Design Of Algorithms Research Articles

Recent Advances in controlled manipulation of micro/nano particles: A review

Abstract This review discusses the transformative impact of micro/nano particle manipulation techniques across scientific and technological disciplines. Emphasizing the pivotal role of precise control at the micro and nanoscale, the paper categorizes manipulation strategies into mechanical/surface force-based, field-control manipulation, and microfluidics manipulation. It addresses challenges specific to the submicrometer scale, highlighting the strengths and limitations of each approach. The unique behaviors exhibited by objects at the micro–nano scale influence the design and operation of manipulators, algorithms, and control systems, particularly in interactions with biological systems. The review covers dielectrophoresis and magnetic manipulation, showcasing their applications in particle manipulation and microfluidics. The evolution of optical tweezers, including holographic, surface plasmon-based, and optical fiber tweezers, is discussed, emphasizing their contributions in various scientific fields. Additionally, the paper also explores the manipulation of micro/nano particle in microfluidic platforms. The comprehensive review underscores the significance of understanding manipulation strategies in diverse environments, anticipating further advancements in science and technology.

Merged-nets enumeration for the systematic design of multicomponent reticular structures

Rational design of intricate multicomponent reticular structures is often hindered by the lack of suitable blueprint nets. We established the merged-net approach, proffering optimal balance between designability and complexity, as a systematic solution for the rational assembly of multicomponent structures. In this work, by methodically mapping node-net relationships among 53 basic edge-transitive nets, we conceived a signature net map to identify merging net pairs, resulting in the enumeration of 53 merged nets. We developed a practical design algorithm and proposed more than 100 multicomponent metal-organic framework platforms. The effectiveness of this approach is commended by the successful synthesis of four classes of materials, which is based on merging three-periodic nets with the four possible net periodicities. The construction of multicomponent materials based on derived nets of merged nets highlights the potential of the merged-net approach in accelerating the discovery of intricate reticular materials.

Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud.

The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.

Enhancing computational efficiency in topology-optimized mode converters via dynamic update rate strategies

In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-performance mode converter through efficient design methods is an urgent requirement. However, traditional design methodologies for these converters face significant computational complexities and inefficiencies. Addressing this challenge, this paper introduces a novel topology optimization design method for mode converters employing a Dynamic Adjustment of Update Rate (DAUR). This approach markedly reduces computational overhead, accelerating the design process while ensuring high performance and compactness. As a proof-of-concept, an ultra-compact dual-mode converter was designed. The DAUR method demonstrated an 80% reduction in computational time compared to traditional methods, while maintaining a compact design (only 1.4 μm × 1.4 μm) and an insertion loss under 0.68 dB across a wavelength range of 1525 nm to 1575 nm. Meanwhile, simulated inter-mode crosstalk remained below − 24 dB across a 40 nm bandwidth. A comprehensive comparison with traditional inverse design algorithms is presented, demonstrating our method’s superior efficiency and effectiveness. Our findings suggest that DAUR not only streamlines the design process but also facilitates exploration into more complex micro-nano photonic structures with reduced resource investment.

Research on supply chain efficiency optimization algorithm based on reinforcement learning

Supply chain efficiency is critical to enterprises and can affect their competitiveness. The supply chain faces an uncertain and complex external market environment, facing the problem of supply chain efficiency optimization; the traditional optimization method is ineffective, which can better face the current environment and deal with problems. It has advantages in optimizing supply chain efficiency and has been widely used. This paper first expounds on the importance of supply chain management status, the limitations of traditional supply chain management methods, and reinforcement learning in the application of supply chain optimization. Then, through experiments, reinforcement learning, supply chain optimization problems, and the analysis of related algorithm design, the optimal algorithm focuses on inventory management optimization. Finally, this paper points out the future research directions and development trend of the supply chain efficiency optimization algorithm based on reinforcement learning.

The Polarization Loop: How Emotions Drive Propagation of Disinformation in Online Media—The Case of Conspiracy Theories and Extreme Right Movements in Southern Europe

This paper examines the influence of emotions on political polarization, looking at online propagation of conspiracy thinking by extreme right movements in Southern Europe. Integrating insights from psychology, political science, media studies, and system theory, we propose the ‘polarization loop’, a causal mechanism explaining the cyclical relationship between extreme messages, emotional engagement, media amplification, and societal polarization. We illustrate the utility of the polarization loop observing the use of the Great Replacement Theory by extreme right movements in Italy, Portugal, and Spain. We suggest possible options to mitigate the negative effects of online polarization in democracy, including public oversight of algorithmic decission-making, involving social science and humanities in algorithmic design, and strengthening resilience of citizenship to prevent emotional overflow. We encourage interdisciplinary research where historical analysis can guide computational methods such as Natural Language Processing (NLP), using Large Language Models fine-tunned consistently with political science research. Provided the intimate nature of emotions, the focus of connected research should remain on structural patterns rather than individual behavior, making it explicit that results derived from this research cannot be applied as the base for decisions, automated or not, that may affect individuals.

Fixed-Ratio Approximation Algorithm for the Minimum Cost Cover of a Digraph by Bounded Number of Cycles

We consider polynomial time approximation for the minimum cost cycle cover problem of an edge-weighted digraph, where feasible covers are restricted to have at most k disjoint cycles. In the literature this problem is referred to as Min-k-SCCP. The problem is closely related to classic Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) and has many important applications in algorithms design and operations research. Unlike its unconstrained variant, the Min-k-SCCP is strongly NP-hard even on undirected graphs and remains intractable in very specific settings. For any metric, the problem can be approximated in polynomial time within ratio 2, while in fixed-dimensional Euclidean spaces it admits Polynomial Time Approximation Schemes (PTAS). In the same time, approximation of the more general asymmetric Min-k-SCCP still remains weakly studied. In this paper, we propose the first fixed-ratio approximation algorithm for this problem, which extends the recent breakthrough Svensson-Tarnawski-Vegh and Traub-Vygen results for the Asymmetric Traveling Salesman Problem.

Construction of multilayered gene circuits using de-novo-designed synthetic transcriptional regulators in cell-free systems

BackgroundDe-novo-designed synthetic transcriptional regulators have great potential as the genetic parts for constructing complex multilayered gene circuits. The design flexibility afforded by advanced nucleic acid sequence design tools vastly expands the repertoire of regulatory elements for circuit design. In principle, the design space of synthetic regulators should allow for the construction of regulatory circuits of arbitrary complexity; still, the orthogonality and robustness of such components have not been fully elucidated, thereby limiting the depth and width of synthetic circuits.ResultsIn this work, we systematically explored the design strategy of synthetic transcriptional regulators, termed switchable transcription terminators. Specifically, by redesigning key sequence domains, we created a high-performance switchable transcription terminator with a maximum fold change of 283.11 upon activation by its cognate input RNA. Further, an automated design algorithm was developed for these elements to improve orthogonality for a complex multi-layered circuit construction. The resulting orthogonal switchable transcription terminators could be used to construct a three-layer cascade circuit and a two-input three-layer OR gate.ConclusionsWe demonstrated a practical strategy for designing standardized regulatory elements and assembling modular gene circuits, ultimately laying the foundation for the streamlined construction of complex synthetic gene circuits.

Bird flock effect-based dynamic community detection: Unravelling network patterns over time

Community structure is essential for topological analysis, function study, and pattern detection in complex networks. As establishing community structure in a dynamic network is difficult, it gives a unique perspective in many interdisciplinary fields. Many researchers have explored the challenging technique that requires parameter specification and optimization for quality result. This study proposed an eco-system conceptual framework based on bird flock effect. Relying on the natural law of rule, we designed a dynamic community detection named DCDBFE. The design of algorithm was based on the three basic rules of bird flock: separation, alignment, and cohesion phase. Then, we provide an explanation of similarity measure used between vertices to identify the modules attraction. DCDBFE employs an incremental community detection approach to repeatedly detect communities in each network snapshot or time step. The contributions are obtained for high quality community detected, free-parameter and well stability. To test its performance, extensive experiments were conducted on both synthetic and real-world networks. The outcomes demonstrate that our approach can effectively find satisfaction from each time step by comparison with the other well-known algorithms.

Tuned African Vultures Optimization Algorithm for Optimal Design of Skeletal Structures Employing Multi-Stage Parameter Adjustment

Tuned African Vultures Optimization Algorithm for Optimal Design of Skeletal Structures Employing Multi-Stage Parameter Adjustment

A Self-Evaluating Collaborative Filtering Recommendation Algorithm: A Case Study of Anime Recommendations

Abstract. This paper introduces a collaborative filtering recommendation algorithm aimed at addressing the issues of information insufficiency and the mismatch of user personalization needs in anime recommendations. Firstly, we review relevant literature to explore the application of collaborative filtering algorithms in recommendation systems and previous research findings. Then, we detail the design and implementation of collaborative filtering algorithms based on anime and user data, calculating similarities between anime and between users respectively for recommendation purposes. Finally, a self-evaluation is conducted to achieve optimal recommendation performance. Experimental results show that when recommending 10 anime titles, the ROC analysis results indicate a high level of precision, with both algorithms performing well in terms of accuracy.

A Morphological Transfer-Based Multi-Fidelity Evolutionary Algorithm for Soft Robot Design

A Morphological Transfer-Based Multi-Fidelity Evolutionary Algorithm for Soft Robot Design

Combined Gradient Boosting Decision Tree and Differential Evolution Algorithm for Optimization Design of Permanent Magnet Arc Motor

Combined Gradient Boosting Decision Tree and Differential Evolution Algorithm for Optimization Design of Permanent Magnet Arc Motor

Round-Robin Beyond Additive Agents: Existence and Fairness of Approximate Equilibria

Fair allocation of indivisible goods has attracted extensive attention over the last two decades, yielding numerous elegant algorithmic results and producing challenging open questions. The problem becomes much harder in the presence of strategic agents. Ideally, one would want to design truthful mechanisms that produce allocations with fairness guarantees. However, in the standard setting without monetary transfers, it is generally impossible to have truthful mechanisms that provide nontrivial fairness guarantees. Recently, Amanatidis et al. [Amanatidis G, Birmpas G, Fusco F, Lazos P, Leonardi S, Reiffenhäuser R (2023) Allocating indivisible goods to strategic agents: Pure Nash equilibria and fairness. Math. Oper. Res., ePub ahead of print November 30, https://doi.org/10.1287/moor.2022.0058 ] suggested the study of mechanisms that produce fair allocations in their equilibria. Specifically, when the agents have additive valuation functions, the simple Round-Robin algorithm always has pure Nash equilibria, and the corresponding allocations are envy-free up to one good (EF1) with respect to the agents’ true valuation functions. Following this agenda, we show that this outstanding property of the Round-Robin mechanism extends much beyond the above default assumption of additivity. In particular, we prove that for agents with cancelable valuation functions (a natural class that contains, e.g., additive and budget-additive functions), this simple mechanism always has equilibria, and even its approximate equilibria correspond to approximately EF1 allocations with respect to the agents’ true valuation functions. Furthermore, we show that the approximate EF1 fairness of approximate equilibria surprisingly holds for the important class of submodular valuation functions as well, even though exact equilibria fail to exist. Funding: This work was supported by the Horizon 2020 European Research Council Advanced Grant “AMDROMA: Algorithmic and Mechanism Design Research in Online Markets” [Grant 788893], the Ministero dell’Università e della Ricerca Research project of national interest (PRIN) “ALGADIMAR: Algorithms, Games, and Digital Markets,” the Nederlandse Organisatie voor Wetenschappelijk Onderzoek Veni Project “Algorithmic Fair Division in Dynamic, Socially Constrained Environments” [Grant VI.Veni.192.153], and the National Recovery and Resilience Plan Greece 2.0 funded by the European Union under the NextGenerationEU Program [Grant MIS 5154714].

Adaptive connectivity-preserving formation control with a dynamic event-triggering mechanism

Connectivity preservation for real-life multi-agent systems is key to designing an effective and reliable control algorithm to achieve desired objectives. But the coexistence of system nonlinearities and uncertainties makes it difficult for the algorithm design, and especially in the event-triggered setting, such difficulty becomes severer due to the demand for integration of multiple ingredients. This paper focuses on proposing an adaptive connectivity-preserving formation control strategy that incorporates a dynamic event-triggering mechanism, in the scenario of the unknown control directions and intrinsic nonlinearities coupling with parameter uncertainties. First, a cluster of potential functions serving as control barrier functions are given to maintain the prescribed connectivity of communication graph. Second, two types of dynamic gains (one type is based on a Nussbaum function) are introduced for agents to cope with the system nonlinearities, uncertainties and the adverse effect of the execution error. As such, an adaptive event-triggered formation protocol is constructed such that the desired formation with connectivity preservation is achieved while a positive minimum inter-execution interval is ensured for each agent to avoid Zeno behavior. Particularly, the threshold in the developed event-triggering mechanism is dynamically adjusted online, rather than static, which better improves resource efficiency. A simulation example is given to demonstrate the effectiveness and advantage of the proposed strategy.

Automatic design generation of trusses from a reused steel stock library using graphic statics

Reuse of undamaged steel components in new structural design offers a means to reduce construction-related waste and carbon emissions. However, designing with reused members significantly differs from conventional methods as the design must conform to a so-called stock constraint. This work presents an automated stock-constrained design algorithm for planar trusses. To promote high efficiency of the resulting designs, graphic statics is used as the backbone of the algorithm. Additionally, optimization is used to reduce the wasted stock material when determining how to cut stock members to form the truss. To address the need for flexibility in reuse design, the underlying engine has inherent stochasticity and is fast (on the order of 1 min). It is herein executed 100 times per design case and shown to generate efficient design options that can either construct the same truss layout in multiple ways or provide the designer with different layout alternatives to select from. Carbon savings are estimated as 83%–90% for an obtained design case when compared to conventionally designed trusses made from recycled steel.

Enhancing IoT data acquisition efficiency via FPGA-based implementation with OpenCL framework

The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.

Exploring Evolutionary Algorithms for Multi-Objective Optimization in Seismic Structural Design

The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field.

New insights into factors affecting the severity of autonomous vehicle crashes from two sources of AV incident records

Superior safety is the main banner value of promoting autonomous vehicle (AV) technology, but it is difficult to responsibly claim it. The potential for AVs to reduce crash and injury risks would be overshadowed by technological limitations, regardless of their ability to mitigate or eliminate human error. This study aims to identify the key factors affecting crash severity by analyzing real-world AV crash data from the U.S. between 2015 and 2022. We integrated two open data sources from the California DMV and NHTSA. Mixed multinomial logit models incorporating the interaction effects were estimated using the crash severity level, addressing the observed and unobserved heterogeneities. Our results show that Advanced Driver Assistance System (ADAS) engagement is associated with a higher likelihood of slight injury crashes, whereas Automated Driving System (ADS) engagements show the opposite effect. In addition, we found that various environmental and road factors, including lighting conditions, weather, road type, and road surface conditions, significantly affect the severity of AV crashes. For instance, daylight conditions contribute to a lower likelihood of slight-injury crashes. On the other hand, driving under unfavorable weather conditions (cloudy, foggy, rainy, or snowy), on the highway, and on non-dry road surfaces are associated with an increase in the likelihood of severe injury crashes. Furthermore, several significant interaction effects were revealed. First, the mitigating effect of ADS engagement on the likelihood of slight injury crashes is reduced by the rear-end collision type. Second, the likelihood of slight injury crashes increases when AV interacts with heavy trucks on highways. Third, the likelihood of severe injuries increases when AVs collide with Vulnerable Road Users (VRUs) on urban streets. Overall, this research is expected to provide policymakers and AV manufacturers with valuable insights into AV safety, stressing that addressing the identified factors will lead to improved AV design and control algorithms.

A gentle introduction to gradient-based optimization and variational inequalities for machine learning

Abstract The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems. In these broader settings, new mathematical challenges emerge that involve equilibria and game theory instead of optima. Gradient-based methods remain essential—given the high dimensionality and large scale of machine-learning problems—but simple gradient descent is no longer the point of departure for algorithm design. We provide a gentle introduction to a broader framework for gradient-based algorithms in machine learning, beginning with saddle points and monotone games, and proceeding to general variational inequalities. While we provide convergence proofs for several of the algorithms that we present, our main focus is that of providing motivation and intuition.