Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for advanced Parkinson's disease. Stimulation of the hyperdirect pathway (HDP) may mediate the beneficial effects, whereas st...
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Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for advanced Parkinson's disease. Stimulation of the hyperdirect pathway (HDP) may mediate the beneficial effects, whereas stimulation of the corticospinal tract (CST) mediates capsular side effects. The study's objective was to suggest stimulation parameters based on the activation of the HDP and CST. This retrospective study included 20 Parkinson's disease patients with bilateral STN DBS. Patient-specific whole-brain probabilistic tractography was performed to extract the HDP and CST. Stimulation parameters from monopolar reviews were used to estimate volumes of tissue activated and to determine the streamlines of the pathways inside these volumes. The activated streamlines were related to the clinical observations. Two models were computed, one for the HDP to estimate effect thresholds and one for the CST to estimate capsular side effect thresholds. In a leave-one-subject-out cross-validation, the models were used to suggest stimulation parameters. The models indicated an activation of 50% of the HDP at effect threshold, and 4% of the CST at capsular side effect threshold. The suggestions for best and worst levels were significantly better than random suggestions. Finally, we compared the suggested stimulation thresholds with those from the monopolar reviews. The median suggestion errors for the effect threshold and side effect threshold were 1 and 1.5 mA, respectively. Our stimulation models of the HDP and CST suggested STN DBS settings. Prospective clinical studies are warranted to optimize tract-guided DBS programming. Together with other modalities, these may allow for assisted STN DBS programming.
The demand for programmers has grown exponentially in recent years, making programming an indispensable skill. However, the complex nature of programming poses various challenges for novice programmers, leading to hig...
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The demand for programmers has grown exponentially in recent years, making programming an indispensable skill. However, the complex nature of programming poses various challenges for novice programmers, leading to high dropout rates in programming courses. The recognition of individual differences, encompassing distinct neurocognitive profiles, is acknowledged to influence the accuracy of predicting programming skill outcomes. As a result, an increasing awareness exists regarding the substantial contribution of human factors, including personality or cognitive ability, to programming performance. This study conducts a Systematic Literature Review (SLR) to explore recent research on individual differentiation or programmer profiling in programming tasks. The primary goal is to examine pertinent research exploring personal characteristics' influence on programming in various contexts. As a result of this review, a taxonomy has been introduced to enhance the comprehension of programmers' multifaceted individual differences, categorising influential factors into nine distinct dimensions. Furthermore, it has been found that individual differences influence performance in programming tasks, as well as in the learning of this discipline. In conclusion, this SLR emphasises human factors' critical role in programming and proposes a taxonomy that is a valuable framework for researchers, educators, and practitioners to enhance their understanding of human factors' influence on programming performance.
Stimuli-responsive material is a kind of intelligent material which is widely used in the field of information programming, due to its physical properties that can change with external stimuli. However, challenges rem...
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Stimuli-responsive material is a kind of intelligent material which is widely used in the field of information programming, due to its physical properties that can change with external stimuli. However, challenges remain in constructing multi-stimuli responsive material systems and their application in programming and reconfiguring complex information. Herein, a multi-stimuli-responsive system was constructed by introducing a cyanostyrene-derivative with aggregation-induced emission (AIE-CSD) into a cholesteric liquid crystal polymer (CLCP). The obtained AIE-CSD-based CLCP (A-CLCP) can achieve programming and reconfiguration of complex information combined with structural and fluorescent color, due to its reversible multi-stimuli-responsive properties to temperature, humidity, photo, and pH. Carboxylic acid derivatives hydrogen bonded liquid crystals in the A-CLCP enable its structural color to respond to temperature and humidity;Chemo- and photo-chromic properties of the AIE-CSD molecule endow the CLCP with programmable fluorescent color;In addition, by adjusting the acidifying condition, the structural and fluorescent information can be programmed either simultaneously or independently. The multi-stimuli-responsive liquid crystalline polymer films with programmable and reconfigurable structural and fluorescent information exhibit tremendous potential in advanced anticounterfeiting and information-encrypted storage.
In this work, we propose a trajectory optimization approach for robot navigation in cluttered 3D environments. We represent the robot's geometry as a semialgebraic set defined by polynomial inequalities such that ...
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In this work, we propose a trajectory optimization approach for robot navigation in cluttered 3D environments. We represent the robot's geometry as a semialgebraic set defined by polynomial inequalities such that robots with general shapes can be suitably characterized. We exploit the collision-free space directly to construct a graph of free regions, search for the reference path, and allocate each waypoint on the trajectory to a specific region. Then, we incorporate a uniform scaling factor for each free region and formulate a Sums-of-Squares (SOS) optimization problem whose optimal solutions reveal the containment relationship between robots and the free space. The SOS optimization problem is further reformulated to a semidefinite program (SDP), and the collision-free constraints are shown to be equivalent to limiting the scaling factor along the entire trajectory. Next, to solve the trajectory optimization problem with the proposed safety constraints, we derive a guiding direction for updating the robot configuration to decrease the minimum scaling factor by calculating the gradient of the Lagrangian at the primal-dual optimum of the SDP. As a result, this seamlessly facilitates the use of gradient-based methods in efficient solving of the trajectory optimization problem. Through a series of simulations and real-world experiments, the proposed trajectory optimization approach is validated in various challenging scenarios, and the results demonstrate its effectiveness in generating collision-free trajectories in dense and intricate environments.
programming is becoming a new literacy, and programming education is being implemented in various settings. One of the issues faced by novice programmers is a lack of problem-solving skills. They have difficulty plann...
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programming is becoming a new literacy, and programming education is being implemented in various settings. One of the issues faced by novice programmers is a lack of problem-solving skills. They have difficulty planning abstract solutions and expressing them in a program. This study proposes a programming learning system that supports novice programmers' thinking processes when solving programming tasks. The system presents candidates for solutions in natural language and allows the user to solve the problem by choosing from a limited number of options. The method allows users to repeat quick trial-and-error solutions without being interrupted by coding-induced problems. We implemented the proposed system as a web application for use on a smartphone and applied it in a university's introductory programming class. After applying the proposed system to 37 students for four weeks, we found a correlation between the number of times a user selected an option using the proposed system and the class task score. We confirmed the possibility that using the proposed system can improve problem-solving skills for programming tasks.
The segmentation of phased array apertures into distinct sets of tile modules, referred to as irregular tiled arrays, is highly appealing in scenarios requiring cost efficiency. However, the limited scanning abilities...
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The segmentation of phased array apertures into distinct sets of tile modules, referred to as irregular tiled arrays, is highly appealing in scenarios requiring cost efficiency. However, the limited scanning abilities restrict broader applications. To mitigate this limitation, we propose a hybrid subarray architecture that enhances flexibility by utilizing the diverse scanning advantages offered by various subarray sizes and shapes. A mixed integer linear programming model is developed to optimize the tiling configuration to adapt the array design to meet different sidelobe level (SLL) and maximum scanning angle requirements. Benders decomposition is employed to efficiently decouple and solve both integer and continuous variables. The proposed array structure exhibits network complexity that falls between traditional irregular arrays and nonadjacent arrays. Compared to traditional arrays, it achieves more than 4 dB sidelobe suppression and a 1.1 dB gain improvement. Compared with nonadjacent arrays, the proposed array not only extends the scanning angle but also achieves significantly lower SLLs, offering superior performance for wide-angle scanning applications.
Genetic programming (GP) based Symbolic Regression (SR) algorithms suffer from the ineluctable effects over model bloat, blind search and diversity loss when determining explicit symbolic models to best depict the con...
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Genetic programming (GP) based Symbolic Regression (SR) algorithms suffer from the ineluctable effects over model bloat, blind search and diversity loss when determining explicit symbolic models to best depict the concealed laws in historical data, which often make them time-consuming and unstable. Most efforts often dealt with one of these effects, such that the algorithms still suffer from other effects. To deal with these effects, we propose a cross-parallel SR algorithm framework based on problem decomposition and multiobjective GP in this paper. The decomposition method is proposed to distill simple subproblems named global and local regression, which can be fast solved to produce various high quality models. In the proposed framework, by expressing the SR problem as the multiobjective optimization model, a number of subproblems are automatically distilled and solved in parallel to reduce model bloat and accelerate the algorithm. Traditional regression methods are employed to produce high quality models to seed the evolving populations for each subtask to maintain population diversity and improve search efficiency. Elite models obtained by each subtasks will be collected and randomly sent to other subtasks to improve the model generalization. Ablation and comparison experiments are conducted to evaluate the performance of the proposed algorithms. The ablation results show that the developed algorithm framework plays a positive role in reducing above ineluctable effects, and can fast determine concise symbolic models for the benchmarks. Comparisons by SRBench demonstrate the effectiveness of the developed algorithm on wide range problems.
With the development of autonomous vehicle technology, planning an efficient trajectory for automatic parking while considering multiple factors such as path length, task time, and passenger comfort is increasingly ne...
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With the development of autonomous vehicle technology, planning an efficient trajectory for automatic parking while considering multiple factors such as path length, task time, and passenger comfort is increasingly necessary. This article proposes a solution to the automatic parking problem using an improved fuzzy goal programming algorithm, a multi-objective technique capable of finding a compromised solution among all objectives using a function transformation. Furthermore, it efficiently solves the optimized automatic maneuver parking problem while considering fuzzy factors. In this way, the proposed solution can compensate for the difficulty of precisely determining excepted values for each optimization objective. The designed multi-objective strategy is validated and analyzed through a series of simulation and experimental studies. The simulation results indicate that the improved fuzzy goal programming approach has a better performance than the general goal programming method, the weighted sum method and single-objective optimization method in searching for a compromised solution of the multi-objective trajectory optimization automatic parking problem. Moreover, experimental results verify the effectiveness of the proposed method in solving multi-objective optimal automatic parking problem.
Libraries that implement Domain-Specific Language (DSL) components keep gaining traction when it comes to developing software for specific application domains. However, creating components that can be organically weav...
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Libraries that implement Domain-Specific Language (DSL) components keep gaining traction when it comes to developing software for specific application domains. However, creating components that can be organically weaved into use cases is an extremely complex task. In this work, we introduce a meta-DSL to assist library development, called Forward-Oriented programming (FOP). This combines lazy evaluation and aspectoriented programming principles to align crosscutting component configurations and alter their execution outcomes depending on usage in subsequent code. Theoretical analysis shows that FOP simplifies component development and makes their combination logic learnable by library users. We realize the paradigm with a Python package, called pyfop , and conduct a case study that compares it with purely functional and objectoriented library implementations. In the study, source code quality metrics demonstrate reduced time and effort to write library components, and increased comprehensibility. Configurations are shared without modifying distant code segments.
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