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Accurate initialization of CONUS convective‐scale forecasting requires a proper estimate of all resolved scales. This study further develops and examines a simultaneous multiscale data assimilation (MDA) approach in EnVar with modulated cross‐scale and cross‐variable covariances. The method is examined using 10 retrospective cases with the assimilation of both in situ and radar reflectivity observations (hereafter, SimMDA). The necessity of the modulated and therefore weakened cross‐covariances in simultaneous MDA for CONUS convective‐scale forecasting is first demonstrated. The relative benefits of increasing the decomposed‐scale number with increased computational cost in SimMDA are also discussed. The impact of the further developed simultaneous MDA method is revealed by comparing it with a commonly adopted DA approach (Baseline), which separately assimilates in situ and reflectivity observations using individual single‐scale localization. During DA cycling, SimMDA improves analysis accuracy for temperature and reflectivity and reduces biases in all variables compared to Baseline. SimMDA yields significantly better forecasts than Baseline for most lead times. Additional experiments are conducted to attribute such improvements in a case study. Specifically, an experiment the same as Baseline except using simultaneous MDA for reflectivity assimilation enhances cold pools and inflows and thus improves storms by making larger‐scale increments. An experiment the same as Baseline except using simultaneous MDA for in situ assimilation more properly constrains small‐scale covariances, leading to more reasonable correlations along the front and more accurate moisture near the dryline and consequently improved analyses and forecasts. Both effects together largely contribute to the overall improvements of SimMDA compared to Baseline. Plain Language Summary: The CONUS domain contains atmospheric flows ranging from synoptic to convective scales and multiple in situ and remote sensing observations sampling a variety of scales. An accurate forecast of CONUS severe weather requires a proper update of all resolved scales. To address this issue, previous studies commonly adopt an approach to separately update a certain range of scales with the assimilation of the corresponding scale‐representative observations. This study further develops and examines a simultaneous multiscale approach to simultaneously update all resolved atmospheric scales with the assimilation of all observations at once. Given the unique challenges associated with a large range of scales in CONUS, this simultaneous approach is further developed by including additional modulation on the cross‐scale and cross‐variable covariances. A total of 10 high‐impact convective events are used to demonstrate the benefits of the further developed simultaneous multiscale DA approach and make diagnostics. Results show that the simultaneous multiscale approach improves the analyses and forecasts compared to the baseline approach. The simultaneous multiscale DA approach yields these improvements by more properly updating different scales. Key Points: This study further develops simultaneous multiscale data assimilation (DA) with adjustable cross‐scale and cross‐variable covariancesThe benefits of simultaneous multiscale DA are demonstrated for CONUS convective‐scale forecasting using 10 retrospective casesSimultaneous multiscale DA of in situ and reflectivity observations separately contribute to different aspects of the benefits [ABSTRACT FROM AUTHOR]

We assess for the first time the impact of in-situ ocean observations on European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecasts of both ocean and atmospheric conditions. A series of coupled reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions. Removal of all ocean in-situ observations in the initial conditions leads to significant degradation in the forecasts of ocean surface and subsurface mean state at lead times from week 1 to week 4. The negative impact is predominantly caused by the removal of the Argo observing system in recent decades. Changes in the mean state of atmospheric variables are comparatively small but significant in the forecasts of lower and upper atmospheric circulation over large regions. Our results highlight the value of continuous, real-time in-situ observations of the surface and subsurface ocean for coupled forecasts in the sub-seasonal range. [ABSTRACT FROM AUTHOR]

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model's insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper is concerned with the linear delayed forward–backward stochastic differential equations (D-FBSDEs). The existence of time delay leads to the infinite-dimensional problem which makes the explicit solvability more challenging. The main contribution is to propose a discretisation approach which gives the explicit solution of the D-FBSDEs and consists of the following three steps: first, we transform the continuous-time D-FBSDEs into the discrete-time form with the aid of interval partition. Second, we derive the solution of the discrete-time D-FBSDEs by applying backward iterative induction. Finally the explicit solution of the continuous-time D-FBSDEs is obtained by taking the limit to the solution of discrete-time form which is also strictly proved under continuous-time framework. The proposed approach is applicable for more general FBSDEs with delay, which would provide a complete solution to the stochastic LQ control with time delay. [ABSTRACT FROM AUTHOR]

Featured Application: The application of this study is all aluminum parts in high-voltage devices and the feasibility of considering additive manufacturing as a manufacturing method. In recent years, additive manufacturing (AM) has made considerable progress and has spread in many industries. Despite the advantages of this technology including freedom of design, lead time reduction, material waste reduction, special tools manufacturing elimination, and sustainability, there are still a lot of challenges regarding finding the beneficial application. In this study, the feasibility of replacing traditional manufacturing methods with additive manufacturing in the energy sector is investigated, with a specific focus on gas-insulated high-voltage switchgear (GIS). All aluminum parts in one specific GIS product are analyzed and a decision flowchart is proposed. Using this flowchart, printability and the best AM technique are suggested with respect to part size, required surface roughness, requirements of electrical and mechanical properties, and additional post processes. Simple to medium complexity level of geometry, large size, high requirements for electrical and mechanical properties, threading and sealing, and lack of a standard for printed parts in the high voltage industry make AM a challenging manufacturing technology for this specific product. In total, implementing AM as a short series production method for GIS aluminum parts may not be sufficient because of the higher cost and more complex supply chain management, but it can be beneficial in R&D cases or prototyping scenarios where a limited number of parts are needed in a brief time limit. [ABSTRACT FROM AUTHOR]

The Great Arctic Cyclone 2012 (AC12) is used to understand the role of initial condition errors in the predictability at 2–3‐day forecast range of a high‐impact summer Arctic Cyclone (AC). Ensemble sensitivity analysis (ESA) is first performed to identify potentially sensitive regions of the cyclone evolution using an ensemble baseline forecast with conventional in situ observations assimilated. A pseudo‐observation method is then introduced to investigate impacts of hypothetical observations in these sensitive but unobserved regions. In the baseline experiments with in situ observations assimilated, the forecasted AC12 reaches its peak intensity 18 hr earlier than in the verifying Global Forecast System Analysis (GFS‐ANL) and the cyclone track is biased toward the southwest. Using ESA, the time of peak intensity and the cyclone track error are identified to be sensitive to the upstream trough, downstream ridge, and the tropopause polar vortex (TPV) to the northeast (NE TPV) of the AC12. These features were not observed by the in situ observation networks. To examine the impact of the observation gaps, pseudo‐observations drawn from GFS‐ANL are assimilated. Pseudo‐observations sample the three features separately to study the impact of the initial condition error on the predictability of AC12. The cyclone peak intensity timing error and track error are greatly reduced when the initial condition error is reduced near the NE TPV. A southward expansion of the NE TPV and the corresponding southward shifting low‐level front lead the forecasted AC12 to progress to the east, which better agrees with the verifying GFS‐ANL. Plain Language Summary: Accurate prediction of strong Arctic cyclones is critical to the scientific and economical activities in the Arctic. However, the sparse observation networks in the Arctic pose challenges to the data assimilation system to constrain the initial conditions in various features of the Arctic Cyclones. This paper used a case study approach to investigate the impact of initial condition errors in different features on the development of the 2012 Great Arctic Cyclone. Synoptic features that are sensitive to the track and timing of the cyclone were first identified using ensemble sensitivity analysis. Assimilating in situ conventional observations did not sufficiently constrain the initial conditions in the sensitive features. Hypothetical pseudo‐observations from an external analysis system were then designed and assimilated to study how each sensitive feature affects the prediction of the physical processes of cyclone development. The results showed that more observations inside the TPV to the northeast of the cyclone brought the most significant forecast improvements at a 2‐day forecast lead time. Key Points: Ensemble sensitivity analysis reveals track and deepening trend forecast biases of Arctic Cyclone 2012 are sensitive to unobserved synoptic scale featuresPseudo‐observation experiments show the northeast tropopause polar vortex (TPV) is the most impactful feature among sensitive featuresSouthward expansion of the northeast TPV impacts the cyclone evolution by shifting the low‐level front southward [ABSTRACT FROM AUTHOR]

Background: Commonly heard statements such as "Christmas comes around more quickly each year" suggest that the passage of time between annual events can become distorted, leading to the sensation of time passing more quickly than normal. At present however, it is unclear how prevalent such beliefs are and, what factors are predictive of it. Aim: To explore the prevalence of beliefs that annual events such as Christmas (Study 1 UK sample) and Ramadan (Study 2 Iraqi sample) feel like they come around more quickly each year. To establish the association between distortions to the passage of time between annual events and emotional wellbeing, event specific enjoyment, memory function and self-reported attention to time. Methods: Participants completed an online questionnaire exploring their subjective experience of time in relation to Christmas and Ramadan. In addition, measures of attention to time, memory function, quality of life and event specific emotion were also taken. Findings: There was widespread agreement that Christmas and Ramadan appeared to come around more quickly each year. In both countries, this belief was associated with greater prospective memory errors, greater attention to time and greater enjoyment of the event. Furthermore, in the UK greater belief that Christmas comes around more quickly was associated with lower social quality of life and in Iraq, greater belief that Ramadan comes around more quickly each year was associated with lower age and female gender. Conclusions: Distortions to the passage of time for annual events are widespread, occur across multiple cultures and are consistently predicted by prospective function, event enjoyment and attention to time. The absence of an association between older age (above 55 years) and a faster passage of time suggests that caution should be taken when concluding that time passes more quickly with increasing age. [ABSTRACT FROM AUTHOR]

Manufacturing key metrics are a useful approach for evaluating shop floor operations. The collaboration between operators and robots is essential in maintaining a resilient performance within smart and flexible manufacturing systems. For effective collaboration, both operators and robots must possess varying degrees of resilience, including full resilience, partial resilience and the ability to handle total disruptions. In this paper, lead time is considered a significant key metric. When the system is fully resilient and dependable, it achieves the optimal lead time. Consequently, lead time serves as a benchmark for evaluating the system's performance. However, if the robot experiences significant performance issues, it can negatively impact the cycle time, resulting in longer lead times. The discrepancy between the optimal lead time and the lead time obtained during partial or complete disruption is subtracted from the optimal lead time. To ensure the validity of the findings, mathematical equations are utilized in combination with other relevant data. This approach contributes to the knowledge base in the field. Finally, the paper will provide suggestions for future research endeavors.. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the effects of process parameters and feature size on filling behavior of polymethyl methacrylate (PMMA) in rapid hot embossing. The filling behavior of PMMA was characterized by analyzing the surface topography of microlenses that were formed on the surface of PMMA after a series of hot embossing experiments, which were conducted by using a universal testing machine that is equipped with a rapid heating module. Experimental results indicated that increasing embossing temperature, embossing force, maintain force and embossing time led to higher average heights of microlenses, and thus improved the filling capacity of PMMA. Furthermore, embossing temperature has the most significant enhancement effect. Whereas, soaking time and demolding temperature shew very slight influence on the filling height of microlens. Therefore, cancelling soaking treatment and increasing demolding temperature are efficient strategies for shortening the cycle time of non-isothermal hot embossing without weakening the filling capacity of PMMA. Moreover, enlarging microhole diameter results in the increase of curvature radius and average height of microlens. Finally, a 5 × 5 microlens array was hot embossed on the PMMA substrate by using a 5 × 5 microhole array SiC mold. It is found that microlens at the center has the maximum height, while microlenses away from the center of PMMA substrate have smaller heights, which is attributed to the decreased contact pressure along the radial direction. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the possible defects and their root causes in a soft-termination multilayered ceramic capacitor (MLCC) when subjected to a thermal reflow process. Design/methodology/approach: Specimens of the capacitor assembly were subjected to JEDEC level 1 preconditioning (85 °C/85%RH/168 h) with 5× reflow at 270°C peak temperature. Then, they were inspected using a 2 µm scanning electron microscope to investigate the evidence of defects. The reliability test was also numerically simulated and analyzed using the extended finite element method implemented in ABAQUS. Findings: Excellent agreements were observed between the SEM inspections and the simulation results. The findings showed evidence of discontinuities along the Cu and the Cu-epoxy layers and interfacial delamination crack at the Cu/Cu-epoxy interface. The possible root causes are thermal mismatch between the Cu and Cu-epoxy layers, moisture contamination and weak Cu/Cu-epoxy interface. The maximum crack length observed in the experimentally reflowed capacitor was measured as 75 µm, a 2.59% difference compared to the numerical prediction of 77.2 µm. Practical implications: This work's contribution is expected to reduce the additional manufacturing cost and lead time in investigating reliability issues in MLCCs. Originality/value: Despite the significant number of works on the reliability assessment of surface mount capacitors, work on crack growth in soft-termination MLCC is limited. Also, the combined experimental and numerical investigation of reflow-induced reliability issues in soft-termination MLCC is limited. These cited gaps are the novelties of this study. [ABSTRACT FROM AUTHOR]

Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly. [ABSTRACT FROM AUTHOR]

According to Logvinovich cavity dependent expansion principle, a cavity developed by cavitator has memory effect, which arises cavity deformation at aft part of a supercavitating vehicle and leads time delay of travel state. This delay affects the dynamic behavior through changing hydrodynamic and moment on the control surface, and even induces instability. This paper focuses on the time delay effect of the underwater supercavitating vehicle, and a simplified dynamic model of underwater supercavitating vehicle with delay was established. First, a planing force was simplified in straight level state, then a simplified model of supercavitating vehicle with delay in longitude plane was obtained. Taking depth, pitch angle, vertical speed and pitch rate as state variant, H ∞ state feedback controller was designed by linear matrix inequality (LMI) and Lyapunov stable theory was exploited to prove the asymptotic stability of the system. Simulation results show that, the controlled system is effectiveness for both simplified model and non-simplified model and it can track reference command signal. [ABSTRACT FROM AUTHOR]

Ocean fog, a meteorological phenomenon characterized by reduced visibility due to tiny water droplets or ice particles, poses significant safety risks for maritime activities and coastal regions. Accurate prediction of ocean fog is crucial but challenging due to its complex formation mechanisms and variability. This study proposes an advanced ocean fog prediction model for the Yellow Sea region, leveraging satellite-based detection and high-performance data-driven methods. We used Himawari-8 satellite data to obtain a lot of spatiotemporal ocean fog references and employed AutoML to integrate numerical weather prediction (NWP) outputs and sea surface temperature (SST)-related variables. The model demonstrated superior performance compared to traditional NWP-based methods, achieving high performance in both quantitative—probability of detection of 81.6%, false alarm ratio of 24.4%, f1 score of 75%, and proportion correct of 79.8%—and qualitative evaluations for 1 to 6 h lead times. Key contributing variables included relative humidity, accumulated shortwave radiation, and atmospheric pressure, indicating the importance of integrating diverse data sources. The study emphasizes the potential of using satellite-derived data to improve ocean fog prediction, while also addressing the challenges of overfitting and the need for more comprehensive reference data. [ABSTRACT FROM AUTHOR]

Accurate prediction of tropical cyclone (TC) intensity is challenging due to the complex physical processes involved. Here, we introduce a new TC intensity prediction scheme for the western North Pacific (WNP) based on a time-dependent theory of TC intensification, termed the energetically based dynamical system (EBDS) model, together with the use of a long short-term memory (LSTM) neural network. In time-dependent theory, TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors, expressed as environmental dynamical efficiency. The LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using best-track TC data and global reanalysis data during 1982–2017. The transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System (GFS) of the National Centers for Environmental Prediction during 2017–21. The predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC intensity. The new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction data. The intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration (CMA) and those by other state-of-art statistical and dynamical forecast systems, except for the 72-h forecast. Particularly at the longer lead times of 96 h and 120 h, the new scheme has smaller forecast errors, with a more than 30% improvement over the official forecasts. [ABSTRACT FROM AUTHOR]

Flood prediction in hilly regions, characterized by rapid flow rates and high destructive potential, remains a significant challenge. This study addresses this problem by introducing a novel machine learning-based approach to enhance flood forecast accuracy and lead time in small watersheds within hilly terrain. The study area encompasses small watersheds of approximately 600 km2. The proposed method analyzes spatiotemporal characteristics in rainfall dynamics to identify historical rainfall–flood events that closely resemble current patterns, effectively "learning from the past to predict the present". The approach demonstrates notable precision, with an average error of 8.33% for peak flow prediction, 14.27% for total volume prediction, and a lead time error of just 1 h for peak occurrence. These results meet the stringent accuracy requirements for flood forecasting, offering a targeted and effective solution for flood forecasting in challenging hilly terrains. This innovative methodology deviates from conventional techniques by adopting a holistic view of rainfall trends, representing a significant advancement in addressing the complexities of flood prediction in these regions. [ABSTRACT FROM AUTHOR]

The Los Alamos Community Ice CodE (CICE) sea ice model is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where it is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed-layer ocean model in CICE. The initial conditions for the sea ice and mixed-layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and the Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model's initial conditions have ice that is too thick in the Beaufort Sea, resulting in excessive ice extent in the Arctic at 6-month lead forecasts and errors in ice volume at all lead times when compared to available observations. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and the ice thickness at the seasonal timescale. This indicates that CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness when initialized with a more realistic sea ice thickness. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, in addition to sea ice coverage, as well as oceanic and atmospheric conditions. [ABSTRACT FROM AUTHOR]

This paper summarizes the investigation of the conversion of an 11-piece steel fabrication to an austempered ductile iron (ADI) casting. It was a collaborative effort involving the component manufacturer, foundry, machinist, local university, and an ADI heat treater. A number of design concepts were developed, with the final, significantly stronger, ADI design being compared to the fabrication using FEA analysis. The resulting conversion, at similar weight and cost, eliminated the failures that were experienced in the fabricated components in addition to reducing the production lead-time. [ABSTRACT FROM AUTHOR]

The duration of extremes in time leads to a phenomenon known as clustering of high values, with a strong impact on risk assessment. The extremal index is a measure developed within Extreme Value Theory that quantifies the degree of clustering of high values. In this work we will consider the cycles estimator introduced in Ferreira and Ferreira (Ann Inst Henri Poincare Probab Stat 54(2):587–605, 2018). A reduced bias estimator based on the Jackknife methodology will be presented. The bootstrap technique will also be considered in the inference and will allow to obtain confidence intervals. The performance will be analyzed based on simulation. We found our proposal effective in reducing bias and it compares favorably with some well-known methods. An application of the methods to real data will also be presented. [ABSTRACT FROM AUTHOR]

IEEE 802.15.4z-compliant ultra-wideband (UWB) devices are becoming ever more popular in contemporary radio engineering systems. Such systems are capable of precisely measuring distances (with their accuracy expressed in centimeters), are immune to interference, offer low latency and transmit data in an energy-efficient manner. Widespread adoption of UWB technology has triggered significant demand for testing integrated circuits these systems rely on, prompting the development of new testing methods to meet the ever increasing requirements in terms of testing speed and reliability. The same applies to sensitivity tests, in the course of which up to 2000 different packets may be received. The process of generating and analyzing such a large number of packets is time consuming. Furthermore, if multiple devices need to be tested simultaneously, the duration of the test will be multiplied accordingly. In such a context, the article investigates the lead time required to generate 2000 UWB packets using conventional methods and proposes a novel approach to significantly reduce packet generation time and improve testing efficiency. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) have been increasingly popular in image categorization in recent years. Hyperparameter optimization is a critical stage in enhancing the effectiveness of CNNs and achieving better results. Properly tuning hyperparameters allows the model to exhibit improved performance and facilitates faster learning. Misconfigured hyperparameters can prolong the training time or lead to the model not learning at all. Manually tuning hyperparameters is a time-consuming and challenging process. Automatically adjusting hyperparameters helps save time and resources. This study aims to propose an approach that shows higher classification performance than unoptimized convolutional neural network models, even at low epoch values, by automatically optimizing the hyperparameters of AlexNet and DarkNet19 with equilibrium optimization, the newest metaheuristic algorithm. In this respect, the proposed approach optimizes the number and size of filters in the first five convolutional layers in AlexNet and DarkNet19 using an equilibrium optimization algorithm. To evaluate the efficacy of the suggested method, experimental analyses were conducted on the pneumonia and COVID-19 datasets. An important advantage of this approach is its ability to accurately classify medical images. The testing process suggests that utilizing the proposed approach to optimize hyperparameters for AlexNet and DarkNet19 led to a 7% and 4.07% improvement, respectively, in image classification accuracy compared to non-optimized versions of the same networks. Furthermore, the approach displayed superior classification performance even in a few epochs compared to AlexNet, ShuffleNet, DarkNet19, GoogleNet, MobileNet-V2, VGG-16, VGG-19, ResNet18, and Inceptionv3. As a result, automatic tuning of the hyperparameters of AlexNet and DarkNet-19 with EO enabled the performance of these two models to increase significantly. [ABSTRACT FROM AUTHOR]

Digital technologies offer a quick and convenient method of communicating across distance, but there are unique benefits to engaging in forms of delayed communication. Long-distance letter writing in the early nineteenth century was often a slow business, and one that Romantic poet, John Keats, capitalised on. In particular, the letter provided Keats with a means of managing the reality of his circumstances when he was faced with inexorable loss and tragedy. In the period of time leading up to the death of his brother, Tom, in 1818, Keats exploited the specific cultures and forms of letter writing to generate a unique consolation for himself and his family members; as he confronted the reality of his own death two years later, the letter played a similarly crucial role in managing his feelings of loss and grief. Letter writing, during these difficult periods, presented Keats with a unique dichotomy: the letter kept him grounded in the cruel reality of his situation while often functioning as a singularly effective means of managing it. How the specific cultures of letter writing and disease intersect, and how Keats's imaginative epistolary narratives coalesce with the increasingly urgent facts of consumption and death, will be examined here. [ABSTRACT FROM AUTHOR]

This document is a compilation of summaries of various research articles in the field of operations research. The articles cover a range of topics including efficient learning algorithms for inventory systems, managing shipping emission control areas, a Lyapunov theory for stochastic approximation, comparing sequential forecasters, static pricing schemes, treatment planning for mass casualty incidents, rational transplant program response to regulations, trade-offs in dynamic allocation problems, augmenting the multinomial logit model, redesigning national organ allocation policies, the robust screening problem, and the impact of residential solar power adoption on electricity prices. [Extracted from the article]

Efficient Learning Algorithms for the Best Capped Base-Stock Policy in Lost Sales Inventory Systems Periodic review, lost sales inventory systems with lead times are notoriously challenging to optimize. Recently, the capped base-stock policy, which places orders to bring the inventory position up to the order-up-to level subject to the order cap, has demonstrated exceptional performance. In the paper "UCB-Type Learning Algorithms with Kaplan–Meier Estimator for Lost Sales Inventory Models with Lead Times," Lyu, Zhang, and Xin propose an upper confidence bound–type learning framework. This framework, which incorporates simulations with the Kaplan–Meier estimator, works with censored demand observations. It can be applied to determine the optimal capped base-stock policy with a tight regret with respect to the planning horizon and the optimal base-stock policy with a regret that matches the best existing result. Both theoretical analysis and extensive numerical experiments demonstrate the effectiveness of the proposed learning framework. In this paper, we consider a classic periodic-review lost-sales inventory system with lead times, which is notoriously challenging to optimize with a wide range of real-world applications. We consider a joint learning and optimization problem in which the decision maker does not know the demand distribution a priori and can only use past sales information (i.e., censored demand). Departing from existing learning algorithms on this learning problem that require the convexity property of the underlying system, we develop an upper confidence bound (UCB)-type learning framework that incorporates simulations with the Kaplan–Meier estimator and demonstrate its applicability to learning not only the optimal capped base-stock policy in which convexity no longer holds, but also the optimal base-stock policy with a regret that matches the best existing result. Compared with a classic multi-armed bandit problem, our problem has unique challenges because of the nature of the inventory system, because (1) each action has long-term impacts on future costs, and (2) the system state space is exponentially large in the lead time. As such, our learning algorithms are not naive adoptions of the classic UCB algorithm; in fact, the design of the simulation steps with the Kaplan–Meier estimator and averaging steps is novel in our algorithms, and the confidence width in the UCB index is also different from the classic one. We prove the regrets of our learning algorithms are tight up to a logarithmic term in the planning horizon T. Our extensive numerical experiments suggest the proposed algorithms (almost) dominate existing learning algorithms. We also demonstrate how to select which learning algorithm to use with limited demand data. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.0273. [ABSTRACT FROM AUTHOR]

In this present study, a single-manufacturer and single-retailer supply chain management model are formulated for a single product. This study specifically looks at a supply chain with variable production rate, stochastic lead time demand, and price- and advertisement-dependent demand. By incorporating these complex aspects into a model, which enables to examine their combined effects on supply chain performance, this study adds to the body of knowledge. The study reveals unique insights into the complex interplay between pricing tactics, advertising efforts, production dynamics, and the variability brought on by stochastic lead times through meticulous study and modelling. Finally, the total system profit is calculated and optimized with all the decision variables. A classical approach is performed to obtain the optimized solution of the joint profit function along with the decision variables. Two models are discussed in the study: (1) the model with normally distributed lead time demand and (2) the model with distribution-free lead time demand. The joint profit of the supply chain is found to be lesser by 1% for the normally distributed lead time demand than the distribution free pattern. The comparison of the shipment policies and the safety factors for the different distribution patterns of the lead time demand are shown. Though the huge increment in the safety factor for unknown leadtime demand distribution may help in reducing the uncertainity factor and disruptions in the supply chain, but also it may unnecessarily tie up more capital which can be invested in other sectors of the supply chain. [ABSTRACT FROM AUTHOR]

One of the lean tools is value stream mapping (VSM), which is used to visually map and analyze the flow of materials, information and processes required to deliver a product or service to the customer. VSM is widely used to streamline processes, reduce lead time and enhance overall operational performance. While VSM is a powerful tool, some challenges are associated with its conventional application in the manufacturing industry. Conventional VSM typically represents a snapshot of the value stream at a specific point in time. This static representation might not capture modern manufacturing environments' dynamic and evolving nature. Hence, this research addresses the problem of static representation of conventional VSM by applying Fuzzy Triangular Distribution (TFN) in the manufacturing industry by introducing a more flexible and dynamic approach. A conveyor manufacturing company was selected as a case study based on the wide variety and low volume type of manufacturing process. TFN approach was used to analyze variabilities in process parameters to identify their mean, minimum, and maximum values and remove all the outliers. Integrating TFN with VSM gives dynamic behavior to the conventional VSM. Based on the identifications, appropriate lean improvement tools were applied to develop an optimized future VSM. As a result, the future state map shows a 71.74% and 19.45% improvement ratio in terms of production lead time and value-added time, respectively, compared to the current VSM. This study can be further extended by investigating how the reduction of lead time and WIP inventory can help reduce costs for a company. [ABSTRACT FROM AUTHOR]

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest. Plain Language Summary: As low earth orbit has become crowded with new satellites and debris, operators have been forced to maneuver satellites to avoid collisions on a regular basis. The drag force on a satellite, which can significantly affect the orbital path, varies depending on solar and geomagnetic activity. Unfortunately, solar activity and resulting impacts at Earth are difficult to predict even just a few days in advance. This paper traces space weather forecasting ability directly to impacts on satellite maneuver decision‐making and finds that forecast uncertainty plays a critical role in making durable maneuver decisions. Key Points: Poor forecasts of space weather drivers inhibit our ability to perform actionable satellite conjunction assessment with advance noticeClimatology‐based probabilistic forecasts are developed for F10.7 and Kp, which are used to assess satellite conjunction scenariosProviding probabilistic forecasts of the space weather drivers makes a meaningful difference in maneuver decision‐making [ABSTRACT FROM AUTHOR]

Accepted by: Konstantinos Nikolopoulos This research studies the pricing strategy of a supply chain with a manufacturer and a retailer. We analyze the customers' purchase behaviour in advance selling with deposit by using utility theory and obtain the pricing decision of the supply chain by using the Stackelberg model. We find that when consumers are not sensitive to the distribution lead time, the retailer's advance selling strategy could Pareto improve the whole supply chain. Better informed consumers and moderate advance selling distribution lead time information could expand the retailer's pre-sale possibilities by enlarging the feasible range of advance selling. [ABSTRACT FROM AUTHOR]

Methylation markers have shown potential for triaging high‐risk HPV‐positive (hrHPV+) women to identify those at increased risk of invasive cervical cancer (ICC). Our aim was to assess the performance of the S5 DNA methylation classifier for predicting incident high‐grade cervical intraepithelial neoplasia (CIN) and ICC among hrHPV+ women in the ARTISTIC screening trial cohort. The S5 classifier, comprising target regions of tumour suppressor gene EPB41L3 and L1 and L2 regions of HPV16, HPV18, HPV31, and HPV33, was assayed by pyrosequencing in archived hrHPV+ liquid‐based samples from 343 women with high‐grade disease (139 CIN2, 186 CIN3, and 18 ICC) compared to 800 hrHPV+ controls. S5 DNA methylation correlated directly with increasing severity of disease and inversely with lead time to diagnosis. S5 could discriminate between hrHPV+ women who developed CIN3 or ICC and hrHPV+ controls (p <.0001) using samples taken on average 5 years before diagnosis. This relationship was independent of cytology at baseline. The S5 test showed much higher sensitivity than HPV16/18 genotyping for identifying prevalent CIN3 (93% vs. 61%, p =.01) but lower specificity (50% vs. 66%, p <.0001). The S5 classifier identified most women at high risk of developing precancer and missed very few prevalent advanced lesions thus appearing to be an objective test for triage of hrHPV+ women. The combination of methylation of host and HPV genes enables S5 to combine the predictive power of methylation with HPV genotyping to identify hrHPV‐positive women who are at highest risk of developing CIN3 and ICC in the future. [ABSTRACT FROM AUTHOR]

Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22. [ABSTRACT FROM AUTHOR]

Value Stream Mapping (VSM) is a key tool in Lean Manufacturing (LM) that helps identify opportunities for process improvement. This research aims to propose an integrated method using LM tools (such as Kanban, VSM, Pareto chart, and Supplier Input Process Output Customer) and Arena simulation to improve the productivity of the production line through reducing lead time, inventory time, including Raw Material (RM) time and Work In Progress (WIP) time and enhancing Process Cycle Efficiency (PCE) ratio. To begin the process, a Pareto chart is employed to identify the main product of the organization. Subsequently, a current VSM is constructed to identify any waste in the production line. The Kanban tool is then used to propose appropriate remedies to improve the manufacturing process. To validate the efficiency of the production capacity in future VSM, Arena simulation is executed by determining Takt Time. Lastly, a future VSM is formulated based on the suggested improvements, and an appraisal is conducted to provide recommendations for prospective enhancements. A real case study of a furniture company in Vietnam is performed to demonstrate the effectiveness of the proposed method. The results reveal impressive benefits, including a 92% reduction in lead time and a corresponding 92% increase in PCE. Additionally, there is a substantial reduction of around 90% in WIP time, while RM time is completely eliminated, resulting in a 100% reduction. These findings highlight the efficacy of the proposed approach and the importance of VSM and other Lean tools in achieving process optimization. [ABSTRACT FROM AUTHOR]

The high cost and low emission frequency of microparticle launchers have resulted in a long lead time for the development of detectors for micro-debris in space. In this paper, two low-cost, high-emission-frequency, small-size, millimeter-sized particle launchers are designed using the principles of gas expansion and surge propulsion by a high-speed air stream. Electrostatic detection is utilized to determine the emission velocity of the microbeads and their deviation from a specific position on the flight trajectory. The emission rate and accuracy of both methods were experimentally evaluated, along with the deviation of the detection system. Both devices emitted microbeads to simulate micro-debris, providing experimental data for the development of a space debris detector and establishing research conditions for studying the impact of micro-debris. [ABSTRACT FROM AUTHOR]

The paper discusses the inventory management problem with a single product stored in two warehouses, where each has its unique suppliers with certain lead times. Moreover, one of the warehouses may act as a backup supplier for the other. In other words, product exchange between two different warehouses within one company is allowed. The first warehouse operates under an a priori known time-variant contractual demand and a bounded random one. Its secondary goal is to accumulate emergency stock that can be delivered to the second warehouse within one time period. For this warehouse we use a desired trajectory generator to shape the required stock level and then utilize a trajectory following control law. The demand in the second warehouse is unknown but bounded, and its suppliers have limited delivery capacity. The challenge is to fulfill the customers' needs, although they might exceed the order limit. Therefore, occasional backup supplies from the first warehouse are necessary. For the control of the second warehouse, a simple sliding mode (SM) scheme is applied. The paper proves that, with appropriate compensation of the emergency deliveries in the first warehouse, our proposed control scheme ensures full demand satisfaction in both warehouses despite the second one's control limit. [ABSTRACT FROM AUTHOR]

In the current study, the Generalized Structure of the Group Method Of Data Handling (GSGMDH) is developed to overcome the main drawbacks of the classical GDMH. The performance of the GSGMDH was checked in two case studies for multi-step flood forecasting at the upstream station (i.e., Saint-Charles station) using the historical records of upstream stations (i.e., Nelson and Croche stations). The results revealed high accuracy in flood forecasting one to six hours ahead for all sample ranges and peak flows, with indices showing R: [0.993, 0.9995], NSE: [0.986, 0.999], RMSE: [0.416, 1.453], NRMSE: [0.0239, 0.152], MAE: [0.146, 0.761], MARE: [0.023, 0.156], and BIAS: [-0.058, 0.01]. Indeed, the descriptive performance of the developed model rates as Very Good for both R and NSE, and Good for NRMSE. The uncertainty analysis of the GSGMDH models demonstrates remarkable precision in flood forecasting, with relative differences between the minimum and maximum uncertainty ranges of less than 1% for both Nelson and Croche upstream stations. Specifically, U95 for Nelson is [0.148, 0.149], and for Croche, it is [0.166, 0.167]. Besides, The reliability analysis of the GSGMDH highlights its effective peak flow forecasting capabilities, with MARE values for various flow discharges remaining below 10% across different lead times, demonstrating the model's precision in predicting high-impact flood events. Moreover, a comparison between the developed GSGMDH and the traditional model reveals that the former surpasses the latter, achieving a maximum relative error of less than 7%, in contrast to the traditional GMDH's minimum MARE exceeding 12%. [ABSTRACT FROM AUTHOR]

Today data is increasingly available in our engineering domains but without an ontology to structure it, one might get drowned in the data lake. Configuration Management data being central to maintaining the configuration of the aircraft throughout its life cycle, need to be well described, understood and interpreted appropriately. This paper presents the effort performed at Airbus in the configuration management domain to structure the data through abstract ontology models of processes, tools and workflows. The ontology objects are then implemented and exposed as a data product to be used in digital transformation initiatives. This ontology based approach has encouraged harmonization of digital initiatives across different aircraft programs, and the associated graph style implementation ensures a more efficient exploration of Configuration Management data, leading to shorter lead time for development of data driven initiatives and knowledge ramp up. [ABSTRACT FROM AUTHOR]

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This work includes the finite element modeling of solidification process within the container with curved top wall. The container has three insulated wall and one cold wall which is situated on top wall and it has sinusoidal configuration. Nanoparticles with low fraction were loaded inside the water to improve the thermal features. In deriving the mathematical model, two main assumptions were incorporated: (1) neglecting the impact of velocity; (2) considering homogeneous mixture for NEPCM. To improve the accuracy of numerical approach, the variable style of grid with time has been employed and also, implicit method was considered for time-dependent two terms. Increasing fraction of nano-powders has its maximum effect when the second level of dp has been selected in which required time reduces around 41.33%. Also, in existence of maximum size of particles, changing ϕ causes reduction of time around 12.41%. With altering dp from 30 to 40 nm, the freezing period reduces around 19.99% while augmenting up to 50 nm can lead to reduction of time around 49.29%. [ABSTRACT FROM AUTHOR]

In order to handle second order lead processes with time delay, this paper provides a unique dominant pole placement based filtered PID controller design approach. This method does not require any finite term approximation like Pade to obtain the quasi-polynomial characteristic polynomial, arising due to the presence of the time delay term. The continuous time second order plus time delay systems with zero (SOPTDZ) are discretized using a pole-zero matching method with specified sampling time, where the transcendental exponential delay terms are converted into a finite number of poles. The pole-zero matching discretization approach with a predetermined sampling period is also used to discretize the continuous time filtered PID controller. As a result, it is not necessary to use any approximate discretization technique, such as Euler or Tustin, to derive the corresponding discrete time PID controller from its continuous time counterpart. The analytical expressions for discrete time dominant pole placement based filtered PID controllers are obtained using the coefficient matching approach, while two distinct kinds of non-dominant poles, namely all real and all complex conjugate, have been taken into consideration. The stabilizable region in the controller and design parameter space for the chosen class of linear second order time delay systems with lead is numerically approximated using the particle swarm optimization (PSO) based random search technique. The efficacy of the proposed method has been validated on a class of SOPTDZ systems including stable, integrating, unstable processes with minimum as well as non-minimum phase zeros. [ABSTRACT FROM AUTHOR]

The present study introduces "WindAware", a wind and turbulence prediction system that provides nowcasts of wind and turbulence parameters every 5 min up to 6 h over a predetermined airway over Chicago, Illinois, USA, based on 100 m high-resolution simulations (HRSs). This system is a long short-term memory-based recurrent neural network (LSTM-RNN) that uses existing ground-based wind data to provide nowcasts (forecasts up to 6 h every 5 min) of wind speed, wind direction, wind gust, and eddy dissipation rate to support the Uncrewed Aircraft Systems (UASs) safe integration into the National Airspace System (NAS). These HRSs are validated using both ground-based measurements over airports and upper-air radiosonde observations and their skill is illustrated during lake-breeze events. A reasonable agreement is found between measured and simulated winds especially when the boundary layer is convective, but the timing and inland penetration of lake-breeze events are overall slightly misrepresented. The WindAware model is compared with the classic multilayer perceptron (MLP) and the eXtreme Gradient Boosting (XGBoost) models. It is demonstrated by comparison to high-resolution simulations that WindAware provides more accurate predictions than the MLP over the 6 h lead times and has almost similar performance as the XGBoost model although the XGBoost's training is the fastest using its parallelized implementation. WindAware also has higher prediction errors when validated against lake-breeze events data due to their under-representation in the training dataset. [ABSTRACT FROM AUTHOR]

Sub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region. Plain Language Summary: Timely and accurate forecasts of weather events before their occurrence is important for decision‐makers in effective planning and actions to cope with weather‐induced disasters. Such forecast plays a crucial role in disaster preparedness over complex terrain regions like Nepal. Since sub‐seasonal to seasonal (S2S) scale prediction can provide forecasts beyond 10 days up to 30 days, is getting popular nowadays. However, their skill needs to be evaluated before any operational use which requires reliable observation data. Since, Nepal lacks the proper ground observing stations: mainly over the high mountains, gridded precipitation datasets produced using multiple sources (like satellites, and numerical models) have been good alternatives. Thus, we evaluated 11 such gridded datasets against the existing ground observing stations. We found that these datasets struggle to estimate extreme precipitation. Additionally, they also fail to capture precipitation characteristics over the complex terrain of the high mountain region compared to flat regions. Overall, gauge interpolated and merged datasets demonstrated better performance in estimating precipitation characteristics over complex terrain; hence, can serve as a benchmark dataset for S2S forecast evaluation in this region. Key Points: Multi‐source precipitation estimates captured the annual to weekly precipitation cycle, but only roughly captured the spatial patternMulti‐source precipitation estimates performed poorly in estimating extreme precipitation and struggled over complex terrainAmong high‐resolution datasets, TPMFD has the potential to be used as a reference dataset for sub‐seasonal to seasonal model evaluation [ABSTRACT FROM AUTHOR]

The shift from traditional on-site to off-site construction marks a significant evolution in the construction industry, characterized by increasing levels of prefabrication. These advancements enhance construction efficiency, reduce lead times, and mitigate environmental impacts, leading to modular integrated construction (MiC). However, MiC presents complex supply chain challenges, particularly in the transportation of prefabricated components and fully integrated modules. This study addresses these challenges by employing a multi-agent simulation using AnyLogic to optimize MiC transport logistics. The simulation models the interactions of various agents involved in the MiC process to improve operational efficiency and reduce costs. Results demonstrate that using three vehicles per supplier minimizes total transport costs, effectively balancing fixed and variable expenses while eliminating penalties for project delays. The findings highlight the cost efficiency of MiC, showing potential savings due to centralized assembly and optimized logistics. These significantly reduce material transportation and related costs, contributing to the overall efficiency and sustainability of construction projects. These insights underscore the value of multi-agent simulation in addressing the complexities of MiC supply chains. [ABSTRACT FROM AUTHOR]

Scalable methods for improving the performance and stability of a field-effect transistor (FET) based on two-dimensional materials are crucial for its real applications. A scalable method of encapsulating the exfoliated MoS2 on the SiO2/Si substrate by hexamethyldisilazane (HMDS) is explored here for reducing the influence of interface traps and ambient contaminants. This leads to 25 times reduction in trap density, three times decrease in subthreshold swing, three times increase in the peak field-effect mobility, and a drastic reduction in hysteresis. This performance remains nearly the same after several weeks of ambient exposure of the device. This is attributed to the superhydrophobic nature of HMDS and the SiO2 surface hydrophobization by the formation of covalent bonds between the methyl groups of HMDS and silanol groups of SiO2. [ABSTRACT FROM AUTHOR]

Summary: The sorting efficiency of the warehouse management and handling robots (automated guided vehicles [AGVs]) in an unmanned warehouse affects the delivery and lead time of orders. Studying the collaborative sorting of multiple AGVs can significantly improve work efficiency. The goal of this paper is to minimize the overall shortest transportation path and time while ensuring the completion of all orders and corresponding recycling or return tasks. This paper conducts experiments on global path planning for multiple AGVs based on the A* algorithm, both without considering collision and deadlock situations, and considering collision and deadlock situations. The results show that without considering collision and deadlock situations, the method of matching fixed storage locations and picking stations can improve overall efficiency, while considering collision and deadlock problems will result in reduced efficiency. In the future, the model will be further optimized taking into account actual conditions to improve overall efficiency. [ABSTRACT FROM AUTHOR]

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game changer for the incremental progress in traditional numerical weather prediction (NWP) known as the "quiet revolution" of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made by increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allows forecasts that require much lower computational costs and that are highly competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training. SIGNIFICANCE STATEMENT: We compare the traditional approach of generating weather forecasts with a new approach based on machine learning (ML). The traditional approach is based on resolving physical equations with numerical methods and can be quite expensive to run on supercomputers. ML models are trained on a very large dataset that combines observations and physically based models to derive our best estimate of the state of the atmosphere hour after hour, over the past decades. In this work, we assess the forecast performance with statistical tools and a focus on extreme events. Our results suggest that ML models, which are much less expensive to run than standard methods, could have a promising future in numerical weather prediction. Our study also points out weaknesses and limitations that would require further investigations and future ML model improvements. [ABSTRACT FROM AUTHOR]

Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a "perfect model," of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific jet. These findings of the source of skillful subseasonal forecasts over the U.S. West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the U.S. West Coast seeking to make informed decisions based on extended weather prediction. Significance Statement: The purpose of this study is to understand the differences between the ability to predict high amounts of the transport of water vapor and precipitation over the North Pacific 3 and 4 weeks into the future. The results indicate that differences do exist in a region that is relevant to precipitation on the U.S. West Coast. To physically explain why differences in predictability exist, the relationship between weekly extremes of the extension of the jet stream, IVT, and precipitation over the North Pacific is explored. These findings may impact decisions relevant to water management on the U.S. West Coast susceptible to drought and flooding extremes. [ABSTRACT FROM AUTHOR]

This paper presents a case study on condition monitoring of power generators at offshore wind turbines. Two fault detection models are proposed for detecting sudden changes in the sensed value of metallic debris at the generator. The first model uses an exponentially weighted moving average, while the second monitors first-order derivatives using a fixed threshold. This is expected to improve the maintenance activities by avoiding late or early part replacement. The economic impact of the proposed approach is also provided with a realistic depiction of the cost structure associated with the corresponding maintenance plan. While the specifics of the case study are supported by real-life data, considering the prevalence of the use of generators not only in offshore wind turbines but also in other production environments, we believe the case study covered in this paper can be used as a blueprint for similar studies in other applications. • Large scale condition monitoring demonstrates economic benefits. • Case study from real wind turbine population with economic impact assessment. • Sensor for monitoring fault mode results in lead time for maintenance planning. • Sensitivity analysis assesses results despite downtime and power price uncertainty. [ABSTRACT FROM AUTHOR]

Frying Gulabjamun in Ghee is key to its distinct taste and texture, but reheating frequency and conditions can affect the sweet's colour and consistency. It is important to understand this dynamic to ensure the desired quality of the final product. Based on the facts as mentioned above, the present study was conducted to analyse the change in colour and viscosity occurring in the Ghee during repetitive frying to cook Gulabjamuns. The change in colour attributes and viscosity of Ghee was monitored at various hours of frying. The results revealed that the Ghee became darker, the green hues became more prominent, and the yellow hues became more intense the longer it was fried. Besides, the most significant color change occurred during the 60-hour frying duration, as indicated by a decrease in lightness (L* value from 43.07 to 41.1) and a noticeable shift towards greener hues (a* value of -7.22334), suggesting that extended frying times lead to the darker and greener side. It attained its maximum viscosity after 12 hours of frying, with a shear stress of 19.10 Pa and viscosity of 0.66 Pa-s, which was correlated with the formation of a scum and the disappearance of steam bubbles. The physicochemical changes taking place in Ghee during the frying of Gulabjamun after repeated heating provide understanding to sweet makers for the selection of the appropriate frying medium and frying techniques, which ultimately assist in the improvement of the overall sensory attributes of the Gulabjamun. [ABSTRACT FROM AUTHOR]

In this comprehensive study, we have delved into the intricate realm of lightning forecasting, a captivating yet challenging pursuit due to the elusive nature of storm electrification. Our focus rested on evaluating the skill of lightning forecasts of NCMRWF Regional Ensemble Prediction System (NEPS-R), based on the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Based on the lightning hotspots in the observed seasonal mean lightning flash count, study regions were identified over East-Northeast India (ENEI) and Peninsular India (PI) during pre-monsoon season and Central and East-Northeast India (CENEI) for monsoon season during the year 2022. Probabilistic and deterministic skill of NEPS-R in lightning prediction using rank histogram, Relative Operating Characteristic (ROC), Brier Skill Score (BSS), Continuous Ranked Probability Skill Score (CRPSS), reliability diagrams, Probability of Detection (POD), False Alarm ratio (FAR), Equitable Threat Score (ETS), and Fractions Skill Score (FSS) were investigated in the present study. Rank histograms brought out the negative bias, which increases with increasing lead time over all the study regions. While ROC and BSS indicate that forecast is skillful on day-1 over ENEI for all the thresholds (> 1, > 5 and > 10) and over PI on day-2 and day-3, CRPSS has a contrasting trend with most skillful forecast over CENEI region. Reliability diagrams indicate under-forecasting for lower probabilities and over-forecasting for higher probabilities for all the thresholds for all the study areas. The overconfidence in the forecast could be at least partially attributed to the sampling error caused by a small ensemble size of NEPS-R, which also displayed similar trend of skill as BSS. Higher POD values in the CENEI region have surpassed those in ENEI and PI. Lower FAR and higher ETS are found over PI region compared to ENEI and CENEI. Furthermore, FSS (threshold > 1) indicates that CENEI region attains better skill (> 0.5) at a neighborhood size of 36 km in day-1 forecast compared to ENEI (68 km) and PI (84 km). The skill scores do emphasize the model's ability in predicting the dominant large scale features of monsoon. This is reflected in the better skill of CENEI region during monsoon as compared to ENEI and PI regions during the pre-monsoon season, where the model is falling short in predicting the prominent local scale features. Also, the model's inadequacy in proper representation of microphysics and kinematics does affect the lightning prediction capability of the model which could be addressed with improved model initial conditions and parameterization schemes and finer horizontal resolution. [ABSTRACT FROM AUTHOR]

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar, making it capable of enhancing tornado monitoring and early warning capabilities. This study analyzed the characteristics and nowcasting signals of a tornado case that occurred on June 16, 2022 in the Guangzhou region. Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation. The X-band phased array radar, with its high temporal and spatial resolution, provided an opportunity to capture early warning signals from polarimetric characteristics. The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature (TVS) features in a 10-minute lead time, surpassing the capabilities of the CINRAD/SA radar. Additionally, due to its higher scanning frequency, the Xband phased-array radar was capable of consistently identifying TVS with shorter intervals, enabling a more precise tracking of the tornado's path. The application of professional radars, in this case, provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals. [ABSTRACT FROM AUTHOR]

The Garhwal region, situated in the central segment of the Himalayas, stands as one of the most seismically active areas in the Uttarakhand Himalaya. The newly established earthquake early warning (EEW) system in the Garhwal region emphasizes the critical need for magnitude scaling relations to enable real-time estimation of earthquake size. This study employs P-wave onset data from both observed and simulated records to develop magnitude scaling relations specific to the Garhwal region. Initially, we modified the semi-empirical technique (SET) for P-wave simulation and validated this modification using the 1991 Uttarkashi (Mw6.8) and 1999 Chamoli (Mw6.4) earthquakes in the Garhwal region. The modified semi-empirical technique (MSET) demonstrates its reliability, as evidenced by the comparatively low root mean square error (RMSE) between observed and simulated records for both earthquakes. Subsequently, we apply MSET to simulate seven additional future earthquakes (Mw7.0–8.5) at the hypocentral locations of the 1991 Uttarkashi and 1999 Chamoli earthquakes. Moreover, conventional EEW parameters, such as average period (τc) and peak amplitude displacement (Pd), are extracted from 107 observed and 63 simulated P-wave onsets, utilizing a 3 s time window. Using this combined dataset of observed and simulated data, we introduce magnitude scaling relations based on the τc and Pd parameters. Subsequently, the developed scaling relations are tested to predict earthquake magnitude using the average values of estimated EEW parameters. The relatively small errors, measuring at 4.47–0.75%, and 2.45–0.60% for τc–Mw and 5.71–1.60% and 4.90–1.57% for Pd–Mw scaling relations, respectively for the Uttarkashi and Chamoli earthquakes suggest the applicability of these proposed relations. However, the scaling relations developed tend to underestimate the magnitude of earthquakes larger than 7.5, potentially attributed to the saturation observed in these relations for big earthquakes. Additionally, the calculated lead time for future earthquakes will range from 4 to 60 s for the sites situated within epicentral distances of 56–200 km. This lead time has the potential to play a substantial role in disaster mitigation in the Garhwal region, especially in the Indo-Gangetic plains and Himalayan foothills. Hence, the development of scaling relations specific to the region is imperative for effective disaster mitigation and risk reduction in the Garhwal region. [ABSTRACT FROM AUTHOR]