GLS Industries (Canada)

Abstract Background Endoscopic activity indices for ulcerative colitis (UC) demonstrate limited accuracy, particularly in acute severe UC (ASUC). Artificial intelligence-based scoring (AI) may enhance this. We investigated whether the DovaVision UC AI tool could detect differences in endoscopic response rates between personalised and standard infliximab (IFX) dosing in ASUC patients enrolled in the prospective randomised TITRATE trial, that were previously not detected with human assessment. Methods In TITRATE, adult IFX-naive steroid-refractory ASUC patients were randomised 1:1 to standard dosing (SD) or personalised dosing (PD) with IFX. After an initial 5 mg/kg IFX infusion, patients in the SD group received 5 mg/kg IFX at week 2 and 6. In the PD arm, additional 5 mg/kg IFX infusions were administered guided by a Bayesian pharmacokinetic algorithm (iDose™) aiming to achieve prospectively defined target IFX serum concentrations until day 42. Endoscopies were recorded at baseline and at week 6. Definitions of endoscopic response were ≥2-point improvement in UC endoscopic index of severity (UCEIS) and ≥1-point improvement in Mayo endoscopic subscore (MES) at week 6 compared to baseline. Patients with missing endoscopic data were classified as non-responders. Endoscopic activity was previously scored on blinded videos by 2 independent human experts, with a third for adjudication (1). Here, we applied the DovaVision UC AI tool to the blinded videos. This scored the UCEIS and MES on individual frames and aggregated results to yield final procedure-level scores. Results In total, 48 patients were randomised to PD (n = 23) or SD (n = 25) (Table 1). Human UCEIS or MES assessments could not distinguish differences in response rates (UCEIS: 13 (56.5%) in the PD and 11 (44.0%) in the SD group (p = 0.540); MES: 14 (60.8%) in PD and 10 (40.0%) in SD group (p = 0.250)) (1).Using AI-assessed UCEIS, 27/48 (56.2%) patients achieved endoscopic response, comprising 18/23 (78.3%) in the PD and 9/25 (36.0%) in the SD group (p = 0.004). Using AI-assessed MES, 24/48 patients (50.0%) responded (16/23 (69.6%) in PD and 8/25 (32.0%) in SD group (p = 0.020)) (Figure 1). Conclusion In ASUC patients treated with IFX, the AI tool was able to detect endoscopic response at 6 weeks of IFX treatment, regardless of the endoscopic activity index that was used. The AI tool could also distinguish response rate differences between personalised and standard dosing of IFX. In the original study, human UCEIS or MES assessments could not distinguish such differences. This is the first IBD study in which AI was able to distinguish treatment effects between different treatment strategies, demonstrating its potential to fundamentally improve clinical trial methodology.

The increasing availability of sophisticated information and communication technology has stimulated new research within the distribution logistics area in the last few decades. Real-time information is crucial to ensure not only the competitiveness of a company but also its survival in the e-commerce era. Companies try to offer delivery to their customers within a few hours of receiving a request. In addition, real-time information can be exploited in systems that operate under emergencies, where response time is critical. We model and solve a multi-vehicle inventory-routing problem in which new service requests are revealed dynamically over time, in real-time or online. For this problem, we propose a class of online algorithms based on iteratively solving integer programming models. These models are solved through a tailored branch-and-cut method, in which several families of valid inequalities are separated and dynamically introduced in the model or through a matheuristic to speed up the solution process. We carry out a competitive analysis that allows us to prove the competitive ratio of the online algorithms we propose and, therefore, to evaluate their performance with respect to the optimal solution of the offline problem, in the worst case. An extensive computational experience on benchmark instances shows that these algorithms are also effective on average and require short computational time when the matheuristic is applied to solve the integer programming models. Additional tests on large real-world instances indicate that the proposed solution methods achieve performance that remains reasonable for the size of these instances.

The capacitated p-median problem (CPMP) involves placing p identical facilities in a network and assigning customer nodes to these facilities to satisfy all customer demands with minimal transportation costs. In practical applications, demand and distance parameters are often uncertain during the planning process, leading to infeasible or excessively costly solutions if these uncertainties are disregarded. This paper addresses the robust CPMP (RCPMP), which incorporates demand uncertainty into the problem using the robust optimization paradigm. We propose a general framework to model and solve the RCPMP, considering different polyhedral uncertainty sets, namely the cardinality-constrained and the knapsack sets. We develop exact approaches encompassing compact models, different families of valid inequalities, and branch-and-cut and branch-and-price algorithms, exploring both implemented uncertainty sets and problem structure. Furthermore, we implement an efficient Variable Neighborhood Search (VNS) heuristic to solve these robust variants, which incorporates state-of-the-art algorithms, parallelization techniques, and optimized data structures. Computational experiments using adapted benchmark instances with up to 400 nodes indicate the effectiveness of the proposed approaches. The results show that using parallelization and hash tables within the VNS heuristic promotes significant performance improvements and yields near-optimal solutions for most instances, as well as outperforming the exact approaches in several instances where the optimal solution was not found. Moreover, these results highlight the benefits of using robust solutions in practical settings, especially when considering different uncertainty sets to generate solutions with advantageous trade-offs between cost and risk.

We explore a series of furan-based non-fullerene acceptors and report their optoelectronic properties, solid-state packing, photodegradation mechanism and application in photovoltaic devices. Incorporating furan building blocks leads to the expected enhanced backbone planarity, reduced band gap and red-shifted absorption of these acceptors. Still, their position in the molecule is critical for stability and device performance. We found that the photodegradation of these acceptors originates from two distinct pathways: electrocyclic photoisomerization and Diels-Alder cycloaddition of singlet oxygen. These mechanisms are of general significance to most non-fullerene acceptors, and the photostability depends strongly on the molecular structure. Placement of furans next to the acceptor termini leads to better photostability, well-balanced hole/electron transport, and significantly improved device performance. Methylfuran as the linker offers the best photostability and power conversion efficiency (>14 %), outperforming all furan-based acceptors reported to date and all indacenodithiophene-based acceptors. Our findings show the possibility of photostable furan-based alternatives to the currently omnipresent thiophene-based photovoltaic materials.

In real time scenario Wireless Sensor Network (WSN) applications involve densely deployed system of sensor nodes. To design an energy aware network challenge lies in efficient resource utilization for accurate reporting of event occurrence. Appropriate connectivity of nodes in WSN with optimal energy routing is the motivation of our work. Vicinity based routing algorithms with varied node deployment is implemented and the results are analyzed in the light of energy and network efficiency. The nodes in the Region of Interest (RoI) are hierarchically categorized into different levels. For multihop communication, nodes search only in their vicinity for the next higher-level nodes. Position of sink and distance between the nodes along with residual energy are the key parameters considered for evaluation and analysis.

A novel numerical approach to solving the shallow-water equations on the sphere using high-order numerical discretizations in both space and time is proposed. A space-time tensor formalism is used to express the equations of motion covariantly and to describe the geometry of the rotated cubed-sphere grid. The spatial discretization is done with the direct flux reconstruction method, which is an alternative formulation to the discontinuous Galerkin approach. The equations of motion are solved in differential form and the resulting discretization is free from quadrature rules. It is well known that the time step of traditional explicit methods is limited by the phase velocity of the fastest waves. Exponential integration is employed to enable integrations with significantly larger time step sizes and improve the efficiency of the overall time integration. New multistep-type exponential propagation iterative methods of orders 4, 5 and 6 are constructed and applied to integrate the shallow-water equations in time. These new schemes enable time integration with high-order accuracy but without significant increases in computational time compared to low-order methods. The exponential matrix functions-vector products used in the exponential schemes are approximated using the complex-step approximation of the Jacobian in the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm. Performance of the new numerical methods is evaluated using a set of standard benchmark tests.

This special issue is dedicated to the international project the Year of Tropics–Midlatitude Interactions and Teleconnections (YTMIT) fostered by the World Meteorological Organization through the S...

Activation energy for charge transport, carrier concentration and recombination rate are identified to strongly affect the device characteristics.

Abstract In this paper, theoretical and numerical analyses of the properties of some complex semi-Lagrangian methods are performed to deal with the issues of the instability associated with the treatment of the nonlinear part of the forcing term. A class of semi-Lagrangian semi-implicit schemes is proposed using a modified TR-BDF2 method, which is the combination of the trapezoidal rule (TR) and the second-order backward differentiation formula (BDF2). The process used for the nonlinear term includes two stages as predictor and corrector in the trapezoidal method and one stage for the BDF2 method. For the treatment of the linear term, the implicit trapezoidal method is employed in the first step, the explicit trapezoidal method in the second step, and the implicit BDF2 method in the third step. The combination of these techniques leads to a family of schemes that has a large region of absolute stability, performs well for the purely oscillatory cases, and has good qualities in terms of accuracy and convergence. The use of the explicit method for the linear term in the second step makes the proposed class of schemes competitive in terms of efficiency compared to some well-known schemes that use two steps. Numerical experiments presented herein confirm that the proposed class of schemes performs well in terms of stability, accuracy, convergence, and efficiency in comparison with other, previously known, semi-Lagrangian semi-implicit schemes and semi-implicit predictor–corrector methods. The potential practical application of the proposed class of schemes to a weather prediction model or any other atmospheric model is not discussed and could be the subject of other forthcoming studies.