Erica L. Green, Samuel M. Kelly, Andrew J. Lucas
et al.
AbstractNear‐inertial motions are common in the coastal ocean, producing significant currents, isopycnal displacements, and turbulent mixing. Unknown fractions of near‐inertial energy are locally dissipated in the mixed layer and converted to offshore propagating internal waves along the coast. Here, we examine near‐inertial motions from July to October 2017 at 10 moorings in Lake Superior, which provides a natural laboratory for the coastal ocean. The lake has an approximate two‐layer structure and is dominated by near‐inertial currents that reach 0.50 m and isopycnal displacements that reach 10 m. Average mode‐1 near‐inertial kinetic energy (KE) and available potential energy (APE) are 320 J and 10 J , respectively. KE is inhibited near the coast and APE has no basin‐wide structure. Velocity is separated into a basin‐averaged inertial oscillation (IO) and a near inertial wave (NIW) residual. A slab model explains 87% of the IO variance, while the NIW field exhibits 5 W offshore energy fluxes along the coasts, a group speed of 0.1 m , and a wavelength of 60 km. The IOs and NIWs contain 200 J and 120 J , respectively. We determine that 1.0 mW of wind work goes into to IOs, and 60% of this power is locally dissipated, while the other 40% is converted to NIWs at the coasts. IOs are found to dissipate more rapidly than NIWs (4.4 vs. 7.2 days residence time). NIWs are hypothesized to be important for catalyzing shear instabilities that drive turbulence.
The concept of life skills is related to the way of life that emphasises the mutual exchange of knowledge, attitudes, and interpersonal skills in education. Its objective is to develop diverse skills among students and prepare them to face life’s challenges with determination. The World Health Organization has defined life skills as “the positive behaviours and tendencies that enable a person to adapt in day-to-day life.” Life skills are the abilities that enable a person to adapt and exhibit positive behaviour, allowing them to deal effectively with the problems and challenges of daily life. Life is a unique gift. Therefore, by equipping life with various skills, happiness, peace, and prosperity are created. In this research, with the objectives of the study in mind, an analytical examination of life skills among secondary-level students has been conducted. This research study examines the effects of living conditions, gender, and social class on students’ life skills and presents the findings. Future researchers can build upon this, and other factors affecting the research can also be explored.
XIA Jinlei, TANG Yijie, WANG Lingling, JIANG Chuanwen, GU Jiu
In the context of “carbon peaking and carbon neutrality”, the large-scale integration and consumption of wind and solar resources is an inevitable trend in future energy development. However, as the capacity of wind and solar power integration increases, the power system also requires more flexible resources to ensure secure operation. To investigate the flexible regulation of hydropower in the system, this study focuses on the downstream stations of the hydro-wind-solar-pumped storage clean energy base in the Yalong River Basin. Considering its flexible regulation capabilities, the study conducts day-ahead optimized operational strategy research for the complementary system. First, to address the challenges of site selection and high costs associated with independent pumped storage, steady-state models for hybrid pumped storage stations in a cascade hydro-wind-solar-pumped storage system are established. To overcome the limitations of traditional models such as low predictive accuracy and the subjective selection of long short-term memory (LSTM) hyperparameters, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of LSTM and the optimized LSTM model is then used to forecast the output of wind and solar power. Next, in order to fully harness the flexible regulation potential of the complementary system, a multi-objective optimal dispatching model is developed considering the economic benefits and flexible regulation margin of the complementary system in the day-ahead time. The normal boundary intersection (NBI) method is employed to solve the multi-objective problem, which can obtain the Pareto optimal solutions with an even distribution. Finally, case studies are conducted based on the actual conditions of the Yalong River Basin. By analyzing different scenarios, the effectiveness of the proposed model and the supportive role of pumped storage in enhancing system flexibility are validated. The results demonstrate that the proposed approach not only balances system profits but also fully exploits the flexible regulation potential of the system, ensuring stable operation of the system.
Engineering (General). Civil engineering (General), Chemical engineering
With the rapid development and accelerated utilization of marine resources, multi-body floating systems have become extensively used in practical applications. This study examines the coupled motions of a side-by-side anchoring system for five fishing vessels in a harbor using ANSYS-AQWA. The system is connected by hawsers and equipped with fenders to reduce collisions between the vessels. It is designed to operate in the sheltered wind-wave combined environment within Ningbo Zhoushan Port, China. Considering the diverse types and quantities of fishing vessels in the anchorage area, this paper proposes a mixed arrangement of three large-scale fishing vessels in the middle and two small-scale vessels on both sides. The time-domain analysis is performed on this system under the combined effects of wind and waves, calculating the motion responses of the five fishing vessels along with the mechanical loads at the hawsers, fenders, and moorings. The results indicate that the maximum loads on these mechanical components remain well within the safe working limits, ensuring reliable operation. In addition, the impact of varying wind-wave angles on the coupled motions of the fishing vessel system are studied. As the wind-wave angle increases, the surge motion of the fishing vessels gradually decreases, while the sway motion intensifies. The forces on the hawsers, fenders, and mooring system exhibit distinct characteristics at different angles.
Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This survey reviews transformer-based LLM applications across scientific fields such as biology, medicine, chemistry, and meteorology, underscoring their role in advancing research. However, the continuous expansion of model size has led to significant memory demands, hindering further development and application of LLMs for science. This survey systematically reviews and categorizes memory-efficient pre-training techniques for large-scale transformers, including algorithm-level, system-level, and hardware-software co-optimization. Using AlphaFold 2 as an example, we demonstrate how tailored memory optimization methods can reduce storage needs while preserving prediction accuracy. By bridging model efficiency and scientific application needs, we hope to provide insights for scalable and cost-effective LLM training in AI for science.
Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.
Composite materials represent the evolution of material science and technology, maximizing the properties for high-end industry applications. The fields concerned include aerospace and defense, automotive, or naval industries. Additive manufacturing (AM) technologies are increasingly growing in market shares due to the elimination of shape barriers, a plethora of available materials, and the reduced costs. The AM technologies of composite materials combine the two growing trends in manufacturing, combining the advantages of both, with a specific enhancement being the elimination of the need for mold manufacturing for composites, or even post-curing treatments. The challenge of AM composites is to compete with their conventional counterparts. The aim of the current paper is to present the additive manufacturing process across different spectrums of finite element analyses (FEA). The first outcomes are building definition (support definition) and the optimization of deposition trajectories. In addition, the multi-physics of melting/solidification using computational fluid dynamics (CFD) are performed to predict the fiber orientation and extrusion profiles. The process modelling continues with the displacement/temperature distribution, which influences porosity, warping, and residual stresses that influence characteristics of the component. This leads to the tuning of the technological parameters, thus improving the manufacturing process.
The International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum useable ionospheric frequency foF2 and the propagation factor M(3,000)F2. At the U.S. Naval Research Laboratory, a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package.
Abstract To what extent does educational gaming add value to more traditional instructional models in learning core concepts of national security and warfighting? This paper presents the results from a quasi-experimental, cross-sectional, and longitudinal study of students taking two standardized courses in the Joint Military Operations department at the US Naval War College. Split into wargaming and non-wargaming sections by instructor preference, subject learning is measured through self-reported and objective measures at three points: prior to the start of the content block on “Operational Art”; after the case study of the WW2 battle of Leyte Gulf but prior to any wargaming; and for subjects in wargaming course sections, after participating in the Leyte Gulf scenario of the “War at Sea” wargame. The results support the hypotheses that wargaming increases learning and alter student preferences in favor of learning through gaming but fail to find evidence that students recognize the value of the debriefing phase of educational gaming. This article adds to existing studies by focusing on an understudied practitioner population—graduate-level career military officers at a professional military education (PME) institution—and mitigating several of the methodological challenges facing many scholarly projects in the study of educational gaming in political science.
This paper explores the integration of advanced machine learning (ML) techniques within simulation-based design optimization (SBDO) processes for naval applications, focusing on the hydrodynamic shape optimization of the DTMB 5415 destroyer model. The use of unsupervised learning for design-space dimensionality reduction, combined with supervised learning through active learning-based multi-fidelity surrogate modeling, allows for significant improvements in computational efficiency while addressing complex, high-dimensional design spaces. By applying these ML techniques to both single- and multi-objective optimizations, aimed at minimizing resistance and enhancing seakeeping performance, the proposed framework demonstrates its practical value in hydrodynamic design. This approach provides a scalable and efficient solution, reducing the reliance on high-fidelity simulations while accelerating the optimization process, without substantial modifications to existing toolchains. A design-space dimensionality reduction of approximately 70% is achieved, reducing the design variables from 22 to 7 while retaining 95% of the original geometric variance. Additionally, computational cost reductions of 65% to 98% are observed, compared to using the full design space and high-fidelity simulations only.
Introduction:
Scrub typhus is an important and widespread cause of acute febrile illness (AFI), which can be diagnosed easily by serological assay.
Methods:
All cases of AFI were sent for Scrub Typhus serology by rapid Enzyme Linked Immuno-Sorbent Assay (ELISA) method. Anyone found positive was admitted for further evaluation.
Results:
In this series 80 % had liver abnormality, 80 % had ARDS, 40 % had renal failure, 40 % had eschar and 80 % of them had MODS. Only 20 % had clinical features of encephalitis with MRI brain haemorrhagic transformation of infarct. All patients responded well to treatment with Doxycycline with hospitalization of around 7-10 days.
Conclusion:
This case series highlights the importance of keeping a high index of clinical suspicion to exclude scrub typhus in all AFI patients.
Nicolae Adrian Visan, Dan Catalin Niculescu, Radu Ionescu
et al.
The impact of fossil fuel utilisation in different combustion systems on climate change due to greenhouse gas accumulation in the atmosphere is rather evident. A part of these gases comes from the large engines used for propulsion in marine applications. In the continuous global effort made by engine manufacturers to mitigate this negative impact, one way is represented by the utilisation of alternative fuels such as biodiesel and methanol, based on dedicated research to fulfil the more stringent regulations concerning pollutant emissions issued by piston heat engines. In this study, a numerical investigation was conducted on a four-stroke large marine diesel engine (ALCO 16V 251C) at several engine speeds and full load conditions. Different blends of diesel–methanol and biodiesel B20–methanol with methanol mass fractions of 10% and 20% were considered for theoretical analysis in two techniques of methanol supply: direct injection mode of a blend of base fuel diesel/biodiesel B20 with methanol and injection of methanol after the intercooler, and direct injection of the base fuel. The results show that, if 10% in power loss can be acceptable, then for diesel–methanol 10%, in the direct injection technology, the NOx emission can be reduced up to 19%, but with a compromise of an 8% increase in SOOT emission, while for biodiesel B20–methanol 10%, with the same direct injection method, the NOx emissions increase by up to 58% with the benefit of reducing SOOT by up to 23% relative to the original diesel fuel operation. For a 20% methanol fraction in blend fuel, the drop in power is more than 10% regardless of the method of methanol supply and the base fuel, diesel, or B20 used.
In this manuscript, we will apply the regularized meshless method, coupled with an error estimation technique, to tackle the challenge of modeling oblique incident waves interacting with multiple cylinders. Given the impracticality of obtaining an exact solution in many real engineering problems, we introduce an error estimation technique designed to achieve reliable solutions. This technique excels in providing dependable solutions that closely approximate analytical solutions. An additional advantage is its capacity to identify the optimal number of points for both source and collocating points, thereby enhancing computational efficiency. The validity of the proposed method will be demonstrated through three numerical cases, presenting results that exhibit substantial agreement.
The Bohai Sea is a semi-enclosed shallow water that is influenced by both natural and anthropogenic stressors. However, the microeukaryotic communities and environmental factors that affect them in different regions remain largely unclear. We investigated microeukaryotic communities in surface sediments from five geographic regions using high-throughput sequencing of the 18S rDNA gene. The Miaodao Archipelago, Yellow River Estuary, and Central Bohai Sea had the highest Shannon and Simpson indices of the eukaryotic communities, while the Yellow River Estuary exhibited the highest Chao1 index. The microeukaryotic communities in surface sediments were mainly composed of Dinoflagellata, Bacillariophyta, Ciliophora, Cercozoa, and Protalveolata. <i>Thalassiosira</i> has a relatively high abundance at the Liaodong Bay and Central Bohai Sea, possessing the proportion of 41.70% and 38.10%, respectively, while <i>Gonyaulax</i> was the most abundant taxa in the Bohai Bay, occupying a proportion of 57.77%. Moreover, a negative correlation between diatoms and dinoflagellates was observed. Phosphorus, nitrogen, salinity, temperature, and silicate were major environmental determinants of microeukaryotic composition. Microeukaryotic communities in the surface sediments, especially for the composition and ratio of diatoms to dinoflagellates, reflected the environmental quality of marine ecosystems. Overall, these microeukaryotic community compositions provide a reliable indicator for monitoring the level of marine eutrophication in the Bohai Sea.
As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI's Data Analysis plugin. While it offers powerful support as a quantitative co-pilot, its limitations demand careful consideration in empirical analysis. This paper assesses the potential of ChatGPT for data science analyses, illustrating its capabilities for data exploration and visualization, as well as for commonly used supervised and unsupervised modeling tasks. While we focus here on how the Data Analysis plugin can serve as co-pilot for Data Science workflows, its broader potential for automation is implicit throughout.
The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenges strain traditional fault diagnostic methods, making it difficult to detect and manage faults across multiple locations while maintaining system stability and performance. This paper proposes a temporal recurrent graph transformer network for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural network uses gated recurrent units to capture temporal features and a multi-head attention mechanism to extract spatial features, enhancing diagnostic accuracy. The approach effectively identifies and evaluates successive multiple faults with high precision. The method is implemented and validated on the MVDC 12kV shipboard system designed by the ESDRC team, incorporating all key components. Results show significant improvements in fault localization accuracy, with a 1-4% increase in performance metrics compared to other machine learning methods.
Abstract In more recent times, research on various aspects of the Wire Arc Additive Manufacturing (WAAM) process has been conducted, and efforts into monitoring the WAAM process for defect identification have increased. Rapid and reliable monitoring of the WAAM process is a key development for the technology as a whole, as it will enable components produced by the process to be qualified to relevant standards and hence be deemed fit for use in applications such as those found in the aerospace or naval sectors. Intelligent algorithms provide inbuilt advantages in processing and analysing data, especially for the large data sets generated during the long manufacturing cycles. Interdisciplinary engineering (IDE) furnishes a concept integrating computer science and industrial system manufacturing engineering together to treat large amounts of process monitoring data. In this work, a WAAM process monitoring and defect detection system integrating intelligent algorithms is presented. The system monitors welding arc current and voltage signals produced by the WAAM process and makes use of a support vector machine (SVM) learning method to identify disturbances to the welding signal which indicate the presence of potential defects. The incremental machine learning models developed in this work are trained via statistical feature analysis of the welding signals and a novel quality metric that improves detection rates is also presented. The incremental learning approach provides an efficient means of detecting welding-based defects, as it does not require large quantities of data to be trained to an operational level (addressing a major drawback of other machine learning methods). A case study is presented to validate the developed system, results show that it was able to detect a set of defects with a success rate greater than 90% F1-score.
Sepsis-associated acute kidney injury (SA-AKI) results in significant morbidity and mortality, and ferroptosis may play a role in its pathogenesis. Our aim was to examine the effect of exogenous H2S (GYY4137) on ferroptosis and AKI in in vivo and in vitro models of sepsis and explore the possible mechanism involved. Sepsis was induced by cecal ligation and puncture (CLP) in male C57BL/6 mice, which were randomly divided into the sham, CLP, and CLP + GYY4137 group. The indicators of SA-AKI were most prominent at 24 h after CLP, and analysis of the protein expression of ferroptosis indicators showed that ferroptosis was also exacerbated at 24 h after CLP. Moreover, the level of the endogenous H2S synthase CSE (Cystathionine-γ-lyase) and endogenous H2S significantly decreased after CLP. Treatment with GYY4137 reversed or attenuated all these changes. In the in vitro experiments, LPS was used to simulate SA-AKI in mouse renal glomerular endothelial cells (MRGECs). Measurement of ferroptosis-related markers and products of mitochondrial oxidative stress showed that GYY4137 could attenuate ferroptosis and regulate mitochondrial oxidative stress. These findings imply that GYY4137 alleviates SA-AKI by inhibiting ferroptosis triggered by excessive mitochondrial oxidative stress. Thus, GYY4137 may be an effective drug for the clinical treatment of SA-AKI.
Future naval warfare has placed high demands on underwater targets’ target detection, target recognition, and opposition resistance, among other things. However, the ocean’s complex underwater acoustic environment and the evolving “stealth” technology of underwater targets pose significant challenges to target detection systems, which has become a hot topic in the field of underwater acoustic signal processing in various countries. This study introduced the mechanism of underwater target radiation noise generation, analyzed the research progress and development of underwater target radiation noise recognition by applying machine learning from three perspectives: signal acquisition, feature extraction, and signal recognition at home and abroad, and elaborated on the challenges of underwater target-radiated noise recognition technology against the backdrop of rapid computing science development, and finally, an integrated signal processing method based on the fusion of traditional feature extraction methods and deep learning is proposed for underwater target radiation noise recognition, which improves the low recognition rate of traditional methods and also circumvents the problem of deep learning requiring high computational cost, and is an important direction for future hydroacoustic signal processing.