The feedback stabilization problem for nonlinear discrete-time systems with a reduced-order observer is investigated, in which the nonlinear terms of the systems satisfy the quasi-one-sided Lipschitz condition. First, a discrete-time reduced-order observer for nonlinear systems is designed. Then, a feedback controller with a reduced-order observer is designed for realizing the stabilization of nonlinear discrete-time systems. We prove that the design of a feedback controller and reduced-order observer of systems can be carried out independently in the case of discrete-time with nonlinear terms, which largely reduces the computational complexity of the observer and controller. The introduction of the quasi-one-sided Lipschitz condition simultaneously enhances the robustness and stability of nonlinear control systems. Finally, the feasibility and effectiveness of the proposed design approach is verified by a numerical simulation. Full article

The reliable transfer of bioprocesses from single-use bioreactors (SUBs) of different scales to conventional stainless steel stirred-tank bioreactors is of steadily growing interest. In this publication, a scale-up study for SUBs with volumes of 200 L and 2000 L and the transfer to an industrial-scale conventional stainless steel stirred-tank bioreactor with a volume of 15,000 L is presented. The scale-up and transfer are based on a comparison of mixing times and the modeling of volumetric mass transfer coefficients k_La, measured in all three reactors in aqueous PBS/Kolliphor solution. The mass transfer coefficients are compared with the widely used correlation of van’t Riet at constant stirrer tip speeds. It can be shown that a van’t Riet correlation enables a robust and reliable prediction of mass transfer coefficients on each scale for a wide range of stirrer tip speeds and aeration rates. The process transfer from single-use bioreactors to conventional stainless steel stirred-tank bioreactors is proven to be uncritical concerning mass transfer performance. This provides higher flexibility with respect to bioreactor equipment considered for specific processes. Full article

This study introduces a novel approach to synthesising a three-dimensional (3D) micro-nanostructured amorphous biosilica. The biosilica is coated with cerium oxide nanoparticles obtained from laboratory-grown unicellular photosynthetic algae (diatoms) doped metabolically with cerium. This unique method utilises the ability of diatom cells to absorb cerium metabolically and deposit it on their silica exoskeleton as cerium oxide nanoparticles. The resulting composite (Ce-DBioSiO₂) combines the unique structural and photonic properties of diatom biosilica (DBioSiO₂) with the functionality of immobilised CeO₂ nanoparticles. The kinetics of the cerium metabolic insertion by diatom cells and the physicochemical properties of the obtained composites were thoroughly investigated. The resulting Ce-DBioSiO₂ composite exhibits intense Stokes fluorescence in the violet–blue region under ultraviolet (UV) irradiation and anti-Stokes intense violet and faint green emissions under the 800 nm near-infrared excitation with a xenon lamp at room temperature in an ambient atmosphere. Full article

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction. Full article

Community green spaces (CGSs) constitute a crucial element of urban land use, playing a pivotal role in maintaining the stability of urban ecosystems and enhancing the overall quality of the urban environment. Through the post-occupancy evaluation (POE) of green spaces, we can gain insights into residents’ actual needs and usage habits, providing scientific evidence for the planning, design, and management of green spaces. This ensures that CGSs better meet residents’ needs and improve their quality of life. The POE of CGSs relies heavily on high-precision data support. However, the current POE system for CGSs faces challenges, such as limited data collection methods, incomplete indicator systems, and excessive manual involvement. To address these limitations in data collection, this study proposes a comprehensive, dynamically monitored, objective, and sustainable POE system for CGSs. This system incorporates a multi-dimensional perception system that integrates the Internet of Things (IoT) and sensors to collect data from various sources. It establishes an evaluation framework from the perspectives of policy guidance and usage needs for CGSs, utilizing neural network systems and artificial intelligence techniques to compute the evaluation results. Using the Jiayuan Sanli Community in Beijing as a case study, this paper demonstrates the feasibility of the proposed system. A comparison between the POE results obtained using the multi-dimensional perception technique and those obtained manually reveals an 87% improvement in the accuracy of the evaluation results based on the multi-dimensional perception system. This system bridges the gap between planning perspectives and user experiences, contributing significantly to future urban land planning and land policy formulation. Full article

Currently, Bixa orellana L. extracts are used as a color source in the food, pharmaceutical, and cosmetic industries because they are important as a potential source of antioxidant activity. The extraction is carried out by conventional methods, using alkaline solutions or organic solvents. These extraction methods do not take advantage of the lipid fraction of annatto (Bixa orellana L.) seeds, and the process is not friendly to the environment. In this work, the objective was to obtain an extract rich in nutraceuticals (bixin and tocols) of high antioxidant power from Peruvian annatto seeds as a potential source for a functional food or additive in the industry using supercritical fluid extraction (SFE). Experiments related to extraction yield, bixin, tocotrienols, tocopherols, and antioxidant activity were carried out. The SFE was performed at 40 °C, 50 °C, and 60 °C, and 100, 150, and 250 bar with 0.256 kg/h carbon dioxide as the supercritical solvent (solvent-to-feed ratio of 10.2). Supercritical extraction at 60 °C and 250 bar presented the best results in terms of global extraction yield of 1.40 ± 0.01 g/100 g d.b., extract concentration of 0.564 ± 0.005 g bixin/g extract, 307.8 mg α-tocotrienol/g extract, 39.2 mg β-tocotrienol/g extract, 2 mg γ-tocopherol/g extract, and IC₅₀ of 989.96 μg extract/mL. Economical evaluation showed that 60 °C, 250 bar, and 45 min presented the lowest cost of manufacturing (2 × 2000 L, COM of USD 212.39/kg extract). This extract is a potential source for functional food production. Full article

Rainwater runoff samples from a range of roofing materials were temporally collected from 19 small-scale roof structures and two commercial buildings through simulated and actual storm events, and the concentrations of polycyclic aromatic hydrocarbons (PAHs), phosphorus flame retardants (PFLs), and pyrethroid insecticides and other water quality parameters were analyzed. In Part I of this research, the concentrations of these contaminants in roof runoff and soils receiving runoff from a range of roofing materials were evaluated. In Part II, recommendations have been developed for a first-flush exclusion to improve the quality of water harvesting for nonpotable uses. Recommendations focus on a first-flush diversion based on mass removals of total suspended solids (TSS) and PAHs linked to conductivity measurements throughout a storm event. Additionally, an upper-confidence limit (UCL) was constructed to determine the minimum diversion required to obtain 50, 75, 90, and 95% mass removal of TSS and PAH contaminants. The majority of TSS were produced during the initial 1.2 mm of runoff. Likewise, the majority of PAHs were removed during the initial 1.2 mm of runoff, except for the asphalt shingle roofs, where high PAHs were observed after 6 mm of runoff. The Texas Water Development Board (TWDB)-recommended first-flush diversion of one gallon for every 100 square feet of rooftop was not always adequate for removing 50% of TSS and PAHs from the roofs. Rainwater runoff conductivity decreased drastically between 1.2 to 2.4 mm of rainwater runoff. Diverting the first flush based on conductivity has the potential to also divert the majority of TSS and PAHs in roof runoff. Full article

The emerging heteropathotype shigatoxigenic (STEC) and extra-intestinal pathogenic Escherichia coli (ExPEC) O80:H2 has been the second leading cause of pediatric HUS in France since the mid-2010s. In contrast with other highly pathogenic STEC serotypes, for which ruminants have clearly been identified as the main human infection source, this heteropathotype’s reservoir remains unknown. In this context, we describe for the first time the isolation of seven STEC O80:H2 strains from healthy cattle on a single cattle farm in France. This study aimed at (i) characterizing the genome and (ii) investigating the phylogenetic positions of these O80:H2 STEC strains. The virulomes, resistomes, and phylogenetic positions of the seven bovine isolates were investigated using in silico typing tools, antimicrobial susceptibility testing and cgMLST analysis after short-read whole genome sequencing (WGS). One representative isolate (A13P112V1) was also subjected to long-read sequencing. The seven isolates possessed ExPEC-related virulence genes on a pR444_A-like mosaic plasmid, previously described in strain RDEx444 and known to confer multi-drug resistance. All isolates were clonally related and clustered with human clinical strains from France and Switzerland with a range of locus differences of only one to five. In conclusion, our findings suggest that healthy cattle in France could potentially act as a reservoir of the STEC-ExPEC O80:H2 pathotype. Full article

Empathy is a crucial component to infer and understand others’ emotions. However, a synthesis of studies regarding empathy and its neuronal correlates in perceptual tasks using event-related potentials (ERPs) has yet to occur. The current systematic review aimed to provide that overview. Upon bibliographic research, 30 studies featuring empathy assessments and at least one perceptual task measuring ERP components in healthy participants were included. Four main focus categories were identified, as follows: Affective Pictures, Facial Stimuli, Mental States, and Social Language. The Late Positive Potential was the most analyzed in Affective Pictures and was reported to be positively correlated with cognitive and affective empathy, along with other late components. In contrast, for Facial Stimuli, early components presented significant correlations with empathy scales. Particularly, the N170 presented negative correlations with cognitive and affective empathy. Finally, augmented N400 was suggested to be associated with higher empathy scores in the Mental States and Social Language categories. These findings highlight the relevance of early perceptual stages of empathic processing and how different EEG/ERP methodologies provide relevant information. Full article

Background/Objectives: The newly emergent COVID-19 pandemic involved primarily the respiratory system and had also major cardiovascular system (CVS) implications, revealed by acute myocardial infarction (AMI), arrhythmias, myocardial injury, and thromboembolism. CVS involvement is done through main mechanisms—direct and indirect heart muscle injury, with high mortality rates, worse short-term outcomes, and severe complications. AMI is the echo of myocardial injury (revealed by increases in CK, CK-MB, and troponin serum markers—which are taken into consideration as possible COVID-19 risk stratification markers). When studying myocardial injury, physicians can make use of imaging studies, such as cardiac MRI, transthoracic (or transesophageal) echocardiography, coronary angiography, cardiac computed tomography, and nuclear imaging (which have been used in cases where angiography was not possible), or even endomyocardial biopsy (which is not always available or feasible). Two-case-series presentations: We present the cases of two COVID-19 positive male patients who were admitted into the Clinical Department of Cardiology in “Sfântul Apostol Andrei” Emergency Clinical Hospital of Galați (Romania), who presented with acute cardiac distress symptoms and have been diagnosed with ST elevation AMI. The patients were 82 and 57 years old, respectively, with moderate and severe forms of COVID-19, and were diagnosed with anteroseptal left ventricular AMI and extensive anterior transmural left ventricular AMI (with ventricular fibrillation at presentation), respectively. The first patient was a non-smoker and non-drinker with no associated comorbidities, and was later discharged, while the second one died due to AMI complications. Conclusions: From this two-case series, we extract the following: old age alone is not a significant risk factor for adverse outcomes in COVID-19-related CVS events, and that the cumulative effects of several patient-associated risk factors (be it either for severe forms of COVID-19 and/or acute cardiac injury) will most probably lead to poor patient prognosis (death). At the same time, serum cardiac enzymes, dynamic ECG changes, along with newly developed echocardiographic modifications are indicators for poor prognosis in acute cardiac injury in COVID-19 patients with acute myocardial injury, regardless of the presence of right ventricular dysfunction (due to pulmonary hypertension). Full article

In this study, a compact and low-power-consumption quantum random number generator (QRNG) based on a laser diode and a hybrid chip with integrated silicon photonics is proposed and verified experimentally. The hybrid chip’s size is 8.8 × 2.6 × 1 ${\mathrm{mm}}^3$, and the power of the entropy source is 80 mW. A common-mode rejection ratio greater than 40 dB was achieved using an optimized 1 × 2 multimode interferometer structure. A method for optimizing the quantum-to-classical noise ratio is presented. A quantum-to-classical noise ratio of approximately 9 dB was achieved when the photoelectron current is 1 $\mathsf{\mu }$A using a balance homodyne detector with a high dark current GeSi photodiode. The proposed QRNG has the potential for use in scenarios of moderate MHz random number generation speed, with low power, small volume, and low cost prioritized. Full article

This paper investigates the influence of the concentration and particle size of h-BN nanoparticles in a nanofluid on the surface integrity of 304 austenitic stainless steel during turning, focusing on the cutting force, friction coefficient, cutting temperature, surface roughness, surface residual stress, work hardening capacity, and 3D surface topography. The results show that, compared to dry cutting, the addition of 3 wt.% h-BN nanofluid can reduce the friction coefficient on the rake face by 38.9%, lower the cutting temperature by 43.5%, decrease the surface roughness by 53.8%, decrease the surface residual stress by 61.6%, and reduce the work hardening degree by 27.5%. Two-dimensional profiles and the 3D surface topography display a more balanced peak–valley distribution. Furthermore, by studying the effect of different h-BN particle sizes in nanofluids on the surface integrity of the machined workpiece, it was found that nanoscale particles have a greater tendency to penetrate the tool–chip interface than submicron particles. Moreover, the h-BN particles in the nanofluid play a “rolling effect” and “microsphere” effect, and the sesame oil will also form a lubricating oil film in the knife-chip contact area, thereby reducing the friction coefficient, reducing the cutting force, and improving the machining surface quality. Full article

Waste management is one of the many major challenges faced by all urban cities around the world. With the increase in population, the current mechanisms for waste collection and disposal are under strain. The waste management problem is a global challenge that requires a collaborative effort from different stakeholders. Moreover, there is a need to develop technology-based solutions besides engaging the communities and establishing novel policies. While there are several challenges in waste management, the collection of waste using the current infrastructure is among the top challenges. Waste management suffers from issues such as a limited number of collection trucks, different types of household and industrial waste, and a low number of dumping points. The focus of this paper is on utilizing the available waste collection transportation capacity to efficiently dispose of the waste in a time-efficient manner while maximizing toxic waste disposal. A novel knapsack-based technique is proposed that fills the collection trucks with waste bins from different geographic locations by taking into account the amount of waste and toxicity in the bins using IoT sensors. Using the Knapsack technique, the collection trucks are loaded with waste bins up to their carrying capacity while maximizing their toxicity. The proposed model was implemented in MATLAB, and detailed simulation results show that the proposed technique outperforms other waste collection approaches. In particular, the amount of high-priority toxic waste collection was improved up to 47% using the proposed technique. Furthermore, the number of waste collection visits is reduced in the proposed scheme as compared to the conventional method, resulting in the recovery of the equipment cost in less than a year. Full article

Differential privacy has emerged as a practical technique for privacy-preserving deep learning. However, recent studies on privacy attacks have demonstrated vulnerabilities in the existing differential privacy implementations for deep models. While encryption-based methods offer robust security, their computational overheads are often prohibitive. To address these challenges, we propose a novel differential privacy-based image generation method. Our approach employs two distinct noise types: one makes the image unrecognizable to humans, preserving privacy during transmission, while the other maintains features essential for machine learning analysis. This allows the deep learning service to provide accurate results, without compromising data privacy. We demonstrate the feasibility of our method on the CIFAR100 dataset, which offers a realistic complexity for evaluation. Full article

Due to the open underwater channels and untransparent network deployment environments, underwater acoustic networks (UANs) are more vulnerable to hostile environments. Security research is also being conducted in cryptography, including authentication based on asymmetric algorithms and key distribution based on symmetric algorithms. In recent years, the advancement of quantum computing has made anti-quantum attacks an important issue in the field of security. Algorithms such as lattice and SPHINCS+ have become a research topic of interest in the field of security. However, within the past five years, few papers have discussed security algorithms for UANs to resist quantum attacks, especially through classical algorithms. Some existing classical asymmetric and symmetric algorithms are considered to have no prospects. From the perspective of easy deployment in engineering and anti-quantum attacks, our research focuses on a comprehensive lightweight security framework for data protection, authentication, and malicious node detection through the Elliptic Curve and Hash algorithms. Our mechanism is suitable for ad hoc scenarios with limited underwater resources. Meanwhile, we have designed a multi-party bit commitment to build a security framework for the system. A management scheme is designed by combining self-certifying with the threshold sharing algorithm. All schemes are designed based on certificate-less and ad hoc features. The proposed scheme ensures that the confidentiality, integrity, and authentication of the system are well considered. Moreover, the scheme is proven to be of unconditional security and immune to channel eavesdropping. The resource and delay issues are also taken into consideration. The simulations considered multiple variables like number of nodes, attackers, and message length to calculate proper values that can increase the efficiency of this scheme. The results in terms of delay, delivery ratio, and consumption demonstrate the suitability of the proposal in terms of security, especially for malicious node detection. Meanwhile, the computational cost has also been controlled at the millisecond level. Full article

Climate change profoundly impacts hydrological systems, particularly in regions such as Croatia, which is renowned for its diverse geography and climatic variability. This study examined the effect of climate change on streamflow rates in two Croatian rivers: Bednja and Gornja Dobra. Using seasonal Mann–Kendall (MK) tests, overall streamflow trends were evaluated. Additionally, innovative polygon trend analysis (IPTA), innovative visualization for innovative trend analysis (IV-ITA), and Bayesian changepoint detection and time series decomposition (BEAST) algorithms were used to assess the trends’ magnitudes and transitions. The seasonal MK analysis identified significant decreasing trends, primarily during summer. The results of IPTA and IV-ITA revealed consistent decreasing trends throughout most months, with a notable increase in September, especially at high flow values. The rivers’ behavior differed between the first and second halves of the month. BEAST analysis detected abrupt changes, including earlier shifts (1951–1968) in the Bednja and more recent ones (2013–2015) in both the Bednja and, to a lesser extent, the Gornja Dobra rivers. This comprehensive approach enhances our understanding of long-term streamflow trends and short-term fluctuations induced by climate change. Full article

Interoperable multi-vendor High-Voltage Direct-Current (HVDC) grids are a key enabler for the integration of renewable energy (in particular offshore wind) and its transmission over longer distances to consumers. However, most HVDC systems today are single-vendor and point-to-point. Various technical and non-technical aspects need to be considered, for example, (real-time) testing, legal aspects (intellectual property and regulation), and the multi-vendor interoperability process. This paper presents findings from the READY4DC project, which is a larger and open European effort involving diverse stakeholders, including HVDC manufacturers, transmission system operators, wind developers, academia, and research institutes. It summarizes key technical recommendations, emphasizing comprehensive interaction studies and the development of a structured legal framework to facilitate the development and operation of a multi-vendor, multi-terminal HVDC grid. The READY4DC project highlights the need for increased harmonization, transparent communication among stakeholders, and future-oriented research to ensure the robustness and interoperability of interconnected grids. Collaborative efforts are key for addressing technical complexities and advancing the deployment of multi-vendor multi-terminal HVDC technology. Full article

Given the energy crisis and escalating environmental pollution, the imperative for developing clean new energy is evident. Hydrogen has garnered significant attention owing to its clean properties, high energy density, and ease of storage and transportation. This study synthesized four types of catalysts—FeS(DI/MB), FeS(ET/MB), Fe(DI/MB), and Fe(ET/MB)—using two distinct solution systems: DI/MB and ET/MB. The FeS(DI/MB) catalyst, synthesized using the layered solution system (DI/MB), demonstrates a uniformly distributed and dense nanosheet structure, exhibiting excellent resistance to strong bases and superior catalytic properties. The FeS(DI/MB) electrode showed OER overpotentials of 460 mV and 318 mV in 1 M and 6 M, respectively, at current densities of up to 500 mA cm⁻². Under industrial electrolysis test conditions, the FeS(DI/MB) electrode required only 262 mV to achieve a current density of 500 mA cm⁻², operating in a high-temperature, strong alkaline environment of 6 M at 60 °C. Furthermore, the FeS(DI/MB) electrode exhibited excellent OER catalytic activity and stability, as evidenced by a 60 h stability test These findings provide valuable insights into the preparation of iron nickel sulfide-based catalysts, and further in-depth and comprehensive exploration is anticipated to yield the excellent catalytic performance of these catalysts in the realm of electrolytic water hydrogen production. Full article

The advancement of massive construction in urban subway projects contributes to the increased use of the artificial ground freezing (AGF) method in the construction of cross passages due to its reliability and environmental friendliness. However, the uplift or subsidence of the ground surface induced by the frost heave and thawing settlement of the soil can be a problem for existing buildings, and the current design method places way too much emphasis on the strength requirement of the freezing wall. In this study, FLAC3D was employed to develop a series of state-of-the-art numerical models of the construction of a typical subway cross passage by the AGF method, utilizing freezing walls with different thicknesses. The results of this study can be used to examine the ground deformation arising from the AGF method and the influence of the thickness of the freezing wall on the AGF method. Full article

The issue of enhancing energy recovery efficiency is a key concern within the European Union’s climate protection efforts. In particular, it applies to all processes and plants for the harvesting, gathering, and conversion of energy. The abandonment of fossil fuels in favour of alternative energy sources, and the increasing of energy efficiency and its recovery, is now a widely accepted direction of energy development. This study focuses on facilities that recover and process energy from municipal waste left after recycling processes, known as waste-to-energy (WtE) plants. These plants’ energy recovery efficiency is governed by the R1 Formula in EU countries. This report is based on an analysis of four years of operational data from selected Polish municipal waste incinerators, supplemented by a discussion of various studies on energy recovery efficiency. The primary objective of this report is to evaluate the effectiveness of these plants in contributing to sustainable waste management and energy recovery. The main effect of the developed report is the set of results of the energy recovery efficiency factor values, determined based on the R1 formula valid in the EU legislation, tabulated and graphically illustrated, and calculated for five selected Polish waste-to-energy plants. The presented results, with their graphical interpretation, discussion, and conclusions, provide insights into several factors influencing the value of the R1 efficiency factor. They can be a valuable contribution to operators of waste-to-energy plants, especially those operating in countries outside the EU. Full article

User experience is a key predictor of future use of goods and services. The presented study collected a combination of qualitative and quantitative data from both experienced users and novices about their perceptions of virtual reality (VR) equipment, any concerns surrounding the data collected by the equipment, and facets that needed to be taken into consideration for future developments. The purpose of this research was to understand the current user experience of VR and ways in which it can be improved. The findings indicated that the majority of people have used VR, albeit infrequently, and that the most common use for it was for entertainment purposes. The most important characteristics of VR systems were judged to be the available content and price. While it was reported to be enjoyable to use, the ways in which it was suggested to be improved were through a reduction in size and weight of the headsets, and incorporating wireless capabilities. Concerns about the use of VR were the potential for sickness, discomfort, and eye strain, the disconnect from the real world and the subsequent risks that this poses, as well as the use and privacy of user data. The findings from this research can be used as a stepping stone toward the advancement of VR technologies. Full article

Within the economic and cultural context of Xinjiang, the tourism industry has rapidly developed as a strategic pillar of the national economy, with the self-driving tour market emerging prominently. However, the uneven spatial layout and insufficient service facilities of self-driving camps limit their development potential. This study aims to enhance the attractiveness of tourism in Xinjiang and improve the visitor service experience by constructing an evaluation system for the layout of self-driving camps based on online travel blog data, utilizing methods such as literature review, surveys, ArcGIS spatial analysis, and web text analysis. The Delphi method and entropy weight method were applied to determine the weights of the influencing factors. The findings reveal spatial imbalances in the layout of Xinjiang’s self-driving camps and propose eight preferred scenic areas for camp location. This study also suggests sustainable development strategies. These insights and recommendations aim to optimize the layout of self-driving camps, enhance the tourism experience, and promote the sustainable development of Xinjiang’s tourism industry. Full article