Operating System Research Articles

Sustainable business operations in technology startups: A model for leadership and administrative excellence

Sustainable business operations have become a critical factor for success in technology startups, where rapid growth and resource constraints pose unique challenges. This review proposes a comprehensive model for sustainable operations management in tech startups, emphasizing the synergy between leadership and administrative excellence. The proposed model aims to guide startups in balancing profitability, scalability, and environmental responsibility while fostering long-term growth. The explores the challenges of achieving sustainability in startups, including limited resources, pressures of rapid scaling, and the environmental impact of tech-driven innovations. It emphasizes the pivotal role of visionary leadership in setting long-term sustainable goals, fostering a culture of innovation, and effectively managing change. Leadership’s ability to align sustainability with operational goals is critical in navigating the dynamic and competitive nature of the tech industry. Additionally, the review highlights the importance of administrative innovations in optimizing resource management, implementing data-driven decision-making, and streamlining processes. By leveraging automation and technology, startups can create more efficient and environmentally friendly operational systems. The model integrates leadership vision with administrative processes, creating a holistic approach that encourages continuous improvement and innovation. The benefits of adopting sustainable operations, such as enhanced financial resilience, competitive advantage, and improved brand reputation, are also discussed. Moreover, sustainability offers tech startups a significant edge in attracting and retaining top talent while fostering customer loyalty. Ultimately, this model serves as a blueprint for technology startups to build scalable, efficient, and responsible operations, ensuring their success in the long term by aligning leadership and administrative strategies with sustainability objectives. Keywords: Sustainable Business, Technology Startups, Leadership, Administrative Excellence.

EddyBot: A multichannel FPGA-based Eddy Current Testing mobile robot

We introduce EddyBot, a novel integration of a multi-channel FPGA-based Eddy Current Testing (ECT) scanner within a mobile robot designed for autonomous non-destructive testing (NDT). EddyBot distinguishes itself by leveraging advanced signal processing, real-time robotics control, and efficient data synchronization to significantly enhance inspection accuracy and efficiency. Our contributions are (i) the deployment of a Zynq-7020 FPGA for fast data acquisition and processing, (ii) alongside the Robot Operating System (ROS) for precise and real-time control with Kalman filtering (KF) sensor fusion for pose estimation. (iii) We further optimize defect detection and localization through a novel data synchronization approach, correlating the robot’s pose with ECT signals to a mean synchronization delay of only 11.77 ms. This allows for precise localization of defects within 0.18 cm, improving the overall reliability. The robot’s performance was evaluated on aluminum and ferromagnetic specimens, highlighting the system’s capability to detect defects with high accuracy.

Fast and full characterization of large earthquakes from prompt elastogravity signals

Prompt ElastoGravity Signals are light-speed gravity-induced signals recorded before the arrival of seismic waves. They have raised interest for early warning applications but their weak amplitudes close to the background seismic noise have questioned their actual potential for operational use. A deep-learning model has recently demonstrated its ability to mitigate this noise limitation and to provide in near real-time the earthquake magnitude (Mw). However, this approach was efficient only for large earthquakes (Mw ≥ 8.3) of known focal mechanism. Here we show unprecedented performance in full earthquake characterization using the dense broadband seismic network deployed in Alaska and Western Canada. Our deep-learning model provides accurate magnitude and focal mechanism estimates of Mw ≥ 7.8 earthquakes, 2 minutes after origin time (hence the tsunamigenic potential). Our results represent a major step towards the routine use of prompt elastogravity signals in operational warning systems, and demonstrate its potential for tsunami warning in densely-instrumented areas.

Methodological approach to quantitative assessment of the security of open operating systems AS of the Internal Affairs Bodies

Objective. The article considers the provisions of the fuzzy logic approach in relation to the method of quantifying the security of open operating systems (OS) of automated systems of the internal affairs bodies of the Russian Federation (AS of the Russian Federation), taking into account possible security threats and the requirements of the standard GOST R ISO/IEC 15408 for leveling possible consequences. Information Message № 240/24/4893 dated October 18, 2016 «On Approval of Information Security Requirements for Operating Systems» of the FSTEC of Russia defines 6 OS security classes. Operating systems corresponding to protection classes 1, 2 and 3 are used in information (automated) systems in which information containing information constituting a state secret is processed, operating systems corresponding to protection classes 4, 5 and 6 are not intended for processing such information. In the presented study, the open operating systems of the AS of the Russian Federation are understood as OS AS, in which information containing information constituting a state secret is not processed. Method. The study was conducted based on the method of analyzing possible security threats to open operating systems, as well as the requirements of GOST R ISO/IEC 15408 standard, using the provisions of fuzzy logic. Result. The result of the automated system for calculating the security index of the analyzed open OS is one of the specified criteria for the degree of security of the OS, based on the provisions of fuzzy logic. Conclusion. The authors propose a method for assessing the security of open OS of the AS of the Russian Federation, based on the provisions of fuzzy logic.

Tracking the Development of Lit Fisheries by Using DMSP/OLS Data in the Open South China Sea

Nightly images offer a special data source for monitoring fishing activities. This study used images from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) to analyze the early development of lit fisheries in the open South China Sea (SCS), which mainly occurred around the Zhong Sha and Xi Sha Islands. Based on peak detection and a fixed threshold, lit fishing positions were extracted well from filtered, high-quality DMSP/OLS images. The results indicated that fisheries experienced an apparent rise and fall from 2005 to 2012, with the numbers of lit fishing boats rising to a maximum of ~60 from 2005 to 2008, almost disappearing in 2009, peaking at ~130 from 2010 to 2011, and starting to decline in 2012. The fish price of major fishing targets declined by ~60% in 2009, which obviously impacted the year’s fishing operations. The reason for declined fishing operations in 2012 was that most of the lit fishing operations shifted farther south to fishing grounds around the Nan Sha Islands. We also explored factors shaping the distribution patterns of lit fisheries by using MaxEnt models to relate fishing positions to environmental variables. Major environmental factors influencing the distribution of lit fishing boats varied with years, of which water depth was the most important factor across years, with an optimal depth range of 1000–2000 m. In addition to depth, the distribution of lit fisheries was also influenced by SST, especially for the years 2005–2008, and a suitable SST was found between 26 and 28 °C. This study fills the knowledge gaps of the inception of lit fisheries and their dynamic changes in the SCS.

Analysis of irradiation in OptiTouch system with optical detection

As part of the research and development project, a multi-touch multimedia system was implemented, working exclusively on the basis of optical technologies. The model of device with a diagonal of 42” was developed and made with 4K TV image technology and simultaneous detection in near infrared. The control of individual system modules was carried out on RaspberryPi microcomputers without typical operating system. The functionality of a conventional large-format display with unlimited multi-touch, QR code scanner, and a document scanner has been achieved in one housing. Luminance distribution tests were carried out in accordance with ANSI requirements and infrared radiation for detection work.

AN IMPROVED COVERAGE PATH PLANNING FOR SERVICE ROBOTS BASED ON BACKTRACKING METHOD

Service robots are of paramount importance in both industrial and residential environments. Nevertheless, these robots frequently encounter obstacles in the form of perilous elements present in their surroundings, which may result in their malfunction and hinder their optimal performance. Hence, autonomous robot applications heavily rely on coverage path planning algorithms, which facilitate the execution of area coverage operations in a streamlined and economical manner. The article presents a coverage path planning algorithm for service robots that is based on a backtracking procedure. The proposed method attempts to strike a balance between the safety of the cleaning automaton and the extent of coverage. Following straight-forward instructions, the mobile robot (MR) initially generates a spiral or zigzag trajectory. It examines each position for possible backtracking locations. When obstacles and visited positions obstruct the path, the improved A* algorithm is utilized to return to known unvisited regions. Subsequently, the robot proceeds to sweep the regions until no remaining back-tracking sites remain. Simulation of the method on the Robot Operating System (ROS) verifies that it is possible to restrict the movement of high-risk robots.

Quality of tracking methods with object reidentification

Objective. This paper presents a study of object tracking algorithms for analyzing sports competitions with a minimum permissible number of frames per second without critical loss of quality of operational systems. The purpose of the study is to determine the optimal tracking method and reidentification model with an increase in the time interval between frames. Method. The study used the Re-Identification MSMT17, Market1501, DukeMTMCreID datasets; specialized neural networks OsNet, ResNet and MobileNet. Tracking algorithms BotSort, Bytetrack, OcSort, DeepOcSort, StrongSort. The NOTA and IDF1 quality metrics were used. Result. The metrics of the experiments are generally quite low, which is due to the nonlinearity of athletes' movement, unlike pedestrians' movement, and a large number of changes in their posture. Conclusion. Depending on the degree of information compression, the optimal tracking methods when using pre-trained reidentification models are ByteTrack and OcSort.

Reversing File Access Control Using Disk Forensics on Low-Level Flash Memory

In the history of access control, nearly every system designed has relied on the operating system (OS) to enforce the access control protocols. However, if the OS (and specifically root access) is compromised, there are few if any solutions that can get users back into their system efficiently. In this work, we have proposed a novel approach that allows secure and efficient rollback of file access control after an adversary compromises the OS and corrupts the access control metadata. Our key observation is that the underlying flash memory typically performs out-of-place updates. Taking advantage of this unique feature, we can extract the “stale data” specific for OS access control, by performing low-level disk forensics over the raw flash memory. This allows efficiently rolling back the OS access control to a state pre-dating the compromise. To justify the feasibility of the proposed approach, we have implemented it in a computing device using file system EXT2/EXT3 and open-sourced flash memory firmware OpenNFM. We also evaluated the potential impact of our design on the original system. Experimental results indicate that the performance of the affected drive is not significantly impacted.

An investigation of ransomware incidents in the maritime industry: Exploring the key risk factors

Ransomware is a subset of malicious cyberattacks that aim to hold an organization’s data or critical infrastructure at ransom, compromising or blocking access. If the attack is public or made public after the initial attack, it can also severely jeopardize an organization’s reputation. Given the direct and immediate impact ransomware attacks can have and the lack of in-depth sharing, additional research is needed to analyze ransomware incidents in order to understand the underlying causes of incidents in addition to the detection and prevention methods. In this paper, 22 public ransomware incidents within the marine industry have been investigated to determine their causal factors and commonalities. To investigate causal factors, DEMATEL (Decision Making Trial and Evaluation Laboratory) and a fuzzy set are used in order to enable an organization to better adhere to operational requirements and cyber risk management strategies to increase cyber resilience against ransomware incidents. The study’s findings highlight the fact that network layer cyber security mitigations, strategies for securely utilizing RDP (Remote Desktop Protocol) protocols, and investments in operating systems (OS) and software security are essential components of preventing future ransomware incidents. This study concludes by suggesting several suitable control and preventative measures to improve system safety.

Network Data Intrusion Detection and Data Feature Extraction of Electromechanical Facilities from Machine Learning

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset’s detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

Pengaruh Diferensiasi, Prestise, dan Performa Sistem Operasi terhadap Keputusan Pembelian Handphone Iphone di Kabupaten Magelang

This study aims to determine the influence of differentiation, prestige, and operating system performance on the purchasing decisions of iPhone smartphones in Magelang Regency. This quantitative research was conducted from May 14, 2024, to June 7, 2024. The study population includes all consumers using iPhone products in Magelang Regency, with a sample of 160 respondents. The sampling technique used was non-probability sampling with a purposive sampling approach, and data were collected through questionnaires. Data analysis was performed using multiple linear regression analysis techniques. The results of the analysis show that differentiation has a positive and significant effect on the purchasing decision of iPhone smartphones in Magelang Regency, with a t-value of 3.813. Prestige also has a positive and significant effect on purchasing decisions, with a t-value of 3.869. The performance of the operating system has a positive and significant effect on purchasing decisions, with a t-value of 10.414. Simultaneously, differentiation, prestige, and operating system performance have a positive effect on the purchasing decision of iPhone smartphones in Magelang Regency, with an f-value of 325.851, which is greater than the f-table value of 2.662. These findings highlight the importance of these three factors in influencing consumer preferences for iPhones in Magelang Regency.

Touchpoints and systems for unmanned autonomous urban ferry operation

Abstract Currently, both experimental and commercial autonomous ferry services have a safety operator onboard who monitors the ferry systems, takes care of the passengers’ safety, and gives them the necessary information. Service touchpoints are all the interaction points between the autonomous ferry and the passengers throughout the passenger journey, from planning the trip to reaching their destination. To reach a state where the ferry service can be unmanned, by moving the safety operator into a remote operation centre, several additional systems and service touchpoints are needed to support the passengers’ safety and provide them with necessary information. This article discusses (1) the service touchpoints and systems needed for an autonomous urban ferry service, (2) how these touchpoints provide the passengers with the necessary information to foster trust and perceived safety, and (3) shows some examples and design suggestions for the experimental autonomous urban ferry milliAmpere2. The service touchpoints presented are (1) a ticket system, (2) an automatic ferry ordering system for on-demand operation, (3) an automatic passenger handling system for safe boarding of the ferry, (4) a passenger information system to provide visual and audio feedback about the ferry service, and (5) a passenger-operator communication system in case contact between the ferry and the remote operation centre need to be established.

Spatio-temporal grid-based storage method for aerospace operation and control data

Abstract The current aerospace operation and control data system has problems such as difficult integration organization and difficult data retrieval in terms of mission planning efficiency, which limits the development of satellite operation and control systems. This paper is oriented to the organization and management of aerospace operation and control data, according to the nature of the current big data indexing model. Based on the principle of earth dissection, we aim to design a grid that can be indexed. First of all, the Earth is dissected to form a hierarchical and uniformly distributed global geographic grid, and then the formed grid is encoded to store satellite attribute information and orbit data for mission planning. Finally, through the data retrieval experiment, the retrieval time is within 100 milliseconds under ten million pieces of data, which proves that the spatial-temporal grid model has the characteristics of efficient and fast retrieval. It can provide the basic information for satellite mission planning to further improve the efficiency of satellite mission planning and management and the efficiency and quality of satellite mission planning.

Application of the VMM ASIC for SiPM-based calorimetry

Highly integrated multichannel readout electronics is crucial in contemporary particle physics experiments. A novel silicon photomultiplier readout system based on the VMM3a ASIC was developed, for the first time exploiting this chip for calorimetric purposes. To extend the dynamic range the signal from each SiPM channel was processed by two electronics channels with different gain. A fully operational prototype system with 256 SiPM readout channels allowed the collection of data from a prototype of the ALICE Forward Hadron Calorimeter (FoCal-H Prototype 2). The design and the test beam results using high energy hadron beams are presented and discussed, confirming the applicability of VMM3a-based solutions for energy measurements in a high rate environment.

Validation of MCNPX Calculational Model for Kartini Reactor Outer Core Neutron Flux

Abstract Kartini reactor in Yogyakarta is a nuclear reactor with a low thermal power of 100 kW that is mainly used for education, training, and research. For that purpose, measuring neutron flux and controlling the power are important operational parameters. The Fission Chamber (FC) type of detector is the main component in the reactor operation and safety system that measures the neutron flux in the outer reactor core. As the FC detector is placed in a fixed location, a numerical calculation model is needed to know the neutron flux in all outer core regions. In this study, the MCNPX code is used. The first step is to validate the model with experimental measurement. The objective of the present study is to obtain a validated model by comparing calculations using MCNPX with measurements using gold foil activation (Neutron Activation Analysis technique). The computational model tested showed promising results with an average difference compared to the gold foil activation method of about 23%. Further research can be done using more gold foil samples or foils from other materials to see the consistency of measurements or minimize the measurement difference.

Temperature adaptive passive daytime radiative cooling: The impact of their essential properties on energy performance of different building types

Passive daytime radiative cooling (PDRC) materials can reduce building cooling demands during hot periods but increase heating needs in colder times, known as heating penalties, limiting their practicality. Temperature adaptive (TA) materials offer a solution, yet optimal TA PDRC properties, such as changes in reflectivity or emissivity from high surface temperature (HST) to low surface temperature (LST) states and the switch temperature at which these changes occur, are under-researched. To address this, a wavelength-dependent PDRC model was coupled with EnergyPlus to identify the optimal reflectivity or emissivity in HST and LST states and determine the appropriate switch temperature. Results show that different reflectivity and emissivity values are needed for various climates to maximize TA PDRC benefits. Switch temperatures from 15 °C to 30 °C primarily affect cooling benefits with minimal impact on heating penalties. Additionally, the heating penalty and cooling benefit of TA PDRC vary across building types, requiring alignment between TA PDRC operational characteristics and HVAC systems to optimize advantages. Among building types, malls benefit the most from PDRC application.

An Empirical Model for Skillful Rogue Wave Forecasts

Abstract One of the leading goals of rogue wave research is to develop a robust rogue wave warning system to mitigate the danger they pose. One such system has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), called the freak wave warning system (FWWS), based on nonlinear wave effects. The FWWS predicts maximum expected wave envelope height as a risk parameter for forecasts. Recently, a data-driven alternative has been proposed by Häfner et al., which was distilled from a neural network using wave buoy observations. However, it has yet to be evaluated by a spectral wave model for application to operational wave forecasting. The data-driven, learned model emphasizes bandwidth-controlled linear superposition as the predominant mechanism in crest-to-trough rogue wave generation, while nonlinear effects are a secondary term. The present work evaluates the performance of the empirical model using output from an ECMWF global wave hindcast. We find that the prediction models based on bandwidth effects have the highest log likelihood scores, with the empirical model outperforming all other tested models. In contrast, the expected maximum envelope wave height from the FWWS does not predict the occurrence of rogue waves. These results indicate that the empirical model with wave model input is a skillful predictor and should be considered for operational implementation to improve rogue wave forecasting. Significance Statement Rogue waves are unexpectedly large and unpredictable waves. Encounters with rogue waves can result in damage to marine vessels and offshore infrastructures. Research is devoted to developing systems to predict the risk of rogue wave events. A novel predictive model has been shown to perform well in predicting rogue wave occurrences based on wave buoy observations. This model is a symbolic expression distilled from an artificial neural network that incorporates known rogue wave dynamics and that can be evaluated alongside traditional risk estimates with minor adjustments to operational forecasting systems. This study evaluates the newly proposed empirical model in a forecasting setting. Our work demonstrates the efficacy of the empirical model for operational forecasting purposes.

Development of a wireless smart sensor system and case study on lifting risk assessment

With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.

Sustainable urban water management index for developing countries. A case study in Puebla City, Mexico

Several indicator and index frameworks have been reported to assess management efficiency and promote transparency, facilitating decision-making and innovation. However, they cannot be applied in cities with limited information, making them impractical. This study proposes a Sustainable Urban Water Management Index (SUWMI) for such cities, selecting six AquaRating indicators plus two indexes recommended in the Sustainable Development Goal 6, yielding a simplified and practical version for diagnosing the management performance rate of the Water Operating Systems (WOS) in cities. Conceptual and mathematical models were defined for SUWMI, and the eight selected terms were hierarchized based on a decision tree with the participation of an expert panel and further multicriteria decision-making methodology by the Analytic Hierarchy Process (AHP) to calculate the weighting parameters. The selected indicators represent five categories on a percentage scale. The highly populated Puebla City Metropolitan Zone in Mexico was the case study. Information was gathered from five Water Operation Systems (WOS) from 2002 to 2020. The tap water area's coverage and physical efficiency improved despite the population growth due to an adequate political strategy after 2014. The SUWMI values of each WOS diagnoses its performance, highlighting their differences and providing a valuable tool for guiding urban water public policy programs. The paper proposes a simple, fast, and easy-to-calculate index that can be helpful for a broader cluster of cities and urban settlements with deficient available information, which is common in developing countries.