Email Notifications Research Articles

LabEmbryoCam: An opensource phenotyping system for developing aquatic animals

Phenomics is the acquisition of high-dimensional data on an individual-wide scale and is proving transformational in areas of biological research related to human health including medicine and the crop sciences. However, more broadly, a lack of accessible transferrable technologies and research approaches is significantly hindering the uptake of phenomics, in contrast to molecular-omics for which transferrable technologies have been a significant enabler. Aquatic embryos are natural models for phenomics, due to their small size, taxonomic diversity, ecological relevance, and high levels of temporal, spatial and functional change. Here, we present LabEmbryoCam, an autonomous phenotyping platform for timelapse imaging of developing aquatic embryos cultured in a multiwell plate format, and while optimised for embryos, the instrument is extremely versatile. The LabEmbryoCam capitalises on 3D printing, single board computers, consumer electronics and stepper motor enabled motion. These provide autonomous X, Y and Z motion, a web application streamlined for rapid setup of experiments, user email notifications and a humidification chamber to reduce evaporation over prolonged acquisitions. Downstream analyses are provided, enabling automated embryo segmentation, heartbeat detection, motion tracking, and energy proxy trait (EPT) measurement. LabEmbryoCam is a scalable, and flexible laboratory instrument, that leverages embryonic and early life stage organisms to tackle key global challenges including biological sensitivity assessment, toxicological screening, but also to support broader engagement with the earliest stages of life.

Dynamic Price Monitoring in E-Commerce: Leveraging Web Scraping, Data Analysis, and Email Automation for Dynamic Pricing Insights

This paper focuses on the development of a web application that leverages web scraping, data storage, and email automation technologies to track real-time price changes for specific products on e-commerce platforms. Built using Next.js for the frontend, MongoDB as the database, and Nodemailer for email notifications, the system aims to provide users with a comprehensive and user-friendly interface to monitor price fluctuations. By scraping data from product pages, the application gathers key pricing metrics, including the average, lowest, highest, and current prices, and presents this information visually to help users make informed purchasing decisions. By utilizing the combination of web scraping and real-time data analysis, this project provides a dynamic solution for both consumers and businesses, offering valuable insights into price changes and trends in the competitive e-commerce landscape.

Development and implementation of an integrated food commodities reservation and management system for the Kasu Majlis cooperative society using structured approach

This paper presents the development and implementation of an integrated food commodities reservation and management system for the KASU Majlis Cooperative Society. Developed using PHP and MySQL within the XAMPP environment, this web-based system addresses inefficiencies in the existing manual process, thereby enhancing operational efficiency and member satisfaction within the cooperative society. This research identifies key challenges, such as delays and inaccuracies in processing requests and inventory management. In response, an automated system has been developed to modernize these processes. The key features include user registration, reservation tracking, and inventory management. The system allows users to confirm their details, view available commodities, reserve items online, check costs, and print reservation forms with email notifications sent upon request submission. These features ensure convenience, transparency, and efficiency. The system was designed using the waterfall model because of its simplicity and ease of implementation. Comprehensive testing was conducted to validate the reliability and performance of the system. The results from the implementation show significant improvements in request-handling accuracy and operational efficiency. Feedback from users indicates increased satisfaction owing to the user-friendly interface and timely updates of the system. The successful deployment of this system demonstrates its potential to enhance operational workflow, reduce waiting times, and improve communication between members and executive officers. This study offers a practical and scalable solution to common challenges in cooperative management, providing a foundation for future advancements in automated systems for similar cooperatives or organizations.

Improved access to genetics for individuals with potential germline mutations identified on somatic testing.

339 Background: Somatic testing is increasingly common in treating cancer and may identify mutations that necessitate confirmatory germline genetic testing. Management of these results varies by institution. From 2021-2024, we completed an iterative approach to determine the most effective method for follow-up germline testing. Methods: We conducted 3 six-month sequential phases to determine the most effective approach to follow up on somatic findings concerning for germline origin. Phase I (3-9/2021), consisted of weekly e-mail notifications sent by the genetics program to the referring provider regarding patients who had somatic mutations of potential germline origin, and recommended providers refer the patient to genetics. Phase 2 (10/2021-4/2022), consisted of monthly e-mail notifications to the referring provider, clinical staff and included cancer center leadership. In phase 3 (10/2023-4/2024), GC assistants and medical secretaries facilitated referrals and direct outreach to schedule patients for germline follow-up. We measured the percentage of patients referred to genetics, who completed germline testing, and had confirmed pathogenic germline mutations. Results: In Phase I, 38% of patients were referred to genetics, 23.5% completed germline testing, and 37% had positive results (Table 1). In Phase 2, 19% of patients were referred to genetics, 4.7% completed germline testing, and 43% had positive results. In Phase 3, using the GC facilitated approach, 87.5% (70/80) of patients were referred. Those not referred had already undergone germline testing, were deceased or transferred care out of state. Of the 70 patients, 38 met with a GC and 30 elected to proceed with germline testing. There are 25 (36%) genetic test results available to date. 37% (26/70) patients did not complete a genetics appointment. 58% (15/26) could not be reached to schedule an appointment. Nine individuals declined or deferred an appointment. Five patients were deceased before a genetics appointment could be coordinated. For those that received referrals to genetics in phase 3, the germline positive results rate was 92% (23/25). Conclusions: The GC driven referral pathway was most effective at facilitating referral and completion of confirmatory testing for patients with somatic findings concerning for germline origin. Follow-up of these results shows an extremely high rate of germline positive results, making subsequent testing imperative for centers treating cancer patients. Given implications for cascade testing, future research is needed to study upfront consenting approaches to automate subsequent germline testing and notification in this population. Phase Intervention Potential Germline n, (%) Referred n , (%) Completed n , (%) Germline Positive n , (%) 1 Weekly Emails 162 62 (38%) 38 (61%) 14 (37%) 2 Monthly Emails 150 28 (19%) 7 (25%) 3 (43%) 3 GC Driven Referrals 80 70 (87.5%) 25 (36%) 23 (92%)

Automated reporting and statistical analysis of test case results in continuous integration: A custom dashboard approach

CI has been one of the cornerstones of modern software development, where teams integrate code changes frequently and have automated testing to receive fast feedback. Running a large amount of test data, however, is overwhelming and can be fatiguing in extracting meaningful insights from traditional reporting methods like email notifications. The paper presents a bespoke dashboard, designed to support the extension of automation in test case result reporting and analysis within a CI environment by real-time data visualization, interactivity in filtering and sorting, and statistical insights that would make trends and anomalies easier for teams to detect. Implemented in a large software project, this solution brought immense improvement in decision-making, team collaboration, and efficiency, since the dashboard reduced time spent on manual report analyses and generations. The flexibility and ability to integrate with a number of CI tools make it a powerful solution for addressing traditional reporting shortcomings and let development teams focus on areas needing improvement, ultimately enhancing software quality.

A Mobile Surveillance System with Face Detection and Recognition Abilities

This paper proposes a mobile surveillance system similar to an electronic dog which has the ability to move around a certain location all while detecting and recognizing the individuals and notifying the respective authorities about the detected person. The system uses an Arduino UNO microcontroller along with IR receiver and remote to function as a remote-controlled vehicle while also providing an option for automatic movement of the vehicle. The system also has an integrated camera that constantly feeds live video to a computer system. Haar-cascades take in the frames of videos and detect appearance of human face. Then, pretrained FaceNet Convolutional Neural Network (CNN) compares the detected face to the authorized faces in an attempt to recognize the face. The system also boasts the feature of email notification for which it employs the SMTP protocol to send an email with the detected face to the authorized personnel. The methods and techniques used to implement these features are presented in depth in this paper.

IoT-based smart irrigation management system to enhance agricultural water security using embedded systems, telemetry data, and cloud computing

Agriculture, the key sector for food production, faces challenges exacerbated by the growing global demand for food and the scarcity of water resources. Traditional irrigation methods often lead to inefficient use of water, resulting in wastage. Moreover, unpredictable weather conditions and the need to adopt sustainable farming practices are driving us to develop advanced irrigation systems. This paper proposed a smart irrigation management system using new technology like 1) embedded systems, 2) Internet of Things (IoT), 3) telemetry data, 4) cloud computing, communication protocol, and 5) sensors to collect and process real-time data of the smart agriculture. This new technology and intelligent algorithm are used in this paper to improve agricultural practices. The architecture of the proposed scheme comprises three layers: 1) IoT devices, 2) ThingsBoard cloud, and 3) the dashboard, each playing a pivotal role in ensuring seamless data flow, secure communication, and enhanced user interaction. The algorithm orchestrates system operations, incorporating sensor data reading, JavaScript Object Syntax (JSON) payload creation, and secure telemetry data transmission via Hypertext Transfer Protocol (HTTP) protocol to the ThingsBoard cloud. Pump activation decisions are governed by predefined thresholds, preventing an over-irrigation system. The evaluation of system performance involves deploying temperature, humidity, moisture, and water level sensors in a testing field. Further, before data is sent to the cloud, sensor values are calibrated using a functional map. Tests carried out with temperature, humidity, and water level sensors demonstrate the system's dynamic efficiency. By visualizing and analyzing environmental information via ThingsBoard, the results provide real-time data on environmental parameters of the system proposed, improving the efficiency of water use and the sustainability of farming practices. In addition, the system features e-mail notifications for alerts. The integration of e-mail notifications reinforces monitoring and management practices by alerting farm owners and users. This study showcases the efficacy of the proposed paper in enhancing sustainability, water usage, and supporting smart agriculture practices. Further, this paper integrates embedded systems, IoT, cloud computing, and advanced algorithms to optimize and enhance crop productivity and contribute to global food and water security.

The email notification frustrates me!

Organizations are increasingly relying on computer-mediated communication, thus, understanding this process’s emotion dynamics is critical. For that purpose, qualitative data is collected from 23 participants. Findings reveal that the activation of the negativity effect is triggered by the receiver’s attitude toward the higher-status sender, emanated from earlier unfair behaviors; which will in turn trigger the receiver’s anxiety and frustration. Moreover, emails receivers are found to be also frustrated and angry anytime higher-level managers are copied on the email. Such negative emotions, along with fear are boosted by their reluctance to reply to such emails as they aim to avoid further escalation and possible retaliation from the sender’s side. Indeed, this study has many theoretical and practical implications, and opens doors to future research. Managers should use the “cc” option with caution, knowing that frustrating working environments diminish employees’ commitment and engagement, and consequently the quality of their service performance.

The effect of athlete endorsement on employees' organizational identification: a case study utilizing an Olympic athlete

PurposeThis study aims to investigate the effects of sponsorship on organizational identification among employees of companies that sponsor individual athletes from the perspective of internal marketing.Design/methodology/approachAn online survey was conducted in July 2021 targeting employees of Company A. Specifically, employees were invited to participate via email notifications sent through the company's intranet. To clarify the impact of company sponsorships for individual athletes on employee organizational identification, this study focused on the relationship between Company A and an athlete they support. Psychometric scales were used to measure the factors of the hypothetical model, such as sponsor fit and attitudes toward sponsorship. The analysis was conducted using IBM SPSS 28.0 and AMOS 25 Graphics, and the hypotheses were tested using structural equation modeling.FindingsWe collected a total of 365 survey responses in two weeks. All the hypotheses in this study were supported, indicating that athlete identification and brand knowledge had a significant positive effect on perceived sponsor fit. In addition, attitudes toward the employer significantly have positive relationships with brand commitment and organizational identification. This study showed that perceived sponsor fit plays an important role in employee perceptions of sponsorship. Athlete endorsement is effective from an internal marketing perspective.Research limitations/implicationsThe limitation of this study is the selection of the research subject. Conducting the same survey targeting individual athletes with different characteristics may lead to discoveries about the impact of corporate sponsorship on employee organizational identification.Practical implicationsThe practical implication of this study is that business executives should also use sponsorship for individual athletes as a means to unify and motivate their employees. In selecting athletes for sponsorship, it is necessary to select individuals who fit the company image. In this process, listening to employees' opinions may help select the most suitable athlete who matches the company's image.Originality/valueMany studies conducted on sports sponsorship have focused on the impact on potential customers, from external marketing. Even in studies examining the effects of sports sponsorship on employees, the sponsored subjects are typically teams or events. Therefore, the originality of this study lies in examining the impact of athlete endorsement on the company's employees from the perspective of internal marketing.

Sustainable Manufacturing in Industry 4.0: Enhancing Safety and Rapid Response in Industrial Control Systems with Smart Edge IoT Devices

In This research, we delve into the dynamic synergy between sustainable energy and Industry 4.0 within the field of "Energy and Sustainable Engineering." It centers on integrating an emergency email generation system into a robust digitalization framework. The study prioritizes hands-on training, emphasizing practical applications that blend sustainable energy practices with the transformative principles of Industry 4.0. By focusing primarily on the intersection of sustainable energy, the project underscores the crucial role of digitization. This seamless integration aligns with the principles of automation, smart technologies, and the evolving landscape of digitization within industries committed to sustainable energy practices. The primary objective in this experiment is to integrate Industrial IoT into the realm of smart manufacturing, effectively incorporating our system into the framework of Industry 4.0, while prioritizing energy sustainability. Through this integration, our experimental results demonstrate that in the event of an emergency condition, instead of the automation system persisting under faulty conditions, an email notification is immediately sent to the operator, facilitated by IoT. This approach not only ensures swift response to critical situations but also contributes to energy conservation and overall sustainability. This initiative significantly contributes to ongoing discussions surrounding sustainable energy, digital transformation, and Industry 4.0, marking a significant step forward in promoting conscientious industrial practices for the future.

The Effect of Playback Speed and Distractions on the Comprehension of Audio and Audio-Visual Materials

In the modern age, we often consume content at faster than its normal speed. Prior research suggests that watching lecture videos at speeds up to 2x does not significantly affect performance, but the mechanisms by which comprehension is preserved at faster playback speeds are not fully understood. Therefore, we sought to investigate whether there is an effect of speed when the content is audio only, varies in modality (audio-only, audio-visual) and content (textual, pictorial), or is accompanied by distractions. In four experiments, we found that: (1) increasing playback speed to 2.5x speed did not impair test performance (though we still do not advise exceeding 2x speed); (2) having a visual aspect (i.e., presentation slides, instructor images) to learning can be advantageous, especially when processing information at faster speeds; (3) there was a small benefit of receiving textual over pictorial presentations, and the effect did not vary by speed; (4) computer-based distractions (i.e., phone calls, text messages, email notifications) did not impact performance at 1x or 2x speed. Hence, students are more adept at learning at faster speeds than conventional wisdom would suggest, even with distractions present, demonstrating intact comprehension at double the natural speed of to-be-learned material. Furthermore, multimedia presentations can help mitigate the negative costs of accelerated speeds, especially when information is processed by separate working memory components (i.e., narration in the auditory channel, text or pictures in the visual channel), which reduces cognitive load.

IoT based monitoring system for DFIG based wind turbines under voltage dips

Due to their partial-rating power converters and dynamic performance, doubly-fed induction generators (DFIGs) are commonly used in wind turbines (WT). However, grid-connected DFIGs are vulnerable to voltage dips, which can cause power quality concerns and possible turbine damage. An Internet of Things (IoT)-based monitoring technique for field-oriented control (FOC)-based DFIG with grid-side converter (GSC) control is proposed in this paper. The proposed approach uses real-time WT voltage and current data to dynamically alter the GSC control settings, allowing for steady operation and minimizing power quality problems during voltage dips. A 2 MW variable-speed pitch-controlled DFIG with a speed limit of 900–2000 rpm is used for performance investigation under voltage dips with crowbar protection. An IoT based monitoring system sends real-time information to a cloud server for additional analysis after detecting anomalies. The ThingSpeak cloud platform sends email notifications of defects, and an MIT App Inventor application is constructed for data visualization. The proposed strategy's effectiveness is confirmed through the simulation results.

Implementing Continuous Integration and Deployment Strategy: Cloversy.id RESTful API Development

The software development cycle involves testing and deployment stages that can be laborious and time consuming, especially in collaborative projects that involve several developers. Implementing Continuous Integration (CI) and Continuous Delivery (CD) offers a solution to streamline this process. This study presents a case study of the Cloversy.id RESTful API project, highlighting challenges encountered during development and the implementation of a new system using GitHub Actions as the DevOps tool. The research resulted in the adoption of a new system, which replaces the conventional practices previously employed by the Cloversy.id development team. Using flow charts, the study meticulously mapped out the development flow, pinpointing bottlenecks and areas for optimization within the cycle. In particular, the implementation of a CI/CD pipeline resulted in a notable improvement, with a 35% increase in speed for CI and a remarkable 39% enhancement for CD. GitHub Actions played a pivotal role in automating critical tasks, reducing the reliance on manual intervention, and minimizing the dependency on team leaders. The platform's features, including detailed logs and email notifications, empowered team leaders and developers alike to take informed actions swiftly. Furthermore, the study highlights the novelty of integrating CI / CD considering factors such as branching strategy, code review practices, testing methodologies, deployment methods, and infrastructure.

REAL-TIME SPEECH TO TEXT CONVERSION & ANSWER EVALUATION

To improve user engagement, a new question-answering system including speech recognition technology is presented in this study. Users react verbally to queries presented to them via a command-line interface built into the system. Advanced speech-to-text conversion techniques are used to convert these spoken responses into text for analysis. The system compares user responses with predefined proper responses using cosine similarity to assess input correctness. An SMTP server is used by the system to automatically send quiz scores to users via email after the quiz is finished. Showcasing the merging of spoken conversation and automated evaluation in educational environments, this ground-breaking solution offers a simplified way to interactive learning and assessment. Keywords—Question-answering, Speech recognition, Command-line interface, Speech-to-text conversion, Cosine similarity [8], User input accuracy, Quiz scores, Email notification, SMTP [4] server, Interactive learning.

Farmland Intrusion Detection using Internet of Things and Computer Vision Techniques

Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.

RecruIT: A cloud-native clinical trial recruitment support system based on Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)

BackgroundClinical trials (CTs) are foundational to the advancement of evidence-based medicine and recruiting a sufficient number of participants is one of the crucial steps to their successful conduct. Yet, poor recruitment remains the most frequent reason for premature discontinuation or costly extension of clinical trials. MethodsWe designed and implemented a novel, open-source software system to support the recruitment process in clinical trials by generating automatic recruitment recommendations. The development is guided by modern, cloud-native design principles and based on Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) as an interoperability standard with the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) being used as a source of patient data. We evaluated the usability using the system usability scale (SUS) after deploying the application for use by study personnel. ResultsThe implementation is based on the OMOP CDM as a repository of patient data that is continuously queried for possible trial candidates based on given clinical trial eligibility criteria. A web-based screening list can be used to display the candidates and email notifications about possible new trial participants can be sent automatically. All interactions between services use HL7 FHIR as the communication standard. The system can be installed using standard container technology and supports more sophisticated deployments on Kubernetes clusters. End-users (n = 19) rated the system with a SUS score of 79.9/100. ConclusionWe contribute a novel, open-source implementation to support the patient recruitment process in clinical trials that can be deployed using state-of-the art technologies. According to the SUS score, the system provides good usability.

Deep Learning Model Based Criminal Identifications System

Criminal record generally contains all the information both personal and criminal with the photograph of the person. In order to recognize Criminal, identification of some sort is required, designated by eyewitnesses. In most cases the resolution or/and quality of the recorded image sections is unsatisfactory and is difficult to recognize the face. Recognition can be achieved in various different ways like DNA, eyes, finger print, etc. One of the ways is face identification. Since facial recognition technology is powered by artificial intelligence, it can provide excellent results in identifying criminals. Even considering that most people, when committing an illicit activity, try to hide their identity: hiding their faces or covering their faces with scarves, masks, etc. In such cases, AI uses deep learning methods to identify the individual. In this project, proposed an Deep Face an automatic criminal identification system for Police Department to enhance and upgrade the criminal distinguishing into a more effective and efficient approach using Convolutional neural network algorithms. In our proposed methodology, a database is created by storing both full and sliced images of the criminals along with all the personal and criminal details. The captured images of the person get compared with the criminal data Law Enforcement Agencies have in their database. The CNN involves mapping the face with some facial points, allowing the true identity of the individual to be revealed. Using technology, this idea will add plus point in the current system while bringing criminals spotting to a whole new level by automating tasks. Law enforcement receive alerts when an individual claimed by the authorities is identified by our technology, speeding up their arrest and preventing new crimes. Customize notifications and alarms based on a variety of detection or recognition events and program automated security response workflows and SMS and email notifications. Index Terms— Criminal records, Database, Facial recognition, Deep face, User Interface, Neural networks, Detection, Automation.

LPG CYLINDER AUTO FILLING AND GAS LEAKAGE DETECTION

The project "LPG Cylinder Auto Filling and Gas Leakage Detection" integrates IoT technology for automating the LPG cylinder filling process and detecting gas leakages. The system utilizes a Load Cell connected to a Node MCU to measure the weight of the cylinder. Concurrently, a gas sensor interfaced with an Arduino board detects any gas leakages. Through serial communication between the Node MCU and Arduino, the weight data from the Load Cell is transmitted to the Arduino, which then displays both the weight information and gas leakage status on an LCD display. Additionally, a Python installed PC is employed to facilitate email notifications. Upon detecting a gas leak or a decrease in weight, the Arduino sends this information to the Python PC via serial communication. Subsequently, email alerts are generated and sent to the designated recipients, notifying them of the detected gas leakage and the need for refilling the cylinder. Moreover, in case of a gas leak, the system activates a pump to initiate the refilling process. Through this integrated approach, the project aims to enhance safety and efficiency in LPG cylinder management. Keywords : Node MCU, Arduino UNO, Gas sensor, Load cell, Python installed PC, Buzzer, Relay, DC Motor.

Web Scraping Application

Abstract: The web scraping application is designed to empower users in tracking and monitoring the prices of products on the Amazon platform. Utilizing advanced web scraping techniques with Cheerio and Axios, the application allows users to add specific products for real-time price monitoring. Leveraging the Next.js framework for efficient web development, the application integrates MongoDB for seamless data storage. To overcome potential access restrictions, the application employs proxy services provided by Bright Data. The inclusion of Cron Jobs facilitates automation and scheduling, ensuring timely updates of product prices. For user engagement and communication, Node Mailer is utilized to send email notifications about price changes. Additionally, the application offers features such as historical price data visualization and user preferences management. Through a combination of these tools and technologies, application aims to provide users with a comprehensive and userfriendly solution for effective price tracking and informed decision-making on the Amazon platform.

LPG gas leakage detection using ESP32

This research paper presents the development of an LPG gas detection system utilizing the capabilities of the ESP32 microcontroller and email notification functionalities. Liquefied Petroleum Gas (LPG), a widely used fuel source, poses a significant threat due to potential fire and explosion hazards in case of leaks. This project addresses this concern by proposing a cost-effective and user-friendly solution for early leak detection. The system employs an MQ-series sensor specifically designed for LPG detection. The sensor's output voltage changes in response to LPG concentration in the surrounding air. This data is acquired by the ESP32 microcontroller and processed to trigger email notifications when gas levels exceed a pre-defined threshold indicative of a potential leak. This allows for remote monitoring and prompt intervention, potentially mitigating safety risks associated with LPG usage in homes and industries. The paper further discusses the system's functionality, development process, and potential applications, along with suggestions for future improvements. Keywords— LPG gas detection, ESP32, MQ-series sensor, email notification, leak detection, safety system, Internet of Things (IoT), microcontroller, sensor data acquisition