Processing Requirements Research Articles

An efficient deep reinforcement learning based task scheduler in cloud-fog environment

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.

A novel method: YOLO-CE and 3D point cloud-based feature extraction for welding seams of tower bases

Abstract Robotic automated welding of non-standard steel structures presents significant challenges, particularly for electric power tower bases. This study introduces a novel approach that integrates the You Only Look Once—Compact Invert Block and Efficient Local Attention (YOLO-CE) model, an enhanced version of YOLOV8 for 2D image segmentation, with 3D point cloud technology. The YOLO-CE model is used to accurately extract point cloud data from the target area, which is then processed using the MSAC algorithm for efficient plane segmentation. Weld lines are identified through plane equations, allowing for initial weld point cloud extraction. To further refine accuracy, an optimized evaluation equation is developed that accounts for both the distance between the weld point cloud and the fitted plane, and the angle between their normal vectors. This enables precise classification of the weld point cloud. From this classification, key weld feature points are identified, and their exact positions are determined by calculating the distances between these points and their intersections with three planes. The reliability of the proposed method was validated using a robot for precise measurements, with a total error margin of less than 1.5084 mm, demonstrating high accuracy and stability. Post-operation inspections confirmed that the welds were filled and free from defects, meeting all process requirements. The YOLO-CE model achieved a mIoU of 96.38% and a precision of 99.8%, highlighting its effectiveness. This method provides an efficient and precise solution for the automated welding of non-standard steel structural components and has promising application potential.

Dependence of cyanobacterium growth and Mars-specific photobioreactor mass on total pressure, pN2 and pCO2

In situ resource utilization systems based on cyanobacteria could support the sustainability of crewed missions to Mars. However, their resource-efficiency will depend on the extent to which gases from the Martian atmosphere must be processed to support cyanobacterial growth. The main purpose of the present work is to help assess this extent. We therefore start with investigating the impact of changes in atmospheric conditions on the photoautotrophic, diazotrophic growth of the cyanobacterium Anabaena sp. PCC 7938. We show that lowering atmospheric pressure from 1 bar down to 80 hPa, without changing the partial pressures of metabolizable gases, does not reduce growth rates. We also provide equations, analogous to Monod’s, that describe the dependence of growth rates on the partial pressures of CO2 and N2. We then outline the relationships between atmospheric pressure and composition, the minimal mass of a photobioreactor’s outer walls (which is dependent on the inner-outer pressure difference), and growth rates. Relying on these relationships, we demonstrate that the structural mass of a photobioreactor can be decreased – without affecting cyanobacterial productivity – by reducing the inner gas pressure. We argue, however, that this reduction would be small next to the equivalent system mass of the cultivation system. A greater impact on resource-efficiency could come from the selection of atmospheric conditions which minimize gas processing requirements while adequately supporting cyanobacterial growth. The data and equations we provide can help identify these conditions.

Automatic detection of landslide impact areas using Google Earth Engine

This research introduces an effective framework for automatically detecting landslide impact areas using Google Earth Engine (GEE). The Asia–Pacific region frequently experiences earthquakes and heavy rainfall, leading to frequent landslides that cause loss of life and property. Focusing on landslide catalogues from Taiwan and Japan, the study proposes an automatic landslide detection process using a new method termed multi-bitemporal images (MBTIs), which involves the collection of accumulated changes over time. First, set the event date and collect all images before and after the event. Second, analyse the change pixels in bi-temporal images. Third, review all change pixels to determine the total amount of accumulated changes. This method includes all bi-temporal image sets in the analysis, unlike traditional methods that only use single pairs of bi-temporal images. Clouds are filtered using a pixel-based approach and machine learning techniques. The landslide areas are analysed statistically, and appropriate thresholds for automatic landslide detection are suggested. Using reproducibility, which indicates the percentage of bi-temporal images that detect vegetation loss in mountainous areas, the proposed method achieves a 99% reduction in false positives with a reproducibility requirement of 24.21%, while maintaining true positives at 66.89%. This study analyzed 28–720 bi-temporal image sets from various regions using Sentinel-2 data, revealing that subsequent landslides can be 7–293 times larger than co-seismic landslides. In comparison, subsequent landslides were found to be 3–12 times larger than rainfall-induced landslides. Additionally, the impact of earthquake event on subsequent landslides is 2.3–24.4 times greater than that of rainfall-induced event. By using GEE, the accumulation of hundreds of satellite images can be completed within 15 min, depending on the processing requirements.

Emerging Frontiers in In Situ Forming Hydrogels for Enhanced Hemostasis and Accelerated Wound Healing.

With a surge in the number of accidents and chronic wounds worldwide, there is a growing need for advanced hemostatic and wound care solutions. In this regard, in situ forming hydrogels have emerged as a revolutionary biomaterial due to their inherent properties, which include biocompatibility, biodegradability, porosity, and extracellular matrix (ECM)-like mechanical strength, that render them ideal for biomedical applications. This review demonstrates the advancements of in situ forming hydrogels, tracing their evolution from injectable to more sophisticated forms, such as sprayable and 3-D printed hydrogels. These hydrogels are designed to modulate the pathophysiology of wounds, enhancing hemostasis and facilitating wound repair. The review presents different methodologies for in situ forming hydrogel synthesis, spanning a spectrum of physical and chemical cross-linking techniques. Furthermore, it showcases the adaptability of hydrogels to the dynamic requirements of wound healing processes. Through a detailed discussion, this article sheds light on the multifunctional capabilities of these hydrogels such as their antibacterial, anti-inflammatory, and antioxidant properties. This review aims to inform and inspire continued advancement in the field, ultimately contributing to the development of sophisticated wound care solutions that meet the complexity of clinical needs.

Unifying Community Efforts on Registration and Data Rights

At the Smithsonian National Museum of Natural History (SI NMNH), the Assistant Registrar and Informatics Manager in the Department of Paleobiology (Paleo) have identified key linkages between registering collections (documentation for specimens and data through acquisitions, loans and disposals) and management of digitized collections, especially for usage right designation. Both roles require extensive collaboration on a shared framework for managing data related to the provenance of fossil specimens and rights associated with any derivative digital assets. This eliminated a disconnect in overlapping responsibilities: those typically siloed in museum registration practices (e.g., provenance data, intellectual property rights) and the data typically siloed in collections management and digitization workflows (e.g., data quality, digital asset management, and usage rights). Paleo shifted to a new practical approach and mindset focused on communal thinking. This propelled implementation of data standard practices and laid the building blocks for a shared community perspective on digitizing paleontological collections. Since 2020, Paleo established processing workflows for digital asset requests of the fossil collections (new digital asset creations vs. use of existing digital assets) and baseline understandings about rights of digital assets. These products led to a comprehensive effort to outline SI NMNH’s options to designate usage rights for digital assets (Table 1) and distribute basic understandings about Smithsonian Terms of Use and Smithsonian Open Access Policy (SI OA) across SI NMNH. Paleo explored this topic further by engaging in virtual meetings, conferences, presentations and informal discussions with the collections management staff and data managers across SI NMNH and externally with domestic and international groups such as the Paleo Data Working Group (PDWG), Society for the Preservation of Natural History Collections (SPNHC) Registrars Group, and Biodiversity Information Standards (TDWG), to develop data standards initiatives for loans, permits and sensitive data (Fig. 1). Through regular community engagement, Paleo forged a concise, yet evolving, perspective on digitizing collections, which aligned both registration and informatics data management needs within the department and museum (Fig. 2). This multi-professional perspective enables the promotion of shared community knowledge and understanding about data rights. The primary purpose is to balance consistent goals, current needs, and long-term outcomes with the flexibility to adapt our practices based on institution-specific policies, new research methods, and varying domestic and international regulations and laws. The notable outcomes from almost four years of work are highlighted and incorporated into Paleo's departmental policy and documentation requirements for processing and fulfilling digital asset creation or use requests workflows and distribution of this information to the public website for visitors and borrowers. However, there are still persisting challenges related to registering and managing these digital assets. There are immediate and future obstacles such as: limitations in effective tracking methods to record creation of digital assets, use of digital assets, and designation of what usage rights and when the decision was made; gaps in community practice, policy and regulations at the institutional level and globally; past practices in workflows and staff structures or lack thereof, which have left digital assets in limbo without means of accessing the data or documentation to identify the appropriate use options and rights. We see a need to continue asking questions. How are varying policies across institutions globally, impacting compliance concerns in registration for sharing sensitive information with other research institutions? How have or will new research techniques and opportunities impact the need for flexibility in practices and decisions? Are there alternative ways to utilize a Collections Management Systems (CMS) or use other platforms to track, store and manage the digital assets? How have others engaged in community discussions to address overlapping needs for data standards and registration of digitized collections? These identify significant inconsistencies across the community that need further discussion with subject-matter experts before effective and comprehensive decisions in data standards for managing digitized collections can be made. Ultimately, there are outstanding questions and challenges at all stages for registering, digitizing and managing digital assets of natural history collections. We continue to unveil nuances in thinking about this topic and find the need for structures of shared knowledge built and actively engaged in by a professionally diverse community. The utilization of subject-matter experts encourages collaboration and alternative perspectives to standardize operations and policy while expanding community efforts and awareness.

Templates-Built Structural Designs for Piezoelectrochemical Pressure Sensors.

Self-powered sensors, capable of detecting static and dynamic pressure without an external power source, are pivotal for advancements in human-computer interaction, health monitoring, and artificial intelligence. Current sensing technologies, however, often fall short of meeting the growing needs for precise and timely pressure monitoring. This article introduces a novel self-powered pressure sensor utilizing electrochemical reactions. The sensor's ion conduction path and internal resistance adjust in response to external stress across a broad range. Its three-dimensional structure, crafted by using a simple template on the electrolyte, enables the efficient and cost-effective detection of various mechanical stimuli. This device not only achieves an optimized power density of approximately 2.34 mW cm-2─surpassing most existing technologies─but also features excellent flexibility, quick response, and recovery times (0.15 and 0.19 s respectively); high durability (2000 cycles); and a broad sensing range (0.23-20 kPa). Moreover, it serves as an ionic touchpad, enhancing data collection and recognition, and integrates seamlessly with a mouthpiece for accurate, real-time monitoring of respiratory activities. This innovative sensor offers minimal cost and simple process requirements while providing multifunctional capabilities for energy harvesting and pressure sensing, marking a significant step forward in the design of next-generation sensors.

Seawater alkalization via an energy-efficient electrochemical process for CO2 capture

Electrochemical pH-swing strategies offer a promising avenue for cost-effective and energy-efficient carbon dioxide (CO2) capture, surpassing the traditional thermally activated processes and humidity-sensitive techniques. The concept of elevating seawater's alkalinity for scalable CO2 capture without introducing additional chemical as reactant is particularly intriguing due to its minimal environmental impact. However, current commercial plants like chlor-alkali process or water electrolysis demand high thermodynamic voltages of 2.2 V and 1.23 V, respectively, for the production of sodium hydroxide (NaOH) from seawater. These high voltages are attributed to the asymmetric electrochemical reactions, where two completely different reactions take place at the anode and cathode. Here, we developed a symmetric electrochemical system for seawater alkalization based on a highly reversible and identical reaction taking place at the anode and cathode. We utilize hydrogen evolution reaction at the cathode, where the generated hydrogen is looped to the anode for hydrogen oxidation reaction. Theoretical calculations indicate an impressively low energy requirement ranging from 0.07 to 0.53 kWh/kg NaOH for established pH differences of 1.7 to 13.4. Experimentally, we achieved the alkalization with an energy consumption of 0.63 kWh/kg NaOH, which is only 38% of the theoretical energy requirements of the chlor-alkali process (1.64 kWh/kg NaOH). Further tests demonstrated the system's potential of enduring high current densities (~20 mA/cm2) and operating stability over an extended period (>110 h), showing its potential for future applications. Notably, the CO2 adsorption tests performed with alkalized seawater exhibited remarkably improved CO2 capture dictated by the production of hydroxide compared to the pristine seawater.

A Comparative Analysis of Machine Learning Algorithms for Big Data Applications in Predictive Analytics

As the volume and complexity of data continue to grow, predictive analytics has emerged as a vital tool for extracting actionable insights from big data, driving decision-making across various domains such as healthcare, finance, and e-commerce. However, selecting an appropriate machine learning algorithm for predictive analytics applications is challenging due to differences in algorithmic performance, computational requirements, and scalability, especially in the context of big data. This paper provides a comprehensive comparative analysis of popular machine learning algorithms utilized in predictive analytics, specifically focusing on their effectiveness and feasibility in big data environments. The study categorizes algorithms based on learning types—supervised, unsupervised, and reinforcement learning—and evaluates their performance across multiple dimensions: prediction accuracy, computational efficiency, scalability, and suitability for real-time analytics. Through a detailed analysis of algorithms, including linear regression, decision trees, support vector machines, neural networks, and clustering techniques, we assess each method’s strengths and limitations in handling large datasets. Additionally, the study introduces a series of metrics, such as accuracy, F1-score, and training time, as benchmarks for assessing the algorithms’ predictive capabilities and computational viability. A hypothetical case study demonstrates the application of these algorithms on a sample big data set, providing insights into their real-world performance across different predictive analytics scenarios. Visual data representations, including comparative tables and performance graphs, offer a clearer perspective on the trade-offs among algorithm choices. The findings highlight that while certain algorithms like random forests and neural networks achieve higher accuracy in prediction tasks, they may also require substantial computational resources, posing limitations for real-time processing in big data applications. This paper concludes with recommendations for selecting machine learning algorithms based on specific predictive analytics objectives, data characteristics, and processing requirements. Furthermore, it discusses the challenges associated with implementing these algorithms in big data contexts and explores potential advancements, such as the integration of deep learning and the use of distributed computing, as promising directions for enhancing predictive analytics performance in future applications.

Point-enhanced convolutional neural network: A novel deep learning method for transonic wall-bounded flows

Low order models can be used to accelerate engineering design processes. Ideally, these surrogates should meet the conflicting requirements of large design space coverage, high accuracy and fast evaluation. Within the context of aerospace applications at transonic conditions, this can be challenging due to the associated non-linearity of the flow regime. Different methods have been investigated in the past to predict the flow-field around shapes such as airfoils or cylinders. However, they usually have reduced spatial resolution, limiting the prediction capabilities within the boundary layer which is of interest for transonic wall-bounded flows. This work proposes a novel Point-Enhanced Convolutional Neural Network (PCNN) method that combines the advantages of the well-established PointNet and convolutional neural network approaches. The PCNN model has relatively low memory requirements in the training process, preserves the spatial correlation in the domain and has the same resolution as a traditional computational method. The architecture is used for the flow-field prediction of civil aero-engine nacelles in which it is demonstrated that the flow features of peak isentropic Mach number (Mis), pre-shock isentropic Mach number and shock location (X/Lnac) are captured within ΔMis = 0.02, ΔMis=0.04, ΔX/Lnac=0.007, respectively. The PCNN model successfully predicts the integral parameters of the boundary layer, in which the incompressible displacement thickness, momentum thickness and shape factor are typically within 5% of the CFD. Overall, the PCNN method is demonstrated for transonic wall-bounded flows for a range of flow physics that include shock waves and shock-induced separation.

Virtual Development of a Single-Cylinder Hydrogen Opposed Piston Engine

A significant challenge in utilizing hydrogen in conventional internal combustion engines is achieving a balance between NOx emissions and brake power output. A lean premixed charge (Lambda ≈ 2.5) allows for efficient and stable combustion with minimal NOx emissions. However, this comes at the cost of reduced power density due to the higher air requirements of the thermodynamic process. While supercharging can mitigate this drawback, it introduces increased complexity, cost, and size. An intriguing alternative is the 2-stroke cycle, particularly in an opposed piston (OP) configuration. This study presents the virtual development of a single-cylinder 2-stroke OP engine with a total displacement of 0.95 L, designed to deliver 25 kW at 3000 rpm. Thanks to its compact size, high thermal efficiency, robustness, modularity, and low manufacturing cost, this engine is intended for use either as an industrial power unit or in combination with electric motors in hybrid vehicles. The overarching goal of this project is to demonstrate that internal combustion engines can offer a practical and cost-effective alternative to hydrogen fuel cells without significant penalties in terms of efficiency and pollutant emissions. The design of this novel engine started from scratch, and both 1D and 3D CFD simulations were employed, with particular focus on optimizing the cylinder’s geometry and developing an efficient low-pressure injection system. The numerical methodology was based on state-of-the-art commercial codes, in line with established engineering practices. The numerical results indicated that the optimized engine configuration slightly surpasses the target performance, achieving 29 kW at 3000 rpm, while maintaining near-zero NOx emissions (<20 ppm) and high brake thermal efficiency (~40%) over a wide power range. Additionally, the cost of this engine is projected to be lower than an equivalent 4-stroke engine, due to fewer components (e.g., no cylinder head, poppet valves, or camshafts) and a lighter construction.

Menakar Kebijakan Pendampingan Self Declare Halal oleh Perguruan Tinggi (Studi Kasus di Universitas Nahdlatul Ulama Sidoarjo dan Uin Maulana Malik Ibrahim Malang)

This study examines various government efforts to encourage halal product assurance, especially through the creation of derivative regulations and the establishment of the Halal Product Assurance Organizing Agency (BPJPH). However, small to micro-scale business actors still need help, especially related to registration fees and understanding the importance of halal certification. The study aims to explain the role of universities in East Java in assisting halal certification for Micro and Small Enterprises (MSEs) while analyzing the legal aspects and maqashid sharia. Using field research methods and qualitative approaches, it was found that the role of universities is as PPH assistants for MSEs who meet the halal certification criteria through the self-declare route. The implementation of self-declare halal assistance is faced with the obstacle of maintaining the activeness of registered PPH assistants, and there are still business actors who need to meet the requirements in the assistance process, such as lack of information on materials and expired material certification. An effective monitoring system is required to overcome potential errors in the halal certification process. Universities are also expected to be able to create program innovations to strengthen and activate the role of PPH assistants. The government, in this case, especially BPJPH, also needs to create a more consistent policy regarding halal certification registration through the application so that the criteria of the Halal Product Assurance System (SJPH), which is built on five basic principle frameworks (arkanul halal) can be met.

Regulatory and legal principles of management of sports schools for children and youth

The activities of sports schools for children and youth play a key role in shaping the physical culture of young people, promoting a healthy lifestyle, and preparing future professional athletes. The successful functioning of these schools relies on appropriate legal frameworks that outline the operational rules, requirements for educational processes, funding, and management of facilities. there is a pressing need to analyze the current legal framework governing the management of sports school for children and youth. Considering the constant changes in the legislation and the growing requirements for the quality of sports institutions, there is a pressing need to analyze the current legal framework governing the management of sports schools for children and youth. The purpose of this article is to analyze the regulatory and legal principles of managing the activities of sports schools for children and youth in Ukraine. The key legislative acts regulating the organizational, financial, and legal aspects of the functioning of sports schools for children and youth have been analyzed, including the Constitution of Ukraine, the Law ‘On Physical Culture and Sports,’ and the orders of the Ministry of Education and Science and the Ministry of Youth and Sports. This resolution provides a comprehensive framework for the operation of sports schools for children and youth as well as defines the forms of ownership, legal foundations of their activities, funding procedures, and requirements for athlete training. Particular attention is given to the state social standard in physical culture and sports, which sets requirements for the quality of services, infrastructure, and athlete training. The mechanisms of management that contribute to increasing the efficiency of sports schools for children and youth are highlighted.

Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming

Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of [Formula: see text] pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.

Meritocracy seen through the eyes of its champions: a comparative study of educational elites

This paper explores the way in which elite students from Germany and Romania understand the role of talent, effort, and structural factors in shaping educational success and failure. The image of a successful student aligns with the requirements of the selection processes, with Romanian students emphasising effort, and German students projecting an image of effortless achievement. Although most students acknowledged structural inequalities played into the selection processes, they did not seem to doubt the intelligence and skills of those who were selected to the most prestigious tracks and streams. By exploring the way in which students at prestigious universities talk about success and failure, this paper captures some ways in which they resist reproducing meritocratic discourses, and other ways in which they perpetuate the myth of meritocracy.

Data protection legislation in Africa and pathways for enhancing compliance in big data health research

BackgroundThe increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing.MethodWe analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation.ResultsIn many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa.ConclusionsWe propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

Research on the Design and Bidirectional Work Process of Metal Diaphragms in Small Double-Pulse Solid Rocket Motors

According to the requirements of the small double-pulse solid rocket motor, a compartmentalized isolation device has been designed. This device consists of a metal diaphragm and a support frame. An experimental study and numerical simulation were used to verify the bidirectional working process of the metal diaphragm during operation of the double-pulse motor. The results show that the pressure-bearing capacity of the metal diaphragm meets the requirements under the working conditions of pulse I without affecting pulse II, because the metal diaphragm can provide insulation and flame retardancy. The metal diaphragm can be cracked in the direction of the preset V-groove in a relatively short time under the working conditions of pulse II, which allows the gas to flow to the first pulse combustion chamber normally. This indicates that the metal diaphragm can meet the requirements of bidirectional working process in dual-pulse motors.

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.

Biosynthesis of calcium oxide nanoparticles by employing Mulberry (Morus nigra) leaf extract as an efficient source for Rhodamine B remediation

Green processes for synthesizing nanocomposites are a hot area of research today as traditional processes are expensive, inefficient, harmful for synthesizing organic and inorganic molecules, and unsuitable for large-scale operations. The present study investigates the capacity of green synthesized Calcium oxide nanoparticles (CaO NPs) for efficiently removing Rhodamine B. Chemical reduction was replaced with Mulberry (Morus nigera) leaf extract as an environmentally friendly reaction mechanism. CaO NPs are characterized by various analytical techniques including EDX, BET, SEM, FTIR, TGA, Zeta Potential, Point of Zero Charge (PZC), and XRD. Maximum adsorption of Rhodamine B by CaO NPs is revealed at an initial concentration of Rhodamine B of 80 ppm, a temperature of 343 K, and contact time of 60 min, 0.4 g of adsorbent at a pH value of 7. Maximum removal of Rhodamine B by CaO NPs was found to be 98.2% which is promising with this small amount of adsorbent (0.4 g). Diverse Kinetic and adsorption isotherms are employed in this study to determine the requirement and significance of the adsorption process. Various adsorption isotherms such as Freundlich, Temkin, Dubinin–Radushkevich (D–R), and Langmuir models have been employed. Among the kinetic adsorption isotherms Elovich, Intraparticle kinetic model, pseudo 1st order, and pseudo 2nd order models were applied. The current study investigates the thorough understanding of the Rhodamine B adsorption process including the mechanism of adsorption using condition optimization, characterization, and model applications. The proposed adsorbent can be employed for the green removal of Rhodamine B from wastewater of industry with maximum efficiency and favorable regeneration properties.

Smart Supervision of Public Expenditure: A Review on Data Capture, Storage, Processing, and Interoperability with a Case Study from Colombia

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.