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Demand-Driven Material Requirements Planning (DDMRP) is a new method of inventory control designed to address the challenges of today's complex and volatile supply chain environment. DDMRP replaces traditional Material Requirements Planning (MRP) with a more agile and responsive approach that is based on a set of colour-coded buffers. DDMRP ensures that the right inventory is in the right place at the right time, enabling companies to respond quickly and effectively to changing customer demand. DDMRP requires the setting of various parameters, such as variability factor (VF) and lead time factor (LTF), which can have a significant impact on systems' performance. Pre-assumed fixed values of DDMRP parameters are used in most of the existing studies. This may lead to either high stockout levels or excess inventory especially in environments involving high level of variability. In this study, we proposed a fuzzy logic-based approach which dynamically adjusts the values of VF and LTF parameters considering demand and lead time variability. The effectiveness of the proposed approach was investigated by comparing its performance with DDMRP based on several numerical experiments. The results showed that the proposed Fuzzy Demand-Driven Material Requirements Planning (FDDMRP) outperforms DDMRP in terms of backorder rate and total cost. [ABSTRACT FROM AUTHOR]

In the current era of turbulence, demand uncertainty and significant supply delays have become recurrent challenges for every global supply chain, which are accentuated by geopolitical issues, regional conflicts, natural disasters, etc. Companies are keen to decide when and where nearshoring and offshoring their supply locations and to adapt their inventory control policies to cope with significant perturbations. To deal with these challenges, we investigate the integration of inventory planning at the strategic level through a novel setting of the location-inventory problem under uncertainty. This problem is characterised by inbound multi-sourcing, order splitting among multiple suppliers, and optimised inventory decisions from sourcing to customers under uncertain demand and lead time. A two-stage stochastic optimisation model is proposed and solved using a Benders decomposition algorithm, supported by a sample average approximation approach. The results show that both lead time and demand uncertainties impact strategic and tactical decisions of the global supply network. Specifically, multi-sourcing and inbound order splitting policies have proven beneficial when the network faces a high lead time uncertainty. Based on an illustrative case of a global automobile spare parts supply network under different supply-side scenarios, managerial insights are gained on the benefit of onshore, nearshore, and offshore decisions. When facing a high lead time uncertainty, as in the pandemic case or any geopolitical episode, our findings suggest the necessity to rely on nearshoring for the supply side. [ABSTRACT FROM AUTHOR]

Supply chains operate in a highly distuptive environment where a SC master plan should be updated in line with disruptions to ensure that a high service level is provided to customers while total cost is minimised. There is an absence of knowledge of how a SC master plan should be updated to cope with disruptions using hybrid modelling. To fill this gap, we present a hybrid modelling framework to update a SC master plan in presence of disruptions. The proposed framework, which is a precursor to a SC digital twin, integrates simulation, machine learning, and optimisation to identify the production, storage, and distribution values that maximise SC service level while minimising total cost under disruptions. This approach proves effective in a SC disrupted by demand increase and lead time extension. Results show that employing hybrid modelling leads to a noticeable improvement in service level and total cost. The outcome of the new knowledge on using hybrid modelling for managing disruptions provides essential learning for the extension of modelling through a digital twin for SC master planning. We observe that in the presence of disruptions it is more economical to keep higher inventory at downstream SC members than the upstream SC members. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research. [ABSTRACT FROM AUTHOR]

Configuring supply chains (SCs) is critical to spare parts retailers' success, entailing two key aspects: stock deployment into distribution centres (DCs) (i.e. inventory centralisation or decentralisation) and stock supply in each DC (how many spare parts to supply and how often). Given the unpredictability of spare parts demand, stock deployment and supply policies should be regularly reviewed, adapting to fluctuations in customer needs. A viable way to do this is to adopt a multi-criteria ABC criticality classification. However, the multi-criteria ABC criticality classification has often been used to plan stock supply policies in a single DC, but only once to plan spare parts deployment. Nevertheless, the available literature methodology presents major limitations, being not applicable in real companies. Therefore, this paper provides a novel methodology, called SP-LACE, which first reviews the configuration of spare parts SCs based on a multi-criteria criticality classification. Then, allows, for the first time, to evaluate the economic benefits of the reviewed SC configuration. SP-LACE was tested on two case studies and compared with the literature methodology. The results indicate that it provides economic benefits (in terms of total SC cost), overcoming the limitations of the literature methodology and ensuring high service levels. [ABSTRACT FROM AUTHOR]

This paper introduces a versatile design method for reconfigurable metasurfaces based on the Pancharatnam–Berry phase theory. Unlike traditional reconfigurable metasurfaces that require designing independent surfaces for specific applications, leading to a significant time investment for designers to learn and create, this study proposes a foundational, invariant metasurface. By selectively metallizing holes or inserting metal cylinders, it achieves nine available functionalities. Regardless of the chosen operating function, the metasurface demonstrates high efficiency at 8.2 GHz, with amplitude loss less than 1 dB. Additionally, when operating in phase modulation mode, the design provides a 360 ° phase adjustment range and a 30 ° phase step. A prototype containing 31 × 31 units (425.6 × 425.6 mm 2) has been fabricated and tested under function 7 (TM and RHCP transmission phase modulation). Measurement results confirm the metasurface's capability for polarization conversion and phase modulation at the frequency of 8.2 GHz. [ABSTRACT FROM AUTHOR]

This paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs. [ABSTRACT FROM AUTHOR]

This paper investigates a single-vendor, multiple-buyer coordinated supply chain under stochastic demand. Each buyer implements a continuous review policy, and the shortage quantity is fully back-ordered. The lead time for the first shipment includes setup, production, and transportation times, while the lead time for the remaining shipments includes only transportation time. The production time is a function of production lot and production rate, which are both decision variables, and the setup and transportation times are controllable as well. For each buyer, the safety stock in the first replenishment cycle is not necessarily identical to that in the remaining cycles. Two successive shipments from the vendor to a buyer may have different sizes, and their ratio is a decision variable. The problem is finding the production and inventory replenishment policy, production rate, and lead times that minimise the long-run expected total cost per time unit. We demonstrate properties satisfied by the cost function and develop an optimisation algorithm, whose performance is compared to a benchmark algorithm based on a commercial solver in a numerical experiment. The experiments also investigate the benefits stemming from the proposed model when compared to models reproducing situations that leverage some or any of the controlled factors. [ABSTRACT FROM AUTHOR]

In this paper, we address a Stochastic-Demand Assembly Job Shop Scheduling Problem (SD-AJSSP) in the presence of the commonality of sub-assemblies across products. We propose a new production methodology, named Assemble-to-Order with Commonality of Sub-Assemblies (ATO-CS) to not only solve the SD-AJSSP, but also, achieve a successful implementation of a mass customisation system by collectively aiming to (1) keep the production costs low by leveraging upon commonality of sub-assemblies in products' BOM and producing sub-assemblies on a mass scale during one of the two stages of production, (2) minimise the loss due to excess inventory build-up in anticipation of stochastic demand of products by postponing the production of certain apex sub-assemblies in products' BOM until the actual demand is realised, and (3) reduce the time of the products' delivery to customers. The ATO-CS method determines optimum production levels as well as schedules assembly operations/jobs over the machines at each stage of production, where the second stage is an assembly job shop and is shown to outperform commonly-used production methodologies. We also develop an algorithm for its implementation and show its efficacy over the use of the state-of-the-art commercial solver CPLEX® in obtaining a lower solution cost and smaller optimality gap. [ABSTRACT FROM AUTHOR]

The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability). [ABSTRACT FROM AUTHOR]

We considered two periodic review perishable inventory systems. The first system is controlled by a periodic review (T , S) policy, where orders are triggered at each review period T and when the stock is less than S. The second one is controlled by a periodic review (s , S) policy, where orders are triggered only if the stock level is below s. For both models, the lifetime of each product was assumed to follow a general distribution, whereas the lead time was constant. The demands followed a Poisson process, and all unsatisfied demand was lost. Using an approximate solution of the steady-state probabilities of the inventory level, we obtained a closed form of cost expression. Next, we conducted a simulation study to identify our solution accuracy. Finally, we performed a sensitivity analysis to evaluate the effect of the lifetime variation and cost parameters on the best policy and its cost. The obtained results were close to those of the optimal policy with an average accuracy of 3.7% for the periodic review (T , S) policy and 3.8% for the periodic review (s , S) policy. For the worst cases, where the accuracy is higher than 7%, we provided an analytical approach to improve the system accuracy. [ABSTRACT FROM AUTHOR]

Prior literature has long examined innovation as a recombination process within or across the boundaries of technological domains. However, limited attention is paid to boundaries per se. Building upon recent development of categorical contrast, this study distinguishes domains with crisp boundaries from those with fuzzy boundaries and examines their effects on innovation outputs. Analyzing a large sample of US patents, we find that spanning crisp boundaries is more likely to generate impactful inventions but at the same time leads to significantly higher recombinant uncertainty. We continue to explore what types of inventors are better able to span such types of domain boundaries. Focusing specifically on the role of inventors' knowledge expertise, we find that while both knowledge depth and breadth enhance the impact of technologies that span crisp boundaries, knowledge breadth is also found to escalate the associated uncertainty. Our emphasis on the contrast of technological domains contributes to the literature on recombinative innovation and boundary spanning. [ABSTRACT FROM AUTHOR]

In this study, we applied the conditional nonlinear optimal perturbation (CNOP) method to generate nonlinear fast‐growing initial perturbations for ensemble forecasting, aiming to assess the effectiveness of the CNOP method in improving the forecast skill of climate events. Our findings reveal a significant improvement in the forecast skill of the Indian Ocean Dipole (IOD) within the CNOP ensemble forecast, particularly at long lead times, thereby extending the skilful forecast lead times. Notably, this improvement is more prominent for strong IOD events, with skilful forecast lead times exceeding 12 months, outperforming many current state‐of‐the‐art coupled models. The high forecast skill of the CNOP method is primarily attributed to its ability to capture the uncertainties in the wind anomaly field in the eastern Indian Ocean (EIO) closely associated with IOD evolution. Consequently, CNOP ensemble members exhibit significant deviations from the control forecast, resulting in a large ensemble spread encompassing IOD evolution. Furthermore, a comparison with the climate‐relevant singular vectors (CSV) method in terms of IOD and El Niño–Southern Oscillation (ENSO) predictions reveals the superior performance of the CNOP ensemble forecast. Despite the initial perturbations for ensemble forecasting being generated aimed at improving IOD forecast skill, the CNOP method significantly improves the forecast skill of both IOD and ENSO events, with a greater improvement for ENSO. Additionally, the CNOP ensemble forecast system provides more reliable estimates of forecast uncertainties and exhibits higher reliability with increasing lead times. In conclusion, the CNOP method effectively captures the nonlinear physical processes of climate events and improve the forecast skill. [ABSTRACT FROM AUTHOR]

Rationale: Heavy‐labelled internal standards increasingly represent the gold standard for absolute quantitation in mass spectrometry (MS)‐based bottom‐up proteomics. The biggest drawbacks of using these standards are that they have high costs and lengthy lead times. Methods: We describe an efficient, low‐cost optimised method to enable 'in‐house' heavy labelling of synthetic tryptic peptides for absolute quantification using tandem LC‐MS/MS mass spectrometry. Our methodology uses 18O water in a trypsin‐catalysed oxygen exchange reaction at the carboxyl terminus with the overall aim of reducing the costs and lead time associated with sourcing heavy standards from commercial vendors. Results: Step‐by‐step instructions are provided on how to execute this protocol with high‐throughput adaptations utilising a 96‐well plate and a liquid‐handling robot. Detailed notes on experimental setup, tips for troubleshooting and suggested improvements to maximise labelling efficiencies are highlighted to achieve the best results. Under optimum conditions, labelling efficiencies of peptides can reach from 95% to 100%. Conclusions: The application of the 'in‐house' labelled standards in generating calibration curves to quantify endogenous peptide concentrations is just as effective as using the synthetically sourced standards while also having great cost reduction implications as well as saving time spent waiting for peptides to arrive. The protocol is highly adaptable and can be customized to fit the specific setup of any laboratory, maximizing achievable labelling efficiencies. [ABSTRACT FROM AUTHOR]

Traffic flow prediction can support proactive control strategies to alleviate queues and delays at expressway toll stations. The prediction should be enough accurate and advanced that leaves sufficient preparation time to implement control measures for upcoming congestions. The accuracy of prediction and the lead time for accurate prediction are contradictory. To balance both aspects, a control-oriented combined prediction method was proposed in this study. This method automatically tuned prediction horizons based on predicted level of congestion. It used long short-term memory (LSTM) neural networks to learn the spatial–temporal characteristics of historical traffic flow and predict short-term and medium-term traffic flow at toll stations. Then, the level of congestion was determined for graded congestion warning and dynamic tuning of prediction horizons. To validate the proposed method, the Ganggou toll station was selected as the study site. The proposed integrated model can facilitate reliable proactive control measures at expressway toll stations. [ABSTRACT FROM AUTHOR]

Background and Aim Methods Results Conclusion The simulation‐based mastery learning (SBML) method holds promise for improving the efficiency and effectiveness of endoscopy training. However, further study is required to establish its advantages over the traditional method. We aim to prospectively compare outcomes between gastrointestinal endoscopy trainees taught using SBML and those trained using conventional apprenticeship methods for upper endoscopy.We performed a blinded, stepwise, comparative study with SBML participants deliberately practicing deconstructed steps of upper endoscopy and apprenticeship participants observing procedures. Three blinded trainers assessed trainees' skills using a validated esophagogastroduodenoscopy (EGD) tool pre‐and post‐training. The minimum pass standard (MPS) was set at a score > 2. We compared MPS of the groups using standard statistics and paired t‐test.Six trainees were enrolled in the SBML group, and six in the conventional group. All trainees in the SBML group passed the minimum standard compared with the conventional group (P = 0.06). All trainees in the SBML group obtained significantly higher scores in overall basic GI endoscopic skills, esophageal, stomach, and duodenal observation skills than those of the conventional apprenticeship group (P < 0.05). The SBML curriculum led to three times more learning (Cohen's d = 6.5) than the conventional method (Cohen's d = 1.8).This prospective study supports SBML for upper endoscopy training compared with the traditional apprentice‐based method. SBML resulted in a steeper learning curve, as trainees learned three times more during the same period. Furthermore, trainees developed a uniform competency level at the end of training. [ABSTRACT FROM AUTHOR]

Validating satellite data using buoys presents a significant challenge due to the differing spatial and temporal representativeness of these data sources. This discrepancy introduces two critical issues in satellite data validation: the selection of the matchup window during the collocation process and the estimation of representativeness error. While numerous studies have addressed these issues independently, research on their interaction remains limited. Utilizing quadruple Sea Surface Salinity (SSS) matchup datasets, this study investigates the impact of the time window on the estimation of SSS representativeness error between two satellite observations: SMOS and SMAP. Our results reveal a clear dependence of representativeness error on the selected time windows. In the global ocean, the variance of representativeness errors within a small time window is 0.040 psu2, which is substantially greater than that of the entire dataset (0.016 psu2). In contrast, the representativeness errors within larger time windows are systematically lower than those within smaller time windows. This research also finds that this dependency is not influenced by the randomness of the matchup data, climatological Sea Surface Salinity, or geographic locations. Instead, our findings suggest that the impact of the time window on representativeness error may reflect the characteristics of satellite data across different time intervals. Satellites are more likely to capture SSS signals within smaller time windows, resulting in larger representativeness errors when comparing satellite SSS with buoy measurements. Conversely, larger time windows lead to significant mismatches between satellite observations and hinder the ability of satellites to detect SSS signals, which can result in smaller or even reversed representativeness errors. [ABSTRACT FROM AUTHOR]

The European North-West shelf seas (NWS) support economic interests and provide environmental services to adjacent countries. Expansion of offshore activities, such as renewable energy infrastructure, aquaculture, and growth of international shipping, will place increasingly complex demands on the marine environment over the coming decades. Skilful forecasting of NWS properties on seasonal timescales will help to effectively manage these activities. Here we quantify the skill of an operational large-ensemble ocean-atmosphere coupled global forecasting system (GloSea), as well as benchmark persistence forecasts, for predictions of NWS sea surface temperature (SST) at 2–4 months lead time in winter and summer. We identify sources of and limits to SST predictability, considering what additional skill may be available in the future. We find that GloSea NWS SST skill is generally high in winter and low in summer. GloSea outperforms simple persistence forecasts by adding information about atmospheric variability, but only to a modest extent as persistence of anomalies in the initial conditions contributes substantially to predictability. Where persistence is low – for example in seasonally stratified regions – GloSea forecasts show lower skill. GloSea skill can be degraded by model deficiencies in the relatively coarse global ocean component, which lacks dynamic tides and subsequently fails to robustly represent local circulation and mixing. However, "atmospheric mode matched" tests show potential for improving prediction skill of currently low performing regions if atmospheric circulation forecasts can be improved. This underlines the importance of coupled atmosphere-ocean model development for NWS seasonal forecasting applications. [ABSTRACT FROM AUTHOR]

The critical role of latent heating (LH) in atmospheric blocking events is well-recognized, yet its influence on the predictability of such events remains underexplored. This study examines the prediction accuracy of winter atmospheric blocking days over the Northern Hemisphere, utilizing 20 years of retrospective forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric blocking events are categorized into three sectors: Atlantic, Continent, and Pacific. Results indicate substantial variations in the LH contribution among different blocking events. During the first 21 days, the Pacific blockings exhibit the most significant impact on prediction skill under strong LH conditions, followed by the Continent blockings, with the Atlantic blockings being least affected. Interestingly, a consistent pattern emerged across sectors: blocking prediction skill is lower under strong LH than under weak LH. During the first 12 days, the Pacific sector typically exhibit lower prediction skill for blocking onset under strong LH, compared to weak LH. For blocking decay, a consistent pattern is observed: lower prediction skill under strong LH than weak LH. Regarding blocking intensity, the forecasting bias escalates with increasing lead time, particularly for strong LH. This study highlights the need to improve moist process simulations in models, aiming to improve atmospheric blocking forecasting accuracy. [ABSTRACT FROM AUTHOR]

We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO. [ABSTRACT FROM AUTHOR]

The study aimed at complete elimination of lead with minimum energy consumption using electrocoagulation process employing concentric electrodes made of aluminium. The process was optimized using RSM for chosen variables: current intensity, initial pH, initial lead concentration and time. Quadratic models were generated and assessed using four factorial CCD. A significant quadratic regression model was obtained and validated using ANOVA. Complete removal was attained for optimised conditions having 6 pH, 0.3A current intensity, 41 mg/L initial Lead concentration,13 minutes of applied time duration using energy of 0.77 watt-hour per gram removal of lead. Adsorption followed pseudo Ist order kinetics with a good fitting of the Langmuir isotherm model. Overall process was well optimised with quite low operational cost of 0.00092 US $. [ABSTRACT FROM AUTHOR]

Problems with task distribution in cloud data centers persist despite earlier research in cloud computing (CC). Particularly in the infrastructure-as-a-service (IaaS) cloud paradigm. In cloud data centers, effective task allocation is essential due to the restricted availability of resources and virtual machines (VMs). IaaS is one of the main CC models since it controls the backend, which includes VMs and data centers. Cloud service providers can ensure satisfactory service delivery performance in these models by preventing situations of host underutilization or overloading. This is because both results increase network execution time and lead to VM failure. To solve these problems, an improved load balancing approaches was proposed in this work. Therefore, this paper suggested an enhanced load balancing approaches to address these issues. The Artificial Bee Colony (ABC) method and the Bat algorithm are combined to create the balancing technique known as the Hybrid BAT and ABC (HBABC) algorithm, which dynamically distributes resources. The suggested HBABC method was assessed using CloudSim and standard workload format (SWF) data sets, which had file sizes of 200 KB and 400 KB. The evaluation was conducted on even workloads ranging from 200 to 20,000, and the performance of the HBABC method was compared with other state-of-the-art algorithms. The implementation of the suggested HBABC method resulted in a reduction of the Makespan (energy level) within the data center and showed improved accuracy in task allocation for VMs in a cloud data center. The ANOVA comparison test revealed a 1.98 percent enhancement in VM accuracy and task distribution, as well as a 0.98 percent decrease in the Makespan or energy level of the cloud data center. The outcomes are in line with various services broker rules that are employed during process of simulating the suggested algorithm in a cloud datacenter. The suggested method will be employed in subsequent studies as a prediction strategy for the resource management system in cloud datacenters. [ABSTRACT FROM AUTHOR]

Retailers are increasingly promising a maximum fulfillment time window for fulfilling customer orders. Motivated by such guarantees, we consider an infinite-horizon periodic-review base-stock inventory system that imposes an endogenously determined fulfillment-time limit for accepted orders. The inventory position is reviewed every fixed T periods and an order is placed to bring the system inventory up to R, the target base stock. The replenishment lead time is a constant of L periods. In each period, the realized consumer demand is accepted only if it can be filled within a guaranteed time limit of τ periods. By incorporating a fulfillment-time limit, the model allows both backorders and lost sales. The objective is to find the strategy that minimizes the long-term average cost per unit time by selecting the base stock and guaranteed-delivery time limit. The behavior of the optimal R with respect to the guaranteed service time τ is investigated, and subsequently, the best value of τ is identified. Interestingly, when τ is greater than or equal to L, the best value of τ is independent of the holding cost and the base stock R. Regularity conditions are developed that assure the cost function is unimodal in R, guaranteeing the optimal base-stock solution can be found easily for a broad family of demand distributions. The solution procedure is extended to the case when the mean customer demand depends on the fulfillment time window promised by the retailer. We find greater customer sensitivity to the fulfillment time leads to a decrease in the optimal fulfillment time guarantee. [ABSTRACT FROM AUTHOR]

Copyright of Scientia Iranica. Transaction E, Industrial Engineering is the property of Scientia Iranica 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.)

Pathology is a subject which demands deep understanding of the fundamentals. This entails regular reading, discussion, viewing the organs and slides to understand the evolution of the disease. For a sound understanding of the subject a robust clinicopathological correlation is a must. However, quite frequently students resort to rote learning of the concepts which over time leads to losing interest in the subject. In this pandemic induced circumstance, the challenge had suddenly increased. We embarked on this project with an aim to compare the effectiveness of the traditional didactic lecture (DL), Online web base learning which is the E learning (EL) and a mix of voice annotated/ audio video presentation followed by interactive large group discussion on the topic , which is a Blended learning (BL) and come to the best possible Teaching-Learning Method (TLM). 124 second year MBBS students participated in the study, of which 41 participated in the DL group, 42 were in EL group and 41 were in BL group. They were taught a topic on alcoholic liver disease. There was significant improvement in the scholastic performance of the BL group. The BL group perceived this method as more interesting and interactive. The study highlights the advantage of BL over EL and DL as the former provides an all round understanding and transforms the learning experience of the students. [ABSTRACT FROM AUTHOR]

Time series classification is a task which deals with temporal sequences, a prevalent data type common in domains such as human activity recognition, sports analytics and general sensing. In this area, interest in explanability has been growing as explanation is key to understand the data and the model better. Recently, a great variety of techniques (e.g., LIME, SHAP, CAM) have been proposed and adapted for time series to provide explanation in the form of saliency maps, where the importance of each data point in the time series is quantified with a numerical value. However, the saliency maps can and often disagree, so it is unclear which one to use. This paper provides a novel framework to quantitatively evaluate and rank explanation methods for time series classification. We show how to robustly evaluate the informativeness of a given explanation method (i.e., relevance for the classification task), and how to compare explanations side-by-side. The goal is to recommend the best explainer for a given time series classification dataset. We propose AMEE, a Model-Agnostic Explanation Evaluation framework, for recommending saliency-based explanations for time series classification. In this approach, data perturbation is added to the input time series guided by each explanation. Our results show that perturbing discriminative parts of the time series leads to significant changes in classification accuracy, which can be used to evaluate each explanation. To be robust to different types of perturbations and different types of classifiers, we aggregate the accuracy loss across perturbations and classifiers. This novel approach allows us to recommend the best explainer among a set of different explainers, including random and oracle explainers. We provide a quantitative and qualitative analysis for synthetic datasets, a variety of time-series datasets, as well as a real-world case study with known expert ground truth. [ABSTRACT FROM AUTHOR]

In this paper, we consider fixed time delays in auctions with resale. The time delay may impact the bidders' valuations of the object. In particular, the auction's winner may obtain some value by depleting the object, either by consuming it or exploiting it, over the interim period and the loser may lose some value by virtue of the object being depleted by the winner. Our main result is that fixed time delays lead to asymmetric bid distributions. For a special family of probability distributions, we show that the first-price auction is revenue superior to the second-price auction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) 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.)

Innovation usually requires time‐consuming exploratory approaches. However, external shocks and related crises, such as the COVID‐19 pandemic, lead to severe time pressures, which require short‐term R&D results. We investigate how organizations' prior collaboration and existing knowledge not only helped them cope with the crisis but also affected the vaccine's development performance. Specifically, we investigate the R&D outcomes of 386 organizations involved in the COVID‐19 vaccine's development within the first 18 months after the pandemic's outbreak. The results reveal that under urgency, organizations with prior scientific collaborations and technological knowledge exhibit a higher R&D performance. Furthermore, a broad network of diverse collaborators strengthened this relationship, thereby calling for more interdisciplinary R&D activities. We therefore extend the literature on innovation speed and strengthen long‐term R&D outcomes' role in organizations with a broad existing knowledge base and collaboration networks. We do so by specifically supporting such organizations' ability to integrate their previous R&D's and collaborations' knowledge to achieve rapid innovative outcomes under urgency. [ABSTRACT FROM AUTHOR]

By considering market demand information, the objective of this study is to measure the bullwhip effect in a two-stage supply chain with one supplier and multiple retailers. First, a model is detailed for measuring the bullwhip effect in which multiple retailers exhibit AR(1) demand processes and the order-up-to inventory level is calculated using market demand information. We find results that the bullwhip effect increases with the market share and lead time and decreases with the contemporaneous correlation and the number of retailers. Second, by comparing the bullwhip effect with or without market information, our model can better control and reduce the bullwhip effect. Third, we calculate the value of market information with the newsvendor model and discover that the value of market information increases with the number of retailers and the contemporaneous correlation and decreases with the market share. In addition, numerical analyses are given to verify the previous results. Moreover, some suggestions are presented to help managers make decisions on the bullwhip effect. [ABSTRACT FROM AUTHOR]

We assess how a retailer can manage inventories and labor under an extreme condition such as a temporary negative demand shock due to a pandemic or an economic turmoil. This analysis incorporates labor market frictions whereby firms incur deadweight costs associated with hiring and firing employees, as well as the option to furlough labor. We examine the impact of these frictions on a retail firm's optimal operating policies around inventory level, furlough, and layoffs. We find that labor market frictions condition the inventory and the level of employment in two ways. First, they lead to underinvestment in inventories, which limits recovery and employment in the postshock period. Second, high labor market frictions motivate the firm to conservatively downsize workforce during the negative demand period leading to higher employment, when compared to downsizing without friction. A significant contribution of our study lies in delineating the optimal furlough decisions and quantifying the impact of the furlough option on inventory and labor decisions. We demonstrate the conditions under which it is optimal for the retailer to either (i) fully downsize labor and leverage the furlough option in the labor market, or (ii) maintain excess labor while also opting to furlough a portion of the workforce. During an extreme event such as a temporary negative demand shock, our results highlight the need for a coordinated effort when implementing governmental subsidy policies on alleviating labor and inventory reductions by accounting for labor market frictions and furlough support. [ABSTRACT FROM AUTHOR]

In this paper, we present the implementation of the Density Functional Theory (DFT) method using the Geant4-DNA framework in the Single Instruction Single Data (SISD) mode. Furthermore, this implementation is improved in terms of execution time within the GeantV project with vectorization techniques such as Single Instruction Multiple Data (SIMD). Within this framework, a set of SIMD strategies in molecular calculation algorithms such as one-electron operators, two-electron operator, quadrature grids, and functionals, was implemented using the VecCore library. The applications developed in this work implement two DFT functionals, the Local Density Approximation (LDA) and the General Gradient Approximation (GGA), to approximate the molecular ground-state energies of small molecules and amino acids. To assess the performance of the implementations, a standard test simulation was performed in multiple CPU platforms. The SIMD vectorization strategy significantly accelerates DFT calculations, leading to time ratios ranging from 1.6 to 5.4 in either individual steps or entire implementations when compared with the scalar process within Geant4. [ABSTRACT FROM AUTHOR]

This research validates tropical cyclone (TC) forecasts from the National Centre for Hydro‐Meteorological Forecasting of Vietnam, the Regional Specialized Meteorological Center (RSMC) Tokyo, and global models (the Japanese Meteorological Agency [JMA], global spectral model [GSM], the National Centers for Environmental Prediction [NCEP], Global Forecast System [GFS], and the European Centre for Medium‐Range Weather Forecasts [ECMWF] Integrated Forecasting System [IFS]) for 72 activated TCs over the Bien Dong Sea. The best quality track forecasts come from the IFS and then GFS and GSM, while the GFS provides a good intensity forecast compared to IFS and GSM. The official guidance and model track forecast tend to be slower and leftward compared to observations (negative biases of along and cross‐track errors). Regarding the ECMWF's ensemble track forecast, the strike probability forecast is quite overforecast/overconfident, especially for a lead time of 72 h and at high probabilities. [ABSTRACT FROM AUTHOR]

The operational sea ice forecasts from the Arctic Ice Ocean Prediction System (ArcIOPS) during 2021–2023 are validated against satellite-retrieved sea ice concentration and drift data, in situ and reanalyzed sea ice thickness data. The results indicate that the ArcIOPS has a reliable capacity on the Arctic sea ice forecasts for the future 7 days. Over the validation period, the root mean square error (RMSE) of the ArcIOPS sea ice concentration forecasts at a lead time of up to 168 h ranges between 8% and 20%, and the integrated ice edge error (IIEE) is lower than 1.6 × 106 km2 with respect to the Hai Yang 2B (HY-2B) sea ice concentration data. Compared to the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS), sea ice volume evolution from the ArcIOPS forecasts is closer to that derived from the CS2SMOS sea ice thickness observations, which have been assimilated into the ArcIOPS. Sea ice thickness comparisons at three locations in the Beaufort Sea between the ArcIOPS forecasts and in situ mooring observations also prove that the sea ice thickness forecasts are credible, which sets a solid basis for supporting ice-breaker navigation in the Arctic thick ice zone. The sea ice drift deviations between the ArcIOPS forecasts and the National Snow and Ice Data Center (NSIDC) data are lower than 4 cm/s in most of the months. Future work will emphasize on developing multi-variable data assimilation scheme and fully coupled air‒ice‒ocean forecasting system for the Arctic sea ice forecasts. [ABSTRACT FROM AUTHOR]

In August 2022, Pakistan experienced unprecedented monsoon rains, leading to devastating floods and landslides affecting millions. While previous research has mainly focused on the contributions of seasonal and synoptic anomalies, this study elucidates the dominant influences of tropical and extratropical intraseasonal oscillations on both the occurrence and subseasonal prediction of this extreme rainfall event. Our scale-decomposed moisture budget analysis revealed that intense rainfall in Pakistan was triggered and sustained by enhanced vertical moisture transport anomalies, primarily driven by interactions between intraseasonal circulation anomalies and the prevailing background moisture field when tropical and mid-latitude systems coincided over Pakistan. Evaluation of subseasonal-to-seasonal prediction models further highlighted the critical role of tropical intraseasonal modes in causing this extreme rainfall event in Pakistan. Models that accurately predicted northward-propagating intraseasonal convection with a forecast lead time of 8–22 days demonstrated good skill in predicting the extreme event over Pakistan. [ABSTRACT FROM AUTHOR]

Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study, we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time, the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination ( R 2 ) of 0.972. In addition, in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time, with a corresponding R 2 of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability. [ABSTRACT FROM AUTHOR]

Drought has extensive, far-reaching, and long-lasting asymmetric effects on vegetation growth worldwide in the context of global warming. However, to date, few scholars have attempted the systematic quantification of the temporal effects of drought on global vegetation across various vegetation types and diverse climate zones. Addressing this gap, we quantitatively investigated the effects of drought on global vegetation growth under various scenarios, considering lagged and cumulative effects as well as combined effects in the 1982–2018 period. Our investigation was based on long-term net primary productivity (NPP) and two multiple-timescale drought indices: the standardised precipitation index (SPI) and the standardised precipitation and evapotranspiration index (SPEI). Our main findings were the following: (1) SPI and SPEI exhibited lagged effects on 52.08% and 37.05% of global vegetation, leading to average time lags of 2.48 months and 1.76 months, respectively. The cumulative effects of SPI and SPEI were observed in 80.01% and 72.16% of global vegetated areas, respectively, being associated with relatively longer cumulative timescales of 5.60 months and 5.16 months, respectively. (2) Compared to the scenario excluding temporal effects, there were increases in the explanatory powers of SPI and SPEI for variations in vegetation NPP based on the lagged, cumulative, and combined effects of drought: SPI increased by 0.82%, 6.65%, and 6.92%, respectively, whereas SPEI increased by 0.67%, 5.73%, and 6.07%, respectively. The cumulative effects of drought on global vegetation NPP were stronger than the lagged effects in approximately two-thirds (64.95% and 63.52% for SPI and SPEI, respectively) of global vegetated areas. (3) The effects of drought on vegetation NPP varied according to climate zones and vegetation types. Interestingly, vegetation in arid zones was the most sensitive and resilient to drought, as indicated by its rapid response to drought and the longest cumulative timescales. The vegetation NPP in tropical and temperate zones exhibited a relatively stronger response to drought than that in cold and polar zones. The strongest correlation of vegetation NPP with drought occurred in shrubland areas, followed by grassland, cropland, forest, and tundra areas. Moreover, for each vegetation type, the correlations between vegetation NPP and drought differed significantly among most climate zones. (4) The vegetation NPP in warming-induced drought regions displayed a higher correlation to drought than that in non-warming-induced drought regions, with shorter lagged and longer cumulative timescales. Our findings highlight the heterogeneity of the lagged, cumulative, and combined effects of drought across various climate zones and vegetation types; this could enhance our understanding of the coupling relationship between drought and global vegetation. [ABSTRACT FROM AUTHOR]

Background: Promoting the use of insecticide-treated mosquito nets (ITNs) is one of the main strategies for reducing malaria-related morbidity. An innovative activity-based contracting (ABC) approach has been implemented in Kwilu Province, Democratic Republic of Congo to optimize ITN mass distribution campaigns, with payments based on contractually defined programmatic outcomes for key campaign activities following independent verification of results. Methods: This internal evaluation was carried out using a mixed methods approach combining qualitative and quantitative document and content analysis from a series of three workshops: validation workshops for campaign results at provincial level for the 2021 and 2022 campaigns; internal evaluation workshop for the Kwilu campaign as part of the ABC approach organized by "Santé pour tous en milieu rural" (SANRU) with its sub-contractors; and national campaign evaluation workshop organized by the National Malaria Control Program. Results: The pilot campaign with the ABC approach in Kwilu has demonstrated better results than campaigns conducted using the standard, non-ABC, approach: better household coverage (99.9% vs. 97.3%) and improved compliance with ITN allocation to households based on the household size (98. 9% vs. 84.7%); lower loss of ITNs (0.3% vs. 0.5%) with immediate penalties for lost ITNs in the province under the ABC approach; shorter campaign lead times (14 vs. 28 weeks from the start of training to the launch of distribution). This last point is crucial, as it is likely to generate efficiencies and contribute to ensuring timely ITN replacement campaigns. Conclusion: The challenges encountered and the lessons learned in the implementation of the pilot ABC approach in Kwilu could guide future distribution campaigns in the DRC and other African countries that would like to engage in distribution campaigns based on performance-based incentive contracts. [ABSTRACT FROM AUTHOR]

The traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the perturbations. Eliminating small scales from the perturbations also yields similar forecast improvements. Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates. Plain Language Summary: This study examines a deep‐learning approach to understanding how small changes to initial conditions impact weather forecasts. Traditionally, a linear approach known as the adjoint method has been used to determine the sensitivity of forecasts to initial conditions. We leverage recent advancements in machine learning to find optimal initial conditions using the backpropagation method within deep‐learning frameworks. This approach iteratively searches for initial conditions that produce the best forecasts. We apply this method to GraphCast forecasts of the June 2021 Pacific Northwest extreme heatwave. We find that small changes to the initial conditions yield nearly perfect 10‐day weather forecasts of the heatwave in both the GraphCast and the Pangu‐Weather models. This research suggests that a significant increase in forecast skill may be possible from existing observations through better estimates of initial conditions. Key Points: We use nonlinear gradient descent to optimize initial conditions for weather forecasting with machine learning modelsApplication to the Pacific Northwest June 2021 heatwave reduces 10‐day forecast error by over 90 percentForecast improvements are not sensitive to the forecast model, and derive mainly from analysis errors on synoptic and larger scales [ABSTRACT FROM AUTHOR]

Accurate and timely water level predictions are essential for effective shoreline and coastal ecosystem management. As sea levels rise, the frequency and severity of coastal inundation events are increasing, causing significant societal and economic impacts. Predicting these events with sufficient lead time is essential for decision-makers to mitigate economic losses and protect coastal communities. While machine learning methods have been developed to predict water levels at specific sites, there remains a need for more generalized models that perform well across diverse locations. This study presents a robust deep learning model for predicting water levels at multiple tide gauge locations along the Gulf of Mexico, including the open coast, embayments, and ship channels, all near major ports. The selected architecture, Seq2Seq, achieves significant improvements over the existing literature. It meets the National Oceanic and Atmospheric Administration's (NOAA) operational criterion, with the percentage of predictions within 15 cm for lead times up to 108 h at the tide gauges of Port Isabel (92.2%) and Rockport (90.4%). These results represent a significant advancement over current models typically failing to meet NOAA's standard beyond 48 h. This highlights the potential of deep learning models to improve water level predictions, offering crucial support for coastal management and flood mitigation. [ABSTRACT FROM AUTHOR]

The Atlantic Niño, a primary climatic variability mode in the equatorial Atlantic Ocean, exhibits pronounced variability not only in boreal summer but also in winter. However, the role of the winter Atlantic Niño in trans-basin interactions remains underexplored compared to its summer counterpart. Through analysis of observational reanalysis data since the mid-twentieth century, here we found that the winter Atlantic Niño significantly influences the development of El Niño–Southern Oscillation (ENSO), surpassing the impact of summer Atlantic Niño, with a longer lead time. This effect is reasonably captured in the CMIP6 Historical simulations from a multi-model ensemble perspective. Further analysis of the global warming scenario projects that the influence of winter Atlantic Niño on ENSO will persist into the future, in contrast to the reduced impact of summer Atlantic Niño. Therefore, these findings underscore the importance of further investigating the winter Atlantic Niño to gain a comprehensive understanding of trans-basin interactions and their future changes. [ABSTRACT FROM AUTHOR]

Ensemble forecast post‐processing is a necessary step in producing accurate probabilistic forecasts. Many post‐processing methods operate by estimating the parameters of a predetermined probability distribution; others operate on a per‐lead‐time or per‐station basis. All of the aforementioned factors either limit the expressive power of the methods in question or require additional models, one for each lead time and station. We propose a novel, neural network‐based method that produces forecasts for all lead times jointly and requires a single model for all stations. We incorporate normalizing spline flows as flexible parametric distribution estimators, which enables us to model complex forecast distributions. Furthermore, we demonstrate the effectiveness of our method in the context of the EUPPBench benchmark, where we conduct 2‐m temperature forecast post‐processing for stations in a subregion of Europe. We show that our novel method exhibits state‐of‐the‐art performance on the benchmark, improving upon other well‐performing entries. Additionally, by providing a detailed comparison of three variants of our novel post‐processing method, we elucidate the reasons why our method outperforms per‐lead‐time‐based approaches and approaches with distributional assumptions. [ABSTRACT FROM AUTHOR]

In this study, the medium‐range predictability of heatwave (HW) onsets in four midlatitude European regions is investigated statistically with the help of ensemble reforecasts for the period 2001–2018. The concept of Euro‐Atlantic weather regimes is adopted to characterise HWs (about 50 in each region) and to study whether forecast skill may depend on the large‐scale dynamical setup. HW onsets over the British Isles and Scandinavia are mainly associated with Scandinavian and European blocking regimes, whereas the "no regime" case is observed more frequently for Central Europe. Stratified by weather regime, the predictability of heatwave onsets is then studied by means of a multiple metric‐based analysis of European Centre for Medium‐Range Weather Forecasts (ECMWF) and Global Ensemble Forecast System Version 12 (GEFSv12) ensemble reforecasts. For two of the regions considered, Central Europe and the British Isles, a conclusive picture is obtained: medium‐range predictive skill is significantly higher for HW onsets associated with Scandinavian or European blocking compared with cases with no pronounced regime. This skill advantage mostly concerns the large‐scale flow and, to some extent, 850‐hPa temperatures, but is generally not reflected in the correct prediction of near‐surface temperatures. Finally, we investigate for two regions how exceptionally good or poor forecasts are related to the atmospheric state during or shortly after forecast initialisation. At 10 days lead time, poor large‐scale flow predictive skill for Central European HW onsets is linked to anomalously high baroclinicity further upstream and an intensified North Atlantic jet stream, whereas good forecasts on average feature an initial state close to climatology. Forecast skill for near‐surface temperatures is not affected by such dynamical precursors, but rather by pre‐existing soil‐moisture anomalies. For the British region, exceptionally good forecasts of both large‐scale flow and near‐surface temperatures are associated with an already established continental blocking. In contrast to Central Europe, pre‐existing soil‐moisture anomalies play less of a role there. [ABSTRACT FROM AUTHOR]

Ankle and foot orthotics are suggested for gait rehabilitation therapy after a stroke. Clinical practice necessitates adjusting the ankle foot orthosis torque to accommodate each patient's unique stride and body function. The objective of this paper is to develop a customized, cost-effective footplate orthosis from carbon fibers, manufactured using 3D printing, for individuals with partial foot amputations. The footplate is designed for use in developing nations and aimed to be made available with a reduced lead time. The experiment work carried out in this research is used to design, analyze, and validate a prosthetic footplate. A foot of size UK 7 with a 2 mm thick model of the human foot was modeled using Unigraphics-NX. The footplate's design is being tested both conceptually and empirically. The footplate manufactured by applying load at toe and heel shows that the carbon fiber successfully restores leg length and reduces pressure on the feet's delicate distal end. [ABSTRACT FROM AUTHOR]

We introduce deep generative diffusion for multivariate and regional surrogate modeling learned from sea‐ice simulations. Given initial conditions and atmospheric forcings, the model is trained to generate forecasts for a 12‐hr lead time from simulations by the state‐of‐the‐art sea‐ice model neXtSIM. For our regional model setup, the diffusion model outperforms as ensemble forecast all other tested models, including a free‐drift model and a stochastic extension of a deterministic data‐driven surrogate model. The diffusion model additionally retains information at all scales, resolving smoothing issues of deterministic models. Furthermore, by generating physically consistent forecasts, previously unseen for such kind of completely data‐driven surrogates, the model can almost match the scaling properties of neXtSIM, as similarly deduced from sea‐ice observations. With these results, we provide a strong indication that diffusion models can achieve similar results as traditional geophysical models with the significant advantage of being orders of magnitude faster and solely learned from data. Plain Language Summary: Thanks to generative deep learning, computers can generate images that are almost indistinguishable from real images. We use this technology to forecast the sea‐ice with models that are solely learned from data, here from simulation data. Doing so for a region North of Svalbard, we enhance the accuracy of the model and maintain their sharpness. The learned model further depicts physical processes as similarly observed for the targeted physical‐driven model. Therefore, this technology could provide us with the necessary tools to learn faster models from data that have similar properties to those based on physical equations. Key Points: We introduce the first denoising diffusion model designed for sea‐ice physicsGenerative diffusion outperforms deterministic surrogates and retains the sharpness in the forecasts as observed in the targeted simulationsOur model generates forecasts that exhibit physical consistency between variables in space and time [ABSTRACT FROM AUTHOR]

To explore tropical cyclone prediction methods that integrate multimodal meteorological data, this study proposes a novel approach. The proposed model employs an LSTM-based temporal branch to extract temporal sequence features from the CMA dataset and a U-Net-based spatial branch to extract three-dimensional spatial features from the ERA5 dataset. These features are then fused through an encoder-decoder structure to integrate high-dimensional spatiotemporal characteristics. Experimental results demonstrate that the spatiotemporal model significantly improves the prediction accuracy for 24-hour lead times. Subsequently, to further optimize the experimental results, the study introduces an ensemble forecasting framework. This framework enhances prediction accuracy by adjusting the outputs of multiple spatiotemporal model prediction members. The optimization is achieved by solving the objective function that reflects the forecast geographical error, thereby optimizing the weighted coefficients. The experimental results indicate that the ensemble forecasting framework can further optimize prediction outcomes. [ABSTRACT FROM AUTHOR]

The traditional computer with von Neumann architecture has the characteristics of separate storage and computing units, which leads to sizeable time and energy consumption in the process of data transmission, which is also the famous "von Neumann storage wall" problem. Inspired by neural synapses, neuromorphic computing has emerged as a promising solution to address the von Neumann problem due to its excellent adaptive learning and parallel capabilities. Notably, in 2016, researchers integrated light into neuromorphic computing, which inspired the extensive exploration of optoelectronic and all-optical synaptic devices. These optical synaptic devices offer obvious advantages over traditional all-electric synaptic devices, including a wider bandwidth and lower latency. This review provides an overview of the research background on optoelectronic and all-optical devices, discusses their implementation principles in different scenarios, presents their application scenarios, and concludes with prospects for future developments. [ABSTRACT FROM AUTHOR]

A single‐panel complex holographic display with a three‐phase covalent macropixel featuring ultralow noise and high‐efficiency 3D image generation is proposed. The essential feature of the proposed three‐phase covalent macropixel is the point‐symmetric covalent concatenation of sub‐pixels and an adaptive computer‐generated hologram encoding algorithm. The proposed single‐panel holographic display exhibits three times signal efficiency enhancement, 1/38 reduction of DC noise, and 1/50 reduction of conjugate noise compared to conventional holographic displays in the generation of full‐color computer‐generated hologram images. This noise reduction leads to three times the optical efficiency enhancement of holographic displays. [ABSTRACT FROM AUTHOR]

Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed. [ABSTRACT FROM AUTHOR]

Conventional metal (sand) casting requires solid patterns consisting of two halves (cope and drag) prepared to remove the pattern. The approach is simple but leads to numerous steps and mismatch errors. Also, sand, a porous material, is very sensitive to vibration and susceptible to cracks and breakage. This research presents a novel approach for investment metal casting, where a water-soluble material is used for pattern generation using material extrusion additive manufacturing (AM). As a proof of concept, a semi-complex non-prismatic geometry with various dimensional features is physically realized using this soluble pattern casting (SPC) technique. The pattern is designed and 3D printed out of a water-soluble acrylonitrile butadiene styrene (ABS) thermoplastic using an indigenously fabricated screw extrusion–based AM setup. A ceramic mould is created from plaster of Paris (PoP) around the soluble pattern, generating the mould cavity on further dissolution. A heated water bath with added turbulence via solid vibrations assisted the dissolution process. The final geometry is realized by firing the mould cavity followed by metal pouring. Various geometrical features and intricate details, such as layer lines, are satisfactorily replicated from the 3D-printed pattern to the final metal casting. The dimensional accuracy and surface finish are analysed along the process, starting from the printed pattern to the ceramic mould cavity and the final metal cast part. The presented method has applications in investment casting (IC) industries as it can help significantly reduce the lead time and provide excellent dimensional conformance and geometrical replication from the pattern to cast. [ABSTRACT FROM AUTHOR]