The work proposes a new approach for evolutionary global optimization which, in simple terms, may be described as a fusion of quantum and simulated annealing. This becomes possible by using basic concepts from Homotopy Theory, conjugated to the already existing Fuzzy Adaptive Simulated Annealing paradigm and other components. In this fashion, it occurs a temporal and nonlinear superposition of the two types of annealing, provoking interesting effects in runtime, including the so-called quantum tunneling, when there is a sudden transition between attraction basins of different minima without "climbing" potential barriers. In addition, the proposal includes a generalization of the "deformation" of Hamiltonians used in quantum annealing by suggesting the use of general homotopies between the surface corresponding to the cost function under processing and another specific landscape, being this possible because of the ability of Fuzzy ASA of handling time-varying objective functions. The proposed paradigm shows that it is possible and beneficial to superpose the two types of annealing, displaying amazing numerical results and suggesting a kind of interlacing or synergic behavior - perhaps we could call it an "annealing entanglement". Finally, given the fast advances in commercial quantum annealing processors, it seems sensible to expect that an implementation of the proposed approach using such devices may occur very soon. In order to demonstrate the efficacy of the proposed algorithm, some significant and detailed examples are included in the text, so as to illustrate and clarify the presented ideas.

Metaheuristic algorithms have been frequently using to tackle optimization problems, however such algorithms in the analysis of health-related data is not commonly used as developing metaheuristic algorithms that work well on health-related data is a difficult task due to complexity of the health data in particular genomics and epigenetics data. One of the important tasks in genomics is to predict genomic elements that are incorporating together to regulate a disease-related genes. Predicting such elements are important as they can be used to develop a personalized cure. In this study, we present for the first time, a multi-object simulated annealing algorithm to identify enhancer-promoter like interactions from Hi-C (chromosome conformation capture) data. These regulatory elements can potentially play vital roles as promoters and/or enhancers in appearance and exacerbation of the regulation of gene.s To evaluate the efficiency of the proposed method, we applied our proposed method and traditional methods on the Hi-C data from mice and compared together. Our results show that the interacting elements identified by our new method are more likely to be functional. The source code of the method is publicly available.

The present work sets out the evaluation of composite laminates through optimization objective functions. Genetic Algorithms (GA), as well as Simulated Annealing (SA), were performed in order to determine the optimal ply orientations of a fiber-reinforced polymer laminate for three given load cases: 1) in-plane loads, 2) combined moments, and 3) in-plane loads and combined moments. It searches the optimal orientation layup, which fulfills at the same time both the maximum strain criterion and the Tsai-Wu failure criterion. Construction of an objective function based on the strains and the stresses involves a minimization process to deformations and a maximization of the safety factor. For the first load case, the initial solution is [±45/0/90/±45]s, and the best solution is [(45/135)2/452]s. For the second load case, [-45/45/45/-45/0/90]s is the initial layup sequence, then the best solution obtained is [(45/-45)2/452]s, where plies at 0° and 90º are not necessary even when axial loads are applied. For the third study case, the original layup sequence is [0/45/-45/45/0/90]s; meanwhile, the best solution calculated is [21/24/145/140/45/49]s. An interesting observation is that each pair of layers has a 5º gap. The simulations show that the qualitative results from the GA are better than the SA, but with a significantly higher computational cost. These kinds of computational tools are expected to be used as a reference guide for an optimal fiber configuration with respect to the common orientations used when composite laminates are designed for a structural application.

Bearings are widely used in various types of electrical machinery and equipment. As their core components, failures will often cause serious consequences . At present, most methods of parameter adjustment are still manual adjustment of parameters. This adjustment method is susceptible to prior knowledge and easy to fall into the local optimal solution, failing to obtain the global optimal solution and requires a lot of resources.Therefore, this paper proposes a new method of bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm.The experimental results show that the method proposed in this paper has a more accurate effect in feature extraction and fault classification compared with traditional bearing fault diagnosis methods. At the same time, compared with the traditional artificial neural network parameter adjustment, this paper introduces the simulated annealing algorithm to automatically adjust the parameters of the neural network, thereby obtaining an adaptive bearing fault diagnosis method. To verify the effectiveness of the method, the Case Western Reserve University bearing database was used for testing, and the traditional intelligent bearing fault diagnosis method was compared. The results show that the method proposed in this paper has good results in bearing fault diagnosis. Provides a new way of thinking in the field of bearing fault diagnosis in parameter adjustment and fault classification algorithms

With the engineering system becoming more and more complex, the uncertainty factors have a more and more profound influence on the reliability and security of the engineering system. In recent years, Reliability-Based Design Optimization (RBDO) has been studied extensively in complex engineering system design. The research on reliability optimization design considering stochastic uncertainty has been comprehensive and widely used. However, the reliability optimization design considering mixed uncertainty has the disadvantages of large computation and imprecise convergence. This study proposes an RBDO method based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to overcome this challenge. In the method, PSO is used to solve the most probable point, while SA has excellent global optimization capability to acquire the global optimal solution. Finally, three examples are given to illustrate the advantages of the proposed method.

The Eucalyptus genus is the most planted forest crop in the world, with Brazil being one of the countries with the greatest productive potential. However, the occurrence of weeds can cause losses in productivity. Chemical control is widely used, but the efficiency of herbicides depends on factors such as the presence of straw in the soil and the occurrence of rainfall. Due to the scarcity of results regarding the interaction between herbicide, straw, and water depth in the forest sector, the objective of this study is to evaluate the efficiency of S-metolachlor + glyphosate in the control of grasses in different densities of eucalyptus straw and with simulated rain after the application of the product. The experiment was conducted in DBC, factorial 3×3×2, with four replications. The first factor represented 0, 50 and 100% of the commercial dose of S-metolachlor + glyphosate, the second 0, 5, and 10 ton ha-2 of straw, and the third 25 and 50mm of water depth applied in soil with a mix of grasses previously sowed. The rainfall simulation was performed 24 hours after herbicide application on each straw volume. The fresh mass of the aerial part of the grasses was collected 43 days after sowing and dried in an oven to determine the dry mass. Visual analyzes of the percentage of control were performed with scales ranging from 0 to 100, where 0 represents no control and 100 efficient control. The fresh and dry mass and the grasses' dry mass/water ratio decreased with increasing herbicide dosage and straw density. The dosage of 2.12 + 1.59 kg i.a. ha-1 of S-metolachlor + glyphosate resulted in greater control of grasses, and the treatments without straw and with the application of the herbicide had the highest percentages of control. Applying different water depths (25 mm or 50 mm) did not influence the control. Despite the control of grasses, the efficiency of the herbicide mixture was affected by the presence of vegetation cover.

Abstract: In this study the semi-distributed model SWAT (Soil and Water Assessment Tool), were applied to evaluate stream flow of Didessa sub basin, which is one of the major sub basins in Abay river basin of Ethiopia. The study evaluated the quality of observed meteorological and hydrological data, established SWAT hydrological model, identified the most sensitive parameters, evaluated the best distribution for flow and developed peak flow for major tributary in the sub basin. The result indicated that the SWAT model developed for the sub basin evaluated at multi hydro-gauging stations and its performance certain with the statistical measures, coefficient about determination (R2) and also Nash coefficient (NS) with values ranging 0.62 to 0.8 and 0.6 to 0.8 respectively at daily time scale. The values of R2 and NS increases at monthly time scale and found ranging 0.75 to 0.92 and 0.71 to 0.91 respectively. Sensitivity analysis is performed to identify parameters those were most sensitive for the sub basin. CN2, GWQMN, CH_K, ALPHA_BNK and LAT_TIME are the most sensitive parameters in the sub basin. Finally, the peak flow for 2-10000 returns periods were determined after the best probability distribution is identified in EasyFit computer program.

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. Wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for the remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. At each clustering tempera-ture, it can obtain the best class centers quickly. The experimental results of the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its ac-ceptable clustering accuracy and high convergence velocity.

This study was carried out to evaluate the effect of exogenous proline on growth, biochemical responses, and plant recovery of drought-stressed oilseed rape plants after renewed irrigation. The experiment was conducted under controlled laboratory conditions. After 21 days of cultivation, 3-4 leaf stage seedlings were sprayed with proline (1 mM), then subjected to prolonged drought stress for 8 days to achieve a severe water deficit, then irrigation was resumed and recovery was assessed after 4 days. The results show that exogenous application of proline reduced drought-induced growth inhibition of seedlings while maintaining relative water content (RWC) and growth parameters closer to those of irrigated plants. Proline had a positive effect on chlorophyll accumulation and membrane permeability while decreasing ethylene, H2O2, and MDA levels. Moreover, after 4 days of recovery, the H2O2 content of the proline-treated plants was significantly lower (2-fold) and the MDA content was close to that of continuously irrigated plants. Thus, all these biochemical reactions influenced plant survival: after drought + proline treatment, the number of surviving plants was 2 times higher than that of drought-treated plants. The findings show that exogenous proline has antioxidant, osmotic, and growth-promoting properties that improve the drought tolerance of winter oilseed rape plants and is, therefore, beneficial for drought adaptation in oilseed rape.

The accumulation of process waste in the production line causes fluctuations, bottlenecks, and increased inventory in workstations disrupting process flow. In this paper, the aim is the use a simulated value stream mapping (SVSM) as a lean assessment tool for decision-making in the continuous improvement process to influence and provide consideration and consistency on productivity improvement in the production system. The proposed methodology applied discrete event simulation for production process operations improvement to eliminate non-value adding times and provides good quality products at the lowest cost and highest efficiency. The results are the analysis of the current state of the production system in a South African truck manufacturing industry small and medium enterprise (SMEs) as a potential solution for the production system future state. The identified non-value adding times in the 6 most critical workstations was eliminated by SVSM which resulted in a productivity improvement of 4%, most importantly bringing the productivity to 95% and total cycle time improvement to 451 for small units and 466 for large units. The results proposed combined VSM and Simulation techniques which enhance the LEAN application by DES to increase productivity and performance improvement to remain competitive in the global economy.

Scientists and astronomers have attached Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

We recently reported that refocusing attention between temporal and spatial tasks becomes more difficult with increasing age, which could impair daily activities such as driving (Callaghan et al., 2017). Here we investigated the extent to which difficulties in refocusing attention extend to naturalistic settings such as simulated driving. 118 participants in five age groups (18-30; 40-49; 50-59; 60-69; 70-91 years) were compared during simulated driving, where they switched from a spatially focal yet temporally complex task (braking due to traffic ahead) to a spatially more distributed task (reading a motorway road sign). Sequential-Task (switching) performance was compared to Single-Task performance (road sign only) to calculate age-related switch-costs. Electroencephalography was recorded in 34 participants (17 in the 18-30 and 17 in the 60+ years groups) to explore age-related changes in the neural oscillatory signatures of refocusing attention while driving. We indeed observed age-related impairments in attentional refocusing, evidenced by increased switch-costs in response times and by deficient modulation of theta and alpha frequencies. Our findings highlight virtual reality (VR) and Neuro-VR as important methodologies for future psychological and gerontological research.

In recent decades, there have been numerous endeavors to develop a novel category of survival distributions possessing enhanced flexibility through the extension of existing distributions. This article constructs and validates the statistical properties of a novel survival distribution in order to obtain an alternative distribution that is suitable for analyzing survival data by presenting the novel mixture of the Fréchet distribution along with statistical properties such as the probability density function (PDF), cumulative distribution function (CDF), rth ordinary moment, skewness, kurtosis, moment-generating function, mean, variance, mode, survival function, hazard function, and asymptotic behavior, as well as constructing the estimators of the unknown parameter by employing the expectation-maximization (EM) algorithm, and simulated annealing. Additionally, the performance of the proposed estimators was compared with bias, mean squared errors (MSE), and simulated variances, and given an illustrative example of the proposed distribution to the survival data set in order to show that the proposed distribution is appropriate for the right-skewed data. This will be extremely advantageous in survival analysis.

Wireless sensor networks (WSN) are mostly utilized in many applications. Indeed, WSN is composed of sensors to evaluate some physical phenomena. However, the information brought by the sensor still missed if we don't know the exact location of the event. Several localization algorithms have been proposed in order to locate the nodes in WSN. The localization algorithms are categorized into range-based and range-free techniques. The range-based techniques use either distance or time to calculate the coordinates of unknown nodes. Nevertheless, those kinds of techniques need some additional material in computation purpose. Therefore, range-based techniques present an expensive solution for positioning in WSN. Alternatively, range-free techniques may do the same task without involving additional material. But they don’t offer a high precision of localization in comparison with range-based techniques. DVHOP is the most popular range-free technique that uses the hop count method in the localization process. In this work, we propose an improvement of DVHOP localization algorithm to create three improved versions of this algorithm and we have achieved that by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and FMINCON solver dedicated to the optimization in the field of WSN nodes localization. The experimental results obtained in this work show clearly the gain and the good impact of our proposition.

Purpose: The present study which addressed adults who stutter (AWS), has been an attempt to investigate power spectral dynamics in stuttering state through answering the written questions using quantitative electroencephalography (qEEG).Materials and Methods: A 64-channel EEG setup was used for data acquisition in 9 AWS. Since speech, and especially stuttering, causes significant noise in the EEG, the three conditions of speech preparation (SP), imagined speech (IS), and simulated speech (SS) in a 7-band format were chosen to source localize the signals using the standard low-resolution electromagnetic tomography (sLORETA) tool in fluent and disfluent states. Results: Having extracted enough fluent and disfluent utterances, significant differences were noted. Consistent with previous studies, the lack of beta suppression in SP, especially in beta2 and beta3 and somewhat in gamma band, was localized in supplementary motor area (SMA) and pre motor area in disfluent state. Delta band frequency was the best marker of stuttering shared in all 3 experimental conditions. Decreased delta power in SMA of both hemispheres and right premotor area through SP, in fronto-central and right angular gyrus through IS, and in SMA of both hemispheres through SS were a notable qEEG features of disfluent speech. Conclusion: The dynamics of beta and delta frequency bands may potentially explain the neural networks involved in stuttering. Based on the above, neurorehabilitation may better be formulated in the treatment of speech disfluency, namely stuttering.

Multiple unique environmental factors such as space radiation and microgravity (µG) pose a serious threat to human gene stability during space travel. Recently, we reported that simultaneous exposure of human fibroblasts to simulated µG and radiation results in more chromosomal aberrations than in cells exposed to radiation alone. However, the mechanisms behind this remain unknown. The purpose of this study was thus to obtain comprehensive data on gene expression using a 3D clinostat synchronized to a carbon (C)-ion or X-ray irradiation system. Human fibroblasts (1BR-hTERT) were maintained under standing or rotating conditions for 3 or 24 h after synchronized C-ion or X-ray irradiation at 1 Gy as part of a total culture time of 2 days. Among 57,773 genes analyzed with RNA sequencing, we focused particularly on the expression of 82 cell cycle-related genes after exposure to the radiation and simulated µG. The expression of cell cycle-suppressing genes (ABL1 and CDKN1A) decreased and that of cell cycle-promoting genes (MKI67, KPNA2, CCNB1, STMN1, and MCM4) increased after C-ion irradiation under µG. The cell cycle may pass through the G1/S and G2 checkpoints with DNA damage due to the combined effects of C-ions and µG, suggesting that increased genomic instability might occur in space.

Different kinds of freshwater fish soups show a diverse range of health functions, due to their different nutritional substances and corresponding bioactivities. Crucian carp soup and snakehead soup have different dietotherapy functions, crucian carp soup is suitable for lactating women and snakehead soup is suitable for postoperative patients. In the current study, the changes of nutrient profiles in the different fish soups, such as chemical composition, free amino acids, mineral and fatty acid contents, were investigated. The antioxidant activities of the fish soups were evaluated by using the DPPH radical scavenging activity, the ferrous ion chelating activity, the hydroxyl radical-scavenging activity and the reducing power effect. In order to learn the theoretical basis of the potential role fish soup plays in diet therapy functions after being digested by the human body, the nutrient profiling and bioactivities of the fish soup samples after simulated gastrointestinal digestion were also explored. The intensive profiles of nutritional composition and antioxidant activities of these two kinds of fish soups were expected to partly provide the theoretical basis of therapeutic effects.

In this paper a variable’s involved in assessing the quality of a distributed generation system are reviewed, aiming to minimize the electric power losses (unused power consumption) and optimize the voltage profile. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA) and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of longer processing time.

We present a very computationally light and fast approximation algorithm and then verify it with genetic algorithm and simulated annealing. We show that our algorithm is on par with GA and SA in terms of output produced while having a tightly bounded time complexity. Our algorithm works best when there is a strong positive correlation between the reliability of a component and its cost. We present two algorithms with the same essence. One of them is system cost bounded and the other is target reliability bounded. Our proposed algorithm works on a subsystem level redundancy instead of component level redundancy.

Purpose - Soil aggregates are of great significance to soil and water conservation and ecological environment construction in arid area of northwest district．Methods - Exploring the effects of different vegetation includes types and land use methods on the stability of soil aggregates in the Loess Plateau, and provide reference for the rational use and management of land, also the improvement of soil structure in the region. Select 9 types of samples of 0-30 cm of typical soil plots as the research objects, compare and analyze the particle size index, stability differences and anti-erodibility of soil aggregates under various vegetation cover. Results - The results show that P value, MWD value, GMD value, D value, and AI value of the 0-10cm surface soil all show the maximum value. As the depth increases, the size distribution of the above index values of each soil sample in the 10-20cm and 20-30cm layers is different; P value in the 0-30cm depth layer is linearly positively correlated with the AI value and MWD value, and linearly negatively correlated with the D value. The correlation coefficient R between each variable is in the range of 0.78-0.97, and the D value reflects the Loess Plateau area stability and erosion resistance of soil aggregates better. GMD and MWD value show an exponential relationship, the correlation coefficient R value of 10-20cm height layer is 0.46; AI and MWD value in 0-10cm, 20-30cm height layer have a power function relationship, 10-20cm height layer has a polynomial function, the correlation coefficient R value is 0.97. The scour coefficient of different soil samples has a high degree of dispersion, the maximum CV value is 1.92, and the minimum value is 0.49. Conclusions - The results of this study can provide a theoretical basis for the ecological and hydrological benefit evaluation of slope erosion control and vegetation restoration on the Loess Plateau.

Collaboration among individuals with diverse skills and personalities is crucial in producing high-quality software. The success of any software project depends on the team’s cohesive functionality and mutual complementation. This study introduces a data-centric methodology for forming Software Engineering (SE) teams centred around personality traits. Our study analyzed data from an SE course where 157 students in 31 teams worked through four project phases and were evaluated based on deliverables and instructor feedback. Using the Five Factor Model (FFM) and a variety of statistical tests, we determined that teams with higher levels of extraversion and conscientiousness and lower neuroticism consistently performed better. We examined team member interactions and developed a predictive model using extreme gradient boosting. The model achieved a 74% accuracy rate in predicting inter-member satisfaction rankings. Through graphical explainability, it underscored incompatibilities among members, notably those with differing levels of extraversion. Based on our findings, we introduce a team formation algorithm using Simulated Annealing (SA), built upon the insights from our predictive model and additional heuristics.

In this paper, a Markov random field model is proposed to determine a site’s light pollution risk level. The specific data of 12 indicators of five typical cities in China is first collected to establish a hierarchical indicator system using an R-type clustering algorithm. Then, the entropy weight method is used to filter, determine 10-factor indicators, three potential impact indicators, and a light pollution risk index, and establish an undirected probability map model. A light pollution measurement based on Markov random field is obtained, and a location-based light pollution risk assessment index (LBLPRAI) is proposed. LBLPRAI of different types of sites is analyzed, and three possible intervention strategies are proposed to solve the light pollution problem: road lighting system planning, increasing vegetation coverage, and building system planning. Finally, the simulated annealing algorithm is used to determine the best intervention strategy, and it is concluded that the use of strategy 1 in the urban community 2 is the most effective measure, which can reduce the risk level of light pollution by 17.2%.

Novel model of biodegradable PHA copolymer films preparation was applied to evaluate biodegradability of various PHA copolymers and discuss its biomedical applicability. In this study, we illustrate the potential biomaterial degradation rate affectability by manipulation of monomer composition via controlling biosynthetic strategies. Within the experimental investigation, we have prepared two different copolymers of 3-hydroxybutyrate and 4-hydroxybutyrate – P(3HB-co-36 mol.% 4HB) and P(3HB-co-66 mol.% 4HB), by cultivating thermophilic bacterial strain Aneurinibacillus sp. H1 and further investigated its degradability in simulated body fluids (SBFs). Both copolymers revealed faster weight reduction in synthetic gastric juice (SGJ) and artificial colonic fluid (ACF) than simple homopolymer P3HB. In addition, degradation mechanisms differed across tested polymers, according to SEM micrographs. While incubated in SGJ, samples were fragmented due to fast hydrolysis sourcing from substantially low pH, which suggest abiotic degradation as the major degradation mechanism. On the contrary, ACF incubation indicated obvious enzymatic hydrolysis. Further, no cytotoxicity of the waste fluids was observed on CaCO-2 cell line. Based on these results in combination with high production flexibility, we suggest P(3HB-co-4HB) copolymers produced by Aneurinibacillus sp. H1 as very auspicious polymers for intestinal in vivo treatments.

The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.

This paper presents an extension of the resuts obtained in previous work by the author concerning the application of global optimization techniques to the design of finite strategic games with mixed strategies. In that publication the Fuzzy ASA global optimization method was applied to many examples of synthesis of strategic games with one previously specified Nash equilibrium, evidencing its ability in finding payoff functions whose respective games present those equilibria, possibly among others. That is to say, it was shown it is possible to establish in advance a Nash equilibrium for a generic finite state strategic game and to compute payoff functions that will make it feasible to reach the chosen equilibrium, allowing players to converge to the desired profile, considering that it is an equilibrium of the game as well. Going beyond this state of affairs, the present article shows that it is possible to "impose" multiple Nash equilibria to finite strategic games by following the same reasoning as before, but with a slight change: using the same fundamental theorem of Richard D. McKelvey, modifying the original prescribed objective function and globally minimizing it. The proposed method, in principle, is able to find payoff functions that result in games featuring an arbitrary number of Nash equiibria, paving the way to a substantial number of potential applications.

The improved understanding of the underlying mechanisms of oxidative damage and/or chronic diseases is of high priority in dietary research. Although the chemical extraction of biofunctional molecules from different fruits and cereals have been studied extensively, the impact of food processing and digestion on bioactivity has not been studied systematically. The aim of this study was to investigate the biofunctional potential of blackcurrant powder incorporated into wholemeal wheat and barley cookies in after simulated in vitro digestion. The incorporation of blackcurrant significantly (p<0.05) increased the total phenolic content (about 60 %), significantly improved oxygen radical absorbance capacity (about 25 %) of the cookie digesta. Additionally cellular antioxidant and anti-proliferative activity (lowest EC₅₀ value 1.02 mg/mL) on human liver cancer cell model, HepG2 was significantly enhanced. Bioactive metabolites of blackcurrant incorporated cookies were significantly supressed the inflammatory cytokine genes IL-1β (about 3-fold), IL-6 (about 0.5-fold) and NF-kB (about 2-fold) regulation and upregulated satiety gene NUCB-2/Nesfatin-1 (about 5-fold) compare with wholemeal wheat and barley control cookies. The exerted synergistic effects of this study suggest that there may be a new and effective option to prevent and control chronic diseases in human.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

One of the hallmarks of microgravity-induced effects in several cellular models is represented by the alteration of oxidative balance with the consequent accumulation of Reactive Oxygen Species (ROS). It is well known that male germ cells are sensitive to oxidative stress and to changes of gravitational force even if published data on germ cell models are scarce. To gain more insights into the mechanisms of male germ cell response to altered gravity, a 3D cell culture model has been established from TCam-2 cells, a seminoma-derived cell line considered the only human cell line available to study in vitro mitotically active human male germ cells. TCam-2 cell spheroids were cultured for 24 hours under unitary gravity (Ctr) or simulated microgravity (s-microgravity) conditions, these last ones were obtained using the Random Positioning Machine (RPM). A significant increase in intracellular ROS and mitochondria superoxide anion levels has been observed after RPM exposure. In line with these results a trend of protein and lipid oxidation increase, and increased pCAMKII expression levels were observed after RPM exposure. The ultrastructural analysis by Transmission Electron Microscopy revealed that RPM-exposed mitochondria appeared enlarged and, even if seldom, disrupted. Notably, even the expression of the main enzymes involved in the redox homeostasis appears modulated by RPM exposure in a compensatory way, being GPX1, NCF1, and CYBB downregulated, whereas NOX4 and HMOX1 upregulated. Interestingly, HMOX1 is involved in the heme catabolism of mitochondria cytochromes, and therefore the positive modulation of this marker can be associated to the observed mitochondria alteration. All together, these data demonstrate TCam-2 spheroid sensitivity to acute SM exposure and indicate the capability of these cells to trigger compensatory mechanisms that allow to overcome the exposure to altered gravitational force.

A new variant of two-echelon routing problem is investigated, where the truck and the drone are used to cooperatively complete the deliveries of all parcels. The truck not only acts as a tool for parcel delivery, but also serves as a moving depot for the drone. The drone can carry several parcels and take off from the truck, while returning to the truck after completing the delivery. The energy consumption model for the routing process of the drone is analyzed, when it is utilized to deliver multiple parcels. A two-stage route-based modelling approach is proposed to optimize both the truck’s main route and the drone’s adjoint flying routes. A hybrid heuristic integrating nearest neighbor and cost saving strategies is developed to quickly construct a feasible solution. The simulated annealing algorithm is applied to improve the quality of the solution, where a Tabu list is employed to improve the search efficiency. Random instances at different scales are used to test the performance of the proposed algorithm. A case study based on the practical road network in Changsha, China, is presented, through which the sensitivity analysis is conducted with respect to some critical factors.

The market for microfluidic chips is experiencing significant growth; however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips’ design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.

The problem of optimal scheduling in a system with parallel queues and a single server has been extensively studied in the queueing theory. However, such systems have mostly been studied by assuming homogeneous attributes of arrival and service processes, or in heterogeneous case a Markov random process was usually assumed. In this paper, we explore the possibility of combining simulation and neural network paradigms for optimal scheduling in a heterogeneous system with an arbitrary inter-arrival and service time distributions. The service of any queue by the server is exhaustive. Further routing of the server after the queue has been emptied is as instructed by the trained neural network. Any change in the queue that currently has a server is accompanied by a switching cost. The simulated annealing optimization algorithm which is used to optimize the parameters of the neural network was adapted for numerically evaluated average cost function. A Markov decision problem was formulated for the corresponding markovian queueing system to verify the results of solving the optimization problem. In addition to comparing the actual values of the average cost, the parameter values of the neural network trained on the optimal control policy of the Markov model are also used to determine the domains of parameters required for the simulated annealing defined on a finite discrete domain. Numerical analysis shows the effectiveness of this approach. Moreover, a comparison of the results for different distributions illustrates the insensitivity of the optimal control policy to the shape of the distributions for the same two first moments. Thus, the optimal control policy for an exponential model can be used as a suboptimal one for the general system.

Generator automatic voltage control device (AVC) is an automatic voltage and reactive power optimization control terminal on the power plant side. Aiming at improving the performance of generator automatic voltage control device, this paper introduces a detection method of AVC substation performance based on virtual dispatching terminal environment. During the testing, the upper computer of redundant power plant is connected to the virtual dispatching terminal environment to simulate the sending instructions of AVC master station, the behavior of AVC sub stations and the excitation system of generators. The performance of AVC substation is evaluated according to the feedback adjustment results, which can effectively detect the abnormal behavior of AVC substation in power plant.

Symbolic pole/zero analysis is an important step when designing an analog operational amplifier. Generally, a simplified symbolic analysis of analog circuits suffers from NP-hardness, i.e., an exponential growth of the number of symbolic terms of the transfer function with the circuit size. In this study, we present a mathematical model combined with a heuristic-metaheuristic solution method for the symbolic pole/zero simplification in operational transconductance amplifiers (OTA). At first, the circuit is symbolically solved and an improved root splitting method is applied to extract symbolic poles/zeroes from the exact expanded transfer function. Then, a hybrid algorithm based on heuristic information and a metaheuristic technique using simulated annealing is performed for the simplification of the derived symbolic pole/zero expressions. The developed method has been tested on three analog OTAs. The obtained results show the effectiveness of the proposed method to achieve accurate simplified symbolic pole/zero expressions with the least complexity.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.

Optical motion capture systems are state-of-the-art in motion acquisition, however as any measurement systems they are not error free -- noise is their intrinsic feature. The works so far mostly employ simple noise model, expressing the uncertainty as a simple variance. In the work we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. For the automated readout of the noise coefficients we solve the multidimensional regression problem using sophisticated metaheuristics in exploration-exploitation scheme. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion in our facility. We had also opportunity to observe the influence of camera failure to the overall performance.

Yogurt ice cream (YIC) containing Bifidobacterum longum subsp. longum TISTR 2195 (B. longum) with different fat contents (2 and 10% fat) and prebiotics (galacto-oligosaccharide, GOS and fructo-oligosaccharide, FOS) was investigated for its physicochemical properties, microstructure, and survival of B. longum during storage time and in a simulated gastrointestinal tract. Fat contents affected pH, fat particle size, hardness, melting rate, and solid-like characteristic of ice cream while FOS seemed to enhance the hardness and elasticity of YIC better than GOS due to less solubility and denser microstructure which exhibited more solid-like properties than YIC containing GOS. The interaction effect of fat content at 10% fat and FOS in YIC seemed to promote more sensorial acceptance than the ice cream containing 2% fat and GOS. The fat content, concentration of prebiotics, and type of prebiotics were attributed to the survival of B. longum in YIC. Moreover, the survival number of B. longum in YIC during storage for 180 days and in the simulated gastrointestinal tract was still in 106 CFU g-1, which is considered to benefit human health. Finally, probiotic YIC in this study could be used as a guide for functional ice cream manufacture future.

The risk related to embankment dam breaches needs to be evaluated in order to prepare emergency action plans. The physical and hydrodynamic parameters of the flood wave generated from dam-failure event correspond to various breach parameters such as width, slope and formation time. This study aimed to simulate dam-breach failure scenario of Yabous dam (NE Algeria) and analyze its influence on areas (urban and natural environments) downstream the dam. The simulation was completed using the sensitivity analysis method in order to assess the impact of breach parameters on the dam-break scenario. The propagation of flood wave associated to dam-break was simulated using the one-dimensional HEC-RAS hydraulic model. This study ap-plied a sensitivity analysis of three breach parameters (slope, width, and formation time) in five sites selected downstream the embankment dam. The simulation showed that the maximum flow of the flood wave recorded at the level of the breach was 8768 m3/s, which gradually attenuated along the river course to reach 1579.2m3/s at about 8.5km downstream the dam. This study estab-lished the map of flood-prone areas that illustrated zones threatened with the flooding wave trig-gered by the dam failure due to extreme rainfall events. The sensitivity analysis showed that flood wave flow, height and width revealed positive and similar changes for the increase in adjustments (±25% and ±50%) of breach width and slope in the 5 sites. However, flood wave parameters of breach formation time showed significant trends that changed in the opposite direction compared to breach slope and width.

This work presents significant results obtained by the application of global optimization techniques to the design of finite, normal form games with mixed strategies. To that end, the Fuzzy ASA global optimization method is applied to several design examples of strategic games, demonstrating its effectiveness in obtaining payoff functions whose corresponding games present a previously established Nash equilibrium. In other words, the game designer becomes able to choose a convenient Nash equilibrium for a generic finite state strategic game and the proposed method computes payoff functions that will realize the desired equilibrium, making it possible for the players to reach the favorable conditions represented by the chosen equilibrium. Considering that game theory is a very significant approach for modeling interactions between competing agents, and Nash equilibrium represents a powerful solution concept, portraying situations in which joint strategies are optimal in the sense that players cannot benefit from individually modifying their current strategies provided that other players do not change their strategies as well, it is natural to infer that the proposed method may be very useful for strategists in general. In summary, it is a genuine instance of artificial inference of payoff functions after a process of global machine learning, applied to their numerical components.

Green infrastructure systems are often overdesigned. This may be a byproduct of static sizing (e.g., accounting for a design storm’s runoff volume but not exfiltration rates) or may be deliberate (e.g., buffering against performance loss through time). Regardless, overdesign may compromise plants’ access to water in systems where soil pits are embedded in infiltration beds. It could raise the storm size required for water to reach soil pits, reducing water availability between storms, which could ultimately induce plant physiological stress. This study investigated the hydrological dynamics and water relations of a tree trench system suspected to have been overbuilt and identified factors contributing to, compounding, and mitigating the risk of plant stress. Results provided strong evidence that the abovementioned processes played out. Water in the infiltration bed reached soil pits only once in three years, with that event occurring during a hydrant release. Moreover, minimal water was retained in the soil pit during the event due to the hydraulic properties of the soil media. Through a growing season, one of the two tree types frequently experienced water stress, while the other did so only rarely. These contrasting responses can likely be attributed to roots either being largely confined to the soil pits or reaching a deeper water source. Implications of these results for green infrastructure design are considered.

Sacroiliac (SI) joint dysfunction can lead to debilitating pain but can be treated with minimally invasive sacroiliac joint fusion (SIJF). This treatment is commonly performed using 2D fluoroscopic guidance. This makes placing the implants without damaging surrounding neural structures challenging. Virtual surgical planning (VSP) using simulated fluoroscopic images may improve perioperative guidance. This article describes a workflow with VSP in SIJF using simulated fluoroscopic images and evaluates achieved implant placement accuracy. Routinely collected survey data on the added value of VSP were also evaluated. Ten interventions were performed on 10 patients by the same surgeon, comprising 30 implants; the median age was 39 years, and all patients were female. The overall mean implant placement accuracy was 4.9 ± 1.26 mm and 4.0 ± 1.44°. There were no malpositioning complications. The VSP helped the surgeon understand the anatomy and determine the optimal position and length of the implants. The average Likert scale score of the survey was 4.7, suggesting a positive role of VSP for SIJF. Planned positions of the implants could be reproduced in surgery with clinically acceptable accuracy and VSP was a valuable asset for the surgeon.

Surgical navigation has evolved as a vital tool in maxillofacial surgery, offering precise and patient-specific data. This study explores the clinical applications and accuracy of intraoperative tool tracking in maxillofacial surgery. The research includes 37 patients with various pathologies who underwent surgery assisted by a surgical navigation system using tracked instruments. The study showcases two representative cases: one involving coronoid hyperplasia with mouth opening deficit and another addressing nose-orbital-ethmoidal-frontal ossifying fibroma resection. The results indicate that surgical navigation with tracked instruments provides high precision (<1.5 mm error), reduced surgical time, and less invasive approaches. The study highlights the potential for reproducible outcomes and increased safety, especially in complex cases. Despite some limitations, the synergy between surgical navigation and tracked instruments offers a promising approach in maxillofacial surgery, expanding its applications beyond current practices.