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In response to the pressing global issue of water scarcity, numerous studies are under way both domestically and internationally to develop water-saving technologies and ensure clean water supply. These efforts heavily rely on water resource measurement data obtained from various sensing instruments operated by government and local agencies. These data are stored in servers and utilized for real-time predictive analyses and services. However, a challenge arises from the unrefined and fragmented nature of the data due to variations in sensing instruments and collection procedures. To address this issue and enable efficient big data analysis of water resource data, this paper introduces a novel approach. It presents the Smart Water Grid-based model for storing and classifying water resource measurement data, along with a comprehensive methodology for constructing a robust big data framework. By implementing this model, researchers and practitioners can enhance the effectiveness of their analyses and derive valuable insights from water resource data, contributing to the development of sustainable water management strategies.

Wetland restoration work is crucial for ecosystem development, and how to scientifically evaluate wetland restoration programmes is the key to improve the effectiveness of wetland restoration. In order to solve the problems of inadequate judgement and human influence in the evaluation of wetland restoration programmes, this paper proposes a wetland restoration programme evaluation model based on Fuzzy Neural Network method, which is based on fuzzy theory and combines the adaptive function and self-learning function of neural network to evaluate three wetland restoration programmes. The results show that programme B is better than programmes A and C and is suitable for long-term application in wetland restoration work in this area. It is concluded that the use of Fuzzy Neural Network model to evaluate the wetland restoration programmes is more accurate, more personalised, and has a better operation rate, which is an important means of evaluating the wetland restoration programmes and an important guideline to carry out the wetland work.

Conventional six-switch inverters, designed for balanced three-phase systems, face challenges in low-voltage networks with mixed single-phase and three-phase loads, leading to voltage imbalances. This imbalance adversely affects equipment, especially protection devices and power electronic converters. The increasing use of power electronic devices in industries with nonlinear loads has driven the adoption of four-leg inverters to handle neutral currents during unbalanced load connections in three-phase networks. This shift introduces challenges in pulse width modulation, particularly in the application of Space Vector Modulation (SVM). This work proposes a novel control strategy using a direct 3D-SVM approach, effectively determining switch states for voltage capacitor balancing and precise output current control. The study's main focus is presenting an advanced control strategy for a four-leg inverter utilizing the 3D-SVM technique to operate efficiently under highly unbalanced loads in distribution networks. The goal is to achieve balanced output voltages and currents under varying load conditions while maintaining the desired variable output frequency. Simulation results validate the proposed control strategy, showing a Total Harmonic Distortion (THD) of output current is 2.06% at 50 Hz and 2.55% at 25 Hz under balanced load, same thing in second case under unbalanced load, the THD is 1.65% at 50 Hz and 2.10% at 25 Hz. with similar under nonlinear load, the THD is 1.94% at 50 Hz and 2.46% at 25 Hz. Importantly, all outcomes fall within IEEE Standard 519-1992 for harmonic limits, highlighting the robustness and reliability of the proposed control approach.

With the rapid development of the global construction industry, not only a large amount of construction waste has been derived, but also a growing shortage of natural sand resources emerges. Aiming at the above problems, this study uses waste concrete and artificial sand as recycled coarse and fine aggregates to form a new type of recycled concrete. 135 standard cubes and prismatic test blocks were produced. Concrete mixtures were prepared with recycled concrete aggregate and artificial sand to fully replace natural aggregates. This study investigates the mechanical properties of recycled concrete containing artificial sand and polypropylene fibers. Polypropylene fiber content varied from 0-1.5% by mass. Experimental results showed the addition of fibers enhanced the compressive strength and ductility. The optimum fiber dosage was 0.6% for strength gain. However, elastic modulus decreased with increasing fiber content due to increased porosity. The findings demonstrate the feasibility of recycled concrete with artificial sand and polypropylene fibers as an environmentally-friendly construction material.

The work proposed a novel model to accurately trace the pollution sources of water pollution incidents based on moth-flame optimization and Metropolis-Hastings sampling algorithms. The model first utilized moth-flame optimization to estimate the parameters of the pollutant migration-diffusion model by minimizing the error between monitored and predicted concentration. It then traced the optimal pollution source location, discharge volume, and time using the M-H sampling algorithm. Simulation experiments demonstrated the model achieved significantly lower errors in tracing pollution source information compared to a previous method, with relative errors within 1.33%. The new model provides an accurate and efficient approach to tracing water pollution incidents and overcomes the limitations of previous methods. It exhibits substantial potential in identifying pollution sources within real-world aquatic environments as well as facilitating prompt responses to mitigate environmental and health impacts.

Excessive use of fossil fuels has an extremely negative impact on the environment and climate, as it results in the emission of harmful gases into the atmosphere. The goal of this research was to compare the use of solar energy and biomass as an energy source for drying tobacco in relation to natural gas. The research covered the drying of Virginia tobacco leaves with batch dryers, namely: thermogen models that use wood chips ("Model 1"), firewood ("Model 2") and pellets ("Model 3"), one model with natural gas thermogen combined with solar cells ("Model 4") and one thermogen with classic tobacco drying with ground gas ("Model 5"). In the first and third drying phase "Model 1" provides enough energy for drying the tobacco leaf but in the second phase it could not provide the required temperature, so it is necessary to increase the capacity of the combustion chamber. "Model 2" fully meets the requirements of tobacco leaf drying technology. Using pellets according to "Model 3" there were no difficulties and the tobacco was dried with good quality. Energy saving by using solar collectors according to "Model 4" is about 20%. Natural gas drying showed as the most expensive one.

E-governance is capable of driving the transition of government from an administrative power allocation of public resources to a citizen-service-oriented governance model. However, during this transformation, various governmental bodies often face the challenge of "data silos" caused by factors such as cross-regional, cross-business, and cross-departmental operations. Without altering the existing information platforms, transparent access technology serves as a key solution for data access within egovernance systems. It enables convenient access to information resources stored in different mediums and formats, facilitating the sharing and consolidation of information and data within and between governmental departments, thereby addressing the issue of "data silos" and enhancing the comprehensive service capabilities of e-governance. This paper firstly provides an overview of the concept, levels, characteristics, and application scenarios of transparent access to government big data. Secondly, it conducts a comprehensive comparative analysis of transparent access technologies in the context of cloud computing and big data. Lastly, based on the requirements of various transparent access technologies and the application of transparent access to government big data, this paper proposes a visionary framework for transparent access to government big data based on cross-domain semantics and channel coupling. This framework includes modules for cross-domain semantic interoperability, coupling of heterogeneous information channels based on knowledge graph, and tracing of multi-source heterogeneous data, aiming to provide innovative solutions for achieving transparent access to government big data.

The irrevocable variations in the thermoelectric properties arise in normally used bare type-K thermocouples that are subjected to high temperatures. Over an extended period of time, these long-term modifications may manifest themselves as a systematic shift in the thermocouple’s characteristics. This paper investigates the temporal instability of thermocouple output at temperatures over 800 °C. This investigation focuses on the change in temperature output that occurs in thermocouples of varying diameters after they have been subjected to high temperatures for more than 365 hours. This data was used to assess the relationship between the amount of temperature drift with different lead diameters and the degradation of the material compositions of thermocouple thermoelements. It is critical to determine the pace at which the thermocouple material degrades over time. This paper concentrates in detail on the drift analysis of thermocouples of different diameters which were tested at high temperatures with their segmental material compositions. Within the scope of this investigation, the elemental analysis of the thermocouple could be carried out using scanning electron microscopy in conjunction with an energy dispersive X-ray (SEM-EDAX) both before and after the thermocouple had degraded. The results show that the thermoelectric response decreases dramatically and the highest drift reaches to about 196.4 µV for (Ø0.51 mm), about 118.6 µV for (Ø1.29 mm) and 79.2 µV for (Ø2.05 mm) thermocouples, respectively at 800 °C after prolonged usage of 365 hours. The highest thermal drift was achieved with [90-10] vol% Ni-Cr / [95-05] vol % Ni-Al, Mn, Si composite thermocouples, which related to the increased amount of the 2-3 oxide layers that were formed in the composite of bare thermoelements.

Last-mile delivery plays a crucial role in improving the service level of express delivery, as it involves direct contact with customers. Providing personalized last-mile delivery services is an important means of improving customer satisfaction. Massive consumer data makes it possible to mine customers’ personalized logistics preferences. The paper studies the vehicle routing problem in last-mile delivery considering customers' preferences. The paper first quantifies customers' preferences for delivery time, location, and mode, and obtains preference probabilities based on historical data. Then, an optimization considering customer satisfaction and enterprise delivery costs is established, and a vehicle routing problem model considering customer preferences is proposed. To solve the problem, we designed an adaptive large neighborhood search (ALNS) algorithm with virtual delivery points to solve the problem and proposed specific destroy and repair operators. Through the case analysis of an express delivery company, this article provides the optimal routs and analyzes the customer preferences on each route. In addition, this article explores the impact of the customer preference constraint and complaint constraint on cost and gives the appropriate customer preference constraint and complaint rate constraint from the perspective of cost-saving.

The aim of this study is to investigate a novel and environmentally friendly method that is an alternative to conventional methods in the production of bi-metallic products. Bi-metallic products are obtained by joining materials with different physical and chemical properties. As a result, the desired properties of different materials can be used together. Numerous conventional methods such as welding, casting and pressing are used in the production of bi-metal materials. However, Cu and AISI 1040 steel materials are almost impossible to be joined by conventional production methods. The original value of this study is that these materials can be joined by a new method, the Mechanical Locking Method. In the joining process, a constant speed (1400 rpm), a constant feed rate (16 mm/min) and different channel depths (10, 13, and 15 mm) were used. The effect of the channel depth, one of the physical design parameters, on the joint quality was investigated. The mechanical and microstructural properties of the joint were determined experimentally. In conclusion, it was determined that the Cu/AISI 1040 material pair was successfully joined by using the Mechanical Locking Method and the most suitable channel depth was 10 mm.

Image denoising is essential for numerous image processing applications, where image noise can profoundly impact processing efficiency and output quality. Addressing the challenge of inflexible reference images in unconditional diffusion probability models and enhancing image denoising performance is of paramount importance. In this research, we propose a novel image denoising model based on component decoupling and introduce sensitivity decoupling operators to prevent entanglement and redundancy among different decoupling models. Additionally, we leverage a model-driven network to fuse image components, resisting noise and model degradation, thereby aiding network convergence. Subsequently, we construct an image adaptive denoising model incorporating diffusion probability and dictionary learning. Experimental results demonstrate the superiority of the proposed approach over other algorithms in grayscale image processing on the Set12 dataset, achieving a peak signal-to-noise ratio (PSNR) of 35.75 dB and an average structural similarity (SSIM) value of 92.68%. Similarly, on the BSD68 dataset, our algorithm outperforms others with a PSNR of 34.35 dB and an average SSIM of 93.89%. Furthermore, for colour image processing, our method yields higher PSNR and average SSIM compared to other approaches. The findings indicate a significant improvement in denoising effectiveness compared to prior methods, highlighting the practical value of the proposed image denoising algorithm.

With the rapid development of Internet technology and digital economy, many manufacturers sell their products through various channels. This paper develops Stackelberg game models by backward induction to analyze dynamic pricing decisions in a dual-channel supply chain enabled with blockchain technology. These models account for strategic consumer purchasing behaviors across sales periods. Analytical and numerical results provide insights into the pricing impacts of blockchain adoption under centralized and decentralized channel structures. Key findings indicate that blockchain adoption incentivizes channels to raise prices in early sales periods but lower prices later on. The results also reveal how consumer strategic behavior influences optimal pricing. This research contributes new knowledge on incorporating emerging blockchain technologies into retail channel design and operations.

Over the past few decades, the use of non-renewable energy has progressively expanded, harming the environment. In this investigation, 4-stroke single-cylinder Reactivity Controlled Compression Ignition (RCCI) engine performance and emission behaviour are reduced with the help of running fuel. 20% Juliflora biodiesel and 80% diesel are used as high-reactive fuel (HRF) and Ethanol is used as the low-reactive fuel (LRF). The RCCI engine is evaluated at different input conditions by varying engine load from 0 to 100 (0, 25, 50, 75, and 100%) and LRF percentage from 30 to 60 (30, 40, 50 and 60%). Additionally Exhaust Gas Recirculation (EGR) is used to enhance the RCCI engine emission behaviour and performance.The studied output performance of RCCI engine are cylinder pressure (CP), brake thermal efficiency (BTE), heat release rate (HRR), and brake-specific fuel consumption (BSFC) respectively. Also, unburned hydrocarbon (HC), carbon monoxide (CO), nitrogen oxides (NOX), and smoke opacity (SO) are calculated on the RCCI engine for all input condition. The test results are further optimized with the help of hybrid deep belief neural network based Aquila optimization method. The proposed hybrid DBN-AO has performed better than conventional DBN method.The predicted optimal value is obtained from the regression and average regression coefficients of 0.99961. The predicted optimum values are load 80%, LRF60%, and EGR 15%, respectively. The confirmatory error analysis has shown BTE (3.7%), BSFC (4%), SO (4.7%), HC (7.775%), CO (3.44%) and NOx (3.46%) respectively. The EGR application reduces the RCCI engine emission behaviour in loading condition.

In this paper, an improved differential evolution algorithm is proposed to improve the positioning accuracy of electronic wristband system. The wristband system is composed of STM32F103 microcontroller, Beidou positioning module, LORA communication module and so on. In the traditional differential evolution algorithm, premature convergence to local optimal will reduce the accuracy of the algorithm. To solve this problem, the method introduces the distance information between known and unknown points and between all unknown points in the fitness function and overall initialization. This reduces the premature convergence of the algorithm and improves the optimization ability of the algorithm. Experimental results using the CICDDoS2019 dataset show that the localization accuracy of the proposed algorithm is improved by 7.6% compared with traditional differential evolution. The method also has good stability. This provides an effective way to improve the positioning accuracy of the wristband, which can be used for applications such as patient monitoring.

This paper examines the challenge of integrated production and distribution, aiming to deliver products to customers precisely on time. Customers, situated within the transportation network, have predefined requirements regarding demand volume and time frames. In the first phase (F1), the problem of planning and allocation of resources is presented as FJSP, while the second phase (F2) addresses the vehicle routing problem as CVRPTW. The first phase, F1, aims to optimize manufacturing processes by appropriately scheduling production tasks to maximize productivity and minimize the time of task execution on machines. Phase 2, F2, encompasses the process of distribution to customers, seeking to minimize the number of vehicles, delivery time, and overall distance travelled. As both problems are among the most challenging in combinatorial optimization, integrating these phases into a single supply chain system poses a significant challenge in problem-solving. A mathematical formulation has been developed to include planning and task allocation in production, as well as vehicle routing, to obtain an optimal solution to the integrated problem. The input data used in the observed case study represent real data in both the first and second phases, forming one integrated supply chain system. Experimental results support the applied methodology.

Pulse signals are often corrupted by noise, compromising signal integrity for downstream analysis. This paper presents an automated denoising technique for pulse waveforms using ensemble empirical mode decomposition (EEMD). The EEMD algorithm decomposes the signal into intrinsic mode functions (IMFs). Statistical metrics of IMF energy and entropy identify noise components for targeted removal via nonlinear filtering. Experiments on simulated pulse echoes demonstrated the approach of accurately eliminated noise regions. Compared to wavelet decomposition and Monte Carlo methods, the EEMD technique exhibited superior noise reduction and over 90% faster processing. This ensemble empirical mode decomposition approach provides an efficient, data-driven methodology for denoising pulse waveforms with applications in biomedical signal analysis.

The essence of computer vision technology is to combine computers with image data to enhance their understanding and perception abilities. One of the major research hotspots in computer vision technology is the object recognition, and in the field of object recognition, a major challenge is the recognition of weak and small target images. In response to the current difficulty in detecting weak and small target images, a visual communication design with image processing models and data fusion was proposed. The Single Shot MultiBox Detector was improved to construct a weak and small target detection model, and the feature fusion method was combined to enhance its weak and small target detection ability. The ablation experiment showed that in contrast with the original model, the improved model improved the detection ability of weak and small targets by 26.54%, and the overall accuracy improved by 11.05%. Two other advanced algorithms were selected for comparison with the research algorithm, and the accuracy of the research algorithm was better, with a higher accuracy of 47.67% -79.56% than the comparison algorithm. The response time was shorter, reaching 0.62 seconds. Visual communication time and success rate performed better, with a communication time lead of 9 s to 19 s and a success rate lead of 16% to 27%. In summary, the algorithm proposed by the research institute has higher accuracy in detecting small and weak images, as well as higher visual communication efficiency and success rate, which has stronger practical significance in the field of computer vision.

Over the previous decade, there has been a significant focus on researching underwater acoustic sensor networks (UW-ASNs) for a diverse range of underwater applications, which in turn has facilitated human exploration of the expansive underwater environment. This research introduces an innovative architectural approach that signifies a noteworthy advancement. By combining both acoustic and optical components, it establishes an underwater wireless sensor network. Additionally, the research introduces an innovative multiple levels Q learning-grounded direction-finding procedure, denoted as the proposed system Multi-layer Guidance Approach (MLGA) which is meticulously tailored for such underwater networks. The network's architecture encompasses both physical grouping and logical division into two tiers: the upper tier is overseen by group leaders responsible for managing routing within the lower tier, where group members execute the actual data packet routing. This design capitalizes on the wider viewpoint of upper-tier group leaders and the concurrent learning processes occurring across all groups, resulting in a substantial enhancement in routing efficiency in comparison with traditional methodologies. The empirical results obtained from experimental tests underscore the robustness of the proposed system when confronted with changes in network topology. Moreover, it showcases the system's ability to achieve higher delivery rates and reduced delays in dynamic networks compared to the established approach of flat Q-learning routing. This innovative strategy holds the potential to significantly push the boundaries of underwater sensor networks, surpassing the constraints of conventional communication methods and providing a more effective and dependable means of transmitting data underwater. This advancement not only contributes to the technical aspects but also holds promise for fostering greater exploration and understanding of underwater environments.

The knowledge of the temperature history during the welding process is the crucial first step for understanding the metallurgical and mechanical effects of welding. The present study focused on finite element method for prediction of the thermal cycles induced during three-pass arc welding of butt plates. Two dimensional numerical analysis was performed by taking into account plane at the mid-section of the welded plate. Considering observed welded plates are symmetrical, half of the model was discretized into mesh of rectangular elements with four nodes. The nodal heat quantities were determined according to the real welding parameters and heat inputs were introduced in numerical analysis for defined finite element nodes. The heat flow in the thickness direction was neglected. The finite element model and heat inputs were defined through ANSYS Parametric Design Language code. The thermal analysis boundary condition was defined as convection which was specified using a convection coefficient of 5ꞏ10‒6 W/mm2K and reference temperature of 25 °C at all nodes. The analysis was carried out for three different finite element models in order to define optimal mesh model. The main difference between observed finite element models is element size and total number of elements. In order to determine the most appropriate finite element model, numerical results of temperature distribution were compared with real experimental values that were measured in transverse direction, during welding process by using thermocouples. Six locations in total, each at varying distances from the weld line, were used as observed points for temperature measuring. The practical application of the used approach was confirmed by establishing that, in the case of all FE models at each observed point, the values of the experimental measurement nearly match the FEA results. The optimal FE model was defined according to cost and time reduction.

With the rapid rise of online classrooms, monitoring student engagement is critical but challenging for educators. This work explores how artificial intelligence (AI) and big data techniques can automatically evaluate student concentration levels in online courses. We developed an end-to-end ResTCN model combining ResNet and temporal convolutional networks (TCN) to extract spatial and temporal video features. Further, we introduced a CutMix data augmentation method and an efficient channel attention (ECA) module to enhance model training. Evaluated on a public dataset of student videos, our approach achieved 63.28% accuracy in classifying student engagement, outperforming state-of-the-art methods. The contributions are a novel spatiotemporal neural architecture, data augmentation strategy, and attention mechanism tailored for the student engagement recognition task. This demonstrates the potential of AI in creating smart education systems.

The hybrid Dielectric Resonator Antenna (DRA) with circular polarization is another key research area since it can provide better antenna characteristics required by modern-day communication standards than the DRA with linear polarization. In this article, a Hybrid rectangular DRA is proposed. The Proposed DRA is excited with a novel feeding structure which is the combination of apex etched rectangular monopole and microstrip feed. The feeding structure is printed on a material with a dielectric constant of 4.4. The DR of 9.8 dielectric constants is placed on top of the patch. The proposed structure resonates at four different bands 1.42-1.52 GHz, 3.76-4.36 GHz, 4.92-5.35 GHz, and 7.02-8.74 GHz. The Critical parameters are analyzed and optimal dimensions are identified using the CST software. The proposed antenna exhibits circular polarization, which is validated with the axial ratio and electrical field distribution. The simulated results of the surface current are presented to validate the performance The measurement result is compared with the simulated results and found to be in agreement. The gain is well maintained above 1.5 dBi in all the resonating bands. A stable radiation pattern across all the resonating bands is achieved.

Using dynamic wireless charging systems in conjunction with electric vehicles (EV) could revolutionize transportation in the future by enabling faster driving and non-stop battery charging. Dynamic wireless power transfer can fix the autonomy problems with electric vehicles. The fact that this charging device gets its power from the electrical grid has some serious drawbacks, too. Utilizing alternate sources, like PV generators, is therefore seen to be a potential answer. This study aims to illustrate the advantages that can arise from the interplay of renewable energy sources with wireless recharge systems for electric vehicles in this environment. Therefore, this paper is built and has regrouped the needed sections that can define the wireless recharge concept based on an alternative energy source. Simulation results were adapted by the Matlab Simulink platform, where more than one situation is tested and examined. This is by testing the influence of the vehicleꞌs speed on the wireless charging performances and by testing what effect can be done if the climatic conditions are varied, especially on the global performances of the wireless recharge tool.

The digital economy has emerged as a significant catalyst for economic expansion on a global scale, and this trend is evident in China. China is emerging as a dominant force in the digital economy and manufacturing industry due to its burgeoning entrepreneurial culture and emphasis on top-notch development. The present study investigates the interplay among the digital economy, entrepreneurship, and the high-quality development of the manufacturing industry in China. This paper comprehensively analyzes the current digital economy and entrepreneurship status in China. It examines the policies and initiatives to foster high-quality development in the manufacturing sector. This paper investigates the influence of digital technologies on productivity and efficiency within the manufacturing industry. It presents an analysis of data and case studies to underscore the notable advancements achieved by China in the digital economy and manufacturing sector while acknowledging the obstacles that remain to be overcome. The results indicate that prioritizing digital technologies, innovation, and entrepreneurship can bolster the manufacturing sector's competitiveness, resulting in sustainable economic expansion and progress. The investigation recognizes the obstacles that necessitate resolution to completely actualize the possibilities of the digital economy and entrepreneurship within the manufacturing sector, utilizing the Multi-Criteria Decision Making (MCDM) approach. The paper advocates for increased cooperation among government, industry, and academia to cultivate a conducive environment for digital entrepreneurship. This collaboration facilitates the manufacturing industry's ability to leverage the digital era's opportunities.

Public emergencies are occurring with increasing frequency worldwide, leading to what is often referred to as a high-risk society. Amid various public emergencies, urban communities take on a more prominent role. However, studies on assessing the emergency preparedness and response capacity of urban communities are currently limited. To evaluate the essential factors, both inherent and external, that affect the emergency management capacities of urban communities, an index system for assessing community emergency preparedness and response capacity was developed by using the theory of the emergency management cycle. Acknowledging the complexity and ambiguity of emergency preparedness and response capacity within urban communities, a multilayer fuzzy comprehensive evaluation model was established using the entropy weight method. This model was then applied to assess the emergency preparedness and response capacity of communities against the backdrop of the COVID-19 public emergency. Results show that the assessment outcomes generated by the multilayer fuzzy comprehensive evaluation model, employing the entropy weight method, align with the real-world situation, signifying the soundness and effectiveness of the selected methods and index system. The conclusion offers a novel basis and methodology for evaluating the emergency preparedness and response capacity of urban communities, holding considerable practical value.

How to transform the business model of enterprises and make it more in line with the new requirements of industrial upgrading is an important part of the government's supply-side reform in the era of Industry 4.0. For the current research on the good management transformation of digital economy in the fourth Industrial revolution, linear function and four commonly used nonlinear function models are first selected to test the good management transformation of digital economy in different technologies, and the key success factors of the strategic transformation from traditional industry to Industry 4.0 business model are explored based on fuzzy hierarchical analysis. Logistic and Gompertz models were used to judge the life cycle stage of the hot technologies of the fourth Industrial revolution, and based on their development trend clustering, the hot technology groups of the fourth Industrial revolution were explored. Secondly, k-means clustering algorithm based on the optimal class center perturbation is proposed, and simulation experiments are carried out. A clustering algorithm based on k-means algorithm is designed, and the moving mode of k-means in the algorithm is changed, and a disturbance strategy is added to strengthen the adaptive intelligent decision-making ability of the algorithm. K-means clustering algorithm based on the optimal class center disturbance is proposed, and simulation experiments are conducted. At the same time, the values of step size factors in the algorithm are compared experimentally. Finally, it explores the function mechanism of digital economy, management transformation and management transformation in the operation of supply chain node enterprises. By analyzing the current situation and problems of digital operation of logistics and supply chain enterprises, it constructs the digital operation system of supply chain enterprises, and proposes the path of digital transformation and upgrading of supply chain enterprises, so as to promote the rapid development of supply chain node enterprises.

Leisure agriculture is a growing trend in the agricultural tourism industry. Human activities negatively affect ecosystem and agricultural output, and these effects worsen as the economy grows. Therefore, most conventional agricultural firms rely on external sources for technological advancement to outsource development or only engage in a limited number of innovation chains. A novel model named the Agricultural and Tourism Dual Chain Model of Leisure Agriculture (ATDCM-LA), is proposed after a thorough examination of the circular economy and presented in this study. Improvements in modern Leisure Agriculture tourism base planning may be attained via ecological agriculture by analyzing the impacts on regional planning. Fuzzy Analytical Hierarchy Technique (FAHT) was used to generate administrators' relative weights for farm leisure growth, and indicators were dynamically ranked by importance. Data Envelope Analysis (DEA) based on Circular Economy (CE) has applied to the existing state of leisure and tourism agriculture, which scales back the expansion of leisure agriculture and prioritizes growth initiatives that will have the most significant impact. This research integrates the goals of promoting rural culture with those of tourism administration and economic theory in agriculture, and it concludes with recommendations for regional growth of leisure agricultural tourist facilities. Leisure agricultural tourist base planning and administration results reveal the necessity to merge rural tradition with the sector's agrarian investment. Finally, the findings indicate that a rural-culture-based Leisure Agriculture tourist basis has some potential for success in regional development.

The paper introduces a fractal geometry utilizing an Octagonal Split Ring Resonator (SRR) hybrid DRA as its basis. The primary antenna consists of a conventional Octagonal patch, which is then scaled down by 0.5 in the first iteration and further by 0.25 in the second and then the DRA is utilized to enhance its performance. Remarkably compact at 6 × 6.2 cm, the antenna demonstrates effectiveness across frequencies of 5.1, 6.8, and 7.2 GHz notably; it maintains a gain above 3.3 dBi and directivity above 4.1 dBi in all operational bands. Analysis employing surface current and return loss substantiates the impact of the fractal design with DR. These simulations and measured results affirm the proposed antenna's suitability for robust C band wireless applications, offering promising performance in antenna parameters.

In order to determine the Lüders band propagation along the deformation zone length and width as affected by the force action direction, a detailed line analysis is performed by applying thermography and digital image correlation (DIC) during static tensile test at different test positions. The test is stopped at the point when the Lüders band propagation reached its middle to analyse temperature changes and deformations occurring in front of, on and behind the Lüders band front. Line analysis of the test samples confirmed that there are changes in the distribution of temperature and deformations. Across the width of the test sample, there are oscillations and differences in the distribution of temperature changes in the deformation zone. However, the same analysis showed that there are no differences in the change of deformations across the width of the test sample, except in the zone behind the Lüders band front where the Lüders band propagated. The influence of microstructure, i.e. the different dislocation density and the interaction of dislocations with fine precipitates, proved to be the cause of change in temperature and deformations measured at the moment when the Lüders band propagation reached its middle.

In order to meet the design requirements of gearbox for different gears, a novel method to verify bolts and gearbox housing is applied, which reduced the time and cost of experimental testing. The finite element analysis of this article is divided into two parts. Firstly, the axial force of the bolt is calculated by finite element method, and the bolts are verified according to VDI2230 standard. Secondly, the force of the gearbox housing is calculated and the strength of the gearbox housing in different gears is verified. Results show that the maximum axial force exerted on the bolt is 4761.8N at the hole 16. The maximum stress value of the gearbox is 234.2 MPa in the first gear state, which closes to the tensile strength 240 MPa. The maximum stress value of the improved gearbox housing is reduced to 133.9 MPa, meeting the usage requirements. In addition, this article established a finite element verification process for the gearbox housing. Although the grid size and boundary conditions apply only to this type of gearbox, the analysis process is applicable to the static strength verification of any gearbox housing.

An automatic annotation method of coal gangue image data based on X-ray acquisition is proposed. Firstly, the manually screened coal and gangue are sent to the X-ray acquisition device for image sampling. Secondly, according to the characteristics of the pure background of coal and gangue images, a gray threshold is used to detect. Further, the mask is used to purify the detected foreground objects, which obtain a single object image. At the same time, the manually screened coal/gangue is sampled and the category information of the currently detected objects is obtained at the same time, and the monomer sample database is established. Finally, based on the monomer sample library established, the monomers randomly were selected in the sample library and combined to generate sample images with classification and location. That is, the automatic annotation of coal gangue data is completed. The results show that the automatic annotation method of coal gangue image data through digital image processing can effectively annotate the collected coal gangue image data, with a high sample generation rate and annotation accuracy of 99%.

On the basis of analyzing the natural frequency of the structure, the identification quantity of each process is constructed with modal parameters and input into the machine learning as characteristic parameters to realize the damage identification. By extracting the median curve of vegetation index time series after 5G filtering in the damaged area of typical civil structures, and comparing it with the actual growth curve of crops in the area, the vegetation index time series monitoring model was constructed, and 10 was selected as the best threshold. The accuracy of the result is verified, and the iteration time is 0.18 hours. A damage detection method based on machine learning is proposed. Good prediction results are obtained for three common surface damage of concrete cracks, spalling and exposed steel bars, which verify the ability of this method to accurately identify and detect structural surface damage at pixel level.

This paper aims to present and deploy an improved task scheduling algorithm for the allocation of user tasks across multiple computing resources. The primary goal of this algorithm is to minimize both execution time and costs while simultaneously enhancing resource utilization within the context of medical applications. Virtual machine scheduling in a heterogeneous cloud environment needs significant attention with the increase in the usage of cloud resources by end users and enterprises. It is one of the significant parameters that affects cloud data centers. The resources requested by every user vary in their configuration. Finding a suitable virtual machine for each process is dynamically a time-consuming process. Virtual machines are classified based on resources such as memory and processing units. Upon the arrival of a request with specific requirements, it can be effortlessly mapped to a corresponding virtual machine. This process is followed by a bilateral method encompassing queuing and scheduling. Queues are formed for requests with different requirements, which are followed by a scheduling algorithm that allocates VMs based on the minimum remaining resources in the resource pool. A scheduling mechanism has been designed to solve the problem of starvation that occurs with the Min-Min fit scheduling policy. The average turnaround time and waiting times are observed to be significantly reduced, which has an impact on the performance of the data center for medical applications. Using the CloudSim Plus tool, the experimental outcomes demonstrated that the proposed approach exhibited remarkable superiority over competing methods in relation to metrics such as average waiting time, turnaround time, and response time. This advantage was observed when compared to multiple algorithms that were examined during the study.

It takes a long time to detect target information in noisy radar information and reduce the probability of false alarm. Therefore, it has become a research direction to reduce the probability of false alarm and the time of effective target detection. This paper introduces a new method to reduce the occurrence of false alarm in non-uniform environment and improve the efficiency of target detection. The proposed method involves a faster and more stable method that involves preprocessing the data set, splitting it into smaller parts, and utilizing a KTH minimum value M determined by an ordered statistics class constant false alarm detection algorithm. Each data point in the small segment is then compared to M, anything above M is classified as a target, and anything below M is ignored as clutter. Then ESVI-CFAR detection was performed on the selected target to obtain the final detection result. Experimental results show that the proposed method is superior to the traditional VI-CFAR and has better target detection performance.

In recent years, the sports environment has realized the significance of physical and psychological features. The sports staffs, athletes, and coaches have shown that new theories and treatments are used to enhance psychology. The basic needs in the individuals' social life are urban public sports. Based on the equalized public services, this paper provides the urban facilities of equalizing public sports. National and consistent rules can provide urban public sports products and services, which are basic to citizens, considering their livelihoods and entertainment needs. In this paper, Semantic Multilevel Structural Equation Modeling (SMSEM) has proposed to assess the sport psychological demand for urban public sports services by artificial intelligence and multimedia data analysis. The goal is to concentrate closely on the demand for sports by the masses, which improves the quality and efficiency of the government's urban public sports service supply. It also promotes the construction of urban sports and leisure cities, allows more people to enjoy urban public sports and guarantees basic sports rights. The growth of positive psychology has a wide range of theories and application fields and enriches new sports psychology theory and application. The relationship between psychological monitoring and sports exercise is closest to the field of competitive sports. The systematic and specialized direction is moving in psychological guidance. In the future application, the results obtained from sports psychology are more applicable.

This paper discusses two different thermo-technical systems for heating and cooling the three-story office building under consideration. Data were collected on natural gas energy consumption over three-year period. The measured data on energy consumption for heating and cooling the building with an absorption heat pump using natural gas as fuel was analyzed. The only device that can replace the absorption heat pump in both the heating and cooling seasons is the vapor compression heat pump.The absorption heat pump has higher energy consumption than the vapor compression heat pump and its price is about 30% higher, but the financial cost of energy from natural gas is about 39% lower than the electricity cost of the vapor compression heat pump. The absorption heat pump is a better solution than the compression heat pump because the total financial costs, which include the investment costs and the energy costs over 20 years of operation, are about 9% lower and because the absorption heat pump heats up to an ambient temperature of ‒20 °C, which is not the case with the vapor compression heat pump.

Congestion control is among the most challenging tasks in enhancing QoS in the Internet of Things (IoT). Currently, wireless networks are able to have a large number of connections but with a limited amount of network resources. Consequently, congestion occurs, which adversely affects throughput, transmission delay, packet losses, power consumption management, and the lifespan of a network. This is certainly relevant in networks where transmissions are controlled by the Routing Protocol for Low-Power and Lossy Networks (RPL), which is commonly employed in the Internet of Things network. To solve this problem, a novel Geodetic fuzzy subgraph-based ranking (GFSR-RPL) for congestion control is proposed. Initially, the proposed GFSR-RPL selects the cluster head using K-means clustering. Then the rank calculation can be done via the final route setting for data transmission. A route setup scheme consists of three elements: 1) a Round Trip Time (RTT) estimator that assesses congestion conditions in a variety of ways; 2) a trend and relative strength indicator analysis; and 3) a geodetic fuzzy subgraph rank calculation method that calculates initial RTO (initial retransmission timeouts) accurately. The proposed GFSR-RPL method reduces the energy consumption of up to 43.58%, 25.8%, 14.82% and 6.85% than existing methods such as RPR, CBR-RPL, ACW and ECLRPL.

Ancestral halls in Canton region are an important part of culture and architectural art in Canton region. Currently, research mostly analyzes from the perspective of clan culture and architectural crafts, and rarely explores architectural characteristics and inheritance from the perspective of cultural gene and constructs the gene map of architectural culture. Taking the theory of cultural gene as the starting point, this paper extensively studies the cultural background and inheritance process of cultural genes of ancestral halls in Canton region through literature research and field research. Based on the thinking method of gene reverse transcription in biology and the cultural three-level hierarchy, the main cultural factors were extracted and classified to establish the cultural gene library, gene hierarchical diagram and gene maps of Cantonese ancestral halls. The Analytic Hierarchy Process method (AHP) was used to screen and rank the cultural factors analyzing their importance in all levels in the cultural gene hierarchical diagram. To summarize, this paper discussed the development and evolution of cultural factors of Cantonese ancestral halls and their contemporary inheritance requires more attention on the innovative translation design of core cultural factors with higher importance, based on the principle of authentic and integral preservation of original, providing new perspective and decision-making references for the inheritance of ancestral halls in Canton region.

Due to advancements in the technology, video transmission is a key feature in several applications. The transmission of the video from the source to the sink is quite different from the transmission of images-based data. The transmission of the video over the wireless sensor network is not an easy task with the available protocols. They have several disadvantages related to the energy efficiency and the reliability. Novel architecture namely high-quality video transmission architecture has been proposed in this paper. This is an energy efficient architecture. To conserve the energy during the transmission, this architecture does not support retransmission of the packets. But the dropping of the packets helps in improving the reliability and the life time of the network. This architecture majorly involves three layers namely the application layer, the network layer and the transport layer. The transmission of the data packets is based on the priority levels. Moving Picture Experts Group (MPEG - 2) technologies is used in the proposed work. Two different frames namely the key frame and the variant frame are used to prioritize the packets. The data packets with the highest priority from the variant frame is transmitted first and then followed by the data packets of the key frame. The data is segmented into smaller units and discrete cosine transform is applied over each unit. The inter link between the transport layer, application layer and the network layer is analysed by means of the state diagram. The results have been tabulated and depicted by means of graphical representations. Comparative analysis has been made between the proposed high quality video transmission architecture and the diversified architecture. The proposed method has been found to have produced comparatively better results and possesses a high quality video transmission with enhanced Peak Signal to Noise Ratio (PSNR) and reduces energy consumption.

With the development of my country's industrial level, the prefabricated building (PB) model has gradually become the mainstream. In order to more scientifically study the development policy of the prefabricated building industry, a system dynamics model with the boundary of resource input, technology research and development, production capacity and market demand system is constructed from the perspective of industry chain development, based on the influence of external subjects of the industry chain on the development of the industry chain. Afterwards, dynamic simulations of the development trend of prefabricated buildings under different policy scenarios are carried out. The results show that the increase in capital investment in the industrial chain and the improvement of the prefabricated assembly rate of government-invested construction projects are strong short-term incentive policies, while corporate tax incentives and prefabricated building purchase loan incentives are long-term incentive policies. Combined with the simulation results, the policy formulation recommendations based on the development trend of the industry chain are put forward.

Borderless e-commerce originated within the international trade domain and has been significantly shaped by the societal backdrop of economic globalization and the rapid advancement of internet technologies. This evolution has given rise to numerous B2C borderless e-commerce platforms. While there has been a growing recognition of the importance of service innovation, there remains an incomplete understanding and a need for further refinement in the service evaluation system. Consequently, the scientific assessment and enhancement of service innovation capabilities have become focal points of research both domestically and internationally. Existing research has predominantly focused on the development models and challenges of import e-commerce retail platforms, with theoretical studies taking precedence. However, there is relatively limited emphasis on the evaluation and empirical analysis of service innovation capabilities in borderless e-commerce platforms. This study aims to bridge the existing research gap by constructing a scientifically accurate evaluation framework and employing statistical methods to comprehensively investigate and assess the service innovation levels of Chinese import borderless e-commerce retail platforms. To address this gap in understanding, this study has selected four import borderless e-commerce retail platforms in China as research subjects. Subsequently, an evaluation model for these import borderless e-commerce retail platforms was constructed through factor analysis to establish an evaluation index system. Then, the service innovation abilities of these four platforms were calculated and ranked within this index system using the TOPSIS model. This process facilitates a deeper exploration of the underlying reasons for their differences and provides clear developmental pathways for these platforms to bolster their service innovation capabilities. The results indicate that there exists a direct correlation between the market share and popularity of e-commerce platforms and their service innovation capabilities. Furthermore, luxury brand marketing and high market share can significantly enhance their service performance. These findings carry valuable implications for guiding the development of borderless e-commerce import platforms.

Food, energy and water are the welfare of the whole mankind, but unfortunately they are often used to blackmail those who suffer from their lack. In this light the influence of the European Union Trading System in the field of mineral fertilizer production is analysed. Based on the available data for mineral fertilizer plant free allocation for the emission of greenhouse gases, predominantly carbon dioxide and in smaller part nitric oxide, is calculated. Determining of the internal mass and energy fluxes is very challenging due to the fact that required pieces of information are not publicly available. Thus, the volume of ammonia and natural gas used in the energy balance is assumed to allow the calculation of preliminary free emissions in the production of ammonia and nitric acid subsection and for heat and fuel. Also, benchmarks and carbon leakage exposure factors are set by the European Commission and taken for the fourth trading period. Historic activity level is calculated from available data. The discussion is aimed at assessing the impact of allotted and measured emissions and their financial impact to agricultural activity.

In order to measure the quality of aluminum alloy laser welding workpiece online, an optical coherence tomography on-line detection system was established. Porosity is one of the most common defects in laser welding of aluminum alloy. The porosity produced during welding will seriously affect the welding quality. Firstly, a test device of laser welding quality detection system is built based on optical coherence tomography algorithm. Then, the theoretical model of the optical coherence tomography detection system is built, and the key parameters affecting the detection device are qualitatively analyzed. Then, deep convolutional neural network algorithm is used to process the image. Finally, the testing equipment is used to test the sample, and the testing results are analyzed. The experimental results show that this method can detect the weld quality of laser welding, and the detection accuracy is 20 μm.

In order to study the environmental vibration characteristics and evaluation of the saturated soil site in the cold northern region caused by the train, vibration observation was carried out on a saturated soil site in a near railway area of the Harbin-Manzhouli Railway, and the environmental vibration caused by different trains at different operating speeds was analyzed in the time and frequency domains. The observation results were evaluated by using the 1/3 octave vibration acceleration level and the maximum weighted plumb vibration level as evaluation index. The results show that the acceleration decreases with the increase of distance, and the acceleration decreases rapidly from 10 m to 30 m, and slowly after 30 m. The vibration energy of the measurement points at 10 m and 20 m is concentrated in 15 - 60 Hz, and the vibration energy of the measurement points after 30 m is concentrated in 15 - 20 Hz. With the increase of the distance from the center of the track, the 1/3 octave vibration acceleration level and the maximum weighted plumb vibration level all show a trend of attenuation. The maximum weighted plumb vibration levels at 30 m are 70.8 dB and 71.3 dB for upward and downward trains, and 77.4 dB and 71.4 dB for upward and downward freight trains, respectively, which are greater than the weighted plumb vibration level limit for special residential buildings. The maximum value of vibration acceleration level at the 10 m measurement point is 88.6 dB, which exceeds the limit by 9.5 dB, and that of 30 m measurement point is 69.8 dB, which meets the specification requirements. The results of the study can provide basic data for the environmental vibration characterization of saturated soil sites caused by train operation and the environmental vibration design of residential, agricultural and water conservancy infrastructures in the near-railway area.

The widespread adoption of online social networks has facilitated content sharing among users. However, privacy controls restrict users' access to only a limited portion of the network, typically limited to direct connections or two-hop friends. Browsing relevant profiles and home pages has become a common practice for users, but the vast amount of data involved often hampers their ability to efficiently retrieve the desired information. This paper presents an efficient keyword search model designed to aid users in accessing the required information effectively. Leveraging advancements in Bloom filter technology, we propose a novel summary index called Friend-based Bloom filter (FBF), which enables large-scale full-text retrieval while reducing inter-server communication costs and query latency. We conduct a comprehensive simulation to evaluate our ranking model, and the results demonstrate the effectiveness of the FBF scheme. Specifically, our approach achieves a reduction of 92.4% in inter-server communication costs and 78.7% in query latency, with a high-search precision condition resulting in a remarkable 98.3% improvement.

The growth of cities induced an increased demand for infrastructure including tunnel constructions. Because the urban ground usually is soft soil therefore tunnel constructions may cause ground settlement and damage the structures around. So, it is very important to carefully consider the tunnel construction effect on the design, construction, operation, and risk assessment of structures around the tunnel. In this paper, the finite element method was used to study the influence of the tunnel excavation on beneath piled buildings at the Hanoi metro line 03. These results indicate that the internal forces and displacements of the pile are greatest for the front pile closest to the tunnel in the group, which is the first pile to be loaded in the group. Two parameters: the tunnel burial depth and the distance from the tunnel centreline to the beneath-piled building have been investigated. These results found that the distance from the tunnel axis to the beneath piled building increases, while both the internal forces and displacements of the pile decrease. These internal forces and displacements increase with greater tunnel burial depth. The magnitude of normal forces and bending moments in the tunnel lining increase as the tunnel burial depth increases, while normal forces increase and bending moments in the tunnel lining decrease when the distance from the tunnel axis to the beneath-piled building decreases.

There is an interaction between subway station and the catchment area around the station, the catchment area of subway station on surrounding land is closely related to the connection modes. At present, subway passengers mainly reach subway stations by walking, cycling and bus, and the catchment area of the three connection modes is different. In order to clarify the catchment area of subway stations, it is necessary to study the catchment area of the three connection modes of subway stations. This study starts with a generalized connection cost for each connection mode, constructing the generalized connection cost models, especially considering the psychological changes of passengers with the increase of connecting distance. On this basis, we use Logit-SUE model to construct the probability density curve of the superior connection distance of different connection modes in different types and different area subway stations. Taking Beijing Metro Line 4 as the research case, through analyzing the probability density curve of each station of Line 4 we calculate the advantageous connection range of each subway station with different connection modes, and take the advantageous connection range as the catchment area for each type of connection mode.

With the normalization of deep mining, the problem of mine thermal damage has been widely concerned. In this paper, 64 logging temperature data of surface boreholes of Zhujidong coalfield in Huainan City, Anhui Province, China, were summarized and sorted. The temperature data of 12 measuring points at the current main working level of −906 m and −965 m were measured. The ground temperature conditions of the vertical direction, horizontal direction, and main coal seam floor in the coalfield were analyzed. The characteristics of the present geothermal field and roadway heat damage, and its influencing factors were discussed. The results showed that the geothermal gradients of this area are between 1.7 ℃/hm and 3.6 ℃/hm, and the average geothermal gradient is 2.607 ℃/hm. It shows the transmission of warming in the vertical direction and horizontal direction shows the ground temperature lowering from south to north, east to west. The geothermal field in this area is mainly affected by the geological structure, and the thermal physical properties of rocks, magmatic rocks and groundwater activities also have a certain effect. At present, most of the main work level has reached the first heat hazard area, and some are in the second heat hazard area, according to Chinese regulations. The working face temperature is keeping above 28 7 ℃ and the relative humidity is being maintained above 70% for a long time. The problem of heat damage is extremely serious. It is suggested to construct the thermal environment control measures combining active and passive ways.

In order to logically prioritize the urgent risks in the heterogeneous mobile network operating environment, we derive environmental factors that can reflect the characteristics of the heterogeneous network operating environment and present them as an improved security risk assessment formula. The prioritized risks derived through this improved risk assessment formula can visually express the severity of the risk by using computer vision. The purpose of this study was to derive environmental factors that can reflect the security control characteristics of various heterogeneous network operating environments and to apply them to security risk evaluation formulas to prioritize urgent risks and easily identify the degree of security risks. In the existing risk assessment formula, risk is calculated based on three indices: the importance of the asset, the vulnerability score, and the threat score. However, two problems were derived from the existing risk assessment. First, the existing risk assessment formula is insufficient to reflect the controlled environment characteristics of each network because the risk level is calculated based on individual assets. Second, if the same systems with the same purpose (same settings) are operated in different heterogeneous network operating environments, they are counted at the same risk level, and action cannot be prioritized quickly. To solve these problems, we propose an indicator called environmental factor (E), which is a combination of three indices. The three indices are "Network Diversity Index (NDI), network Zone Separation Index (ZSI) and Control Level Index (CLI)". NDI expressed the diversity of networks numerically. ZSI is a numerical expression of the complexity of the network zone. CLI is a numerical expression of the degree of network control level. Results of the study showed that the risk assessment formula applying the proposed risk assessment factors can quickly identify urgent risks and act quickly. In heterogeneous mobile network operating environment in which numerous systems are operated, really urgent risks among the risks calculated through the proposed risk assessment will be handled quickly and logically.

Digital agricultural innovation ecosystems defined the notion of agricultural innovation ecosystems in regional areas. Developing a data economy for agriculture based on digital spaces necessitates an awareness of and proficiency with digital innovation ecosystems. The digital formation of agriculture has played a great role in enhancing agrarian production, encouraging the ecological development of the agricultural economy, and accomplishing sustainable economic goals. The profound integration of the digital economy and the agriculture industry has become a major concern. A multifaceted technology expansion across the agricultural economy, a Remote Sensing Assisted Digital Agriculture Innovation Ecosystem (RS-DAIE) has been developed to enhance country-level digital agriculture requirements. Therefore, simple guidelines for building an efficient marketing strategy are crucial for expanding access to healthy food options and fostering the growth of organic farmers locally and internationally. The trial findings show that RS-DAIE has the finest accuracy by 98.9%, reliability rate by 99.3%, data transmission by 97.3%, and moisture content ratios, which are better than other technologies.

As a result of relevant policies and regulations, most wastewater treatment plants are faced with upgrading to further improve the level of effluent targets. To this end, this paper conducts an experimental study on deep denitrification in the sulfur Oyster shells mixotrophic nitrification filter process using sulfur as filler. During the experiments, when the water temperature in the mixotrophic pool was 15 °C, the nitrogen load of the inflow was 7.3 × 10−3kg/m3·d and HRT equaled to 3.5 h, the average TN concentration in the effluent is 3.42 mg/L, and the TN removal rate reaches 54.49%, which can stably meet the core control area standard in the "Discharge Standard of Water Pollutants in Daqing River Basin" (DB13/2795-2018) and are the best operating parameters during the experimental period. The test results show that oyster shells can provide a large amount of alkalinity, alleviating the pH drop in the water column and effectively mitigating the acidification of the water column. Based on experimental calculations, without considering the loss of packing material, the operating cost of the sulphur-mixed denitrification filter process is reduced by $ 0.191 per tonne of water compared with the existing deep treatment unit in the WWTP. The above results show that the sulphur mixer denitrification filter has the ability to degrade the secondary effluent TN in depth, which provides some experimental basis for the sulphur mixer denitrification filter to be used as a deep treatment unit.