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Cyber range environments are key platforms for cybersecurity training, research and testing. This can enable the emulation of realistic cyberattacks and incident response scenarios. Most of the traditional approaches to simulation are based on predefined or rule-based models. These approaches do not allow for adaptation and fail to account for the complexity of evolving threats. An artificial Intelligence-Driven Multi-Agent System (MAS) has been proposed in this paper. The framework autonomously simulates sophisticated cyberattacks and coordinates automated incident response within a cyber range. CICIDS2017 and UNSW-NB15 datasets are combined and integrated into a cyber range simulator CyDER 2.0. Reinforcement learning and anomaly detection methods are used to enable attack and defence agents for adaptive behaviours. The MAS architecture implements realistic attack vectors and response strategies. A set of experiments demonstrate that the AI-driven MAS achieves much higher simulation realism and responsiveness than the traditional static systems. This method also has higher detection accuracy with minimal mitigation times. The model undergoes rigorous validation and acceptance testing to assess robustness and generalizability.

Most existing adaptive information security approaches focus on simplified behavioral patterns and work as isolated models. This limits their effectiveness against advanced and dynamic cyber threats. Therefore, there is an emergent requirement for a mathematically unified framework that can dynamically capture and forecast the aggregate behavior of both the attacker and the defender in a complex environment. The paper proposes a mathematical modeling approach that combines composite behavior models into adaptive information security strategies. The framework encapsulates heterogeneous behavioral patterns into a unified dynamic model that can adapt to an ever-changing threat landscape. This result in novel adaptation rules derived from system dynamics and game theory, with the aim of enabling proactive defense mechanisms that can adapt to real-time challenges posed by adversary actors. The outcomes presented in this paper demonstrate strong improvements in threat detection, mitigation speed, and resource optimization through systematic model implementation, comprehensive simulation, and positive statistical hypothesis testing. The comparison reveals that the proposed method is generally superior to existing methods in scalability and effectiveness. It presents a new class of adaptive cybersecurity models that have deeper behavioral insights and enhanced resilience in complicated threat environments.

Pesticides residue poses serious concerns to human health. The present study was carried out to determine the pesticide residues of peri-urban bovine milk (n = 1183) from five different sites (Bangalore, Bhubaneswar, Guwahati, Ludhiana and Udaipur) in India and dietary exposure risk assessment to adults and children. Pesticide residues were estimated using gas chromatography with flame thermionic and electron capture detectors followed by confirmation on gas chromatography-mass spectrometer. The results noticed the contamination of milk with hexachlorocyclohexane (HCH), dichloro-diphenyl trichloroethane (DDT), endosulfan, cypermethrin, cyhalothrin, permethrin, chlorpyrifos, ethion and profenophos pesticides. The residue levels in some of the milk samples were observed to be higher than the respective maximum residue limits (MRLs) for pesticide. Milk samples contamination was found highest in Bhubaneswar (11.2%) followed by Bangalore (9.3%), Ludhiana (6.9%), Udaipur (6.4%) and Guwahati (6.3%). The dietary risk assessment of pesticides under two scenarios i.e. lower-bound scenario (LB) and upper-bound (UB) revealed that daily intake of pesticides was substantially below the prescribed acceptable daily intake except for fipronil in children at UB. The non-cancer risk by estimation of hazard index (HI) was found to be below the target value of one in adults at all five sites in India. However, for children at the UB level, the HI for lindane, DDT and ethion exceeded the value of one in Ludhiana and Udaipur. Cancer risk for adults was found to be in the recommended range of United States environment protection agency (USEPA), while it exceeded the USEPA values for children.

This study examined a room temperature operative, highly sensitive, stable, and selective PANI ammonia (NH 3 ) gas sensor using multiwalled carbon nanotubes (MWCNTs) and copper nanocomposites (Cu). The silicon substrate was coated with the sensing materials using the drop casting technique. To synthesize PANI, PANI@Cu@MWCNT nanocomposites chemical polymerisation method and ultrasonication techniques were used. In comparison to three PANI nano-composite sensor, which demonstrated sensing responses of 18%, 28%, and 43%, respectively, the PANI@Cu 3 @MWCNT 3 -based sensor demonstrated a greater sensing response of 116% under the room temperature conditions of NH 3 (100 ppm). The resistance variation of all the sensors is 62 kΩ, 78 kΩ, 89 kΩ, and 90 kΩ respectively. The PANI@Cu 3 @MWCNT 3 based sensor exhibited excellent results in term of resistance (90 kΩ). The stability, response time (10 s), and recovery time (13 s) of PANI@Cu 2 @MWCNT 2 is measured and has better results in terms of time than all other sensors. Pure PANI nano-composite sensor has shown the sensing response of 18%, resistance variation of 62 kΩ, response time (45 s), recovery time (48 s) respectively. The sensing materials were characterized using FTIR, XRD, EDX, and FESEM techniques. PANI and PANI@Cu@MWCNT nanocomposites’ gas sensing capabilities were examined using a Keithley 6514 multimeter.

Cotton fabric has been extensively used as the substrate of inkjet printing to manufacture traditional garments as well as emerging e-textiles due to its comfort, renewability, good dyeability, biodegradability and relatively low cost. In present work, the spreading and coalescence of ink drops on a cotton fabric as well as their effects on the image quality were investigated. A reactive orange 13 dye was selected as the colorant to make it convenient to observe the depositing morphologies of ink drops. The impacting and wetting processes of an ink drop on a cotton fiber were observed through a high-speed camera. Depositing morphologies of an ink drop, coalescing structures of ink drops and patterns printed with different drop spacings were observed through a microscope. The results show that the ink drop stably deposited on the cotton fabric and formed a long strip pattern after wetting. That indicates the inkjet printing pattern on a cotton fabric should be composed of “line segments” instead of round points. The edges of the pattern printed with a small drop spacing appeared bleeding phenomenon due to the ink drops excessively accumulated on the gaps between cotton fibers. Ink drops could not coalesce at a large drop spacing resulting in the printed pattern being discontinuous. The ideal pattern was printed at an intermediate drop spacing, which was 20 µm in this experiment.Graphic abstract

The exponential growth of Artificial Intelligence (AI) has led to the emergence of cutting edge methods and a plethora of new tools for media editing. The use of these tools has also facilitated the spread of false information, propaganda, and harassment through the creation of sophisticated fake video and audio content, commonly referred to as deepfakes. While existing efforts exist for identifying deepfakes videos, they have received less attention when it comes to social media videos. This paper presents 59-Layer Fake Dense Inception Network (FDINet59), designed to detect deepfakes contents. The dataset generated by Multi Task Cascaded Convolutional Networks (MTCNN) crop for training the system and evaluated its ability to spot deepfakes on datasets. The results show that FDINet59 provides impressive performance in identifying deepfakes material, achieving a maximum accuracy of 70.02% with a loss of 0.688 log units while using the training dataset. The ability of FDINet59 to detect deepfakes content generated by auto encoders and Generative Adversarial Network (GAN), commonly used to create deepfake videos. The results show that FDINet59 is 94.95% accurate with a log loss of 0.205. The proposed model can play an important role in preventing the spread of deceptive deepfake videos on social media. The development of more sophisticated deepfake detection algorithms is crucial to counter the negative impacts of this technology on society.

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The ‘cleaning’ factor has the highest weight, and ‘updating’ is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

In modern communication networks, particularly satellite-based systems, data security faces significant challenges from vulnerabilities such as signal interception, jamming, and latency during long distance transmissions. Traditional cryptographic methods are increasingly vulnerable to quantum computing threats, underscoring the need for advanced solutions to protect data integrity, confidentiality, and availability. This research investigates the fusion of quantum cryptography and Machine Learning (ML) to improve security in satellite communication. The Quantum Key Distribution (QKD), which is grounded in quantum mechanics, enables unbreakable encryption by detecting eavesdropping via quantum state disturbances. The CatBoost ML algorithm is applied to a dataset of 10,000 records featuring categorical attributes for prioritizing security elements such as anomaly detection, encryption types, and access controls. The model yields an accuracy of 89.23% and Area under Curve the Receiver Operating Characteristic (AUC-ROC) score of 94.56%, effectively predicting threat levels. Feature importance reveals anomaly detection (28.5%) and quantum encryption (22.3%) as primary contributors. While hurdles such as high implementation costs and transmission range limitations persist, this quantum ML synergy provides a proactive, adaptive framework for resilient, future-ready communication networks.

This study explores the factors influencing Informal Investment Intentions among potential investors, focusing on Risk Propensity and Subjective Norms. It examines the mediating effect of Entrepreneurial Alertness and the moderating role of gender, aiming to understand how these elements shape investment decisions among management students in India. A survey-based cross-sectional research design is followed to evaluate a 340 cross-sectional sample. Global Entrepreneurship Monitor (GEM) database, SEM, and the PROCESS macro have been utilized to understand the synchronization between the variables. CFA, RMSEA, GFI, and TLI tests are employed to check the fitness and validity of the model, and CMB, together with PFA, have been applied for variance testing. The study found that Subjective Norms significantly influence Informal Investment Intentions, mediated by Entrepreneurial Alertness. Gender exuberates the relationship between Risk Propensity and Informal Investment Intentions through Self-Efficacy, with stronger effects observed among male students. Findings offer insights for policymakers and educators to design interventions fostering Informal Investment Intentions by enhancing Entrepreneurial Alertness and addressing gender-specific differences, particularly among management students. This study adds to the literature by examining gender's moderating role in the relationship between Risk Propensity and Informal Investment Intentions, providing a nuanced understanding of informal investment behavior in an Indian context.

The primary goal of this research study, in the field of information technology (IT), is to improve the security and durability of software. A quantum computing-based security algorithm springs quite a lot of symmetrical approaches and procedures to ensure optimum software retreat. The accurate assessment of software’s durability and security is a dynamic aspect in assessing, administrating, and controlling security for strengthening the features of security. This paper essentially emphasises the demarcation and depiction of quantum computing from a software security perspective. At present, different symmetrical-based cryptography approaches or algorithms are being used to protect different government and non-government sectors, such as banks, healthcare sectors, defense, transport, automobiles, navigators, weather forecasting, etc., to ensure software durability and security. However, many crypto schemes are likely to collapse when a large qubit-based quantum computer is developed. In such a scenario, it is necessary to pay attention to the security alternatives based on quantum computing. Presently, the different factors of software durability are usability, dependability, trustworthiness, and human trust. In this study, we have also classified the durability level in the second stage. The intention of the evaluation of the impact on security over quantum duration is to estimate and assess the security durability of software. In this research investigation, we have followed the symmetrical hybrid technique of fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The obtained results, and the method used in this estimation, would make a significant contribution to future research for organising software security and durability (SSD) in the presence of a quantum computer.