Operating System Research Articles

Statistical Response of ENSO Complexity to Initial Condition and Model Parameter Perturbations

Abstract Studying the response of a climate system to perturbations has practical significance. Standard methods in computing the trajectory-wise deviation caused by perturbations may suffer from the chaotic nature that makes the model error dominate the true response after a short lead time. Statistical response, which computes the return described by the statistics, provides a systematic way of reaching robust outcomes with an appropriate quantification of the uncertainty and extreme events. In this paper, information theory is applied to compute the statistical response and find the most sensitive perturbation direction of different El Niño–Southern Oscillation (ENSO) events to initial value and model parameter perturbations. Depending on the initial phase and the time horizon, different state variables contribute to the most sensitive perturbation direction. While initial perturbations in sea surface temperature (SST) and thermocline depth usually lead to the most significant response of SST at short and long ranges, respectively, initial adjustment of the zonal advection can be crucial to trigger strong statistical responses at medium range around 5–7 months, especially at the transient phases between El Niño and La Niña. It is also shown that the response in the variance triggered by external random forcing perturbations, such as the wind bursts, often dominates the mean response, making the resulting most sensitive direction very different from the trajectory-wise methods. Finally, despite the strong non-Gaussian climatology distributions, using Gaussian approximations in the information theory is efficient and accurate for computing the statistical response, allowing the method to be applied to sophisticated operational systems. Significance Statement The purpose of this work is to better understand how El Niño–Southern Oscillation (ENSO) responds to changes in its initial state and internal dynamics or external forcings. A statistical quantification of this response allows for the comprehension of the triggering conditions and the effect of climate change on the occurrence frequency and strength of each type of ENSO event. Such a study also allows to detect the most sensitive perturbation directions, which has practical significance in guiding anthropogenic activities. The approach used to study the response in this work is through the framework of information theory, which allows for an unbiased and robust assessment of the statistical response that is not affected by the turbulent dynamics of the system.

Assessing the Need for Additional Evacuation Measures in Deep Underground Stations

Underground railroad stations face evacuation challenges owing to the rapid spread of smoke and extended escape routes during fires. Evacuation in deeply underground domestic railroad stations relies solely on stairways, with no legal standards for additional evacuation methods. In contrast, international standards such as NFPA 130 and Japan’s Fire Protection Engineering Association (JAFPE) recommend using evacuation elevators as a supplementary means of evacuation. This study analyzed smoke behavior in deep underground railroad stations through fire simulations at the Soongsil University and Guryong station. It also examined additional evacuation measures to enhance evacuation safety through evacuation simulations. The results show that during a fire, smoke spread follows the same path as the passenger evacuation routes because of the chimney effect. Even with smoke control and ventilation systems in operation, smoke follows the same evacuation routes, such as stairways or escalators. Therefore, the study concluded that evacuation elevators should be installed as an additional means to improve evacuation efficiency, and placed in smoke-protected evacuation routes. The study also found that combining evacuation stairways and elevators significantly enhances the evacuation efficiency.

Plant Disease Detection Using Machine Learning

In this project, we have developed a plant disease detection model using a machine learning approach on a dataset named “Plant Disease Expert” which is available on Kaggle. The dataset contains 199,611 images across 58 subdirectories. The splitting of the dataset in training, validation and testing is done in ratio 90:5:5. The data preprocessing included resizing the images, normalizing pixel values and applying augmentation techniques. We have used different libraries throughout the project such as NumPy, Pandas, Operating system, Time, Matplotlib, OpenCV, Shutil, TensorFlow, Keras, Seaborn, etc. EfficientNetB3 is used as the base model. EfficientNetB3 is a convolutional neural network architecture developed by Google Brain researchers [1]. It is known for its efficiency and effectiveness in image classification tasks. Additional custom layers such as max pooling, batch normalization, dense layers, and activation functions are used to optimize the performance. The adamax optimizer and categorical cross-entropy were used during training. The model was trained for 10 epochs with a batch size of 20 and a learning rate of 0.01. For evaluation, classification report, accuracy, precision, recall, F1-score and support were used. The model achieved an accuracy of 98.76% [1].

Operating Systems (OS): An Insight Investigative Research Analysis and Future Directions

In the realm of technological computing, the pivotal interface of operating systems (OS) governs the orchestration of machinery, orchestrating seamless human interactions with the swiftly advancing array of device peripherals. Over decades, the intricacies of computing have undergone a profound metamorphosis, embracing monumental leaps facilitated by the progressive proliferation of operating system distributions. From the erstwhile colossal processing units to the present-day intricately crafted nano-fabricated microcontrollers, motherboards, and chipsets, all human-computer interactions gravitate towards the nuanced tapestry of OS distributions and intricately woven source-coded programming. This comprehensive research endeavors to undertake a meticulous exploration of the myriad typologies of operating systems, intricately dissecting their distinctive functionalities and performance metrics, with a discerning focus on aligning specific user profiles with the most fitting OS distributions. Moreover, this investigation seeks to unravel the labyrinthine landscape of OS distributions, illuminating the optimal pathways for both seasoned users and neophytes alike.

Raspberry Pi based IOT Weather Station

This paper presents a real-time system for monitoring and updating weather conditions online. The system tracks three key parameters: temperature, humidity, and rainfall. These values are displayed on an LCD screen and stored in a database. When the area is dry, the rainfall reading shows zero. If raindrops are detected, the rainfall value updates accordingly. As the temperature changes, it also reflects on the display. Users can check the weather status of a specific area from anywhere. We use an ARM-based Raspberry Pi 3 board, running the Raspbian operating system with a Linux kernel. The programming is done in Python, which is supported by the IDLE environment. This system provides users with a clear understanding of local weather conditions, making it a valuable tool for monitoring the climate in a specific area.

Comparison Performance Analysis of HDD, SSD and NVME for OLTP Database Server

Databases are an essential component of life in modern society. The database has become an online Transaction Processing (OLTP) operational support company. This study discusses the storage configuration in a virtual server database for OLTP Database with a benchmark against three storage configurations for four databases: SQL Server, MySQL, PostgreSQL, and MariaDB for Windows Server virtualization. Benchmarks using TPC-C to get the best performance from all three configurations have been tested. The results of this study show that the storage configuration of HDD as a virtual disk drive OS and NVME as a virtual disk drive database get better performance as online transaction processing (OLTP) compared to HDD as a virtual disk drive OS and HDD or SSD as a virtual disk drive database. Using the TPC-C benchmark, the study tested the performance of four databases (SQL Server, MySQL, PostgreSQL, and MariaDB) in various virtual storage configurations. The results of 36 experiments involving different numbers of virtual users (10, 50, and 100) showed that a hybrid configuration that used HDDs for the operating system and NVME for database storage provided the best performance. On SQL Server, SSD configurations show better results for smaller user loads. The results suggest that the combination of HDD and NVME is the optimal configuration for most databases in OLTP tasks, and it provides useful guidance in efficient storage investment decision-making. Further research is expected to be able to conduct tests.

Wazuh SIEM for Cyber Security and Threat Mitigation in Apparel Industries

Security of IT infrastructure is critical in the modern digital environment, particularly for industries that manufacture clothing. The application of the Wazuh Security Information and Event Management (SIEM) system to improve security monitoring and compliance for an IT system in the apparel industry is the main topic of this study. To improve the apparel industry cyber threat monitoring system in real-time monitoring, detecting the threat vectors that support the centralized management system, to meet these objectives of the company, an SIEM security management system has been installed and configured with the components of Wazuh manager, indexer, Wazuh agent, and Wazuh dashboard as well. An Oracle virtual lab environment was created to install and configure the Wazuh system a robust Linux operating system was installed at the server level and Windows 10 was installed on the client end to the authenticity failure of the security system. To implement the Wazuh system, the project management guideline was followed which ensured the planning and implementation of the project, testing phases, and along with review and maintenance. Wazuh security management system successfully identified the authentical failure reports and vulnerabilities and generated automated reports from the system that showed the result of 1 critical, 19 high, and 5 medium levels of vulnerabilities shown in Figure 6, and the necessary patch recommended to solve the critical issues. On the other hand, the system also generated security events as well as integrity monitoring system reports. The deployment of the Wazuh security management system ensured the robust cost-effective security management system with high quality for detecting cyber threats which supports the regulatory and compliance requirements and is a viable tool for the apparel industry to protect the stakeholders’ interest.

Data-Driven Insights for a Holistic Understanding of Research Culture

Research environments (and their measurement and tracking) are becoming increasingly complex, with rapid Artificial Intelligence (AI) advancements, interdisciplinary collaboration, and global connectivity shaping the way research knowledge is created and disseminated. Within this dynamic landscape, universities hold an ever increasing collection of valuable data, which are stored within core operational systems including research information systems, research management systems or grants databases, human resource systems and course management systems. This treasure trove of information, often overlooked and underutilised, not only serves as a valuable tool in guiding strategic decisions, but also could be further used to provide a comprehensive approach to monitoring research integrity and culture. This critical reflection follows extensive conversations and debates around defining and assessing ‘research culture’. How can we possibly measure something that has, up until now, been viewed as merely a concept? How can we generate useful metrics that reflect the culture of a research institution? Our reflections will draw attention to the potential of leveraging readily accessible information to gauge and benchmark research integrity and culture practices. We discuss how regular habitual integration of these data sources enables continuous monitoring and measurement of research culture as well as the ability to assist in the assessment of interventions or initiatives designed to improve it. We reflect on how this approach ensures that University leaders have a consistent and up-to-date understanding of the research environment through which they can identify strengths, pinpoint areas for improvement, and cultivate a more robust, inclusive, and transparent research ecosystem. The current paper, through illustrative examples from one UK-based institution, explores the potential in harnessing existing data, such as collaborative trends and prevailing research practices, to gain valuable insights into the dynamics of academic research. In addition, we explore the advantages and drawbacks of using this data to develop potential metrics that can be used to recognise and reward a healthy research culture.

Analysis of the internal environment when creating an innovative university

Subject. The article discusses the creation of innovative universities. Objectives. The purpose is to analyze the internal environment in the formation of an innovative university and to investigate the factors of resistance to organizational changes. Methods. The survey was attended by scientific and pedagogical staff of two innovative universities: 170 people from the Institute of Economics and Entrepreneurship of the National Research Lobachevsky State University of Nizhny Novgorod and 50 people. from the economics faculties of the Nizhny Novgorod Branch of the Higher School of Economics. Results. We analyzed internal environment and studied organizational changes aimed at creating new social mechanisms and channels of interaction with the State, industry and society. Furthermore, we considered the factor of resistance to organizational changes and the possibility of its reduction. The paper presents the results of diagnostics of the resistance force in two universities. Conclusions. The leading organizational factor (prerequisite) for the emergence of resistance is an imperfect system of organizational communications. The priority task of university management is to create an operational and objective system for informing employees about the goals, methods and results of changes. The most significant psychological reasons for the emergence of resistance are the fear of negative consequences of changes and misalignment of changes with traditional academic values.

AI security in different industries: A comprehensive review of vulnerabilities and mitigation strategies

Artificial intelligence has quickly and gradually spread across all sectors and fields, achieving exceptional performance in operations and decision-making. Yet, as industries process their tasks with the help of AI systems, new threats appear that require proper solutions. In the following section of this paper, the use of security solutions involving AI in various industries, which are finance, health, manufacturing, and government, as well as a discussion of risks involving artificial intelligence in particular fields and the corresponding measures to be taken against them will be discussed. AI is often applied to detect fraud and credit scoring in the financial sector but has potential risks, such as adversarial attacks and data manipulation. Similarly, the healthcare industry uses AI for predictive healthcare diagnostics and patient information protection; threats are data theft and adversarial examples attacking diagnostic models. In manufacturing, AI individuals contain predictive servicing and excellent control, though they are constantly exposed to industrial espionage and the sabotage of AI models. In retail, however, AI is used to improve marketing and predict consumer behavior. While these benefits exist, questions about using Algorithms that could be biased and privacy infringements continue to raise their ugly head. Defensive and governmental organizations that apply AI for surveillance or automatic operating systems are most vulnerable to adversarial intervention with key control systems and innate security systems. To counter such threats, this paper provides details of industry-specific measures, including adversarial training data sanitization, AI model audit, and privacy-preserving approaches. We illustrate examples linked to every one of these methods to show how they can be deployed in a variety of industries and to guard AI systems. To this end, the following review of the relationship between AI and cybersecurity demonstrates that security measures that rely on artificial intelligence must be regularly checked and updated. This reveals the call for proper, protective, and industry-specific measures to manage the risks and protect AI applications to ensure that the world’s modern, interconnected world is safe for AI.

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.