Original Strategy Research Articles

Optimum design of contra-rotating wind turbines with adjacent rotors

A downstream installed contrarotating rotor can efficiently extract the residual kinetic energy of a wind turbine wake, thus increasing the overall power coefficient. A significant interaction between the two rotors exists so that, to completely exploit the device potential, it is not advisable to adopt a classical design procedure conceived for a single-rotor configuration, as commonly seen in the existing literature. In particular, current approaches also oversimplify the design of the back rotor by merely mirroring the front rotor geometry, rather than optimising it for its power extraction duties. To fill this knowledge gap, the paper presents an original design strategy expressly conceived for contra-rotating wind turbines. This new approach optimises the geometry of both rotors, fully accounting for the interaction between them and the wake rotation at the outlet of the front wheel. The procedure adopts a calculus of variations approach aimed at maximising the overall power coefficient. It relies on a classical actuator disk model and is it valid for two adjacent rotors with the same diameter. As a result, the optimal chord and pitch distributions of the two rotors are evaluated for any combination of the front and back tip speed ratios. The procedure can be applied to marine turbines, too.

Optimizing floating crane operations for efficient bulk product transshipments on inland waterways

In the realm of global supply chains, the optimization of floating crane operations for bulk product transshipment via inland waterways emerges as a crucial necessity to address economic, operational, and environmental imperatives. This research identifies a significant gap in existing methodologies for the scheduling, routing, and assignment of floating cranes, which are essential for improving efficiency and sustainability in maritime logistics. To bridge this gap, we propose the Reinforcement Learning Variable Neighbourhood Strategy Adaptive Search (RL-VaNSAS) algorithm, a novel integration of reinforcement learning with variable neighbourhood search strategies. This advanced model aims to holistically minimize energy consumption, labor costs, and penalty costs, while simultaneously enhancing service efficiency. Through rigorous simulations, RL-VaNSAS was benchmarked against conventional methods such as Differential Evolution (DE), Genetic Algorithm (GA), and the original Variable Neighbourhood Search Adaptive Strategy (VaNSAS), revealing its superior capability in significantly reducing annual energy costs to $1,211,948, labor costs to $270,948, penalty costs to $19,948, and operational hours to 12,087. Demonstrating notable advancements in operational efficiency and cost reduction, RL-VaNSAS offers a sustainable solution to the dynamic challenges of maritime logistics, characterized by fluctuating vessel arrivals and diverse cargo requirements. The findings illuminate the critical need for innovative optimization techniques in enhancing the sustainability and efficiency of maritime logistics operations. RL-VaNSAS not only fills the identified research gap but also sets a new standard for future endeavors in global supply chain management, underlining the importance of adopting advanced optimization strategies for sustainable production and economic growth.

Data-Driven Morozov Regularization of Inverse Problems

The solution of inverse problems is crucial in various fields such as medicine, biology, and engineering, where one seeks to find a solution from noisy observations. These problems often exhibit non-uniqueness and ill-posedness, resulting in instability under noise with standard methods. To address this, regularization techniques have been developed to balance data fitting and prior information. Recently, data-driven variational regularization methods have emerged, mainly analyzed within the framework of Tikhonov regularization, termed Network Tikhonov (NETT). This paper introduces Morozov regularization combined with a learned regularizer, termed DD-Morozov regularization. Our approach employs neural networks to define non-convex regularizers tailored to training data, enabling a convergence analysis in the non-convex context with noise-dependent regularizers. We also propose a refined training strategy that improves adaptation to ill-posed problems compared to NETT’s original strategy, which primarily focuses on addressing non-uniqueness. We present numerical results for attenuation correction in photoacoustic tomography, comparing DD-Morozov regularization with NETT using the same trained regularizer, both with and without an additional total variation regularizer.

Optimal Algorithms for Online b-Matching with Variable Vertex Capacities

AbstractWe study the b-matching problem, which generalizes classical online matching introduced by Karp, Vazirani and Vazirani (STOC 1990). Consider a bipartite graph $$G=(S\dot{\cup }R,E)$$ G = ( S ∪ ˙ R , E ) . Every vertex $$s\in S$$ s ∈ S is a server with a capacity $$b_s$$ b s , indicating the number of possible matching partners. The vertices $$r\in R$$ r ∈ R are requests that arrive online and must be matched immediately to an eligible server. The goal is to maximize the cardinality of the constructed matching. In contrast to earlier work, we study the general setting where servers may have arbitrary, individual capacities. We prove that the most natural and simple online algorithms achieve optimal competitive ratios. As for deterministic algorithms, we give a greedy algorithm RelativeBalance and analyze it by extending the primal-dual framework of Devanur, Jain and Kleinberg (SODA 2013). In the area of randomized algorithms we study the celebrated Ranking algorithm by Karp, Vazirani and Vazirani. We prove that the original Ranking strategy, simply picking a random permutation of the servers, achieves an optimal competitiveness of $$1-1/e$$ 1 - 1 / e , independently of the server capacities. Hence it is not necessary to resort to a reduction, replacing every server s by $$b_s$$ b s vertices of unit capacity and to then run Ranking on this graph with $$\sum _{s\in S} b_s$$ ∑ s ∈ S b s vertices on the left-hand side. Additionally, we extend this result to the vertex-weighted b-matching problem. Technically, we formulate a new configuration LP for the b-matching problem and conduct a primal-dual analysis.

The enhancement of primary frequency regulation ability of combined water and power plant based on nuclear energy: Dynamic modelling and control strategy optimization

The combined water and power plant based on nuclear energy (CWPN) is a potential way with significant economic and environmental benefits. To accommodate high penetration of intermittent renewable power within the power grid, it is essential to enhance the primary frequency regulation (PFR) ability of CWPN, and many factors, including the reactor operation safety, energy efficiency, and the water-power coupling mechanism of CWPN should be considered. In this study, a dynamic model of CWPN was developed, and two new strategies, the constant turbine power method (CPM) and the classic-PI-decoupling method (CDM), were proposed to enhance PFR capability. The results show that both strategies maintain the reactor power at full load during PFR, with CDM providing superior control, as evidenced by a maximum overshoot of 0.56 % compared with 3.58 % for CPM. Under typical step disturbances of ±1 % full power (FP), ±2%FP, and ±3%FP, the PFR performance of the original and new strategies was evaluated on the basis of settling time and the apex/nadir frequency. Compared with the original strategy (OS), the maximum settling time under CDM and CPM was reduced by 3.3 s and 8.8 s, respectively. The proposed strategies demonstrated improved PFR performance, with smaller apex/nadir frequency values than those of the OS.

Validation and optimization of a solar district heating system with large scale heat storage

Solar district heating systems with large-scale heat storage can contribute to a sustainable future. Currently, there are few mature systems and a lack of suitable tools, hindering large-scale applications. High-precision system models and efficient control strategies are necessary for designing optimal plants. This study developed a TRNSYS model of a large solar heating plant with components like a solar collector field, pit heat storage, heat pump, boilers, piping, pumps, and a MATLAB control system. The model is validated using a solar district heating system in Dronninglund, Denmark, showing good agreement with the measurement, with a difference in the solar thermal production of only 0.3% and an average temperature difference in the pit storage of 1 K. The model of the absorption chiller is improved for wide heat pump simulation, yielding a difference of approx. 5% compared to the measured coefficient of performance. The predicted solar fraction is 0.51, differing by 1.5% from the measurement. The study also proposed two new control strategies: adaptive low-temperature operation strategy and full-time heat pump operation strategy. These optimized strategies are compared with the original control strategy. Results show that the low-temperature and full-time optimization strategies improve the heat pump efficiency by 20%, the collector efficiency by 10%, and the solar utilization by 15%. The economic and environmental performance of the solar heating plant will also be enhanced with a maximum of 27% lower heat costs and 32 kg/MWh lower carbon emissions compared to the original system. In contrast, without the absorption heat pump, the solar heat production decreases by 10%. The system does not meet carbon neutrality if natural gas is used as the auxiliary heat source. This study aims to provide new simulation models and control strategies, which could be utilized for design of optimal large-scale solar thermal applications.

Control strategy calibration of a direct-expansion solar-assisted heat pump

As known, the performance of a direct-expansion solar-assisted heat pump (DX-SAHP) is affected by environmental and operational parameters significantly, and the variable capacity system has distinct advantages. The calibration of the control strategy will be very useful for the thermal performance improvement of DX-SAHP systems. Based on the test rig which is used to supply domestic hot water, the original variable capacity control strategy has been calibrated with the quasi-dynamic mathematical model under different conditions. The calibration results show that the optimum range of the degree of superheat is 7–12 °C. For winter and spring/autumn conditions, the optimum range of operation duration are 360–420 min and 300–360 min, and the corresponding optimum range of average compressor speed are 3750–4500 r·min−1 and 2750–3750 r·min−1, respectively. Experiments prove that the actual operation parameters can be maintained at the optimum range well and the average coefficient of performance (COPm) during the whole heating process has been improved further. For winter and spring/autumn, the average operation duration and compressor speed are 395 min and 3970 r·min−1, 345 min and 3066 r·min−1, respectively. Moreover, the values of COPm could reach 3.44 and 4.67, respectively. Even at solar radiation intensity of 577.6 W·m−2 with ambient temperature of −9.1 °C, the COPm could reach 2.38.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Improving diffusion kinetics of zinc ions/stabilizing zinc anode by molecular slip mechanism and anchoring effect in supramolecular zwitterionic hydrogels

The severe hydrogen evolution reaction and parasitic side reaction on Zn anode are the key issues which hinder the development of aqueous Zn-based energy storage devices. Herein, a polyacrylamide/carboxylated cellulose nanofibers/betaine citrate supramolecular zwitterionic hydrogels with molecular slip effects are proposed for enhancing Zn2+ diffusion and protecting Zn anodes. Non-covalent interactions within supramolecular hydrogels forms the skeleton for molecular slip and the strong coordination of carboxyl and amino groups with Zn2+ further facilitates the rapid Zn2+ transfer. Additionally, anchoring carboxyl and amino groups at the anode promotes the uniform deposition of Zn2+and protects Zn anode. On the basis of molecular slip mechanism and anchoring effect in the supramolecular zwitterionic hydrogels, Zn||Zn symmetric batteries undergo 800 h of stable electroplating stripping at a depth of discharge of 80 %. Zn||Cu asymmetric batteries exhibit an impressive average coulombic efficiency of 99.4 % over a remarkable span of 900 cycles at a current density of 15 mA cm−2. Furthermore, Zn||NH4V4O10 batteries successfully undergo over 1,000 cycles at a current density of 0.5 A g−1. Intrinsic ion diffusion mechanism of supramolecular hydrogel electrolytes provides an original strategy for the application of high-performance Zn-based energy storage devices.

Jansenist Education and Racine’s Intertextuality in European Context: Can an Early Modern Translation of Andromaque Help Revisit an Old Concept?

Abstract The article deals with the intertextual dimensions of Jean Racine’s tragedy Andromaque and its rendering by the seventeenth–century Polish translator Stanisław Morsztyn. The analysis is based on an old hypothesis concerning the link between Racine’s intertextual literary practice and his erudite education at the Petites écoles de Port-Royal. The translator, although he had not received Jansenist education, was able to identify correctly and develop the original strategy of quotations and allusions. The article attempts to seek the factors that helped the Polish translator render the intertextuality of Andromaque in another cultural context. Three such factors have been proposed—the tradition of neo-Latin poetry developed both in France and in Poland, the Italian Petrarchism (along with the idea of sprezzatura), perceived not as a store of topics but as a pattern of free operation with Ancient allusions. The similarity between Racine’s education and that of Morsztyn, which combined the influence of the Polish Brethren with homeschooling, could have been the third factor. The article closes with a question concerning the perspectives of cultural studies in intertextual strategies of poets connected with various religious circles of the seventeenth century.

Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking

This paper proposes an original hybrid control strategy for a three-phase multilevel flying capacitor inverter (FCI) used in wind energy conversion system (WECS). A state feedback law, computed using a Lyapunov function and an invariance principle, is designed to manage grid current control and capacitor voltage balancing, guaranteeing the asymptotic stability of the closed-loop system. Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). The use of fractional-order calculus leads to improved convergence and more seamless performance while preserving the robust characteristics of the system. The proposed control strategies are validated through simulation, demonstrating improved transient response and stability under varying wind conditions and parameter uncertainties. These results highlight the potential of advanced control algorithms to enhance the efficiency and reliability of wind energy systems, contributing to global efforts to achieve sustainable energy goals.

MDverse, shedding light on the dark matter of molecular dynamics simulations.

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

M[formula omitted]ixup: A multi-modal data augmentation approach for image captioning

Despite the great success, most models in image captioning (IC) are still stuck in the dilemma of generating simple and non-discriminative captions. In this paper, we study this problem from the perspective of data augmentation and propose a novel method called Multi-modal Mixup (M3ixup). Compared with the original Mixup strategy designed for image classification, the proposed M3ixup has three novel designs to mix IC samples from the aspects of visual features, sentence embeddings and loss values, respectively. In practice, M3ixup can not only enrich the diversity of IC training data, but also enforce the model to focus more on visual information for captioning, thereby alleviating the negative effect of dataset bias and addressing the issue of simple captioning. To validate M3ixup, we apply it to three baseline models and conduct extensive experiments on MS COCO. The experimental results demonstrate that our proposed M3ixup can not only improve the discriminability and quality of generated captions, but also help the baseline models obtain obvious performance gains, i.e., improving the CIDEr scores of the state-of-the-art model from 133.8 to 135.3 on off-line testing and 135.4 to 137.1 on online testing.

A retrospective study: Long term prognosis in adults with PA-VSD-MAPCAs

BackgroundPulmonary Atresia, Ventricular Deptal Defect, and Major Aortopulmonary Collateral Arteries (PA-VSD-MAPCAs) is a congenital cyanotic heart defect with poor prognosis. Due to its complex and highly variable anatomy, the best treatment plan is not clear. We aimed (1) to investigate the survival of PA-VSD-MAPCAs patients according to the underlying original anatomy and treatment strategy, and (2) to evaluate life expectancy between patients with or without severe hypoplastic native pulmonary arteries (NPAs) after surgical versus non-surgical treatment. MethodsA prospectively established database of 169 PA-VSD-MAPCAs patients treated and followed up at University Hospitals Leuven was accessed. Patients were divided into three groups according to the treatment strategy. Kaplan-Meier survival curves were plotted, and Log Rank tests were used for comparison. ResultsThe overall mean survival for patients with PA-VSD-MAPCAs was 38.5 years (95%-CI: 33.1–43.9). Patients with complete intracardiac repair had the longest mean survival of 43.8 years (95%-CI: 38.1–49.6) versus the other groups (p < 0.001). A longer mean event-free survival time was found in patients with normal, well-developed NPAs (p = 0.047). Finally, patients with poorly developed or absent NPAs had worse survival rates when a surgical approach was followed. Systemic-pulmonary shunt placement or unifocalisation had limited effect on prognosis in the absence of total repair (p = 0.167). ConclusionsPatients with PA-VSD-MAPCAs who underwent complete intracardiac repair and/or with well-developed native pulmonary arteries had the best prognosis. Our analyzed data suggest that incomplete surgical repair resulted in survival rates comparable to those seen with a non-surgical approach.

Synthesis and Characterization of Transition Metal Complexes Supported by Phosphorus Ligands Obtained Using Hydrophosphination of Cyclic Internal Alkenes.

The design and study of rich, bulky phosphorus ligands is a key area of research for homogeneous catalysis. Here, we describe an original strategy using a hydrophosphination reaction to produce phosphines of interest for coordination chemistry and homogenous catalysis. In particular, the phosphine obtained by reacting diphenylphosphine with acenaphthylene (ligand 2) gives a ligand that adopts an unusual spatial geometry. The coordination chemistry of the ligand has been investigated with Au(I), Ag(I), Cu(I), and Pd(II), for which a complete characterization could be made, particularly in X-ray diffraction studies. The reactivity of some of these complexes has been demonstrated, particularly in Pd-catalyzed cross-coupling reactions and Au-catalyzed hydroaminations and in the hydration of alkynes.

Getting the intermolecular forces correct: introducing the ASTA strategy for a water model.

Having a force field for water providing good bulk properties is paramount for modern studies of most biological systems. Some of the most common three-site force fields are TIP3, SPC/ε or OPC3, providing a decent range of bulk properties. That does not mean though, that they have realistic inter-atomic forces. These force fields have been parameterized with a top-down approach, meaning, by fitting the force field parameters to the experimental bulk properties. This approach has been the governing strategy also for many variants of four- and more-site models. We test a bottom-up approach, in which the force field is parameterized by optimizing the non-bonded inter-atomic forces. Our philosophy is that correct inter-atomic forces lead to correct geometrical and dynamical properties. The first system we try to optimize with the accurately system tailored atomic (ASTA) approach is water, but we aim to eventually probe other systems in the future as well. We applied our ASTA strategy to find a good set of parameters providing accurate bulk properties for the simple three-site force field forms, and also for AMOEBA, a more detailed and polarizable force field. Even though our bottom-up approach did not provide satisfactory results for the simple three-site force fields (with fixed charges), for the case of the AMOEBA force field it led to a modification of the original strategy, giving very good intra- and inter-molecular forces, as compared to accurate quantum chemically calculated reference forces. At the same time, important bulk properties, in this study restricted to the density and diffusion, were accurately reproduced with respect to the experimental values.

KNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation

Abstract. Random and spatial cross-validation (CV) methods are commonly used to evaluate machine-learning-based spatial prediction models, and the performance values obtained are often interpreted as map accuracy estimates. However, the appropriateness of such approaches is currently the subject of controversy. For the common case where no probability sample for validation purposes is available, in Milà et al. (2022) we proposed the nearest-neighbour distance matching (NNDM) leave-one-out (LOO) CV method. This method produces a distribution of geographical nearest-neighbour distances (NNDs) between test and training locations during CV that matches the distribution of NNDs between prediction and training locations. Hence, it creates predictive conditions during CV that are comparable to what is required when predicting a defined area. Although NNDM LOO CV produced largely reliable map accuracy estimates in our analysis, as a LOO-based method, it cannot be applied to the large datasets found in many studies. Here, we propose a novel k-fold CV strategy for map accuracy estimation inspired by the concepts of NNDM LOO CV: the k-fold NNDM (kNNDM) CV. The kNNDM algorithm tries to find a k-fold configuration such that the empirical cumulative distribution function (ECDF) of NNDs between test and training locations during CV is matched to the ECDF of NNDs between prediction and training locations. We tested kNNDM CV in a simulation study with different sampling distributions and compared it to other CV methods including NNDM LOO CV. We found that kNNDM CV performed similarly to NNDM LOO CV and produced reasonably reliable map accuracy estimates across sampling patterns. However, compared to NNDM LOO CV, kNNDM resulted in significantly reduced computation times. In an experiment using 4000 strongly clustered training points, kNNDM CV reduced the time spent on fold assignment and model training from 4.8 d to 1.2 min. Furthermore, we found a positive association between the quality of the match of the two ECDFs in kNNDM and the reliability of the map accuracy estimates. kNNDM provided the advantages of our original NNDM LOO CV strategy while bypassing its sample size limitations.

Damage identification in scale sensitive beam-like nano-joints in the framework of space-fractional Euler–Bernoulli theory

In the paper, a fundamental question about damage identification in scale-sensitive bodies is posed. For this purpose, nano-scale beam-like joints are analyzed using dedicated computational schemes. The theoretical description is led in the framework of non-local space-fractional Euler–Bernoulli (s-FEBB) theory — a special case of space-Fractional Continuum Mechanical model generalizing classical continuum mechanics utilizing fractional calculus. The problem of damage identification is stated as an optimization task where the original numerical strategy is implemented. What is crucial, the s-FEBB model assumes constitutive parameters identified and validated for the silver nanobeam in previous studies. Based on tens of thousands of analyses, we arrive at the general conclusion that localization of fracture/damage/deterioration in material bodies whose dimensions are of the order of intrinsic microstructure is possible but requires advanced modeling — otherwise, damage can be identified in a wrong place or more places, or local damage can become distributed one and/or with wrong intensity.

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials.

Improving the efficiency of interim assessments in phase III trials should reduce trial costs, hasten the approval of efficacious therapies, and mitigate patient exposure to disadvantageous randomizations. We hypothesized that in silico Bayesian early stopping rules improve the efficiency of phase III trials compared with the original frequentist analysis without compromising overall interpretation. Cross-sectional analysis. 230 randomized phase III oncology trials enrolling 184,752 participants. Individual patient-level data were manually reconstructed from primary endpoint Kaplan-Meier curves. Trial accruals were simulated 100 times per trial and leveraged published patient outcomes such that only the accrual dynamics, and not the patient outcomes, were randomly varied. Early stopping was triggered per simulation if interim analysis demonstrated ≥ 85% probability of minimum clinically important difference/3 for efficacy or futility. Trial-level early closure was defined by stopping frequencies ≥ 0.75. A total of 12,451 simulations (54%) met early stopping criteria. Trial-level early stopping frequency was highly predictive of the published outcome (OR, 7.24; posterior probability of association, >99.99%; AUC, 0.91; P < 0.0001). Trial-level early closure was recommended for 82 trials (36%), including 62 trials (76%) which had performed frequentist interim analysis. Bayesian early stopping rules were 96% sensitive (95% CI, 91% to 98%) for detecting trials with a primary endpoint difference, and there was a high level of agreement in overall trial interpretation (Bayesian Cohen's κ, 0.95; 95% CrI, 0.92 to 0.99). However, Bayesian interim analysis was associated with >99.99% posterior probability of reducing patient enrollment requirements ( P < 0.0001), with an estimated cumulative enrollment reduction of 20,543 patients (11%; 89 patients averaged equally over all studied trials) and an estimated cumulative cost savings of 851 million USD (3.7 million USD averaged equally over all studied trials). Bayesian interim analyses may improve randomized trial efficiency by reducing enrollment requirements without compromising trial interpretation. Increased utilization of Bayesian interim analysis has the potential to reduce costs of late-phase trials, reduce patient exposures to ineffective therapies, and accelerate approvals of effective therapies. Question: What are the effects of Bayesian early stopping rules on the efficiency of phase III randomized oncology trials?Findings: Individual-patient level outcomes were reconstructed for 184,752 patients from 230 trials. Compared with the original interim analysis strategy, in silico Bayesian interim analysis reduced patient enrollment requirements and preserved the original trial interpretation. Meaning: Bayesian interim analysis may improve the efficiency of conducting randomized trials, leading to reduced costs, reduced exposure of patients to disadvantageous treatments, and accelerated approval of efficacious therapies.

Site‐Selective Ortho/Ipso C−H Difunctionalizations of Arenes using Thianthrene as a Leaving Group

AbstractSite‐selective ortho/ipso C−H difunctionalizations of aromatic compounds were designed to afford polyfunctionalized arenes including challenging 1,2,3,4‐tetrasubstituted ones (62 examples, up to 97 % yields). To ensure the excellent regioselectivity of the process while keeping high efficiency, an original strategy based on a “C−H thianthenation/Catellani‐type reaction” sequence was developed starting from simple arenes. Non‐prefunctionalized arenes were first regioselectively converted into the corresponding thianthrenium salts. Then, a palladium‐catalyzed, norbornene (NBE)‐mediated process allowed the synthesis of ipso‐olefinated/ortho‐alkylated polyfunctionalized arenes using a thianthrene as a leaving group (revisited Catellani reaction). Pleasingly, using a commercially available norbornene (NBE) and a unique catalytic system, synthetic challenges known for the Catellani reaction with aryl iodides were smoothly and successfully tackled with the “thianthrenium” approach. The protocol was robust (gram‐scale reaction) and was widely applied to the two‐fold functionalization of various arenes including bio‐active compounds. Moreover, a panel of olefins and alkyl halides as coupling partners was suitable. Pleasingly, the “thianthrenium” strategy was successfully further applied to the incorporation of other groups at the ipso (CN/alkyl/H, aryl) and ortho (alkyl, aryl, amine, thiol) positions, showcasing the generality of the process.