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Enterprises today face an increasing need for seamless data exchange across various information systems, both internally and with their partners. Addressing challenges in information system and data interoperability is essential. Unfortunately, this issue is often underrecognized by many stakeholders, leading to time wasted on non-value-added tasks and a significant decline in data quality. Our contribution comprises two essential components. Firstly, we introduce and implement extensions to BPMN 2.0 to visually represent data exchanges that encounter interoperability issues as well as those successfully resolved. These extensions also provide performance metrics such as cost, duration, quality, and data availability for tasks affected by these exchanges. By doing so, they gauge the extent of the interoperability challenge and underscore the need to address it for all stakeholders within the enterprise. Secondly, we propose a method derived from FMECA, enabling users to meticulously examine each exchanged piece of data and compute its criticality. This approach empowers the prioritization of corrective actions to enhance data quality, establishing a continuous improvement process that ensures optimal data quality over time. [ABSTRACT FROM AUTHOR]

The instability (in processing time) in the flow-drill screwing process is undesired but inescapable due to variations in material property, gauge, and process parameters. A substantial number of materials and lab labor need to be used to test and control the variability of the real manufacturing joining process. To enhance the stability and efficiency of the screwing process, this study seeks multi-disciplinary collaboration by applying linear-regression modeling. Six hundred and forty-eight data points were collected and split into an 80% training set for model building and a 20% test set for model validation. A multiple linear-regression model was built. The results indicated that, compared to variable base level (6000 rpm rotational speed and 1100 N downforce), higher rotational speed (8000 rpm, 7000 rpm), greater downforce (1200 N, 1300 N), and their interaction were significantly associated with passage (processing) time, while the switch point did not significantly affect passage time. The interaction plot and effect size were adopted to provide measurements of the effect magnitude on processing time. The coefficient of determination indicated that 86% of the variability in the passage time can be explained by this model. Statistical analysis, such as data visualization, statistical modeling, and other data-driven analysis methods, can be used to detect underlying relationships between variables, investigate variations, and make predictions in the manufacturing process. The outcomes from the data-driven analysis can benefit from improving the economical manufacturing system, refining the processing setting, and reducing test material costs, labor, and lead time. [ABSTRACT FROM AUTHOR]

Motivated by a long-standing gap between inventory theory and practice, we study online inventory models with unknown cyclic demand distributions. We design provably efficient reinforcement learning (RL) algorithms that leverage the structure of inventory problems to achieve optimal theoretical guarantees that surpass existing results. We apply the standard performance measure in online learning literature, regret, which is defined as the difference between the total expected cost of our policy and the total expected cost of the clairvoyant optimal policy that has full knowledge of the demand distributions a priori. This paper analyzes, in the presence of unknown cyclic demands, both the lost-sales model with zero lead time and the multiproduct backlogging model with positive lead times, fixed joint-ordering costs and order limits. For both models, we first introduce episodic models where inventory is discarded at the end of every cycle, and then build upon these results to analyze the nondiscarding models. Our RL policies HQL and FQL achieve O˜(T) regret for the episodic lost-sales model and the episodic multiproduct backlogging model, matching the regret lower bound that we prove in this paper. For the nondiscarding models, we construct a bandit learning algorithm on top that governs multiple copies of the previous RL algorithms, named Meta-HQL. Meta-HQL achieves O˜(T) regret for the nondiscarding lost-sales model with zero lead time, again matching the regret lower bound. For the nondiscarding multiproduct backlogging model, our policy Mimic-QL achieves O˜(T5/6) regret. Our policies remove the regret dependence on the cardinality of the state-action space for inventory problems, which is an improvement over existing RL algorithms. We conducted experiments with a real sales data set from Rossmann, one of the largest drugstore chains in Europe, and also with a synthetic data set. For both sets of experiments, our policy converges rapidly to the optimal policy and dramatically outperforms the best policy that models demand as independent and identically distributed instead of cyclic. This paper was accepted by J. George Shanthikumar, data science. Funding: X.-Y. Gong was partially supported by an Accenture Fellowship. The work of X.-Y. Gong and D. Simchi-Levi was partially supported by the MIT Data Science Lab. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2023.4947. [ABSTRACT FROM AUTHOR]

Efficient order allocation and inventory management are essential for the success of supply chain operations in today's dynamic and competitive business environment. This research introduces an innovative decision-making model incorporating dependability factors into redesigning and optimizing order allocation and inventory management systems. The proposed model aims to enhance the overall reliability of supply chain operations by integrating stochastic factors such as demand fluctuations, lead time uncertainty, and variable supplier performance. The system, named Dynamic Reliability-Driven Order Allocation and Inventory Management (DROAIM), combines stochastic models, reliability-based supplier evaluation, dynamic algorithms, and real-time analytics to create a robust and flexible framework for supply chain operations. It evaluates the dependability of suppliers, transportation networks, and internal procedures, offering a comprehensive approach to managing supply chain operations. A case study and simulations were conducted to assess the efficacy of the proposed approach. The findings demonstrate significant improvements in the overall reliability of supply chain operations, reduced stockout occurrences, and optimized inventory levels. Additionally, the model shows adaptability to various industry-specific challenges, making it a versatile tool for practitioners aiming to enhance their supply chain resilience. Ultimately, this research contributes to existing knowledge by providing a thorough decision-making framework incorporating dependability factors into order allocation and inventory management processes. Practitioners and experts can implement this framework to address uncertainties in their operations. [ABSTRACT FROM AUTHOR]

The push for more battery-powered electric vehicles, the COVID-19 pandemic, and other global supply chain destabilizing events (e.g., the war in Ukraine) have made it necessary for manufacturers to consider innovative approaches to sourcing and materials management. This study assesses the value of sharing delivery due date updates, which are then used to convert spent time into a fraction of the expected total time for order fulfillment (i.e., progress updates), to synchronize the delivery of components for an automotive manufacturer who, amid long and variable lead times for semiconductor chips, maintains a strategy of waiting for the delivery of all needed parts before commencing production. Having progress updates in a fractional form puts numerical bounds (i.e., 0% to 100%) on the shared information, and this facilitates closed-form analytical comparisons. We develop analytical models to show decreases in expected assembly-related costs based on the progress update information shared between two suppliers—one supplier has a shorter and more reliable order fulfillment lead time than the other. When there are more than two suppliers, we use a simulation model to estimate the expected value of sharing progress updates. Our results show that increasing the frequency of shared progress updates, and having more suppliers share progress updates, results in increased cost-savings percentages. However, the opposite occurs with increasing uncertainty in suppliers' delivery lead times. The computational study provides support for the existence of a critical mass level of suppliers needed to achieve a given percentage cost saving. This study can help automakers to make informed decisions on encouraging information sharing among suppliers, by first creating a critical mass of suppliers to share progress updates. Our investigations also help show the conditions in which to best leverage such information sharing strategies while waiting for the delivery of all needed components prior to production. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Cauchy problem of the hyperbolic Keller-Segel equations in H s (T d) on torus with d ≥ 1. Firstly, developing the dissipative mechanism through translation, we establish the global well-posedness in H s (T d) (s > 1 + d 2) with initial data near some equilibrium state. Secondly, by capturing the feature of the preservation of zero directional derivative, we give a class of initial date that lead to finite time blow-up. It's worth noting that our method of proving blow-up phenomenon does not require any conservation law. Finally, the characterization of this blow-up motivates us to show the ill-posedness of this system in H 3 2 (T d) in the sense of "norm inflation", which implies that our ill-posedness result for this system is sharp on one dimensional torus. [ABSTRACT FROM AUTHOR]

We show that a simple telescoping sum trick, together with the triangle inequality and a tensorisation property of expected-contractive coefficients of random channels, allow us to achieve general simultaneous decoupling for multiple users via local actions. Employing both old (Dupuis et al. in Commun Math Phys 328:251–284, 2014) and new methods (Dupuis in IEEE Trans Inf Theory 69:7784–7792, 2023), we obtain bounds on the expected deviation from ideal decoupling either in the one-shot setting in terms of smooth min-entropies, or the finite block length setting in terms of Rényi entropies. These bounds are essentially optimal without the need to address the simultaneous smoothing conjecture, which remains unresolved. This leads to one-shot, finite block length, and asymptotic achievability results for several tasks in quantum Shannon theory, including local randomness extraction of multiple parties, multi-party assisted entanglement concentration, multi-party quantum state merging, and quantum coding for the quantum multiple access channel. Because of the one-shot nature of our protocols, we obtain achievability results without the need for time-sharing, which at the same time leads to easy proofs of the asymptotic coding theorems. We show that our one-shot decoupling bounds furthermore yield achievable rates (so far only conjectured) for all four tasks in compound settings, which are additionally optimal for entanglement of assistance and state merging. [ABSTRACT FROM AUTHOR]

Real-time flood forecasting is crucial for early flood warnings. It relies on real-time hydrological and meteorological data. Satellite Precipitation Products offer real-time global precipitation estimates and have emerged as a suitable option for rainfall input in flood forecasting models. This study first compared the daily Satellite Precipitation Products of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) with observed rainfall data of India Meteorological Department from the year 2001 to 2009 using contingency tests. Rainfall data of IMERG are used to build four Real-time flood forecasting models based on machine learning: feedforward neural network (FFNN), extreme learning machine (ELM), wavelet-based feedforward neural network, and wavelet-based extreme learning machine. The models consider the IMERG gridded data at 1 h resolution as input to predict water level at Hayaghat gauging station of Bagmati River with lead times from 1 h to 10 days. These models have been trained and tested with the observed water level data. The model performance was also evaluated using various statistical criteria. Results showed good correlation between IMERG and observed data with a probability of detection of 85.42%. Overall, wavelet-based models outperformed their singular counterparts. Among the singular models, the FFNN model performed better than ELM with satisfactory predictions up to 5 days of lead time. For a 7 days lead time, only wavelet-based-FFNN performs well, whereas none of the models produced satisfactory results for 10 days lead time. [ABSTRACT FROM AUTHOR]

We explored the influence of temporal distances on order allocation between a low‐cost, less reliable supplier and a reliable, high‐cost supplier. We posited that a far temporal distance increases the preference for the unreliable, low‐cost supplier. We conducted a vignette‐based experiment where participants placed orders between the suppliers with different reliability and cost levels. Our experiment showed that a longer lead time led to larger orders for the low‐cost unreliable supplier. Our insights help explain how temporal distances inherent in supply chains can elicit differing evaluations of suppliers, altering ordering decisions. [ABSTRACT FROM AUTHOR]

Platform-based product development has been applied extensively in discrete manufacturing industry to accommodate changing market demands. Nevertheless, while process industry manufacturers face similar market demands, the topic is only sparsely covered in literature. Through a systematic review of the literature, this study uncovers the definitions used, drivers behind, approaches and methods applied, and industry examples of platform-based product development in the process industry. Based on these analyses, a research agenda is then proposed to further the knowledge of this topic. The study identified existing definitions of key platform-related terms used in several studies and furthermore discovered new definitions for some terms. The most prominent drivers behind pursuing platform-based product development was found to be cost reduction and productivity of product development, with development lead time reduction playing a less significant role. Literature related to platform-based product development focuses primarily on product design and development issues, with less attention given to market, manufacturing, and supply chain issues. Only few industrial cases were identified within the process industry while multiple anecdotal descriptions were discovered. For future research, further insight into key platform concepts, applicability of existing methods, broader value chain focus and detailed industrial cases are considered relevant. [ABSTRACT FROM AUTHOR]

Robotic manufacturing systems have proven to be an effective solution for modern manufacturing enterprises to deal with increasing in customer demands and market competition. However, these systems may be unable to completely satisfy user requirements because of the difference between user and design perspectives. Thus, designing robotic manufacturing systems requires iterative processes that significantly increase development costs and lead time. A user-customised design approach is needed that enables users to customise robotic manufacturing systems as well as alleviate the burden on designers of eliciting user requirements. However, most users may not be able to customise their systems because of a lack of engineering knowledge. The authors propose a knowledge-based engineering approach to aid users in customising the architectures of robotic manufacturing systems. Two models — an ontological knowledge model and a multi-attribute decision-making model — are defined and integrated in the proposed KBE architecture definition method. A rule-based reasoning process is proposed in the ontological knowledge model based on explicit semantic descriptions of users' unstructured or semi-structured requirements and the components of robotic manufacturing systems, which infers the possible architecture of the required system. The MADM model is adopted to evaluate the architecture alternatives to determine the optimal solution. [ABSTRACT FROM AUTHOR]

AbstractAll-pair shortest path routing within stochastic road networks is often more complicated and computationally challenging than routing in deterministic networks because uncertainties in travel time are introduced. We develop and test a GPU-enabled approach to resolve this computational challenge. This approach is based on a simulation framework that includes four steps: simulation of road networks impedances, computation of candidate paths using an all-pair shortest path algorithm, travel time estimation across candidate paths, and all-pair optimal path calculations. We developed GPU algorithms to accelerate the second and third steps, which are computationally demanding for large road networks. We used the road network of Wuhan, China as a case study. Our experiments indicate that the GPU approach leads to thousands of times in improvement of acceleration, which makes all-pair shortest path routing within stochastic road networks computationally feasible. [ABSTRACT FROM AUTHOR]

The innovative approach used for the design and fabrication of a UAV tails through additive manufacturing (AM) completely changes the concept of designing and manufacturing products through conventional methods. AM based processes can reduces costs, lead times, and increases design freedom, allowing parts to be tailored to specific needs. A key benefit of AM processes is the ability to produce optimized designs with reduced mass and without compromising structural effectiveness. This is possible thanks to the combination of high‐performance AM materials and the extraordinary manufacturing capability of AM technologies combined with an appropriate design‐for‐additive manufacturing (DfAM) approach, overcoming traditional manufacturing techniques. This article presents the development process of a UAV tails, based on a Design for Additive Manufacturing (DfAM) approach in order to reduce structural mass and guarantee safe operation under service loads. Therefore, by replacing the metallic component of the UAV with a techno‐polymeric one, several configurations have been developed. The optimal additive configuration achieved a weight reduction of 60% compared to a metal configuration of equivalent volume, without loss of mechanical properties. In conclusion, a feasibility study of the proposed configuration was carried out by 3D printing the vertical tail redesigned. Highlights: An innovative approach by combining DfAM processes with FFF technology.Metal replacement of UAV tails with advanced technopolymers.Efficient Weight Reduction about 60% compared to metal structure.Single‐piece design simplifies assembly and manufacturing.UAV redesigned tails with enhanced efficiency, adaptability, and feasibility. [ABSTRACT FROM AUTHOR]

Forecast error growth as a function of lead time of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. The question under investigation is whether omitting these atmospheric phenomena will improve the predictability of the resulting value. The topic is studied in the extended Lorenz (2005) system. This system shows that omitting small spatiotemporal scales that significantly affect prediction ability will reduce predictability more than modeling it. In other words, a system with model error (omitting phenomena) will not improve predictability. A hypothesis explaining and describing this behavior is developed, with the difference between systems (model error) produced at each time step seen as the error of the initial conditions. The resulting model error is then defined as the sum of the increments of the time evolution of the initial conditions so defined. The hypothesis is compared to the fit parameters that define the model error in certain approximations of the average forecast error growth. Parameters are interpreted in this context, and the approximations are used to estimate the errors described in the hypothesis. A method is proposed to distinguish increments of prediction error growth from small-spatiotemporal-scale phenomena and model errors. Results are presented for the error growth of the ECMWF system, where a 40 % reduction in model error between 1987 and 2011 is calculated based on the developed hypothesis, while over the same period the instability (error growth rate) of the system with respect to initial condition errors has grown. [ABSTRACT FROM AUTHOR]

Accurate predictions are crucial for addressing the challenges posed by climate change. Given South Korea's location within the East Asian summer monsoon domain, characterized by high spatiotemporal variability, enhancing prediction accuracy for regions experiencing heavy rainfall during the summer monsoon is essential. This study aims to derive temporal weighting functions using hybrid surface rainfall radar-observation data as the target, with input from two forecast datasets: the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) and the KLAPS Forecast System. The results indicated that the variability in the optimized parameters closely mirrored the variability in the rainfall events, demonstrating a consistent pattern. Comparison with previous blending results, which employed event-type-based weighting functions, showed significant deviation in the average AUC (0.076) and the least deviation (0.029). The optimized temporal weighting function effectively mitigated the limitations associated with varying forecast lead times in individual datasets, with RMSE values of 0.884 for the 1 h lead time of KLFS and 2.295 for the 4–6 h lead time of MAPLE. This blending methodology, incorporating temporal weighting functions, considers the temporal patterns in various forecast datasets, markedly reducing computational cost while addressing the temporal challenges of existing forecast data. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) enables the automated production of complex geometries with low waste and lead time, notably through Material Extrusion (MEX). This study explores Large Format Additive Manufacturing (LFAM) with carbon fiber-reinforced polyaryletherketones (PAEK), particularly a slow crystallizing grade by Victrex. The research investigates how extrusion parameters affect the mechanical properties of the printed parts. Key parameters include line width, layer height, layer time, and extrusion temperature, analyzed through a series of controlled experiments. Thermal history during printing, including cooling rates and substrate temperatures, was monitored using thermocouples and infrared cameras. The crystallization behavior of PAEK was replicated in a Differential Scanning Calorimetry (DSC) setup. Mechanical properties were evaluated using three-point bending tests to analyze the impact of thermal conditions at the deposition interface on interlayer bonding and overall part strength. The study suggests aggregated metrics, enthalpy deposition rate and shear rate under the nozzle, that should be maximized to enhance mechanical performance. The findings show that the common practice of setting fixed layer times falls short of ensuring repeatable part quality. [ABSTRACT FROM AUTHOR]

Summer Arctic sea ice is declining rapidly but with superimposed variability on multiple time scales that introduces large uncertainties in projections of future sea ice loss. To better understand what drives at least part of this variability, we show how a simple linear model can link dominant modes of climate variability to low-frequency regional Arctic sea ice concentration (SIC) anomalies. Focusing on September, we find skillful projections from global climate models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) at lead times of 4–20 years, with up to 60% of observed low-frequency variability explained at a 5-yr lead time. The dominant driver of low-frequency SIC variability is the interdecadal Pacific oscillation (IPO) which is positively correlated with SIC anomalies in all regions up to a lead time of 15 years but with large uncertainty between GCMs and internal variability realization. The Niño-3.4 index and Atlantic multidecadal oscillation have better agreement between GCMs of being positively and negatively related, respectively, with low-frequency SIC anomalies for at least 10-yr lead times. The large variations between GCMs and between members within large ensembles indicate the diverse simulation of teleconnections between the tropics and Arctic sea ice and the dependence on the initial climate state. Further, the influence of the Niño-3.4 index was found to be sensitive to the background climate. Our results suggest that, based on the 2022 phases of dominant climate variability modes, enhanced loss of sea ice area across the Arctic is likely during the next decade. Significance Statement: The purpose of this study is to better understand the drivers of low-frequency variability of Arctic sea ice. Teasing out the complicated relationships within the climate system takes a large number of examples. Here, we use 42 of the latest generation of global climate models to construct a simple linear model based on dominant named climate features to predict regional low-frequency sea ice anomalies at a lead time of 2–20 years. In 2022, these modes of variability happen to be in the phases most conducive to low Arctic sea ice concentration anomalies. Given the context of the longer-term trend of sea ice loss due to global warming, our results suggest accelerated Arctic sea ice loss in the next decade. [ABSTRACT FROM AUTHOR]

Currently, the development of the health care industry is very rapid. The rapid development of health services will have an impact on increasing the elements of their supporting activities. One of the supporting activities is the procurement of medical equipment. Procurement cases often do not go well, so procurement is not on time. This is due to the very long lead time. This has an impact on decreasing customer satisfaction. Companies try to overcome this problem to be able to compete in the market. This study aims to help companies eliminate waste that occurs in the procurement process so that customer satisfaction is obtained. The approach used is Lean Service with VSM as a process flow mapping tool. Focus Group Discussion was used in the analysis of problems and solutions for improvement. Based on the FGD, there are three problems, namely, the delivery of goods, project creation, and billing documents. The results showed that the overall lead time of the critical care procurement process decreased from 216 days to 110 days, or by 45. [ABSTRACT FROM AUTHOR]

This paper examines the adoption of brutalism in the work of Israeli landscape architects. Since the 1960s, Brutalism made its way from Europe to Israel by leading architects at the time. In parallel, landscape architects also considered Brutalism appropriate to the young state, and the use of concrete as a cheap and readily available construction material. They demonstrated creativity by constructing plazas, outdoor housing complexes, playgrounds and parks, essentially laying a cement carpet across the landscape. The absence of a well-established local outdoor material tradition, coupled with climatic and maintenance considerations, encouraged the burgeoning prominence of outdoor Brutalism in Israel. [ABSTRACT FROM AUTHOR]

The prediction of Indian monsoon rainfall variability affecting a country with a population of billions remained one of the major challenges of the numerical weather prediction community. While in recent years, there has been a significant improvement in predicting the synoptic scale transients associated with the monsoon circulation, the intricacies of rainfall variability remained a challenge. Here, an attempt is made to develop a global model using a dynamic core of a cubic octahedral grid that provides a higher resolution of 6.5 km over the global tropics. This high-resolution model has been developed to resolve the monsoon convection. Reforecasts with the IITM High-resolution Global Forecast Model (HGFM) have been run daily from June through September 2022. The HGFM model has a wave number truncation of 1534 in the cubic octahedral grid. The monsoon events have been predicted with a ten-day lead time. The HGFM model is compared to the operational GFS T1534. While the HGFM provides skills comparable to the GFS, it shows better skills for higher precipitation thresholds. This model is currently being run in experimental mode and will be made operational. [ABSTRACT FROM AUTHOR]

Temperature correlation network has been commonly used in climate monitoring, diagnosing and prediction using reanalysis data, however its application in the network analysis of climate dynamical models hasn't been deeply studied. We construct a temperature correlation network based on near-surface 2m air temperature of four climate models by comparing their capability of properly capturing the structural characteristics of the temperature networks, and further conduct a comparative analysis of the topological differences among different models. The features of temperature correlation networks varied significantly among the four models, in which the ECMWF-SYSTEM5 model network has the highest connectivity among the four models, while the NCEP_CFS2 model has the lowest one. It is also revealed that the model with higher connectivity normally has a stronger correlation between the nodes of the air temperature correlation network, which is likely attributed to the model's stronger teleconnection, regional consistency and smaller standard deviation between predicted temperature series of most two grid points. It is also implied that the model's prediction skills have a probable relationship with the network structure. For each model, the 1-month lead prediction has the highest prediction skill corresponding to the model having the connectivity close to the observation. With the increase of prediction lead times, connectivity bias has a quick rise, and the prediction skill has an obvious decrease. However, for different models at the same prediction lead time, it is not the case that the larger the connectivity deviation the lower the prediction skill, for example, the ECMWF_SYSTEM5 model has the highest prediction skill and the largest connectivity deviation, we find that the ECMWF_SYSTEM5 model network possesses significantly higher connectivity and more distinctive small-world characteristics, which implies a stable network structure helps to improve the prediction skills of the model. Therefore, this study can deepen our understanding of climate models and provide guidance for improving the ability of models to properly simulate climate features. [ABSTRACT FROM AUTHOR]

The present study comprehensively investigates the practical and intrinsic predictability of the sea surface temperature (SST) in the Northern Tropical Atlantic (NTA) based on the 138-year-long coupled hindcasts with a recently developed seasonal ensemble prediction system. This system can yield skillful deterministic predictions for the prominent warm and cold events at least 6 months ahead. Notably, it excels in providing probabilistic predictions for below- and above-normal events rather than for neutral events. The predictability of SST in the NTA undergoes remarkable seasonal variation with two peaks of predictability targeted at April and October regardless of the lead time. Various sources of predictability for these target months are revealed. For the target month of April, the preceding remote forcing from the El Niño–Southern Oscillation (ENSO) in the tropical Pacific Ocean combined with local signal results in the phase locking of the SST variation and seasonality of signal component over the NTA. This ultimately contributes to the high predictability targeted at April. However, From the perspective of potential predictability of the predictability targeted at October, which has been rarely mentioned in previous studies. It is also encouraging that, similar to the Indian Ocean Dipole, ENSO and the signal-to-noise ratio of the system mainly contribute to predictability beyond persistence at long lead times for the spring SST in the NTA. This indicates that potential future ENSO improvements may leave much room for improvement in the current SST prediction in the NTA. [ABSTRACT FROM AUTHOR]

The influence of travel time on perceived traveled distance has often been studied, but the results are inconsistent regarding the relationship between the two magnitudes. We argue that this is due to differences in the lengths of investigated travel distances and hypothesize that the influence of travel time differs for rather short compared to rather long traveled distances. We tested this hypothesis in a virtual environment presented on a desktop as well as through a head-mounted display. Our results show that, for longer distances, more travel time leads to longer perceived distance, while we do not find an influence of travel time on shorter distances. The presentation through an HMD vs. desktop only influenced distance judgments in the short distance condition. These results are in line with the idea that the influence of travel time varies by the length of the traveled distance, and provide insights on the question of how distance perception in path integration studies is affected by travel time, thereby resolving inconsistencies reported in previous studies. [ABSTRACT FROM AUTHOR]

Extreme precipitation over a 2-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal time frame may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted three workshops over 6 years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study, experimental subseasonal-to-seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S time frame. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification that allows for more flexibility in spatial uncertainty might be beneficial for evaluating S2S forecasts. These results help to create a foundation for the design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers. SIGNIFICANCE STATEMENT: There has been an increase in demand for forecasts between the 10- and 14-day weather forecast time frame and 1–3-month seasonal time frame with respect to periods of extreme precipitation among decision-makers, yet this relies on trustworthy and usable forecasts. In this study, experimental forecast products were designed and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Participants preferred a combination of map products, daily regional products, and text narratives with anticipated impacts when making decisions at long lead times. These results help to create a foundation for the design and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers. [ABSTRACT FROM AUTHOR]

Three-dimensional printing technology in construction is a rapidly growing field that offers innovative opportunities for design and construction execution. A key component of this process is the automated production of high-performance construction mixtures that meet specific requirements for strength, fluidity, and setting speed. This overview article outlines the history and development of 3D printing technology in the construction industry, describes various printing technologies, and discusses the properties and requirements for construction mixes. Special attention is given to automated systems for batching and mixing ingredients, which increase the precision and efficiency of production. The different types of construction mixes used in 3D printing and the main technical and operational challenges associated with their application are also presented. The article's conclusions highlight the potential of this technology to revolutionize the construction industry by improving efficiency and reducing costs and project lead times. [ABSTRACT FROM AUTHOR]

The scarcity of cryogenic thermometers often stems from their high cost and lengthy lead times for calibration. Establishing an in-lab temperature calibration system is necessary to quickly make use of uncalibrated sensors or self-made sensors. This paper introduces a straightforward and high-accuracy thermometer calibration system. By employing copper screws as thermal links between the sensor platform and the cryogen-free refrigerator, temperature oscillation on the sensor platform is suppressed to a few millikelvins. In addition, this paper presents a data processing model based on clustering algorithms. These algorithms sort and group data based on distance, which is similar to human visual judgment of data. This paper discusses the parameter optimization process of the clustering algorithm to interpret the automated data process. The cryogenic temperature sensors calibrated by this system exhibited high accuracy, with relative errors of less than 1% compared to standard thermometers. Moreover, automatically processing calibration data from two uncalibrated thermometers takes just over 10 min, highlighting the effectiveness of this calibration system. [ABSTRACT FROM AUTHOR]

This study assesses the skill of existing indices and newly introduced sea surface temperature (SST) indices in predicting rainfall over East Africa (EA) during short rains from September to December (SOND) and long rains from March to May (MAM). The existing and newly introduced indices were selected based on grid point correlation and spatial mean correlation. West–east SST gradient over the Indian Ocean (WEIO), north–south SST gradient over the Pacific Ocean (NSPO), anomalies over the western Indian Ocean (WIO) and anomalies over the eastern Pacific Ocean (EPO) from newly developed indices, and bivariate ENSO time series (BEST), Oceanic Niño Index (ONI), Niño3.4, western Pacific gradient (WPG) and dipole index moment (DMI) from existing indices have a positive correlation with SOND rainfall at 5% (p<0.05) significance level. West–east SST gradient over the Pacific Ocean (WEPO) and SST anomalies over the southern Pacific Ocean (SPO) from new indices and Southern Oscillation Index (SOI) from existing indices have a significant negative correlation with SOND rainfall over EA. SST anomalies over central and eastern Pacific and Indian oceans significantly correlate with SOND rainfall over southern EA (SEA) and equatorial EA (EEA). Rainfall over northern EA (NEA) has a significant negative correlation with SST over the east and central Pacific oceans. Negative correlation extends northeast and southeast from North of Australia over the Pacific Ocean during MAM. WEIO, WIO, NSPO, Niño3.4, Atlantic Oscillation (AO) and WPG have shown strong and significant (p<0.05) relations at least over one subregion. The new SST and existing oceanic indices have a stronger and more significant correlation with short rains than long rains over EA, except in the northern part of EA. Correlation, RMSE and skill score of regression models revealed that new indices showed higher performance than existing indices over EEA and SEA in predicting rainfall, unlike over NEA during both seasons. [ABSTRACT FROM AUTHOR]

Over the East Africa region forecasts of the onset of the rainy seasons have the potential to support decision‐making, especially in the largely rain‐fed agricultural sector. However, the understanding of key features of onset remains limited. Here, we analyse the variability of onset and associated drivers at interannual and subseasonal timescales, using several onset definitions. Results show that the onset date is especially variable from year to year in some of the high‐potential agricultural areas (standard deviation >20 days), which has implications for agricultural risk management. The choice of onset definition metric matters; agronomic definitions have limited applicability at the regional scale and are also highly sensitive to the spatial scale of analysis and to the choice of rainfall data. Onset information provided at coarse scales should be used with caution for decision‐making at the local scale; the "hit rate" of coarse‐scale tercile onset information at the local scale is less than 40% on average. To varying degrees, onset is related to total seasonal rainfall and thus to dominant interannual drivers of rainfall, including the Indian Ocean Dipole and ENSO modes in October–December and the western Pacific "V‐gradient" pattern in March–May. However, by analysing the dominant proportion of onset variance unrelated to total rainfall during the climatological season we show a substantial influence of subseasonal drivers, notably the Madden–Julian Oscillation. As such, there is an opportunity for rainfall onset information to be provided across seasonal and subseasonal timescales. Our work reinforces the need for enhanced co‐production of such onset information with stakeholders, especially regarding the choice of metric, alignment of forecasts with livelihood calendars, interpretation of the credibility of information content for local‐level decision‐making, as well as appropriate strategies for staggered risk management interventions informed by forecasts over "seamless" lead times. [ABSTRACT FROM AUTHOR]

Utilizing observed wind speed and direction data from observation stations near the Taiwan Strait and ocean buoys, along with forecast data from the EC model, GRAPES_GFS model, and PanGu weather prediction model within the same period, RMSE, MAE, CC, and other parameters were calculated. To comparatively evaluate the forecasting performance of the PanGu weather prediction model on the sea surface wind field near the Taiwan Strait from 00:00 on 1 June 2023, to 23:00 on 31 May 2024. The PanGu weather prediction model is further divided into the ERA5 (PanGu) model driven by ERA5 initial fields and the GRAPES_GFS (PanGu) model driven by GRAPES_GFS initial fields. The main conclusions are as follows: (1) over a one-year evaluation period, for wind speed forecasts with lead times of 0 h to 120 h in the Taiwan Strait region, the overall forecasting skill of the PanGu weather prediction model is superior to that of the model forecasts; (2) different initial fields input into the PanGu weather prediction model lead to different final forecast results, with better initial field data corresponding to forecast results closer to observations, thus indicating the operational transferability of the PanGu model in smaller regions; (3) regarding forecasts of wind speed categories, the credibility of the results is high when the wind speed level is ≤7, and the PanGu weather prediction model performs better among similar forecasts; (4) although the EC model's wind direction forecasts are closer to the observation field results, the PanGu weather forecasting model also provides relatively accurate and rapid forecasts of the main wind directions within a shorter time frame. [ABSTRACT FROM AUTHOR]

This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available. [ABSTRACT FROM AUTHOR]

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77–0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4–6 months: 0.82; 7–9 months: 0.75; 10–12 months: 0.71), accuracy (0.68–0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to examine the association between household energy poverty (EP) and trajectories of emotional and behavioural difficulties during childhood. Methods: The Growing up in Ireland study is two nationally representative prospective cohorts of children. The Infant Cohort (n = 11,134) were recruited at age 9 months (9 m) and followed up at 3, 5, 7 and 9 years (y). The Child Cohort (n = 8,538) were recruited at age 9 y and followed up at 13 y and 17/18 y. EP was a composite of two relative measures of EP. Emotional and behavioural difficulties were repeatedly measured using the strengths and difficulties questionnaire (SDQ). Linear spline multilevel models were used, adjusted for confounders to examine the association between (1) EP (9 m or 3 y) and trajectories of emotional and behavioural difficulties from 3 to 9 y in the Infant Cohort and (2) EP at 9 y and the same trajectories from 9 to 18 y in the Child Cohort. Results: In adjusted analyses, EP at 9 m or 3 y of age was associated with higher total difficulties score at 3 y (0.66, 95% CI 0.41, 0.91) and 5 y (0.77, 95% CI 0.48, 1.05) but not at 7 y or 9 y. EP at 9 y was associated with higher total difficulties score at 9 y (1.73, 95% CI 1.28, 2.18), with this difference reducing over time leading to 0.68 (95% CI 0.19, 1.17) at 17/18 y. Conclusions: Our study demonstrates a potential association between early life EP and emotional and behavioural difficulties that may be transient and attenuate over time during childhood. Further studies are required to replicate these findings and to better understand if these associations are causal. [ABSTRACT FROM AUTHOR]

The Turkish straits system (TSS), consisting of the Marmara Sea, Dardanelles and Bosphorus, transfers the Black Sea water and the Mediterranean water. To our best knowledge, finding the lag time of the Bosphorus to Dardanelles and estimating upper layer discharge of the Dardanelles using Bosphorus upper layer discharge have not been studied before in the existing literature. With this motivation, continuous wavelet transform (CWT) was applied to the upper layer discharge data of the Dardanelles and Bosphorus as a pre-processing instrument and the noise in the data was eliminated by separating them into sub-series. The wavelet coherence (WTC) and the cross-wavelet transform (XWT) were applied to detect the lag time of the Bosphorus to the Dardanelles. The upper layer flow lag time of the Bosphorus to the Dardanelles is calculated to be about 5 days. In addition, there are not sufficient studies to predict Dardanelles flow by using Bosphorus flow. In order to fill this gap in the literature, the wavelet-ANN (WANN) and stand-alone ANN were employed to estimate the upper layer discharges of the Dardanelles from Bosphorus. Performances of model were estimated by RMSE and Pearson's R2. ANN and WANN models were developed to predict discharges with lead times of up to 7 days (168 h). Using the wavelet transform as a pre-processing tool significantly enhanced estimations in comparison to the stand-alone ANN model. While stand-alone ANN model cannot make accurate predictions for any lead time, the WANN model can make correct forecasts of Dardanelles upper layer discharges for up to 3 days (72 h). [ABSTRACT FROM AUTHOR]

Flash floods pose a distinct challenge compared to traditional fluvial flooding, with infrastructure-based solutions proving less effective. Effective responses hinge on advanced early warning systems providing actionable information, emphasising the necessity for computational flood forecasting models. However, hydrodynamic models, renowned for accuracy and completeness, face limitations due to computational intensity. This study explores two 2D flood forecasting models, RIM2D and SERGHEI, both with GPU implementations which allow us to maximise the forecast lead time. While RIM2D is less computationally intensive, suitable for operational use, SERGHEI, with higher computational costs, targets large-scale high-performance computing (HPC) systems. The assessment of applicability and trade-offs is carried out on the 2021 Eifel flood event, particularly in the lower Ahr valley. A set of simulations were performed at various resolutions from 1 to 10 m, which reveal similar accuracy among both models at coarser resolutions, yet discrepancies arise at finer resolutions due to the distinct formulations. Both models exhibit a rapid computational cost escalation, but at resolutions equal to or coarser than 5 m, forecasts are remarkably faster than the real-time ideal for operational use, paving the way for their use in early warning systems. However, higher resolutions necessitate multi-GPU and HPC capabilities, underlining the importance of embracing such technology in addressing broader flood domains. [ABSTRACT FROM AUTHOR]

In this study, we present a machine‐learning model capable of predicting food insecurity in the Horn of Africa, which is one of the most vulnerable regions worldwide. The region has frequently been affected by severe droughts and food crises over the last several decades, which will likely increase in future. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food‐insecurity risk. We present a XGBoost machine‐learning model to predict food‐security crises up to 12 months in advance. We used >20 data sets and the FEWS IPC current‐situation estimates to train the machine‐learning model. Food‐security dynamics were captured effectively by the model up to 3 months in advance (R2 > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 96) and 20%–50% of crisis onsets in agro‐pastoral regions (n = 22) with a 3‐month lead time. We also compared our 8‐month model predictions to the 8‐month food‐security outlooks produced by FEWS NET. Over a relatively short test period (2019–2022), results suggest the performance of our predictions is similar to FEWS NET for agro‐pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop‐farming regions than FEWS NET. With the well‐established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine‐learning methods into operational systems like FEWS NET. Plain Language Summary: In the face of increasing droughts and food crises, this study explored the use of machine learning to provide predictions of food crises in the Horn of Africa, up to 12 months in advance. We used an algorithm called "XGBoost," which we fed with over 20 data sets of potential food security drivers. After training the model, we found that food security dynamics were accurately predicted up to 3 months in advance, especially in pastoral and agro‐pastoral regions. The model accurately predicted 20% of crisis onsets in pastoral areas and 20%–50% in agro‐pastoral regions with a 3‐month lead time. In agro‐pastoral and pastoral regions, our machine learning algorithm showed a similar performance to the established early warning system from FEWS NET. The machine‐learning model did not show good performance in crop‐farming areas. Nonetheless, this study underscores the potential of integrating machine‐learning methods into existing operational systems like FEWS NET. By doing so, it paves the way for improved early warning capabilities, crucial in mitigating the looming threat of food insecurity in the Horn of Africa. Key Points: A machine‐learning model is presented to predict food‐security crises in the Horn of AfricaThe model demonstrates high overall performance, and performs similarly to FEWS NET outlooks in the (agro‐) pastoral regionsThis study can be utilized to integrate machine learning into existing early warning systems, thereby creating hybrid solutions for the future [ABSTRACT FROM AUTHOR]

Copyright of Journal of Faculty of Educational Sciences is the property of Ankara University, Faculty of Educational Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mainline coordinated control is usually based on fixed speed and statistical traffic flow by period. However, in actual operation, the vehicles parked in front of the intersection and the arriving vehicles often fluctuate, and the through-traffic green time is wasted due to phase transition, which leads to mismatches between the signal plans and actual traffic flow requirements, affecting the traffic efficiency of the intersection. To address the above issues, using vehicle–road collaborative control (VRCC), by calculating the phase difference lead time and phase difference of adjacent intersections, the green extension time for the green wave through-traffic phase, and the guiding vehicle speed, the goal of reducing the detention volume of through traffic, reducing the waste of through-traffic green time caused by phase transitions and improving the throughput of through traffic can be achieved. The speed of the green wave traffic flow is increased by guiding vehicles to form saturated platoons during green periods. Finally, PTV VISSIM 4.3 was used for simulation verification, and the results showed that compared to not implementing the control strategy, the average delay on the arterial road was reduced by 85.1%, the average number of stops was reduced by 84.3%, the average travel time was reduced by 34%, and the average queue length was reduced by 62.6%. This significantly improved the efficiency of traffic on the arterial road and effectively reduced congestion. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Efficient inventory management is essential for maintaining a balance between supply and demand in various industries. This research study aims to quantitatively examine the impact of supply chain delays, with a specific emphasis on lead times and in-transit amounts, inventory levels, and the ability to meet sales demands. Mathematical modeling and statistical analysis are utilized to create prediction models that assess the impact of variations in lead time and quantities in transit on inventory stability and fulfillment rates. The study used regression analysis to ascertain the relationships between the indicated parameters and inventory outcomes. Also, machine learning algorithms like Random Forest and Linear Regression are applied to predict possible disruptions and optimize inventory levels. The methodology followed focuses on the Tri-Model Fusion Stacking approach, which combines various models to improve the predicted accuracy and offer a more comprehensive analysis. The main goal of this research is to provide practical insights that help organizations optimize their inventory management techniques, resulting in cost reduction and enhanced service levels. The findings aim to simplify the modification of inventory management techniques in light of up-to-date supply chain information, providing a notable improvement in the resources available to supply chain experts. [ABSTRACT FROM AUTHOR]

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors. The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency. The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations. It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion. The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions. The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons. Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered. The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented. The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view. The practical problems of determining the threshold for the number of votes, based on which a classification decision is made, have been researched. Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy. Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy. The speed of the analysis increases in proportion to the degree of reduction. The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification, which guarantees a decent level of accuracy. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to systematically review the literature on digital supply chain (DSC), big data (BD) and manufacturing lead time (MLT) in industrial companies. Design/methodology/approach: This study provides a systematic review of the 99 research on this subject that was published between 2015 and 2022. Studies were found in the Scopus database. This review also identifies gaps in the literature, highlights conflicting results, examines prospective data sources for empirical researchers and offers suggestions for choosing promising research subjects in the future. Findings: This study performed a thorough literature review to a developing field of inquiry in order to identify the impact of the digital supply chain, BD and manufacturing lean time, an area that has received little attention in the literature. Future pathways and ramifications are also offered based on the literature content search. The results showed that BD improves DSC performance through resilience and innovation of the DSC. MLT and DSC integration were found to be positively correlated, according to the results. Originality/value: Although the production lead time is preferable to boost customer value and supply reliability, the long lead time hurts the DSC's ability to compete. DSC integration also improves coordination and streamlines processes. The researchers suggest fostering organizational flexibility, information exchange to accomplish DSC integration and adaptable behaviors including responsiveness and alertness. [ABSTRACT FROM AUTHOR]

Background and Objectives: The number of cardiac pacemakers being used has increased in recent decades, and this increase has led to a rise in device-related complications, requiring percutaneous device extraction. Our aim was to present our single-center clinical experience in percutaneous lead extractions. Materials and Methods: We analyzed an observational retrospective cohort study of 93 patients for the transvenous removal of a total of 163 endocardial leads. We evaluated the device details, indications, lead characteristics, extraction methods, complications, reimplantation procedure, follow-up data, effectiveness, and safety. Results: Patients' mean age was 68.6 ± 11.6 years. Lead extraction indications were pocket infection in 33 (35.5%), lead dysfunction in 33 (35.5%), and system upgrade in 21 (23%) cases, and lead endocarditis in 6 (6%) cases. The duration from implantation to extraction time was a detected median of 43 (24–87) months. The most common retracted lead type was the RV defibrillator lead (62%), and the lead fixation type was active for one hundred (61%) patients. A new device was inserted in 74 (80%) patients, and the device type was most commonly a CRT-D (61%). Patients were followed up at a median of 17 (8–36) months, and 18 patients (19%) died at follow-up. Complete procedural success was obtained in 78 (84%) patients, and clinical procedural success was obtained in 83 (89%) patients. Procedural failure was detected in 15 (16%) patients. Major and minor complications were detected in 10 (11%) and 6 (6.5%) patients, respectively. The most common minor complication was pocket hematoma. Conclusions: Our experience suggests that transvenous lead extraction has a high success rate with an acceptable risk of procedural complications. The simple manual traction method has a high rate of procedural success, despite a high dwell time of the lead. [ABSTRACT FROM AUTHOR]

Background: A fixed 10-year lead-time in composite time-trade-off (C-TTO) tasks might compromise the precision of utility values below − 1. This study explored how alternative lead-times (ALTs) influence EQ-5D-5L value sets and their implications in economic evaluations. Methods: Leveraging data from Taiwan's EQ-5D-5L valuation and capitalizing on its exploratory willingness-to-accept question, we explored participants' quantification of "worse-than-dead (WTD)" health states with ALTs up to 50 years. We then derived alternative value sets incorporating these ALTs through interval regression and compared them against those from conventional models. To evaluate their impact on health change valuation, we simulated utility differences for all possible EQ-5D-5L health-state-pairs using each value set. Results: With a salient floor effect observed in the C-TTO values, the model with ALT led to a wider range of predicted utilities (– 2.3897 ~ 1), compared with those of conventional models (generalized least squares (GLS): – 0.7773 ~ 1; Tobit-GLS: – 0.9583 ~ 1). Compared to the Tobit-GLS model, the model with ALT increased the numerical distance in 80% of health-state-pairs, with 11% decreasing and 9% altering direction (e.g., positive to negative) in utility differences. Conclusions: While ALTs offer insights into patient preferences, their integration into economic evaluations might require rescaling. Future research should prioritize advanced rescaling methods or enhanced elicitation strategies for populations with substantial censoring. This is pivotal for improving the elicitation of extreme WTD states and accurately discerning the relative distances between health states. Countries developing EQ-5D-5L value sets should consider pilot studies and incorporating region-specific questions on social determinants, especially where pronounced floor effects are suspected. [ABSTRACT FROM AUTHOR]

We study the nature and consequences of IBM's invention pledge program, the largest of its kind in US history. Invention pledges involve the open disclosure of patentable inventions without seeking patent protection, unlike patent pledges for which firms waive patent rights after patenting. Competing theoretical views characterize invention pledges either as low-quality inventions with poor organizational fit or as patentable inventions pledged openly by firms with weak firm-specific patent rights and lead-time advantages. We test these theoretical claims using a novel method that compares the quality of subsequent patents citing IBM invention pledges with similar others. Citing IBM invention pledges is positively correlated with patent quality, inconsistent with theories of adverse selection. IBM patents cite its invention pledges on average two years before others, consistent with private benefits of open disclosure. Cited invention pledges spur follow-on innovation, further highlighting their social benefits. We discuss the generalizability of our results obtained using the sample of cited invention pledges to uncited invention pledges. Overall, our results highlight the tradeoffs firms face between knowledge protection and disclosure and generalize to a small number of other disclosing firms. [ABSTRACT FROM AUTHOR]

Using model projections to study the emergence of observable climate signals presumes omniscient knowledge about the climate system. In reality, observational knowledge suffers from data quality and availability issues, for instance data gaps, changes in instrumentation, issues due to gridding and retrieval algorithms. Overlooking such deficiencies leads to misrepresentations of the time of emergence (ToE). We introduce a new definition of ToE that accounts for observational limitations, and show that significant corrections to the ToE may be necessary to achieve the same statistical confidence as would be afforded by omniscient knowledge. We also show how our method can inform future observational needs and observing systems design. Plain Language Summary: Long‐term planning for climate change adaptation requires accurate forecasts of climate impacts. Such forecasts are produced using computer models, which provide omniscient knowledge of the climate states they simulate. However, real‐world knowledge is based on incomplete and sometimes flawed observational data. Ignoring these flaws yields a distorted view of the timing of observable climate impacts. We propose a method to address this issue by accounting for observational limitations such as data gaps, changes in measuring equipment, data post‐processing, etc. We also show how to use the method to plan future data collection. Key Points: The degree of confidence placed in observed climate trends is misrepresented when overlooking observational limitationsWe provide a nonparametric method to account for such limitationsThe method can also inform the design of future observing platforms [ABSTRACT FROM AUTHOR]

Atmospheric prediction at 2–6 weeks lead time (so‐called subseasonal‐to‐seasonal timescales) entails large forecast uncertainty. Here we investigate the flow‐dependence of this uncertainty during Boreal winter. We categorize the large‐scale flow using North Atlantic‐European weather regimes. First, we show that forecast uncertainty of near‐surface geopotential height (Z1000) and temperature (T2m) are strongly sensitive to the prevailing regime. Specifically, forecast uncertainty of Z1000 reduces over northern Europe following Greenland Blocking (enhanced predictability) due to a southward shifting eddy‐driven jet. However, due to strong temperature gradients and variable flow patterns, Greenland blocking is linked to increased forecast uncertainty of T2m over Europe (reduced predictability). Second, we show that forecast uncertainty of weather regimes is modulated via the stratospheric polar vortex. Weak polar vortex states tend to reduce regime‐uncertainty, for example, due to more frequent predicted occurrence of Greenland blocking. These regime changes are associated with increased T2m uncertainty over Europe. Plain Language Summary: Weather is chaotic, and forecasts several weeks ahead are quite uncertain. Nevertheless, the degree of uncertainty varies, which can be relevant for long‐term planning in various sectors, including agriculture, energy supply, and public health. Here we show that the degree of uncertainty depends on the weather at the time of forecast start, which we categorize using eight characteristic weather patterns for the North Atlantic and Europe. We analyze a large set of forecasts during winter, with lead times up to 6 weeks. For example, persistent high‐pressure systems over Greenland are known to favor low temperatures over northern Europe. Our results indicate that, in addition, temperatures are highly variable in these cases, leading to unusually high forecast uncertainty. In contrast, uncertainty of near‐surface air pressure tends to decrease due to less frequent storms over the North Atlantic. Furthermore, we show that forecast uncertainty of the weather patterns themselves varies, which is useful when large‐scale flow conditions are more critical than local weather. We analyze forecasts under different circulation conditions in the Arctic stratosphere, as these can have long‐lasting impacts on surface weather. We find forecasts of weather patterns become less uncertain when the circumpolar winds in the Arctic stratosphere are weak. Key Points: Ensemble spread of near‐surface weather in subseasonal forecasts is sensitive to the prevailing North Atlantic‐European weather regimeGreenland blocking is linked to the smallest ensemble spread of geopotential but to the largest spread of temperature over northern EuropeWeak polar vortex states are followed by reduced forecast uncertainty of weather regimes but increased uncertainty of European temperature [ABSTRACT FROM AUTHOR]