Complex Analysis Research Articles

Trends in tourism supply and demand in the micro-regions of the Southern Great Plain region

According to the Statistical Office of the European Union (EUROSTAT), the Southern Great Plain region was one of the poorest regions in the European Union in 2022, and tourism development could be one of the breakthrough points. The Southern Great Plain region has a special role for spas and related health tourism, and its rich historical heritage provides a good basis for cultural tourism. The main purpose of the research was to examine the tourism situation in the micro-regions of the Southern Great Plain region, using a complex tourism performance indicator and cluster analysis. Based on the results of the complex tourism performance indicator, the Gyulai micro-region performed the best in the region. The results of the indicator show that tourism is not only concentrated in the tourist areas defined by the government in the region. These micro-region are thus excluded from the target areas for tourism investment. In the future, it would be important to examine the situation of areas that are not included in the defined tourism areas and to integrate them into the tourism target areas, since tourism is a means of balanced development of the areas and can contribute to the convergence of disadvantaged areas.

Complexity Analysis and Control of Output Competition in a Closed-Loop Supply Chain of Cross-Border E-Commerce Under Different Logistics Modes Considering Chain-to-Chain Information Asymmetry.

To investigate the dynamic complexity of chain-to-chain output decisions in a closed-loop supply chain system of cross-border e-commerce (CBEC), this study decomposes the system into four product-market (PM) chains, based on the e-commerce platform's information-sharing strategy and the manufacturer's selected logistics mode (direct mail or bonded warehouse). By combining game theory with complex systems theory, discrete dynamic models for output competition among PM chains under four scenarios are constructed. The Nash equilibrium solution and stability conditions of the models are derived according to the principles of nonlinear dynamics. The stability of the model under the four scenarios, as well as the impacts of the initial output level and comprehensive tax rates on the stability and stability control of the system, are analyzed using numerical simulation methods. Our findings suggest that maintaining system stability requires controlling the initial output levels, the output adjustment speeds, and tariff rates to remain within specific thresholds. When these thresholds are exceeded, the entropy value of the model increases, and the system outputs decisions to enter a chaotic or uncontrollable state via period-doubling bifurcations. When the output adjustment speed of the four PM chains is high, the direct-mail logistics mode exhibits greater stability. Furthermore, under increased tariff rates for CBEC, the bonded warehouse mode has a stronger ability to maintain stability in system output decisions. Conversely, when the general import tax rate increases, the direct-mail mode demonstrates better stability. Regardless of the logistics mode, the information-sharing strategy can enhance the stability of system output decisions, while increased e-commerce platform commission rates tend to reduce stability. Interestingly, the use of a non-information-sharing strategy and the direct-mail logistics mode may be more conducive to increasing the profit levels of overseas manufacturers. Finally, the delayed feedback control method can effectively reduce the entropy value, suppress chaotic phenomena in the system, and restore stability to output decisions from a fluctuating state.

Integrating Scenario Analysis and Participatory Modeling to Generate Plausible Future Narratives for Water Resources: A Case Study in the Middle Rio Grande River Basin

In addressing complex water management issues, participatory modeling (PM) and scenario analysis (SA) have emerged as pivotal tools for fostering stakeholder engagement, social learning, and collaborative decision-making. This study explores the efficacy of integrating PM and SA within a series of online workshops focused on the Middle Rio Grande River Basin. Data were collected using a mixed-methods approach, including surveys, workshop transcripts, and participant outputs from workshop activities. Through these workshops, participants from diverse backgrounds engaged in collaborative scenario-building exercises, interactive discussions, and hands-on simulations using the SWIM 2.0 model, fostering shared learning and deeper insights into regional water challenges. Our findings highlight the importance of group-level activities in enhancing participants’ understanding, engagement, and trust in modeling tools, which can lead to more informed decision-making processes. The workshops demonstrated significant improvements in SA completion and a deeper connection between participants and policy discussions. However, challenges remain in bridging the gap between SA and active decision-making, particularly in contexts where participants do not perceive themselves as decision-makers. This study underscores the potential of participatory approaches to address complex environmental issues, while also identifying areas for further refinement to enhance stakeholder empowerment and tool usability in collaborative settings.

High-throughput platform for label-free sorting of 3D spheroids using deep learning

End-stage liver diseases have an increasing impact worldwide, exacerbated by the shortage of transplantable organs. Recognized as one of the promising solutions, tissue engineering aims at recreating functional tissues and organs in vitro. The integration of bioprinting technologies with biological 3D models, such as multi-cellular spheroids, has enabled the fabrication of tissue constructs that better mimic complex structures and in vivo functionality of organs. However, the lack of methods for large-scale production of homogeneous spheroids has hindered the upscaling of tissue fabrication. In this work, we introduce a fully automated platform, designed for high-throughput sorting of 3D spheroids based on label-free analysis of brightfield images. The compact platform is compatible with standard biosafety cabinets and includes a custom-made microscope and two fluidic systems that optimize single spheroid handling to enhance sorting speed. We use machine learning to classify spheroids based on their bioprinting compatibility. This approach enables complex morphological analysis, including assessing spheroid viability, without relying on invasive fluorescent labels. Furthermore, we demonstrate the efficacy of transfer learning for biological applications, for which acquiring large datasets remains challenging. Utilizing this platform, we efficiently sort mono-cellular and multi-cellular liver spheroids, the latter being used in bioprinting applications, and confirm that the sorting process preserves viability and functionality of the spheroids. By ensuring spheroid homogeneity, our sorting platform paves the way for standardized and scalable tissue fabrication, advancing regenerative medicine applications.

Law of Ukraine “on administrative procedure”: new terminology and legal value of the law

Goal. The purpose of the article is to analyze the key terms of the Law of Ukraine “On Administrative Procedure”, especially new for Ukrainian legislation, which will contribute to ensuring legal certainty, unity of understanding of such categories for the purposes of administrative and judicial practice. It is worth noting that although the purpose of this work is devoted to the outline of the main terms of the Law of Ukraine “On Administrative Procedure”, they are important for Ukrainian legislation as a whole, given the fundamental nature of this law. Method. The methodology includes a complex analysis of the issues, theoretical elaboration of the current legal material on the raised issue and formation of relevant conclusions and recommendations. During the study, the following methods of scientific knowledge were used: terminological, systemic-structural, comparative-legal, and the method of analysis. The results. Based on the results of the research, the importance of ensuring proper legal understanding of the provisions of the Law of Ukraine “On Administrative Procedure” for its further effective legal application was revealed, the main novelties in terminology were analyzed and the importance of further scientific work on the analysis of both the provisions of the Law itself and the results of its legal application was noted. Separately, the historical component of the adoption of the Law of Ukraine “On Administrative Procedure” is briefly described, and attention is drawn to the relevant regulatory documents that became the “legal foundation” for its adoption. Emphasis is placed on the importance of legal analysis of the provisions of this legislative act, including monitoring the results of its application in practice. The presented analysis of individual terms of the normative legal act proved that legal regulation in the field of administrative procedure has deep roots, which, in turn, can serve as a basis for work in the direction of increasing theoretical studies in this field. Scientific news. The study emphasized the importance of the adoption of the Law of Ukraine “On Administrative Procedure”, its significance both for the legal system of Ukraine as a whole and separately in the field of administrative procedure. The article analyzes the main provisions of the Law of Ukraine “On Administrative Procedure” from the point of view of novelties in terminology. Emphasis is placed on the fact that the procedure of interaction between the state, on the one hand, and citizens and business, on the other, should become unified, effective and, of course, transparent, with an orientation to the interests of the latter. It was found that the implementation of the general administrative procedure at the legislative level has undoubtedly brought Ukraine closer to the standards of the European Union, because the “right to proper administration” is an integral part of a highly developed legal state. Practical significance. The result of this work is aimed at further encouraging an in-depth theoretical study of the field of administrative procedure as a “novel of Ukrainian legislation” and finding ways to effectively implement the Law of Ukraine “On Administrative Procedure” into the legal realities of our country today. In the process of consideration of amendments to the legislation, a generalization of the key terminology was carried out, the understanding of which will make it possible to effectively implement the regulatory act in practice.

Photo-Excited Carrier Dynamics in Ammonothermal Mn-Compensated GaN Semiconductor.

We investigated the carrier dynamics of ammonothermal Mn-compensated gallium nitride (GaN:Mn) semiconductors by using sub-bandgap and above-bandgap photo-excitation in a photoluminescence analysis and pump-probe measurements. The contactless probing methods elucidated their versatility for the complex analysis of defects in GaN:Mn crystals. The impurities of Mn were found to show photoconductivity and absorption bands starting at the 700 nm wavelength threshold and a broad peak located at 800 nm. Here, we determined the impact of Mn-induced states and Mg acceptors on the relaxation rates of charge carriers in GaN:Mn based on a photoluminescence analysis and pump-probe measurements. The electrons in the conduction band tails were found to be responsible for both the photoconductivity and yellow luminescence decays. The slower red luminescence and pump-probe decays were dominated by Mg acceptors. After photo-excitation, the electrons and holes were quickly thermalized to the conduction band tails and Mg acceptors, respectively. The yellow photoluminescence decays exhibited a 1 ns decay time at low laser excitations, whereas, at the highest ones, it increased up to 7 ns due to the saturation of the nonradiative defects, resembling the photoconductivity lifetime dependence. The fast photo-carrier decay time observed in ammonothermal GaN:Mn is of critical importance in high-frequency and high-voltage device applications.

Multivariate distance dispersion entropy: a complexity analysis method capturing intra- and inter-channel signal variations for multichannel data

Multivariate distance dispersion entropy: a complexity analysis method capturing intra- and inter-channel signal variations for multichannel data

A Temporal Convolutional Neural Network Fusion Attention Mechanism Runoff Prediction Model Based on Dynamic Decomposition Reconstruction Integration Processing

Accurate and reliable runoff forecasting is of great significance for hydropower station operation and watershed water resource allocation. However, various complex factors, such as climate conditions and human activities, constantly affect the formation of runoff. Runoff data under changing environments exhibit highly nonlinear, time-varying, and stochastic characteristics, which undoubtedly pose great challenges to runoff prediction. Under this background, this study ingeniously merges reconstruction integration technology and dynamic decomposition technology to propose a Temporal Convolutional Network Fusion Attention Mechanism Runoff Prediction method based on dynamic decomposition reconstruction integration processing. This method uses the Temporal Convolutional Network to extract the cross-temporal nonlinear characteristics of longer runoff data, and introduces attention mechanisms to capture the importance distribution and duration relationship of historical temporal features in runoff prediction. It integrates a decomposition reconstruction process based on dynamic classification and filtering, fully utilizing decomposition techniques, reconstruction techniques, complexity analysis, dynamic decomposition techniques, and neural networks optimized by automatic hyperparameter optimization algorithms, effectively improving the model’s interpretability and precision of prediction accuracy. This study used historical monthly runoff datasets from the Pingshan Hydrological Station and Yichang Hydrological Station for validation, and selected eight models including the LSTM model, CEEMDAN-TCN-Attention model, and CEEMDAN-VMD-LSTM-Attention (DDRI) for comparative prediction experiments. The MAE, RMSE, MAPE, and NSE indicators of the proposed model showed the best performances, with test set values of 1007.93, 985.87, 16.47, and 0.922 for the Pingshan Hydrological Station and 1086.81, 1211.18, 17.20, and 0.919 for the Yichang Hydrological Station, respectively. The experimental results indicate that the fusion model generated through training has strong learning ability for runoff temporal features and the proposed model has obvious advantages in overall predictive performance, stability, correlation, comprehensive accuracy, and statistical testing.

A Differentiable Physics-Informed Machine Learning Approach to Model Laser-based Micro-Manufacturing Process

Abstract Advanced manufacturing processes are often based on complex multiphysics phenomena that are either poorly understood or are computationally too expensive to simulate in the context of process design, control or planning. Traditionally, simplified physics models with prescribed heuristics or purely data-driven surrogate models are used as alternatives in such applications. The concept of physics-informed machine learning (PIML) has been shown to have unique advantages over both of these alternatives in various fields of complex system analysis. In this paper, a new physics-informed machine learning (PIML) approach is presented to model the geometry of the cut produced by a Magnetically-assisted Laser Induced Plasma Micro Machining (M-LIPMM) process. This PIML architecture uses a neural network to auto-adapt the parametric boundary condition and physical properties used in a simplified finite difference based physics model (of 2D heat conduction), as a function of the inputs namely the laser settings. This network also estimates the scaling and shifting parameters used by a convolutional neural network that takes the temperature profile predicted by the simplified heat conduction model to predict the width and depth of the machined cut. Trained on physical experiment data, the PIML approach compares favorably to a pure data-driven neural network model in extrapolation tests, while also providing physical insights (that the latter cannot). The PIML approach also provides an 85% better accuracy overall compared to the simplified physics model with heuristic settings.

Morphology of the Human Pineal Gland Studied by Freeze-Fracturing in Scanning Electron Microscopy.

The human pineal gland is the largest producer of the hormone melatonin. Pineal acervuli (brain sand), calcified concretions in the pineal gland, have long been studied because of their association with ageing, melatonin production, and neurological disorders. The solid inorganic matter of the hydroxyapatite crystals often renders sample sectioning impossible, to the extent that the sections lose value. Technically, freeze-fracturing has revealed the detailed structure and cell relationships without tissue damage. In our electron microscopic study, samples of the human pineal gland were obtained during autopsy from 20 donors with mean age 69 years. Samples underwent freeze-fracturing and standard histological procedures, and were analysed by scanning electron microscopy (SEM) in high vacuum. Based on our results, freeze-fracturing enabled identification of a mulberry-like acervulus topography. The acervuli were situated in specific "nest-like" structures, which were surrounded by pinealocytes, interstitial cells, and nerve fibres. A fractured surface of the intrapineal acervuli exhibited a regular lamellar structure. Freeze-fracturing the pineal gland and imaging by SEM enabled complex structural analysis. This approach permits viewing the surface acervuli spherical and internal lamellar architecture. Our results confirmed that the parenchyma of this small but important gland contains two types of acervuli, depending on their size: non-aggregated and aggregated. We propose to include these forms of acervuli in the new edition of the Terminologica Histologica. In conclusion, pineal gland freeze-fracturing by SEM is suitable for complex structural analysis. Our description of our methods can be a guide for other scientists who want to study the pineal gland with electron microscopy methods.

Exploring sex-specific genetic architecture of coronary artery disease in Tehran: a cardiometabolic genetic study

ABSTRACT Background The development of coronary artery disease (CAD) is influenced by sex and genetic factors. Genome-wide association studies (GWAS) have linked genetic loci to CAD, mostly in European populations. The study aims to find sex-related genetic differences in the Iranian population. Research design and methods We conducted a sex-stratified GWAS with 4519 subjects (1832 males and 2687 females) in the discovery group and 922 subjects (495 males and 427 females) in the confirmation group of an Iranian cohort. We analyzed 9,141,124 variants using a genome-wide complex trait analysis (GCTA) tool. Results We detected distinct genetic variants associated with CAD in males: rs34952209 [OR = 1.79; p = 5.216E–8], rs1432687863 [OR = 1.95; p = 8.477E–8], and in females, rs7314741 [OR = 1.67; p = 7.142-8E] positively influenced CAD risk. The CAD-associated SNPs that were obtained have been confirmed using independent samples. Rs3495229 May impact histone mark and Pou2f2 motifs, while rs7314741 in the LEM Domain Containing 3 (LEMD3) promoter may affect a regulatory motif for the STAT transcription factor. According to Roadmap and ENCODE data, Rs1432687863 is a new variant affecting CAD in males, potentially through H3K9me3 in the heart. Conclusions Our findings highlight the role of sex-specific genetic differences in CAD development, providing novel insights into disease pathways which is not appropriate using a sex-combined strategy.

A MALDI-TOF MS-based multiple detection panel of drug resistance-associated multiple single-nucleotide polymorphisms in Candida tropicalis.

Candida tropicalis is one of the main causes of invasive candidiasis. Rapid identification of antifungal resistance is crucial for selection of an appropriate antifungal to improve patient outcomes. Mutations at specific loci are strongly correlated with resistance to antifungal agents. In this study, we developed a multi-single-nucleotide polymorphism (SNP) panel to accurately identify 36 mutation sites across seven genes of C. tropicalis that are associated with resistance to azoles and/or echinocandins. Ten isolates were selected to test repeatability, and another 20 isolates of C. tropicalis were selected to validate consistency. Intra-assay and inter-assay repeatability of the panel was 100%, with the loci accuracy being 99.44% (716 of 720). Furthermore, 109 isolates were examined for clinical research, and the most commonly detected mutations were G751A and A866T of UPC2, A491T of TAC1, and A395T and C461T of ERG11. The G751A and A866T mutations of UPC2 as well as the A395T and C461T mutations of ERG11 co-existed. The SNP panel enables identification of specific mutations at critical sites of drug-resistant strains to facilitate the rapid selection of appropriate antifungal agents and efficient monitoring of the regional epidemiological trends of resistance of C. tropicalis.IMPORTANCEC. tropicalis infections pose a growing global public health challenge, with mortality rates approaching 40%. C. tropicalis is one of the top four Candida spp. responsible for candidiasis, particularly in the Asia-Pacific region and Latin America, notably affecting patients with neutropenia and malignancies. The azole resistance rate of C. tropicalis ranges from 0% to 30%. Between 2009 and 2018, the China Hospital Invasive Fungal Surveillance Network reported an increase in fluconazole and voriconazole resistance from 5.7% to ~30%. Although resistance to echinocandins and amphotericin B remains low, multi-resistance to echinocandins and azoles has been observed. Current methods for detecting drug resistance are limited by the long turnaround time of antifungal susceptibility testing, low throughput of Sanger sequence to target resistance mutations, complex data analysis, and high costs of second-generation sequencing. We developed and validated a rapid, high-throughput, and cost-effective panel to detect and monitor drug-resistance mutations of C. tropicalis.

Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication

Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication

Quantum implementation of bilinear interpolation algorithm based on NEQR and center alignment

Abstract In the field of quantum image processing, scaling techniques have been extensively studied as a critical subfield. Predominantly, these techniques leverage bilinear interpolation method. However, the current quantum bilinear interpolation algorithm remains in its initial version, resulting in a significant flaw: the centers of the source image and the interpolated image are misaligned. This flaw not only leads to results identical to nearest neighbor interpolation during integer multiple image scaling-down but also causes excessive qubit usage. To tackle these issues, our study introduces an innovative bilinear interpolation method with the following key advancements. Firstly, by integrating the NEQR representation with an enhanced bilinear interpolation from OpenCV, the central misalignment is effectively rectified. Secondly, resolving the central misalignment enables our method to prevent the degradation to nearest neighbor interpolation during integer multiple image reduction. Finally, based on the characteristics of optimization algorithms and quantum computing, along with simpler quantum modules and quantum convolution algorithms, the steps of the quantum image scaling algorithm have been optimized to reduce circuit complexity. Through a series of simulation experiments and complexity analyses, it has been demonstrated that our method, compared to the initial bilinear interpolation method, exhibits lower circuit complexity and significantly reduces qubit usage.

Comparative analysis of accuracy and computational complexity across 21 swarm intelligence algorithms

Nonlinear, complex optimization problems are prevalent in many scientific and engineering fields. Traditional algorithms often struggle with these problems due to their high dimensionality and intricate nature, making them time-consuming. Many researchers have proposed new metaheuristic algorithms inspired by biological behaviors in nature, which comparatively show higher performance and accuracy than traditional optimization algorithms. Nature-inspired algorithms, particularly those based on swarm intelligence, offer adaptable and efficient solutions to these challenges. In recent years, swarm intelligence algorithms have made significant advancements. Classical and CEC benchmark suits are immersively useful for studying the performance of optimization algorithms. According to our literature survey, we identified that many algorithms were evaluated based on accuracy. Currently, swarm intelligence algorithms are used in many applications, and efficiency and computational complexity need to be evaluated. A broad-level study of the computational complexity and accuracy of popular swarm intelligence algorithms has not been done recently. Therefore this study we comprehensively evaluate and compare 21 bio-inspired swarm intelligence algorithms on eight non-separable unimodal, eight separable unimodal, five non-separable multimodal, seven separable multimodal functions, and two CEC 2018 many objective functions. We study the structure and mathematical model of the selected algorithms. Then we categorized selected algorithms into six different behavioral groups. We calculated the root mean square error between expected and actual values. Then we performed an RMSE cross-validation statistical test to understand how accurately an algorithm resolves an average problem. We found that Artificial Lizard Search Optimization (ALSO) is the most prominent algorithm in accuracy and efficiency. Besides that, Cat Swarm Optimization (CSO), Squirrel Search Algorithm (SSA), and Chimp Optimization Algorithm (CHOA-B) are also considered more universal algorithms. The Squirrel Search Algorithm (SSA) is ALSO’s second-best algorithm in time complexity. Wasp Swarm Algorithm (WSO), and Bat-Inspired Algorithm (BA) presented the lowest time complexity. Finally, several important issues and research directions are discussed.

PyPCG: a Python toolbox specialized for phonocardiography analysis.

Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. The development of methods which analyse phonocardiographic data tries to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. In these approaches there is little to no standardization and multiple parts of these models have to be reimplemented on a case-by-case basis. Datasets containing heart sound recordings also lack standardization in both data storage and labeling, especially in fetal phonocardiography. We are presenting a toolbox that can serve as a basis for a future standard framework for heart sound analysis. This toolbox contains some of the most widely used processing steps and with these, complex analysis pipelines can be created. These functions can be tested individually. Due to the interdependence of the steps, we validated the current segmentation stage using two phonocardiogram datasets, a fetal dataset comprising 50 one-minute abdominal PCG recordings, which include 6758 S1 and 6729 S2 labels and a filtered version of the dataset used in the 2022 PhysioNet Challenge, containing 413 records with 9795 S1 and 9761 S2 labels. Our results were compared to other common and publicly available segmentation methods, such as peak detection with the Neurokit2 library, and the Hidden Semi-Markov Model by Springer et al. Our best model achieved a 96.1% F1 score and 11.7 ms mean absolute error for fetal S1 detection, and 81.3% F1 score and 50.5 ms mean absolute error for PhysioNet S1 detection. Our detection method outperformed all other tested methods on the fetal dataset and
achieved results comparable to the state of the art on the PhysioNet dataset. Accurate segmentation of signals is critical for the calculation of accurate statistical measures and the creation of classification models. Our toolbox contains functions for both feature extraction and calculation of statistics which are compatible with the previous steps. All of our methods can be fine tuned for specific datasets. pyPCG is available on https://pypcg-toolbox.readthedocs.io/en/latest/.

Optimizing Urban Traffic Flow through Advanced Tensor Analysis and Multilinear Algebra: A Computational Approach to Enhancing Smart City Dynamics

Growing traffic congestion is a worldwide problem that collides against the aims of environmental sustainability, economic productivity, and the quality of life in cities. This research proposes a new computational framework for traffic management that integrates advanced tensor analysis and methods from multilinear algebra. We have developed and validated a new predictive model that greatly improves the optimization of traffic flows by synthesizing the naturally complex multi-dimensional traffic data analysis. Our results demonstrate that, compared with existing systems, the proposed approach results in higher accuracy of prediction, much improved computational efficiency, and provides scalable and adaptable solutions for application in a wide range of urban habitats. Such research may push the boundaries further on the smart city infrastructures to provide a very well-founded mathematical framework for the dynamics of improved urban mobility through high-level data-oriented information.

Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends

Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i.e., by differencing or detrending). However, many empirical researchers have a limited understanding of stationarity, which can lead to the use of incorrect research practices and misleading substantive conclusions. In this paper, we address this problem by answering these questions in an accessible way. To this end, we study how researchers can use detrending and differencing to model trends in time series analysis. We show via simulation the consequences of modeling trends inappropriately, and evaluate the performance of one popular approach to distinguish different trend types in empirical data. We present these results in an accessible way, providing an extensive introduction to key concepts in time series analysis, illustrated throughout with simple examples. Finally, we discuss a number of take-home messages and extensions to standard approaches, which directly address more complex time-series analysis problems encountered by empirical researchers.

A complex adaptive system analysis of emerging tourism work paradigms

ABSTRACT This conceptual paper offers a novel conceptual analysis of emerging work paradigms that have the potential to reshape the tourism professions, underscoring the need for a comprehensive perspective. It emphasises the necessity of examining the increasingly complex tourism professions through the lens of Complex Adaptive Systems (CAS). Additionally, we examine how a CAS analysis of tourism work paradigms can be effectively conducted. First, five fundamental tenets that guide a CAS exploration of work dynamics were identified. Subsequently, the conceptual foundation of the CAS approach was established through the development of a framework synthesised from existing literature on work paradigms and tourism work. This framework elucidates the mechanisms and implications of evolving tourism work dynamics, with the primary objective of addressing their inherent complexities while offering a precise structure for CAS-based simulation modelling. Moreover, we provide guidance on constructing a simulation model based on this framework to address 12 identified gaps in the current tourism work literature. The theoretical contributions, methodological advancements, and potential research directions arising from the CAS approach benefit in predicting and advancing the future of tourism professions. Finally, practical implications for organisational strategy development and policy-making toward fostering a sustainable tourism work ecosystem are discussed.

Deep Learning-Based Control System Design for Emergency Vehicles through Intersections

This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.