Irregular Behavior Research Articles

Isochoric heat capacity, phase transition and derived key thermodynamic properties of methyl decanoate

In the present work single – and two–phase isochoric heat capacity (CV) and liquids-gas phase transition temperature (TS) and density (ρS) of methyl decanoate as component of biodiesel derived from coconut oil or babassu oil have been studied. The 15 liquid and vapor isochores between (152 and 834) kg·m−3 at temperatures from (300 to 463) K and at pressures up to 16 MPa were determined using high-temperature and high-pressure nearly constant-volume adiabatic calorimeter. For each experimental isochores the measurements were conducted in the two-phase region in the immediate vicinity of the phase-transition temperatures (TS) to precisely determine the phase boundary density, one-phase and two-phase isochoric heat capacities (ρS,CV1, and CV2) using isochoric heat -capacity discontinuity behavior technique. For ten liquid isochores, the TSwas experimentally determined usingCVdiscontinuity method (irregular behaviour of CV). For vapor (152.09 and 204.55) kg·m−3 and near-critical (235.13 and 300.83) kg·m−3 isochores we have never reached isochoric heat-capacity discontinuity temperatures TSdue to thermal decomposition of the methyl decanoate at high temperatures (above 473 K). The measured CV2 as a function of specific volume (V) along the various isotherms (below 473 K) were used to accurately estimate the values of the second temperature derivatives of chemical potential and vapour-pressure using theoretically based Yang-Yang relation. The contributions of the vapour-pressure and the chemical potential to the measured total two-phase CV2 of methyl decanoate were estimated. The measured CV2ρS, and TS were used for determination many key thermodynamic properties of methyl decanoate along the saturation curve as well as the critical property data (TC=669.5 ± 5 K andρC = 287 ± 5 kg·m−3).

An approach to pedestrian walking behaviour classification in wireless communication and network failure contexts

Despite the wide range of research on pedestrian safety, previous studies have failed to analyse the real-time data of pedestrian walking misbehaviour on the basis of either pedestrian behaviour distraction or movements during specific activities to realise pedestrian safety for positive (normal) or aggressive pedestrians. Practically, pedestrian walking behaviour should be recognised, and aggressive pedestrians should be differentiated from normal pedestrians. This type of pedestrian behaviour recognition can be converted into a classification problem, which is the main challenge for pedestrian safety systems. In addressing the classification challenge, three issues should be considered: identification of factors, collection of data and exchange of data in the contexts of wireless communication and network failure. Thus, this work proposes a novel approach to pedestrian walking behaviour classification in the aforementioned contexts. Three useful phases are proposed for the methodology of this study. In the first phase involving factor identification, several factors of the irregular walking behaviour of mobile phone users are established by constructing a questionnaire that can determine users’ options (attitudes/opinions) about mobile usage whilst walking on the street. In the second phase involving data collection, four different testing scenarios are developed to acquire the real-time data of pedestrian walking behaviour by using gyroscope sensors. In the third phase involving data exchange, the proposed approach is presented on the basis of two modules. The first module for pedestrian behaviour classification uses random forest and decision tree classifiers part of machine learning techniques via wireless communication when a server becomes available. The developed module is then trained and evaluated using five category sets to obtain the best classification of pedestrian walking behaviour. The second module is based on four standard vectors for classifying pedestrian walking behaviour when a server is unavailable. Fault-tolerant pedestrian walking behaviour is identified and is initiated when failures occur in a network. Two sets of real-time data are presented in this work. The first dataset is related to the questionnaire data from 262 sampled respondents, and the second dataset comprises data on 263 sampled participants with pedestrian walking signals. Experimental results confirm the efficacy of the proposed approach relative to previous ones.

Observing Brief and Irregular Behaviour of Animals—The Validity of Short Observation Periods

There are efficient sampling methods to accurately estimate behaviour with a moderate or long duration. For short behaviour, observing animals continuously is recommended although there is no recommended minimum observation time. In most studies, sampling method and observation time per day is determined by practical considerations. Thus, this study analysed the validity of behavioural observations in different observation periods using continuous sampling (CS) or time sampling (TS) based on biting behaviour. Tail-biting and ear-biting of weaned piglets in six pens were continuously observed for 12 h per day for 4 days to form a reference. Shorter observation periods of CS and TS were simulated by taking subsets of this reference. The amount of behaviour per hour of each observation period was compared to the reference and to other observation period of the same kind and length. Four different measurements were defined to calculate accuracy scores (AS; 0–1; higher values are better). Comparison to the reference shows better AS for observation periods with longer observation time in total (0.5 h of CS: 0.2; 6 h of CS: 0.6). Additionally, TS covers longer time periods without decreasing AS. However, focus on activity time results in an overestimation of irregular behaviour. Comparing AS among observation periods of the same kind and length show overall low agreement. This study indicated problems of different observation periods of CS and TS to accurately estimate behaviour. Therefore, validity of behavioural observations should be analysed in greater detail to determine optimal sampling methods.

Geochemical behavior of Mo and W in the CO2-rich mineral waters occurring in the East Elbrus area

Molybdenum and tungsten concentrations in cold CO2-rich mineral groundwaters of east Elbrus area lie in two ranges: 0.01-0.43 ppb and 0.01-410 ppb. The temperature of the water varies from 7.9 to 17.6 °C. Mo concentrations have irregular behaviour with temperature and do not demonstrate a significant correlation with any parameters. Tungsten concentrations are strongly and positively correlated only with As, indicating either common sources or common processes controlling their grades. The highest W concentrations are exclusively associated with Na–HCO3–Cl water types, with a circum-neutral condition (6<pH<7) and lower Eh (-60-+45 mV). The dominant species of Mo and W in mineral waters of eastern Elbrus are molybdate ion (MoO4), and [W7O24]6- polyanion. Based on the risk assessment results, for two potential contaminants (Mo and W), the concentration level of W exceeded the environmental quality standard, which suggests a severe to moderate ecological risk and, therefore, needs to be considered as the priority pollutant for the studied area.

Multifunctions of fine root phenology in vegetative and reproductive growth in mature beech forest ecosystems

AbstractMasting, a large interannual variation of reproductive investment spatially synchronized within perennial plants, is an important factor for understanding the use and storage of carbon and nutrient resources in various forest ecosystems. Fine roots play essential roles in water and nutrient uptake but also consume a large amount of net primary production (NPP) during growth. Understanding masting mechanisms requires knowledge of how masting trees succeed in abundant fructification by adjusting resource acquisition via fine roots and how mast seeding influences fine root dynamics. The aims of the present study were to estimate interannual variations in NPP, including belowground production, over several years while evaluating fine root phenology and to suggest possible relationships between fine root growth and the production of nuts, leaves, and woody biomass in mature forest ecosystems. Nut and leaf production was quantified using litter traps in two beech forests located at different altitudes of 550 and 1500 m, and woody biomass increment was estimated using allometric equations. Seasonal and mass‐based annual fine root production was determined using minirhizotrons and soil cores. Correlations between seasonal fine root and aboveground production were analyzed using the state‐space model, which can verify masting effects on fine root production between flower bud formation and mast seeding. NPP estimates exhibited almost biennial or irregular masting behavior. Nut production was positively correlated with fine root production in autumn of the previous year in the lower site but showed negative correlations with that in the early growing season of the same year in the upper site. The former indicated enhanced fine root production to absorb nutrients prior to mast seeding, and the latter suggested the suppression of fine root production due to resource depletion during masting. Fine root production increased with increasing leaf production in the early growing season in both sites, indicating the corresponding water transport between them. Thus, fine root phenology has distinct multiple functions to ensure water and nutrient resources in supporting aboveground vegetative and reproductive growth, resulting in variable seasonal patterns of fine root production.

Efficient Detection Using Soft Computing Approach of Modified Fuzzy C-Means Based Outlier Detection in Electronics Patient Records Systems

Outliers are the data objects or data elements which are deviated from the observation or population in the dataset. Normally outliers are considered as noise data but in recent days outliers are taken vital role in the applications like healthcare systems to energetically check for any malignant, irregular, or abnormal behaviour. Accuracy of the outlier detection is purely based upon the efficiency of outlier detection methods and application where the outlier detection is involved. In this paper we design hybrid three phase modified fuzzy c-means and diverse distance based outlier detection method for distributed dataset to detect the unusual usage of data and illegitimate approach in large-scale integral networks, especially in healthcare sectors (EPR systems). The proposed algorithm combining the features of modified C-Means Fuzzification, Z-score and Manhattan distance in outlier identification. The proposed algorithm provides efficiency in outlier detection on univariate EPRdata. This paper also mainly focuses the application intrusion detection in healthcare data. The algorithm is tested on real world dataset of machine learning database repository (UCIML).

Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions. However, DL models are vulnerable to adversarial examples, which may lead to increased predictive error and wrong operational decisions. This work proposes a new scheme to detect adversarial examples and mitigate their impact on DL forecasting models. This approach is based on one-class classifiers and features extracted from the data inputted to the forecasting models. Tests were performed using data collected from a real-world PV power plant along with adversarial samples generated by the Fast Gradient Sign Method under multiple attack patterns and magnitudes. One-class Support Vector Machine and Local Outlier Factor were evaluated as detectors of attacks to Long-Short Term Memory and Temporal Convolutional Network forecasting models. According to the results, the proposed scheme showed a high capability of detecting adversarial samples with an average F1-score close to 90%. Moreover, the detection and mitigation approach strongly reduced the prediction error increase caused by adversarial samples.

Random Matrix Theory in Cd isotopes

Random matrix theory (RMT) is used to provide a measure of the chaoticity (q) of calculated results for the spectra for various Cd isotopes. The goal is to gain a better understanding of the internal dynamics in play; namely, whether it tracks with regular or irregular (chaotic) behavior as determined through an RMT analyses of calculated spectra. The basis-state configurations used to determine the spectra includes all positive, negative, natural (J π = 1−, 2+, …), and unnatural parity configurations (J π = 0−, 1+, 2−, …), unless suppressed for comparative purposes. The results show that when intruder-state configurations are in play, regular behavior emerges, but when not in play, chaotic behavior seems to dominate the dynamics.

Effects of toxicity and zooplankton selectivity on plankton dynamics under seasonal patterns of viruses with time delay

Intricate functional diversities in marine ecosystems often lead to exhibit a variety of dynamical behaviors, including chaos. To bring order into such chaotic situations, the system develops different mechanisms of self‐adaptability. Our present investigation is based on the study of an eco‐epidemiological model for virally infected toxic phytoplankton and zooplankton system. Both healthy and infected phytoplankton are assumed to be exposed to grazer zooplankton with a varying degree of exposure. An extension is made by introducing the fact that viral replication process in a lysis event is mediated by some time lag. To achieve more realistic and explicit outcomes of the existing phenomena correlated with our model, we consider the force of infection and replication of free viruses as seasonally forced. We find that time delay accounts for recurrent stability switching event in the system. The chaotic behavior is observed in the seasonally forced delayed system, which indicates the emergence of harmful blooms. The main motive is to gain a clear insight into the field of synchronization and stabilization of the irregular behaviors of the system. Our investigations suggest that increased strength of toxic compounds exuded by phytoplankton species may suppress the prevalence of chaotic disorder and drives the system into a zooplankton‐free zone. The intensity of selective grazing of zooplankton changes the state of chaos to order and promotes the disease‐free system.

Fault-Adaptive Autonomy in Systems with Learning-Enabled Components.

Autonomous Cyber-Physical Systems (CPS) must be robust against potential failure modes, including physical degradations and software issues, and are required to self-manage contingency actions for these failures. Physical degradations often have a significant impact on the vehicle dynamics causing irregular behavior that can jeopardize system safety and mission objectives. The paper presents a novel Behavior Tree-based autonomy architecture that includes a Fault Detection and Isolation Learning-Enabled Component (FDI LEC) with an Assurance Monitor (AM) designed based on Inductive Conformal Prediction (ICP) techniques. The architecture implements real-time contingency-management functions using fault detection, isolation and reconfiguration subsystems. To improve scalability and reduce the false-positive rate of the FDI LEC, the decision-making logic provides adjustable thresholds for the desired fault coverage and acceptable risk. The paper presents the system architecture with the integrated FDI LEC, as well as the data collection and training approach for the LEC and the AM. Lastly, we demonstrate the effectiveness of the proposed architecture using a simulated autonomous underwater vehicle (AUV) based on the BlueROV2 platform.

Investigating the Relationship between Political Uncertainty and Market Irregularities: With Emphasis on the Risky Information Environment

investors and managers are always faced with uncertainty in the information environment, which uncertainty can be due to factors such as the synchronization of stock returns, extraordinary fluctuations in stock returns and the number of equations. The purpose of this study is to investigate the political uncertainty caused by the size of the firm under the influence of risky information environment, the irregular behavior of accruals anomaly and the anomaly behavior of the cost of normal stock equity of companies. For this purpose, the data of 99 companies listed on the Tehran Stock Exchange and Iran TSETMC during the years 2009 to 2019 were examined and tested through combined data. The results showed that the political uncertainty caused by the firm size affected the concurrency stock returns with the optional accrual anomaly behavior and the cost of normal stock equity behavior of companies has a positive and signification relationship. The results also showed that the political uncertainty caused by the firm size is affected by the extraordinary fluctuation of stock returns with the optional accrual anomaly behavior and the cost of normal stock equity behavior of companies has a positive and signification relationship. In addition, the political uncertainty caused by the firm size is affected by the number of equations with the optional accrual anomaly behavior and the cost of normal stock equity behavior of companies has a positive and signification relationship.

Relation between ionic surfactant concentration and thickness of foam film stabilized by ionic – nonionic surfactant mixed adsorbed films

In this paper, we compared the thickness of common black films stabilized by four different ionic-nonionic surfactant mixed adsorbed films. For the cetyltrimethylammonium chloride (CTAC) - pentaethyleneglycol monododecyl ether (C12E5), SDS - tetraethyleneglycol monodecyl ether (C10E4), and dodecyltrimethylammonium bromide (DTAB) - tetraethyleneglycol monooctyl ether (C8E4) systems, the foam film thickness measured by using Scheludko–Exerowa glass ring method coincided very well when the foam film thickness was plotted as a function of bulk ionic surfactant (1:1 electrolyte) concentration. In addition, by using the phase diagram of mixed adsorbed film obtained from the thermodynamic analysis of the surface tension data, it was also demonstrated that the surface charge density and surface tension have a minor effect on the equilibrium film thickness. On the other hand, the foam film stabilized by the 1-decyl-3-methyl imidazolium tetrafluoroborate (DMIMBF4) - C10E4 mixed adsorbed film showed much smaller thickness values compared to the above-mentioned systems. Irregular behavior observed in the DMIMBF4 - C10E4 system was discussed based on the specific interaction between imidazolium head groups and BF4- ions through hydrogen bonding in an ion-concentrated environment in the Stern layer.

Hydrosedimentological Response to Estuarine Deepening: Conceptual Analysis

This paper describes the effects of anthropogenic deepening of tidal rivers in a conceptual way, with focus on tidal distortion and the residual transport of coarse sediment, driven by asymmetries in peak velocity. The rivers under consideration are fairly small, with small river discharge, and may have irregular hypsometry, with substantial intertidal area, or not. Residual sediment transport is driven by asymmetries in tidal velocity (horizontal tide), which is, however, difficult to establish in general. This paper discusses how and under which cases asymmetries in tidal elevations (vertical tide) can provide appropriate information on residual sediment transport. It is argued that deepening may induce a competition between an increase in tidal amplitude by amplification and a reduction in the asymmetry itself. Linear analysis shows that tidal asymmetry may show irregular behavior locally even for regular river configurations. It is therefore expected that these irregularities become larger in natural and engineered rivers. Analysis of local asymmetries may therefore be misleading in assessing the river's response to deepening with respect to the overall residual sediment transport and the river's morphology. Thus analysis of the overall morphodynamic response of a tidal river to tidal asymmetry, as affected by deepening, requires integration of the nonlinear effects along the entire river. It is argued that tidal asymmetry can be quantified by determining the difference in travel times of the high and low waters at any location within the river. This also implies that tidal water level variations and their asymmetries are governed by the entire tidal volume up-river of the cross section under consideration. River discharge further complicates the analyses by affecting residual water flows, effective hydraulic drag, tidal asymmetry, and mean water level. These effects reduce in response to deepening. However, salinity intrusion and gravitational circulation increase with deepening. We believe that assessing the (long-term) effects of deepening a fairway in a tidal river or estuary requires the use of process-based numerical models to account for all these nonlinear interactions, next to appropriate data collection. The current paper may help in analyzing and interpreting the numerical results.

Factors Affecting the Outcome of Endoscopic Endonasal Transsphenoidal Surgery for Functioning Pituitary Adenomas

Objective: Analysis of preoperative predictors for pituitary adenoma operations and their impact on results after endoscopic endonasal transsphenoidal surgery (EETSS) Methods: We carried out a prospective study of all the patients (99), who were endoscopically endonated transphenoidal at the Department of Neurosurgery, Benha University, Egypt and Wuhan Union Hospital, and China between January 2018 and December 2019. Medical data were gathered and evaluated, including information on sex, age, tumour size, hormone level, preoperative MRI brain and sella, resection scope, result (clinical and endocrinological) and complications. Results: 99 patients with PA were endoscopic transsphenoidal endonasal treatments, 52 males (52.5%) and 47 females (47.5 percent ). Average age 42.5 ± 11.2 years, Irregular menstrual behaviour was the highest in the study populations in 22 patients, followed by visual disturbance in 17 patients (17.2%), acromegaly and increased intracranial pressure in 14 patients, followed by 11 (11.1%), 8 (8.1%) accidents, and 5 (8.1%) cases (11.1%), followed by headache with 11 (12.1%). Conclusion: the size and scope of the tumour excision are only important postoperative determinants (hormonal, radiological & postoperative complications).

Elucidating the impact of feeding behaviour on body composition in finishing pigs fed ad libitum using an integrative feeding behaviour index

Large variability in body composition is observed in pigs fed and raised in similar conditions. Part of this variation might be explained by the feeding behaviour of pigs, given its effect on metabolic hormones associated with energy regulation and protein metabolism. The objective of this study was to elucidate this relationship by examining body composition with a new index describing feeding behaviour in finishing pigs. Feeding behaviour and body composition information on 138 pigs during the last 28 d of the finishing phase from three trials were used. For each pig, the sum of the areas between the observed relative cumulative feed intake and the regression line for the evolution of relative feed intake over time was used to calculate the new feeding behaviour index. This index quantifies the irregularity of feed intake (IIFI) by integrating different components of feeding behaviour into a single measurement. Body lipids and protein composition were estimated by dual X-ray densitometry at the beginning (day 1) and end of the finishing phase (day 28). The relationship of IIFI with performance and body composition variables was examined using correlation tests and factor analysis within each dataset. In datasets where IIFI was correlated with body composition, linear models were used to quantify the contribution of IIFI to the total variation of body composition. Weak to moderate correlations (r = ±0.31 to ±0.44; P < 0.05) of IIFI with body composition and performance variables were found only in datasets 1 and 2. However, in dataset 2, IIFI was also correlated with daily feed intake (r = - 0.54; P < 0.001). Irregular feeding behaviour is associated to increased proportion of protein (PdDG) and decreased proportion of lipids (LdDG) in body gain. However, IIFI only accounted for 8% to 14% and 10% to 12% of the total variation of PdDG and LdDG in datasets 2 and 1, respectively. Additionally, factor analysis showed that the number of meals and IIFI had high loadings in a factor separate from factors associated with body composition or performance, suggesting that body composition and performance are independent of feeding behaviour. In conclusion, factors other than feeding behaviour might be involved in modulating body composition of finishing pigs fed ad libitum.

How Interpersonal Distance Between Avatar and Human Influences Facial Affect Recognition in Immersive Virtual Reality.

Purpose: The purpose of this study was to determine the optimal interpersonal distance (IPD) between humans and affective avatars in facial affect recognition in immersive virtual reality (IVR). The ideal IPD is the one in which the humans show the highest number of hits and the shortest reaction times in recognizing the emotions displayed by avatars. The results should help design future therapies to remedy facial affect recognition deficits.Methods: A group of 39 healthy volunteers participated in an experiment in which participants were shown 65 dynamic faces in IVR and had to identify six basic emotions plus neutral expression presented by the avatars. We decided to limit the experiment to five different distances: D1 (35 cm), D2 (55 cm), D3 (75 cm), D4 (95 cm), and D5 (115 cm), all belonging to the intimate and personal interpersonal spaces. Of the total of 65 faces, 13 faces were presented for each of the included distances. The views were shown at different angles: 50% in frontal view, 25% from the right profile, and 25% from the left profile. The order of appearance of the faces presented to each participant was randomized.Results: The overall success rate in facial emotion identification was 90.33%, being D3 the IPD with the best overall emotional recognition hits, although statistically significant differences could not be found between the IPDs. Consistent with results obtained in previous studies, identification rates for negative emotions were higher with increasing IPD, whereas the recognition task improved for positive emotions when IPD was closer. In addition, the study revealed irregular behavior in the facial detection of the emotion surprise.Conclusions: IVR allows us to reliably assess facial emotion recognition using dynamic avatars as all the IPDs tested showed to be effective. However, no statistically significant differences in facial emotion recognition were found among the different IPDs.

Discrete Superior Hyperbolicity in Chaotic Maps

In the last few decades, the dynamics of one-dimensional chaotic maps have gained the tremendous attention of scientists and scholars due to their remarkable properties such as period-doubling, chaotic evolution, Lyapunov exponent, etc. The term hyperbolicity, another important property of chaotic maps is used to examine the regular and irregular behavior of the dynamical systems. In this article, we deal with the hyperbolicity and stabilization of fixed states using a superior two-step feedback system. Due to the superiority in the chaotic evolution of one-dimensional maps in the superior system we are encouraged to examine the hyperbolicity and stabilization in chaotic maps. The hyperbolic notion, hyperbolicity in periodic states of prime order, stabilization, and the hyperbolic set of the chaotic maps are studied. The numerical, as well as experimental simulations, are carried out, followed by theorems, examples, remarks, functional plots, and bifurcation diagrams.

Nonbearing Wall infill Under Seismic Impacts In Terms Of Non-Linear Static Analysis

The presence of wall infill during seismic impacts is one of the key factors that significantly affect the seismic stability of buildings. Also, irregularity of configuration in plan and height can often lead to more serious damage comparing to regular buildings, due to torsional oscillations and stress concentration on individual elements. The purpose of this article is to study the behavior of various irregularities in the shape of buildings under seismic impacts in terms of non-linear static analysis, taking into account the non-bearing wall infill. For this purpose, five typical geometric shapes of the building plan used in Ukraine and abroad (rectangular, + shape, L-shaped, T-shaped and 4-shaped) were adopted.The calculations were performed using the Pushover Analysis (nonlinear static analysis) method in CAD Lira-SAPR 2019. The main parameter that forms the basis for seismic stability analysis is the story drift under seismic impacts. Obtained results indicate the need to take into account the effect of wall infill, as this can lead to an increase in the seismic stability of the building and stress concentration on individual elements, which can lead to their collapse.

Products of Interest

Products of Interest

WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks

Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors.