Algorithmic Framework Research Articles

Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG metrics. Last, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitatory-inhibitory balance, highlighting the explanatory power of our framework to generate mechanistic insights.

Evidence for the extent and oncological benefit of lymphadenectomy for pancreatic cancer

Pancreatic cancer is usually diagnosed at a late stage and is characterized by early systemic metastases, which can also be present in the form of micrometastases that are not primarily visible. Lymphatic metastases in pancreatic cancer are common. The extent of lymph node removal (lymphadenectomy, LAD) in pancreatic cancer is defined in the guidelines of the Association of the Scientific Medical Societies in Germany (AWMF) and according to currently available data has more diagnostic and prognostic relevance than therapeutic relevance; however, within the framework of modern multimodal treatment algorithms, radical surgery is the most relevant of all components of multimodal treatment with LAD playing an important role. According to current data, extended LAD without technical necessity in the surgery of the primary tumor brings no advantages for the patients but numerous limitations in the quality of life and should therefore not be performed as the standard. Important aspects of LAD for pancreatic cancer are the lymph node ratio, extended vs. standard LAD and innovations in LAD in the field of interaortocaval lymph nodes and the so-called triangle operation.

Integrated Sensing and Communication Target Detection Framework and Waveform Design Method Based on Information Theory

Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed. This framework adopts the OTFS waveform as its fundamental signal. The target detection is implemented through a relative entropy test (RET) comparing echo signals against target presence/absence hypotheses. Furthermore, to enhance the system’s target detection capability, the iterative OTFS-ISAC waveform design (I-OTFS-WD) method which maximizes the relative entropy is proposed. This method utilizes the minorization-maximization (MM) algorithm framework and semidefinite relaxation (SDR) technique to transform the non-convex optimization problem into an iterative convex optimization problem for resolution. The simulation results demonstrate that, under sufficient sample conditions, the RET algorithm achieves a 9.12-fold performance improvement over LRT in low-SNR scenarios; additionally, the optimized waveform reduces the sample requirements of the RET algorithm by 40%, further enhancing the target detection capability of the OTFS-ISAC system.

A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior

This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-inspired optimization algorithms and a mechanism that creates a continuing variance within population groupings, while attempting to maintain levels of computational simplicity that have helped nature-inspired optimization algorithms gain notoriety within the field of feature selection. Through this functionality, the proposed framework seeks to increase search diversity within the population swarm to address issues such as premature convergence, and oscillations within the swarm. The proposed frameworks have shown promising results when implemented into the Bat algorithm (BA), Firefly algorithm (FA), and Particle Swarm Optimization algorithm (PSO), all of which are popular when applied to the field of feature selection, and have been shown to perform well in a variety of domains, gaining notoriety due to their powerful search capabilities.

Personalized learning path based on graph attention mechanism deep reinforcement learning research on recommender systems

In e-learning, the rapid expansion of learning resources poses challenges for learners in finding suitable materials due to their diverse preferences and cognitive abilities. Consequently, personalized learning path recommendation has emerged as a pivotal research area, especially for advancing e-learning systems. This paper introduces an algorithmic framework that integrates deep reinforcement learning with a graph attention mechanism to tailor learning paths to individual learners. The online course dataset is selected and a series of controlled experiments are conducted on the common recommendation models proposed in the past, and the experimental results are analyzed using a combination of two evaluation indices, such as data evaluation results and model variance. The experimental results show that adding the attention mechanism can significantly improve the accuracy of the model recommendation, compared with the deep reinforcement learning model without adding the graph attention mechanism, the comprehensive scores of the students in the test set were improved by 5.8 and 12.8 points, respectively, and the accuracy was improved by 5.3% compared with the previous deep learning model; the deep reinforcement model used in this paper with the addition of the labeling feedback mechanism was improved by 5.3% compared with the deep learning with feedback mechanism. In the recommendation model, the final scores of the students were improved by 3.7 and 8.2 points, respectively. In addition, the Advanced test set in the recommendation model of the learning path recommended by the student score improvement is more than two times of the Middle test set’s scores improvement, indicating that more learning recommended object knowledge points the richer, the model recommendation accuracy rate is higher. By merging graph attention mechanisms with deep reinforcement learning, our system provides precise recommendations, offering insights into the development of efficient personalized learning path systems and accelerating their educational applications.

Switching Time Optimization for Binary Quantum Optimal Control

Quantum optimal control is a technique for controlling the evolution of a quantum system and has been applied to a wide range of problems in quantum physics. We study a binary quantum control optimization problem, where control decisions are binary-valued and the problem is solved in diverse quantum algorithms. In this paper, we utilize classical optimization and computing techniques to develop an algorithmic framework that sequentially optimizes the number of control switches and the duration of each control interval on a continuous time horizon. Specifically, we first solve the continuous relaxation of the binary control problem based on time discretization and then use a heuristic to obtain a controller sequence with a penalty on the number of switches. Then, we formulate a switching time optimization model and apply sequential least-squares programming with accelerated time-evolution simulation to solve the model. We demonstrate that our computational framework can obtain binary controls with high-quality performance and also reduce computational time via solving a family of quantum control instances in various quantum physics applications.

Two-step inertial ADMM for the solution of Nonconvex Nonsmooth Optimization Problems with Nonseparable Structure

Abstract In this paper, we propose an algorithmic framework called two-step inertial alternating direction methods of multipliers (TIADMM) to solve a class of nonconvex and nonsmooth optimization problems with nonseparable structure. The new algorithm adds two-step inertial effect to each subproblem and introduces three relaxed terms into the dual update step to improve the convergence. This work employs two assumptions; (1) the auxiliary function satisfies the Kurdyka-Łojasiewicz property, and (2) the parameters satisfy some conditions to prove the global convergence of the sequence generated by the algorithm. Furthermore, the algorithm is extended to a linearization vision for solving nonconvex optimization problems. Finally, tests are conducted on two numerical examples to show the effectiveness.

Systematic Review and Framework for AI-Driven Tacit Knowledge Conversion Methods and Machine Learning Algorithms for Ontology-Based Chatbots in E-Learning Platforms

The conversion of tacit knowledge, which is deeply rooted in personal experience and often difficult to articulate, presents a significant challenge within knowledge management systems. Ontology-based chatbots offer a promising solution by leveraging structured knowledge representations and advanced natural language processing (NLP) techniques to facilitate this transformation. This paper explores the various methods and algorithms used in developing ontology-based chatbots, with a particular focus on their role in converting tacit knowledge into more accessible forms. Additionally, it provides a comparative analysis of the algorithms employed, highlighting their respective strengths and weaknesses. Ultimately, this study addresses the critical challenge of managing and converting tacit knowledge, with the aim of enhancing the overall effectiveness of knowledge management systems.

A Primal-Dual Perspective on Program Verification Algorithms

Many algorithms in verification and automated reasoning leverage some form of duality between proofs and refutations or counterexamples. In most cases, duality is only used as an intuition that helps in understanding the algorithms and is not formalized. In other cases, duality is used explicitly, but in a specially tailored way that does not generalize to other problems. In this paper we propose a unified primal-dual framework for designing verification algorithms that leverage duality. To that end, we generalize the concept of a Lagrangian that is commonly used in linear programming and optimization to capture the domains considered in verification problems, which are usually discrete, e.g., powersets of states, predicates, ranking functions, etc. A Lagrangian then induces a primal problem and a dual problem. We devise an abstract primal-dual procedure that simultaneously searches for a primal solution and a dual solution, where the two searches guide each other. We provide sufficient conditions that ensure that the procedure makes progress under certain monotonicity assumptions on the Lagrangian. We show that many existing algorithms in program analysis, verification, and automated reasoning can be derived from our algorithmic framework with a suitable choice of Lagrangian. The Lagrangian-based formulation sheds new light on various characteristics of these algorithms, such as the ingredients they use to ensure monotonicity and guarantee progress. We further use our framework to develop a new validity checking algorithm for fixpoint logic over quantified linear arithmetic. Our prototype achieves promising results and in some cases solves instances that are not solved by state-of-the-art techniques.

A Cross-Layer Game-Theoretic Approach to Resilient Control of Networked Switched Systems Against DoS Attacks.

This article investigates the resilient control strategies of networked switched systems (NSSs) against denial-of-service (DoS) attacks and external disturbance. In the network layer, both the defender and the attacker allocate energy over multiple channels. Considering the impact of switching characteristic in the physical layer on the network layer, a dynamic regulating factor is proposed to adjust the total energy of the defender. To optimize the signal-to-interference-noise ratio and energy consumption simultaneously at each player's side, a multiobjective game problem is formulated. Furthermore, a nondominated sorting genetic algorithm framework is employed, incorporating the knee point selection mechanism to attain the Pareto-Nash equilibrium, based on which the optimal defense strategy can be derived to achieve resilience against DoS attacks. In the physical-layer, taking the dynamic packet loss caused by DoS attacks and external disturbance into account, an H∞ minimax controller containing control inputs and the switching signal is designed to guarantee the optimal performance for NSSs through the dynamic game-theoretic approach. Finally, the networked continuous stirred tank reactor system is provided to verify the effectiveness of the proposed method.

An Innovative algorithm framework for cardiovascular risk assessment based on ECG data

Background:Cardiovascular disease (CVD) is a primary universal physical problem, with conventional prediction systems frequently being persistent and expensive. Modern advancements in machine learning (ML)offer a hopeful option for accurate CVD risk assessment by leveraging multifaceted relations among diverse risk factors.Aim:Their search proposes a novel deep learning (DL) system, Dynamic Owl Search algorithm-driven Adaptive Long Short-Term Memory (DOS-ALSTM), to enhance cardiovascular risk prediction utilizing electrocardiogram (ECG) data.Method:The study utilizes ECG data from a diverse population group to train and assess the proposed model. Data is cleaned and normalized employing standard techniques to handle lost values and ensure reliability. Relevant features are extracted using statistical and signal processing technique to detain crucial features from the ECG data. The DOS-ALSTM system integrates a DOS optimization algorithm for optimized parameter regulation and ALSTM networks to detain sequential dependencies in ECG data for accurate risk prediction. The recognized method is evaluated using Python software.Result:The DOS-ALSTM system demonstrates superior performance with superioraccuracy of 99%, recall of 98%, F1-Score of 97.9% and Precision of 98.8% in CVD risk assessment compared to traditional methods

Heuristic-based vehicle arrangement for ro-ro ships

In this paper, a two-level search strategy fused with an improved no-fit polygon algorithm and improved bat algorithm is proposed to obtain the layout points of multiple vehicles. Additionally, a space–time scheduling strategy is proposed using the Improved D*Lite Algorithm (ID*Lite) and improved Bezier curve to generate the trajectories of individual vehicles. Furthermore, a conflict resolution strategy is introduced to address the collision conflict problem during multi-vehicle scheduling. The proposed strategies aim to achieve the objectives of shortest scheduling time and smallest array space in order to reduce the time and space cost of vehicle loading on ro-ro carriers. Through a comparison with other algorithms (e.g., at high loading pressure, Scenario 3), the strategies presented in this paper demonstrate advantages such as higher deck utilization (92.234%), shorter path length, fewer collision conflicts, and more balanced weighting. Mainstream methods only focus on a certain part of vehicle transportation, we not only use more stable parking spot generation algorithm and parking order generation algorithm, but also more high-speed multi-vehicle path-finding algorithm, path optimization strategy, multi-vehicle scheduling strategy, which innovatively form a complete vehicle planning process, and at the same time, as an algorithmic framework that can be generalized to any 2d or 3d scenarios.

Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery

This study explored the optimization of groupage intercity delivery using a two-stage genetic algorithm (GA) framework, developed with the BaumEvA Python library. The primary objective was to minimize the transportation costs by strategically positioning regional branch warehouses within a logistics network. In the first stage, the GA selected optimal branch warehouse locations from a set of candidate cities. The second stage addressed the vehicle routing problem (VRP) by employing a combinatorial GA to optimize the delivery routes. The GA framework was designed to minimize the total costs associated with intercity and last-mile deliveries, factoring in warehouse locations, truck routes, and vehicle types for last-mile fulfillment while ensuring capacity constraints are adhered to. By solving both line haul and last-mile delivery subproblems, this solution adjusted variables related to warehouse placement, cargo volumes, truck routing, and vehicle selection. The integration of such optimization techniques into the logistics workflow allowed for streamlined operations and reduced costs.

Energy-efficient artificial fish swarm-based clustering protocol for enhancing network lifetime in underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs) face significant challenges, such as limited energy resources, high propagation delays, and harsh underwater environments. Efficient clustering can help address these challenges by grouping nearby nodes to minimize network fragmentation and balance energy consumption. However, placing gateways near the sink node can result in increased communication overhead and higher energy consumption in regions with concentrated data flow. To address these issues, we propose an energy-efficient artificial fish swarm-based clustering cognitive intelligence protocol (EAFSCCIP). EAFSCCIP leverages the collective behavior of artificial fish within a Bees algorithm framework, using a combination of heuristic and metaheuristic approaches for optimal cluster-head (CH) selection in each round. The protocol focuses on reducing energy consumption and extending network lifetime by considering real-time energy levels and the proximity of nodes for CH selection. Simulations have been executed in NS3 to validate and compare the performance of the proposed algorithm with the existing clustering protocols. The results indicate that EAFSCCIP significantly enhances the packet delivery ratio (PDR) by an average of 5.33% over existing methods and improves network lifetime by 6.54% compared to traditional protocols. It also reduces energy consumption by 25.6% and decreases packet loss by 50.5%, while achieving 20.4% higher throughput at the initial stage. These improvements make EAFSCCIP a promising solution for applications like acoustic monitoring in UWSNs, providing a balance between energy efficiency and reliable data transmission.

A novel seismic inversion method based on multiple attributes and machine learning for hydrocarbon reservoir prediction in Bohai Bay Basin, Eastern China

As the demands for hydrocarbon exploration continue to rise, the identification of thin sand bodies becomes significantly important for subsequent petroleum exploration and development efforts. However, traditional inversion techniques struggle with complex subsurface structures because of the low frequency seismic data. To characterize the architecture of hydrocarbon reservoir precisely, a novel seismic inversion method is applied to improve the resolution of seismic data for a high interpretation accuracy. In this study, we take the X Oilfield in Eastern China as an example, adopted a novel approach combining spectral decomposition with convolutional neural networks (CNNs) within a genetic algorithm (GA) framework for inversion. The CNNs are adept at recognizing and interpreting the spatial configurations in the data, thereby establishing a high correlation between seismic attributes and sand body distributions. GA helps CNNs to get an optimal solution in a fast speed. The results reveal that the model's sand thickness predictions closely match the actual measurements at wells, with a new horizontal well's alignment with the predicted output reaching an accuracy of 85.1%. Compared to traditional seismic inversion methods, our method requires less data. This approach may find a wider application, especially at offshore oilfields with few wells data and low quality seismic data.

SeqDPI: A 1D-CNN approach for predicting binding affinity of kinase inhibitors.

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction. The proposed SeqDPI model extract the relevant drug and protein features from the one dimensional Sequential representation of the dataset considered using optimized CNN networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to L2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. The proposed model reduces the convolution strain typically encountered in existing in silico models that utilize complex 3D structures of drug protein datasets. The result shows that the SeqDPI model achieves a mean square error MSE of (0.167) across cross validation folds, outperforming baseline models such as KronRLS (0.406), Simboost (0.226), and DeepPS (0.214). Additionally, SeqDPI attains a high CI score of 0.9114 on the benchmark KIBA dataset, demonstrating its statistical significance and computational efficiency compared to existing methods. This gives the relevance and effectiveness of SeqDPI model in accurately predicting binding affinities while working with simpler one-dimensional data, making it a robust and computationally cost-effective solution for drug-target interaction prediction.

Revisiting microgenderome: detecting and cataloguing sexually unique and enriched species in human microbiomes

BackgroundMicrogenderome or arguably more accurately microsexome refers to studies on sexual dimorphism of human microbiomes aimed at investigating bidirectional interactions between human microbiomes, sex hormones, and immune systems. It is important because of its implications to disease susceptibility and therapy, in which men and women demonstrate divergence in many diseases especially autoimmune diseases. In a previous report [1], we presented analyses of several key ecological aspects of microgenderome by leveraging the large datasets of the HMP (human microbiome project) but failed to offer species-level composition differences such as sexually unique species (US) and enriched species (ES). Existing approaches, for such tasks, including differential species relative abundance analysis and differential network analysis, possess certain limitations given that virtually all rely on species abundance alone or are univariate, while ignoring species distribution information across samples. Obviously, it is both species abundance and distribution that shape/drive the structure and dynamics of human microbiomes, and both should be equally responsible for the universal heterogeneity of microbiomes including the sexual dimorphism.ResultsHere, we fill the gap by taking advantages of a recently developed computational algorithm, species specificity, and specificity diversity (SSD) framework (refer to the companion article) to reanalyze the HMP and complementary seminovaginal microbiome datasets. The SSD framework can randomly search and catalogue the sexually specific unique/enriched species with statistical rigor, guided by species specificity (a synthetic metric of abundance and distribution) and specificity diversity (SD). The SSD framework reveals that men seem to have more unique species than women in their gut and reproductive system microbiomes, but women seem to have more unique species than men in the airway, oral, and skin microbiomes, which is likely due to sexual dimorphism in the hormone and immune systems. We further investigate co-dependency and heterogeneity of those sexually unique/enriched species across 15 body sites, with core/periphery network analyses.ConclusionsThis study not only produced sexually unique/enriched species in the human microbiomes and analyzed their codependency and heterogeneity but also further validated the robustness of the SSD framework presented in the companion article, by performing all negative control tests based on the HMP gut microbiome samples.

The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach

The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach

HSF-IBI: A Universal Framework for Extracting Inter-Beat Interval from Heterogeneous Unobtrusive Sensors.

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion. Specifically, harmonic summation (HarSum) is employed for calculating the average heart rate, which in turn guides the selection of the optimal band selection (OBS), the basic sequential algorithmic scheme (BSAS)-based template group extraction, and the template matching (TM) procedure. The optimal IBIs are determined by evaluating the signal quality index (SQI) for each heartbeat. The algorithm is morphology-independent and can be adapted to different sensors. The proposed algorithm framework is evaluated on a self-collected dataset including 19 healthy participants and an open-source dataset including 34 healthy participants, both containing heterogeneous sensors. The experimental results demonstrate that (1) the proposed framework successfully integrates data from heterogeneous sensors, leading to detection rate enhancements of 6.25 % and 5.21 % on two datasets, and (2) the proposed framework achieves superior accuracy over existing IBI extraction methods, with mean absolute errors (MAEs) of 5.25 ms and 4.56 ms on two datasets.

Unsupervised attribute reduction algorithm framework based on spectral clustering and attribute significance function

Unsupervised attribute reduction algorithm framework based on spectral clustering and attribute significance function