Operating System Research Articles

Present and Future Actions of CETYS University to Mitigate Climate Action

The strategic location of CETYS University, in Baja California state in the northwest of Mexico, gives it exceptional conditions for the use of renewable energies. With an installed photovoltaic solar energy capacity of 1.37 MW, it is the academic institution with the greatest generation potential in Latin America. The importance that CETYS University gives to the subject has stimulated the institution since 2015 to offer a degree and master's degree in renewable energy to have greater and better collaboration with solar and wind energy technology companies located in the state. With a vision of the future, in 2023 the CETYS Institute for Sustainability Studies (ISS) was created to promote and coordinate, among other actions, those related to energy and climate change. With a planning horizon until 2036, the ISS, looking forward, has proposed the following actions: a) an internal audit to increase the energy efficiency of their buildings and classrooms; b) support for research projects to build on-campus operational small-scale wind energy systems; c) the deployment of the 300 x 500 program, which seeks to plant at least 300 native trees in the region with low water consumption each year, to offset the atmospheric emissions of 500 vehicles; d) a policy to achieve 80% carbon neutrality, and e) the incorporation of CETYS to the network of Mexican universities for climate action. Among many others, these actions position CETYS as a leading institution in sustainable development at the regional level.

Near-real-time atmospheric and oceanic science products of Himawari-8 and Himawari-9 geostationary satellites over the South China Sea

Abstract. The initial release of near-real-time (NRT) atmospheric and oceanic science products from Japanese Himawari-8 and Himawari-9 (H8/9) geostationary (GEO) satellites over the South China Sea (SCS) was unveiled in 2024. The primary objective behind crafting these NRT H8/9 satellite products is to facilitate weather and marine environment monitoring, enhance maritime security, and aid ocean navigation, among other purposes. As part of this investigation, a novel NRT data processing system was devised to generate a variety of regional H8/9 GEO satellite science products within a resolution of 10 min and a gridded resolution of 0.05° × 0.05° from 3 November 2022 to the present. This algorithm system was built upon the preceding Fengyun (FY) geostationary satellite algorithm test bed (FYGAT), which was the prototype of the FY-4 GEO meteorological satellite science product operational processing system. These regional H8/9 GEO satellite science products encompass a range of crucial data such as cloud mask, fraction, height, phase, optical, and microphysical properties; layered precipitable water; and sea surface temperature. We subjected these products to rigorous evaluations against high-quality analogous satellite products and reanalysis data spanning 1 year in 2023. The validations underscore a strong consistency between the H8/9 GEO satellite atmospheric and oceanic science products over the SCS and the referenced products. Nevertheless, slight discrepancies in these satellite science products were identified, primarily stemming from variations in sensor/dataset characteristics, retrieval algorithms, and geometric conditions. These outcomes demonstrate the suitability of the first edition of NRT atmospheric and oceanic science products of H8/9 satellites over the SCS in supporting the intended quantitative applications. This NRT GEO satellite data record is publicly accessible through the File Transfer Protocol (FTP) provided by the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) in China. Free access to the dataset is possible via https://doi.org/10.6084/m9.figshare.25015853 (Liu et al., 2024).

4.B. Round table: Closing the loop in the European Health Data Space: relation between primary and secondary data use

Abstract Currently, data collection processes in healthcare institutions across Europe remain exclusively geared towards the needs of primary use. Neither medical professionals’ clinical workflows, nor the data infrastructures and data management processes established within different organisations allow for the kind of highly standardised and structured data collection, processing and segregation from operational systems that secondary use requires. The development of a European Health Data Space offers large possibilities for research and innovation. High quality or primary data and secure pathways are required for the secondary use of data. Closing the loop and making relevant information from secondary use of data available to clinicians and health care professionals is a crucial step to build trust and enable a cultural shift that will enable health innovations and targeted treatments reach all those who could benefit from them without delay. Closing the loop between primary and secondary use of data will accelerate the shift from treatment of illness to prediction and prevention in the way healthcare is delivered - a shift that will need to be accompanied by an overhaul of European health systems’ current funding and reimbursement models. Establishing trustworthy sources of information and guidance for the use of novel health technologies, as well as involving patients and their caregivers early in the development of concrete applications, could help to foster better understanding and build trust in this area. To ensure acceptance, it will be particularly important for these developments to be driven by identified problems and defined needs in healthcare. Efforts should be made to address and include all parts of the population, not just the highly engaged and usually highly educated patients who typically participate in collaborative initiatives. The workshop will provide a holistic understanding of the interconnectedness between primary data and secondary use of health data. High quality of data and secure pathways are required for the secondary use of data. The workshop will explore paths for the evaluation of data-driven innovation and its introduction into clinical pathways that need to be further developed and standardised across the EU and the role that different actors in the health data system must play to meet the needs for secure and high-quality of data for research and policy development. The importance of actors like healthcare professionals, patients and citizens as well as the role of industry, public-private partnerships and relevance of health data innovative solutions will be addressed by each of the panellists and collaboration pathways for closing the loop will be further explored. The discussion will use and build on the findings and solutions identified in the EIT Health Think Tank report ‘Implementing the EHDS across Europe’. Key messages • In order to move to a data sharing environment, a cultural shift is needed to foster an environment of equality and openness to share data. • Healthcare professionals, patients and citizens are active participants and public-private partnerships could be instrumental to foster novel solutions and trust in this cultural transformation. Speakers/Panelists Nienke Schutte Sciensano, Brussels, Belgium Milana Trucl European Patient Forum, Etterbeek, Belgium Sinead O’Connor Trinity College Dublin, Dublin, Ireland Aneta Tyszkiewicz EFPIA, Brussels, Belgium Elina Drakvik Sitra, Helsinki, Finland

Managing a 4000-participant cohort with a novel mobile app: data from the RisCoin study in Germany

Abstract Issue RisCoin, a prospective monocentric longitudinal observational study, aimed to identify risk factors for COVID-19 vaccine failure. Secondary aim is to monitor symptoms, SARS-CoV-2 infection, vaccination status of 3816 enrolled healthcare workers (HCW) and 180 patients with inflammatory bowel disease (IBD). Since the LMU University Hospital Munich served both as study sponsor and employer of the HCW, a secured approach was required. Description of the solution Participants accessed the study app (CentraXX, KAIROS GmbH, Germany) via pseudonymized Contact-ID with irreversible anonymization after 6 months. Key app features were the serological results report, bidirectional messaging, and weekly survey for self-reported data on vaccinations, infections, and symptoms. Active app use was defined as submitting >1 weekly survey during the study period 10.2021-12.2022. Results Over 15 months, our study team maintained 1964 two-way communications with 958 participants via the app. Of 3979 participants with app access, 3622 (91%) were active users, 2606 (65%) submitted 1 to 11 surveys, 1016 (26%) made ≥12 submissions (P75, “frequent users”). Frequent users were more likely to be IBD patients (p = 0.001), female (p < 0.001), aged 60 or older (p < 0.001). Staff in administration and nurses were more likely to be frequent users compared to physicians (p < 0.001). Main problems included the initial operational system disparities resulting in app crashes and continuous need for app reactivation. Both led to loss of active participants and required substantial staff resources to onboard and support participants during the study. Lessons The study app enabled secure, flexible, bidirectional communication with all participants. Despite technical issues, the majority actively used the app and provided valuable longitudinal data through weekly survey submissions. Key messages • A study-specific app provides a flexible and secure bidirectional anonymous communication for the study team and participants. • Adherence to app-based communication and data collection for pandemic monitoring was particularly high among patients, nurses and administrative staff.

Xiaomi: An Analysis of Market Strategy under the High Cost-Performance Ratio

Abstract: Xiaomi Corporation, which was established in 2010 in Beijing, is well-known for its smartphones along with other smart hardware as well as IoT devices. It is the second-largest manufacturer of smartphones in the world, behind Samsung, with a market share of about 12%, most of which run on the MIUI (now HyperOS) operating system. It mainly focuses on providing products that are of excellent quality and performance but still maintain affordability which attracts customers from around the world. This research delves into Xiaomi's adaptive marketing tactics as a reaction to evolving market dynamics and changing consumer demands, and examines Xiaomis market plan by looking at three clear instances. The first is how Xiaomi keeps its strong position in the smartphone market by bringing out improved models every year with a steady price tag. This helps to maintain their image of being very good value for money. Second, the choice of the company to put $300,000 for three years into remaking its logo with Japanese designer Kenya Hara. This highlights how redesigning has a strategic effect on brand image and worldwide identification. Thirdly, this study talks about Xiaomis start in the car industry when it introduced the first car model called Xiaomi SU7 in month April. It shows a major shift from making only smartphones to also producing electric cars.

Optimizing electrokinetic remediation for pollutant removal and electroosmosis/dewatering using lateral anode configurations

Soil electrokinetics (SEK) research has been widely used in various fields such as soil remediation, dewatering, land restoration, geophysics, sedimentation, pollution prevention, consolidation, and seed germination. According to our most recent published research on SEK process design modifications during the last 30 years (1993–2022), more than 150 designs have been introduced to assure SEK’s maximum performance. Incorporating lateral electrodes/anodes was not documented in the existing literature, which motivated us to investigate the output of this design. In this study, we aimed to enhance the performance of the perforated cathode pipe soil electrokinetic remediation (SEKR) system (PCPSS) for removing inorganic pollutants by installing lateral anodes (LA-PCPSS) using two approaches. In the first approach, the LA-PCPSS was connected to different sources of applied voltages (DSAV) from different power supplies, while in the second approach, the entire operation system was connected to the same source of applied voltage (SSAV). We used the Taguchi approach (L9OA) to determine the optimal levels of applied voltages for the DSAV system. The results indicated that the DSAV-(LA-PCPSS) could be optimized at an applied voltage of 1 V cm−1 for the surface and the first and second lateral anodes. The indigenous Sr (elements found in the tested soil without artificial pollution) in kaolinite showed the best response among other elements (Ni and other indigenous elements) when optimizing the DSAV-(LA-PCPSS) using the Taguchi approach. Installing lateral anodes (position B) supplied to low applied voltage (0.5 V cm−1) improved the electroosmosis (EO) rate/dewatering. Reverse migration of ions was observed during the remediation of real contaminated soil using the SSAV-(LA-PCPSS). The DSAV-(LA-PCPSS) is considered an appropriate design for the SEKR of inorganic pollutants, and increases the EO flow/dewatering. Additionally, the increased energy consumption employing the DSAV-(LA-PCPSS) was extremely minimal compared to the traditional PCPSS, which is an economic advantage for SEKR research. The DSAV-(LA-PCPSS) is still under optimization/intensification process, and subsequent processes will be examined to achieve high efficiency.

Resources for Teaching Operating Systems: A Survey of Instructors and a Literature Review

Objectives : Faculty new to teaching operating systems or those looking to refresh their course need to understand the state of the art in operating system education. Toward this goal, we conducted a survey of operating system instructors to understand how they approach teaching the course and what textbook and software platforms they use in their classrooms. We also conducted a literature review examining two decades of papers focused on teaching operating systems to undergraduates. Survey : We surveyed people who teach operating systems at the undergraduate level to determine which textbook they use, on which software they base projects, and how they approach teaching the course. Literature Review : We searched the ACM Digital Library for publications focused on undergraduate education about operating systems. We selected a total of 51 papers. For each selected paper, we identified which approach (i.e., concrete vs. abstract) and perspective (i.e., internal or external) on teaching operating systems the authors used, which Curriculum 2023 topics they covered, and on what type of system (e.g., educational, production, research) their projects were based. We also looked at the evaluation of the methods and the impact these papers have had on the field. Findings: Instructors teaching operating systems tend to balance both their approach and their perspective, with a slight leaning toward an internal perspective, whereas authors supporting operating system education overwhelmingly support an internal, concrete approach to the course. In addition, authors also tend to focus on a few key topics, including System Calls and Processes, Concurrency, Scheduling, Virtual Memory, File Systems API and Implementation, and Performance Evaluation. Conclusions : This work will help faculty teaching operating systems to consider which approach and perspective they wish to take in their course and to find the resources most relevant to their preferred approach and perspective. It can also help focus future research in the area of operating system education, to align it better with current practices.

Predictability of the 2020 Strong Vortex in the Antarctic Stratosphere and the Role of Ozone

AbstractThe Antarctic vortex of October–December 2020 was the strongest on record in the satellite era for the season in the mid‐ to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology's operational seasonal climate forecast system of that time, ACCESS‐S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS‐S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to understand the potential role of ozone in the event and its associated anomalies of the Southern Annular Mode (SAM) and rainfall over south–eastern Australia and western Patagonia. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring but suddenly appeared as a result of the record‐low upward propagating wave activity in September 2020. The ACCESS‐S2 forecasts significantly underestimated the negative wave forcing in September even at zero lead time, irrespective of the ozone configuration, therefore falling short in predicting the record strength of the polar vortex in late spring 2020. Nevertheless, ACCESS‐S2 with prescribed realistic ozone that had large anomalies in the Antarctic stratosphere significantly better predicted the strong vortex and the subsequent positive SAM and related rainfall anomalies over south–eastern Australia and western Patagonia in the austral summer of 2020–21. This highlights the potentially important role of ozone variations for seasonal climate forecasting as a source of long‐lead predictability.

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.

New design of operational MEMS bridges for measurements of properties of FEBID-based nanostructures.

Focused electron beam-induced deposition (FEBID) is a novel technique for the development of multimaterial nanostructures. More importantly, it is applicable to the fabrication of free-standing nanostructures. Experimenting at the nanoscale requires instruments with sufficient resolution and sensitivity to measure various properties of nanostructures. Such measurements (regardless of the nature of the quantities being measured) are particularly problematic in the case of free-standing nanostructures, whose properties must be separated from the measurement system to avoid possible interference. In this paper, we propose novel devices, namely operational micro-electromechanical system (opMEMS) bridges. These are 3D substrates with nanometer-scale actuation capability and equipped with electrical contacts characterised by leakage resistances above 100 GΩ, which provide a platform for comprehensive measurements of properties (i.e., resistance) of free-standing FEBID structures. We also present a use case scenario in which an opMEMS bridge is used to measure the resistance of a free-standing FEBID nanostructure.

Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

Static analysis framework for permission-based dataset generation and android malware detection using machine learning

Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques. However, the issues presented by modern malware cannot be met by traditional signature or heuristic-based malware detection methods. Previous research suggests that machine-learning classifiers can be utilised to analyse permissions, making it possible to differentiate between malicious and benign applications on the Android platform. There exist machine-learning methods that utilise permission-based attributes to build models for the detection of malware on Android devices. Nevertheless, the performance of these detection methods is dependent on the raw or feature datasets. Android malware research frequently faces a major obstacle due to the lack of adequate and up-to-date raw malware datasets. In this paper, we put forward a systematic approach to generate an Android permission-based dataset using static analysis. To create the dataset, we collect recent raw malware samples (APK files) and focus on the reverse engineering approach and permission-based features extraction. We also conduct a thorough feature analysis to determine the important Android permissions and present a machine-learning-based Android malware detection mechanism. The experimental result of our study demonstrates that with just 48 features, the random forest classifier-based Android malware detection model obtains the best accuracy of 97.5%.

Analysis and Comparison of Social Media Applications Using Forensic Software on Mobile Devices

With the integration of mobile systems into daily life, social media applications used especially on Android and iOS platforms contain a significant amount of sensitive information. Social media applications on mobile systems have huge personal and sensitive content. Therefore, it is important to design effective techniques for forensic analysis of social media applications and to detect personal data. In this research, three different paid mobile forensic software and 4 different brands and models of smartphones with different operating systems were used and analyzed. The study shows that private messages, e-mails, time information, shared data, location and time information, and other personal data can be obtained by a forensic expert who performs an examination, and it is seen that one software can access the deleted data, but another software cannot access it. In proportion to the technology used in today’s world, mobile forensics incidents are increasing day by day, and a competitive environment is created among the software used to illuminate these incidents. With this competition, software companies dealing with forensic informatics are trying to obtain different data to illuminate forensic events that may occur due to the active use of social media accounts with the developing technology, and software that does not meet the needs in the face of this situation remains in the background. The criminal elements in the investigation areas of mobile forensics differ daily, and the scope of crimes in the virtual environment is expanding with the developing technology. Therefore, mobile forensic analysis applications should be successful in social media applications other than standard data.

Single and entangled photon pair generation using atomic vapors for quantum communication applications

Significant progress has been achieved in leveraging atomic systems for the effective operation of quantum networks, which are essential for secure and long-distance quantum communication protocols. The key elements of such networks are quantum nodes that can store or generate both single and entangled photon pairs. The primary mechanisms leading to the production of single and entangled photon pairs revolve around established techniques such as parametric down-conversion, four-wave mixing, and stimulated Raman scattering. In contrast to solid-state platforms, atomic platforms offer a more controlled approach to the generation of single and entangled photon pairs, owing to the progress made in atom manipulation techniques such as trapping, cooling, and precise excitation schemes facilitated by the use of lasers. This review article delves into the techniques implemented for generating single and entangled photon pairs in atomic platforms, starting with a detailed discussion of the fundamental concepts associated with single and entangled photons and their characterization techniques. The aim is to evaluate the strengths and limitations of these methodologies and offer insights into potential applications. Additionally, the article will review the extent to which these atomic-based systems have been integrated into operational quantum communication networks.

Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.

Efficiency analysis in bi-level on fuzzy input and output

To enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA’s ’black box’ approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell’s method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model’s parameters.

Increasing the efficiency of railway rolling stock operation with induction traction motors due to implementation of the operational system for diagnostic condition of rotor

Increasing the efficiency of railway rolling stock operation with induction traction motors due to implementation of the operational system for diagnostic condition of rotor

Unmanned Agricultural Machine Operation System in Farmland Based on Improved Fuzzy Adaptive Priority-Driven Control Algorithm

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

Optimal replacement of coal with wind and battery energy storage

Abstract Electric utilities are considering replacing their coal power plants with renewables and energy storage to reduce emissions. However, they have also expressed concerns about operational changes and system reliability brought by these replacements. Utilities in remote rural areas face more challenges as they also face energy insecurity while having limited interconnections to wider systems and reliance on imported fuels. Therefore, it is critical for remote utilities to understand different coal replacement approaches and their impacts on system expansion, operation and energy security. In this paper, we define and investigate three approaches to replace coal using wind and batteries: 1) replacing exact coal generation, 2) replacing at least coal generation, and 3) replacing total energy provided by coal. We develop a case study inspired by the small remote grid in Fairbanks, Alaska, which has a single limited interconnection with the grid south of it. We utilize a power system expansion and economic dispatch model that co-optimizes the capacities of wind and batteries required for each approach and the hourly dispatch of energy and reserves for one year. We further analyze the operational cost variability under fixed and fluctuating fuel prices. We find that replacing the exact coal generation requires minimal operational changes, but also significantly more wind and battery capacities. In contrast, replacing total energy provided by coal induces more cycling in other resources, challenging grids with limited flexibility-providing resources. However, replacing total energy provided by coal allows more generation variability in response to fuel price fluctuations, enhancing energy security.