In recent years, blockchain research has drawn attention from all across the world. It is a decentralized competence that is spread out and uncertain. Several nations and scholars have already successfully applied blockchain in numerous arenas. Blockchain is essential in delicate situations because it secures data and keeps it from being altered or forged. In addition, the market’s increased demand for data is driving demand for data scaling across all industries. Researchers from many nations have used blockchain in various sectors over time, thus bringing extreme focus to this newly escalating blockchain domain. Every research project begins with in-depth knowledge about the working domain, and new interest information about blockchain is quite scattered. This study analyzes academic literature on blockchain technology, emphasizing three key aspects: blockchain storage, scalability, and availability. These are critical areas within the broader field of blockchain technology. This study employs CiteSpace and VOSviewer to understand the current state of research in these areas comprehensively. These are bibliometric analysis tools commonly used in academic research to examine patterns and relationships within scientific literature. Thus, to visualize a way to store data with scalability and availability while keeping the security of the blockchain in sync, the required research has been performed on the storage, scalability, and availability of data in the blockchain environment. The ultimate goal is to contribute to developing secure and efficient data storage solutions within blockchain technology. Full article

The computational capabilities of computer tools expand the student’s search capabilities. Conducting computational experiments in the classroom is no longer an organizational problem. This raises the “black box” problem, when the student perceives the computational module as a magician’s box and loses conceptual control over the computational process. This article analyses the use of various computer tools, both existing and specially created for “key” computational experiments, that aim at revealing the essential aspects of the introduced concepts using specific examples. This article deals with a number of topics of algebra and calculus that are transitional from school to university, and it shows how computational experiments in the form of a “transparent” box can be used. Full article

This paper presents the original results of research on an inline jet mixer being an alternative to other, conventional mixing apparatuses used for extraction processes. In particular, researched novel geometry of a jet mixer was subjected to testing of either hydraulic performance or a liquid–liquid extraction process. Inline jet mixers are well suited for mixing gases and liquids and can be used in such processes as extraction, heat exchange, and reaction. In such an apparatus, mixing of liquids takes place by high-velocity injection of one stream into another through a series of small holes placed peripherally to a concentrically mounted inner tube. The literature lacks the data to allow for the design of these types of mixers. Extraction experiments were performed for the ethyl acetate–ethanol–water system. The research results presented in this paper enable the calculation of mixing power and the selection of optimal mixer operating parameters. Equations describing the flow resistance for both streams were developed. The mixing power was calculated and compared with other types of contactors. The data on overall volumetric mass transfer coefficients obtained by this study showed that the considered extractor is competitive with other conventional contactors at almost identical or even lower energy consumption. Full article

Phase-change materials (PCMs) are attractive materials for storing thermal energy thanks to the energy supplied/returned during the change in matter state. The encapsulation of PCMs prevent them from connecting into large clusters, prevents the chemical interaction of the PCM with the walls of the tank and the exchanger material, and allows the phase change to be initiated in parallel in each capsule. The microencapsulation of PCMs (mPCMs) and the nanoencapsulation of PCMs (nPCMs) entail that these particles added to the base liquid can act as a slurry used in heat exchange systems. PCM micro-/nanocapsules or mPCM (nPCM) slurry are subjected to numerous physical, mechanical, and rheological tests. However, flow tests of mPCM (nPCM) slurries are significantly limited. This paper describes the results of detailed adiabatic flow tests of mPCM slurry in a tube with an internal diameter of d = 4 mm and a length of L = 400 mm. The tests were conducted during laminar, transient, and turbulent flows (Re < 11,250) of mPCM aqueous slurries with concentrations of 4.30%, 6.45%, 8.60%, 10.75%, 12.90%, 15.05%, and 17.20%. The mPCM slurry had a temperature of T = 7 °C (the microcapsule PCM was a solid), T = 24 °C (the microcapsule PCM was undergoing a phase change), and T = 44 °C (the microcapsule PCM was a liquid). This work aims to fill the research gap on the effect of the mPCM slurry concentration on the critical Reynolds number. It was found that the concentration of the mPCM has a significant effect on the critical Reynolds number, and the higher the concentration of mPCM in the base liquid, the more difficult it was to keep the laminar flow. Additionally, it was observed that, as yet unknown in the literature, the temperature of the slurry (and perhaps the physical state of the PCM in the microcapsule) may affect the critical Reynolds number. Full article

In December 2014, a novel foot and mouth disease (FMD) virus was introduced to a pig farm in South Korea, despite the animals being vaccinated. A marginal antigenic matching between the novel and vaccine strains potentially led to the infection of the vaccinated animals. To understand the impact of using an FMD vaccine on the transmission dynamics of an unmatched field strain, simulation models were employed using daily reported data on clinical cases from the farm. The results of this study indicated that immunisation with the FMD vaccine reduced the shedding of the novel FMD virus in pigs. However, there was no evidence to suggest that the immunisation had a significant effect in reducing the development of clinical signs. These findings highlight that the use of an unmatched FMD vaccine can confound the outbreak by altering the disease dynamics of the novel virus. Based on this study, we emphasise the importance of continuous testing to ensure antigenic matching between the circulating strains and the vaccine pool. Full article

This study proposes a new method to accurately evaluate the overall building envelope thermal performance considering the window–wall correlation, providing a new tool for building thermal design. Firstly, a non-stationary room heat transfer model is established based on the Resistance-Capacity Network method. The influence of solar heat gain through the windows on the heat transfer process of the walls in the actual environment is considered, and the room’s integrated thermal resistance and integrated heat capacity indexes describing the overall room thermal resilience performance are proposed. Then, a field research test is conducted around Lhasa to obtain the local dwelling information, climate conditions, and indoor thermal environment. Numerical simulations using EnergyPlus are made to verify the effectiveness of the indexes in describing the overall building (maximum difference within 3.67% MBE and 2.92% CVRMSE) based on the field test results. Finally, the proposed envelope thermal performance index is used to analyze the local residential buildings around Lhasa. The results show that the lack of consideration of window–wall correlation has led to the failure of a local newly built building’s actual envelope performance to meet the design requirements. These findings could help to develop the thermal design method of the building envelope. Full article

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures. Full article

The dynamic surface properties of aqueous dispersions of α-lactalbumin (ALA) amyloid fibrils differ noticeably from the properties of the fibril dispersions of other globular proteins. As a result, the protocol of the application of ALA fibrils to form stable foams and emulsions has to be deviate from that of other protein fibrils. Unlike the fibrils of β-lactoglobulin and lysozyme, ALA fibrils can be easily purified from hydrolyzed peptides and native protein molecules. The application of the oscillating barrier method shows that the dynamic surface elasticity of ALA fibril dispersions exceeds the surface elasticity of native protein solutions at pH 2. ALA fibrils proved to be stable at this pH, but the stability breaks at higher pH levels when the fibrils start to release small peptides of high surface activity. As a result, the dynamic surface properties of ALA coincide with those of native protein solutions. The ionic strength strongly influences the adsorption kinetics of both fibril dispersions and native protein solutions but have almost no impact on the structure of the adsorption layers. Full article

The Japanese National Database (NDB), a useful data source for epidemiological studies, contains information on health checkups, disease diagnoses, and medications, which can be used when investigating common cardiometabolic diseases. However, before the initiation of an integrated analysis, we need to combine several pieces of information prepared separately into an all-in-one dataset (AIOD) and confirm the validation of the dataset for the study. In this study, we aimed to confirm the degree of agreement in data entries between diagnoses and prescribed medications and self-reported pharmacotherapy for common cardiometabolic diseases in newly assembled AIODs. The present study included 10,183,619 people who underwent health checkups from April 2018 to March 2019. Over 95% of patients prescribed antihypertensive and antidiabetic medications were diagnosed with each disease. For dyslipidemia, over 95% of patients prescribed medications were diagnosed with at least one of the following: dyslipidemia, hypercholesterolemia, or hyperlipidemia. Similarly, over 95% of patients prescribed medications for hyperuricemia were diagnosed with either hyperuricemia or gout. Additionally, over 90% of patients with self-reported medications for hypertension, diabetes, and dyslipidemia were diagnosed with each disease, although the proportions differed among age groups. Our study demonstrated high levels of agreement between diagnoses and prescribed medications for common cardiometabolic diseases and self-reported pharmacotherapy in our AIOD. Full article

This paper discusses the results of a study to determine the effect of the chemical composition of two tin-based bearing alloys (B89 and B83) on their tribological properties. The tribological properties were tested using a T05 block-on-ring tester under technically dry and wet friction conditions. The research includes the determination of the wear rates, loss of mass, coefficients of friction, and changes in the coefficient of friction as a function of the process and material parameters. A study of the microstructure and base properties of such alloys, which affect the tribological properties and wear, are also presented. The study showed that chemical composition has a significant effect on the tribological properties; increasing the proportion and changing the morphology of the SnSb precipitates to rhomboidal in the B83 alloy results in an increase in wear resistance represented by loss of mass. Decreasing the size and proportion of these precipitates results in a stabilization of the frictional force variation and a slight decrease in the coefficient of friction. The research showed that SnSb phase precipitation is mainly responsible for the wear resistance of the investigated bearing alloys. Full article

For engines operating using heavy fuel oil (HFO), the nozzle rings of turbocharger turbines are prone to severe degradation because of contamination with unburned fuel deposits. This contamination may lead to increased excitation of blade resonance. A previous study provides technical guidelines on how to extract the relevant information from pulsation spectra using a single probe installed away from the turbine trailing edge and some sound experimental proofs of integral mode turbine vibration detection. These theoretical discussions only allude to the effects of mistuning and interferences due to classical blade passing frequencies on sound radiation patterns emitted by integral blade vibration modes. In this study, both effects are thoroughly discussed. Combining the knowledge of theoretical study and further experimental results, the application range of this blade vibration detection method can be remarkably extended. Full article

Long-term dependence is an essential feature for the predictability of time series. Estimating the parameter that describes long memory is essential to describing the behavior of time series models. However, most long memory estimation methods assume that this parameter has a constant value throughout the time series, and do not consider that the parameter may change over time. In this work, we propose an automated methodology that combines the estimation methodologies of the fractional differentiation parameter (and/or Hurst parameter) with its application to Recurrent Neural Networks (RNNs) in order for said networks to learn and predict long memory dependencies from information obtained in nonlinear time series. The proposal combines three methods that allow for better approximation in the prediction of the values of the parameters for each one of the windows obtained, using Recurrent Neural Networks as an adaptive method to learn and predict the dependencies of long memory in Time Series. For the RNNs, we have evaluated four different architectures: the Simple RNN, LSTM, the BiLSTM, and the GRU. These models are built from blocks with gates controlling the cell state and memory. We have evaluated the proposed approach using both synthetic and real-world data sets. We have simulated ARFIMA models for the synthetic data to generate several time series by varying the fractional differentiation parameter. We have evaluated the proposed approach using synthetic and real datasets using Whittle’s estimates of the Hurst parameter classically obtained in each window. We have simulated ARFIMA models in such a way that the synthetic data generate several time series by varying the fractional differentiation parameter. The real-world IPSA stock option index and Tree Ringtime series datasets were evaluated. All of the results show that the proposed approach can predict the Hurst exponent with good performance by selecting the optimal window size and overlap change. Full article

Breast cancer is currently the most diagnosed form of cancer and the leading cause of death by cancer among females worldwide. We described the family of long non-coding mitochondrial RNAs (ncmtRNAs), comprised of sense (SncmtRNA) and antisense (ASncmtRNA) members. Knockdown of ASncmtRNAs using antisense oligonucleotides (ASOs) induces proliferative arrest and apoptotic death of tumor cells, but not normal cells, from various tissue origins. In order to study the mechanisms underlying this selectivity, in this study we performed RNAseq in MDA-MB-231 breast cancer cells transfected with ASncmtRNA-specific ASO or control-ASO, or left untransfected. Bioinformatic analysis yielded several differentially expressed cell-cycle-related genes, from which we selected Aurora kinase A (AURKA) and topoisomerase IIα (TOP2A) for RT-qPCR and western blot validation in MDA-MB-231 and MCF7 breast cancer cells, as well as normal breast epithelial cells (HMEC). We observed no clear differences regarding mRNA levels but both proteins were downregulated in tumor cells and upregulated in normal cells. Since these proteins play a role in genomic integrity, this inverse effect of ASncmtRNA knockdown could account for tumor cell downfall whilst protecting normal cells, suggesting this approach could be used for genomic protection under cancer treatment regimens or other scenarios. Full article

Mobile service robots often have to work in dynamic and cluttered environments. Multiple safety hazards exist for robots in such work environments, which visual sensors may not detect in time before collisions or robotic damage. An alternative hazard alert system using tactile methods is explored to pre-emptively convey surrounding spatial information to robots working in complex environments or under poor lighting conditions. The proposed method for robot-inclusive tactile paving is known as Passive Auto-Tactile Heuristic (PATH) tiles. These robot-inclusive tactile paving tiles are implemented in spatial infrastructure and are aimed to allow robots to pre-emptively recognize surrounding hazards even under poor lighting conditions and potentially provide improved hazard cues to visually impaired people. A corresponding Tactile Sensing Module (TSM) was used for the digital interpretation of the PATH tiles and was mounted onboard a mobile audit robot known as Meerkat. The experiment yielded a 71.6% improvement in pre-emptive hazard detection capabilities with the TSM using a customized Graph Neural Network (GNN) model. Full article

Improving rice yield is one of the most important food issues internationally. It is an undeniable goal of rice breeding, and the effective panicle number (EPN) is a key factor determining rice yield. Increasing the EPN in rice is a major way to increase rice yield. Currently, the main quantitative trait locus (QTL) for EPN in rice is limited, and there is also limited research on the gene for EPN in rice. Therefore, the excavation and analysis of major genes related to EPN in rice is of great significance for molecular breeding and yield improvement. This study used japonica rice varieties Dongfu 114 and Longyang 11 to construct an F₅ population consisting of 309 individual plants. Two extreme phenotypic pools were constructed by identifying the EPN of the population, and QTL-seq analysis was performed to obtain three main effective QTL intervals for EPN. This analysis also helped to screen out 34 candidate genes. Then, EPN time expression pattern analysis was performed on these 34 genes to screen out six candidate genes with higher expression levels. Using a 3K database to perform haplotype analysis on these six genes, we selected haplotypes with significant differences in EPN. Finally, five candidate genes related to EPN were obtained. Full article

Due to their effective and affordable joining capabilities, resistance spot-welded (RSW) structures are widely used in many industries, including the automotive, aerospace, and manufacturing sectors. Because spot-welded structures are frequently subjected to cyclic stress conditions while in service, fatigue failure is a serious concern. It is essential to comprehend and predict their fatigue behavior in order to guarantee the dependability and durability of the relevant engineering products. The analysis of fatigue failure in spot-welded structures is the main topic of this paper, along with the prediction of fatigue life (N_f) and identification of failure mechanisms. Also, the effects of parameters such as the amount of cyclic load applied, the load ratio, and size of the spot-welding on the N_f were investigated. To achieve this, the fatigue performance of spot-welded joints was simulated using the extended finite element method (XFEM). The XFEM method is particularly suited for capturing intricate crack patterns in spot-welded structures because it allows for the modeling of crack propagation without the need for remeshing. It was observed that when the cycling load was decreased by 20%, N_f increased by around 250%. On the other hand, the fatigue life of the structure, and, hence, the crack propagation rate, was significantly affected by the load ratio and diameter of the spot-welding. This paper presents the details of the novel approach to studying spot-weld fatigue characterization using XFEMs to simulate crack propagation. Full article

African swine fever (ASF) is an infectious disease that affects both domestic pigs (DPs) and wild boar (WB). The WB population plays an important role in the spread of ASF as the WB acts as a natural reservoir of the virus and transmits it to other susceptible wild and domestic pigs. Our study was aimed at revealing the areas with a high concentration of the WB population, and their potential relationships with the grouping of ASF cases in WB during the course of the ASF spread in the Russian Federation (2007–2022). We collected the annual data on WB numbers by municipalities within the regions of the most intensive ASF spread. We then conducted spatiotemporal analysis to identify clustering areas of ASF cases and compare them with the territories with a high density of WB population. We found that some of the territories with elevated ASF incidence in WB demonstrated spatial and temporal coincidence with the areas with a high WB population density. We also visualized the zones (“emerging hot spots”) with a statistically significant rise in the WB population density in recent years, which may be treated as areas of paramount importance for the application of surveillance measures and WB population control. Full article

Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets. Full article

The fundamental stages in designing a stock portfolio are each stock’s selection and capital weighting. Selection and weighting must be conducted through diversification and price movement analysis to maximize profits and minimize losses. The problem is how the technical implementations of both are carried out. Based on this problem, this study aims to design these selection and weighting mechanisms. Stock selection is based on clusters and price movement trends. The optimal stock clusters are formed using the K-Means algorithm, and price movement analyses are carried out using the moving average indicator. The selected stocks are those whose prices have increasing trends with the most significant Sharpe ratio in each cluster. Then, the capital weighting for each preferred stock is carried out using the mean-variance model with transaction cost and income tax. After designing the mechanism, it is applied to Indonesia’s 80 index stock data. In addition, a comparison is conducted between the estimated portfolio return and the actual one day ahead. Finally, the sensitivity of investors’ courage in taking risks to their profits and losses is also analyzed. This research is expected to assist investors in diversification and price movement analysis of the stocks in the portfolios they form. Full article

This study examines the effect of mining pollutants and wear products on the rheological and tribological properties of a lubricating grease working in the microclimate of the Polkowice-Sieroszowice mine belonging to the KGHM Polska Miedź Group (Polkowice, Poland). The material under investigation is a commercial lubricating grease thickened with complex lithium soap, based on mineral oil with a molybdenum disulfide (MoS₂) addition. A sample of the grease was taken from one of the friction junctions of a self-propelled drilling jumbo operated in the mine. Comparative tests of the fresh grease and the spent grease were carried out. For the two greases, rheological tests, i.e., dynamic oscillation tests and tests in steady flow conditions, were carried out at a constant shear rate. The rheological tests were conducted using a rotational rheometer. Moreover, the tribological characteristics of the tested greases under different friction junction loads were carried out using a ball-on-disc tribometer. Besides friction resistance, the lubrication ability of the two greases was also evaluated through an analysis of the wear of the steel disks after the friction process. Contour and topographic maps of the wear traces of the discs together with their wear profiles were compared. Full article

P-glycoprotein (encoded by the ABCB1 gene) has a dual role in regulating inflammation and reducing chemotherapy efficacy in various diseases, but there are few studies focused on pulmonary TB patients. In this study, our objective was to identify a list of genes that correlate with high and low levels of ABCB1 gene expression in the lungs of pulmonary TB patients with different activity of chronic granulomatous inflammation. We compared gene expression in two groups of samples (with moderate and high activity of tuberculomas) to identify their characteristic gene signatures. Gene expression levels were determined using quantitative PCR in samples of perifocal area of granulomas, which were obtained from 65 patients after surgical intervention. Subsequently, two distinct gene signatures associated with high inflammation activity were identified. The first signature demonstrated increased expression of HIF1a, TGM2, IL6, SOCS3, and STAT3, which correlated with high ABCB1 expression. The second signature was characterized by high expression of TNFa and CD163 and low expression of ABCB1. These results provide insight into various inflammatory mechanisms and association with P-gp gene expression in lung tissue of pulmonary TB patients and will be useful in the development of a host-directed therapy approach to improving the effectiveness of anti-TB treatment. Full article

Introduction: Asthma and bronchiectasis are often partners in a complex but uneven relationship with asthma receiving more attention. The aim of this study is to describe how bronchiectasis is investigated in some Severe Asthma (SA) Centers, scattered throughout the Italian territory. Materials and Methods: We enrolled 92 patients with SA and bronchiectasis from eight Italian SA Centers and recorded diagnostic approaches to investigate SA and bronchiectasis at the time of enrollment (T0), at the 6-month (T1), and at the 12-month (T2) follow-up visits. Results: A statistically significant heterogeneous diagnostic approach emerged across the centers under study. In fact, while, as expected, all involved centers made an in-depth investigation of SA, only a few of them provided a complete investigation of bronchiectasis in order to provide specific treatment. Discussion: This real-life multicenter study confirmed that patients with coexistent SA and bronchiectasis are mainly investigated for pheno-endotyping asthma but rarely for the complete assessment of bronchiectasis. We believe that the diagnostic flowchart of SA patients with suspicion or confirmed bronchiectasis needs to be clarified and implemented as the association of these conditions strongly influences the final outcome and management of these patients. Full article

This study investigated the replacement of butter with soy wax (SW)/rice bran oil (RBO) oleogel in varied proportions in cookie dough and the resulting cookies. The study mainly evaluates the physical, textural, and chemical properties of the butter cookie dough and cookies by replacing butter with SW/RBO oleogel. The dough was assessed using moisture analysis, microscopy, FTIR Spectroscopy (Fourier Transform Infrared) and impedance spectroscopies, and texture analysis. Micrographs of the dough showed that D-50 (50% butter + 50% oleogel) had an optimal distribution of water and protein. D-0 (control sample containing 100% butter) showed the lowest impedance values. Moisture content ranged between 23% and 25%. FTIR spectroscopy suggested that D-50 exhibited a consistent distribution of water and protein, which CLSM and brightfield microscopy supported. Texture analysis revealed that the dough samples exhibited predominantly fluidic behavior. As the amount of oleogel was raised, the dough became firmer. The prepared cookies showed a brown periphery and light-colored center. Further, a corresponding increase in surface cracks was observed as the oleogel content was increased. Cookies moisture analysis revealed a range between 11 and 15%. Minute changes were observed in the texture and dimensions of the cookies. In summary, it can be concluded that replacing butter with oleogel by up to 50% seems to be feasible without significantly compromising the physicochemical properties of cookie dough and cookies. Full article

Community outdoor public spaces are indispensable to urban residents’ daily lives. Analyzing community outdoor public spaces from a behavioral perspective is crucial and an effective way to support human-centered development in urban areas. Traditional behavioral analysis often relies on manually collected behavioral data, which is time-consuming, labor-intensive, and lacks data breadth. With the use of sensors, the breadth of behavioral data has greatly increased, but its accuracy is still insufficient, especially in the fine-grained differentiation of populations and behaviors. Computer vision is more efficient in distinguishing populations and recognizing behaviors. However, most existing computer vision applications face some challenges. For example, behavior recognition is limited to pedestrian trajectory recognition, and there are few that recognize the diverse behaviors of crowds. In view of these gaps, this paper proposes a more efficient approach that employs computer vision tools to examine different populations and different behaviors, obtain important statistical measures of spatial behavior, taking the Bajiao Cultural Square in Beijing as a test bed. This population and behavior recognition model presents several improvement strategies: Firstly, by leveraging an attention mechanism, which emulates the human selective cognitive mechanism, it is capable of accentuating pertinent information while disregarding extraneous data, and the ResNet backbone network can be refined by integrating channel attention. This enables the amplification of critical feature channels or the suppression of irrelevant feature channels, thereby enhancing the efficacy of population and behavior recognition. Secondly, it uses public datasets and self-made data to construct the dataset required by this model to improve the robustness of the detection model in specific scenarios. This model can distinguish five types of people and six kinds of behaviors, with an identification accuracy of 83%, achieving fine-grained behavior detection for different populations. To a certain extent, it solves the problem that traditional data face of large-scale behavioral data being difficult to refine. The population and behavior recognition model was adapted and applied in conjunction with spatial typology analysis, and we can conclude that different crowds have different behavioral preferences. There is inconsistency in the use of space by different crowds, there is inconsistency between behavioral and spatial function, and behavior is concentrated over time. This provides more comprehensive and reliable decision support for fine-grained planning and design. Full article