Alarm Features Research Articles

Sistem Informasi Penyewaan Arena Futsal dan Alarm Permainan dengan Menggunakan Laravel Berbasis Web di Kota Kraksaan Kabupaten Probolinggo

The advancement of information technology has transformed the management of futsal businesses, simplifying processes such as court reservations, schedule management, payments, and providing automated notifications to users through a game alarm feature. This system also includes an automatic reporting feature that generates accurate and real-time rental summaries. This study aims to design and implement a web-based futsal rental information system using the Laravel framework, featuring a game alarm to enhance management efficiency. The system is designed to address limitations in futsal rental management and time scheduling, as well as simplify the reporting process, which is currently done manually. The system development follows the waterfall model, which includes requirements analysis, system design, implementation, testing, and maintenance stages. The results show that the implementation of this web-based system significantly improves the efficiency of futsal court management in Kraksaan City, particularly in managing game time, rental reporting, and real-time financial management. The game alarm feature effectively reduces time conflicts between users, while the automatic reporting feature helps business owners track revenue and rental trends easily. The implementation of this system is expected to improve service quality and provide a better user experience, while assisting business owners in managing their operations more efficiently.

Evaluating the Efficacy of Fecal Immunochemical Test, Fecal Calprotectin, and Serum C-Reactive Protein in Diagnosing Patients With Chronic Lower Gastrointestinal Symptoms.

Accurate early detection of ileocolonic lesions in patients with chronic lower gastrointestinal symptoms (LGISs) is often difficult due to the rarity of early-stage alarm signs. This study assesses the effectiveness of noninvasive blood and stool biomarkers in diagnosing ileocolonic lesions in patients with chronic LGISs undergoing colonoscopy. We conducted a prospective study between April 2020 and July 2022 involving patients with LGISs lasting a month or more. Before colonoscopy, we gathered clinical data, blood samples for C-reactive protein (CRP) and stool samples for fecal immunochemical test (FIT) and fecal calprotectin (FC) analysis. Of 922 participants analyzed (average age 62 years, 37% male), 130 (14.1%) had significant colonoscopy findings, including cancer, advanced adenoma, and inflammatory conditions. Test effectiveness showed an area under the curve of 0.630 for alarm features, 0.643 for CRP, 0.781 for FIT, and 0.667 for FC. Combining stool tests with alarm features improved diagnostic precision. Those without alarm features had a high negative predictive value of 0.97 with low threshold FIT and FC, missing minimal significant lesions, and no cancer. For patients with alarm features, dual high-cutoff test positivity showed a positive predictive value of 0.67. Adding CRP to fecal tests did not enhance accuracy. FIT and FC are valuable in evaluating LGISs. Negative results at low cutoffs can delay colonoscopy in limited resource settings while positive results at dual high cutoffs substantiate the need for the procedure.

Yield of alarm features in predicting significant endoscopic findings among hospitalized patients with dyspepsia.

Dyspepsia is a very prevalent upper gastrointestinal tract symptoms complex. Some of these symptoms might arise from serious underlying diseases, so the promotion of evidence-based guidelines could potentially better align evaluation and treatment. To determine the value of alarm features as a predictive factor for significant endoscopic findings (SEFs) among hospitalized patients presenting with dyspepsia. We conducted a retrospective case-control study including information about 6208 endoscopic procedures performed for hospitalized patients. Patients were divided into two groups, with and without SEFs, and compared to elucidate the ability of the different alarm features to predict SEFs. During the study, 605 patients fulfilled the inclusion criteria. When the demographics and clinical characteristics of the two groups were compared, tachycardia (P < 0.05), normocytic anemia, (P < 0.05), leukocytosis (P < 0.05), and hypoalbuminemia (P < 0.05) documented on admission prior to endoscopy were strong predictors of SEFs. Among the alarm features, upper gastrointestinal bleeding, persistent vomiting, odynophagia [odds ratio (OR) = 3.81, P < 0.05; OR = 1.75, P = 0.03; and OR = 7.81, P = 0.07, respectively] were associated with SEFs. Unexplained weight loss was strongly associated with malignancy as an endoscopic finding (OR = 2.05; P < 0.05). In addition, long-term use of anti-aggregate medications other than aspirin (P < 0.05) was correlated to SEFs. Novel predictors of SEFs were elucidated in this study. These parameters could be used as an adjunctive in decision making regarding performing upper endoscopy in hospitalized patients with dyspepsia.

Diagnosis of Chronic Constipation.

Patients with chronic constipation (CC) usually complain of mild to severe symptoms, including hard or lumpy stools, straining, a sense of incomplete evacuation after a bowel movement, a feeling of anorectal blockage, the need for digital maneuver to assist defecation, or reduced stool frequency. In clinical practice, healthcare providers need to check for 'alarm features' indicative of a colonic malignancy, such as bloody stools, anemia, unexplained weight loss, or new-onset symptoms after 50 years of age. In the Seoul Consensus on the diagnosis and treatment of chronic constipation, the Bristol stool form scale, colonoscopy, and digital rectal examination are useful for objectively evaluating the symptoms and making a differential diagnosis of the secondary cause of constipation. If patients with CC improve to lifestyle modification or first-line therapies, the effort to determine the subtypes of CC is usually not considered. On the other hand, if conventional therapeutic strategies fail, diagnostic testing needs to be considered to distinguish between the different subtypes of functional constipation (normal-transit constipation, slow transit constipation, or defecatory disorder) because these subtypes of constipation have different therapeutic implications and a correct diagnosis is critical. In the Seoul consensus, physiological testing is recommended for patients with functional constipation who have failed to respond to treatment with available laxatives (for a minimum of 12 weeks and recommended a therapeutic regimen) or who are strongly suspected of having a defecatory disorder. The Seoul consensus contains statements of physiological testing, including balloon expulsion test, anorectal manometry, defecography, and colon transit time.

Android-Based Lifestyle Management Application for Type-2 Diabetes Mellitus Patients

Diabetes Mellitus (DM) Type-2 is a chronic disease that occurs due to uncontrolled blood glucose levels in the body. Uncontrolled glucose levels in people with Type-2 Diabetes Mellitus can cause various kinds of complications, therefore this research aims to create an Android application that can help people with Type-2 Diabetes Mellitus in controlling blood glucose levels. This application creation method includes the stages of preparation and application creation by conducting previous literature studies, which produce a theoretical model. The result of this research is a lifestyle management application for type 2 diabetes mellitus patients called Selfcare Glucose Management (SCGM), which has 4 kinds of features, including a feature that can calculate daily calorie needs by displaying various types of food with a predetermined weight and number of calories, an alarm feature that functions to remind patients to take medication and inject insulin, a physical activity training feature and a feature that contains educational material about Diabetes Mellitus. This application has been tested using application measurement methods in the form of repeated manual calculations or calibration and obtained results that match the expected values. It is hoped that this application can be developed to be more interactive and communicative in its use.

THE ROLE OF RADIOLOGIC IMAGING IN IRRITABLE BOWEL SYNDROME (IBS): A SYSTEMATIC REVIEW

Background: The role of radiologic imaging in the investigation of irritable bowel syndrome (IBS) remains a subject of debate and there is some evidence, from recent studies of utilization of imaging in IBS, which focused on associated costs and radiation exposure, that imaging is being used relatively widely in these patients. The aim: This study aims to show the role of radiologic imaging in irritable bowel syndrome (IBS). Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2013 and 2023 were taken into account. Several different online reference sources, like Pubmed and SagePub, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 112 articles, whereas the results of our search on SagePub brought up 179 articles. The results of the search conducted for the last year of 2013 yielded a total 84 articles for PubMed and 91 articles for SagePub. The result from title screening, a total 18 articles for PubMed and 25 articles for SagePub. In the end, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: In patients presenting with IBS symptoms and alarm features, radiologic testing may be used to exclude an alternative diagnosis and the imaging modality should be chosen based on the most likely alternative diagnosis.

Efficient Intrusion Detection System Data Preprocessing Using Deep Sparse Autoencoder with Differential Evolution

A great amount of data is generated by the Internet and communication areas’ rapid technological improvement, which expands the size of the network. These cutting‐edge technologies could result in unique network attacks that present security risks. This intrusion launches many attacks on the communication network which is to be monitored. An intrusion detection system (IDS) is a tool to prevent from intrusions by inspecting the network traffic and to make sure the network integrity, confidentiality, availability, and robustness. Many researchers are focused to IDS with machine and deep learning approaches to detect the intruders. Yet, IDS face challenges to detect the intruders accurately with reduced false alarm rate, feature selection, and detection. High dimensional data affect the feature selection methods effectiveness and efficiency. Preprocessing of data to make the dataset as balanced, normalized, and transformed data is done before the feature selection and classification process. Efficient data preprocessing will ensure the whole IDS performance with improved detection rate (DR) and reduced false alarm rate (FAR). Since datasets are required for the various feature dimensions, this article proposes an efficient data preprocessing method that includes a series of techniques for data balance using SMOTE, data normalization with power transformation, data encoding using one hot and ordinal encoding, and feature reduction using a proposed deep sparse autoencoder (DSAE) with differential evolution (DE) on data before feature selection and classification. The efficiency of the transformation methods is evaluated with recursive Pearson correlation‐based feature selection and graphical convolution neural network (G‐CNN) methods.

Getting the BS out of Irritable Bowel Syndrome with Diarrhea (IBS-D): Let's Make a Diagnosis.

Irritable bowel syndrome with diarrhea (IBS-D) is diagnosed when chronic symptoms of abdominal pain accompany loose stools, and alarm features, such as fever, anemia, rectal bleeding, and weight loss are absent. This combination of symptoms makes structural disorders, such as inflammatory bowel disease or cancer, unlikely, but does not exclude other conditions that cause these symptoms. The question is whether making a "positive diagnosis" of IBS-D based on symptoms alone and instituting therapy based on that diagnosis still makes sense. Clinical observations suggest that at least two-thirds of cases of IBS-D can be explained by three mechanisms: a) food intolerances (~ 30-40%), b) bile acid diarrhea (~ 20-30%), and c) disturbed microbial flora (~ 15-20%). Other conditions that are less frequent but can cause IBS symptoms or be confused with IBS include: celiac disease, microscopic colitis, mastocytosis/mast cell activation, and drug side-effects. Many cases of IBS-D have a discoverable, underlying cause that can direct therapy more efficiently.

Design of Additional Safety Locks for Motor Vehicles Using RFID

Indonesia, facing the dual challenges of rapid economic development and rising vehicle theft rates due to economic inequalities, has prompted a need for innovative security solutions in the automotive sector. This research aims to address this growing concern by introducing a sophisticated motor vehicle security system utilizing Radio Frequency Identification (RFID) technology. The proposed system employs RFID keys, which use ID tag cards as unique identifiers to activate motor vehicles. The methodology centers around the integration of RFID technology with a microcontroller, creating a security system that is both advanced and user-friendly. This system includes an alarm feature that is triggered when an ID tag card not registered with the vehicle's RFID is used, providing an additional layer of security against unauthorized access. The results of implementing this system have been promising. Testing revealed that the RFID locks, when combined with the microcontroller, effectively recognized authorized ID tag cards and successfully denied access when unauthorized cards were used. The alarm system also functioned as intended, activating when discrepancies in ID tag data were detected. This integration of RFID with a microcontroller not only enhanced the security of motor vehicles but also contributed to a significant reduction in vehicle thefts in the community, showcasing the potential of RFID technology as a viable solution to the prevalent issue of vehicle security in Indonesia.

Next-Generation Railway Safety: Designing an Automatic Gate System with Voice-Activated Alarms

Railway crossings are high-risk areas that demand constant vigilance and attention to ensure the safety of passengers and other road users. Automatic gate systems have been widely used to mitigate these risks, but their effectiveness is limited by the reliance on visual cues and their inability to alert drivers or pedestrians who are deaf or hard of hearing. To address this limitation, we propose a voice-activated automatic gate railway system that uses voice commands to control the gates and employs an audio alarm to alert road users of the approaching train and will give also alarm who walk on the railway. Our system consists of a voice recognition module, a control unit, and a network of sensors that detect the presence of trains and road users. When a train is detected, the gate closes automatically, and the system generates a voice alarm to alert nearby road users. The voice recognition module ensures that the system can be operated hands-free, making it convenient for drivers and pedestrians. Moreover, the audio alarm feature ensures that people with hearing impairments are alerted to the approaching train. We evaluated the performance of the proposed system using a prototype installed at a railway crossing in a high-traffic area. Our results demonstrate that the system is reliable and effective, with a high degree of accuracy in voice recognition and a low false positive rate. Our findings suggest that the voice-activated automatic gate railway system with an audio alarm is a promising solution to enhance the safety and security of railway crossings.

Next-Generation Railway Safety: Designing an Automatic Gate System with Voice-Activated Alarms

Railway crossings are high-risk areas that demand constant vigilance and attention to ensure the safety of passengers and other road users. Automatic gate systems have been widely used to mitigate these risks, but their effectiveness is limited by the reliance on visual cues and their inability to alert drivers or pedestrians who are deaf or hard of hearing. To address this limitation, we propose a voice-activated automatic gate railway system that uses voice commands to control the gates and employs an audio alarm to alert road users of the approaching train and will give also alarm who walk on the railway. Our system consists of a voice recognition module, a control unit, and a network of sensors that detect the presence of trains and road users. When a train is detected, the gate closes automatically, and the system generates a voice alarm to alert nearby road users. The voice recognition module ensures that the system can be operated hands-free, making it convenient for drivers and pedestrians. Moreover, the audio alarm feature ensures that people with hearing impairments are alerted to the approaching train. We evaluated the performance of the proposed system using a prototype installed at a railway crossing in a high-traffic area. Our results demonstrate that the system is reliable and effective, with a high degree of accuracy in voice recognition and a low false positive rate. Our findings suggest that the voice-activated automatic gate railway system with an audio alarm is a promising solution to enhance the safety and security of railway crossings.

Next-Generation Railway Safety: Designing an Automatic Gate System with Voice-Activated Alarms

Railway crossings are high-risk areas that demand constant vigilance and attention to ensure the safety of passengers and other road users. Automatic gate systems have been widely used to mitigate these risks, but their effectiveness is limited by the reliance on visual cues and their inability to alert drivers or pedestrians who are deaf or hard of hearing. To address this limitation, we propose a voice-activated automatic gate railway system that uses voice commands to control the gates and employs an audio alarm to alert road users of the approaching train and will give also alarm who walk on the railway. Our system consists of a voice recognition module, a control unit, and a network of sensors that detect the presence of trains and road users. When a train is detected, the gate closes automatically, and the system generates a voice alarm to alert nearby road users. The voice recognition module ensures that the system can be operated hands-free, making it convenient for drivers and pedestrians. Moreover, the audio alarm feature ensures that people with hearing impairments are alerted to the approaching train. We evaluated the performance of the proposed system using a prototype installed at a railway crossing in a high-traffic area. Our results demonstrate that the system is reliable and effective, with a high degree of accuracy in voice recognition and a low false positive rate. Our findings suggest that the voice-activated automatic gate railway system with an audio alarm is a promising solution to enhance the safety and security of railway crossings.

Smart CCTV Surveillance System with Visitor Management System, Face Detection, Fire Alarm, Parking Assistant for Residential Areas

Abstract: The "Smart CCTV Surveillance System" stands as a revolutionary solution at the nexus of security, cutting-edge technology, and user-centric design. Anchored by features such as real-time facial recognition, trustworthiness recognition, and comprehensive threat detection, the system redefines the paradigm of surveillance and access control. Beyond addressing the critical challenges of unauthorized access and security threats, this project introduces a robust fire alarm feature. By integrating sophisticated algorithms for real-time fire detection, the system ensures swift response to potential emergencies. Alongside this, the project streamlines visitor management through automation, optimizing registration, check-in, and check-out processes. The addition of parking assistance enhances the overall user experience, guiding visitors to available parking spaces and mitigating congestion. In essence, the "Smart CCTV Surveillance System" not only establishes an impregnable security infrastructure but also envisions a future where technology seamlessly collaborates with human needs, fostering environments that are not just secure but also smarter and more user-friendly

GTFP: Network Fault Prediction Based on Graph and Time Series

With the explosion of 5G network scale, the network structure becomes increasingly complex. During the operation of the network devices, the probability of anomalies or faults increases accordingly. Network faults may lead to the disappearance of important information and cause unpredictable losses. The prediction of network faults can enhance the quality of network services and reduce economic loss. In this paper, we propose the concept of 4D features and use the BERT algorithm to extract semantic features, the graph neural network algorithm to extract network topology information, and the Temporal Convolutional Network (TCN) algorithm to extract time series. Based on this, we propose Fault Prediction based on GraphSage and TCN (GTFP), an end-to-end solution of network fault alarm prediction, which is based on GraphSage and TCN (GTCN), a hybrid algorithm of a graph neural network and the TCN model. Our solution takes the historical alarm data as input. First, we filter out the alarm noises irrelevant to the faults through data cleaning. Then, we employ feature engineering to extract the valid alarm features, including the statistical features of the network alarm information, the semantic features of the alarm texts, the sequential features of the alarms and the network topology features of the nodes where the alarms are located. Finally, we use GTCN to predict future fault alarms based on the extracted features. Experiments on the alarm data of a real service system show that GTFP performs better than the state-of-the-art algorithms of fault alarm prediction.

The Impact of the Zakat Calculator Application on Zakat and Waqf Management Study Program Students

Regarding the zakat calculator application using a programming language that can be used for smartphones/mobile phones with a simple program appearance according to the type of zakat you want to calculate so that it can be used by students. This application makes it easier for people, especially students, to carry out zakat calculation activities according to their types. From the system that has been implemented, the results of calculating zakat according to its type will be easier because the system used is a practical system in the form of a smartphone/cell phone application that can be easily accessed and the results can be accepted by the community, especially students. The zakat calculator application is an application that is applied to an Android smartphone which can help Muslims in calculating zakat that must be issued according to the regulations of Allah and His Messenger which can be used anytime, anywhere, and by anyone, besides that this application is also equipped with a menu feature in which there is an explanation of the history of zakat, zakat law, types of zakat, and mustahik zakat. This application uses the formula for calculating zakat assets and professional zakat which is applied to a coding program that will calculate the amount of zakat assets and professional zakat that must be issued by Muslims. This zakat calculation application can help Muslims in calculating zakat assets and professional zakat that must be issued and with the zakat alarm feature it can be used as a reference for reminders in paying zakat in a timely manner.

Calculation of Android-Based Zakat on Assets and Profession Zakat

The ease of the android operating system based on open source, android becomes easy and free to develop. One of them is the use of applications on android-based smartphones related to the fulfillment of obligations on special assets, namely the calculation of zakat. Android-based zakat calculation application is an application that is applied to android smartphones which can help Muslims in calculating zakat that must be issued according to the rules of Allah and His Rosul which can be used anytime, anywhere, and by anyone, besides that this application is also equipped with a zakat alarm feature that can be a reference reminder time in paying zakat on time. The system development method used in making this application is the prototype method. This zakat calculation application is made using java as a programming language with Android Studio as an Integrated Development Environment (IDE). This application uses the calculation formula for property zakat and professional zakat which is applied to the program coding that will calculate the amount of property zakat and professional zakat that must be paid by Muslims. This zakat calculation application can help Muslims in calculating property zakat and professional zakat that must be issued and with the zakat alarm feature can be used as a reminder time reference in paying zakat on time.

Are There Ethnic Differences in Recorded Features among Patients Subsequently Diagnosed with Cancer? An English Longitudinal Data-Linked Study.

English population-based cancer-registry-linked primary care data were analysed. We identified the coded features of six cancers (breast, lung, prostate, colorectal, oesophagogastric, and myeloma) in the year pre-diagnosis. Logistic regression models investigated ethnic differences in first-incident cancer features, adjusted for age, sex, smoking status, deprivation, and comorbidity. Of 130,944 patients, 92% were White. In total, 188,487 incident features were recorded in the year pre-diagnosis, with 48% (89,531) as sole features. Compared with White patients, Asian and Black patients with breast, colorectal, and prostate cancer were more likely than White patients to have multiple features; the opposite was seen for the Black and Other ethnic groups with lung or prostate cancer. The proportion with relevant recorded features was broadly similar by ethnicity, with notable cancer-specific exceptions. Asian and Black patients were more likely to have low-risk features (e.g., cough, upper abdominal pain) recorded. Non-White patients were less likely to have alarm features. The degree to which these differences reflect disease, patient or healthcare factors is unclear. Further research examining the predictive value of cancer features in ethnic minority groups and their association with cancer outcomes is needed.

EE12 Diagnosing Irritable Bowel Syndrome (IBS): A Real-World Study to Assess Resource Utilization

IBS is a common gastrointestinal disorder affecting over 10% of the US population. Accurately diagnosing IBS with current available invasive tests may be difficult since the focus is to rule out alarm features. Patients are subjected to invasive diagnostic tests that are costly and may not provide additional information. A real-world study has been initiated to evaluate the impact of a non-invasive 2nd generation blood test and breath test in terms of reducing the need for further testing and directing treatment vs control. The present analysis reports on 39 control subjects being seen by a gastroenterologist.

HealthDote: A blockchain-based model for continuous health monitoring using interplanetary file system

The Healthcare industry demands increased privacy and security to protect confidential patient information and comply with regulations. Both these features can be incorporated into the existing systems using Blockchain technology. The only challenge faced here is the ease of users, but this can be quickly resolved by integrating the Internet of Things (IoT) and blockchain. IoT-based devices overcome limited computing capacity for personal intelligent health devices. Cloud-assisted IoT devices also require limited storage capacity for devices like wearable sensors. However, it must be considered that this system still has drawbacks, leading to its inefficiency. These problems include Data Privacy and Data sharing. This paper proposes an Interplanetary File System (IPFS) based solution to these problems. Here, the users continually upload the health data collected by IoT devices and add them to blockchain transactions that the other user nodes, such as physicians, pharmacists, insurance companies, hospital authorities, etc., can access. This system ensures the well-being of the users by monitoring the data gathered every 5 min and daily. It also facilitates the alarm feature in an emergency, making it reliable. The user receives daily notifications regarding his lifestyle, and the family members receive the notifications on his behalf if there are some chances of an emergency. The authorized doctors are also notified immediately in case an emergency is detected. Apart from this feature, the user can get consultations from doctors, prescriptions from the pharmacist, funds from insurance authorities, and hospital supplies, all through the transaction on the six blockchains of HealthDote using the cryptocurrency DoteCoins, which are designed specifically for this system.

Value of Alarm Features in Dyspepsia for Predicting Significant Organic Lesions in Endoscopy

Background: For a long older age and the presence of alarm features are regarded as indications for prompt endoscopy in patients with dyspepsia. We aimed to find out the value of alarm features in diagnosing serious organic upper gastrointestinal lesions in patients with dyspepsia. Material &amp; methods: In this observational study clinical variables and endoscopic findings of consecutive patients with dyspepsia were recorded in a semi-structured questionnaire. Univariate and multivariate logistic regression was done stepwise to identify predictors for endoscopic findings. A simplified predictor model was built with the age and the presence of any predictor alarm feature to find out the diagnostic accuracy of this model for the significant endoscopic lesion. Results: Among 304 patients (M=134, F=170) one or more alarm features were present in 193 cases (63.5%). Significant organic lesions were found in 84(27.6%) cases. Age ≥ 45 years (OR 2.608), abdominal lump (OR 4.489) and family history of upper gastrointestinal cancer (OR 3.880) were found as independent predictors of major endoscopic findings. Using a simplified predictor model of age ≥ 45 years or the presence of any predictive alarm feature, sensitivity, specificity, positive predictive value, and negative predictive value were 32.4 %, 82.5 %, 79.8% and 36.4% respectively for a significant endoscopic lesion. For upper gastrointestinal cancers, these values were 6%, 100.0%, 100.0% and 47.3% respectively. Conclusion: The predictive value of the age and the presence of alarm features alone are not optimal for significant endoscopic findings in patients with dyspepsia. A newer and more accurate predictive model is a time demand for organic UGI lesions, especially for malignancies. J Bangladesh Coll Phys Surg 2023; 41: 132-140