Computational Solution Research Articles

SEGURANÇA RESIDENCIAL INTELIGENTE: DETECÇÃO DE COMPORTAMENTO SUSPEITO POR MEIO DE ANÁLISE DE VÍDEO

This work presents the development of a computational solution for intelligent home monitoring, designed to detect people, analyze their behaviors and issue alerts for abnormal activities. YOLOv8-Pose technology was used to detect people's key points and a modified MLP (Multilayer Perceptron) neural network with LSTM (Long Short-Term Memory) characteristics was used to classify behavior. The training dataset was created by recording videos using a smartphone strategically positioned to capture the frontal area of a residence. This resulted in 154 video clips where the actor exhibited normal behaviors by walking along the scene and 154 videos where the actor performed actions that could lead to home invasion, such as attempting to climb the fence or break the garage gate. To carry out the experiments, videos with multiple actors and different behaviors were analyzed to evaluate the effectiveness of the developed methodology. The results indicated a high success rate in detecting normal behaviors, although challenges remain in scenarios with partial occlusion. The model's accuracy in classifying behaviors was 91.6%, reflecting its effectiveness in correctly identifying normal and abnormal activities.

Numerical Analysis of Enhanced Forced Convection in Perforated Surface Wavy Plate-Fin Core

Abstract Steady, periodically fully developed forced convection through three-dimensional perforated sinusoidal wavy-fin cores with uniform wall temperature is computationally investigated. Constant property air flow (Pr ≈ 0.71) with a Reynolds number range of 50 – 4000 covering both laminar and turbulent regimes is considered. The computational solutions, validated with in-house experimental data for continuous and perforated wavy-fin coupons, highlight the effects of perforation locations (characterized by phase angle β), fin porosity, inter-fin spacing, and corrugation amplitude on the thermal-hydraulic performance of the fins. The local temperature, velocity, and pressure variations, and the corresponding local heat transfer coefficient and friction drag (or the Fanning friction factor f and Colburn factor j) are reported. Fluid flows from the adjacent channels through wavy-surface perforations induce secondary flow and interrupt the boundary layer leading to an increase in f and j in both laminar and turbulent regimes. Decrease in corrugation amplitude and inter-fin spacing leads to the suppression of recirculation zones, whereas higher porosity yields increased f and j. Perforated fins nevertheless require less surface area to fulfill a specified heat load condition with a fixed pressure drop as compared to the continuous wavy fins. Furthermore, the perforation location has a noticeable effect on the local heat transfer and flow dynamics and, except for Re <∼200, wavy fins with perforations at relatively higher phase angle β perform better.

Variable Dose-Constraints Method for Enhancing Intensity-Modulated Radiation Therapy Treatment Planning

The conventional approach to intensity-modulated radiation therapy treatment planning involves two distinct strategies: optimizing an evaluation function while accounting for dose constraints, and solving feasibility problems using feasibility-seeking projection methods that incorporate inequality constraints. This paper introduces a novel iterative scheme within the framework of continuous dynamical systems, wherein constraint conditions dynamically evolve to enhance the optimization process. The validity of dynamically varying dose constraints is theoretically established through the foundation of continuous-time dynamical systems theory. In particular, we formalize a system of differential equations, with both beam coefficients and dose constraints modeled as state variables. The asymptotic stability of the system’s equilibrium is rigorously proven, ensuring convergence to a solution. In practical terms, we leverage a discretized iteration formula derived from the continuous-time system to achieve rapid computational speed. The mathematical structure of the proposed approach, which directly incorporates dose-volume constraints into the objective function, facilitates significant computational efficiency and solution refinement. The proposed method has an inherent dynamics that approaches more desirable solutions within the set of solutions when the solution to the optimization problem is not an isolated point. This property guarantees the identification of optimal solutions that respect the prescribed dose-volume constraints while enhancing accuracy when such constraints are feasible. By treating dose constraints as variables and concurrently solving the optimization problem with beam coefficients, we can achieve more accurate results when compared with using fixed values for prescribed dose conditions.

A generalized bilevel optimization model for large-scale task scheduling in multiple agile earth observation satellites

Scheduling large-scale tasks within a multi-agile earth observation satellite system poses a formidable challenge. Given the complexity of this optimization problem, the responding solving strategy should exhibit both efficiency and effectiveness in terms of computational time and solution quality. The current strategies can broadly be categorized into all-in-one and two-stage methodologies. The latter, more conducive to real-world scenarios, owing to reducing the problem complexity and practical operational flexibility, dissects this challenge into task allocation and single-satellite scheduling. However, existing two-stage strategies still consider the objectives and constraints at the equivalent level and only adapt to specific algorithms. In this way, the pivotal issues concerning the solution complexity and strategy compatibility remain fundamentally unaddressed. To address this limitation, we proposed a generalized bilevel optimization model called Question-and-Answer model, which establishes two distinct optimization model with different contains and objectives. In this model, the upper questions are highly indispensable in the response from the lower level and constraints are considered separately at two stages. To ascertain the generalization of this framework, we conduct a comprehensive range of experiments employing various proposed strategies and algorithms in two stages. Diverse algorithms ranging from heuristic principles, and evolutionary strategies, to reinforcement learning can be seamlessly combined and integrated within this framework. The results demonstrate that the scale of scenarios does not affect the effectiveness of any amalgamated algorithms within this framework. Furthermore, the transition of upper questions according to the lower answers indeed improves the objectives but concurrently intensify the computational time.

A Biochemistry-Inspired Algorithm for Path Planning in Unmanned Ground Vehicles

Unmanned ground vehicles (UGVs) have gained significant attention due to their extensive applications in both military and civilian sectors. For effective UGV deployment, path planning algorithms must prioritize computational efficiency, solution reliability, and runtime performance while maintaining path quality. Autonomous path planning remains a critical challenge in UGV navigation, as conventional methods, while effective, often suffer from considerable computational overhead. To address this issue, we propose a novel biochemistry-inspired path planning algorithm designed specifically for static grid-based scenarios. MetaPath demonstrates remarkable computational efficiency while maintaining solution quality across different obstacle densities in benchmark environments. Specifically, the algorithm achieves path lengths within ±5% of all benchmark algorithms while dramatically reducing the exploration space, visiting up to 10% of the cells explored by conventional approaches such as A*. This superior efficiency translates into exceptional runtime performance, executing up to 3000 times faster than bio-inspired algorithms like Ant Colony Optimization (ACO) and the Genetic Algorithm (GA), performing nearly three times faster than the widely used A* algorithm, and maintaining competitive performance with efficient algorithms like Breadth-First Search (BFS) and Particle Swarm Optimization (PSO), thereby establishing the algorithm as a highly efficient pathfinding solution. Most notably, MetaPath introduces a novel approach as the first chemistry-inspired pathfinding algorithm, guaranteeing path discovery when one exists within reasonable computational time, a crucial advantage over some benchmark algorithms that may fail to converge or require excessive computational resources in complex scenarios.

Snap-through of functionally graded graphene origami-enabled auxetic metamaterial doubly curved nonlinear shells

The lightweight design of thin-walled curved structures as spherical shells are frequently implemented in architecture, aerospace, mechanical, automotive, nuclear, and defense structures. Under the transverse loads, shells may largely deform and hence snap from one equilibrium position to the other. Thus, this work aims to develop a mathematical model and computational solution to investigate the nonlinear bending and snap-through behavior of doubly curved auxetic metamaterial shell subjected to transversal loading, for the first time. The shell is composed of several layers through the shell thickness, each layer is manufactured from a copper (Cu) matrix reinforced with a specified weight fraction of graphene origami auxetic metamaterial (GOAM). The mechanical properties of the GOAM shell panel are presented and described by functions of the GOAM volume fraction and folding degree. Three types of GOAM-distributions are considered, which are U-type, X-type, and O-type. The theoretical framework of Kirchhoff–Love hypotheses for thin shells and von Karman type nonlinearity are used to derive the governing equations. The differential quadrature method (DQM) is implemented to discretize the space domain and convert the nonlinear partial differential equation to nonlinear algebraic equations in terms of displacement field. An efficient incremental iterative procedure is developed to solve the nonlinear equations and predict the snap-through behavior. A model conversion and validation with isotropic spherical shell is considered. Several numerical results are conducted considering the effects of changing GOAM content, distribution pattern, folding degree, and shell curvature and thickness. For panels exhibiting snap-through behavior, increasing the shell curvature leads to higher snap-through limiting load; however, the instability gap is enlarged.

A Study of the Capacitated Vehicle Routing Problem with Time-Window and Three-Dimensional Loading Constraints in Land–Sea Transport

This paper addresses the capacitated vehicle routing problem with time-window and three-dimensional loading constraints in land–sea transport (3L-CVRPTWLS, which is an extension of the 3L-CVRP) to minimize the total cost of land–sea transport. The 3L-CVRPTWLS considers the online packing environment and port-of-destination (POD) constraint, which are clearly of practical significance in freight distribution. Due to its high degree of combinatorial complexity, the literature on this problem is very limited. To solve the 3L-CVRPTWLS, we develop a general deepest-bottom-left-fill (DBLF) and layer heuristic for packing and a hybrid variable-neighborhood tabu search for the routing phase (HLVNTS) based on the “packing first, routing second” (P1R2) strategy. HLVNTS reduces the average total number of vehicles by 2.51% and the average total travel distance by 27.62% in a shorter amount of time. The experimental results show that the proposed algorithm performs well in the tested instances in terms of both computational efficiency and solution quality. Moreover, we evaluate the impact of the POD constraint on the total transportation cost. This study may provide some important support for the sustainable development of land–sea transport and help to protect the environment.

Using neural networks for the detection of textual patterns in academic texts: A case study at the university of Nariño

Dealing manually with large volumes of textual information leads to problems in qualitative research, such as the time spent reading and processing the data and the possibility of losing critical details. In education sciences, investigations are commonly based on the qualitative paradigm, requiring substantial amounts of data to be processed textually. In this situation, an alternative for processing text to detect patterns using neural networks in the computational linguistics scenario is proposed. In this regard, qualitative research using a quasi-experimental method is performed. The quasi-experiment is done within the Qualitative Information Analysis course of the master's degree in Higher Education Teaching in 2022 with Class XIV; in such a course, six students evaluated the computational solution using material related to their working master’s thesis. The results from the application test are satisfactory due to the positive user acceptance, providing a plausible alternative to commercial computational solutions on the market.

Distributed Methodology for a Cell-Transmission-Based System Optimal Dynamic Traffic Assignment

This study introduces a distributed methodology for system optimal dynamic traffic assignment formulated using the cell transmission model (CTM). Using CTM increases the methodology’s accuracy in predicting link flows and its dynamics; however, it increases the computational complexity significantly. Our methodology is specifically designed to address this challenge and provide a balance between the quality of the solution and computational complexity while ensuring first-in-first-out (FIFO) queuing discipline. The methodology follows a receding horizon framework and distributes the network-level traffic assignment into several intersection-level sub-problems and solves them in parallel. Therefore, this heuristic significantly reduces the computational complexity and finds solutions in real-time. The distribution is achieved by relaxing coupling constraints among different sub-problems. The sub-problems coordinate their decisions with each other by sharing information and implementing it in the re-introduced constraints that were previously relaxed. This process was used to avoid infeasible solutions, reduce the likelihood of finding local solutions, and promote system-level optimality. The information that needs to be exchanged is estimated using CTM simulation runs. All optimal sub-problem solutions are implemented in network-level CTM simulations, and cell occupancies and flows are obtained. The FIFO discipline is also approximated in CTM. We have tested the methodology in networks of 20 and 40 intersections with 15 and 25 origin-destination pairs, respectively, under various demand levels, and compared the performance with a benchmark approach capable of finding the optimal solutions. The maximum observed optimality gap in case studies with 20 and 40 intersections was 3.1%, and the solutions were found in real-time in all scenarios.

Overcoming the preferred-orientation problem in cryo-EM with self-supervised deep learning.

While advances in single-particle cryo-EM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the 'preferred' orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep learning-based software to address map anisotropy and particle misalignment caused by the preferred-orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's ability to generate near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, β-galactosidases and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred-orientation problem.

Innovative Analytical and Semi-Analytical Approaches to the Nonlinear Fisher-KPP Model: Accurate Computational Solutions and Applications

Innovative Analytical and Semi-Analytical Approaches to the Nonlinear Fisher-KPP Model: Accurate Computational Solutions and Applications

A new biomechanical approach to cranial suture function: the role of contact elements in linear and nonlinear models

Abstract Understanding cranial sutures and how they relieve and dissipate stress is essential to assess their role in cranial biomechanics and to develop highly accurate predictive models. This involves examining how ontogeny affects cranial sutures, as well as their morphology and function, and how these changes through time may impact essential biomechanical loadings such as chewing or direct biting. In this work, we study the cranial sutures of Crocodylus niloticus in detail using contact elements under finite element analysis. Contact elements permit the creation of a physical relationship between two bones that are in contact and even the configuration of these relationships, for example, in terms of movement or flexibility. The definition of bone contacts may require linear and/or nonlinear computational solutions to attain higher accuracy. Herein, skull geometry is tested to determine how bones may be altered by different types of contacts under various conditions. As predicted, the absence of sutures or cranial kinesis leads to a reduction in stress distribution across the skull, whereas sutures and cranial kinesis help the skull relieve stress and prevent certain bones from sustaining high stress levels. The type of contact used in individual sutures has a significant effect on model outcomes. Additionally, feeding behaviors significantly impact cranial biomechanics, reflecting the influence of other variables that may be applied to the models. As highlighted by the results, in order to obtain accurate results when analyzing fossil taxa, the nature of the cranial sutures should be taken into account. Therefore, developing predictive models based on living taxa is invaluable, because it facilitates the study of extinct taxa for which there is a lack of information on the fibrous joints due to poor or no preservation in the fossil record.

Visualizing the human olfactory projection and ancillary structures in a 3D reconstruction

Visualizing in 3D the histological microanatomy of the human olfactory projection from the olfactory mucosa in the nasal cavity to the olfactory bulbs in the cranial cavity necessitates a workflow for handling a great many sections. Here, we assembled a 3D reconstruction of a 7.45 cm3en-bloc specimen extracted from an embalmed human cadaver. A series of 10 µm coronal sections was stained with quadruple fluorescence histology and scanned in four channels. A trained anatomist manually segmented six structures of interest in a subset of the sections to generate the ground truth. Six convolutional neural networks were then trained for automatic segmentation of these structures in 1234 sections. A high-performance computing solution was engineered to register the sections based on the fluorescence signal and segmented structures. The resulting 3D visualization offers several novel didactic opportunities of interactive exploration and virtual manipulation. By extrapolating manual counts of OSNs in a subset of sections to the calculated volume of the envelope of the entire olfactory epithelium, we computed a total of ~2.7 million OSNs in the specimen. Such empirically derived information helps assess the extent to which the organizational principles of the human olfactory projection may differ from those in mice.

A Bilevel Approach to the Facility Location Problem with Customer Preferences Under a Mill Pricing Policy

This paper addresses the facility location problem under a mill pricing policy, integrating customers’ behavior through the concept of preferences. The problem is modeled as a bilevel optimization problem, where the existence of ties in customers’ preferences can lead to an ill-posed bilevel problem due to the possible existence of multiple optima to the lower-level problem. As the commonly employed optimistic and pessimistic strategies are inadequate for this problem, a specific approach is proposed bearing in mind the customers’ rational behavior. In this work, we propose a novel formulation of the problem as a bilevel model in which each customer faces a lexicographic biobjective problem in which the preference is maximized and the total cost of accessing the selected facility is minimized. This allows for a more accurate representation of customer preferences and the resulting decisions regarding facility location and pricing. To address the complexities of this model, we apply duality theory to the lower-level problems and, ultimately, reformulate the bilevel problem as a single-level mixed-integer optimization problem. This reformulation incorporates big-M constants, for which we provide valid bounds to ensure computational tractability and solution quality. The computational study conducted allows us to assess, on the one hand, the effectiveness of the proposed reformulation to address the bilevel model and, on the other hand, the impact of the length of the customer preference lists and fixed opening cost for facilities on the computational time and the optimal solution.

Machine Learning-Based Optimization Models for Defining Storage Rules in Maritime Container Yards

This paper proposes an integrated approach to define the best consignment strategy for storing containers in an export yard of a maritime terminal. The storage strategy identifies the rules for grouping homogeneous containers, which are defined simultaneously with the assignment of each group of containers to the available blocks (bay-locations) in the yard. Unlike recent literature, this study focuses specifically on weight classes and their respective limits when establishing the consignment strategy. Another novel aspect of this work is the integration of a data-driven algorithm and operations research. The integrated approach is based on unsupervised learning and optimization models and allows us to solve large instances within a few seconds. Results obtained by spectral clustering are treated as input datasets for the optimization models. Two different formulations are described and compared: the main difference lies in how containers are assigned to bay-locations, shifting from a time-consuming individual container assignment to the assignment of groups of containers, which offers significant advantages in computational efficiency. Experimental tests are organized into three campaigns to evaluate the following: (i) The computational time and solution quality (i.e., space utilization) of the proposed models; (ii) The performance of these models against a benchmark model; (iii) The practical effectiveness of the proposed solution approach.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

Learning-Based Genetic Algorithm to Schedule an Extended Flexible Job Shop.

This work considers an extended flexible job-shop scheduling problem from a semiconductor manufacturing environment. To find its high-quality solution in a reasonable time, a learning-based genetic algorithm (LGA) that incorporates a parallel long short-term memory network-embedded autoencoder model is proposed. In it, genetic algorithm is selected as a main optimizer. A novel autoencoder model is trained offline via end-to-end unsupervised learning without relying on labeled data. This model captures the major linkages among decision variables and generates promising solutions in an informative low-dimensional space, striking a balance between computational efficiency and solution quality. To further improve its search ability, a co-evolving framework is designed, which includes both a network-embedded subpopulation and a regular one. The former focuses on its global search while the latter ensures LGA's convergence. An information exchange method between the two subpopulations balances global and local search, improving its overall optimization ability. This work conducts various numerical experiments to compare LGA with the CPLEX optimizer, several classical heuristics, and some popular methods. Results show that LGA outperforms its peers in finding high-quality solutions in a reasonable time.

Dynamic Routing and Scheduling Approach for Aircraft Taxi Automation with Adaptive Surface Situation

The emergence of surface trajectory-based operation (STBO) has promoted the development of taxi automation systems to plan conflict-free aircraft trajectories for efficient airport operations. Unlike traditional airport ground movement, aircraft guided by the taxi automation system possess a significant degree of freedom during taxiing, requiring the system to coordinate aircraft movement on the surface with timely responses to uncertainty. This study proposes a dynamic routing and scheduling approach with an adaptive surface situation characterization mechanism. A link-level unimpeded taxi time estimation method is designed to fine-grain characterize airport operation. A comprehensive evaluation system is then developed to identify similar situation patterns. Finally, a dynamic routing and scheduling approach is proposed to optimize airport ground movement with an adaptive and refined surface situation mechanism. The case study of actual operational data collected from a hub airport reveals that the proposed approach achieves efficient movement plans and collaborative control for all aircraft, as well as a more balanced utilization of the runway. The embedded adaptive situation mechanism also makes a better tradeoff between computational efficiency and optimal solutions. The study provides a promising approach to generating conflict-free four-dimensional trajectories for airport operations in future STBO scenarios.

Machine Learning-Driven Analysis of Heat Transfer in Chemically Reactive Fluid Flow Considering Soret-Dufour Effects

ObjectiveThe study investigates tangent hyperbolic nanofluid flow across an extended media, emphasizing magnetohydrodynamic influences, mixed convection, and nanoparticle transport with the help of an innovative and advanced technique. MethodologyIt employs a sophisticated Gaussian Neural Network and Hybrid Cuckoo Search to mimic complex fluid dynamics accurately, ensuring efficient computation and solution precision. By transforming the governing system of nonlinear partial differential equations into ordinary differential equations via similarity transformation, the methodology attains computational efficiency and solution precision. Core FindingsStatistical measures confirm the model's resilience, indicating absolute error variations from E−05 to E−12 and mean squared errors continuously between E−02 − E−07, highlighting the reliability of the model, while the Error in Nash-Sutcliffe Efficiency values fall within the interval E−05 − E−15. The Theil's Inequality Coefficient values lie in the range of E−01 − 10−07. Future Direction and ApplicationsThe findings underscore the model's capacity to improve thermal management, energy efficiency, and process dependability in sectors like chemical processing, environmental engineering, and sustainable energy production. This study positions the Gaussian Neural Networks framework as a robust computational instrument, providing a foundation for subsequent investigations into intricate geometries, multi-physics phenomena, and magnetohydrodynamic systems for novel technology applications.

Heat transfer enhancement in magnetohydrodynamic hybrid nanofluids over a Bi-directional extending sheet with slip and convective conditions

The hybrid nanofluids can enhance cooling systems in microelectronics by enhancing heat dissipation from processes which makes them vital for maintaining optimal performance. They are also beneficial in solar systems where efficient heat transfer is essential for exploiting the energy detention. Thus, this study has studied the three-dimensional flow of a water-based hybrid nanofluid comprising of Fe3O4 and CuO nanoparticles on a bi-directional extending sheet. The bi-directional extending sheet is subjected to thermal convective and velocity slip conditions. In addition, the effects of heat source, magnetic field, thermal radiation, viscous dissipation, and Joule heating are employed. The partial differential equations (PDEs) that represent the mathematical model are subsequently converted to ordinary differential equations (ODEs) by applying the appropriate similarity variables. The shooting technique is incorporated to determine the computational solution of the converted ODEs. The effectiveness of the current study is confirmed by published results, which also validate the current outcomes. Based on the results, it is concluded that CuO and Fe3O4 solid nanoparticle volume fractions improved the thermal distribution while decreasing the velocity distributions along x and y-axes. The thermal distribution is improved by the thermal heat source, thermal radiation, thermal Biot number, and thermal Eckert numbers. CuO-water nanofluid has a higher velocity panel than CuO-Fe3O4/water hybrid nanofluid. Conversely, the CuO-Fe3O4/water hybrid nanofluid has a higher thermal profile than the CuO-water nanofluid. CuO-water nanofluid flow has higher surface drags than CuO-Fe3O4/water hybrid nanofluid flow. CuO-water nanofluid has a lower heat transfer rate than CuO-Fe3O4/water hybrid nanofluid.