Classical Strategies Research Articles

Reducing betrayal behavior in green building construction: A quantum game approach

The issue of energy consumption in buildings is becoming increasingly pertinent. Society highly values energy conservation and environmental protection, viewing the adoption of green buildings as an inevitable trajectory in the evolution of the construction sector. However, a challenge emerges during the progression of green building construction concerning betrayal within the supply chain. This paper employs a quantum game approach to tackle the issue of green building betrayal within a two-tier green building supply chain comprising developers and contractors. Initially, a non-zero-sum classical game model is formulated based on the green-building construction process between developers and contractors Subsequently, the classical strategy space is extended to the quantum strategy space. The findings indicate that in classical games, and without considering quantum entanglement, even if both parties adopt comprehensive strategies, the party exerting complete effort still faces the risk of betrayal. Conversely, in scenarios of maximum quantum entanglement, the loss incurred by the party not exerting complete effort does not affect the party making a complete effort. Consequently, when accounting for quantum entanglement, both parties tend to opt for complete effort. Lastly, an entanglement treaty is proposed to constrain the strategic choices of both parties and ensure that neither has an incentive to betray. The findings of this study provide good suggestions for green building project managers to improve the effort degree of the developer and general construction contractor in green buildings to promote the development of green buildings.

Iterated fractional Tikhonov method for recovering the source term and initial data simultaneously in a two-dimensional diffusion equation

In this paper, an inverse problem of a two-dimensional diffusion equation is considered. The purpose here includes recovering not only the source term but also the initial value from given observations at two fixed times t=T1 and t=T2. The uniqueness of the solutions for the inverse problem is given. Instead of the classical Tikhonov regularization strategy, we propose an iterated fractional Tikhonov regularization method to solve this problem, an a priori and an a posterior parameter selection rules and corresponding convergence rates are derived. For verification of the theoretical estimates, several numerical examples are constructed and compared with the standard iterated Tikhonov regularization method.

Model predictive-based DNN control model for automated steering deployed on FPGA using an automatic IP generator tool

With the increase in the non-linearity and complexity of the driving system’s environment, developing and optimizing related applications is becoming more crucial and remains an open challenge for researchers and automotive companies alike. Model predictive control (MPC) is a well-known classic control strategy used to solve online optimization problems. MPC is computationally expensive and resource-consuming. Recently, machine learning has become an effective alternative to classical control systems. This paper provides a developed deep neural network (DNN)-based control strategy for automated steering deployed on FPGA. The DNN model was designed and trained based on the behavior of the traditional MPC controller. The performance of the DNN model is evaluated compared to the performance of the designed MPC which already proved its merit in automated driving task. A new automatic intellectual property generator based on the Xilinx system generator (XSG) has been developed, not only to perform the deployment but also to optimize it. The performance was evaluated based on the ability of the controllers to drive the lateral deviation and yaw angle of the vehicle to be as close as possible to zero. The DNN model was implemented on FPGA using two different data types, fixed-point and floating-point, in order to evaluate the efficiency in the terms of performance and resource consumption. The obtained results show that the suggested DNN model provided a satisfactory performance and successfully imitated the behavior of the traditional MPC with a very small root mean square error (RMSE = 0.011228 rad). Additionally, the results show that the deployments using fixed-point data greatly reduced resource consumption compared to the floating-point data type while maintaining satisfactory performance and meeting the safety conditions

First experimental results on lifetime-aware wind farm control

The study presents the methodology, implementation, and first results of the experimental test of a lifetime-aware wind farm control (WFC) approach. The novel strategy goes beyond the conventional WFC formulations and aims to maximise profit while ensuring fulfilment of a desired lifetime. Damage due to fatigue is formulated using a cost model and is also limited by a lifetime constraint. The control strategy is based on yaw-induced wake steering and utilises a tool chain of power and damage estimators. The experiment was conducted with three model turbines in a boundary layer wind tunnel, which allowed the simulation of a realistic dynamic variation of the wind direction. The novel strategy is compared to conventional greedy operation, as well as to the classical maximum power strategy. The results show that the novel control strategy can improve the economic performance of the farm.

Effect of Input Subsidy Reduction on Greenhouse Emission Reduction Potential in Paddy Production Systems in Karnataka State of India

Increasing concerns and call for reduction in Greenhouse gas (GHG) emission have necessitated the search for broader and all-inclusive policy initiatives, extending into agricultural production, where high carbon energy inputs are used. One classical policy strategy for GHG emission reduction, has been taxation. However, given the critical role of agriculture, especially in developing economies, policies that directly or indirectly increase agricultural inputs costs and reduce their demand require stronger theoretical, conceptual and empirical support to ensure that while agri-environmental quality is promoted, welfare of farming households, food security and overall economic growth are not compromised. Using paddy production in Karnataka state in India, the study assessed effects of agricultural input taxation (reduction in rice input subsidy) on future demand for such inputs and their effect on GHG emission reduction, vis-a-viz production and welfare losses. In microeconomic modelling framework, we applied quadratic almost ideal demand system and stochastic efficiency functions in the analysis of the data. Data for the study, a micro-level farm data, was obtained from Cost of Cultivation Scheme (CSS) for irrigated and non-irrigated production systems, covering the period 2009 -2018 production seasons. Specifically, the study used three future tax regime scenarios- 10%, 20% and 30% input subsidy reduction rates, to model an optimum greenhouse emission reduction potential. The results revealed that inputs evaluated were normal with inelastic demand functions; many input coefficients implied significant complementary relationships; irrigated paddy production system had higher estimates of GHG emissions. Input taxation (reduction in subsidy) under all the three scenarios effectively, resulted in declined inputs consumption patterns, and subsequently led to significant decrease in greenhouse emissions. The highest GHG emission reduction potential was observed in irrigated farming system. Greenhouse emission reduction potential was optimal at moderate subsidy reduction policy rate of 10%. It is recommended that, given the inelastic estimates derived, moderate tax (reduction in subsidy) policy option on inputs would yield effective greenhouse mitigation with appropriate compensation through effective integrative schemes.

Improving algorithmic trading consistency via human alignment and imitation learning

Research on algorithmic trading using reinforcement learning has become increasingly popular in recent years. Although most of the current reinforcement learning methods are employed to train the agent for some kind of modeling or data problem, it is worthwhile to explore in aligning agents with human behavior in applications as crucial as financial trading. Achieving such consistency by incorporating human expert experience into agent behavior is a key for potential improvements in this field. Imitation learning learns directly from examples of humans or other agents performing tasks. However, using imitation learning alone suffers from the problem of transitionally fitting expert example data. By combining the advantages of imitation learning and the Advantage Actor–Critic method, the Human Alignment Advantage Actor–Critic (HA3C) algorithm is proposed, to enhance single-asset trading strategy. First, by adding daily and weekly frequency trading data as input features to TimesNet, which is specifically designed to extract correlated temporal patterns from time-series data, it can capture both short-term and long-term features, thus capturing time-series features more comprehensively. Second, an expert action labeling method is proposed to train a strategy prediction network through supervised learning of behavior imitation. Third, a pre-trained strategy network is transferred to balance the exploration and exploitation of the agent’s behavior. Imitation learning techniques leverage finance-specific knowledge to enhance algorithmic trading consistency. This approach enables algorithms to mimic and adapt human decision-making patterns in finance, ultimately improving overall performance. This paper introduces a novel return-based function that efficiently balances short-term and long-term returns over flexible time horizons. It considers the maximum return from different positions and uses flexible time windows to capture trends while maximizing returns. Finally, evaluation on six commonly used datasets, such as DJI and SP500, demonstrates the advantages of the proposed HA3C algorithm compared with other classical and reinforcement learning-based strategies. Notably, on the HSI dataset, the HA3C strategy significantly outperforms other methods, achieving an impressive cumulative return of 681.55% and a Sharpe ratio of 5.07. These results show the superior performance of the HA3C algorithm in enhancing stock trading strategies and its potential to impact algorithmic trading consistency through aligning agent behavior with human expertise.

In-situ detection of Pb2+ based on the ternary dihydrogen phosphate complex by surface-enhanced Raman scattering

Surface-enhanced Raman scattering (SERS) is a convenient and fast technique to detect toxic heavy metals such as lead ions (Pb2+). In this study, a silver nanoparticle-decorated hydrogel substrate by a one-pot method is prepared, addressing the instability of traditional sol substrates and the non-tunability of localized surface plasmon resonance (LSPR) of solid substrates, which can achieve in-situ detection of Pb2+ in real systems using a portable Raman spectrometer. Meanwhile, the characteristic SERS peaks attributed to the ternary complex of 4-MBA/H2PO4-/Pb2+ formed by the dihydrogen phosphate, 4-mercaptobenzoic acid, and Pb2+ are used firstly for Pb2+ detection to our knowledge, decreasing the cost of the classical strategy. The method is highly anti-interference, simple, and cost-effective, which exhibits a good linear correlation (y= 21.6x + 2711, R2=0.993) in the range of 3–500 μg/L with a detection limit of 1.6 μg/L that is one order of magnitude lower than that of the EPA regulations. This method is expected to realize the rapid and accurate on-site SERS detection of Pb2+ by the new strategy.

Strategies in Communicating and Enforcing Covid-19 Measures: A Rhetorical Analysis

The emergence of the COVID-19 virus in late 2019 initiated a global crisis, leading to millions of infections and hundreds of thousands of deaths worldwide. Governments worldwide implemented various measures to curb the spread of the virus, including lockdowns, social distancing, and hygiene protocols. In response to these measures, leaders employed persuasive language techniques to encourage compliance and reassure citizens. This study explores the linguistic strategies used by four national leaders—Donald Trump, Vladimir Putin, Boris Johnson, and Uhuru Kenyatta—in their speeches addressing the COVID-19 pandemic. The analysis focuses on the rhetorical strategies employed, including ethos, logos, and pathos, as well as linguistic devices such as metaphors and enumeration. The findings of this study show that leaders strategically utilize persuasive language to convey information, evoke emotions, and inspire collective action. The study underscores the importance of understanding linguistic persuasion in effective communication during crises and highlights the relevance of classical rhetorical strategies in contemporary political discourse.

A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product

Balancing adaptability, reliability, and accuracy in vision technology has always been a major bottleneck limiting its application in appearance assurance for complex objects in high-end equipment production. Data-driven deep learning shows robustness to feature diversity but is limited by interpretability and accuracy. The traditional vision scheme is reliable and can achieve high accuracy, but its adaptability is insufficient. The deeper reason is the lack of appropriate architecture and integration strategies between the learning paradigm and empirical design. To this end, a learnable viewpoint evolution algorithm for high-accuracy pose estimation of complex assembled products under free view is proposed. To alleviate the balance problem of exploration and optimization in estimation, shape-constrained virtual–real matching, evolvable feasible region, and specialized population migration and reproduction strategies are designed. Furthermore, a learnable evolution control mechanism is proposed, which integrates a guided model based on experience and is cyclic-trained with automatically generated effective trajectories to improve the evolution process. Compared to the 1.69°,55.67 mm of the state-of-the-art data-driven method and the 1.28°,77.67 mm of the classic strategy combination, the pose estimation error of complex assembled product in this study is 0.23°,23.71 mm, which proves the effectiveness of the proposed method. Meanwhile, through in-depth exploration, the robustness, parameter sensitivity, and adaptability to the virtual–real appearance variations are sequentially verified.

Neural network survivability approach of a wave energy converter considering uncertainties in the prediction of future knowledge

To tune the wave energy converter (WEC) controller parameters such as damping to reduce the line force during extreme wave conditions, future knowledge of the line force is required. To achieve this, the incoming wave and system state should be predicted for a few seconds in the future. It is rather an arduous task to predict the future knowledge of waves and the system’s dynamic when dealing with breaking and steep waves, and the system is subject to various nonlinear forces. The classical model-based control strategies often rely on linear assumptions to estimate the WEC dynamics for the sake of simplicity. Unlike the model-based, the data-driven approaches are free from modeling errors and the algorithms are trained over the true and noisy data to predict non-linear system behaviors. Using data-driven approaches, we are able to model nonlinear dynamics. However, new questions emerge on the accuracy of the future wave and system state predictions, and how this uncertainty propagates into the final prediction of the line force. As incorrect damping may lead to excessive line force and detrimental damage to the system, these are the knowledge gaps that need to be addressed. The main purpose of this paper is to answer these questions through a survivability strategy for wave energy converters by providing a realistic perspective on the implementation of the neural network approaches by accounting for the errors in the input data. For this purpose, a series of neural networks is designed that first predicts the surface elevation for 0.36 s ahead, i.e. corresponding to 2 s in the full-scale WEC. This future knowledge of the wave elevation is then used to predict the system state (i.e. power take-off (PTO) translator position) for the same prediction horizon based on the PTO damping. This information is then fed to a convolutional neural network (CNN) that predicts the peak line force 0.36 s ahead. This paper also evaluates the predictive capabilities of neural network (NN) models, comparing them to classical autoregressive (AR) and Kalman filter methods. While the AR model exhibits slightly higher accuracy in fixed PTO system configurations, it falters in providing real-time predictions when system parameters undergo variations. In contrast, the NN prediction model excels by establishing a robust relationship between system configuration and output signals. Especially noteworthy is its ability to outperform classical methods with minimal training on a selective set of system configurations. Then, the sensitivity of the peak line force prediction to the uncertainties in the input data from NN models and the prediction horizon is analyzed. The neural network models are trained over the experimental data subjected to extreme sea states for a point absorber wave energy converter. The results suggest that the accuracy of the surface elevation prediction has an insignificant direct effect on the peak force prediction model. However, these uncertainties are reflected in the PTO translator position prediction, and the model is considerably sensitive to the accuracy of this prediction. This sensitivity nonetheless is less notable for higher PTO damping values.

Parameter-setting heuristic for the quantum alternating operator ansatz

The quantum alternating operator ansatz (QAOA) is a generalized approach for solving challenging optimization problems that builds on the alternating structure of the quantum approximate optimization algorithm. Finding high-quality parameters efficiently for QAOA remains a major challenge in practice. In this work, we introduce a classical strategy for parameter setting, suitable for cases in which the number of distinct cost values grows only polynomially with the problem size, such as is common for constraint-satisfaction problems. The crux of our strategy is that we replace the cost function expectation value step of QAOA with a classical model that can be efficiently evaluated classically and has parameters which play an analogous role to the QAOA parameters. This model is based on empirical observations that, in some QAOA states, variable configurations with the same cost have the same amplitudes from step to step. We define this class of states as homogeneous states. For problems with particular symmetries, QAOA states are guaranteed to be homogeneous. More generally, high overlaps between QAOA states and homogeneous states have been empirically observed in a number of settings. Building on this idea, we define a classical homogeneous proxy for QAOA in which this property holds exactly and which yields information describing both states and expectation values. We then classically determine high-quality parameters for this proxy and then use these parameters in QAOA, an approach we label the homogeneous heuristic for parameter setting. We numerically examine this heuristic for MaxCut on unweighted Erdős-Rényi random graphs. For up to three QAOA levels we demonstrate that the heuristic is easily able to find parameters that match approximation ratios corresponding to previously found globally optimized approaches. For levels up to 20 we obtain parameters using our heuristic with approximation ratios monotonically increasing with depth, while a strategy that uses parameter transfer instead fails to converge with comparable classical resources. These results suggest that our heuristic may find good parameters in regimes that are intractable with noisy intermediate-scale quantum devices. Finally, we outline how our heuristic may be applied to wider classes of problems. Published by the American Physical Society 2024

Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method

The research presents a novel approach to inventory modelling, emphasizing stochastic demand and dynamic pricing strategies for a seasonal sales framework. The methodology divides the sales season into intervals, each associated with distinct pricing strategies influenced by stochastic factors. The study employ exponential smoothing for demand forecasting, optimizing inventory replenishment and dynamic pricing strategies through developed algorithms. Notably, the study determine the optimal smoothing parameter for demand forecasting, balancing responsiveness to recent demand patterns with long-term stability. Proposed research achieves a comprehensive framework empowering businesses to enhance competitiveness and profitability by addressing challenges of stochastic demand and dynamic pricing. Dynamic pricing strategies outperformed classical strategies, allowing businesses to respond promptly to demand fluctuations and optimize profit margins during sales seasons. Incorporating stochastic demand models enabled organizations to implement safety stock and buffer inventory levels effectively, mitigating risks associated with uncertain demand patterns. Real-time data analysis was crucial for adjusting price dynamics and making effective management decisions, leading to improved financial performance. The iterative nature of dynamic pricing strategies emphasized the importance of continuous refinement to adapt to evolving market dynamics. This approach enables data-driven decisions, adaptation to market fluctuations, and improved inventory management despite unpredictable demand. Ultimately, this study provides valuable insights and methodologies applicable across industries for efficient and profitable inventory management.

Fuzzy inference logic assisted rendezvous points based effective routing strategy in wireless sensor networks

SummaryIn this proposed strategy, an effective routing strategy is introduced for routing based on Rendezvous Points assisted fuzzy inference logics. First, sensor nodes in the cluster are created using the mean‐shift clustering mechanism (MS‐ClusT). A cluster head is selected for the clusters to gather aggregated data from the nodes of clusters. The cluster head selection is done using the Manta Ray optimization‐assisted modified network‐based fuzzy inference logic (MMFiL) approach. Rendezvous points are then selected for routing to minimize the delay and energy consumption for visiting all cluster heads. The rendezvous points are used to define the shortest route for routing. The shortest path from clusters to the base station by selecting rendezvous points is implemented using an integrated neural network with an Archer‐Fish optimization algorithm (InnAFo). In the end, the performance attained by the model is compared with the results of the classical strategy and returns a 0.976563 packet delivery rate and 0.029297 loss rate in 500 nodes. Furthermore, the throughput attained by the proposed model is 0.992874 Mbps, and the delay is 67.30 ms at 500 nodes. The analysis of the proposed model will result in less time needed for cluster head selection.

Weak Interaction Activates Esters: Reconciling Catalytic Activity and Turnover Contradiction by Tailored Chalcogen Bonding.

The activation of esters by strong Lewis acids via the formation of covalent adducts is a classic strategy to give reactivity; however, this approach frequently incurs limited turnover due to the low efficiency in the dissociation of catalyst from a stable catalyst-product complex. While the use of some weak interaction catalysts that can easily dissociate from any bonding complexes in the reaction system would solve this catalyst turnover problem, the poor catalytic activity in the ester activation that can be provided by these noncovalent forces in turn sets up a formidable challenge. Herein, we describe the activation and catalytic transformation of esters by weak interactions, which provides a promising platform to reconcile the catalytic activity and turnover problems. Several tailored chalcogen-bonding catalysts were developed for the activation of esters, enabling achieving several inherently low reactive Diels-Alder reactions as well as the ring-opening polymerization of lactones through weak chalcogen bonding interactions. This supramolecular catalysis approach is particularly highlighted by its capability to promote some uncommon Diels-Alder reactions involving using dienes bearing electron-withdrawing groups coupled by α,β-unsaturated ester as dienophiles and substrate incorporating competitive Lewis basic sites, in which typical strong Lewis acids showed low catalytic efficiency, while representative hydrogen and halogen bonding catalysts were inactive.

Aliovalent Substitution Tunes Physical Properties in a Conductive Bis(dithiolene) Two-Dimensional Metal-Organic Framework.

Two-dimensional conductive metal-organic frameworks have emerged as promising electronic materials for applications in (opto)electronic, thermoelectric, magnetic, electrocatalytic, and energy storage devices. Many bottom-up or postsynthetic protocols have been developed to isolate these materials or further modulate their electronic properties. However, some methodologies commonly used in classic semiconductors, notably, aliovalent substitution, are conspicuously absent. Here, we demonstrate how aliovalent Fe(III) to Ni(II) substitution enables the isolation of a Ni bis(dithiolene) material from a previously reported Fe analogue. Detailed characterization supports the idea that aliovalent substitution of Fe(III) to Ni(II) results in an in situ oxidation of the organic dithiolene linker. This substitution-induced redox tuning modulates the electronic properties in the system, leading to higher electrical conductivity and Hall mobility but slightly lower carrier densities and weaker antiferromagnetic interactions. Moreover, this aliovalent substitution improves the material's electrochemical stability and thus enables pseudocapacitive behavior in the Ni material. These results demonstrate how classic aliovalent substitution strategies in semiconductors can also be leveraged in conductive MOFs and add further support to this class of compounds as emerging electronic materials.

Optimal risk management considering environmental and climatic changes.

Climate change presents challenges to policy and economic stability, necessitating effective trading strategies to reduce environmental risks. This article addresses gaps in existing studies by using a Markov-switching model to consider climate risk. Backward stochastic differential equationsare used to optimize utility with three hedging strategies based on the concept of risk aversion. Numerical scenarios confirm the model's superiority in incorporating exogenous events, with our risk-averse strategy outperforming classical approaches. Our strategy outperforms classical strategies by taking a flexible risk trading when investors face risk-averse behavior due to climate risk events. The findings presented in this article have important implications for the development of more resilient investment portfolios and can contribute to climatepolicy.

Post-transplant-cyclophosphamide and short-term Everolimus as graft-versus-host-prophylaxis in patients with relapsed/refractory lymphoma and myeloma-Final results of the phase II OCTET-EVER trial.

Conditioning regimens and the choice of immunosuppression have substantial impact on immune reconstitution after allogeneic hematopoietic stem cell transplantation (aHSCT). The pivotal mechanism to maintain remission is the induction of the graft-versus-tumor effect. Relapse as well as graft versus host disease remain common. Classic immunosuppressive strategies implementing calcineurin inhibitors (CNI) have significant toxicities, hamper the immune recovery, and reduce the anti-cancer immune response. We designed a phase II clinical trial for patients with relapsed and refractory lymphoid malignancies undergoing aHSCT using a CNI-free approach consisting of post-transplant cyclophosphamide (PTCy) and short-term Everolimus after reduced-intensity conditioning and matched peripheral blood stem cell transplantation. The results of the 19 planned patients are presented. Primary endpoint is the cumulative incidence and severity of acute GvHD. Overall incidence of acute GvHD was 53% with no grade III or IV. Cumulative incidence of NRM at 1, 2, and 4 years was 11%, 11%, and 16%, respectively, with a median follow-up of 43 months. Cumulative incidence of relapse was 32%, 32%, and 42% at 1, 2, and 4 years after transplant, respectively. Four out of six early relapses were multiple myeloma patients. Overall survival was 79%, 74%, and 62% at 1, 2, and 4 years. GvHD-relapse-free-survival was 47% after 3 years. Using PTCy and short-term Everolimus is safe with low rates of aGvHD and no severe aGvHD or cGvHD translating into a low rate of non-relapse mortality. Our results in this difficult to treat patient population are encouraging and warrant further studies.

Chromosome Transplantation: Opportunities and Limitations.

There are thousands of rare genetic diseases that could be treated with classical gene therapy strategies such as the addition of the defective gene via viral or non-viral delivery or by direct gene editing. However, several genetic defects are too complex for these approaches. These "genomic mutations" include aneuploidies, intra and inter chromosomal rearrangements, large deletions, or inversion and copy number variations. Chromosome transplantation (CT) refers to the precise substitution of an endogenous chromosome with an exogenous one. By the addition of an exogenous chromosome and the concomitant elimination of the endogenous one, every genetic defect, irrespective of its nature, could be resolved. In the current review, we analyze the state of the art of this technique and discuss its possible application to human pathology. CT might not be limited to the treatment of human diseases. By working on sex chromosomes, we showed that female cells can be obtained from male cells, since chromosome-transplanted cells can lose either sex chromosome, giving rise to 46,XY or 46,XX diploid cells, a modification that could be exploited to obtain female gametes from male cells. Moreover, CT could be used in veterinary biology, since entire chromosomes containing an advantageous locus could be transferred to animals of zootechnical interest without altering their specific genetic background and the need for long and complex interbreeding. CT could also be useful to rescue extinct species if only male cells were available. Finally, the generation of "synthetic" cells could be achieved by repeated CT into a recipient cell. CT is an additional tool for genetic modification of mammalian cells.

DKK3 Expression in Glioblastoma: Correlations with Biomolecular Markers.

Glioblastoma is the most common malignant primary tumor of the CNS. The prognosis is dismal, with a median survival of 15 months. Surgical treatment followed by adjuvant therapies such as radiotherapy and chemotherapy characterize the classical strategy. The WNT pathway plays a key role in cellular proliferation, differentiation, and invasion. The DKK3 protein, capable of acting as a tumor suppressor, also appears to be able to modulate the WNT pathway. We performed, in a series of 40 patients, immunohistochemical and Western blot evaluations of DKK3 to better understand how the expression of this protein can influence clinical behavior. We used a statistical analysis, with correlations between the expression of DKK3 and overall survival, age, sex, Ki-67, p53, and MGMT and IDH status. We also correlated our data with information included in the cBioPortal database. In our analyses, DKK3 expression, in both immunohistochemistry and Western blot analyses, was reduced or absent in many cases, showing downregulation. To date, no clinical study exists in the literature that reports a potential correlation between IDH and MGMT status and the WNT pathway through the expression of DKK3. Modulation of this pathway through the expression of DKK3 could represent a new tailored therapeutic strategy in the treatment of glioblastoma.

New strategy for anti-loop formulations

AbstractThis paper presents a strategy based on binary labelling of nodes for the creation of anti-loop formulations from existing strategies. This strategy prevents by default the formation of odd cycles, therefore it can have important role in iterative procedures based on generating subtour elimination constraints. It can also be used to modify the classic strategies used in problems associated to graphs. In this paper we focus on this last application. The behavior of this strategy is analyzed with two problems associated with graphs, the Asymmetric Traveling Salesman Problem (ATSP) and the Steiner Problem, where two configurations that modify the Miller-Tucking-Zemlig proposal to avoid cycles are compared. The experimental analysis shows that this strategy keep a good convergence, highlighting its use for the Steiner problem.