thomas LR. “I wish they had asked”: a qualitative study of emotional distress and peer support during internship. J Gen Intern Med. 2020; 35: 3443-3448https://doi.org/10.1007/s11606-020-05803-4 Crossref PubMed Scopus (12) Google Scholar Interventions including positive psychology coaching, peer support, mindfulness curricula, and web-based cognitive behavioral therapy have demonstrated modest but inconsistent improvements in well-being; however, they may be considered burdensome. 2 Guille C Zhao Z Krystal J et al. Web-based cognitive behavioral therapy intervention for the prevention of suicidal ideation in medical interns: a randomized clinical trial. JAMA Psychiatr. 2015; 72: 1192-1198https://doi.org/10.1001/jamapsychiatry.2015.1880 Crossref PubMed Scopus (91) Google Scholar , 3 Ireland MJ Clough B Gill K et al. A randomized controlled trial of mindfulness to reduce stress and burnout among intern medical practitioners. Med Teach. 2017; 39: 409-414https://doi.org/10.1080/0142159X.2017.1294749 Crossref PubMed Scopus (72) Google Scholar , 4 Bommarito S Hughes M. Intern mental health interventions. Curr Psychiatry Rep. 2019; 21: 55https://doi.org/10.1007/s11920-019-1035-y Crossref PubMed Scopus (3) Google Scholar , 5 Meeks LM Ramsey J Lyons M et al. Wellness and work: mixed messages in residency training. J Gen Intern Med. 2019; 34: 1352-1355https://doi.org/10.1007/s11606-019-04952-5 Crossref PubMed Scopus (19) Google Scholar By contrast, mobile phone-based interventions are less onerous and have been well-accepted. 6 Scielzo SA Weigle DC Kazi SD. Resident fuel levels: reframing, assessing, and addressing well-being. J Grad Med Educ. 2018; 10: 198-202https://doi.org/10.4300/JGME-D-17-00536.1 Crossref PubMed Scopus (3) Google Scholar , 7 Weis HB Clark AT Scielzo SA et al. The fuel gauge: a simple tool for assessing general surgery resident well-being. J Surg Educ. 2020; 77: 27-33https://doi.org/10.1016/j.jsurg.2019.07.011 Crossref PubMed Scopus (7) Google Scholar , 8 Nathan J McCray L Feldman N. The text effect: stress management and resiliency training pilot for resident physicians. Fam Med. 2021; 53: 139-144https://doi.org/10.22454/FamMed.2021.847102 Crossref PubMed Scopus (2) Google Scholar No interventions, however, address abrupt or acute distressing events.

CNN Based Wildlife Intrusion Detection and Alert System

Wildlife intrusion detection and alert systems are designed to detect and alert wildlife intrusion events, such as animals crossing highways, entering farms or protected areas, and approaching human settlements. This system often uses advanced technologies, such as cameras, sensors, and machine learning algorithms, to detect and identify animal species and behaviors. Convolutional neural networks (CNNs) are a type of deep learning algorithm commonly used for image and video analysis tasks, including object detection and classification. CNNs can learn to extract features from images and videos automatically and accurately, making them well-suited for detecting animals in wildlife intrusion detection systems.

Simulating the Effectiveness of an IoT Parcel Alert System for Enhancing Delivery Efficiency and Safety During Covid-19

The Internet of Things (IoT) has revolutionized the way devices communicate and interact with one another. Malaysia has witnessed a substantial increase in online purchasing in recent years. The COVID-19 pandemic and the Malaysian government's mobility control order (MCO) have contributed to the rise in online sales. This circumstance has resulted in a substantial increase in the number of packages that Malaysian delivery firms must manage, producing issues for both homeowners and delivery services. Unattended parcel delivery, parcel loss, and unsuccessful delivery efforts have become widespread. This paper proposes an IoT-based Parcel Delivery Alert System to address the challenges associated with unattended parcel delivery, parcel loss, and failed delivery attempts. The system comprises a parcel safe box that integrates with IoT sensors, such as weight and load sensors, image sensors, and light sensors. The IoT sensors provide real-time information about the delivery status and alert the recipient once the delivery has been made. The system's efficiency and effectiveness were evaluated using the MARS simulator, demonstrating a significant improvement in parcel delivery performance. This paper presents the design, development, and simulation of an IoT-based Parcel Delivery Alert System that can enhance the delivery experience while minimizing delivery-related problems. The paper describes the design and development of the parcel safe box and the eventual evolution of the system. One can acquire access to the safe box by scanning the tracking number on the package's delivery label using a QR code.

Antibacterial Efficacy of N-(4-methylpyridin-2-yl) Thiophene-2-Carboxamide Analogues against Extended-Spectrum-β-Lactamase Producing Clinical Strain of Escherichia coli ST 131.

Development in the fields of natural-product-derived and synthetic small molecules is in stark contrast to the ongoing demand for novel antimicrobials to treat life-threatening infections caused by extended-spectrum β-lactamase producing Escherichia coli (ESBL E. coli). Therefore, there is an interest in the antibacterial activities of synthesized N-(4-methylpyridin-2-yl) thiophene-2-carboxamides (4a-h) against ESBL-producing E. coli ST131 strains. A blood sample was obtained from a suspected septicemia patient and processed in the Bactec Alert system. The isolate's identification and antibacterial profile were determined using the VITEK 2® compact system. Multi-locus sequence typing of E. coli was conducted by identifying housekeeping genes, while ESBL phenotype detection was performed according to CLSI guidelines. Additionally, PCR was carried out to detect the blaCTX-M gene molecularly. Moreover, molecular docking studies of synthesized compounds (4a-h) demonstrated the binding pocket residues involved in the active site of the β-lactamase receptor of E. coli. The result confirmed the detection of E. coli ST131 from septicemia patients. The isolates were identified as ESBL producers carrying the blaCTX-M gene, which provided resistance against cephalosporins and beta-lactam inhibitors but sensitivity to carbapenems. Among the compounds tested, 4a and 4c exhibited high activity and demonstrated the best fit and interactions with the binding pocket of the β-lactamase enzyme. Interestingly, the maximum of the docking confirmations binds at a similar pocket region, further strengthening the importance of binding residues. Hence, the in vitro and molecular docking studies reflect the promising antibacterial effects of 4a and 4c compounds.

Assessing Automated Flood Disaster Alert Systems in Zambia: Case of Mbeta Island in Sioma, Western Province

Globally, as the advancement and mainstreaming of Technology become more perversive, ICTs have become agents of change transforming the way we conduct and perceive human activities to sustain and promote the quality of life. It is also no surprise that today’s technology pros control everything from telephone system to software compliance and disaster recovery that seem to become sole candidates for change. For instance, todays ICTs have been integrated in modern lifestyles and has become perversive in everything from telephone networking, automated security systems, software compliance and backups to disaster recovery platforms and Internet of Things. Additionally, one may argue that it is no longer feasible or rapidly becoming obsolete to solely rely on manual systems to predict potential threats such as natural and man-made disasters. Thus implementing early warning systems, communities play an important role in leveraging ICTs potential to generate warning hazards through automated systems that escalates emergency notifications to rescue and mitigation teams. Using a positivist approach with methodological triangulation at data collection and analysis, this study assessed automated flood disaster alert systems in Zambia with specific reference to Mbeta Island in Sioma. The study was informed by Unified Theory of Acceptance and Use of Technology (UTAUT) theoretical framework with questionnaires and interviews as measurement instruments to assess factors that led to the adoption of the alert system installed in Mbeta Island. Study findings showed varying impact of independent variables: Effort expectancy 6.2%, Performance expectancy 11.8%, Facilitating conditions 4.4% and Social influences 2.6% respectively. The total impact showed there were factors left out in the implementation of the alert system and that there is a need to adopt a new and robust disaster mitigation system that would allow for integration of community needs and everyday challenges in relation to natural and man-made disasters.

Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques

Social relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments.

Increasing Utilization and Improving Documentation in a Radiology Critical Alert System

Effective communication of critical imaging findings is an important patient safety issue. Despite an increase in exam volumes, our institution saw a decrease in the number of alerts sent through our critical alert system, indicating that critical findings were not being communicated. The purpose of our interventions was to increase the number of critical alerts, while also improving documentation and improving our provider database. We used a program of education for our radiologists and systematic reinforcement to increase the usage of our critical alert system. We also implemented a new time-stamp macro in our dictation system to improve documentation of emergency alerts, and engaged with other departments to improve the contact information in our provider database. Our interventions led to an increase in the monthly number of critical alerts, most notably for findings that require clinical or imaging follow-up (17 alerts per month). There was also a steady improvement in documentation (96.9% compliance), along with an increase in the number of alerts to providers with current contact information (0.5% per month). Our efforts show that educational and collaborative efforts can result in improved communication of radiologic critical results.

Electronic Alert System Significantly Increases HBV Screening Rates Before Immunosuppressive Treatments.

Hepatitis B Virus (HBV) screening rates before starting immunosuppressive treatments are suboptimal. The aim of the study was to evaluate the efficacy of a new electronic alert system in increasing HBV screening rates. The electronic alert system, HBVision2, identifies patients at risk of HBV reactivation when a pre-determined International Classification of Diseases (ICD)-10 code is entered into the hospital's database or immunosuppressive treatment is prescribed. The system evaluates the prior Hepatitis B Surfage Antigen (HBsAg) and anti-Hepatitis B Core Immunglobulin G (HBc IgG) results and sends an alert code to the clinician for screening if serology is not completely available or consult a specialist in case of positive serology. The HBV screening and consultation rates of patients before (control group) and after HBVision2 were retrospectively compared. The clinical course of unscreened and/or unconsulted patients was determined, and the clinical efficacy of HBVision2 in preventing HBVr was predicted. Control group included 815 patients (52.6% male, mean age: 60 ± 12, 82.5% with oncologic malignancy) and study group included 504 patients (56% male, mean age: 60 ± 13, 91.4% with oncologic malignancy). Groups were similar with respect to gender, mean age, and HBVr risk profile of the immunosuppressive treatment protocols. Overall, both HBsAg (from 55.1% to 93.1%) and anti- HBc IgG screening rates significantly increased (from 4.3% to 79.4%) after the electronic alert system (P < .001, for both). Consultation rates of anti-HBc IgG-positive patients significantly increased from 40% to 72.7% (P = .012). HBVr developed in 2 patients (2.6%) who were not screened and/or consulted after the alert system. Alert program prevented the development of HBVr in 10 patients (1.9%) of the study group and decreased the development of HBVr by 80%. Electronic alert system significantly improved HBsAg and anti-HBc IgG screening rates before starting immunosuppressive treatment and prevented the development of HBVr to a great extent. However, screening rates are still below optimal and need to be improved.

Applying a Responsible Innovation Framework in Developing an Equitable Early Alert System:

The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and implementation of data analytics in higher education. In a case study of creating an early-alert retention system at James Madison University, a working group of ~20 faculty, staff, and students creatively utilized the AIRR framework. The present study discusses how the AIRR framework was utilized to observe and enhance group processes, and the outcomes of those enhanced processes.

Clinical Impact of a Sepsis Alert System Plus Electronic Sepsis Navigator Using the Epic Sepsis Prediction Model in the Emergency Department

BackgroundThe Epic Sepsis Prediction Model (SPM) is a proprietary sepsis prediction algorithm that calculates a score correlating with the likelihood of an International Classification of Diseases, Ninth Revision code for sepsis. ObjectiveThis study aimed to assess the clinical impact of an electronic sepsis alert and navigator using the Epic SPM on time to initial antimicrobial delivery. MethodsWe performed a retrospective review of a nonrandomized intervention of an electronic sepsis alert system and navigator using the Epic SPM. Data from the SPM site (site A) was compared with contemporaneous data from hospitals within the same health care system (sites B–D) and historical data from site A. Nonintervention sites used a systemic inflammatory response syndrome (SIRS)–based alert without a sepsis navigator. ResultsA total of 5368 admissions met inclusion criteria. Time to initial antimicrobial delivery from emergency department arrival was 3.33 h (interquartile range [IQR] 2.10–5.37 h) at site A, 3.22 h (IQR 1.97–5.60; p = 0.437, reference site A) at sites B–D, and 6.20 h (IQR 3.49–11.61 h; p < 0.001, reference site A) at site A historical. After adjustment using matching weights, there was no difference in time from threshold SPM score to initial antimicrobial between contemporaneous sites. Adjusted time to initial antimicrobial improved by 2.87 h (p < 0.001) at site A compared with site A historical. ConclusionsImplementation of an electronic sepsis alert system plus navigator using the Epic SPM showed no difference in time to initial antimicrobial delivery between the contemporaneous SPM alert plus sepsis navigator site and the SIRS-based electronic alert sites within the same health care system.

Abstract Number ‐ 42: Patterns of Alert and Management of Cerebral Aneurysms using an Incidental Aneurysm Alert System (IAAS)

Introduction Incidentally discovered cerebral aneurysms have become an increasingly more common finding as patients undergo more frequent surveillance imaging. To aid in clinical management of these critical findings, we sought to develop an automated alert system for these vascular lesions. Here we describe the effectiveness of the alert system and subsequent management of incidentally discovered cerebral aneurysms on MR or CT angiography using an Incidental Aneurysm Alert System (IAAS). Methods IAAS received neuroradiology reports of MRA and CTA generated by the UCLA Department of Radiology via an HL7 feed. Reports were parsed using natural language processing to identify mentions of ‘aneurysm’. Each identification automatically generated an alert email to the interventional neuroradiology division for physician triage. Indication for imaging, specialty of ordering physician, aneurysm location and size, aneurysm development risk factors, PHASES score, patient ethnic background and subsequent management of vascular lesion were assessed. Results A total of 129 consecutive reports were reviewed from March‐December of 2020. Ten duplicate patients were excluded. Of the unique reports, 5 alerted for non‐vascular lesions and 7 did not demonstrate an aneurysm after additional imaging, resulting in a 90% detection accuracy of suspected cerebral aneurysms for a total of 107 unique cases included in the analysis. The median age was 65 years and 65% were female. Self‐identified race was 54% non‐Hispanic White, 20% other, 8% Asian, 6% Black, and 6% unknown/declined to answer. One quarter (25%) were of Hispanic ethnicity. The most common indication for imaging was acute stroke (27%). Of the detected aneurysms, 48% resulted in consultation with a neuro‐interventionalist. Of the referrals generated, 75% originated from a hospital facility and 25% from community practice. The most common referring specialty was Neurology (37%). Of those referred, just over 50% subsequently underwent diagnostic and/or therapeutic angiography. Seventy three percent of patients who underwent cerebral angiography were intervened upon either immediately or within 2 years from discovery. Eight percent of detected aneurysms were ruptured on presentation. Aneurysms were treated with flow diversion (37%), primary coiling (37%), clipping (16%) and vessel sacrifice/surgical trapping (10%). The mean PHASES score of all patients referred was 4.5, conferring a 5‐year rupture risk of ∼0.9‐1.3%. Asian and Hispanic populations had higher mean PHASES scores on presentation of 7.2 (∼2.4% 5‐year rupture risk) and 5.2 (∼1.3% 5‐year rupture risk) respectively, compared with Non‐Hispanic Caucasians of 4.1 (∼0.9% 5‐year rupture risk). For Hispanics, mean age was 54 years and mean aneurysm size 9.5 mm as compared to 65 years and 5.7 mm in non‐Hispanic Caucasians on presentation. There were no significant differences in aneurysm risk factors including HTN, smoking, fibromuscular dysplasia or family history of cerebral aneurysms. Conclusions IAAS is an effective method of alerting neuro‐interventionalists of incidentally identified cerebral aneurysms. Of those detected in our pilot series, Hispanics were younger with larger aneurysms on detection. IAAS may have potential value in connecting general physicians with cerebrovascular specialists, improving the management of patients with incidentally discovered cerebral aneurysms.

Emotional Responses to Wireless Emergency Alerts for COVID-19 and Predictors of Public Health Compliance

This study explored perceptions and effects of the Wireless Emergency Alert (WEA) system for COVID-19 public health messaging after a message was sent to Pennsylvania residents in November 2020. Survey and interview research were conducted to understand the targeted publics’ reactions to this message and factors impacting potential behavior change. Findings showed residents who received the WEA expressed greater feelings of anger and surprise about the COVID-19 threat compared to those who did not. Additionally, for participants who did not receive the WEA message, higher arousals of fear and perceptions of threat severity predicted a higher likelihood that they would have changed their Thanksgiving plans. Interview data suggested positive emotions toward using WEAs for public health crises in general.

Home Surveillance and Alert System using Raspberry Pi Zero W and GSM Modem with MQTT Protocol

Today’s modern era has made it essential for everyone, even for the privacy of their homes, to have controllable, affordable, and easy-to-use security systems. The creation of a cost-effective, straightforward, and feature-rich home security alarm system using a Raspberry Pi Zero W microprocessor and MQTT protocol is discussed in this paper. The proposed system is primarily intended for outlying areas with limited or insufficient network bandwidths. Together with sensors such a PIR (passive infrared sensor), sound sensor, gas leakage sensor, obstacle/proximity sensor (vibration sensor), fire/flame sensor, GSM module, and a surveillance pi camera, it makes use of a Raspberry Pi Zero W as a microprocessor. [1]. In addition, the system will promptly alert the homeowner through phone calls, SMS, or mail anytime it senses the presence of an intruder or other safety hazards. Hence, anyone who wants to make their space (bank, home, workplace, jewelry shops, and cabins) safe and to protect themselves from theft and infiltration can use this system at a fair price.