Multilayer Approach Research Articles

Fixed-time formation tracking for unmanned surface vehicles: A multi-layer neural networks approach

This paper investigates the distributed fixed-time formation problem of unmanned surface vehicles (USVs) in the presence of external disturbances, model uncertainties, input saturation and quantization constraints. To deal with the problem, a fixed-time sliding-mode control algorithm is proposed, where multi-layer neural networks (MNNs) are designed to approximate the unknown dynamics and composite disturbances of the system. The proposed MNNs combine the advantages of fuzzy neural networks (FNNs) and radial basis function neural networks (RBFNNs), exhibiting robust dynamic characteristics. Furthermore, the non-singular fast terminal sliding mode (NFTSM) is integrated into the fixed-time control framework to improve the robustness and speed of convergence for uncertain USV systems. Comparative simulations conducted with USVs demonstrate the superiority and effectiveness of the proposed algorithm.

Factors that influence the criminal justice response to human trafficking: a systematic review of North American and European studies

Human trafficking, the recruitment and transportation of persons by use of force, coercion, or deception for exploitation is one of the largest illicit industries worldwide. In the execution of anti-trafficking policies to combat this phenomenon, nation-states have assigned a decisive role to criminal justice actors as they are not only responsible for the detection, investigation, and prosecution of perpetrators but also for the identification and (legal) protection of victims of human trafficking. However, research has shown that criminal justice actors encounter many challenges whilst carrying out their tasks, which is also reflected in the low numbers of successful prosecutions and identified victims. To our knowledge, the reasons behind this observation have never been studied systematically. This systematic review aims to collect and synthesise empirical literature on factors that influence the criminal justice response to human trafficking to better understand and identify gaps in knowledge. In total, 35 empirical studies were included. Findings were discussed in light of theory and previous research and were used to provide recommendations for policy and future research. These studies suggest that factors such as prevailing stereotypes, an absence of legal guidance and a lack of capacity and expertise interact and even amplify each other and that strengthening the criminal justice response to human trafficking requires a multi-layered approach that simultaneously addresses several challenges.

Participation in non-formal adult education within the European context: examining multilayer approach

Participation in non-formal adult education (NFE) is considered an essential part of lifelong learning, aiming to foster the development of individuals and societies. This significance is particularly evident today, given the era of extensive digitalization and disruptive technological advances. Nevertheless, not all adults participate in organized learning and have equal chances. Therefore, this study addresses the absence of up-to-date comparative findings on participation in NFE in the post-COVID-19 world. To this end, we examine current trends in NFE participation in four European countries: Sweden, Germany, the United Kingdom, and the Czech Republic (RQ1), where we explore the impact of key micro (social and behavioral), meso (job-related), and macro-level (country-specific) factors on this participation (RQ2). Our findings indicate that NFE participation remained relatively stable in 2022 compared to 2016, except for Sweden and Germany, which achieved higher participation rates. However, differences in participation between countries are diminishing. Simultaneously, NFE participation is becoming increasingly job-oriented and receives more support from employers. However, it is important to note that inequality in access to NFE persists, as the main predictors of NFE participation, such as learning intentions, educational attainment, economic and occupational status, remain consistent, regardless of the participants’ economic activity and country of birth. This underscores the enduring significance of a key concept behind this study: the willingness to engage in organized NFE exhibits a complex structure with multiple layers.

Achieving exceptional strain-hardening ability in nanocrystalline Ni–W coatings through compositionally modulated multilayer approach

Nanocrystalline Ni–W coatings show exceptional strength; however, they exhibit poor toughness due to limited ductility and high residual stresses which impede their use in practical applications as a potential hard chrome replacement. In the present study, compositionally modulated multilayer (CMM) coatings with alternate layers of Ni-1 at% W (soft) and Ni-15 at% W (hard) of three different layer thicknesses of 1, 0.5, and 0.1 μm are developed by pulse reverse electrodeposition to achieve the strength-ductility synergy, thereby enhanced wear resistance. Microcompression test results reveal that all the CMM coatings exhibit remarkably higher strain-hardening ability than the homogenous coatings. Significant fluctuations in the strain-hardening rate and a positive shift in the onset stress for the work-hardening stages were observed with the layer thickness. This suggests a strong interplay between the hard and soft layers on the deformation characteristics of multilayer coatings. Detailed electron microscopy of the cross-section of the deformed micropillars shows substantial evidence of co-deformation of both layers. A mechanism is proposed to explain the enhanced strain-hardening in multilayers involving the strength incompatibility arising in the layers leading to lateral constraints, resulting in strain gradient plasticity, stabilizing of local shear by the soft layers, and the barrier effect of the interfaces.

Quantification of spatial heterogeneity and its influence on particle migration

Suffusion experiments were utilised to quantify the evolving degree of heterogeneity in gap-graded soils. Upward flow was imposed on test specimens comprising a mixture layer with varying finer fraction contents overlaid by a coarse layer. A coaxial permeameter cell enabled the local permeability to be obtained using spatial time domain reflectometry. Three methods (coefficient of variation, Dykstra-Parsons coefficient, and Lorenz coefficient) were employed to quantify heterogeneity based on spatial variability in local permeability. For the two-layered specimen in this study, all three methods demonstrated that suffusion had a homogenising effect with particle migration from the mixture layer to the coarse layer. Detailed insights were obtained from a multi-layered approach, where the Dykstra-Parsons coefficient was found to be more sensitive to spatial variations, while the Lorenz coefficient was less dependent on the amount of data. The key observation was that an increase in heterogeneity led to a reduction in particle migration. This is an important finding as prior studies focused on homogeneous specimens, while this study demonstrates that small amounts of heterogeneity can significantly impact particle migration characteristics. This reinforces the need to quantify the evolving degree of heterogeneity.

The Preparation Phase of the 2022 ML 5.7 Offshore Fano (Italy) Earthquake: A Multiparametric–Multilayer Approach

This paper presents an analysis of anomalies detected during the preparatory phase of the 9 November 2022 ML = 5.7 earthquake, occurring approximately 30 km off the coast of the Marche region in the Adriatic Sea (Italy). It was the largest earthquake in Italy in the last 5 years. According to lithosphere–atmosphere–ionosphere coupling (LAIC) models, such earthquake could induce anomalies in various observable variables, from the Earth’s surface to the ionosphere. Therefore, a multiparametric and multilayer approach based on ground and satellite data collected in each geolayer was adopted. This included the revised accelerated moment release method, the identification of anomalies in atmospheric parameters, such as Skin Temperature and Outgoing Longwave Radiation, and ionospheric signals, such as Es and F2 layer parameters from ionosonde measurements, magnetic field from Swarm satellites, and energetic electron precipitations from NOAA satellites. Several anomalies were detected in the days preceding the earthquake, revealing that their cumulative occurrence follows an exponential trend from the ground, progressing towards the upper atmosphere and the ionosphere. This progression of anomalies through different geolayers cannot simply be attributed to chance and is likely associated with the preparation phase of this earthquake, supporting the LAIC approach.

Development and Validation of a Novel Human-Fixed Cadaveric Model Reproducing Cerebrospinal Fluid Circulation for Simulation of Endoscopic Endonasal Skull Base Surgery.

Endoscopic endonasal surgery is a well-established surgical approach to the skull base. Surgeons need a reusable long-lasting tool to acquire the skills needed for skull base reconstruction. The aim of this study was to elaborate and validate a human formalin-fixed cadaveric model that reproduces a realistic cerebrospinal fluid (CSF) circulation and that adequately renders a CSF leak. An external ventricular drain that connects with a peristaltic pump is placed in the subarachnoid space, which allows a water circulation that reproduces CSF circulation. Intracranial pressure is measured in real time. Endoscopic endonasal skull base approaches are performed, to create different skull base openings and CSF leaks. Participants were tasked with reconstruction of the defects using a standardized multilayered approach, with the goal of obtaining a watertight closure under normal intracranial pressure ranges. Compiled data included time of reconstruction, years of experience of participants, and success/failure to achieve a watertight reconstruction. A Likert questionnaire was also used. The cadaveric model reproduced CSF circulation in 4 types of dural defects: sellar, suprasellar, transcribriform, and transclival. Intracranial pressures were similar to physiological conditions and were reproducible. Each model was tested multiple times, over several months. Success rates concurred with training levels (r = .8282 and P = .0017). A strong inverse correlation was also found between years of experience and time of reconstruction (r = .4977 and P < .0001). Participants agreed that the model was realistic (median Likert score of 4), and they strongly agreed that it allowed for the improvement of their surgical skills (median Likert score of 5). This novel human-fixed cadaveric model for CSF circulation is efficient and adequately reproduces surgical conditions for skull base approaches. The model is unique, easy to reproduce, and reusable. It can be used as a tool for teaching and for research purposes.

Highly flexible and temperature-tolerant phase change devices for dual-band camouflage

Here, we have developed a more temperature-tolerant emitter with a gradient emittance, which can enable adaptation to changing environmental conditions. Such a thermal emitter is mainly constructed by multilayered films composed of nitrogen (N)-doped Ge2Sb2Te5 (N-GST) and an underlying metal film. The proposed device not only possesses special wavelength selectivity in the middle infrared range but can also dynamically adjust average emissivity (from 0.13 to 0.83) through the degree of crystallization. Besides, N doping can elevate the phase transition temperature of GST and enhance its thermal resistance, which renders it particularly well-suited for applications in higher temperature environments than pure GST. This emitter also shows strong adhesion capability and high flexibility against bending, enabling more practical and widespread situations. By using a multi-layer structure, we combined the more temperature-tolerant and dynamically modulating N-GST emitter with an optical thin film, successfully achieving dual camouflage for both infrared and visible light. The element doping technology and multi-layer stacking approach presented in this research will provide valuable insight for the development of dynamic emissive materials in multi-spectral camouflage scenarios.

Improving the reliability of nanosatellite swarms by adopting blockchain technology

Satellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%.

Medical Image Classification using a Many to Many Relation, Multilayered Fuzzy Systems and AI

One of the research gaps in the medical sciences is the study of orphan diseases or rare diseases, due to limited data availability of rare diseases. Our previous study addressed this successfully by developing an Artificial Intelligence (AI)-based medical image classification method using a multilayer fuzzy approach (MFA), for detecting and classifying image abnormalities for large and very small datasets. A fuzzy system is an AI system used to handle imprecise data. There are more than three types of fuzziness in any image data set: 1) due to a projection of a 3D object on a 2D surface, 2) due to the digitalization of the scan, and 3) conversion of the digital image to grayscale, and more. Thus, this was referred to in the previous study as a multilayer fuzzy system, since fuzziness arises from multiple sources. The method used in MFA involves comparing normal images containing abnormalities with the same kind of image without abnormalities, yielding a similarity measure percentage that, when subtracted from a hundred, reveals the abnormality. However, relying on a single standard image in the MFA reduces efficiency, since images vary in contrast, lighting, and patient demographics, impacting similarity percentages. To mitigate this, the current study focused on developing a more robust medical image classification method than MFA, using a many-to-many relation and a multilayer fuzzy approach (MCM) that employs multiple diverse standard images to compare with the abnormal image. For each abnormal image, the average similarity was calculated across multiple normal images, addressing issues encountered with MFA, and enhancing versatility. In this study, an AI-based method of image analysis automation that utilizes fuzzy systems was applied to a cancer data set for the first time. MCM proved to be highly efficient in detecting the abnormality in all types of images and sample sizes and surpassed the gold standard, the convolutional neural network (CNN), in detecting the abnormality in images from a very small data set. Moreover, MCM detects and classifies abnormality without any training, validation, or testing steps for large and small data sets. Hence, MCM may be used to address one of the research gaps in medicine, which detects, quantifies, and classifies images related to rare diseases with small data sets. This has the potential to assist a physician with early detection, diagnosis, monitoring, and treatment planning of several diseases, especially rare diseases.

Enhancing communication: Deep learning for Arabic sign language translation

Abstract This study explores the field of sign language recognition through machine learning, focusing on the development and comparative evaluation of various algorithms designed to interpret sign language. With the prevalence of hearing impairment affecting millions globally, efficient sign language recognition systems are increasingly critical for enhancing communication for the deaf and hard-of-hearing community. We review several studies, showcasing algorithms with accuracies ranging from 63.5 to 99.6%. Building on these works, we introduce a novel algorithm that has been rigorously tested and has demonstrated a perfect accuracy of 99.7%. Our proposed algorithm utilizes a sophisticated convolutional neural network architecture that outperforms existing models. This work details the methodology of the proposed system, which includes preprocessing, feature extraction, and a multi-layered CNN approach. The remarkable performance of our algorithm sets a new benchmark in the field and suggests significant potential for real-world application in assistive technologies. We conclude by discussing the impact of these findings and propose directions for future research to further improve the accessibility and effectiveness of sign language recognition systems.

Dynamic interaction in sustainable energy: Learning from South Sumatra in transforming conflict into collaboration to accelerate energy mix targets

Abstract Energy transition is an alternative solution that emerged as an effort to achieve zero net emissions. The countries that signed the Paris Agreement are expected to reach their targets by 2030. Indonesia targets this agreement by performing the energy mix set at 25% nationally. Many provinces have difficulties achieving its implementation due to conflicts between related stakeholders, including industry. However, some provinces have shown promising transition changes with accelerated target achievement. This research aims to identify how the government of South Sumatra can reach beyond the National Target by turning the conflict into a collaboration between government and industry. The collaboration impacts accelerating the achievement of the energy mix target. A multi-layered approach was used to analyze the dynamics of the energy transition by demonstrating the energy transition process in Indonesia using Actor Interaction analysis, Dilemma analysis, and System Analysis. Therefore, the bottom-up approach chosen, systematically uncovering actor interactions within and across systems. Interviews were carried out for data collection. The results of the interviews were identified until a series of methods emerged that were used to identify the collaboration that occurred. The analysis found that local governments encounter dilemmatic situations to balance a smooth energy transition process and ensure stable socioeconomic conditions. The system dynamic simulation has proven to be a vital addition to Drama Theory and Graph Model for Conflict Resolution, offering a temporal perspective that is fundamental for the decision-making process that concerns the ever-changing environmental situation.

Attention-deficit/hyperactivity disorder (ADHD) in adults: a multilayered approach to a serious disorder of inattention to the future.

Attention-deficit/hyperactivity disorder (ADHD) affects people of all ages, yet its presentation varies as the person matures and social demands increase from childhood into adulthood. Interestingly, when analyzing the disorder in adults, it is not immediately clear what the 'attention deficit' in the ADHD denomination exactly means. Adults with ADHD have a broad range of difficulties, far beyond an attentional deficit, that impact negatively their social functioning and often lead to failures in all walks of life. Therefore, in this review, I attempt to reconcile the notion of attention deficit with the protean manifestations of ADHD in adults through a proposal that ADHD symptoms have as a common denominator an inattention to the future. I build this construct through a multilayered approach, progressing from the epidemiological and clinical considerations for Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnosis, to a deeper understanding of the disorder, discussing how these patients fail to anchor the present into the future (i.e., to be attentive to future consequences), thus failing to approximate future goals from present action. Integrating cognitive observations with imaging abnormalities, it is possible to propose that ADHD in adults is perhaps the most prevalent frontal lobe disorder in humans, ultimately impacting upon psychosocial management and treatment strategies.

Improved Intrusion Detection in the Internet of Things: A Multi-Layered Neural Network Approach and Analysis

The (IoT) Internet of Things is a complex notion that refers to the interconnection of several individual devices over a network (IoT). The data gathered by these interconnected devices have the potential to have far-reaching consequences for human society, the economy, and the environment. The IoT is especially vulnerable to a variety of vulnerabilities in hostile environments like the internet. High-end security solutions are not adequate to safeguard an IoT system due to adequate storage and less processing capabilities. This emphasizes the need for ascendable, strewn, and robust smart security solutions. In this study, IoT networks are safeguarded depleting a multiple-layered security strategy centered on deep learning. The proposed architecture employs the use of three intrusion detection datasets CIC-IDS, BoT-IoT, and ToN-IoT to weigh the performance of the insinuated multiple-layered approach. Irrevocably, compared to 92% accuracy for the existing methodologies, the new layout obtained 98% accuracy.

RFID-GSM Automatic Gate Control for Highly Secure Organizations

Abstract: In the current dynamic global landscape, security is paramount for organizations, particularly those entrusted with safeguarding sensitive data and valuable assets. Automatic gate control systems are pivotal in fortifying security and optimizing the operational efficiency of access points for highly secure organizations. By integrating Radio-Frequency Identification (RFID) and Global System for Mobile Communications (GSM) technologies, these systems provide sophisticated features that bolster security measures while enabling seamless access for authorized personnel. These automatic gate control systems provide a multi-layered approach to security, addressing various vulnerabilities and potential threats highly secure organizations face. By leveraging RFID technology, these systems enable efficient and reliable authentication of authorized personnel, minimizing the risk of unauthorized access or breaches. Additionally, the integration of GSM technology enhances the system's responsiveness and remote management capabilities, allowing administrators to monitor gate activities in real time and respond promptly to any security incidents or anomalies.

The Effectiveness of a Comprehensive threat Mitigation Framework in NETWORKING: A Multi-Layered Approach to Cyber Security

The rapidly evolving landscape of cyber threats necessitates robust and adaptable strategies to safeguard networking. This study explores the development and evaluation of a comprehensive framework for threat mitigation, integrating advanced methodologies from cybersecurity, risk management, and threat intelligence. Leveraging a mixed-methods approach, including surveys and secondary data analysis, the research assesses current practices and identifies critical gaps in existing frameworks. The proposed multi-layered defense mechanism incorporates proactive and reactive measures, aligning real-time threat intelligence with sophisticated incident response strategies. Key components such as anomaly detection systems, employee training, and continuous security audits are highlighted as essential elements of the framework. Through extensive validation, including empirical tests and case studies, the framework demonstrates its efficacy in enhancing organizational resilience against complex cyber threats. The findings provide valuable insights into the practical application of integrated cybersecurity measures, offering a scalable and flexible solution tailored to the dynamic nature of digital security challenges. This study addresses the critical need for an adaptable and holistic approach to threat mitigation, contributing to the field of cybersecurity with actionable strategies for managing and mitigating digital threats

Unveiling surgical expertise through machine learning in a novel VR/AR spinal simulator: A multilayered approach using transfer learning and connection weights analysis

BackgroundVirtual and augmented reality surgical simulators, integrated with machine learning, are becoming essential for training psychomotor skills, and analyzing surgical performance. Despite the promise of methods like the Connection Weights Algorithm, the small sample sizes (small number of participants (N)) typical of these trials challenge the generalizability and robustness of models. Approaches like data augmentation and transfer learning from models trained on similar surgical tasks address these limitations. ObjectiveTo demonstrate the efficacy of artificial neural network and transfer learning algorithms in evaluating virtual surgical performances, applied to a simulated oblique lateral lumbar interbody fusion technique in an augmented and virtual reality simulator. DesignThe study developed and integrated artificial neural network algorithms within a novel simulator platform, using data from the simulated tasks to generate 276 performance metrics across motion, safety, and efficiency. Innovatively, it applies transfer learning from a pre-trained ANN model developed for a similar spinal simulator, enhancing the training process, and addressing the challenge of small datasets. SettingMusculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Montreal, Canada. ParticipantsTwenty-seven participants divided into 3 groups: 9 post-residents, 6 senior and 12 junior residents. ResultsTwo models, a stand-alone model trained from scratch and another leveraging transfer learning, were trained on nine selected surgical metrics achieving 75 % and 87.5 % testing accuracy respectively. ConclusionsThis study presents a novel blueprint for addressing limited datasets in surgical simulations through the strategic use of transfer learning and data augmentation. It also evaluates and reinforces the application of the Connection Weights Algorithm from our previous publication. Together, these methodologies not only enhance the precision of performance classification but also advance the validation of surgical training platforms.

Pre-Earthquake Oscillating and Accelerating Patterns in the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) before the 2022 Luding (China) Ms6.8 Earthquake

The coupling processes among the lithosphere, atmosphere, and ionosphere (LAI) during the earthquake preparation phase are still an open scientific debate. Comprehensive LAI coupling effects around the 2022 Ms6.8 Luding earthquake in China are investigated with a multi-parameter and multi-layer approach, including the b-value, revised accelerated moment release, Earth resistivity, ELF magnetic field emissions, atmospheric electric field, surface temperature, foF2 from ionosonde, GNSS TEC, Ne and magnetic field from CSES and Swarm satellites, and energetic electrons from CSES and NOAA satellites. It is found that the anomalies start from the lithospheric parameters as Earth resistivity and b-values 1–2 years before to reflect the local stress loading in the seismic region, then the ionospheric and atmospheric disturbances occur and accelerate −50 days before and −15 days before, and finally the electrons precipitate a few days before. The simultaneous perturbations in LAI illustrate the thermodynamic coupling channel, such as on 24 August, −12 days before. Meanwhile, the abundant developed ionospheric anomalies without atmospheric disturbances demonstrate the electromagnetic coupling way from the lithosphere to the ionosphere directly. Finally, the results demonstrate a two-way model of LAIC: one way is characterized by a slow chain of processes, of thermodynamic nature, starting from the ground and proceeding to the above atmosphere and ionosphere, showing an exponential trend in the cumulative number of anomalies; the second way is characterized by oscillating electromagnetic coupling between the lithosphere and ionosphere, showing intermittent fluctuations in the corresponding cumulative number of anomalies.

Numerical investigation of turbulence effect on flight trajectory of spherical windborne debris: A multi-layered approach

Accurate modeling of the turbulent wind field is a crucial component of risk analysis for structures to windborne debris damage. Existing studies typically simplify the complexities of wind turbulence, and the potential influence on the accuracy of debris flight modeling has not been systematically demonstrated. This study takes a multi-layered approach to numerically simulate the flight trajectory of spherical debris in a turbulent wind field. Complexities are incrementally added to the simulated wind field to systematically investigate the influence of spatial correlation and non-Gaussian features of turbulence on debris flight behavior. The sensitivity of debris flight behavior to turbulent wind features will inform the design of debris flight tracking wind tunnel tests and building façade debris vulnerability modeling efforts.

Antiviral drugs preformulation: thermal energy effects by multilayer perceptron network approach and Hirshfeld surface properties

Antiviral drugs preformulation: thermal energy effects by multilayer perceptron network approach and Hirshfeld surface properties