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Dynamic reordering and inspection for the multi-item Inventory Record Inaccuracy problem
Get PDFInventory Record Inaccuracy (IRI) is a significant challenge in inventory management, caused by discrepancies between actual stock and inventory records due to factors such as spoilage, theft, and obsolescence. Despite extensive academic focus on robust ordering and inventory inspection planning, current literature either considers these in isolation, or focuses on stylized single-item scenarios, not addressing the full dynamics of reordering and inspection in warehouses with many SKUs. We consider such a large warehouse with several heterogeneous items that are subject to shrinkage and limited inspection opportunities. Our approach jointly optimizes reordering and inspection decisions in a dynamic decision framework, incorporating both inspection and travel times. We introduce an algorithmic pipeline for dynamic reordering and inspection that combines a neural network to decide on replenishments and a mixed-integer program to determine an optimal inspection subset. Our results show that ignoring shrinkage can increase costs by up to 95.3% and inspection already becomes viable at a relatively low level of IRI. Our proposed policy saves 8.3% in total costs compared to a static reordering and inspection benchmark, and 21.8% compared to a no-inspection policy.</p
The relationship of digital transformation and corporate sustainability:Synergies and tensions
Get PDFScholars within management disciplines have shown a growing interest in digital transformation and sustainability phenomena to address global societal challenges. Indeed, previous studies have investigated the initial analysis of the intersection between these two emerging and intertwined topics. However, there has been no comprehensive or critical analysis of the relationships among these concepts. Nevertheless, a clear understanding of this phenomenon is key to developing rigorous and meaningful knowledge and enabling future research. Our critical review analyses 91 articles on digital transformation and sustainability research to address this issue. The findings propose a synthesis of the definition types of digital transformation and sustainability in four categories, from which only 16 articles show a relationship between both concepts. This study theoretically contributes to management research by uncovering issues and assumptions around the conceptualizations of digital transformation and sustainability at the corporate level. By doing so, we present a consolidation of conceived knowledge and clarify these interrelated concepts. Moreover, understanding and assessing these relationships will lead to a future research agenda and implications for practitioners.</p
Great saphenous vein arterialisation in patients with severe chronic limb threatening ischemia:a last resort for limb salvage
Get PDFBackground: In one-fifth of patients with chronic limb-threatening ischemia, there are no revascularization options. In those cases, venous arterialization could be a last resort for limb salvage. This study examines the clinical outcome of 17 patients with nonhealing wounds (Fontaine 4), who underwent great saphenous vein (GSV) arterialization, leaving the distal saphenous side branches open and avoiding incisions in the lower leg and foot. Methods: In this retrospective study, all the patients who underwent GSV arterialization between January 1, 2020, and October 1, 2023, were included. During the procedure, a small incision was made in the groin to identify the GSV and superficial femoral artery. The GSV was detached from the deep venous system, the upper leg side branches were ligated, and the side branches distal to the knee joint were left open to create a pressure drop. Valvulotomy of the GSV down to the foot was performed in all patients. This approach does not necessitate the use of grafts or stents so that a severely infected foot can also be treated. For this study, we have analysed the limb salvage, wound healing, and mobility after treatment and observed preoperative and postoperative skin oxygenation over a 1-year period. Results: This intervention resulted in a limb salvage rate of 46% and a secondary patency of 88% after 1 year in cases where amputation would have otherwise been necessary. Preoperative and postoperative TcPO2 measurements indicated a gradual but consistent increase in skin oxygenation in 71% of the patients with a GSV larger than 3 mm, highlighting the importance of future patient selection. All patients who successfully received arterialization achieved complete wound healing and improved mobility. Conclusions: This study introduced a promising approach to venous arterialization in patients with severe chronic limb threatening ischemia who have no other treatment options, with the potential to significantly reduce disease burden and improve quality of life.</p
Collaboratively Increasing the DDoS-Resilience of Digital Societies Through Anti-DDoS Coalitions
Get PDFDistributed denial-of-service (DDoS) attacks continue to plague the Internet and are a risk to the availability of critical digital systems that we increasingly depend on in our daily lives, such as financial services and the Internet infrastructure. To curb this problem, we propose the novel concept of an Anti-DDoS Coalition (ADC), which is a group of network operators that collaboratively increase their DDoS-readiness by sharing fingerprints of the DDoS attacks they handle, and carries out DDoS exercises together. The novelty of an ADC is that it combines the technical systems for both of these activities with the legal and governance means to deploy ADCs in practice. This multidisciplinary approach is unlike previous work on collaborative DDoS mitigation that focused on technology development (and largely failed). We make three contributions. First, we develop a multidisciplinary blueprint for ADCs in terms of their activities (sharing DDoS fingerprints and carrying out DDoS exercises) and supporting legal agreements and governance mechanisms. Second, we design two open-source technical systems for ADCs: a "DDoS Clearing House" for sharing DDoS fingerprints, and a "DDoS-CH Cyber Range" for carrying out small-scale DDoS exercises, both of which extend network operators' existing scrubbing services and other standard anti-DDoS measures. Third, we validate the concept of an ADC in practice with the Netherlands' national Anti-DDoS Coalition (Dutch ADC), a joint effort of 22 network operators from industry, government, and academia that are currently deploying the DDoS-CH and the Cyber Range in production.</p
A sparse optimization approach to infinite infimal convolution regularization
No full textIn this paper we introduce the class of infinite infimal convolution functionals and apply these functionals to the regularization of ill-posed inverse problems. The proposed regularization involves an infimal convolution of a continuously parametrized family of convex, positively one-homogeneous functionals defined on a common Banach space X. We show that, under mild assumptions, this functional admits an equivalent convex lifting in the space of measures with values in X. This reformulation allows us to prove well-posedness of a Tikhonov regularized inverse problem and opens the door to a sparse analysis of the solutions. In the case of finite-dimensional measurements we prove a representer theorem, showing that there exists a solution of the inverse problem that is sparse, in the sense that it can be represented as a linear combination of the extremal points of the ball of the lifted infinite infimal convolution functional. Then, we design a generalized conditional gradient method for computing solutions of the inverse problem without relying on an a priori discretization of the parameter space and of the Banach space X. The iterates are constructed as linear combinations of the extremal points of the lifted infinite infimal convolution functional. We prove a sublinear rate of convergence for our algorithm and apply it to denoising of signals and images using, as regularizer, infinite infimal convolutions of fractional-Laplacian-type operators with adaptive orders of smoothness and anisotropies.</p
Tailoring Nickel Oxide Thin Films:Comparative Study of Oxidizing Agents in Thermal and Plasma-Enhanced Atomic Layer Deposition
Get PDFThermal atomic layer deposition (TALD) and plasma atomic layer deposition (PALD) were used for producing thin NiOx films from nickel(II) acetylacetonate Ni(acac)2, employing different oxidizing agents (deionized water H2O, ozone O3, and molecular oxygen O2). The films were deposited at 300 °C (TALD) and 220 °C (PALD) over glass substrates; their physical and chemical properties were considerably influenced by the choice of oxidizing agents. In particular, ALD(H2O) samples had a low growth per cycle (GPC) and a high concentration of defects. The best NiOx parameters were achieved with PALD(O2), featuring high GPC (0.07 nm/cycle), high optical transparency in the visible region, electrical resistivity (1.18 × 104 Ω·cm), good carrier concentration (8.82 × 1013 cm–3), and common mobility (5.98 cm2/V·s). The resulting NiOx films are polycrystalline and homogeneous in thickness and composition. According to ultraviolet photoelectron spectroscopy (UPS), work function φ and the valence band maximum EV can be tuned by the choice of the coreactant employed, with variations of up to ∼1 eV between TALD and PALD synthesis. Our results suggest that PALD permits one to achieve a better energy band alignment of NiOx and CsFAMAPbBrI perovskite, which is promising for solar cell applications
Pore formation and pore inter-connectivity in plasma electrolytic oxidation coatings on aluminium alloy
Get PDFThe porosity and microstructure of plasma electrolytic oxidation (PEO) coatings are key factors in determining their properties and applications. Despite advances in understanding the PEO process, the mechanisms driving pore formation and their correlation with process parameters remain unclear due to the complex interplay between these variables. This study investigates the effects of treatment time and duty cycle on the microstructure of PEO coatings produced on an aluminium alloy in an alkaline electrolyte, with a particular focus on pore formation. Our findings reveal that longer treatment durations lead to the significant development of sub-surface pores at the interface between the inner and outer layers. Additionally, a lower duty cycle leads to an increase in sub-surface pores, while a higher duty cycle favours the formation of surface pores. Morphological, 3D microstructural mapping, and chemical analyses reveal that pore formation is driven by the micro-discharges, gas generation, and the preferred gas escape path within the micro-melt pools formed during the PEO formation process. The preferred gas escape path is closely linked to the characteristics and lifetime of local micro-melt pools, elucidating the mechanisms behind pore formation
Uncertainty quantification in sequential hybrid deep transfer learning for solar irradiation predictions
Get PDFHybrid deep learning model with multi-frequency capabilities is presented for simulating solar irradiation. Utilizing hourly recorded solar irradiation and climate data, model employs Convolutional Neural Network (CNN) to capture spatial and Long Short-Term Memory (LSTM) network to capture temporal characteristics. Initially, local models are developed individually, trained on single datasets. Additionally, global models are trained collectively by fusing multiple datasets. Furthermore, the study delves into transfer learning. To evaluate the uncertainty of prediction, Prediction Intervals (PIs) are computed using the bootstrap method and its results are compared with those of Bayesian Neural Network (BNN). The findings indicate that hybrid CNN-LSTM model surpasses single models in point predictions up to 25% in test. Local models demonstrate efficacy when deployed within their respective regions but exhibit notable errors when data scarcity hampers training. Global models exhibit adaptability to individual locations at expense of training efforts. Pre-training models on comprehensive and diverse source dataset, followed by transfer to a target dataset, generally yields superior performance. Concerning the model's performance in terms of PIs, CNN-LSTM model demonstrates the highest efficacy with average Prediction Interval Coverage Probability (PICP) of 0.82 and average Normalized Mean Prediction Interval Width (NMPIW) of 0.043. Also, in strategies involving limited or no available data for training, CNN-based models exhibit superior performance with lower uncertainty
Community of inquiry:A bridge linking motivation and self-regulation to satisfaction with E-learning
Get PDFLearner satisfaction is a key metric that encapsulates the overall e-learning experience. While numerous studies have explored the “what” (i.e., the factors that predict satisfaction), there has been less focus on the “how” (i.e., the mechanisms through which these factors are associated to satisfaction). This study seeks to address this gap by elucidating how two key individual factors, motivation and self-regulation, are associated with satisfaction. It postulates that while these factors are directly associated with learner satisfaction, they also have an indirect relationship through their association with the Community of Inquiry (CoI) presences—social, cognitive, and teaching—which serve as mediating factors. Data were collected from 247 master's students enrolled in online programs at three state universities in Iran. Path analysis was performed to study the interactions between these variables. The findings provide valuable insights into their complex relationships, revealing that self-regulation had a more substantial predictive role in learner satisfaction than learner motivation. Furthermore, while both motivation and self-regulation were directly associated with satisfaction, they also had indirect associations through their relationship with the three CoI presences. Within this process, the perception of cognitive presence emerged as a central mediator, highlighting its crucial role in enhancing the e-learning experience. The paper concludes with suggestions for theoretical advancement and practical implications for future e-learning practices
