Spatial Analysis of the Aging Population and Socio-economic Factors of China:Global and Local Perspectives

Population aging has become an inevitable trend and exerted profound influences on socio-economic development in China.In this study,we utilized data from national population census and statistical yearbooks in 2010 and 2020 to explore spatio-temporal patterns of aging population and its coupling correlations with socio-economic factors from both global and local perspectives.The results from Local Indicators of Spatial Association(LISA)uncover notable spatial disparities in aging population rates,with higher rates concentrated in the eastern regions and lower rates in the western areas of the Chinese mainland.The results from the global correlation analysis with the changes in aging population rates show significant positive correlations with government interventions and industrial structures,but negatively correlated with economic development,social consumption,and medical facilities.From a local perspective,a Geographically Weighted(GW)correlation analysis is employed to uncover local correlations between aging trends and socio-economic factors.The insights gained from this technique not only underscore the complexity and diversity of economic implications stemming from population aging,but also provide invaluable guidance for crafting region-specific economic policies tailored to various stages of population aging.

Application and Promotion of Non-pesticide Replacing High-toxic Pesticide Techniques and Its Benefit Analysis in Kunming City

[ Objective] The paper was to operate the application and promotion of non-pesticide replacing high-toxic pesticides techniques in Kunming City, and to analyze its benefit. [ Method ] Through application and promotion of frequency trembler grid lamps, sticking plate trapping technology, construction of treatment ponds for field waste vegetable leaves, standardized （accurate） cultivation techniques, cultivation techniques of disease-resistant varieties and diverse cultivation technologies, the promotion benefit of non-pesticide replacing high-toxic pesticides techniques was comprehensively investigated and evaluated. [ Result ] The appli- cation and promotion area of non-pesticide replacing high-toxic pesticides techniques in Kunming City during 2006 -2010 reached 94 667 hm2. The investigation on control efforts and quantitative analysis of cost/benefit showed that the beneficial result of application and promotion of non-pesticide replacing high-toxic pesticides techniques was higher than the direct benefit of application and promotion of traditional pesticide replacing techniques. This improvement innovated the traditional pesticide replacing method in replacement work of high-toxic pesticides, reducing the usage volume of pesticide in Kunming City. [ Conclusion] The application and promotion of non-pesticide replacing high-toxic pesticides techniques improved the economic, social and ecological benefit of replacement work of high-toxic pesticides, protected the agricultural ecological environment and promoted the sustainable development of agricultural production.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

Innovative Techniques Unveiled in Advanced Sheet Pile Curtain Design

This thorough review explores the complexities of geotechnical engineering, emphasizing soil-structure interaction (SSI). The investigation centers on sheet pile design, examining two primary methodologies: Limit Equilibrium Methods (LEM) and Soil-Structure Interaction Methods (SSIM). While LEM methods, grounded in classical principles, provide valuable insights for preliminary design considerations, they may encounter limitations in addressing real-world complexities. In contrast, SSIM methods, including the SSI-SR approach, introduce precision and depth to the field. By employing numerical techniques such as Finite Element (FE) and Finite Difference (FD) analyses, these methods enable engineers to navigate the dynamics of soil-structure interaction. The exploration extends to SSI-FE, highlighting its essential role in civil engineering. By integrating Finite Element analysis with considerations for soil-structure interaction, the SSI-FE method offers a holistic understanding of how structures dynamically interact with their geotechnical environment. Throughout this exploration, the study dissects critical components governing SSIM methods, providing engineers with tools to navigate the intricate landscape of geotechnical design. The study acknowledges the significance of the Mohr-Coulomb constitutive model while recognizing its limitations, and guiding practitioners toward informed decision-making in geotechnical analyses. As the article concludes, it underscores the importance of continuous learning and innovation for the future of geotechnical engineering. With advancing technology and an evolving understanding of soil-structure interaction, the study remains committed to ensuring the safety, stability, and efficiency of geotechnical structures through cutting-edge design and analysis techniques.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Accumulation of heavy metals in two crop seeds due to soil contamination as determined by neutron activation analysis techniques

Samples of two crop seeds (corn and sesame) grown on different contaminated soil sources were collected and prepared for neutron activation analysis. Soil contamination sources were organic waste compost applied to soil amendments namely, sewage sludge(Bs) and municipal solid waste (MSW) at 4%, 6% and 8% respectively. The non destructive NAA technique was used to determine some trace elements accumulation in plant seeds. Results revealed that increasing rate of tested organic waste resulted in dramatic increase in tested heavy metals levels in seeds(i.e., Fe, Zn, Co, Cr, Sc and Hg). Sesame seeds showed higher affinity to accumulate trace elements than corn grains in most tested elements. Moreover, MSW addition enhanced the accumulation of tested metals in seeds more than Bs compost.

Analysis of Pneumonia Model via Efficient Computing Techniques

Pneumonia is a highly transmissible disease in children.According to the World Health Organization(WHO),the most affected regions include south Asia and sub-Saharan Africa.Worldwide,15%of pediatric deaths can be attributed to pneumonia.Computing techniques have a significant role in science,engineering,and many other fields.In this study,we focused on the efficiency of numerical techniques via computer programs.We studied the dynamics of the pneumonia-like infections of epidemic models using numerical techniques.We discuss two types of analysis:dynamical and numerical.The dynamical analysis included positivity,boundedness,local stability,reproduction number,and equilibria of the model.We also discusswell-known computing techniques including Euler,Runge Kutta,and non-standard finite difference(NSFD)for the model.The non-standard finite difference(NSFD)technique shows convergence to the true equilibrium points of the model for any time step size.However,Euler and Runge Kutta do not work well over large time intervals.Computing techniques are the suitable tool for crosschecking the theoretical analysis of the model.

Determination of Inorganic Antimony Species Conversions during Its Speciation Analysis in Soil Using Isotope Dilution Techniques

A new method was developed in this work to account for inorganic Sb species interconversion during soil sample preparation and subsequent separation steps. The Sb(III) and Sb(V) concentrations at each investigated step in the analytical procedure were determined using species specific spikes (121Sb(III) with 81.18% and 123Sb(V) with 74.04% enrichment). The spiking of these enriched isotopes species solutions was done separately before soil sample extraction and before HPLC separation. Simply by subtracting the final concentration of each species done by on-line isotope dilution (ID) from its concentration at different stages of the analytical procedure done by species specific ID, the influence of each step on species transformation can be estimated. After optimization, the extraction procedure for inorganic Sb species 6% Sb(III) (1.3 RSD, n = 3) and 43.2% Sb(V) (2.9% RSD, n = 3) as percent of total Sb were detected in the examined soil sample using online ID. Using the above described methodology we found that there was no reduction of Sb(V) to Sb(III) during sample preparation or species separation. While about 9.3% of extractable Sb (4.6% of total) was converted from Sb(III) to Sb(V) during the extraction step, no conversion during HPLC separation step was observed. By compensating for Sb(III) transformation during the sample preparation step;the extractable Sb(III) and Sb(V) as percent of total Sb yielded 10.6% and 38.7%, respectively.

Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.

An Analysis on the Discourse Cohesion Techniques in Children's English Books

Discourse cohesion techniques analysis attracts much attention both at home and abroad and many scholars have conducted their research in this field, however, few of them focus on children's English books which has its own characteristics and cohesion techniques and deserves our research.

Volatile profile and multivariant analysis of Sanhuang chicken breast in combination with Chinese 5-spice blend and garam masala

Sanhuang chicken is a popular native breed in China and well-known for delicious flavour.Spices could enhance the chicken meat flavour and work well in preservation.Chinese 5-spice blend(CS)and garam masala(GM)are routinely using spices in China and Pakistan,respectively.The flavour profiles of Sanhuang chicken breast(CB)and its blends with CS and GM were obtained by electronic nose(E-nose),solid-phase microextraction gas chromatography-mass spectrometry(SPME GC-MS)and GC-ion mobility spectrometry(GC-IMS).Principal component analysis(PCA)efficiently discriminated the aroma profiles of three chicken formulations.The GC-chromatographs revealed the significant aroma alterations of chicken breast meat after marination with spices.Aldehydes were the major contributors of chicken aroma,while most of the aromatic hydrocarbons were generated by spices.Almost all chicken key-compounds produced by oxidation reaction were either reduced or eliminated by marination,showing the antioxidation capacity of spices leading to meat preservation.GC-IMS is not only a rapid and comprehensive detection method,but also proved to be more sensitive than GC-MS.The substantial role of both traditional spices in enhancing flavour quality of chicken meat,and their exposure as functional ingredients in Chinese and Pakistan cuisines could lead to the cross-cultural meat trade opportunities.

Entropy generation analysis from the time-dependent quadratic combined convective flow with multiple diffusions and nonlinear thermal radiation

Diffusions of multiple components have numerous applications such as underground water flow, pollutant movement, stratospheric warming, and food processing. Particularly, liquid hydrogen is used in the cooling process of the aeroplane. Further, liquid nitrogen can find applications in cooling equipment or electronic devices, i.e., high temperature superconducting(HTS) cables. So, herein, we have analysed the entropy generation(EG), nonlinear thermal radiation and unsteady(time-dependent) nature of the flow on quadratic combined convective flow over a permeable slender cylinder with diffusions of liquid hydrogen and nitrogen. The governing equations for flow and heat transfer characteristics are expressed in terms of nonlinear coupled partial differential equations. The solutions of these equations are attempted numerically by employing the quasilinearization technique with the implicit finite difference approximation. It is found that EG is minimum for double diffusion(liquid hydrogen and heat diffusion)than triple diffusion(diffusion of liquid hydrogen, nitrogen and heat). The enhancing values of the radiation parameter R_(d) and temperature ratio θ_(w) augment the fluid temperature for steady and unsteady cases as well as the local Nusselt number. Because, the fluid absorbs the heat energy released due to radiation, and in turn releases the heat energy from the cylinder to the surrounding surface.

Exploring the Application of Vibration Analysis Technology in the Medical Field

In the field of engine maintenance and assurance, the technology of unit condition detection through vibration analysis is relatively mature. More and more patents and technical products have been released, proving the practical value of the technology in mechanical vibration from the application level. In medical science, signals such as heart sounds and pulses are also vibration signals in nature, in order to expand the application of the technology and explore the value of the technology in medical applications. In order to extend the application of the technology and to explore the value of the technology in medical applications, the wavelet analysis technology was used to program the Labview2022 software to implement the corresponding analysis program for the analysis of the collected physiological signals. Finally, the wavelet transform-based analysis of the physiological signals was successfully implemented. It is demonstrated that the design concept can be achieved by applying this technique, which makes it valuable in the field of physiological signal detection and analysis.

EFFECT OF TROPICAL CYCLONE PRECIPITATION ON ALLEVIATING THE DROUGHT SITUATION IN THE SOUTHEAST COASTAL REGIONS OF CHINA DURING SUMMER AND AUTUMN:USING THE IMPROVED OBJECTIVE SYNOPTIC ANALYSIS TECHNIQUE

To quantitatively study the role of tropical cyclone precipitation(TCP) on alleviating the drought in the southeast coastal region of China(SCR) during summer and autumn,the objective synoptic analysis technique(OSAT),improved for consistency and rationality,was used to separate the TCP data on the summers and autumns of 1963-2005 on the basis of daily precipitation data from stations and tropical cyclone best track data.After defining the season drought index,the actual drought distribution and the assumed drought distribution without TCP were acquired.The results showed that within 1 000 km from the southeast coastline of China,TCP accounted for 11.3%of natural precipitation(NP).Without TCP,the drought index in the SCR during summer would have increased from 0.2 to 0.6 or even above 1.0 in some regions whereas the drought index during autumn would have increased from 0.4 to 0.6 or above 1.2 in some regions.The impact of TCP on drought decreases progressively from the southeast coastline to the inland regions.The TCP proportion(TCPP) showed a significant negative correlation with the drought index in many regions of the southeast,and the significant region is wider in autumn than in summer.TCP relieved the drought most significantly within a range of 0-500 km from the southeast coastline.This drought relief showed different characteristics for the interannual variability in summer and autumn,and the cross wavelet transform indicated that the impact of TCP on drought mainly lies in 2-4-year time scales.In particular,there was a significant effect during the summers of 1977-1985 and in the autumns following that of 1985.Therefore,TCP has indeed largely alleviated drought in the SCR during summer and autumn.

New Trends in the Modeling of Diseases Through Computational Techniques

The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance.The handling strategies of real-world problems are artificial neural networks(ANN),evolutionary computing(EC),and many more.An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually,with only 25%to 45%reported to the World Health Organization(WHO).It remains one of the top parasitic diseases with outbreak and mortality potential.In 2020,more than ninety percent of new cases reported to World Health Organization(WHO)occurred in ten countries:Brazil,China,Ethiopia,Eritrea,India,Kenya,Somalia,South Sudan,Sudan,and Yemen.The transmission of visceral leishmaniasis is studied dynamically and numerically.The study included positivity,boundedness,equilibria,reproduction number,and local stability of the model in the dynamical analysis.Some detailed methods like Runge Kutta and Euler depend on time steps and violate the physical relevance of the disease.They produce negative and unbounded results,so in disease dynamics,such developments have no biological significance;in other words,these results are meaningless.But the implicit nonstandard finite difference method does not depend on time step,positive,bounded,dynamic and consistent.All the computational techniques and their results were compared using computer simulations.

An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.

Extraction and Analysis of Coronal High-temperature Components Based on Outlier Detection

The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging Assembly.However,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the predictions.In this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del's method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many differences.In order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del's method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 MK.It is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del's method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.

Fast Tensor Principal Component Analysis via Proximal Alternating Direction Method with Vectorized Technique

This paper studies the problem of tensor principal component analysis (PCA). Usually the tensor PCA is viewed as a low-rank matrix completion problem via matrix factorization technique, and nuclear norm is used as a convex approximation of the rank operator under mild condition. However, most nuclear norm minimization approaches are based on SVD operations. Given a matrix , the time complexity of SVD operation is O(mn2), which brings prohibitive computational complexity in large-scale problems. In this paper, an efficient and scalable algorithm for tensor principal component analysis is proposed which is called Linearized Alternating Direction Method with Vectorized technique for Tensor Principal Component Analysis (LADMVTPCA). Different from traditional matrix factorization methods, LADMVTPCA utilizes the vectorized technique to formulate the tensor as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. In the experiment part, synthetic tensor data with different orders are used to empirically evaluate the proposed algorithm LADMVTPCA. Results have shown that LADMVTPCA outperforms matrix factorization based method.

Optimization of Baosteel's digital automatic analysis technique for maceral specification

Baosteel developed a digital automatic analysis technique for maceral specification in 2002. This analysis system combines digital image processing, graphics, databases, expert systems, artificial intelligence and other advanced technologies. After 6 years of application in coke production, the system proved itself successful in coal quality testing and coal blending guidance on maceral. However,during this long process, some inadequacies were found that impacted the precision and accuracy of the analysis. So ,in 2008 Baosteel began to work on improving the coal maceral analysis system. The improvements included the following：further upgrading and enhancing the analysis performance of microscopic images ;extending the gray levels to increase the reflectance measurement accuracy 64 times;changing the focus method and effectively eliminating the interference of halo. In addition, an improved image recognition method was adopted to make the extraction of vitrinite more accurate and a new model of coal constituent algorithm was added which can accurately determine the composition of maceral （exinite, vitrinite,inertinite）. Since these improvements were completed, the system has achieved higher automation, speed and accuracy, collected more information and performed more accurate maceral analysis for coke production. Meanwhile, the improved system has provided a reliable analytical basis for the further study on the relationship between coke quality and coal blending.

Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

This paper presents a procedure of sing le gear tooth analysis for early detection and diagnosis of gear faults. The objec tive of this procedure is to develop a method for more sensitive detection of th e incipient faults and locating the faults in the gear. The main idea of the sin gle gear tooth analysis is that the vibration signals collected with a high samp ling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of i ndividual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the locat ion of a failed tooth can be indicated in terms of the position of the segment t hat deviates from the normal segments. An experimental investigation verified th e advantages of the single gear tooth analysis.