Application of natural language machine learning for generalization of geoecological parameters of pelagic biological resources in the Atlantic and South-Eastern Pacific oceans

Abstract. Mesoscale eddies are important, frequent, and persistent features of the circulation in the eastern South Pacific (ESP) Ocean, transporting physical, chemical and biological properties from the productive shelves to the open ocean. Some of these eddies exhibit subsurface hypoxic or suboxic conditions and may serve as important hotspots for nitrogen loss, but little is known about oxygen consumption rates and nitrogen transformation processes associated with these eddies. In the austral fall of 2011, during the Tara Oceans expedition, an intrathermocline, anticyclonic, mesoscale eddy with a suboxic (< 2 µmol kg−1 of O2), subsurface layer (200–400 m) was detected ∼ 900 km off the Chilean shore (30° S, 81° W). The core of the eddy's suboxic layer had a temperature-salinity signature characteristic of Equatorial Subsurface Water (ESSW) that at this latitude is normally restricted to an area near the coast. Measurements of nitrogen species within the eddy revealed undersaturation (below 44 %) of nitrous oxide (N2O) and nitrite accumulation (> 0.5 µM), suggesting that active denitrification occurred in this water mass. Using satellite altimetry, we were able to track the eddy back to its region of formation on the coast of central Chile (36.1° S, 74.6° W). Field studies conducted in Chilean shelf waters close to the time of eddy formation provided estimates of initial O2 and N2O concentrations of the ESSW source water in the eddy. By the time of its offshore sighting, concentrations of both O2 and N2O in the subsurface oxygen minimum zone (OMZ) of the eddy were lower than concentrations in surrounding water and “source water” on the shelf, indicating that these chemical species were consumed as the eddy moved offshore. Estimates of apparent oxygen utilization rates at the OMZ of the eddy ranged from 0.29 to 44 nmol L−1 d−1 and the rate of N2O consumption was 3.92 nmol L−1 d−1. These results show that mesoscale eddies affect open-ocean biogeochemistry in the ESP not only by transporting physical and chemical properties from the coast to the ocean interior but also during advection, local biological consumption of oxygen within an eddy further generates conditions favorable to denitrification and loss of fixed nitrogen from the system.

A cost effective, easily scalable, and application independent FPGA cluster co-processing platform for machine learning (ML) applications is proposed. The work in this paper focuses on delivering an economical platform for the researchers and developers without the knowledge of FPGAs who seek budget friendly solutions to increase the performance of their ML applications without relying on the highly expensive solutions available in the today's FPGA market. The approach consists of two main parts, with a CPU based host machine learning application and the FPGA cluster sharing the heavy workload through an Ethernet protocol. An experiment was conducted using a perceptron layer implementation on the hardware and the test results show the execution time improvements due to the utilization of the proposed cluster approach. The solution proposed is easily scalable with any FPGA platform with an Ethernet support.

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review

The oil and gas industry faces constant pressure to enhance operational efficiency, reduce costs, and minimize equipment downtime. Machine learning (ML) applications in predictive maintenance have emerged as a transformative approach to achieving these goals. This review explores the role of machine learning in predictive maintenance, highlighting its potential to revolutionize maintenance strategies and improve asset management across the industry. Predictive maintenance leverages advanced algorithms to analyze historical and real-time data from equipment and sensors, enabling the identification of patterns and anomalies that precede equipment failures. By utilizing techniques such as supervised learning, unsupervised learning, and reinforcement learning, organizations can forecast equipment malfunctions and schedule maintenance activities proactively, thereby reducing unexpected downtimes and extending asset lifecycles. The implementation of ML in predictive maintenance provides several key benefits. Firstly, it enhances operational efficiency by optimizing maintenance schedules and minimizing unplanned outages, which are critical in the capital-intensive oil and gas sector. Secondly, it enables cost savings through more efficient resource allocation and reduced labor costs associated with reactive maintenance strategies. Thirdly, machine learning algorithms can continuously learn from new data, refining their predictive capabilities and improving accuracy over time. Several case studies illustrate the successful application of machine learning in predictive maintenance within the oil and gas industry. For example, ML models have been employed to predict pump failures, optimize drilling operations, and improve pipeline integrity monitoring. These applications not only lead to significant financial savings but also enhance safety by reducing the risk of catastrophic failures. In conclusion, machine learning applications in predictive maintenance represent a crucial advancement for the oil and gas industry. By harnessing the power of data-driven insights, organizations can enhance operational efficiency, reduce costs, and ultimately drive sustainable growth. This review emphasizes the transformative potential of machine learning in predictive maintenance, establishing it as a key strategy for success in the ever-evolving oil and gas landscape.

Purpose Although there are studies analyzing barriers related to new technological concepts, it turns out that there are only a few studies on barriers to machine learning (ML) applications, and none of them consider the implications for smart logistics. Therefore, the purpose of this study is to reveal and analyze the barriers to ML applications in smart logistics from both industry and academic perspectives. Design/methodology/approach To achieve this aim, first, various barriers to smart logistics activities based on the Industry 4.0 perspective are identified. Later, the relative importance of these barriers critical to the success of smart logistics activities is determined. Finally, the interval-valued fuzzy (IVF) DEMATEL method is used to analyze the cause-and-effect relationship between each barrier based on industry and academic perspective. Findings Eleven barriers related to ML applications in smart logistics were evaluated by seven experts who are working in different positions. Results show that, the most crucial cause-and-effect barriers are integration and connection problems with value chain/network systems (B6), requirements of adapting new infrastructures (B11) and lack of transparency, safety and security (B3). Originality/value There is no study about determining barriers with merging smart logistics activities with the Industry 4.0 perspective. It is expected that the results of this study will contribute to the use of ML in the logistics sector by revealing significant concepts to which businesses should pay attention to prevent these barriers and by suggesting practical solutions to these problems.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.

The application of machine learning and nature-inspired optimization methods, like for example genetic algorithms (GA) and particle swarm optimization (PSO) can be found in various scientific/technical areas. In recent years, these approaches are finding application in accelerator physics to a greater extent. In this paper, nature-inspired optimization as well as the machine learning will be shortly introduced and their application to the accelerator facility at GSI/FAIR will be presented. For the heavy-ion synchrotron SIS18 at GSI, the multi-objective GA/PSO optimization resulted in a significant improvement of multi-turn injection performance and subsequent transmission for intense beams. An automated injection optimization with genetic algorithms at the CRYRING@ESR ion storage ring has been performed. The usage of machine learning for a beam diagnostic application, where reconstruction of space-charge distorted beam profiles from ionization profile monitors is performed, will also be shown. First results and the experience gained will be presented.

Airport scheduling has an important optimization value in air transportation, and its complexity and multiple constraints make it difficult for existing methods to deal with it comprehensively. This paper is a review paper, that systematically summarizes the application of genetic algorithms and machine learning in the scheduling of flights, the scheduling of airport resource allocation, and the scheduling of airport external resource allocation. Through the analysis of many studies, this paper summarizes the advantages and disadvantages of different algorithms in various scheduling scenarios and points out the advantages of genetic algorithms in dealing with complex multi-objective optimization problems. In addition, this paper explores the potential of combining machine learning with genetic algorithms to further enhance the effect of scheduling optimization. This paper provides an important reference for future research and helps the innovation and practical application of methods in the field of airport scheduling.

Machine learning applications have momentously enhanced the quality of human life. The past few decades have seen the progression and application of machine learning in diverse medical fields. With the rapid advancement in technology, machine learning has secured prominence in the prediction and classification of diseases through medical images. This technological expansion in medical imaging has enabled the automated recognition of anatomical landmarks in radiographs. In this context, it is decisive that machine learning is capable of supporting clinical decision support systems with image processing and whose scope is found in the cephalometric analysis. Though the application of machine learning has been seen in dentistry and medicine, its progression in orthodontics has grown slowly despite promising outcomes. Therefore, the present study has performed a critical review of recent studies that have focused on the application of machine learning in 3D cephalometric analysis consisting of landmark identification, decision making, and diagnosis. The study also focused on the reliability and accuracy of existing methods that have employed machine learning in 3D cephalometry. In addition, the study also contributed by outlining the integration of deep learning approaches in cephalometric analysis. Finally, the applications and challenges faced are briefly explained in the review. The final section of the study comprises a critical analysis from which the most recent scope will be comprehended.

We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compositional structure of KAN and is based on block-encodings, constructed recursively from a single layer using quantum singular value transformation. We demonstrate the algorithmic utility of QKAN in two applications. First, we introduce and analyze QKAN as a quantum learning model, treating the eigenvalues of block-encoded matrices as neurons and applying parametrized activation functions on the edges of the network. We show that QKAN is a wide-and-shallow neural architecture, where shallow depth is compensated by exponentially wide layers whenever efficient block-encodings of inputs are available. We further discuss how to parametrize and train QKAN using parametrized quantum circuits and quantum linear algebra subroutines. Second, we demonstrate that QKAN can serve as a multivariate quantum state-preparation protocol for functions with shallow compositional structure. We demonstrate this by efficiently preparing a multivariate Gaussian quantum state using a two-layer QKAN. Looking forward, we anticipate that QKAN's compositional and modular design will enable new applications in quantum machine learning and quantum state preparation.

The Internet is a primary breeding ground for various network attacks, presenting a persistent challenge to cybersecurity. Machine learning has emerged as a powerful tool for automating the detection of cyber attacks and threats. Unlike traditional algorithms that rely on predefined signatures or rules, machine learning excels at uncovering complex patterns within vast volumes of Internet data. This capability enables the development of more intelligent and effective security solutions. The integration of machine learning into security systems has become a critical focus in both academia and industry. Nevertheless, the integration of machine learning and cybersecurity remains in its infancy. A significant gap exists between theory and practice: while machine learning may perform well in controlled or simulated environments, it may not work as expected in complex and dynamic real-world scenarios. Currently, machine learning security solutions are often evaluated using publicly available datasets in controlled laboratory settings. These lab-based evaluations introduce uncertainty about their practical effectiveness, which may hinder their adoption by security practitioners in real-world contexts. Proactive security measures aim to prevent threats before they cause harm, offering greater efficiency and effectiveness compared to reactive security, which relies on responsive strategies. Machine learning can enhance proactive security by automatically identifying potential vulnerabilities and predicting emerging threats, thereby strengthening the protection of Internet infrastructures. This dissertation aims to facilitate the application of machine learning in real-world cybersecurity settings, with a focus on addressing five core research questions: 1) What general challenges and limitations arise in the design, development, and deployment of practical ML-based security applications? 2) How can ML-based security solutions be effectively implemented in real-world scenarios? 3) What specific challenges and limitations are encountered when implementing such solutions in practical environments? 4) What current technologies, approaches, or solutions can be used to address these challenges and limitations, thereby facilitating the implementation of practical ML-based security systems? 5) What future research directions hold promise for advancing the practical application of machine learning in the cybersecurity field? We begin with a comprehensive literature review, identifying potential challenges and limitations in the practical application of machine learning (ML) within the field of cybersecurity. Building on these insights, we develop two practical tools designed to enhance existing proactive security mechanisms, directly addressing specific real-world challenges. Subsequently, we offer key recommendations regarding methodologies and solutions to mitigate obstacles in the development and deployment of realistic ML-based security systems. Finally, we propose future research directions, highlighting promising technologies and concepts with the potential to advance the field of practical ML applications in cybersecurity. This dissertation represents the first comprehensive investigation into the application of machine learning in realistic cybersecurity scenarios, with significant implications for the field of ML-based cybersecurity. It provides security researchers and practitioners with a deeper understanding of practical challenges and obstacles in this field. The development of two practical tools not only advances state-of-the-art proactive security mechanisms but also delivers actionable recommendations to assist researchers in addressing these challenges. Finally, the proposed future research directions highlight the potential of emerging technologies to enable more effective ML-driven security solutions in real-world environments. Overall, the insights presented in this dissertation help bridge the gap between theory and practice in ML-based cybersecurity, promoting the practical implementation of ML security systems that can replace or complement traditional approaches.

Abstract. Air/sea fluxes of dimethylsulfide (DMS) were measured by eddy correlation over the Eastern South Pacific Ocean during January 2006. The cruise track extended from Manzanillo, Mexico, along 110° W, to Punta Arenas, Chile. Bulk air and surface ocean DMS levels were also measured and gas transfer coefficients (kDMS) were computed. Air and seawater DMS measurements were made using chemical ionization mass spectrometry (API-CIMS) and a gas/liquid membrane equilibrator. Mean surface seawater DMS concentrations were 3.8±2.2 nM and atmospheric mixing ratios were 340±370 ppt. The air/sea flux of DMS was uniformly out of the ocean, with an average value of 12±15 μmol m−2 d−1. Sea surface concentration and flux were highest around 15° S, in a region influenced by shelf waters and lowest around 25° S, in low chlorophyll gyre waters. The DMS gas transfer coefficient exhibited a linear wind speed-dependence over the wind speed range of 1 to 9 m s−1. This relationship is compared with previously measured estimates of k from DMS, CO2, and dual tracer data from the Atlantic and Pacific Ocean, and with the NOAA/COARE gas transfer model. The model generated slope of k vs. wind speed is at the low end of those observed in previous DMS field studies.

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.