Operating System Research Articles

Research on Production Operation System of Sucker Rod Pumping Units

Oil is the most important raw material in modern industry, and its extraction mainly relies on natural flow and artificial lifting. Sucker rod pumping is currently the most widely used method of artificial lifting for mechanical oil extraction, accounting for about 60% to 70% of all methods. This paper deeply explores the theoretical methods involved in the production operation process of sucker rod pumping units. From the perspective of sucker rod pumping unit production operation, a literature review is conducted on the flow patterns in the wellbore, pump efficiency analysis, and intermittent oil extraction systems, clarifying the optimization objectives of liquid production and system efficiency, and sorting out from the aspects of dynamic liquid level, pump efficiency, reasonable submergence depth, and intermittent operation systems. Through in-depth analysis of these key areas, a solid methodological foundation is provided for the future design and development of sucker rod pumping unit production operation systems.

Evaluation of the incident management system of a bigger city during the winter flood 2023/24 in northern Germany: Organized (ir)responsibility in the pre-disaster phase

Incident management systems operate within complex and uncertain conditions during disasters. Therefore, researching their activities is challenging as there is no clear right or wrong. The winter 2023/2024 flood event in the city of Oldenburg, Germany, provided an opportunity to evaluate crisis management under high uncertainty, although no disaster has eventually occurred. Three stakeholder groups of the Incident Management System were examined through After-Action Reviews: The Emergency Operation Center (EOC), specialist advisors, and the Area Command (AC). Data on performance shaping factors (descriptions and judgements) were collected and analyzed using the Countenance Evaluation Model as a general framework in combination with a Systems-Thinking approach. The study aimed to identify variables within and between groups as well as factors in the environment that either hindered or facilitated work transactions. It emerged that the work organization in bodies of public administration is at odds with the uncertain, fast-paced, and problem-oriented structure of Incident Management System operations, causing stress and adding workload to employees. Additionally, the separation of strategic and tactical decision making led to coordination issues due to divergent time structures and task logics of EOC and AC. Furthermore, although specialist advisors played a crucial role in risk assessments and setting the direction of risk mitigation activities, it became clear that this person related resource is scarce and difficult to embed into the Incident Command System. A final finding is the importance of the larger Emergency Operation Center working environment. The spatial constraints and noise levels caused stress.

Exploring Marine-Based Food Production: The Challenges for a Sustainable and Fast Biotechnology-Based Development

Marine-derived nutrients and bioactive compounds may offer a myriad of biological benefits, such as anticancer and anti-inflammatory properties, and technological potential, enhancing food quality as additives. Their role in the sustainable development of food technology is fundamental, especially in advancing the knowledge of functional foods and related technologies. Algae are considered one of the major sources of marine-derived ingredients and the subject of several recent studies. Despite their potential, the translation of marine ingredients’ potential into a marine-based competitiveness of the food industry faces hurdles in the extraction process and operational systems scale-up that the industry needs to tackle. The complexity of marine matrices with diverse compounds and solubilities adds complexity to extraction processes and may lead to low yields or bioactivity loss. Contaminants, like heavy metals and pesticide residues in marine organisms, require rigorous purification processes for product safety. The use of biorefinery systems in marine-based ingredients’ production, particularly cascade processes, offers zero-waste solutions, contributing to the blue economy and aligning with UN sustainability goals. Sustainability assessment tools are critical for evaluating marine-based food production’s environmental, social, and economic impacts. A continued exploration and collaboration are essential for the future, fostering innovation and sustainability to create a resilient, equitable, and eco-friendly food system.

Numerical study of fluid flow and entropy optimization subject to second order slip condition across a permeable curved surface

The entropy generation analysis has great significance in the industrial sectors with heat transmission and fluid flows, for evaluating the irreversibility aspect of a system in heat transfer operations. The numerical simulation of entropy generation and the nanofluid flow across a permeable curved surface subject to cross-diffusion and irregular heat source/sink is reported in the current investigation. The thermodynamics 2nd law is used to simulate the entropy optimization. The flow phenomena are mathematically described by partial differential equations (PDEs), which are derived in a curvilinear coordinate system. The system of ODEs (ordinary differential equations) is derived by using the similarity conversion, which is further numerically calculated through the parametric continuation method (PCM) using MATLAB software. The results reveal that the velocity slip and curvature parameters improve the velocity profile whereas the inverse effect is observed against the surface permeability parameter. It can also be noticed that the entropy optimization enhances with the variation in Brinkman number and temperature ratio parameter. The impact of Schmidt number and chemical reaction decline the mass transmission ratio.

Outdoor light at night is a modifiable environmental factor for metabolic syndrome: The 33 Communities Chinese Health Study (33CCHS)

Metabolic syndrome (MetS) is a significant public health problem and presents an escalating clinical challenge globally. To combat this problem effectively, urgent measures including identify some modifiable environmental factors are necessary. Outdoor artificial light at night (LAN) exposure garnered much attention due to its impact on circadian rhythms and metabolic process. However, epidemiological evidence on the association between outdoor LAN exposure and MetS remains limited. To determine the relationship between outdoor LAN exposure and MetS, 15,477 adults participated the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. Annual levels of outdoor LAN exposure at participants' residential addresses were assessed using satellite data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). Generalized linear mixed effect models were utilized to assess the association of LAN exposure with MetS and its components, including elevated waist circumference (WC), triglycerides (TG), blood pressure (BP), fasting blood glucose (FBG), and reduced high-density lipoprotein cholesterol (HDL-C). Effect modification by various social demographic and behavior factors was also examined. Overall, 4701 (30.37 %) participants were defined as MetS. The LAN exposure ranged from 6.03 to 175.00 nW/cm2/sr. The adjusted odds ratio (OR) of MetS each quartile increment of LAN exposure were 1.43 (95 % CI: 1.21–1.69), 1.44 (95 % CI: 1.19–1.74) and 1.52 (95 % CI: 1.11–2.08), respectively from Q2–Q4. Similar adverse associations were also found for the components of MetS, especially for elevated BP, TG and FBG. Interaction analyses indicated that the above associations were stronger in participants without habitual exercise compared with those with habitual exercise (e.g. OR were 1.52 [95 % CI: 1.28–1.82] vs. 1.27 [95 % CI, 1.04–1.55], P-interaction = 0.042 for MetS). These findings suggest that long-term exposure to LAN can have a significant deleterious effect on MetS, potentially making LAN an important modifiable environmental factor to target in future preventive strategies.

Revitalisasi Bank Sampah Gesit Desa Rumah Gerat melalui Edukasi dan Penataan Sistem Operasional Bank Sampah

This community service activity aims to enhance the effectiveness of the "Bank Sampah Gesit" in Rumah Gerat Village through educational initiatives and operational system restructuring. The program includes educating participants on waste management concepts, types of recyclable materials, and effective operational management, followed by a Focus Group Discussion (FGD) to design improved operational systems. Using a one-shot case study approach with pretest-posttest assessments, this initiative successfully increased participants' knowledge and understanding of waste management principles and established a functional waste management system. This system includes the types of waste managed, waste deposit recording systems, and the roles of waste collectors and sponsors in sustaining the bank. Fully supported by the Research and Community Service Institute (LPPM) of Universitas Negeri Medan and in collaboration with Roda Hijau Kota Medan, this community service program encourages active community participation in environmental conservation efforts. The goal is for this initiative to inspire the residents of Rumah Gerat Village to manage waste more efficiently, positively impacting the local environment, and creating a cleaner and sustainable environment for future generations.

Development of Ecological Low-Carbon Agriculture with Chinese Characteristics in the New Era: Features, Practical Issues, and Pathways

Agriculture is not only the source of carbon emissions, but also an important carbon sink. The development of low-carbon agriculture in China is closely related to achieving the national strategic goal of “dual carbon.” Reducing carbon emissions in China’s agricultural sector and accelerating the development of ecological low-carbon agriculture (ELA) are extremely important and urgent, both from the perspective of the long-term common welfare for mankind and from the perspective of maintaining the sustainable development of agriculture itself. However, ELA is still an emerging concept in China, and its connotation and characteristics are not yet clear. There is a lack of effective paths for orderly and scientific promotion of the development of ELA in China. Based on this, this paper focuses on the emerging concept of ELA and examines ELA with Chinese characteristics from the dimensions of connotation, features, practical issues, and pathways, aiming to provide guidance for the sustainable development of ELA in China. The research results indicate that there are regional development imbalances, low willingness of farmers to participate, insufficient technological reserves, and difficulties in realizing the value of ecological products in China’s ELA. It is necessary to improve the modern agricultural production and operation system, tap into the potential of agricultural emission reduction and carbon sequestration, build a technical system to support the development of ELA, and establish a sound mechanism for realizing the value of ecological products. By continuously improving the regional organizational capacity and performance of ELA, accelerating the advancement of ELA technology and demonstrating its regional promotion, and by establishing and continuously improving the support system for ELA, one can promote the sustainable development of ELA in China. In addition, the research findings of this paper can also provide reference for the ecological low-carbon development of the global agricultural sector, supporting the contribution of the agricultural sector to achieving high-quality global sustainable development goals.

Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA

Abstract We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System (UFS) at the National Oceanic and Atmospheric Administration (NOAA), part of the US National Weather Service (NWS) operational forecasting suite. The operational implementation of the atmosphere-wave coupled Global Ensemble Forecast System version 12 (GEFSv12) in September 2020 was a critical step in NOAA’s transition to the broader community-based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact events. The integration of a coupled wave component with higher spatial and temporal resolution and optimized physics parameterizations notably enhanced forecast skill and predictability, particularly benefiting winter storm predictions of wave heights and peak wave periods. This successful endeavor encountered challenges that were addressed by the simultaneous development of new features that enhanced wave model forecast skill and product quality and facilitated by a multidisciplinary team collaborating with NOAA’s operational forecasting centers. The GEFSv12 upgrade marks a pivotal shift in NOAA’s global forecasting capabilities, setting a new standard in wave prediction. We also describe the coupled GEFSv12-Wave component impacts on NOAA operational forecasts, and ongoing experimental enhancements, which altogether represent a substantial contribution to NOAA’s transition to the fully-coupled UFS framework.

Design and Validation of an Ambulatory User Support Gait Rehabilitation Robot: NIMBLE

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

Investigating Human-Robot Interactions with a Quadruped Robot

Robots are becoming more prevalent in our daily lives. As robots become more common in society, we must understand how people react and interact with them. This study leverages Boston Dynamics' Spot robot to examine human responses to quadruped robots. Previous studies had Spot walking alongside participants at fixed speeds, independent of their gait. Improving upon this method, I developed a program that enables Spot to detect and follow fiducial markers (AprilTags). Using the Robot Operating System (ROS), the depth and position of the tag within the camera’s frame were extracted and translated into motion commands for the robot. The tag is attached to an apparatus on the hip that extends the tag near the subject’s knee, allowing Spot to track the participant’s trunk speed and maintain a consistent distance, offering a more adaptive walking experience. The program allows for variations in the acceleration, height, and camera selection (front/back, left/right) through a custom Graphical User Interface (GUI), allowing for diverse trial conditions. Extensive testing identified considerations to optimize the walking experience. Factors included gradual acceleration to allow Spot to react and maintain detections, and the set requirement to run only a single camera at a time for optimized processing. Additionally, keeping the tag unobstructed and square to the camera is critical to avoid failures in detection. Overall, Spot's new capabilities will assist in further studies of human and robot interactions.

FP2.12 - 'To Evaluate Lower Threshold of Polyp Size for Tattooing Routinely, a Single Trust Observational Study.'

Abstract Aim The goal of the study is to establish the lowest polyp size for clinically effective tattooing routinely. Method Retrospective data collection of colonoscopy and flexible sigmoidoscopy for bowel screening and non-bowel screening patients; 450 patients underwent polypectomy of <20mm from September 2021 to September 2022 in MTW NHS Trust. Patients were included according to 'single patient single polyp' criteria. We excluded 20+ mm polyps, multiple polyp, known FAP, lynch syndrome and other genetic predisposed colorectal polyps. Histology was obtained from electronic patient records (Allscripts). SPSS version 25.0 was used for the statistical analysis, which also included ROC curve analysis and Chi-Square test. Results A total of 450 participants, 288 males (64.0%) and 162 females (36.0%), were enrolled. Polyp sizes: 0-9mm (42.2%) and 10-19mm (57.8%). 8.0% were tattooed. Polyps found in right- hemicolon, transverse, left-hemicolon, sigmoid and rectum accordingly 29.78%, 20.5%, 9.1%, 28.4% and 12.22%. Pedunculated polyps were found in 143 individuals (31.8%), while sessile polyps were observed in 256 individuals (56.9%). Furthermore, in histological analysis, 251 individuals (55.8%) exhibited tubular adenomas and the majority of the cohort (77.6%) presented with low-grade dysplasia. Malignant invasion was detected in 27 subjects (6%). Receiver operating system (ROC) curve analysis indicated that polyp size was a predictive factor for malignancy development, with a cut-off lower point identified at 8.5 mm. Conclusions The findings confirm the advice to tattoo polyps bigger than 8-9 mm to make tracing and follow- up easier.

Human neural stem cell-derived artificial organelles to improve oxidative phosphorylation

Oxidative phosphorylation (OXPHOS) in the mitochondrial inner membrane is a therapeutic target in many diseases. Neural stem cells (NSCs) show progress in improving mitochondrial dysfunction in the central nervous system (CNS). However, translating neural stem cell-based therapies to the clinic is challenged by uncontrollable biological variability or heterogeneity, hindering uniform clinical safety and efficacy evaluations. We propose a systematic top-down design based on membrane self-assembly to develop neural stem cell-derived oxidative phosphorylating artificial organelles (SAOs) for targeting the central nervous system as an alternative to NSCs. We construct human conditionally immortal clone neural stem cells (iNSCs) as parent cells and use a streamlined closed operation system to prepare neural stem cell-derived highly homogenous oxidative phosphorylating artificial organelles. These artificial organelles act as biomimetic organelles to mimic respiration chain function and perform oxidative phosphorylation, thus improving ATP synthesis deficiency and rectifying excessive mitochondrial reactive oxygen species production. Conclusively, we provide a framework for a generalizable manufacturing procedure that opens promising prospects for disease treatment.

Overview of Embedded Rust Operating Systems and Frameworks.

Embedded Operating Systems (OSs) are often developed in the C programming language. Developers justify this choice by the performance that can be achieved, the low memory footprint, and the ease of mapping hardware to software, as well as the strong adoption by industry of this programming language. The downside is that C is prone to security vulnerabilities unknowingly introduced by the software developer. Examples of such vulnerabilities are use-after-free, and buffer overflows. Like C, Rust is a compiled programming language that guarantees memory safety at compile time by adhering to a set of rules. There already exist a few OSs and frameworks that are entirely written in Rust, targeting sensor nodes. In this work, we give an overview of these OSs and frameworks and compare them on the basis of the features they provide, such as application isolation, scheduling, inter-process communication, and networking. Furthermore, we compare the OSs on the basis of the performance they provide, such as cycles and memory usage.

Using Mixed Reality for Control and Monitoring of Robot Model Based on Robot Operating System 2

This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the Unity game engine alongside the Mixed Reality Toolkit (MRTK) to create an immersive mixed reality application. The project uses the Turtlebot 3 Burger model robot, simulated within the Gazebo virtual environment, as a representative mechatronic system for demonstration purposes. Communication between the mixed reality application and ROS 2 is facilitated through a publish–subscribe mechanism, utilizing ROS TCP Connector for message serialization between nodes. This interface not only enhances the user experience by allowing for the real-time monitoring and control of the robotic system but also aligns with the principles of Industry 5.0, emphasizing human-centric and inclusive technological advancements. The practical outcomes of this research include a fully functional mixed reality application that integrates seamlessly with ROS 2, showcasing the potential of mixed reality technologies in advancing the field of industrial automation and human–machine interaction.

PHIGrader: Evaluating the effectiveness of Manifest file components in Android malware detection using Multi Criteria Decision Making techniques

The popularity of the Android operating system has itself become a reason for privacy concerns. To deal with such malware threats, researchers have proposed various detection approaches using static and dynamic features. Static analysis approaches are the most convenient for practical detection. However, several patterns of feature usage were found to be similar in the normal and malware datasets. Such high similarity in both datasets’ feature patterns motivates us to rank and select only the distinguishing set of features. Hence, in this study, we present a novel Android malware detection system, termed as PHIGrader for ranking and evaluating the efficiency of the three most commonly used static features, namely permissions, intents, and hardware components, when used for Android malware detection. To meet our goals, we individually rank the three feature types using frequency-based Multi-Criteria Decision Making (MCDM) techniques, namely TOPSIS and EDAS. Then, the system applies a novel detection algorithm to the rankings involving machine learning and deep learning classifiers to present the best set of features and feature type with higher detection accuracy as an output. The experimental results highlight that our proposed approach can effectively detect Android malware with 99.10% detection accuracy, achieved with the top 46 intents when ranked using TOPSIS, which is better than permissions, hardware components, or even the case where other popular MCDM techniques are used. Furthermore, our experiments demonstrate that the proposed system with frequency-based MCDM rankings is better than other statistical tests such as mutual information, Pearson correlation coefficient, and t-test. In addition, our proposed model outperforms various popularly used feature ranking methods such as Chi-square, Principal Component Analysis (PCA), Entropy-based Category Coverage Difference (ECCD), and other state-of-the-art Android malware detection techniques in terms of detection accuracy.

Ambience: an operating system for IoT microservices

Increasingly, the heterogeneity of devices and software that comprise the Internet of Things (IoT) is impeding innovation. IoT deployments amalgamate compute, storage, networking capabilities provisioned at multiple resource scales, from low-cost, resource constrained microcontrollers to resource rich public cloud servers. To support these different resource scales and capabilities, the operating systems (OSs) that manage them have also diverged significantly. Because the OS is the “API” for the hardware, this proliferation is causing a lack of portability across devices and systems, complicating development, deployment, management, and optimization of IoT applications. To address these impediments, we investigate a new, “clean slate” OS design and implementation that hides this heterogeneity via a new set of abstractions specifically for supporting microservices as a universal application programming model in IoT contexts. The operating system, called Ambience, supports IoT applications structured as microservices and facilitates their portability, isolation, and deployment time optimization. We discuss the design and implementation of Ambience, evaluate its performance, and demonstrate its portability using both microbenchmarks and end-to-end IoT deployments. Our results show that Ambience can scale down to 64MHz microcontrollers and up to modern x86_64 servers, while providing similar or better performance than comparable commodity operating systems on the same range of hardware platforms.

Modeling Android Security Vulnerabilities: Insights from Statistical Distributions

Android operating system is a mobile operating system that supports multimedia features. Android offers a wide range of applications and integrated features for playing, recording, editing and sharing audio, video, images and other multimedia content. Most Android devices include cameras, speakers, microphones, and other multimedia components. In software security, vulnerabilities are critical concerns that often emerge during software development. Predicting these vulnerabilities post-release is essential for risk assessment and mitigation. While various models have been explored, the Android operating system remains relatively uncharted. This study delves into modeling Android security vulnerabilities using different statistical distributions, comparing their suitability to the widely-used Alhazmi-Malaiya Logistic (AML) model. Data from the National Vulnerability Database (NVD) spanning 2016 to 2018, along with Common Vulnerability Scoring System (CVSS) scores, was analyzed. The study evaluates several distribution models, including Logistic, Weibull, Nakagami, Gamma, and Log-logistic, for monthly vulnerability counts and average monthly impact values. Goodness-of-fit tests and information criteria were applied for model robustness assessment. The findings offer valuable insights for researchers and Android software developers, aiding prediction, risk assessment, resource allocation, and research direction. Logistic and Nakagami distributions emerged as the best-fit models for average monthly impact values and monthly vulnerability counts, respectively. Finally, statistical methods perform better against known artificial intelligence methods for small data sets or more clearly defined data due to their flexible features such as comprehensibility, amount of data, need for calculation, and data independence.

Feasibility and foundational elements of CBM+ technology application in weapon systems

The maintenance strategies of weapon systems have evolved from corrective maintenance post-failure to time-based preventive maintenance, driven by the growing adoption of diagnostic technologies and data utilization. Currently, there is a transition to Condition-Based Maintenance (CBM) and, where applicable, Condition-Based Maintenance Plus (CBM+). This study investigated the feasibility of applying CBM+ technology using sensor data from weapon systems and explored methods for implementing the related foundational elements. First, the feasibility of CBM+ technology was confirmed by integrating data collection devices into the engine components of selected weapon systems, enabling sensor data collection and monitoring. Second, empirical evidence was gathered on the foundational elements necessary for the broader adoption of CBM+ technology. Practical application methods were proposed based on this evidence, including criteria for implementing CBM+ technology in major operational weapon systems, estimation of sensor data capacity, network methods for data transmission and collection, and the standardization of sensor data. The study’s findings are expected to provide reference points for selecting target weapon systems for the development and application of CBM+ technology, as well as outline essential foundational elements, such as security, networking, and data standardization, critical for leveraging related technologies.

DewROS2: A platform for informed Dew Robotics in ROS

With the shift from Cloud to Fog and Dew Robotics a lot of emphasis of the research community has been devoted to task offloading. Effective and efficient resource monitoring is however necessary for such offloading and it is also fundamental for other important safety and security tasks. Despite this, robot monitoring has received little attention in general and also for Robot Operating System (ROS) the most employed framework in robotics. In this paper DewROS2 is presented, a platform for Dew Robotics that comprises entities to monitor the system status and to share it with interested applications. The design and implementation of the platform is presented together with the monitoring entities created. DewROS2 has been deployed on different real devices, including an unmanned aerial vehicle and an industrial router, to move from theory to practice and to analyze the impact of monitoring on robot resources. DewROS2 has also been tested in a search and rescue use case where robots are used to collect and transmit videos to spot signs of humans in trouble. Results in controlled and uncontrolled conditions show that the monitoring nodes do not have a significant impact on the performance while providing important and measurable benefits to the applications. Accurately monitoring of robot resources, for example, allows the search and rescue application to almost double the utilization of the network, therefore collecting video at a much higher resolution.

A global flash drought inventory based on soil moisture volatility

Flash droughts, characterized by rapid onset and development, present significant challenges to agriculture and climate mitigation strategies. Operational drought monitoring systems, based on precipitation, soil moisture deficits, or temperature anomalies, often fall short in timely detection of these events, underscoring the need for customized identification and monitoring indices that account for the rapidity of flash drought onset. Recognizing this need, this paper introduces a global flash drought inventory from 1990 to 2021 derived using the Soil Moisture Volatility Index (SMVI). Our work expands the application of the SMVI methodology, previously focused on the United States, to a global scale, providing a tool for understanding and predicting these rapidly developing phenomena. The dataset encompasses detailed event characteristics, including onset, duration, and severity, across diverse climate zones. By integrating atmospheric variables through their impact on soil moisture, the inventory offers a platform for analyzing the drivers and impacts of flash droughts, and serves as a large, consistent dataset for use in training and evaluating flash drought prediction models.