Inherent Constraints Research Articles

Development of a Natech Social Vulnerability Index: A Comprehensive Multi-Hazard Risk Assessment for a Case Study in Colombia

Natech refers to complex scenarios where secondary technological accidents are triggered by natural disasters, having the potential to cause severe societal loss, especially in developing countries such as Colombia, potentially due to its vulnerable social system (Ran, MacGillivray, Gong, & Hales, 2020) (Porto & Freitas, 2003). However, one major identified research gap is the lack of a specific approach to assess the Natech social vulnerabilities. A popular vulnerability assessment tool: social vulnerability index, widely used for natural and industrial hazards, shows promising usability for Natech vulnerability assessment. The current study has proposed a novel methodology to assess Natech social vulnerability by establishing a Natech social vulnerability index based on identification of the Natech scenarios and investigation of multi-hazard intercorrelations. To validate the introduced methodology, the author utilized rainfall-induced, landslide-triggered chemical Natech incidents in Colombia as a representative case. The study embarked on identifying distinct hazards present in varying Natech situations to determine which social elements amplify vulnerability. Subsequently, composite indicators, or social vulnerability indices, were formulated to quantitatively amalgamate these pinpointed social elements. These were mapped within diverse vulnerability conceptual paradigms and then refined and scrutinized using Structural Equation Modelling (SEM). The outcomes, portrayed via a Geological Information System (GIS), predominantly resonate with preceding studies. Nonetheless, certain fit index values signal potential ambiguities in model alignment. The paper concludes into the inherent challenges and constraints of the devised methodology. Grounded on these analyses, policy recommendations tailored for the Colombian government’s vulnerability assessment have been proposed. These include advisories on refining the national census, databanks, and Natech reports. Additionally, pivotal research directions have been highlighted to address and mitigate the acknowledged research shortfalls in impending studies. In summary, this research, encompassing its innovative methodology, findings, and advisories, serves as a pivotal reference for succeeding investigations on social vulnerability assessments related to Natech and other multi-hazard events.

Herbal Cigarette Sales Information System Case Study at Stockis Sin Sriwijaya

There is still space for improvement in the application and usage of technology and information in the sales business, as indicated by the significant percentage of clients who continue to make manual purchases. An example would be the present Sriwijaya Herbal Sin Cigarettes retailer. The Sin Cigarette Herbal Sriwijaya stockist shop employs a traditional sales system where consumers physically visit the shop to make purchases. This research aims to enhance consumer convenience by developing a sales information system for herbal cigarettes, enabling online access to sales. This study employs a descriptive research methodology and a waterfall system development approach. This approach involves sequential steps, including needs analysis, design, code production, testing, and support. Data collection from the users was followed by the design and development stage of the program, employing the PHP programming language for implementation. Subsequently, an evaluation was conducted on the functioning system. The outcome is a sales information system that is anticipated to possess the capability to process data into high-quality information efficiently, seamlessly integrate various components, and serve as a repository for data storage. With the existence of this sales information system for herbal cigarettes, the processing of sales data has been computerized automatically, enabling automated report generation. This study introduces an internet-based sales information system for the Sriwijaya Herbal Cigarette Stockist Shop. It replaces traditional sales procedures, expands market reach, streamlines report generation, provides comprehensive buyer information, and maximizes profitability. The innovation employs the waterfall system development methodology and the PHP programming language to enhance retail operations' effectiveness and efficiency. An inherent constraint of this study is the need for the online-based sales information system to address all challenges the Sriwijaya Herbal Cigarette Stockist Shop faces, including technological infrastructure and user proficiency issues. Reliable internet connectivity is crucial for the system's effectiveness, which may require improvements in certain regions. Additionally, employees and customers may require additional training to use the new systems proficiently, which should be considered in terms of time and resources for this research.

Causalities-multiplicity oriented joint interval-trend fuzzy information granulation for interval-valued time series multi-step forecasting

Interval-valued time series (ITSs) multi-step forecasting research is still in its infancy. Two cruces here lie in counterintuitive or conservative nature of semantic descriptors for ITSs, and disregard for multiplicity of causalities resulting from uncertainty in causalities between data or between trends within a set of interval-valued data. In this paper, we put forth a type of joint interval-trend fuzzy information granules, which takes non-loss of within-interval information as the main design criterion. A modified fuzzy information granulation method carries originality in portraying intuitive and accurate interval-trends, directly linked with inherent relational constraints such as lower bound data should not be greater than upper bound data. Furthermore, we formulate a legible format of multi-factor fuzzy IF-THEN rules, which exhibits interesting interpretations to causalities between interval-trends at a higher level of multiplicity. The forecasting process is fuzzy rules-based, resulting in wise results by calculating rule firing weights. Thus, we develop a well construct of accuracy and interpretability for multi-step forecasting of ITSs, manifested in: (a) reducing cumulative errors by operating at the granular level, and (b) perceiving interval-trends in an intelligible manner and emphasizing multiple causalities via transparent fuzzy logic inference. Experimental results convincingly confirm the validity of the model.

Adaptive Bounded Bilinear Control of a Parallel‐Flow Heat Exchanger

ABSTRACTThis work investigates the adaptive constrained control design for a parallel‐flow heat exchanger represented by a system of two coupled linear first‐order hyperbolic partial differential equations (PDEs). This system incorporates structured uncertainty involving unknown in‐domain parameters that characterize neglected dynamics in the heat exchanger model. These parameters may encompass unmodeled heat transfer phenomena, variations in fluid properties, and modeling simplifications. The objective is to regulate the internal fluid outlet temperature to track a desired reference trajectory by adjusting the external fluid velocity. Due to inherent physical constraints, this manipulated variable is upper and lower‐bounded. Accordingly, the control problem is bounded and bilinear. Using the set‐invariance principle and an energy‐like framework, we first develop a bounded state‐feedback controller. Then, since the measurements are considered only at the boundaries, we propose an adaptive boundary observer using a swapping scheme and a recursive least squares identifier. The proposed adaptive observer provides online estimates of the distributed state and the unknown parameters. Next, the state‐feedback controller is associated with the boundary observer and parameter identifier, and the exponential stability of the closed‐loop system is guaranteed using Lyapunov's stability theory. Finally, we provide numerical simulations to demonstrate the efficiency of the proposed control scheme.

Construction of interpretable ensemble learning models for predicting bioaccumulation parameters of organic chemicals in fish

Accurate prediction of bioaccumulation parameters is essential for assessing exposure, hazards, and risks of chemicals. However, the majority of prediction models on bioaccumulation parameters are individual models based on a single algorithm and lack model interpretation, resulting in unsatisfactory prediction accuracy due to inherent constraints of the algorithm and weak interpretability. Ensemble learning (EL) that combine multiple algorithms, coupled with SHapley Additive exPlanation (SHAP) method, may overcome the limitations. Herein, EL models were constructed for three bioaccumulation parameters using datasets covering 2496 chemicals. The EL models demonstrated superior prediction accuracy compared to both individual models developed in this study and those from previous research, achieving a coefficient of determination of up to 0.861 on the validation sets. Applicability domains were characterized using a structure-activity landscape-based (abbreviated as ADSAL) methodology. The optimal EL models, together with the ADSAL, were successfully used to predict bioaccumulation parameters for 4374 chemicals included in the Inventory of Existing Chemical Substances of China. Model interpretation using the SHAP method offered insight into key features influencing bioaccumulation potential, including hydrophobicity, water solubility, polarizability, ionization potential, weight, and volume of molecules. Overall, the study provides data and models to support the sound management and risk assessment of chemicals.

Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning

Superalloys are indispensable materials for the fabrication of high-temperature components in aircraft engines. The discovery of a novel class of γ/γ′ Co-Al-W alloys has ignited a surge of interest in Co-based superalloys, with the aspiration to transcend the inherent constraints of their Ni-based counterparts. However, the conventional methodologies utilized in the design and advancement of new γ/γ′ Co-based superalloys are frequently characterized by their laborious and resource-intensive nature. In this study, we employed a coupled Density Functional Theory (DFT) and machine learning (ML) approach to predict and analyze the stability of the crucial γ′ phase, which is instrumental in expediting the discovery of γ/γ′ Co-based alloys. A dataset comprised of thousands of reliable formation (Hf) and decomposition (Hd) energies was obtained through high-throughput DFT calculations. Through regression model selection and feature engineering, our trained Random Forest (RF) model achieved prediction accuracies of 98.07% for Hf and 97.05% for Hd. Utilizing the well-trained RF model, we predicted the energies of over 150,000 ternary and quaternary γ′ phases within the Co-Ni-Fe-Cr-Al-W-Ti-Ta-V-Mo-Nb system. The energy analyses revealed that the presence of Ni, Nb, Ta, Ti, and V significantly reduced the Hf and the Hd of γ′, while Mo and W deteriorate the stability by increasing both energy values. Interestingly, although Al reduces the Hf, it increases Hd, thereby adversely affecting the stability of γ′. Applying domain-specific screening based on our knowledge, we identified 1049 out of >150,000 compositions likely to form stable γ′ phases, predominantly distributed across 11 Al-containing systems and 25 Al-free systems. Combining the analysis of CALPHAD method, we experimentally synthesized two new Co-based alloys with γ/γ′ dual-phase microstructures, corroborating the reliability of our theoretical prediction model.

Nanoimprinting and backside ultraviolet lithography for fabricating metal nanostructures with higher aspect ratio

This paper introduces an innovative approach to increasing the aspect ratio of metal nanostructures fabricated using nanoimprint lithography (NIL). Although conventional NIL and metal lift-off processes can fabricate metal nanostructures, the achievable aspect ratio is often limited by the inherent constraints of NIL. In this study, we demonstrate that for an ultraviolet (UV) transparent substrate, metal nanostructures patterned via NIL can serve as a photomask. A negative-tone photoresist (PR) layer was then deposited on top of the patterned metal nanostructures. By illuminating the substrate from the backside with UV light and subsequently developing the PR, PR structures complementary and self-aligned to the metal layer were obtained. This enabled a second round of metal deposition and lift-off, thereby increasing the height of the metal structures and enhancing the aspect ratio. Experimentally, we demonstrated that this method can improve the aspect ratio from less than 1.0 to as high as 2.1. This paper also addresses the further developments and potential applications of this technique.

Enhancing Security in Low-power Wide-area (LPWA) IoT Environments: The Role of HSM, Tamper-proof Technology, and Quantum Cryptography

Low-power wide-area (LPWA) networks are integral to expanding Internet of Things (IoT) applications, offering extensive coverage with low power consumption. However, these networks face significant security challenges due to their widespread deployment and inherent constraints. In order to provide secure services in an LPWA IoT environment, important information stored in IoT devices (encryption keys, device unique numbers, etc.) must be safely protected from external hacking or theft by physical access, and it is necessary to develop tamper-proof technology to enhance physical security. Meanwhile, with so many ruggedized IoT devices processing and transmitting sensitive information, security systems are essential to protect the integrity and privacy of IoT data. This paper explores the critical role of hardware security modules (HSMs), tamper-proof technology, and quantum cryptography in enhancing the physical, network, and data security of LPWA IoT environments. We propose operational strategies for HSMs, tamper-proof technology in ruggedized LPWA IoT settings, and a quantum key distribution (QKD)-based IPsec solution for robust network and data security.

Exploring Inherent Consistency for Semi-Supervised Anatomical Structure Segmentation in Medical Imaging.

Due to the exorbitant expense of obtaining labeled data in the field of medical image analysis, semi-supervised learning has emerged as a favorable method for the segmentation of anatomical structures. Although semi-supervised learning techniques have shown great potential in this field, existing methods only utilize image-level spatial consistency to impose unsupervised regularization on data in label space. Considering that anatomical structures often possess inherent anatomical properties that have not been focused on in previous works, this study introduces the inherent consistency into semi-supervised anatomical structure segmentation. First, the prediction and the ground-truth are projected into an embedding space to obtain latent representations that encapsulate the inherent anatomical properties of the structures. Then, two inherent consistency constraints are designed to leverage these inherent properties by aligning these latent representations. The proposed method is plug-and-play and can be seamlessly integrated with existing methods, thereby collaborating to improve segmentation performance and enhance the anatomical plausibility of the results. To evaluate the effectiveness of the proposed method, experiments are conducted on three public datasets (ACDC, LA, and Pancreas). Extensive experimental results demonstrate that the proposed method exhibits good generalizability and outperforms several state-of-the-art methods.

R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution.

High-resolution microscopy hyperspectral (HS) images can provide highly detailed spatial and spectral information, enabling the identification and analysis of biological tissues at a microscale level. Recently, significant efforts have been devoted to enhancing the resolution of HS images by leveraging high spatial resolution multispectral (MS) images. However, the inherent hardware constraints lead to a significant distribution gap between HS and MS images, posing challenges for image super-resolution within biomedical domains. This discrepancy may arise from various factors, including variations in camera imaging principles (e.g., snapshot and push-broom imaging), shooting positions, and the presence of noise interference. To address these challenges, we introduced a unique unsupervised super-resolution framework named R2D2-GAN. This framework utilizes a generative adversarial network (GAN) to efficiently merge the two data modalities and improve the resolution of microscopy HS images. Traditionally, supervised approaches have relied on intuitive and sensitive loss functions, such as mean squared error (MSE). Our method, trained in a real-world unsupervised setting, benefits from exploiting consistent information across the two modalities. It employs a game-theoretic strategy and dynamic adversarial loss, rather than relying solely on fixed training strategies for reconstruction loss. Furthermore, we have augmented our proposed model with a central consistency regularization (CCR) module, aiming to further enhance the robustness of the R2D2-GAN. Our experimental results show that the proposed method is accurate and robust for super-resolution images. We specifically tested our proposed method on both a real and a synthetic dataset, obtaining promising results in comparison to other state-of-the-art methods.

Segmental-porosity regulation of nanochannel membranes with anomalously improved ion selectivity for enhanced osmotic power generation

Nanochannel-based osmotic energy conversion can directly convert salinity gradient energy between solutions with different ion concentrations into electric energy. In the energy conversion process, the high-concentration side of the nanochannel membrane has high ion permeability but low ion selectivity, whereas the low-concentration side exhibits opposite properties, which is the inherent limitation of osmotic power. However, the relationship between ion selectivity and permeability is unclear, which hinders the improvement of the output power density. This paper reports a segmental-porosity regulation method to break through the inherent constraints of the ion selectivity–permeability trade-off. The nanochannel membrane was divided into three regions along the ion transport direction as upstream, midstream, and downstream. The porosities of these three regions were separately regulated to optimize the structures for ion selectivity–permeability matching. When a high porosity was adopted for the downstream region and a small and mediate porosity combination was used for the upstream and midstream regions, the highest output power density was achieved, which was 78.44% higher than that of a straight nanochannel with homogeneous porosity under a 100-fold concentration ratio. The outstanding osmotic power generation performance of the optimized segmental structure was attributed to the alleviated ion concentration polarization and unexpectedly improved ion selectivity. Finally, the process and underlying mechanisms of segmental-porosity regulation are also proposed. This paper reports a novel methodology for designing the structure and porosity of nanochannel membranes to enhance osmotic power generation.

Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration Framework

Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers’ fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD Logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec .

An Integrated Multi-Model Framework Utilizing Convolutional Neural Networks Coupled with Feature Extraction for Identification of 4mC Sites in DNA Sequences

N4-methylcytosine (4mC) is a chemical modification that occurs on one of the four nucleotide bases in DNA and plays a vital role in DNA expression, repair, and replication. It also actively participates in the regulation of cell differentiation and gene expression. Consequently, it is important to comprehend the role of 4mC in the epigenetic regulation for revealing the complications of the gene expression and their associated governing cellular operations. However, the inherent resource requirements and time constraints of the experimental procedure, present challenges to the cellular culture process. While data-driven methodologies present promising solutions to mitigate the demand for extensive experimental efforts, their performance relies on the suitability and existence of high-quality data. This study presents a multi-model framework that integrates convolutional neural network (CNN) with the distributed k-mer and embedding feature extraction techniques to enhance the identification of 4mC sites in DNA sequences. The integration of k-mers ensures the effective representation of the local sequence patterns, while the utilization of embedding enables a more holistic encoding by considering the broader context and semantics of the sequence data. Following the initial step, the obtained distributed representation of the DNA sequence seamlessly enters the CNN, triggering a crucial convolution operation wherein a set of adaptable filters systematically convolves across the sequence to detect vital local patterns. The proposed integrated multi-model framework was applied to six publicly available datasets and evaluated against the cutting-edge 4mCPred, 4mCCNN, iDNA4mC, Meta-4mCpred, DeepTorrent, 4mCPred-SVM, and DMKL-HFIS methods. The evaluation was based on accuracy, specificity, sensitivity, and Matthews Correlation Coefficient. The results demonstrated that the proposed multi-model framework outperformed the state-of-the-art methods, as well as one-hot encoding and the hybrid of one-hot & TNC features, in accurately identifying 4mC sites.

Are acute hospital trust mergers associated with improvements in the quality of care?

This study aims to assess the extent to which acute hospital trust mergers in England are associated with quality improvements. We apply an event study design using difference-in-difference (DID) and coarsened exact matching to compare the before-and-after performance of eight mergers from 2011 to 2015. We find little evidence that mergers contribute to quality improvements other than some limited increases in the proportion of patients waiting a maximum of 18weeks from referral to treatment. We postulate that financial incentives and political influence could have biased management effort towards waiting time measures. Inherent sample size constraints may limit generalisability. Merger costs and complexity mean they are unlikely to offer an efficient strategy for helping to clear elective care backlogs. We recommend further research into causal mechanisms to help health systems maximise benefits from both mergers and emerging models of hospital provider collaboration. This paper is the first to study the quality impact of a new wave of acute hospital mergers taking place in the English National Health Service from 2011 onwards, applying a group-time DID estimator to account for multiple treatment timings.

Localization of underground pipeline intrusion sources using cross-correlation CNN: application in pile-driving model test

Preserving the structural integrity of critical infrastructure systems necessitates a heightened focus on fortifying the protection of underground pipelines. To this end, this paper presents an innovative approach, namely the Multi-Sample Joint Localization Method (MSJLM) utilizing Cross-Correlation Convolutional Neural Networks (CC-CNN), aimed at precisely localizing intrusion sources in the vicinity of underground pipelines. Traditional techniques for detecting and pinpointing pipeline intrusions primarily rely on a single sensor monitoring point, which is susceptible to inherent errors and constraints. In contrast, the MSJLM proposed in this study leverages data from multiple samples, integrating diverse data sources through correlation analyses to elevate precision and reliability. The utilization of the CC-CNN framework for processing aggregated data has proven highly successful in extracting spatial features and identifying patterns. Furthermore, the effectiveness of this method is corroborated through validation via a pile-driving model test.

Validation of gut transit rate assessment methodology and the mitigation of sampling stress in Atlantic salmon, Salmo salar

Gut transit rate is an important component of the digestive process in fish, particularly in the context of aquaculture, where factors such as rearing temperature and dietary formulation impact on-farm performance. As such, developing and validating research methodologies that explore these critical physiological responses in fish is vital in the context of precision aquaculture practices. However, there are inherent methodological constraints of gut transit rate assessments, particularly when assessing the effect of sampling on the stress response of fish. In light of this, the present study aimed to assess the gut transit rate and plasma cortisol levels of Atlantic salmon (Salmo salar) subjected to the more commonly applied, repeated sampling model that results in multiple tanks disturbances (e.g., 2, 4, 8, 16 and 24 h post-feed) in comparison to one requiring only a singular tank disturbance at one of the designated time points. An inert beaded marker was integrated into diet formulations to determine the transit rate of digesta in three major gastrointestinal tract regions: the stomach, mid-intestine, and distal intestine over a period of 48 h following the provision of food. A significantly higher plasma cortisol concentration was recorded in fish subjected to a repeated disturbance, particularly at 16 h post feed (22.99 ± 6.88 ng mL−1), compared to fish sampled from tanks subjected to a single sampling event (11.35 ± 0.92 ng mL−1). Despite this, gut transit rate was generally unaffected by sampling design over the duration of the study with both groups of fish displaying a similar rate of gut transit. This suggests that repeated tank sampling (i.e. sampling multiple fish from the same tank at multiple timepoints) is suitable to assess gut transit rate in fish as it does not significantly confound gut transit rate assessments. It is envisioned that this data will contribute to the growing understanding of the digestive process of Atlantic salmon and the relationship with stress response in fish and experimental design. Moreover, it should strengthen future studies aimed at assessing key digestive processes in cultured species.

Real-sea validation of a model predictive controller's inherent robustness for medium-scale unmanned trimaran heading

The development of medium-to large-scale and high-performance unmanned surface vehicles (USVs) is a burgeoning trend in intelligent marine systems. The heading controller is crucial for USVs to execute diverse missions, especially given their inherent underactuation characteristics and constraints. Recent efforts have focused on enhancing the robustness of controllers against external disturbances and employed two main strategies: one converges the control error to a bounded residual set through robust modifications, while the other eliminates the error by modelling disturbances. Notably, these enhancements have primarily catered to small USVs, where disturbances significantly impact their manoeuvrability, necessitating such robust control strategies. This focus has somewhat overshadowed the inherent robustness of closed-loop control systems. Compared with small USVs, medium-to large-scale USVs are less affected by external disturbances, despite undertaking more complex missions. Leveraging the intrinsic robustness of controllers presents an opportunity to simplify controller design, thereby reallocating computational resources towards enhancing mission capabilities. Model Predictive Control (MPC) has attracted significant attention recently, and its receding horizon and optimality theoretically provides a new level of inherent robustness, which remains under-explored in real sea. This paper focuses on the inherent robustness of MPC in managing the heading of a medium-scale unmanned trimaran subjected to the thrust angle and angular velocity constraints. A model predictive controller considering the constraints is designed based on the identified Nomoto model and the asymptotic stability is ensured with a terminal cost. Conducted real-sea experiments and comparative analyses with a Proportional-Integral-Derivative (PID) controller, the most widespread and dominant control algorithm in practical USV engineering, underscore the superiority of MPC in maintaining satisfactory closed-loop performance. Furthermore, the MPC controller is also successfully applied to real-sea path-following missions and demonstrated good tracking performance in various sea conditions ranging from level 3 to 5 and wind speeds spanning from level 6 to 8. This validation opens up new avenues for motion control strategies in the evolving landscape of larger-scale USVs.

The SME R&D intensity and product innovation relationship: the mediating role of quality management in the context of a developing country

SMEs engage in product innovation despite their inherent resource constraints, lack of financial slack, and the under‐development of competitively viable strategic configurations around the globe. While progress has been made in identifying the antecedents and capabilities attributed to successful innovation outcomes and the performance of these firms, there remain disparate and often paradoxical observations on the factors that affect SME innovation performance across geographies (i.e. developed vs developing countries) and operational contexts (R&D intensity and quality management practices). This study collected data from 241 resource‐constrained small and medium‐sized enterprises (SMEs) in a developing country (Ghana) to contribute to this debate. The results of structural equation modeling show that quality management mediates the relationship between R&D intensity and product innovation. The results also reveal the effects of knowledge integration and financial slack on the relationship between R&D intensity and product innovation, with a high level of knowledge integration enhancing the effect but a high financial slack hinders it. This study sheds light on a broader range of contextual and financial variables when seeking contingencies of SME product innovation performance within theory and practice.

Enhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode control

The rise in robotics technology has increased interest in ThreeWheeled Mobile Robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraints, which restrict independent movement in all directions. Factors like sensor noise, nonlinear system dynamics, and uncertain system parameters also add to the complexity of controlling TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between Backstepping Sliding Mode Control (BSMC) and Backstepping Fuzzy Sliding Mode Control(BFSMC) across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improvements in performance across various trajectory types when considering the integral time absolute error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. Integrating the Extended Kalman Filter further improves accuracy, particularly in noisy conditions.

The Intersection of Machine Learning and Wireless Sensor Network Security for Cyber-Attack Detection: A Detailed Analysis.

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML's potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment.