Application Integration Research Articles

Detection of SQL Injection Attacks Based on Supervised Machine Learning Algorithms: A Review

In the ever-changing world of cybersecurity, it is becoming more important to ensure integrity of web applications as well as securing sensitive data. Among a variety of vulnerabilities, SQL injection is considered a significant risk with severe consequences. Addressing this crucial threat has always attracted the researchers to explore various approaches to identify and detect SQL injection attacks. The machine learning has captured the attention of the researchers to explore its potential due to its success in several different fields and the limitation of other rule-based approaches. This study provides a comprehensive review on a variety of the most recent researches that have been carried out using supervised learning algorithms. The study reveals that machine learning has a huge potential in the process of identification and detection of SQL injection attacks.

Accurate internal cavities and kissing bond sizing in metal plates by using the time-of-flight of laser-induced ultrasound waves

This paper presents a nondestructive method for accurately identifying internal flaws in metal plates, which is crucial for ensuring structural integrity in safety-critical applications. The technique relies on analyzing laser-induced ultrasound (LIU) longitudinal wave time-of-flight, as demonstrated through a theoretical five-layer model. Experimental validation was conducted using a piezo-sensor in contact with a slab containing millimetric artificial cavities immersed in air, resulting in a discrepancy of 5.05%. In contrast, experiments performed in a water medium exhibited a lower discrepancy of 2.5%. (Discrepancy refers to differences between measurements obtained through an experimental time-of-flight analysis and caliper measurements.) The results obtained in water-based experiments affirm the accuracy of the proposed model. B-scan measurements and the five-layer model were utilized to generate 2D reconstructed images, enabling precise localization and sizing of cavities and kissing bonds between plates, finding an average size of kissing bond of 30 µm. In conclusion, the proposed five-layer model, based on a longitudinal wave time-of-flight analysis, provides a straightforward framework for an easy cavity and kissing bond measurements in metal plates.

Simulation of High-Gain Microstrip Patch Antennas for 5G Applications

This paper presents a single-band Microstrip Patch Antenna (MPA) with L, T, and other suitable slots, specifically designed for high-gain performance in fifth-generation (5G) mobile communication systems. Incorporating these innovative slots onto a single rectangular patch antenna yielded substantial improvements in gain and bandwidth, addressing the requirements of 5G applications. The investigation was conducted within the Computer Studio Suite (CST) simulation environment, utilizing simulated structures to identify optimized slot dimensions. The performance and dimensions of the newly modified patch antennas are systematically assessed and compared against those of a previously developed single microstrip patch antenna and an array designed for operation at 28 GHz. The results of the proposed antennas exhibited significant gain and directivity, noticeable bandwidth, Voltage Standing Wave Ratio (VSWR), and efficiency at the selected resonance frequency. The successful implementation of this proposed design holds the potential to greatly simplify the integration of 5G applications into handheld devices.

Задачное обучение русскому языку в Китае в контексте теории усвоения второго языка

As the political, economic, cultural, educational, and scientific-technical collaboration between China and Rus-sia continues to evolve, China's demand for Russian-speaking professionals steadily increases. There is a pressing need for new teaching methodologies that are tailored to national conditions and grounded in second language acquisition theory. One such method is task-based Russian language teaching (Task-based Lan-guage Teaching), which can ensure the preparation of teaching staff capable of meeting the needs of the PRCЭs societal development – that is, Chinese speakers proficient in Russian for teaching it as a second lan-guage within China. The study aims to illuminate how task-based methods facilitate the learning of Russian among Chinese students by promoting active engagement, authentic language use, and the integration of cul-tural insights. Task-based learning emerges as a potent pedagogical approach for teaching Russian in China, aligning with second language acquisition theory by promoting interactive, context-rich language use. The study underscores the need for further research into task design and implementation strategies to maximize the benefits of TBLT for learners of diverse linguistic backgrounds. This exploration into task-based Russian language instruction in China demonstrates the potential of task-based methodologies to significantly enhance second language acquisition through a focus on practical application, interactive learning, and cultural integra-tion. As such, it reinforces the call for educators to incorporate task-based approaches in language teaching programs, tailor-made to the specific linguistic and cultural context of Chinese students learning Russian.

Autonomous integration of TSN-unaware applications with QoS requirements in TSN networks

Modern industrial networks transport both best-effort and real-time traffic. Time-Sensitive Networking (TSN) was introduced by the IEEE TSN Task Group as an enhancement to Ethernet to provide high quality of service (QoS) for real-time traffic. In a TSN network, applications signal their QoS requirements to the network before transmitting data. The network then allocates resources to meet these requirements. However, TSN-unaware applications can neither perform this registration process nor profit from TSN’s QoS benefits.The contributions of this paper are twofold. First, we introduce a novel network architecture in which an additional device acts as a central user configuration (CUC) for TSN-unaware applications and autonomously signals their QoS requirements to the network. Second, we propose a processing method to detect real-time streams in a network and extract the necessary information for the TSN stream signaling. It leverages a Deep Recurrent Neural Network (DRNN) to detect periodic traffic, extracts an accurate traffic description, and uses traffic classification to determine the source application. As a result, our proposal allows TSN-unaware applications to benefit from TSNs QoS guarantees. Our evaluations underline the effectiveness of the proposed architecture and processing method.

MgMnxOy-GAC particle electrode for effective peroxone oxidation: High salinity organic wastewater treatment with synergistic electrocatalytic ozonation

A three-dimensional electro-peroxone (3D-EP) system achieved efficient degradation of high salinity organic wastewater and showed significant implications for potential application. Herein, an MgMn-granule-activated carbon (GAC) particle electrode was developed through impregnation calcination and applied in multipollutant removal. The integration of electrolysis, ozone application, and MgMn-GAC in the 3D-EP system manifested strong synergy, leading to successful elimination of all phenol within 30 min due to ample hydroxyl radical production. The energy consumption of MgMn-GAC was 1.86 kWh·m−3 and the specific energy consumption was 0.033 kWh·g−1 COD, exhibiting high electrical energy utilization efficiency under low applied current and low O3 exposure conditions. After six consecutive reaction experiments, COD removal reached 95.6 %, demonstrating remarkable catalytic activity and cycling stability. The study findings indicate that this material could be used as a low-cost particle electrode for practical wastewater treatment.

Optimization of Recombinant Protein Production in Synechococcus elongatus PCC 7942: Utilizing Native Promoters and Magnetic Fields.

The cyanobacterium Synechococcus elongatus PCC 7942 holds significant potential as a biofactory for recombinant protein (RP) production due to its capacity to harness light energy and utilize CO2. This study aimed to enhance RP production by integration of native promoters and magnetic field application (MF) in S. elongatus PCC 7942. The psbA2 promoter, which responds to stress conditions, was chosen for the integration of the ZsGreen1 gene. Results indicated successful gene integration, affirming prior studies that showed no growth alterations in transgenic strains. Interestingly, exposure to 30 mT (MF30) demonstrated a increase in ZsGreen1 transcription under the psbA2 promoter, revealing the influence of MF on cyanobacterial photosynthetic machinery. This enhancement is likely attributed to stress-induced shifts in gene expression and enzyme activity. MF30 positively impacted photosystem II (PSII) without disrupting the electron transport chain, aligning with the "quantum-mechanical mechanism" theory. Notably, fluorescence levels and gene expression with application of 30 mT were significantly different from control conditions. This study showcases the efficacy of utilizing native promoters and MF for enhancing RP production in S. elongatus PCC 7942. Native promoters eliminate the need for costly exogenous inducers and potential cell stress. Moreover, the study expands the scope of optimizing RP production in photoautotrophic microorganisms, providing valuable insights for biotechnological applications.

Classroom Management

Efficient classroom management is crucial for fostering an environment conducive to learning and student development. This paper introduces a novel approach to classroom management through the integration of a user-friendly web application. The system aims to streamline various administrative tasks, enhance communication between educators, students, and parents, and promote student engagement. The web application offers a centralized platform for educators to manage attendance records, assignments, and grades digitally, reducing the time spent on administrative tasks. Additionally, it facilitates real-time communication through messaging features, enabling quick updates on class schedules, assignments, and announcements.

Integration of an online application to optimise multi-disciplinary meetings: a retrospective analysis.

Multi-disciplinary Meetings (MDM) play a crucial role in complex patient care, involving input from various specialties to formulate evidence-based management plans. However, traditional unidirectional approaches and reliance on manual processes have led to inefficiencies in the MDM pathway. This study identified and aimed to improve four critical moments where Information Communication Technologies (ICTs) could enhance the MDM process. Initial referral, information synthesis, meeting presentation and the creation of actionable/auditable items. Microsoft Office Forms, a customisable survey platform, was implemented to streamline MDM processes. Forms were created to gather patient information, develop agendas, and track outcomes. Automation through Excel scripting further optimised data organisation and agenda creation. Referrals using Forms takes an average of 7min and 21s. Over 15months the submission time has reduced and is trending towards under 5min for each referral. The system's scalability has allowed 1744 cases to be discussed over a 15-month period. Active departments using Forms is expanding to seven from two prior to the pilot. Microsoft Office Forms proved to be a valuable and adaptable tool for MDMs, offering benefits such as streamlined information gathering, real-time collaboration, and scalability. The study highlights the potential of existing tools within Microsoft licenses for healthcare process optimisation, providing a cost-effective and customisable solution for MDM agendas. While recognising some limitations, the study concludes that leveraging Microsoft Office Forms can significantly improve system efficiency in a multi-disciplinary setting.

The Experiences of Non-PE Teachers in Teaching First Aid and Emergency Response: A Phenomenological Study

This phenomenological study aimed to explore and understand the experiences of non- PE teacher in teaching First Aid and Emergency Response in District I Asuncion, Davao Del Norte. The participants’ experiences, coping mechanisms, and insights were gathered. The theory and the idea of the study were gleamed through the Skills Acquisition Theory of Fitts and Posner (1967), Pedagogical Content Knowledge (PCK) by Shulman (1987), and Experiential Learning Theory by Kolb (1970). Out of the fourteen (14) participants who participated in this study, seven (7) non-PE teachers underwent in- depth interviews (IDI), and another seven (7) for focus group discussion (FGD); (7) participants of them participated virtually through Zoom application while the other (7) participants underwent to a face-to-face interview. They selected to purposive sampling technique. As to the experiences of the non-PE teachers, the following themes emerged: experiencing lack of resources; inadequacy of training opportunities; giving value to learning first aid and emergency response; having struggles in teaching due to insufficient knowledge and skills; provision of practical knowledge and personal growth; having lack of confidence and uncertainty in teaching first aid; and having students who actively participating. To address the issues and challenges, the non-PE teachers mentioned the following: application of experiential learning in practical knowledge skills education; collaboration with people in first aid and emergency response; being optimistic and enthusiastic; sustaining creative adaptation and comprehensive readiness; and utilizing online resources for knowledge enhancement. They also cited insights they could share with others through the themes that emerged; provisions of support, training, and seminar- workshop for non-PE teachers; integration of practical application in real scenarios; continuous professional development in first aid and emergency response; and practice of being calm in emergency response. The results are deemed essential to non–PE teachers, students, the Department of Education, School Administrators, and other stakeholders to give awareness and create best practices that would provide great help in delivering quality teaching of First Aid and Emergency Response, training techniques that would motivate and encourage the non- PE teachers.

Sun Stats: An IoT Node MCU Integrated Web Application with Real-Time Energy Monitoring and Cost Forecasting

Rising global concerns about energy consumption and its environmental impact have fueled interest in renewable and sustainable energy sources, with solar energy emerging as a promising solution. In response to climate change challenges and the depletion of finite fossil fuel resources, there is a pressing need for innovative energy management solutions. This paper introduces "SunStats," an IoT NodeMCU integrated web application designed for real-time energy monitoring and cost (consumption) forecasting.
 SunStats combines weather data and cost forecasting to provide tailored capabilities for local residential users. Integrating smart technologies and data-driven approaches, it aims to empower consumers with valuable insights into their energy consumption patterns.
 It has a real-time energy monitoring feature, facilitated by the use of NodeMCU, allowing users to continuously track their energy consumption patterns. The user-friendly interface enables consumers to access real-time data on their electricity usage, facilitating informed decisions about energy management. Additionally, the application's integration of weather data offers contextual insights into how weather conditions influence energy usage, empowering users to adjust their consumption behavior accordingly.
 Beyond real-time energy monitoring and weather integration, SunStats incorporates cost forecasting. Utilizing historical energy usage patterns and weather data, the application predicts future energy consumption for users. This forecasting feature provides consumers with valuable information about potential fluctuations in energy consumption, enabling effective planning and informed decision-making.
 The primary goal of SunStats is to empower users to become proactive energy managers, offering transparency into real-time energy data, weather insights, and cost forecasts. This knowledge enables users to implement energy-saving strategies, optimize efficiency, and contribute to a more sustainable future.

Torsional shear strength of adhesive joined steel in saline environments

This study investigates the impact of exposure to aggressive environments, specifically saline conditions per ASTM B117 standard, on the shear strength of adhesive‐bonded joints, crucial for ensuring structural integrity in various industrial applications like automotive, aerospace, and construction. Employing an epoxy‐based adhesive to bond AISI304 steel, specimens undergo 14‐ and 28‐day salt spray treatments to simulate harsh working conditions. Torsional shear strength assessment of steel‐to‐steel adhesive joints is conducted using a torsion test methodology, with an analytical approach used to extrapolate adhesive joint shear strength. This research contributes to understanding adhesively bonded joint performance in challenging environmental conditions, and how the shear strength of the bonded joints changes in relatively small amount of time, offering valuable insights for robust structural designs across industrial sectors.This article is protected by copyright. All rights reserved.

JAVA Based Quiz Application

The Java-based Quiz Application is a versatile, standalone program designed to operate independently of an internet connection, making it ideal for offline use in various settings such as training sessions, schools, and areas with limited internet access. It boasts a comprehensive set of features for quiz creation, administration, and participation. Key among its features is the offline user authentication system, enabling users to securely create accounts and log in without internet connectivity. This ensures accessibility and autonomy for users regardless of their location or internet availability. The application offers a user-friendly interface for adding various question types, including multiple choice, to offline quizzes, allowing users to customize quizzes to suit their needs and cover diverse topics. Furthermore, users can manage quizzes offline, facilitating real-time adjustments by educators, trainers, or organizers without the need for an internet connection. The application provides players with a seamless offline quiz experience, offering swift performance feedback and navigation to enhance engagement. Users can select quizzes from offline categories, attempt them offline, and receive immediate scoring and feedback. Emphasizing security, the Java Quiz Application implements robust encryption techniques to safeguard user data and ensure confidentiality offline. This commitment to security ensures the protection of sensitive information even in offline environments, maintaining user trust and confidence in the application's integrity. Key Words: Java, No Internet Connection, Offline Quiz, user Friendly

An Urban Intelligence Architecture for Heterogeneous Data and Application Integration, Deployment and Orchestration.

This paper describes a novel architecture that aims to create a template for the implementation of an IT platform, supporting the deployment and integration of the different digital twin subsystems that compose a complex urban intelligence system. In more detail, the proposed Smart City IT architecture has the following main purposes: (i) facilitating the deployment of the subsystems in a cloud environment; (ii) effectively storing, integrating, managing, and sharing the huge amount of heterogeneous data acquired and produced by each subsystem, using a data lake; (iii) supporting data exchange and sharing; (iv) managing and executing workflows, to automatically coordinate and run processes; and (v) to provide and visualize the required information. A prototype of the proposed IT solution was implemented leveraging open-source frameworks and technologies, to test its functionalities and performance. The results of the tests performed in real-world settings confirmed that the proposed architecture could efficiently and easily support the deployment and integration of heterogeneous subsystems, allowing them to share and integrate their data and to select, extract, and visualize the information required by a user, as well as promoting the integration with other external systems, and defining and executing workflows to orchestrate the various subsystems involved in complex analyses and processes.

Influence of physico-chemical properties of two lipoxin emulsion-loaded hydrogels on pre-polarized macrophages: a comparative analysis.

Inflammation, a crucial defense mechanism, must be rigorously regulated to prevent the onset of chronic inflammation and subsequent tissue damage. Specialized pro resolving mediators (SPMs) such as lipoxin A4 (LXA4) have demonstrated their ability to facilitate the resolution of inflammation by orchestrating a transition of M1 pro-inflammatory macrophages towards an anti-inflammatory M2 phenotype. However, the hydrophobic and chemically labile nature of LXA4 necessitates the development of a delivery system capable of preserving its integrity for clinical applications. In this study, two types of emulsion were formulated using different homogenization processes:mechanical overhead stirrer (MEB for blank Emulsion and MELX for LXA4 loaded-Emulsion) or Luer-lock syringes (SEB for blank Emulsion and SELX for LXA4 loaded-Emulsion)). Following characterization, including size and droplet morphology assessment by microscopy, the encapsulation efficiency (EE) was determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS). To exert control over LXA4 release, these emulsions were embedded within silanized hyaluronic acid hydrogels. A comprehensive evaluation, encompassing gel time, swelling, and degradation profiles under acidic, basic, and neutral conditions, preceded the assessment of LXA4 cumulative release using LC-MS/MS. Physicochemical results indicate that H-MELX (Mechanical overhead stirrer LXA4 Emulsion loaded-Hydrogel) exhibits superior efficiency over H-SELX (Luer-lock syringes LXA4 Emulsion loaded-Hydrogel). While both formulations stimulated pro-inflammatory cytokine secretion and promoted a pro-inflammatory macrophage phenotype, LXA4 emulsion-loaded hydrogels displayed a diminished pro-inflammatory activity compared to blank emulsion-loaded hydrogels. These findings highlight the biological efficacy of LXA4 within both systems, with H-SELX outperforming H-MELX in terms of efficiency. To the best of our knowledge, this is the first successful demonstration of the biological efficacy of LXA4 emulsion-loaded hydrogel systems on macrophage polarization. These versatile H-MELX and H-SELX formulations can be customized to enhance their biological activity making them promising tools to promote the resolution of inflammation in diverse clinical applications.

Learning in the Context of (Digital) University Teaching: Flexibilisation and Individualisation

This article explores the challenges and developmental landscapes of digital university education, focusing on fostering flexibility and individualization in learning. It examines how integrating online and in-person formats can serve these objectives and how evaluation techniques can enhance the quality of teaching offerings. The shift towards lifelong learning (LLL) and catering to individual student needs are regarded as pivotal concerns. The paper presents various developmental fields within higher education, emphasizing the need to cultivate a learner-centric culture that supports self-directed learning. The integration of online and in-person formats as part of a flexible teaching concept is discussed as a solution for individualizing and flexibilizing learning. Additionally, the evaluation of teaching as a quality measure is deliberated, emphasizing the importance of effective evaluation methodologies. The work concludes with a contemplation on the future of university education, advocating for a balanced blend of online and in-person formats. This strategy aims to foster flexibility and individualization in learning to meet the demands of self-directed and lifelong learning. The article notes that experiences from the pandemic should be integrated into future university teaching rather than reverting to previous norms. The open questions, particularly regarding the integration of AI applications like chatbots and their impact on instruction, are highlighted as pivotal for further developments in higher education.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration

With an emphasis on classifying diseases of sugarcane leaves, this research suggests an attention-based multilevel deep learning architecture for reliably classifying plant diseases. The suggested architecture comprises spatial and channel attention for saliency detection and blends features from lower to higher levels. On a self-created database, the model outperformed cutting-edge models like VGG19, ResNet50, XceptionNet, and EfficientNet_B7 with an accuracy of 86.53%. The findings show how essential all-level characteristics are for categorizing images and how they can improve efficiency even with tiny databases. The suggested architecture has the potential to support the early detection and diagnosis of plant diseases, enabling fast crop damage mitigation. Additionally, the implementation of the proposed AMRCNN model in the Android phone-based application gives an opportunity for the widespread use of mobile phones in the classification of sugarcane diseases.

Impact of Post Quantum Digital Signatures On Block Chain: Comparative Analysis

The emergence of quantum computing poses a substantial risk to the security of block chain technology, requiring a transition to post-quantum cryptography (PQC) to protect the future integrity and security of block chain systems. This study provides a comprehensive assessment of integrating post-quantum digital signatures into block chain frameworks, aiming to mitigate quantum vulnerabilities while maintaining the reliability, scalability, and integrity of block chain applications. A comprehensive evaluation is conducted to assess the efficacy of post-quantum signature algorithms recommended by NIST in comparison to conventional cryptographic benchmarks like ECDSA. The evaluation primarily centers on the speed of transaction processing, network scalability, and the overall upgrade of security. The results indicate that enhancing block chain systems to counter quantum attacks is intricate. Nevertheless, it is a crucial measure in guaranteeing block chain applications’ enduring security and dependability amidst the ever-changing technical risks. The findings of our analysis indicate that the adoption of post-quantum signatures poses significant technical and operational challenges. However, it also offers opportunities to strengthen the resilience of block chain systems against quantum threats, drive advancements in secure digital transactions, and establish a new benchmark for cryptographic practices in the era of quantum computing. This study provides significant insights and ideas for developers and politicians interested in developing and administering secure, quantum-resistant block chain networks, contributing to the critical conversation on preparing block chain technology for a post-quantum future.

Assessing the Impact of Mobile Applications on Student Engagement in ICT and Computer Science Education

This study investigates the impact of mobile applications on student engagement in the realms of Information and Communication Technology (ICT) and Computer Science Education. As the integration of mobile applications in educational settings becomes increasingly prevalent, understanding their influence on student engagement is crucial for optimizing learning experiences. The study employs assess the effectiveness of selected mobile applications aligned with the curriculum. Data analysis focuses on measuring and evaluating student engagement levels. The results reveal shedding light on the implications for enhancing ICT and computer science education. This research contributes to the existing literature by addressing gaps and providing insights into the practical use of mobile applications in educational settings.