constraint handling Dotaz Zobrazit nápovědu
Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.
- Klíčová slova
- constraint handling, crossover, grey wolf optimizer, mutation, precedence constraints, process planning optimization, selection,
- Publikační typ
- časopisecké články MeSH
The estimation of foraging parameters is fundamental for understanding predator ecology. Predation and feeding can vary with multiple factors, such as prey availability, presence of kleptoparasites and human disturbance. However, our knowledge is mostly limited to local scales, which prevents studying effects of environmental factors across larger ecological gradients. Here, we compared inter-kill intervals and handling times of Eurasian lynx (Lynx lynx) across a large latitudinal gradient, from subarctic to the Mediterranean ecosystems, using a standardised dataset of predicted adult ungulate kills from 107 GPS-collared lynx from nine distinct populations in Europe. We analysed variations in these two foraging parameters in relation to proxies reflecting prey availability, scavengers' presence and human disturbance, to improve our understanding of lynx predation at a continental scale. We found that inter-kill intervals and handling times varied between populations, social status and in different seasons within the year. We observed marked differences in inter-kill intervals between populations, which do not appear to be driven by variation in handling time. Increases in habitat productivity (expressed by NDVI, used as a proxy for prey availability) resulted in reduced inter-kill intervals (i.e. higher kill rates). We observed less variation in handling (i.e. feeding) times, although presence of dominant scavengers (wild boars and brown bears) and higher human impact led to significantly shorter handling times. This suggests that kleptoparasitism and human disturbance may limit the energetic input that lynx can obtain from their prey. We also observed that the human impact on foraging parameters can be consistent between some populations but context-dependent for others, suggesting local adaptations by lynx. Our study highlights the value of large-scale studies based on standardised datasets, which can aid the implementation of effective management measures, as patterns observed in one area might not be necessarily transferable to other regions. Our results also indicate the high degree of adaptability of these solitary felids, which enables them to meet their energy requirements and persist across a wide range of environmental conditions despite the constraints imposed by humans, dominant scavengers and variable prey availability.
- Klíčová slova
- Eurasian lynx, Europe, foraging, handling time, human impact, inter‐kill interval, prey availability, scavengers,
- MeSH
- ekosystém MeSH
- Lynx * fyziologie MeSH
- potravní řetězec MeSH
- predátorské chování * MeSH
- roční období MeSH
- stravovací zvyklosti * MeSH
- zvířata MeSH
- Check Tag
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
The anatomical structure of mesophyll tissue in the leaf is tightly connected with many physiological processes in plants. One of the most important mesophyll parameters related to photosynthesis is the internal leaf surface area, i.e. the surface area of mesophyll cell walls exposed to intercellular spaces. An efficient design-based stereological method can be applied for estimation of this parameter, using software-randomized virtual fakir test probes in stacks of optical sections acquired by a confocal microscope within thick physical free-hand sections (i.e. acquired using a hand microtome), as we have shown in the case of fresh Norway spruce needles recently. However, for wider practical use in plant ecophysiology, a suitable form of sample storage and other possible technical constraints of this methodology need to be checked. We tested the effect of freezing conifer needles on their anatomical structure as well as the effect of possible deformations due to the cutting of unembedded material by a hand microtome, which can result in distortions of cutting surfaces. In the present study we found a higher proportion of intercellular spaces in mesophyll in regions near to the surface of a physical section, which means that the measurements should be restricted only to the middle region of the optical section series. On the other hand, the proportion of intercellular spaces in mesophyll as well as the internal needle surface density in mesophyll did not show significant difference between fresh and frozen needles; therefore, we conclude that freezing represents a suitable form of storage of sampled material for proposed stereological evaluation.
Trophic specialists are expected to possess adaptations that increase the efficiency of handling preferred prey. Such adaptations may constrain the ability to utilise alternative prey. Here we tested whether the ant-eating spider Euryopis episinoides possesses metabolic specialisations with increased efficiency in utilising preferred prey and decreased efficiency in utilising alternative prey. In addition, we investigated the contribution of genetic variation via maternal effects. We reared E. episinoides spiders from the first instar on two different diets, either ants (preferred prey) or fruit flies (alternative prey). Spider survival rate and increases in body mass were significantly higher on the ant diet. The total development time did not differ between diet groups, nor did the number of egg sacs per female or the incubation period. However, the number of eggs per egg sac and hatching success were higher on the ant diet. There was a genetic variation in several offspring traits. Our data support the hypothesis that stenophagous ant-eating E. episinoides have a metabolic specialisation on ant utilisation indicated by higher efficiency in utilising ants than fruit flies. While most individuals of E. episinoides were able to capture fruit flies, only very few spiders were able to develop and reproduce on a pure fruit fly diet, suggesting the existence of within-species genetic variation regarding the tolerance to alternative prey.
- Klíčová slova
- Ant-eating spider, Euryopis episinoides, Stenophagy, Trophic specialisation,
- MeSH
- Drosophila melanogaster fyziologie MeSH
- druhová specificita MeSH
- energetický metabolismus fyziologie MeSH
- Formicidae fyziologie MeSH
- ovum fyziologie MeSH
- pavouci růst a vývoj fyziologie MeSH
- predátorské chování fyziologie MeSH
- stravovací zvyklosti MeSH
- tělesná hmotnost MeSH
- zvířata MeSH
- Check Tag
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.
- Klíčová slova
- Energy management, Micro-grid (MG), Multi-objective optimization, Photovoltaic (PV), Renewable energy sources (RESs), Sine cosine algorithm, Wind turbine (WT),
- Publikační typ
- časopisecké články MeSH
Power management for embedded devices in Fifth Generation (5G) networks is mandatory for synchronizing the communication between the devices. In such cases, the need for integration power optimization is recommended aiding lossless and high-speed communications. To suppress the issues in embedded hardware-based power failures during transmissions, this article proposes a Compressive Transmission Scheme (CTS) through Power Regulation (PR). The proposed scheme identifies multiple transmission possibilities under low power and high throughput constraints of 5G in a single interval. The device integrations are decided by the available devices under power-efficient transmission slots. Such allocation slots are defined for integrated transmission using neural-diffracted networks. The learning network defines the objectives for transmission between embedded hardware and the 5G device under low power. This is pursued until the transmission is completed; the adverse energy drain impact is handled by offloading the slots to the active hardware available. This balances the power management to prevent communication loss satisfying the 5G constraints. For the maximum slots/device, the proposed scheme achieves 11.46% high slot allocation, 12.47% low latency, and 9.99% less power consumption.
- Klíčová slova
- 5G, Compressed transmission, Embedded devices, Neural network, Power management,
- Publikační typ
- časopisecké články MeSH
This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.
Imbalanced datasets are prominent in real-world problems. In such problems, the data samples in one class are significantly higher than in the other classes, even though the other classes might be more important. The standard classification algorithms may classify all the data into the majority class, and this is a significant drawback of most standard learning algorithms, so imbalanced datasets need to be handled carefully. One of the traditional algorithms, twin support vector machines (TSVM), performed well on balanced data classification but poorly on imbalanced datasets classification. In order to improve the TSVM algorithm's classification ability for imbalanced datasets, recently, driven by the universum twin support vector machine (UTSVM), a reduced universum twin support vector machine for class imbalance learning (RUTSVM) was proposed. The dual problem and finding classifiers involve matrix inverse computation, which is one of RUTSVM's key drawbacks. In this paper, we improve the RUTSVM and propose an improved reduced universum twin support vector machine for class imbalance learning (IRUTSVM). We offer alternative Lagrangian functions to tackle the primal problems of RUTSVM in the suggested IRUTSVM approach by inserting one of the terms in the objective function into the constraints. As a result, we obtain new dual formulation for each optimization problem so that we need not compute inverse matrices neither in the training process nor in finding the classifiers. Moreover, the smaller size of the rectangular kernel matrices is used to reduce the computational time. Extensive testing is carried out on a variety of synthetic and real-world imbalanced datasets, and the findings show that the IRUTSVM algorithm outperforms the TSVM, UTSVM, and RUTSVM algorithms in terms of generalization performance.
OBJECTIVE: The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum. APPROACH: We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts. MAIN RESULTS: Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts. SIGNIFICANCE: These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the relationships between EEG activity and brain hemodynamics.
- MeSH
- algoritmy MeSH
- artefakty MeSH
- Bayesova věta MeSH
- dospělí MeSH
- elektroencefalografie statistika a číselné údaje MeSH
- faktorová analýza statistická MeSH
- hemodynamika fyziologie MeSH
- kyslík krev MeSH
- lidé MeSH
- magnetická rezonanční tomografie statistika a číselné údaje MeSH
- mladý dospělý MeSH
- mozkový krevní oběh fyziologie MeSH
- multimodální zobrazování MeSH
- nervová síť fyziologie MeSH
- psychomotorický výkon fyziologie MeSH
- skalp MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- kyslík MeSH
