Optimization Strategy Research Articles

Design Strategy and Consideration for Oncology Dose-Optimization: An Industry Perspective

The conventional more-is-better dose selection paradigm is often inappropriate for novel molecularly targeted agents. In response, the US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. Furthermore, the FDA recently also published draft guidance “Optimizing the dosage of human prescription drugs and biological products for the treatment of oncologic diseases guidance for industry,” providing overviews on the rationale, importance, and principles of dose optimization from the regulatory perspective. However, detailed guidance on how to design dose optimization trials is still lacking; it is a key issue faced by drug developers. The article discusses statistical and design strategies of dose optimization in oncology by sharing current industry practice on dose-finding, delineating key statistical components for dose optimization trials, describing available design strategies, and providing recommendations and future directions. This article is a joint work of the Statistical Methods in Oncology Scientific Working Group (Dose-Optimization Workstream) of the American Statistical Association Biopharmaceutical Section.

Alternating size field optimizing and parameterization domain CAD model remeshing

Tessellating CAD models into triangular meshes is a long-lasting problem. Size field is widely used to accommodate varieties of requirements in remeshing, and it is usually discretized and optimized on a prescribed background mesh and kept constant in the subsequent remeshing procedure. Instead, we propose optimizing the size field on the current mesh, then using it as guidance to generate the next mesh. This simple strategy eliminates the need of building a proper background mesh and greatly simplifies the size field query. For better quality and convergence, we also propose a geodesic distance based initialization and adaptive re-weighting strategy in size field optimization. Similar to existing methods, we also view the remeshing of a CAD model as the remeshing of its parameterization domain, which guarantees that all the vertices lie exactly on the CAD surfaces and eliminates the need for costly and error-prone projection operations. However, for vertex smoothing which is important for mesh quality, we carefully optimize the vertex's location in the parameterization domain for the optimal Delaunay triangulation condition, along with a high-order cubature scheme for better accuracy. Experiments show that our method is fast, accurate and controllable. Compared with state-of-the-art methods, our approach is fast and usually generates meshes with smaller Hausdorff error, larger minimal angle with a comparable number of triangles.

CAR-macrophage versus CAR-T for solid tumors: The race between a rising star and a superstar.

Adoptive cell therapy (ACT) has been demonstrated to be one of the most promising cancer immunotherapy strategies due to its active antitumor capabilities in vivo. Engineering T cells to overexpress chimeric antigen receptors (CARs), for example, has shown potent efficacy in the therapy of some hematologic malignancies. However, the efficacy of chimeric antigen receptor T cell (CAR-T) therapy against solid tumors is still limited due to the immunosuppressive tumor microenvironment (TME) of solid tumors, difficulty in infiltrating tumor sites, lack of tumor-specific antigens, antigen escape, and severe side effects. In contrast, macrophages expressing CARs (CAR-macrophages) have emerged as another promising candidate in immunotherapy, particularly for solid tumors. Now at its nascent stage (with only one clinical trial progressing), CAR-macrophage still shows inspiring potential advantages over CAR-T in treating solid tumors, including more abundant antitumor mechanisms and better infiltration into tumors. In this review, we discuss the relationships and differences between CAR-T and CAR-macrophage therapies in terms of their CAR structures, antitumor mechanisms, challenges faced in treating solid tumors, and insights gleaned from clinical trials and practice for solid tumors. We especially highlight the potential advantages of CAR-macrophage therapy over CAR-T for solid tumors. Understanding these relationships and differences provides new insight into possible optimization strategies of both these two therapies in solid tumor treatment.

A Hybrid Cascaded Multilevel Converter Based on Si and SiC Devices and Its Regulation Optimization Strategy

A Hybrid Cascaded Multilevel Converter Based on Si and SiC Devices and Its Regulation Optimization Strategy

Dynamic Coding-Based Control Scheme Under Lossy Digital Network: An Optimized Time-Varying Packet Length Approach.

This work proposes a design scheme of the desired controller under the lossy digital network by introducing a dynamic coding and packet-length optimization strategy. First, the weighted try once-discard (WTOD) protocol is introduced to schedule the transmission of sensor nodes. The state-dependent dynamic quantizer and the encoding function with time-varying coding length are designed to improve coding accuracy significantly. Then, a feasible state-feedback controller is designed to attain that the controlled system subject to possible packet dropout is exponentially ultimately bounded in the mean-square sense. Moreover, it is shown that the coding error directly affects the convergent upper bound, which is further minimized by optimizing the coding lengths. Finally, the simulation results are provided via the double-sided linear switched reluctance machine systems.

Research on Regulatory Strategies of Green Finance and Optimization of Corporate Green Behavior under the Framework of Circular Economy

<p>The green finance market, as an integral part of the circular economy, significantly influences environmental protection and sustainable development. This study explores regulatory issues concerning corporate "greenwashing" behavior in this market, aiming to devise effective regulatory strategies to promote genuine green economic activities. By applying evolutionary game theory, this paper constructs a game model of interaction between the government and enterprises. Through numerical simulation analysis, it reveals the dynamic characteristics of the green finance market in three different stages: initial, growth, and maturity, and their impact on system stability. The study suggests that strengthening regulatory measures, enhancing transparency in information disclosure, establishing unified green assessment standards, and improving incentive policies are key measures to curb corporate greenwashing behavior and promote green transformation. The implementation of these strategies not only enhances the market's green reputation but also encourages active participation of enterprises in circular economic activities, thereby achieving a win-win situation for environmental and economic benefits. The research findings of this paper have important theoretical and practical implications for guiding the healthy development of the green finance market and formulating circular economy policies.</p>

Comprehensive Flow Path Design Method for the Adaptive Cycle Engine Considering the Coupling Relation of Multiple Components

Abstract The adaptive cycle engine (ACE) has multiple coupled components on the same spool and complex bypass system, which makes it have more complex intercomponent coupling relation and hard to coordinate in the flow path design. In this study, the coupling relation of the ACE components and the component reference conditions are analyzed and determined, a multicomponent collaborative optimization design method is proposed to enable the quantitative evaluation of flow path design solutions. In this method, two optimization strategies are presented based on the different priorities of the intercomponent size coupling parameters, the intercomponent aerodynamic coupling parameter and the component performance in the optimization problem. ACE flow path solutions for various feasible design speed combinations are generated automatically considering the component performance and intercomponent coupling relation. According to an ACE flow path design case study, the design physical rotational speeds of low-pressure spool (NL,d) and high-pressure spool (NH,d) should be 7000 to 7600 r/min and 10,000 to 15,000 r/min, respectively. At NH,d = 12,000 r/min and NL,d = 7200 r/min, the high-pressure compression components and the fan components could be designed with the lowest aerodynamic load, respectively. NH,d is the key factor affecting the axial length of ACE. This method can be applied to other gas power plant designs.

Harnessing the Potential of Zn Powder Anodes: Innovations and Future Directions in Aqueous Zinc‐Ion Batteries

AbstractThe pursuit of alternative anode materials featuring inexpensive, efficient, and adaptable is shaping the future of rechargeable zinc‐ion batteries. The Zn powder anodes stand out among contenders due to their cost‐effectiveness, exceptional processability, and adjustability. However, their widespread use is hampered by significant challenges, including volume expansion and dendrite growth. To counter these issues, a gamut of optimization strategies for Zn powder anodes has been explored and developed. This review systematically encapsulates these research findings, offering a comprehensive understanding of various enhancement methods, such as three‐dimensional printing, in‐situ surface engineering, scalable electrostatic self‐assembly, and epoxy oligomer binding. These methodologies are discussed in detail in the context of their unique synthesis methods and underlying mechanisms. The paper concludes by outlining prospective research trajectories aimed at further optimizing the use of Zn powder anodes in aqueous zinc‐ion batteries, thereby illuminating potential avenues for future exploration and development.

A Hybrid PWM Strategy for Switching Loss Reduction in Three-Level Inverters

The advanced bus-clamping PWM (ABCPWM) sequences are well known to reduce line current THD in a three-level neutral point-clamped inverter. This paper deals with ABCPWM sequences employed optimally to achieve minimum switching loss throughout the linear operating region in a three-level inverter. The work extensively analyzes the performance of these sequences in terms of inverter switching loss and compares them with centered space vector modulation PWM (CSVPWM). Based on the analysis, a hybrid PWM scheme termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">switching loss reduction</i> is proposed by optimally employing ABCPWM sequences in conjunction with variation in power factors. At a given average switching frequency, the proposed hybrid scheme reduces inverter switching loss compared to CSVPWM and the recently proposed discontinuous PWM-based optimization strategy at all power factors and modulation indices. With this scheme, switching loss reduces to 22% to 36% compared to CSVPWM and a maximum of 12% reduction than the existing optimization strategy. Furthermore, the scheme gives lower harmonic distortion compared to these two existing PWM schemes near high modulation indices and near unity power factor load. The theoretical, simulated, and real-time simulation results are presented with experimental results using TMS320F28335 DSP controller for validating the proposed work.

Graph Convolutional Network Discrete Hashing for Cross-Modal Retrieval.

With the rapid development of deep neural networks, cross-modal hashing has made great progress. However, the information of different types of data is asymmetrical, that is to say, if the resolution of an image is high enough, it can reproduce almost 100% of the real-world scenes. However, text usually carries personal emotion and it is not objective enough, so we generally think that the information of image will be much richer than text. Although most of the existing methods unify the semantic feature extraction and hash function learning modules for end-to-end learning, they ignore this issue and do not use information-rich modalities to support information-poor modalities, leading to suboptimal results, although they unify the semantic feature extraction and hash function learning modules for end-to-end learning. Furthermore, previous methods learn hash functions in a relaxed way that causes nontrivial quantization losses. To address these issues, we propose a new method called graph convolutional network (GCN) discrete hashing. This method uses a GCN to bridge the information gap between different types of data. The GCN can represent each label as word embedding, with the embedding regarded as a set of interdependent object classifiers. From these classifiers, we can obtain predicted labels to enhance feature representations across modalities. In addition, we use an efficient discrete optimization strategy to learn the discrete binary codes without relaxation. Extensive experiments conducted on three commonly used datasets demonstrate that our proposed method graph convolutional network-based discrete hashing (GCDH) outperforms the current state-of-the-art cross-modal hashing methods.

Data-Driven Modeling and Operation Optimization With Inherent Feature Extraction for Complex Industrial Processes

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

Biomass derived PANI/BPC composite with enhanced polarization loss for efficient electromagnetic wave absorption

Based on the strategies of heterogeneous interface construction and impedance matching optimization, the polyaniline/biomass derived porous carbon (PANI/BPC) has been synthesized. The attachment of PANI on the surface of BPC prompts a relatively amount of heterogeneous interface endowing sufficient interfacial polarization loss. Meanwhile, the coralline-like PANI acts as bridges on the surface of BPC to construct complex conductive networks enhancing conduction loss and multi-reflections & scattering. The balances between conduction loss and polarization loss, impedance matching and attenuation capability have been established. Thanks to the synergistic effect of strong dielectric loss and optimized impedance matching, the PANI/BPC composite achieves the optimal absorption capacity with the minimum reflection loss (RL) of -40.89 dB at 2.6 mm and the effective absorption bandwidth (EAB) of 4.24 GHz at 2.1 mm. This satisfactory result may provide a new approach for the effective development and utilization of biomass derived porous carbon as absorbing materials.

The Design of FIR Filter Based on improved DA Algorithm and its FPGA implementation: REVIEW

The Design of FIR Filter Based on improved DA Algorithm and its FPGA implementation: REVIEW

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Optimizing dental screening protocols for children with special healthcare needs: Enhancing access and prevention

Children with special healthcare needs face significant barriers to accessing dental care, resulting in higher rates of untreated dental disease and poorer oral health outcomes compared to their peers. Optimizing dental screening protocols tailored to the unique needs of this population is crucial to enhancing access to care and preventing dental disease. This paper explores the challenges in accessing dental care for children with special healthcare needs, reviews current screening protocols, and proposes strategies for optimization. The literature review highlights the multifactorial nature of barriers to care, including socioeconomic factors, physical and cognitive limitations, and cultural considerations. Despite existing challenges, success stories and emerging technologies offer promising avenues for improvement. Multidisciplinary collaboration, tailored screening protocols, technology integration, community outreach, and policy advocacy are identified as key strategies for optimization. The proposed strategies are underpinned by the principles of equity, inclusivity, and person-centered care. Implementation plans include pilot programs, professional training, and integration with existing healthcare systems. Evaluation metrics will assess the effectiveness and sustainability of optimized screening protocols. Expected outcomes include increased access to dental care, reduced prevalence and severity of dental disease, improved oral health-related quality of life, and cost-effectiveness. The conclusion emphasizes the importance of ongoing evaluation and adaptation to meet the evolving needs of children with special healthcare needs. This paper calls for concerted efforts from stakeholders across healthcare, education, policy, and advocacy sectors to implement and support the proposed strategies. By optimizing dental screening protocols, we can make significant strides towards enhancing access to care and improving oral health outcomes for children with special healthcare needs.

Principles, applications, and advancements of the Segment Anything Model

The Segment Anything Model (SAM) is a prominent computer vision model discussed in a review paper focusing on image segmentation. This paper explores the concepts, applications, and advancements of SAM, which excels at accurately separating diverse object types and managing visual data. It leverages convolutional neural networks (CNNs), an encoder-decoder architecture, skip connections, and spatial attention mechanism to capture fine details and contextual information across different scales. SAM finds versatile applications in various domains, including medical imaging for precise anatomical structure delineation and pathology identification. It improves recognition and classification by precise positioning and segmentation. However, the SAM model faces challenges such as complex object shapes and computational requirements for real-time deployment in resource-constrained environments. To tackle these limitations, researchers have proposed advancements like feature enhancement, network architecture modifications, and regularization techniques. Future directions may involve lightweight network designs, optimization strategies, and integration of external information to enhance accuracy, efficiency, and robustness of the SAM model.

Advanced Exergy Assessment of an Air Source Heat Pump Unit

Conventional exergy-based analysis methods are used for evaluating the performance of the energy conversation systems. Conventional exergy-based analyses identify the sources, amounts, and reasons of irreversibilities (exergy destructions), costs and environmental effects, and provide a general direction for improvement. However, interactions between system components (endogenous/exogenous) and technical limitations (avoidable/unavoidable) cannot be identified with any conventional analysis. Hence, the real potential for improvement and optimization strategies can be misguided. Advanced exergy based analysis seeks to overcome this limitation. An air source heat pump unit was assessed applying conventional and advanced exergy analysis approaches respectively. Avoidable/unavoidable and endogenous/exogenous exergy destructions, modified exergy efficiencies and modified exergy losses ratios were calculated for every single component of the system. The results showed that while the evaporator and condenser efficiencies could be upgraded via constructional enhancements to the overall system and other system components, internal operating conditions were mainly responsible of the inefficiencies regarding with the compressor. The analysis demonstrated that while it was possible to improve evaporator and condenser efficiency by making constructive enhancements to whole system design, the efficiency of the compressor was mainly determined by the internal conditions in which the compressor operated.

Learning to Learn Better Visual Prompts

Prompt tuning provides a low-cost way of adapting vision-language models (VLMs) for various downstream vision tasks without requiring updating the huge pre-trained parameters. Dispensing with the conventional manual crafting of prompts, the recent prompt tuning method of Context Optimization (CoOp) introduces adaptable vectors as text prompts. Nevertheless, several previous works point out that the CoOp-based approaches are easy to overfit to the base classes and hard to generalize to novel classes. In this paper, we reckon that the prompt tuning works well only in the base classes because of the limited capacity of the adaptable vectors. The scale of the pre-trained model is hundreds times the scale of the adaptable vector, thus the learned vector has a very limited ability to absorb the knowledge of novel classes. To minimize this excessive overfitting of textual knowledge on the base class, we view prompt tuning as learning to learn (LoL) and learn the prompt in the way of meta-learning, the training manner of dividing the base classes into many different subclasses could fully exert the limited capacity of prompt tuning and thus transfer it power to recognize the novel classes. To be specific, we initially perform fine-tuning on the base class based on the CoOp method for pre-trained CLIP. Subsequently, predicated on the fine-tuned CLIP model, we carry out further fine-tuning in an N-way K-shot manner from the perspective of meta-learning on the base classes. We finally apply the learned textual vector and VLM for unseen classes.Extensive experiments on benchmark datasets validate the efficacy of our meta-learning-informed prompt tuning, affirming its role as a robust optimization strategy for VLMs.

Oppositional based artificial rabbits optimization applied for optimal allocation of nonlinear DG in distribution networks considering total harmonic distortion limit

This paper proposes a new optimization approach to identify the optimal placement and sizes of distributed generation (DG) units in a radial distribution network (RDN). Inverter-based DG also known as nonlinear DG (NLDG) units inject nonlinear current into the system, which can cause harmonic distortion. This harmonic distortion can limit the penetration of DG in the system by affecting the stability and reliability of the power system. Therefore, it is essential to consider harmonic distortion when identifying the optimal placement and sizes of DG units in RDNs to ensure DG’s safe and effective integration into the power system. The aim of the proposed work is to minimize real power loss, voltage deviation (VD), and total harmonic distortion limit (THD), and improve the voltage stability index (VSI) by integrating the search strategies of opposition-based learning and artificial rabbits optimization (ARO) to achieve better performance. The proposed algorithm, called opposition-based artificial rabbits optimization (OARO), considers different types of DG units like DG TYPE I and DG TYPE III and is suggested for two familiar RDNs: IEEE 33-bus, and 118-bus systems. The Pareto optimality concept has been introduced to solve the multi-objective problems of the systems. The simulation outcomes have been analyzed by comparing several optimization techniques in various cases. OARO has been shown to produce high-quality results with minimal iterations, reducing the time needed to solve the issue and conserving energy. Overall, the proposed approach offers a promising solution for identifying DG units’ optimal placement and sizes in RDNs while considering various operating scenarios.

GIPC: Fast and Stable Gauss-Newton Optimization of IPC Barrier Energy

Barrier functions are crucial for maintaining an intersection- and inversion-free simulation trajectory but existing methods, which directly use distance can restrict implementation design and performance. We present an approach to rewriting the barrier function for arriving at an efficient and robust approximation of its Hessian. The key idea is to formulate a simplicial geometric measure of contact using mesh boundary elements, from which analytic eigensystems are derived and enhanced with filtering and stiffening terms that ensure robustness with respect to the convergence of a Project-Newton solver. A further advantage of our rewriting of the barrier function is that it naturally caters to the notorious case of nearly parallel edge-edge contacts for which we also present a novel analytic eigensystem. Our approach is thus well suited for standard second-order unconstrained optimization strategies for resolving contacts, minimizing nonlinear nonconvex functions where the Hessian may be indefinite. The efficiency of our eigensystems alone yields a 3× speedup over the standard Incremental Potential Contact (IPC) barrier formulation. We further apply our analytic proxy eigensystems to produce an entirely GPU-based implementation of IPC with significant further acceleration.