Analysis Of Behavior Research Articles

Nitrogen-mediated volatilisation of defensive metabolites in tomato confers resistance to herbivores.

Plants synthesise a vast array of volatile organic compounds (VOCs), which serve as chemical defence and communication agents in their interactions with insect herbivores. Although nitrogen (N) is a critical resource in the production of plant metabolites, its regulatory effects on defensive VOCs remain largely unknown. Here, we investigated the effect of N content in tomato (Solanum lycopersicum) on the tobacco cutworm (Spodoptera litura), a notorious agricultural pest, using biochemical and molecular experiments in combination with insect behavioural and performance analyses. We observed that on tomato leaves with different N contents, S. litura showed distinct feeding preference and growth and developmental performance. Particularly, metabolomics profiling revealed that limited N availability conferred resistance upon tomato plants to S. litura is likely associated with the biosynthesis and emission of the volatile metabolite α-humulene as a repellent. Moreover, exogenous application of α-humulene on tomato leaves elicited a significant repellent response against herbivores. Thus, our findings unravel the key factors involved in N-mediated plant defence against insect herbivores and pave the way for innovation of N management to improve the plant defence responses to facilitate pest control strategies within agroecosystems.

DEVELOPMENT OF NETWORK SIMULATION MODEL FOR EVALUATING THE EFFICIENCY OF DISTRIBUTED CONSENSUS TAKING INTO ACCOUNT THE INSTABILITY OF NETWORK CONNECTIONS

The dynamic and unpredictable nature of network environments poses a significant challenge for distributed systems, particularly those relying on consensus algorithms for state management and fault tolerance. To address this challenge, this article introduces a novel simulation model designed to study the impact of unstable network connections on clusters running consensus algorithms. The model is engineered to mimic varying degrees of network instability, including latency fluctuations and connection disruptions, which are characteristic of real-world distributed systems. Our proposed model represents a significant advancement in the simulation of distributed networks. It employs a sophisticated network emulation layer capable of generating a wide spectrum of unstable network conditions. The core of the model is a highly configurable consensus mechanism simulator that allows for the adjustment of key parameters such as heartbeat intervals, election timeouts, and message loss rates. This level of configurability enables a comprehensive analysis of consensus behaviors under different network scenarios. The article focuses on the methodology behind the development of the model, detailing the theoretical underpinnings and the implementation strategies used to ensure a realistic representation of network instability. We also discuss the potential applications of the model, which extend beyond academic research into practical domains where distributed ledger technologies and distributed databases are prevalent. Through the deployment of this model, researchers and system architects can gain deeper insights into the resilience and adaptability of consensus algorithms. The model serves as a tool for preemptively identifying and addressing potential issues in distributed systems, facilitating the development of more robust and reliable technologies. In summary, the article showcases the design and capabilities of a new model that enables an in-depth understanding of the delicate interplay between network instability and consensus efficiency. By focusing on the model itself, the article aims to lay a foundation for future studies and improvements in the field of distributed systems.

ДОСЛІДЖЕННЯ КОНТЕКСТНО-ЧУТЛИВОГО АЛГОРИТМУ МОНІТОРИНГУ КІБЕРБЕЗПЕКИ НА ОСНОВІ РЕКУРЕНТНИХ НЕЙРОННИХ МЕРЕЖ

The most common problems faced by modern information and communication systems (ICS) in the context of combating cyber threats were examined in the paper. The importance of ensuring the reliable operation of ICS, and protecting their users' private data from unauthorized interception or destruction was emphasized. The main principles of effective protection of ICS systems against possible interference in their work were defined. The classification of cyber threats and their impact on the functioning of information systems was presented. Features of the use of modern information technologies were determined, such as machine learning (ML), and recurrent neural networks (RNN) for increasing the effectiveness of detecting and preventing such threats, speeding up the process of calculating large volumes of information about various aspects of the work of information and communication systems. The parameters of the analysis of ICS behavior, which indicate the presence of problems in cyber security, were studied. The features and advantages of deploying RNN in ICS were analyzed, which makes it possible to simplify the tasks of cyber defense. A modified context-sensitive algorithm for cyber security monitoring (CCM-RNN) was proposed, which is based on RNN and allows taking into account the dynamics of system changes in the established context, for example, the type or volume of traffic from users, etc. The method of selecting the most effective parameters and properties of ICS for detecting cyber threats was improved. The results of the study of the effectiveness of the use of the modified CCM-RNN algorithm demonstrated its broad capabilities for fast and accurate detection of anomalies in the operation of ICs that may threaten their cyber security. By changing the number of properties of the CCM-RNN algorithm, which correspond to the characteristics of various aspects of the IC, it is possible to achieve the maximum accuracy of cyber threat detection. The modified algorithm also allows for the reduction of the duration of calculations during analysis. Based on the research results, a conclusion was made about the feasibility of using the proposed modified CCM-RNN algorithm for the ability to detect cyber security threats in ICS by flexibly adjusting the number and type of learning parameters of neural networks. In this way, the accuracy and duration of calculations were optimized, as well as the peculiarities and contexts of information and communication systems were taken into account.

A Mesoporous Silica-Loaded Multi-Functional Hydrogel Enhanced Tendon Healing via Immunomodulatory and Pro-Regenerative Effects.

Tendon injuries are pervasive orthopedic injuries encountered by the general population. Nonetheless, recovery after severe injuries, such as Achilles tendon injury, is limited. Consequently, there is a pressing need to devise interventions, including biomaterials, that foster tendon healing. Regrettably, tissue engineering treatments have faced obstacles in crafting appropriate tissue scaffolds and efficacious nanomedical approaches. To surmount these hurdles, an innovative injectable hydrogel (CP@SiO2), comprising puerarin and chitosan through in situ self-assembly, is pioneered while concurrently delivering mesoporous silica nanoparticles for tendon healing. In this research, CP@SiO2 hydrogel is employed for the treatment of Achilles tendon injuries, conducting extensive in vivo and in vitro experiments to evaluate its efficacy. This reults demonstrates that CP@SiO2 hydrogel enhances the proliferation and differentiation of tendon-derived stem cells, and mitigates inflammation through the modulation of macrophage polarization. Furthermore, using histological and behavioral analyses, it is found that CP@SiO2 hydrogel can improve the histological and biomechanical properties of injured tendons. This findings indicate that this multifaceted injectable CP@SiO2 hydrogel constitutes a suitable bioactive material for tendon repair and presents a promising new strategy for the clinical management of tendon injuries.

Comparative analysis of thermal behaviour in functionally graded material fin under convective-radiative heat transfer: Homotopy perturbation method

In this study, the thermal characteristics of a fully wet, moving longitudinal fin made from linear Functionally Graded Material (FGM) is explored. The fin response to convective-radiative heat transfer is examined through three distinct FGM scenarios namely, a Homogeneous material (HM), Functionally Graded Material I (FGM I) having greater thermal grading near the base, and Functionally Graded Material II (FGM II) having increased thermal grading towards the tip. The derived energy equation is a second-order nonlinear ordinary differential equation is solved using the Homotopy Perturbation Method (HPM) compared with results found in the existing literature. The fin thermal profile has been graphically analysed for the thermal conductivity grading parameter (β), Peclet number (Pe) and other pertinent parameters. It is found that the temperature distribution along the fin is highest for the FGM II fin and lowest for the HM fin, with the FGM I fin displaying intermediate values. Efficiency of the fin (η) is discovered to be lowest in the case of HM fin structures followed by FGM I and FGM II fin structures at the base and vice versa at the tip. The results show significant potential of HPM which benefits the manufacture and design of FGM fin structures.

Experimental analysis of the compressive behaviour of perforated brick masonry using digital image correlation

The paper presents an experimental program on the characterisation of the mechanical behaviour of perforated brick masonry (PBM) under uniaxial compression. The study is aimed to provide a better understanding on the compressive strength and elastic behaviour of a kind of masonry hardly dealt with in the literature. The mechanical response of the investigated masonry shows actually two complexities, linked respectively to the perforated nature of the bricks and the asymmetric cross-section of the units. The latter feature is particularly recurrent in exposed brickwork composed of perforated bricks, whenever one of the face shells has a larger thickness than the other one. For this purpose, an experimental campaign was carried out on units and PBM subjected to uniaxial compressive loading. The mechanical response of PBM is investigated to accurately determine the masonry Young’s modulus and the compressive strength. Measurements obtained with conventional sensors are compared with those derived from the Digital Image Correlation (DIC) technique. Lastly, the experimental findings in terms of compressive strength and Young’s modulus are cross-referenced with the estimations derived from empirical criteria available in Eurocode 6 and ACI 530.1–02.

Cozy den or winter walk: the effects of climate and supplementary feeding on brown bear winter behavior

AbstractHibernation is a key adaptation for coping with unfavorable climatic conditions and low food availability in areas with severe winter conditions. While understanding the physiology and phenology of this adaptation has received considerable attention, comparatively little information is available on how hibernation will be affected by changing climate conditions. We used GPS telemetry data from 20 free‐ranging brown bears monitored over 31 winters between 2007 and 2022, to identify behavioral strategies of bears during winter. We applied behavioral change point analysis to quantify brown bears’ hibernation phenology in a population close to the bear's southern latitudinal range limit in Europe where supplementary food is available to bears year‐round. We observed winter behavior patterns that varied across age and reproductive classes but also within individuals between winters. Among 31 winter events, we registered six cases in which bears exhibited a single hibernation/stationary period and 19 events where hibernation was split into up to five stationary periods. Moreover, six winter events did not show behaviors consistent with hibernation and individuals remained partly or completely active throughout winter. The movement of these active bears decreased with increasing snow depth. In addition, these winter‐active bears showed higher fidelity to supplementary feeding sites during the winter period compared to the rest of the year. Our data suggest that an abundance of human‐provided food resources during winter may facilitate the emergence of different wintering strategies in brown bears. Furthermore, supplemental feeding sites in combination with predicted mild winters and prolonged natural food availability suggest that the use of hibernation as an energy‐saving strategy to overcome severe environmental conditions may decrease in the future.

Transient dynamic stress behavior analysis of the axial flow pump as turbine at part loads

To explore the structural dynamic characteristics of the impeller and shaft system of the axial flow pump as turbine, the variation law of impeller transient equivalent stress, deformation, and the impeller and shaft system mode are analyzed by the transient fluid-structure interaction method. Findings indicate that the equivalent stress is centralized at the blade root around the hub and tends to decrease as it moves towards the shroud. The maximum equivalent stress at 1.0Qbep and 1.1Qbep is 1.26 times and 2.0 times higher than that at 0.8Qbep. Blade deformation is significantly influenced by flow rate. The maximum deformation of the blade at 1.0Qbep and 1.1Qbep is 1.6 times and 3.6 times higher than that at 0.8Qbep. The impeller and shaft system with prestress in dry mode has an average natural frequency of the first 10 orders that is 16.7 Hz higher than without prestress. However, with the fluid adding mass and flow damping, the average natural frequency of the first 10 orders of the impeller and shaft system in wet mode is reduced by 278.08 Hz compared with dry mode. In fact, the impeller and shaft system of the axial flow pump as turbine is not prone to vibration.

Bibliometric Analysis of Scientific Publications on Communicative Behavior Problems

Purpose of research. The purpose of the study is to analyze the dynamics of the publication activity of scientific research in the field of studying communicative behavior. Communicative behavior is an object with an interdisciplinary research status. It falls into the area of research interest of various scientific fields. A bibliometric analysis of publication activity was carried out in connection with solving several tasks: to find out areas of knowledge in which the greatest publication activity is concentrated, to study the methods that are used in various fields of communicative behavior analysis, to identify organizations that are included in this research search.Methods. The study was conducted in two scientific electronic databases: the Russian Scientific Electronic Library Elibrary.ru and the international open platform for researchers and scientific organizations Lens. The bibliometric analysis was used as the research method. It involves the quantification of documentary information flows and it is based on quantitative research indicators reflecting the state of science as a whole or its individual branches.Results. As a result of the bibliometric analysis of publication activity on the problems of scientific research in the field of communicative behavior, communicative behavior in the digital environment and communicative behavior in social networks, a significant increase in publication activity was revealed in both international and Russian information databases over the past 10 years (2013 - 2023). It is also noted that the predominant number of publications on the issues studied is concentrated in the field of psychology. However, we can trace a shift in the focus of foreign research on communicative behavior towards digitalization in information technology, and Russian research towards the field of digital technologies in the educational environment.Conclusion. The results obtained indicate the high relevance, as well as the scientific and practical significance of the research topic on the problems of communicative behavior in general, as well as communicative behavior in the digital environment and social networks, in particular.

Seismic behavior analysis of long-span cable-stayed bridge under bi-directional near-fault ground motions

In this paper, the seismic behaviors of a long-span cable-stayed bridge subjected to bi-directional near-fault ground motions are analyzed. The cable-stayed bridge with a main span of 432 m is selected as the research object and appropriately modeled using the OpenSees software. Ten sets of historical near-fault and far-field ground motions associated with the same earthquake event and site class are utilized as input excitations. Nonlinear time-history analyses are conducted to explore three key aspects: the influence of ground motion directionality, the effectiveness of the percentage rule and simplified Square-Root-of-Sum-of-Squares (SRSS) rule, and the main influential factors. The findings reveal that near-fault ground motions induce larger seismic responses compared to far-field ground motions. Moreover, the effects of ground motion directionality are more pronounced in near-fault scenarios than in far-field events. The percentage rule and simplified SRSS rule demonstrate minor significance in predicting the critical seismic responses. The bi-directional peak ground velocity (PGV) of seismic motions in the principal directions of the cable-stayed bridge may emerge as the primary factor influencing the seismic responses, making it a suitable intensity measure for bi-directional seismic fragility assessment.

A Comprehensive Exploration of Text, Web, Social Media, and Geospatial Analytics for Informed Decision Making

Text Analytics is the process of turning unstructured text data into useful information for analysis, to gauge consumer opinions, feedback, and product evaluations. It also provides search functionality, entity modelling, and emotional analysis to enable fact-based decision making. Analysis of website visitor behaviour is done using Web Analytics. The number of websites and users on the internet is growing daily. It is the process of tracking website information that can help to improve the web application features and evaluate the behaviour of users. By performing social media analytics on social media for example twitter, Instagram, Facebook. Data such as likes, comments, shares and saves can be obtained and analyse to know how the society think about the product. Geospatial analysis is used to make visualizations that include maps, graphs, statistics that show data according to geographic location. This is important to be analysed as it tells which area or country has the highest product sold or lowest subscription of the service. To make the data much easier for human brain to analyses or to make a conclusion, data visualization design is the process of putting all data information collected into a visual context. For instance, graph or map. The main objectives of data visualization are to make it simpler or easier to spot the outliers and patterns trends in big data sets. There are a variety of clustering processes or techniques available to arrange the data efficiently to its related data. The clustering process that is used in data mining is presented in this work.

A study of pedestrian wayfinding behavior based on desktop VR considering both spatial knowledge and visual information

Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.

Detection and analysis of student behavior in college labor education courses based on YOLOv5 network

To improve the application of behavior detection technology in college education, the study proposes a new model built on deep CNN, which is used for student behavior detection and analysis in college labor education courses. The study first analyzed the target detection algorithm, and optimized the selected You Only Look Once version 5 (YOLOv5) algorithm and its network structure with a series of improvements, and based on this, embedded the attention module into the algorithm structure to finally obtain a new model, namely YOLOv5-O. After a series of experiments, YOLOv5-O reached an average accuracy of 90.1% on the test set, while the application test in the actual teaching environment showed that its average accuracy was 86.7%. This result is obviously superior to the existing technology, which proves the validity of the study and provides strong data support for the automatic detection of student behavior. In addition, in the teaching experiment, YOLOv5-O assisted teaching achieved the most significant teaching effect, and students’ achievement improved the most. The feasibility of this method is verified.

Phase Transition Heat Transfer Enhancement of a Graphene-Coated Microporous Copper Surface Using Two-Step Electrodeposition Method

Abstract Owing to their exceptionally high thermal conductivity, there is a growing demand for graphene nanoparticles in phase transition heat transfer applications. This research delves into the exploration of various critical phenomena within the realm of surface science, specifically focusing on interactions at solid-liquid and liquid-liquid interfaces. In this work, graphene nanoparticles at varying concentrations are subject to electrochemical deposition on a microporous copper substrate to form graphene coated over microporous copper (GCOMC). The study encompasses a comprehensive analysis of surface characteristics, such as porosity, roughness, and wettability. Furthermore, the study involves the calculation of two key heat transfer metrics, the critical heat flux (CHF) and the boiling heat transfer coefficient (BHTC), through the execution of pool boiling experiments. The findings of this research underscore the remarkable superiority of GCOMC surfaces over their uncoated copper counterparts in terms of boiling performance. Particularly, the GCOMC surface showcases an impressive 87.5% enhancement in CHF and a 233% increase in BHTC compared to the bare copper surface. Furthermore, this investigation delves into a detailed quantitative analysis of bubble behavior, encompassing parameters such as bubble departure diameter, bubble departure frequency, and nucleation site density, employing high-speed camera techniques to comprehensively understand the underlying processes.

Analysis of Corrosion Polarization Behaviour in GNP/TiB2 Reinforced Hybrid Al–Mg-Si-MMNCs for Marine Environments

Analysis of Corrosion Polarization Behaviour in GNP/TiB2 Reinforced Hybrid Al–Mg-Si-MMNCs for Marine Environments

Molecular Intercalation‐Induced Two‐Phase Evolution Engineering of 1T and 2H‐MS2 (M = Mo, V, W) for Interface‐Polarization‐Enhanced Electromagnetic Absorbers

AbstractPolarization at interfaces is an important loss mechanism for electromagnetic wave (EMW) attenuation, though the motion behavior of carriers in interfaces composed of different types of conductors has yet to be investigated. Tuning the phase structure of transition metal dichalcogenides (TMDs) MS2 (M = Mo, V, W) by organics small molecule intercalation to achieve the modulation of interfacial types is an effective strategy, where 1T‐MS2 exhibits metallic properties and 2H‐MS2 has semiconducting properties. To exclude the contribution of the intrinsic properties of TMDs materials, three TMDs (MoS2, VS2, and WS2), which also possess phase transitions, are investigated. Among them, the 1T‐MS2 composite exhibits excellent EMW absorption performance under the synergistic effect of interfacial polarization and conduction loss. 1T‐MoS2/MOF‐A exhibits the best EMW absorption performance with an RLmin of −61.07 dB at a thickness of 3.0 mm and an EAB of 7.2 GHz at 2.3 mm. The effectiveness of the modulation of the interfacial polarization using 1T‐phase and 2H‐phase MS2 is demonstrated, which is important for the analysis of the carrier motion behavior during the interfacial loss.

On the qualitative behaviour and numerical analysis of a Calcium oscillation model through Atangana-Baleanu operator in Caputo sense

On the qualitative behaviour and numerical analysis of a Calcium oscillation model through Atangana-Baleanu operator in Caputo sense

SPOTTING OF CYBERBULLYING ON SOCIAL MEDIA USING MACHINE LEARNING

Cyberbullying occurs when someone harasses, threatens, or mistreats someone else online or on social media. Cyberbullying leads to threats, public embarrassment, and reprimands. Cyberbullying has caused a rise in youth suicide and mental health issues. It has lowered my self-esteem and escalated my suicidal thoughts. If cyberbullying continues, an entire generation of young adults will suffer from low self-esteem and mental health issues. Many machine learning algorithms are used to automatically identify social media cyberbullying. Social media monitoring allows this. These models don't account for all the factors that could make a comment or post bullying. Bullying is still ambiguous. This study presents a cyberbullying diagnosis model based on several factors. Cyberbully is a misuse of technology advantage to bully a person. This project is used Dataset namely,’ Cyberbullying-tweets’. This project used Machine Learning Classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Neural Networks (NN), Logistic Regression(LR) Algorithms and calculated performance results for comparing the performance for dataset. Keywords: Cyberbullying Detection, Social Media Analysis, Machine Learning Models, Natural Language Processing (NLP), Text Classification , Feature Extraction, Sentiment Analysis, Textual Data Mining, Online Harassment, User Profiling, Toxic Language Detection, Hate Speech Detection, Content Moderation, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Neural Network Algorithms, Lexicon-based Features, Syntactic Features, N-gram Analysis, Arabic Social Media (if focusing on Arabic language),User Behavior Analysis, Feature Engineering, Cross- Platform Analysis, Real-time Monitoring, Social Network Analysis

Nexus of knowledge oriented leadership and knowledge worker performance in Chinese higher education institutions: a post-pandemic behavioral analysis

PurposeIn the wake of the COVID-19 outbreak understanding the behavioral dynamics and role of knowledge-oriented leadership (KOL) in driving knowledge worker performance (KWP) is paramount for organizations striving to adapt and thrive in the post-pandemic era. Drawing on the path-goal theory, this study explores the impact of KOL on KWP. In addition, this study seeks to examine both the direct and indirect influences of KOL on KWP via digital orientation (DO) and digital citizenship practices (DCP), especially for higher education institutions (HEIs) located in China.Design/methodology/approachThe sample consisted of 319 academic staff from HEIs in China. The study used Smart-PLS 4.0 for analytical examination.FindingsThe results indicate that the impact of knowledge-oriented leadership on KWP is negligible. Furthermore, DCP and DO partially mediate the relationship between KOL and KWP. The findings of this research have practical implications for Chinese HEIs and organizations across industries, providing insights into how to navigate the complexities of the post-pandemic work environment and effectively harness digitalization to optimize the KWP.Research limitations/implicationsThe implications of this research extend beyond the HEIs, providing organizations in various industries with evidence-based strategies to promote effective leadership, foster digital skills, and cultivate a culture of digital citizenship in the post-pandemic work landscape.Originality/valueDespite the increasing significance of KOL in the higher education sector, there is a dearth of studies establishing a relationship between KOL and KWP in HEIs. The present study seeks to fill this gap by examining the association among KOL, KWP, DO, and DCP in HEIs during the post-pandemic. This research provides significant contributions to the existing literature on the topic by scientifically exploring the association between KOL and KWP by examining the intervention mechanism of both digital citizenship practices and digital orientation.

Experiment and simulation study on the functional fatigue of shape memory alloy beam actuators

Abstract The functional fatigue behavior of shape memory alloy (SMA) beam actuators is gaining importance as their utilization in engineering applications becomes more widespread. However, research on the functional fatigue behavior of SMA beam actuators under bending conditions is not as extensive as that on SMA wires. In this paper, an experimental study and theoretical analysis of the functional fatigue behavior of SMA beam actuators were conducted. A measuring method for bending deflection and an automatic thermal cyclic test bench was designed. A series of functional fatigue tests were conducted on SMA beam actuators under different bias load conditions and the functional fatigue patterns were obtained. The material damage factor is defined and calculated through specimen tests. By incorporating the damage factor to modify the existing constitutive model, a model considering functional fatigue and tension-compression asymmetry is obtained. Finite Element Analysis (FEA) is performed based on this modified model to simulate the actuating performance of SMA beam actuators and to compute the deflection at different numbers of cycles. Additionally, a small parameter study on the actuator shape is conducted using FEA. By comparing the FEA results with functional fatigue experimental data, the effectiveness of the modified model is validated, demonstrating its capability to describe functional fatigue and predict actuator lifespan.