Integrated Development Environment Research Articles

A Collaborative Code Platform with Advanced AI Features and Real-Time Collaboration Tools

Abstract: Good collaboration and code management are crucial in today's software development environment. Our research has launched a new collaborative process that combines AI resources, including AI and global research, to improve products and enhance and facilitate collaboration. The platform features real-time collaboration tools such as dynamic text with multilanguage support, dialog functionality, and notepad functionality. With OAuth authentication, users can seamlessly perform CRUD operations on files and directories, share code repositories, and participate in live coding sessions. The platform supports compilation and execution of a code file and follows good design principles such as caching, microservices architecture, and containerization to ensure scalability and performance. The platform focuses on user experience by providing simple user interface design and easy interface navigation. Users can create workspaces, share them collaboratively, and seamlessly import repositories from Git. The platform's intelligent data manager helps manage files and directories, while the collaborative platform allows developers to collaborate on code libraries efficiently. The platform is modern web-based, using React on the frontend and Node.js on the backend; Supported with syntax highlighting and smart initialization along with Socket.io for runtime with SQL and NoSQL databases. Provides comprehensive language support. All progress is saved, providing users with peace of mind. While the basic design is for desktops, future iterations are expected to support mobile devices.

#2892 The role of physical activity in onco-nephrology; a new weapon in the management of urological malignancies

Abstract Background and Aims New clinical evidence demonstrated that Physical Activity (PA) brings numerous benefits both at the level of Health-RelatedQuality of Life (HRQoL), biological mechanisms and at the psychological level. Indeed, it is well known that cancer treatments results in a series of symptoms and side effects that debilitates the patients with cancer and can be counteracted with physical activity. PA has an effect on several aspects of biological mechanisms: sex hormones, metabolic hormones, inflammation and adiposity, and immune function. Furthermore, strong evidence shows that aerobic or combined (aerobic and endurance) training reduces symptoms of anxiety, depression and fatigue. Combined training also leads to improvements in HRQoL, preventing and improving muscle loss and sarcopenia. This study aims to assess the influence of physical activity on this improved scenario. Method A consecutive cohort of 82 pts was enrolled in the Urological Department at San Raffaele Scientific Institute between 2018-2021. The control group (CT) was composed of 31 patients affected by CKD alone (GFR < 60 ml/min/1.73 m2, K-DIGO 2012 classification) and 51 patients with CKD and a recent history of urological malignancies (within 12 months) represented the case group (CS). Inclusion criteria were: Age (>18 years old), eGFR (<60 ml/min/1.73), Malnutritional Screening Tool (MST<2), Urological Cancer aggressiveness (no metastatic process), Informed consent (signed). Each patient underwent an initial nephrological and nutritional evaluation during which a conventional CKD Mediterranean-like diet with a controlled protein intake (MCPD) (0.6-1 g total protein/kg body weight/die and 30-35 kcal/kg body weight/die) was prescribed together with physical activity recommendation according to The National Kidney Foundation (NKF) for a period of 6 months (±2 months). Physical activity recommendations included some aerobic exercise for 30 min on most days of the week, plus other activities to increase muscle strength (exercises with resistance bands) e/o to improve flexibility (such as stretching) once or twice a week. To assess the influence of physical activity within the collaborative nephrologist-nutritionist approach (NNCA) on enhanced kidney function, the Rapid Assessment of Physical Activity (RAPA) test was administered to each patient. The test consists of two indicators: RAPA 1 will indicate aerobic physical activity levels (from 0=sedentary to 7=physically active), while RAPA 2 will indicate other types of exercises (0=none; 1=strength; 2=flexibility; 3=both). Subsequently, Bioelectrical Impedance Analysis (BIA) was conducted on all participants in the cohort to collect data on Body Cell Mass per height squared (BCM/h2), Phase Angle (PA), Extracellular Mass (ECM)/BCM, and Extracellular to Intracellular water ratio (ECW/ICW). Statistical analysis: Linear regression and Pearson's Chi-Squared test; Data analysis: R programming language and RStudio integrated development environment. Results Table 1 shows the patient's baseline characteristics. Table 2 shows anthropometric indices and parameters at pre- and post-dietary and nephrological intervention. Table 3 underlines the poor condition prevalent in CKD patients with or without oncological history, also confirmed by the low level of usual physical activity detected by the RAPA test at T0. Sure enough, our population was mainly inactive and sedentary. Results show that, after the intervention, our patients became regularly active (RAPA 1 score T0: 2.47; RAPA 1 score T1: 3.17; Delta = + 0.7, p < 0.0001); the same trend was observed for RAPA 2 score (observed increase 14%, p < 0.002). Conclusion Based on these findings, it can be inferred that standard clinical care should incorporate thorough assessment of both nutritional status and PA. The adoption of a MCPD, coupled with exercise training within a collaborative nephrologist-nutritionist approach, emerges as a potential strategy to enhance the HRQoL of this particular kind of patient.

Data models in ship design and construction – insights from 4D BIM

The lack of a cohesive understanding of a ship product data model, from design to operations, is currently a limiting factor in realizing more efficient ship lifecycle management and design processes. The paper sheds light on the history and gaps in realizing an integrated, interoperable, and multi-domain ship product data model. It also explores practices from BIM (Building Information Modelling) as an inspiration for solutions to overcome challenges related to information modeling, integrated design environment, and 4D engineering and planning.

An integrated development environment based situational awareness for operational reliability evaluation in wind energy systems incorporating uncertainties

Wind power fluctuations impact power system flexibility and reliability, emphasizing situational awareness (SA) in wind power integration. Monitoring wind speed and power generation forms the core of SA, enabling insight into wind system conditions. An Integrated Development Environment (IDE) becomes pivotal in assessing the SA of wind-integrated systems, which is vital for forecasting operational reliability in power system planning. The IDE facilitates Simulink-based wind model development and is linked to dSPACE hardware through a real-time interface (RTI) for validation. This IDE-driven integration utilizes cloud-based Internet of Things (IoT) capabilities, acquiring wind speed and power generation data for precise SA evaluations. A multi-state wind speed model is employed to incorporate energy generation fluctuations. To improve this, the artificial neural network (ANN) model, i.e., a time series-based non-linear autoregressive with exogenous input (NARX) ANN, is utilized to predict future wind speed and power generation, strengthening SA-based operational reliability. The study investigates the effectiveness of a suggested operational planning strategy under wind generation uncertainties. This assessment is carried out within a real-time deployment of wind energy systems seamlessly integrated with IoT on the dSPACE platform. The primary objective is to evaluate the system's ability to maintain SA effectively.

Hybrid feature selection in a machine learning predictive model for perioperative myocardial injury in noncoronary cardiac surgery with cardiopulmonary bypass.

Perioperative myocardial injury (PMI) is associated with increased mobility and mortality after noncoronary cardiac surgery. However, limited studies have developed a predictive model for PMI. Therefore, we used hybrid feature selection (FS) methods to establish a predictive model for PMI in noncoronary cardiac surgery with cardiopulmonary bypass (CPB). This was a single-center retrospective study conducted at the Fuwai Hospital in China. Patients aged 18-70years who underwent elective noncoronary surgery with CPB at our institution from December 2018 to April 2021 were enrolled. The primary outcome was PMI, defined as the postoperative cardiac troponin I (cTnI) levels exceeding 220 times of upper reference limit (URL). Statistical analyses were conducted by Python (Python Software Foundation, version 3.9.7 and integrated development environment Jupyter Notebook 1.1.0) and SPSS software version 26.0 (IBM Corp., Armonk, New York, USA). A total of 1130 patients were eventually eligible for this study. The incidence of PMI was 20.3% (229/1130) in the overall patients, 20.6% (163/791) in the training dataset, and 19.5% (66/339) in the testing dataset. The logistic regression model performed the best AUC of 0.6893 (95 CI%: 0.6371-0.7382) by the traditional selection method, and the random forest model performed the best AUC of 0.6937 (95 CI%: 0.6416-0.7423) by the union of Wrapper and Embedded method, and the CatBoost model performed the best AUC of 0.6828 (95 CI%: 0.6304-0.7320) by the union of Embedded and forward logistic regression technique, and the Naïve Bayes model achieved the best AUC with 0.7254 (95 CI%: 0.6746-0.7723) by forwarding logistic regression method. Moreover, the decision tree, KNeighborsClassifier, and support vector machine models performed the worse AUC in all selection forms. Furthermore, the SHapley Additive exPlanations plot showed that prolonged CPB, aortic clamp time, and preoperative low platelets count were strongly related to the PMI risk. In total, four category feature selection methods were utilized, comprising five individual selection techniques and 15 combined methods. Notably, the combination of logistic regression and embedded methods demonstrated outstanding performance in predicting PMI risk. We also concluded that the machine learning model, including random forest, catboost, and Naive Bayes, were suitable candidates for establishing PMI predictive model. Nevertheless, additional investigation and validation are imperative for substantiating these finding.

The impact of a chatbot working as an assistant in a course for supporting student learning and engagement

AbstractNowadays, many digital learning tools can be integrated into a course to enhance the teaching–learning process. It is a fact that technology is changing how students learn and how professors teach. The growing utilization of AI chatbots in education to support students' learning underscores the importance of identifying critical aspects related to students' acceptance of this technology. Teachers require a deeper understanding of how students perceive and respond to chatbot assistance, as well as methods to measure levels of satisfaction, engagement, and knowledge acquired while using these technological tools. The aim of this study was, on the one hand, to illustrate the integration of a chatbot into a course to assist students in solving specific learning activities. On the other hand, it aimed to analyze the correlation between the factors influencing university students' intention to use chatbots and their perceived usefulness. It is noteworthy that the learning activity integrated two technologies: an Integrated Development Environment and a chatbot, offering additional educational value to students. The Technology Acceptance Model was used to measure the students' perceived usefulness and attitude toward this digital learning tool. The findings of this study provided insights to faculty on how to integrate and use chatbots to enhance students' engagement and motivation. The results revealed that the perceived usefulness of chatbots does, in fact, influence students' intention to use them. Most of the students expressed satisfaction with having a chatbot to assist them in solving exercises. Students highlighted that one of the most significant benefits was the 24/7 chatbot's support availability and its user‐friendly interface.

UTILIZING DIGITAL COMPETENCES FOR THE DEVELOPMENT OF LEADERSHIP AND MANAGEMENT POTENTIAL IN HIGHER MEDICAL EDUCATION STUDENTS

Introduction. Building a robust healthcare system aimed at improving overall population health and a reliable electronic health system requires a corresponding level of proficiency in digital competences (DC) among future healthcare professionals. The relevance of this research lies in the understanding that the effective execution of medical and managerial responsibilities is impossible without an adequate level of DC, and mastering them must occur during the education of future healthcare professionals. The aim. To investigate the levels of proficiency in DC and the understanding of their importance among students enrolled in institutions of higher medical education (IHME) for the development of leadership and management potential during the educational process of future healthcare professionals. Materials and methods. A cross-sectional study was conducted among 265 students (1st-6th years) at Dnipro State Medical University. Bibliosemantic and sociological methods were used. Students were grouped by academic year: junior (1-2), middle (3-4), and senior (5-6). Results were analyzed using widely accepted statistical approaches and the integrated development environment Jupyter Notebook (https://jupyter.org/install). A significance level of p<0.05 was considered statistically significant for all hypotheses. Results. According to the obtained data all students, regardless of their academic year, understand the importance of studying digital competences. On average, the overall importance level for all DC was 6.99 (95% CI 6.72-7.26) on a ten-point scale. Proficiency in DC significantly (p=0.002) varied depending on the academic year, with seniors scoring higher than middle (in all aspects) and junior students (for most aspects). No significant differences were found between the proficiency levels of junior and middle students (p>0.05). Proficiency in general DC was significantly higher compared to special medical and cybersecurity DC, both within each group and for all respondents. More than 80% of respondents stated that mastering DC is important for performing managerial functions, with no differences between groups (p>0.05). Conclusions. Present-day healthcare professionals require a proper level of proficiency in DC. The study demonstrates that students' motivation to learn DC is more than sufficient, but proficiency in special medical and cybersecurity DC needs improvement. The majority of respondents consider mastering DC an integral element in the formation of health-oriented leadership and effective management in healthcare. Mastering of DC should occur systematically with active involvement of IHME in the process.

Exosomal non-coding RNA exhibit sex-specific profiles in salt sensitive hypertension

Exosomes are secreted by various living cells and can be found in all body fluids, especially in cerebrospinal fluid (CSF) and plasma, thereby providing a source of biomarkers for cardiovascular diseases that can be used for patients’ diagnostic. Using high-throughput sequencing technologies, exosomes have been found to contain different RNA populations particularly non-coding RNA. Recently, many non-coding RNA have been recognized as key regulators in the progression of hypertension. In this study we hypothesized that female and male C57BI/6J mice exhibit different microRNA (miR) profiles after deoxycorticosterone acetate (DOCA)+salt hypertension treatment. CSF (10 μl) and plasma (100 μl) were collected from 3-month-old mice and again after 3 weeks of DOCA-salt treatment before exosomes isolation and processing for high throughput small RNA sequencing (Creative Biolabs). Analysis was performed using R language and RStudio ( https://rstudio.com/ ) as an integrated development environment. Global miRNA analysis of CSF exosomes shows different expression miRNA profiles in DOCA+salt male mice compared with their own baseline values. In addition, we observed a remarkable difference in global miRNA expression profile between DOCA+salt male mice and DOCA+salt females (p-value < 2.2e-16), while no significant difference was detected between DOCA+salt females and their baseline values (p-value = 0.6679). Moreover, we observed that there are three clusters of around 200 miR that have different expression levels in DOCA+salt males. Our analysis identified around 74 upregulated miRNA in DOCA+salt males compared to DOCA+salt females. The maximum level of expression belonged to the muscle-specific miRNA (myomiR) miR-1a-3p, miR-206-3p and miR-26a-5p which were highly accumulated in CSF derived exosomes. These top three upregulated miRNA were associated with 282 validated target genes among which, two pivotal genes, ACE2 and ADAM17, were identified in the PPI network, miRTargetLink, and by KEGG analysis. miR-26a-5p was mostly associated with ACE2 and ADAM17 and the mRNA expression of ADAM17 was associated with reduced miR-206-3p. In conclusion, our bioinformatic analysis identified three miRNA and associated them with two key molecules involved in salt sensitive hypertension. Further analysis is likely to highlight more diagnostic biomarkers and potential candidates for the development of new therapeutic approaches. This work was supported in part by a research grant from the National Institutes of Health (HL163588). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Mining for cost awareness in the infrastructure as code artifacts of cloud-based applications: An exploratory study

Context:Cloud computing’s rise as the primary platform for software development and delivery is largely driven by the potential cost savings. However, it is surprising that no empirical evidence has been collected to determine whether cost awareness permeates the development process and how it manifests in practice. Objective:This study aims to provide empirical evidence of cost awareness by mining open source repositories of cloud-based applications. The focus is on Infrastructure-as-Code artifacts that automate software (re)deployment on the cloud. Methods:A systematic examination of 152735 repositories yielded 2010 relevant hits. We then analyzed 538 relevant commits and 208 relevant issues using inductive and deductive coding and corroborated findings with discussions from Stack Overflow. Results:The findings indicate that developers are not only concerned with the cost of their application deployments but also take actions to reduce these costs beyond selecting cheaper cloud services. We also identify research areas for future consideration. Conclusion:Although we focus on a particular Infrastructure-as-Code technology (Terraform), the findings can be applicable to cloud-based application development in general. The provided empirical grounding can serve developers seeking to reduce costs through service selection, resource allocation, deployment optimization, and other techniques.

GMAX: A Geant4-Based Monte Carlo Advanced X-ray Simulation Platform

X-ray digital radiography plays a pivotal role in accessing the internal structure of objects. Nevertheless, key imaging parameters, such as source-to-target distance or number of projected images needed for three-dimensional construction, mostly rely on real-life trials thus increasing radiation exposure risks. This paper introduces a recently developed software platform known as GMAX (Geant4-based Monte Carlo Advanced X-ray) that helps addressing above issues from a simulation perspective. GMAX employs Geant4, a Monte Carlo simulation code at its core and facilitates high-fidelity X-ray imaging, requiring no prior user experience. Compared to CAD models that only reflect object’s geometrical information, GMAX simulates how objects interact with photon and can accurately evaluate important imaging parameters, such as target object to detector distance. It also provides three-dimensional construction functionality for images and therefore could be used as an effective tool for X-ray non-destructive testing and inspection.

DEVS: Secure and optimal decentralized energy trading scheme for Electric Vehicle and Charging Station using game theory

SummaryWith the advent and popularity of electric vehicles (EVs), the intelligent transportation system has adopted them as an alternative to fossil fuel or gasoline vehicles owing to their benefits of reduced greenhouse gas emissions. However, it becomes critical to schedule the vast EVs at the charging station (CS) efficiently while maintaining security, privacy, and optimality during the energy trading. Therefore, we propose a blockchain and zero‐sum game theory‐based energy trading scheme, that is, DEVS, for optimal and secure EV charging at the CS. We incorporate the Interplanetary File System with the blockchain to ameliorate the cost‐efficiency and reliability of EV energy trading. Furthermore, we implement the zero‐sum game theory between players EV and CS to yield an optimal payoff based on pure and mixed strategies during the energy trading procedure. Moreover, we deploy and execute the smart contract (SC) of the blockchain‐based proposed DEVS scheme in the Remix integrated development environment to secure EV energy trading transactions. Additionally, the security of the proposed DEVS scheme is checked and analyzed using a fuzzing‐based Echidna tool for protected energy trading. Finally, the performance evaluation of the proposed DEVS scheme is analyzed using various standard metrics such as profit comparison based on strategies, energy price comparison, CS profit comparison for pure strategy (based on arrival time), convergence comparison, and profit (EV and CS) at saddle point and mixed strategy to avail preserved and optimal energy trading for EVs.

Cost-Efficient Deployment Strategies: A Comparative Analysis of Feature Flagging Services and Blue/Green Deployments

Aims: This study provides a detailed comparison of two leading feature flagging services, LaunchDarkly and ConfigCat, and also examines the cost-efficiency and operational implications of using Blue/Green deployment strategies. It seeks to aid stakeholders in understanding the trade-offs and benefits of each approach to make informed decisions based on project-specific needs, budget constraints, and developmental objectives. Study Design: The study employs a comparative analysis framework, focusing on features, usability, scalability, architectural design, and the financial impacts of adopting feature flagging services versus Blue/Green deployment strategies. Place and Duration of Study: The analysis was conducted over a period of two years, encompassing a broad range of software development environments and project scenarios to ensure comprehensive coverage and relevance. Methodology: The methodology employed in this paper includes a detailed examination of LaunchDarkly and ConfigCat's service offerings, an evaluation of Blue/Green deployment strategies, and an analysis of cost-efficiency of each approach. The study synthesizes information from product documentation, user feedback, and performance metrics, alongside interviews with industry experts and case studies from diverse software development projects. Results: The results highlight the nuanced differences between feature flagging services in terms of scalability, ease of use, and the suitability for various project sizes. LaunchDarkly emerges as optimized for large-scale, complex projects due to its extensive feature set and scalability, while ConfigCat is favored for its simplicity and ease of use in smaller projects. The analysis also uncovers the cost benefits of feature flagging over Blue/Green deployments, emphasizing the savings on infrastructure and operational expenses while offering dynamic feature management capabilities. Conclusion: The study concludes that the choice between feature flagging services like LaunchDarkly and ConfigCat, and the utilization of Blue/Green deployment strategies, should be guided by specific project requirements, financial constraints, and desired operational efficiency. Feature flagging services provide a cost-effective, flexible solution for dynamic feature management, whereas Blue/Green deployments offer a straightforward, though potentially more resource-intensive, approach to minimizing deployment risks. This comparative analysis aims to assist stakeholders in selecting the most appropriate deployment strategy to meet their development goals efficiently.

Comparative Evaluation of Python Integrated Development Environments (IDEs) on Android Mobile Devices: Implications for Teaching Programming Concepts

Comparative Evaluation of Python Integrated Development Environments (IDEs) on Android Mobile Devices: Implications for Teaching Programming Concepts

Enhanced Security and Efficiency in Attendance Management: A Novel RFID and Arduino Integrated System

The advent of Radio Frequency Identification (RFID) technology has ushered in a new paradigm in the domain of automated attendance systems, offering a sophisticated yet user-friendly approach to personnel management. This paper presents a comprehensive study on the design and deployment of an RFID-based attendance system powered by the versatile Arduino platform, elucidating its operational tenets, system architecture, and practical implementations. At the heart of the system lies the MFRC522 RFID reader, which synergizes with an Arduino microcontroller to facilitate the identification and logging of attendance data. The system is enhanced by the inclusion of an SD Card Module for data storage and a Real-Time Clock (RTC) Module to ensure accurate timestamping of attendance events. The seamless integration of these components results in a robust mechanism that not only simplifies the attendance tracking process but also fortifies the security framework by leveraging unique identifiers for each user. The study spans the detailed process of assembling the hardware, crafting the software in the Arduino Integrated Development Environment (IDE), and meticulously testing the integrated system to affirm its efficacy. The resulting attendance system embodies a significant stride towards refining attendance management practices, eliminating the shortcomings of manual tracking while providing a scalable and reliable solution adaptable to various organizational settings.

Interactive Abstract Interpretation with Demanded Summarization

We consider the problem of making expressive, interactive static analyzers compositional . Such a technique could help bring the power of server-based static analyses to integrated development environments (IDEs), updating their results live as the code is modified. Compositionality is key for this scenario, as it enables reuse of already-computed analysis results for unmodified code. Previous techniques for interactive static analysis either lack compositionality, cannot express arbitrary abstract domains, or are not from-scratch consistent. We present demanded summarization, the first algorithm for incremental compositional analysis in arbitrary abstract domains that guarantees from-scratch consistency. Our approach analyzes individual procedures using a recent technique for demanded analysis, computing summaries on demand for procedure calls. A dynamically updated summary dependency graph enables precise result invalidation after program edits, and the algorithm is carefully designed to guarantee from-scratch-consistent results after edits, even in the presence of recursion and in arbitrary abstract domains. We formalize our technique and prove soundness, termination, and from-scratch consistency. An experimental evaluation of a prototype implementation on synthetic and real-world program edits provides evidence for the feasibility of this theoretical framework, showing potential for major performance benefits over non-demanded compositional analyses.

Research on the System Design and Target Recognition Method of the Rebar-Tying Robot

In the construction industry, the construction process of rebar tying is highly dependent on manual operation, which leads to a wide range of work areas, high labor intensity, and limited efficiency. Therefore, robot technology for automatic rebar tying has become an inevitable trend in on-site construction. This study aims to develop a planar rebar-tying robot that can achieve autonomous navigation, precise positioning, and efficient tying on a plane rebar mesh without boundaries. Our research covers the overall design of the robot control systems, the selection of key hardware, the development of software platforms, and the optimization of core algorithms. Specifically, to address the technical challenges of accurately recognizing the tying position and status, we propose an innovative two-stage identification method that combines a depth camera and an industrial camera to obtain image information about the area to be tied. The effectiveness of the planar rebar-tying robot system, including the recognition method proposed in this study, was verified by experiments on a rebar mesh demonstration platform. The following application of our robot system in the field of the Shenyang Hunnan Science and Technology City Phase IV project achieved satisfactory performance. It is shown that this research has made a unique and significant innovation in the field of automatic rebar tying.

DESIGN AND IMPLEMENTATION OF IOT BASED HOTEL ROOMS OCCUPANCY REPORTING SYSTEM

Advances in technology applications are becoming more visible in everyday activities as the globe becomes more technologically skilled and evolved. Being able to receive and execute control is critical in the age of Internet of Things technology and control. This pattern eventually led to the development of an Internet of Things (IoT) system for reporting room occupancy as a promising technique or system for determining the availability of rooms and buildings. This method lowers human mistake, mismanagement, and energy use, all of which directly translate to decrease hotel running costs, possibly to zero. As a result, it is conceivable that the number of guests staying in Nigerian hotels has risen significantly. If this is the case, good management will most certainly result in an immense rise in profits. However, sharp practices among hotel employees tend to divert the profit met for the owners, thus this project is designed to automatically report hotel occupancy easily accessible to owners. An infrared detection circuit was used detect human movement and a lithium-ion battery that supplied 3.7 volts was used as the power supply for the circuit while a GPRS module (a SIM 800L GSM module) was utilized to receive and transmit data messages via radio waves, and an LGT8F328P microcontroller controlled every other component in the circuit. An android application was developed and installed on a mobile phone to gather all of the real-time readings and events that take place throughout the project’s system. The programming of the microcontroller was done using the Arduino Integrated Development Environment (IDE) software and C++ programming language. When the project was turned on, I saw that all of the LEDs turned on automatically. The first two LEDs (brown and green) were automatically turned on, as were the corresponding room LEDs. After that, the green LED turned off. When the infrared LEDs’ visibility is obscured, their input to the phototransistors in each room and the LEDs attached to each room automatically turn on, indicating that the rooms are occupied and vice versa. The various room compartments’ check-ins and check-outs are tracked, managed, and updated using the proposed hardware. The data is then transmitted to the IoT cloud and eventually to the Android application

A Short Range Radar System using Arduino

RADAR is a detection system that uses radio waves to determine the characteristics of the detected objects such as: range, height, direction, or the speed of objects. This paper, is aimed at designing a radar system that uses an ultrasonic sensor to detect objects. The ultrasonic sensor is used to measure the distance between the radar and any object-based non-contact technology. This system is controlled through Arduino. Arduino UNO board is sufficed to control ultrasonic sensor and also to interface the sensor and display device. Whereas, the movement of the sensor is controlled by using a small servo motor. This radar is controlled using the Arduino Uno board as a microcontroller. The signal received from the sensor is processed using “Processing Development Environment Software". Ultra-sonic sensor is attached to the servo motor it rotates about 180 degree and gives visual representation on the software called processing IDE. Processing IDE gives graphical representation and it also provides angle or position of the object and distance of the objects on the PC or monitor.

Intelligent Program Correction and Evaluation System

The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder’s lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct short code blocks containing mistakes. This makes code composition easier and more time-efficient, thus improving the efficacy of large-scale development environments. Because Python is becoming more popular, the goal of our study is to enhance the efficiency of writing Python code by offering an automatic code-correcting approach. Furthermore, automatic program evaluation has been performed to assist in the debugging of small code blocks, which will ultimately be employed in the creation of real-time computer applications. The proposed technique would be useful for new learners who wish to create small Python code blocks for ease of writing and debugging on online platforms (like edX, Coursera, and Udacity). One of the major contributions of the project is to create an erroneous dataset of Python coding that contains all potential forms of probable syntax errors. The dataset induces variety through the use of multiple coding templates and is used to train deep learning models. We used the state-of-the-art text-to-text T5 transformer network model to automatically repair and evaluate the incorrect code. The outcomes of auto-correction are examined using the ROUGE and BLEU scores, as well as accuracy. The model corrects Python code with single, double, and the multiple number of errors with greater than 80% accuracy. Similarly, the performance of the basic T5 transformer network for program auto-evaluation with and without mistakes has been examined, and the model achieves greater than 65% accuracy in both cases. The proposed T5 base transformer outperforms the SOTA auto-correction models in terms of accuracy, according to a comparison study of the proposed method with the earlier techniques for auto-correcting codes.

DSP in the loop implementation of an improved sliding mode control for induction motor drive

This paper deals with the design and processor in the loop implementation of an improved sliding mode control of three-phase induction motor. Studied control strategy uses a state-space model expressed in the rotor flux frame. Proposed control strategy is designed in two steps based in cascaded method. This controller is based on new smooth functions replacing sign functions to reduce chattering phenomenon. This work integrates also a calculation of controller parameters to ensure set response times. It focuses on the study of these proposed techniques and their effect on motor waveforms. For code generation of the control algorithm, implementation and validation by processor in the loop technique we have created a platform based on MATLAB/Simulink with code composer studio as integrated development environment and the development board LAUNCHXL-F28069M from Texas instruments. Obtained results show that the proposed strategy reduces chattering using a simple algorithm.