Scalability Of Applications Research Articles

On-chip high-dimensional entangled photon sources

Abstract High-dimensional quantum entanglement is an important resource for emerging quantum technologies such as quantum communication and quantum computation. The scalability of metres-long experimental setups limits high-dimensional entanglement in bulk optics. Advancements in quantum technology hinge on reproducible, and reconfigurable quantum devices — including photon sources, which are challenging to achieve in a scalable manner using bulk optics. Advances in nanotechnology and CMOS-compatible integration techniques have enabled the generation of entangled photons on millimeter-scale chips, significantly enhancing scalability, stability, replicability, and miniaturization for real-world quantum applications. In recent years we have seen several chip-scale demonstrations with different degrees of freedom including path, frequency-bin, time-bin, and transverse modes, on many material platforms. A complete quantum photonic integrated circuit requires the generation, manipulation, and detection of qudits, involving various active and passive quantum photonic components which further increase the degree of complexity. Here, we review and introduce the nonlinear optical processes that facilitate on-chip high-dimensional entangled photon sources and the currently used material platforms. We discuss a range of current implementations of on-chip high-dimensional entangled photon sources and demonstrated applications. We comment on the current challenges due to the limitations of individual material platforms and present future opportunities in hybrid and heterogeneous integration strategies for the next generation of integrated quantum photonic chips.

First-principles investigation of structural, mechanical, and electronic properties of AMgX3 (A=Ga, In, Tl, and X=Cl, Br, I) perovskites for optoelectronic applications

Abstract Metal-halide perovskites have emerged as a revolutionary material in solar energy technology, offering exceptional light-harvesting efficiency, eco-friendly characteristics, and low production costs. These materials are paving the way for next-generation photovoltaic devices with their outstanding optoelectronic properties and scalability for commercial applications. To determine the various features of the halide perovskites AMgX3 (where A stands for Ga, In, Tl, and X for Cl, Br, and I), we utilized DFT with the (Generalized Gradient Approximation) GGA-PBE (Perdew–Burke–Ernzerhof) exchange and correlation approximation to examine the structural, mechanical, electronic, and optical behaviors of the perovskite materials. Structurally, these materials exhibit cubic stability, vital for high-performance durability in photovoltaic devices. Mechanically, the calculated elastic constants verify their strength, suitable for environments where mechanical stability is critical, such as in aerospace electronics. The band gap range (1.22–3.69 eV) shows how versatile the materials are. TlMgI3 is suitable for infrared (IR) detection, whereas GaMgCl3 and InMgCl3 are optimal for ultraviolet (UV) applications. These findings support applications from IR sensors to UV photo detectors. The compounds’ optical properties, such as their high absorption coefficients, dielectric constants, and reflectivity, show how well they can collect and send light, which is important for solar cells and LEDs. The mechanical and optoelectronic properties collectively enhance their suitability for photonic and thermoelectric devices, offering scalable solutions for renewable energy and advanced photonics applications.

Employee attendance system using face recognition and GPS using local binary pattern histogram

Tracking employee attendance is an integral part of running a company in an organized and economical manner. Conventional approaches such as manual sign-ins and RFID cards or fingerprint scanning have shown important weaknesses, especially with regard to proxy attendance (buddy punching). We chose the LBPH algorithm since it has a higher flexibility against changes of light, which means that we can use it in many situations like indoor or outdoor cases. The system performances for various conditions were also noteworthy, achieving 96.4% recognition accuracy with FAR = 0.05 %, FRR = 1 % in normal lighting conditions and maintaining a 94.1 % near-accurate performance under low-light environmental settings whilst sustaining the performance at 90.6 % in outdoor environments, which resulted in detection time of approximately between 1.3–2.3 seconds respectively. For further peace of mind, the system incorporated GPS tracking to provide location verification with a 90% to 94% accuracy rate—logging attendance only when students were present in a designated area. This integrated system is especially useful in contemporary hybrid workplaces, as it minimizes attendance fraud and enhances operational efficiency. Although the system is capable of functionally robust performance under normal conditions, tests point to possible scalability and performance improvements in extreme lighting conditions and outdoor applications, thus establishing future development paths for environmental adaptation.

Cloud data centers and networks: Applications and optimization techniques

As the volume and complexity of big data continue to escalate, optimizing the performance, scalability, and energy efficiency of big data applications within cloud data centers has become increasingly crucial. This journal presents a comprehensive survey of current optimization techniques, focusing on data placement, job scheduling, and network configurations tailored for cloud environments. We explore the impact of various data center topologies on the performance of big data frameworks like Hadoop, emphasizing the trade-offs between performance and energy efficiency. Advanced methodologies, including dynamic data placement strategies, locality-aware scheduling, and innovative reduce task placement techniques, are reviewed in depth. Additionally, we highlight the importance of network power effectiveness (NPE) and examine the role of optical and electronic switching technologies in enhancing data center efficiency. By synthesizing findings from recent studies, this paper provides valuable insights into the optimization of cloud data centers, offering recommendations for improving resource utilization and reducing job completion times while maintaining energy efficiency. The findings contribute to the ongoing efforts to scale and adapt cloud data infrastructures for the rapidly growing demands of big data applications.

Improving healthcare application scalability through microservices architecture in the cloud

This review paper explores the scalability of healthcare applications by adopting microservices architecture in cloud environments. As healthcare systems face increasing demands for efficient and flexible application performance, traditional monolithic architectures often struggle to accommodate fluctuating workloads and rapidly evolving technological landscapes. This paper highlights the fundamental characteristics of microservices, emphasizing their advantages over monolithic systems, particularly in enhancing agility and resource optimization. It further examines the role of cloud computing in providing scalable solutions, enabling healthcare organizations to allocate resources based on real-time demands dynamically. The paper discusses essential strategies and best practices for implementing microservices, addressing common challenges and offering solutions to facilitate a smooth transition. Ultimately, the findings underscore the transformative potential of microservices architecture in improving operational efficiency and patient care within the healthcare sector.

Real-Time Soil Nutrient Monitoring Using NPK Sensors: Enhancing Precision Agriculture

Prediction of various parameters in the agriculture field using sensors is a significant topic nowadays. However, in many scenarios, the sensor data does not accurately detect the real parameter(/s) in the agriculture field. The sensor data may vary due to various external factors, whereas the real parameters don’t vary too much for a particular agriculture field. The present work introduces a modified neural network approach to predict real agricultural parameters from sensor data with accuracy caused by several external factors and demonstrates enhanced predictive accuracy and adaptability. The neural network takes the sensor data as input in various weather conditions and tries to find out the original real parameters of that sensor data. The real-time sensor data was collected from multiple agricultural sites. The results demonstrated high predictive accuracy, with the neural network outperforming traditional statistical methods in forecasting soil moisture and other vital variables. Additionally, the model’s ability to generalize across different environmental conditions enhances its applicability in various crop management scenarios. The study concludes that neural networks hold significant potential for improving the efficiency of smart agriculture systems by providing timely, data driven insights for farmers and agronomists. Further research will explore the integration of deep learning models and edge computing to enhance scalability and realtime responsiveness in field applications. The aforementioned research highlights the significance of NPK sensors in sustainable farming methods, namely in enabling accurate nutrient management via real-time data.

Utilization of Pseudomonas fluorescens Bacteria in Weed Control and Phosphate Supply in Oil Palm (Elais Guineensis Jacq.) Plantations

With growing environmental concerns, sustainable management practices in oil palm plantations are becoming increasingly essential. This review examines the potential of Pseudomonas fluorescens as a biological agent for both weed control and phosphate solubilization in oil palm (Elaeis guineensis) cultivation. A systematic literature search was conducted across Google Scholar, Scopus, and ScienceDirect databases, focusing on studies published between 2007 and 2023. Studies that investigated the role of Pseudomonas fluorescens in enhancing plant growth through weed suppression or improving phosphate uptake were selected. The findings reveal that Pseudomonas fluorescens can significantly benefit plant health by minimizing weed competition and enhancing the availability of phosphorus in the soil. However, challenges such as the variability in environmental conditions, strain specificity, and scalability of application persist. The review highlights the importance of further field trials and experimental research to refine the practical use of Pseudomonas fluorescens in achieving more sustainable oil palm production.

Photothermal Synergistic Hydrogen Production via a Fly-Ash-made Interfacial Vaporific System.

Employing UV-vis spectrum for hydrogen generation and vis-IR spectrum to elevate reaction temperatures and induce phase transitions effectively enhances yield and purifies water, demonstrating a judicious strategy for solar energy utilization. This study presents an interfacial photothermal water splitting system that utilizes all-inorganic, economical industrial by-products known as fly ash cenospheres (FAC) for solar-driven hydrogen generation. In this system, the yield reaches 254.8µmol h-1cm-1, representing an 89% augmentation compared to that of the three-phase system. In situ experiments, combined with theoretical calculation, reveal the system's robust light absorption capacity, facilitating rapid gas separation, thus improves the solar-to-hydrogen (STH) efficiency. Furthermore, the system demonstrates strong performance in turbid water and scalability for expansive applications, achieving a hydrogen yield exceeding 50 L h-1 m-2 from various water sources. Facilitating large-scale hydrogen production and water purification, it thereby establishing its potential as a viable solution for sustainable energy generation.

A Comprehensive Study of Feature Selection Techniques in Machine Learning Models

This paper explores the importance and applications of feature selection in machine learning models, with a focus on three main feature selection methods: filter methods, wrapper methods, and embedded methods. By comparing their advantages and limitations, the paper highlights how feature selection can improve model performance, reduce redundant features, minimize overfitting, and enhance computational efficiency. Additionally, the paper discusses the applications of feature selection across various domains, including healthcare, finance, and image processing, and examines how metrics such as accuracy, precision, and recall can assess the effectiveness of feature selection. As the complexity of datasets increases, the integration of feature selection with deep learning and explainable AI emerges as a key future direction, particularly in addressing scalability and fairness issues in large-scale and real-time applications. Finally, the paper concludes with an outlook on the future development and potential of feature selection in machine learning.

Methods for Detection, Extraction, Purification, and Characterization of Exopolysaccharides of Lactic Acid Bacteria-A Systematic Review.

Exopolysaccharides (EPSs) are large-molecular-weight, complex carbohydrate molecules and extracellularly secreted bio-polymers released by many microorganisms, including lactic acid bacteria (LAB). LAB are well known for their ability to produce a wide range of EPSs, which has received major attention. LAB-EPSs have the potential to improve health, and their applications are in the food and pharmaceutical industries. Several methods have been developed and optimized in recent years for producing, extracting, purifying, and characterizing LAB-produced EPSs. The simplest method of evaluating the production of EPSs is to observe morphological features, such as ropy and mucoid appearances of colonies. Ethanol precipitation is widely used to extract the EPSs from the cell-free supernatant and is generally purified using dialysis. The most commonly used method to quantify the carbohydrate content is phenol-sulfuric acid. The structural characteristics of EPSs are identified via Fourier transform infrared, nuclear magnetic resonance, and X-ray diffraction spectroscopy. The molecular weight and composition of monosaccharides are determined through size-exclusion chromatography, thin-layer chromatography, gas chromatography, and high-performance liquid chromatography. The surface morphology of EPSs is observed via scanning electron microscopy and atomic force microscopy, whereas thermal characteristics are determined through thermogravimetry analysis, derivative thermogravimetry, and differential scanning calorimetry. In the present review, we discuss the different existing methods used for the detailed study of LAB-produced EPSs, which provide a comprehensive guide on LAB-EPS preparation, critically evaluating methods, addressing knowledge gaps and key challenges, and offering solutions to enhance reproducibility, scalability, and support for both research and industrial applications.

Application of Bayesian Neural Networks in Healthcare: Three Case Studies

This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs offering a probabilistic framework that addresses the inherent uncertainty and variability in healthcare data. This study demonstrates the real-world applicability of BNNs through three key case studies: personalized diabetes treatment, early Alzheimer’s disease detection, and predictive modeling for HbA1c levels. By leveraging the Bayesian approach, these models provide not only enhanced predictive accuracy but also uncertainty quantification, a critical factor in clinical decision making. While the findings are promising, future research should focus on optimizing scalability and integration for real-world applications. This work lays a foundation for future studies, including the development of rating scales based on BNN predictions to improve clinical outcomes.

Combining federated learning and control: A survey

AbstractThis survey provides an overview of combining federated learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real‐world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state‐of‐the‐art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modelling over controller design, focusing on adaptive control, to knowledge transfer in multi‐agent decision‐making systems.

The Minimotor Ltd: Pioneering digital transformation in production and operations

This teaching case examines the complexities encountered by a global industrial company undergoing digital transformation, particularly due to its heterogeneous production landscape. It highlights the critical factors for effectively orchestrating digital transformation initiatives and utilizing the scalability of technological applications. This industry example is relevant for delegates, students and practitioners aiming to implement maturity models and to realize the digital transformation within the context of Industry 4.0.

Microbial Bioremediation of Spent Engine Oil: Current Advances, Challenges, and Future Directions

Study’s Excerpt Recent advancements in microbial bioremediation of spent engine oil (SEO) are presented. Specifically, the efficiency of bacterial and fungal species in degrading SEO hydrocarbons are reviewed. Key factors that influence degradation rates, such as strain specificity and environmental parameters in relation to different microorganisms are also highlighted. Full Abstract The extensive pollution of soil and water by spent engine oil (SEO) presents a considerable environmental hazard, especially because of its poisonous and recalcitrant characteristics. Traditional remediation approaches typically fall short in managing these toxins successfully, leading to an increased interest in bioremediation as a sustainable option. This review aims to compile current achievements/advancements in the microbial bioremediation of SEO, with emphasis on the effectiveness of several bacterial and fungal species in degrading the hydrocarbons in SEO under diverse environmental settings. Various studies published in the last decade (accessible via Google Scholar, Google, Scopus, and PubMed), reporting on microbial degradation rates on SEO, the impact of environmental conditions, and the efficacy of microbial consortia were objectively selected and analysed. Key findings showed that various genera such as Alcaligenes, Acinetobacter, Bacillus, Candida, Flavobacterium, Pseudomonas, and Rhodococcus were the most commonly reported microorganisms with potential SEO remediation because they have been reported to significantly degrade and used hydrocarbons in SEO as an energy source. Notably, Pseudomonas alcaligenes and Klebsiella aerogenes exhibited significant degradation rates of SEO, up to 68%, over 21 days under optimal conditions; Acinetobacter, Bacillus, Micrococcus, Flavobacterium, and Pseudomonas were found to exhibit a total hydrocarbon reduction of 86.7% over 60 days; while species like Ochrobactrum thiophenivorans were found to effectively degrade waste lubricating oil. However, there are significant differences in degradation rates among the reported species due to physiological factors such as temperature, pH, and available nutrients. Hence, it is inferential to conclude that microbial bioremediation shows promise in SEO management but its effectiveness is highly dependent on the type of microbial strain used and optimizing environmental conditions. Therefore, this review identifies several key knowledge gaps and proposed future research directions, such as the integration of bioremediation with emerging technologies to improve and ensure efficiency and scalability in real-world applications.

Building the Modern Web: A Comparative Study of MERN And FERN Technology Stacks

This paper compares the MERN and FERN technology stacks, focusing on their core components, strengths, and limitations for modern full-stack development. MERN (MongoDB, Express.js, React.js, Node.js) provides flexibility and scalability for large-scale applications requiring robust backend control. FERN (Firebase, Express.js, React.js, Node.js), on the other hand, leverages Firebase’s real-time capabilities, making it ideal for applications requiring real-time functionality with minimal backend setup. Key differences between these stacks include backend complexity, real-time synchronization, scalability, and cost considerations. The comparison aims to help developers select the most appropriate stack based on project requirements

Qubit: The Leap into Quantum Computation

Abstract. This article delves into the fundamental aspects of qubits in quantum computation, emphasizing their coordination, mathematical representation through the Bloch Sphere, and the unique advantages and challenges of superconducting, trapped ion, and photonic qubits. It highlights the transformative potential of quantum algorithms in solving complex problems in cryptography and information security, while addressing key sources of quantum errors such as decoherence and quantum noise. The study discusses advanced error correction methods and underscores the necessity of improving qubit coherence, stability, scalability, and integration for the practical application and commercialization of quantum computers. Ongoing research and collaboration are deemed essential for overcoming technical challenges and unlocking the full potential of quantum computation across various industries.

Detection and Mitigation of DDoS Attacks : A Review of Robust and Scalable Solutions

Distributed Denial-of-Service (DDoS) attacks have emerged as a critical threat to network security, causing significant disruptions by overwhelming systems with malicious traffic. The motivation behind this review is the growing sophistication and frequency of DDoS attacks, which demand more robust and scalable detection and mitigation techniques. While numerous methods have been proposed, limitations such as high false positive rates, resource constraints, and the evolving nature of attacks continue to challenge existing solutions. This review aims to analyze and evaluate various robust detection mechanisms, including machine learning, anomaly detection, and hybrid models, with a focus on scalability and adaptability in real-world applications. The objective is to identify key strengths and weaknesses in current approaches, highlighting future research directions for building more resilient DDoS defense systems capable of operating efficiently under high-traffic conditions.

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

The role of APIs in modern software development

Application Programming Interfaces (APIs) play a critical role in modern software development, enabling seamless communication between disparate systems and enhancing the scalability, modularity, and security of applications. This research investigates the impact of APIs on software architecture, focusing on their use in facilitating interoperability across distributed environments. Using a mixed-methods approach, the study combines a literature review, a survey of 50 software developers, and case studies from industry leaders such as Amazon Web Services (AWS), Google, and Facebook. The findings reveal that REST APIs are the most widely used (75%), while GraphQL is gaining popularity for its ability to optimize data retrieval in complex systems. Security challenges, particularly vulnerabilities in REST APIs, were highlighted by 35% of developers, underscoring the need for stronger authentication and encryption methods like OAuth. This study also explores how APIs foster innovation by enabling third-party integrations, driving ecosystem growth in cloud computing and other sectors. The research concludes that while APIs are vital for the future of software development, addressing security risks and improving API management practices will be essential for maximizing their benefits in emerging technologies such as IoT and AI.

Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy

This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and angle encoding to amplitude encoding; for encoding classical data. We conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead.This study underscores the potential of quantum data embedding to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.