Explosive Development Research Articles

Exploring Spatiotemporal Interaction Patterns of Dockless Shared Bicycle Networks: A Case Study of Shenzhen, China

Abstract. The explosive development of shared bicycles has changed the way people travel. In parallel with the urbanization process, shared bicycles have emerged as a significant mode of transportation, especially for the first and last mile transit. With the aim of obtaining a comprehensive understanding of the spatiotemporal patterns underlying shared bicycle usage, this study takes Shenzhen, China as a case study. Drawing on the principles and techniques of complex network analysis, the research adopts a spatial grid framework, treating each grid as a node, the flow of shared bicycles as an edge, and the flow frequency as the corresponding weight. Subsequently, the spatiotemporal interaction networks of different periods of working days and weekends are constructed respectively, based on which the topological characteristics are analysed. The results show that the spatiotemporal interaction networks exhibit high regional connectivity and node interdependence, suggesting the presence of a small-world phenomenon.

Introduction and Development of Surface-Enhanced Raman Scattering (SERS) Substrates: A Review.

Since its discovery, the phenomenon of Surface Enhanced Raman Scattering (SERS) has gradually become an important tool for analyzing the composition and structure of substances. As a trace technique that can efficiently and nondestructively detect single molecules, the application of SERS has expanded from environmental and materials science to biomedical fields. In the past decade or so, the explosive development of nanotechnology and nanomaterials has further boosted the research of SERS technology, as nanomaterial-based SERS substrates have shown good signal enhancement properties. So far, it is widely recognized that the morphology, size, composition, and stacking mode of nanomaterials have a very great influence on the strength of the substrate SERS effect. Herein, an overview of methods for the preparation of surface-enhanced Raman scattering (SERS) substrates is provided. Specifically, this review describes a variety of common SERS substrate preparation methods and explores the potential and promise of these methods for applications in chemical analysis and biomedical fields. By detailing the influence of different nanomaterials (e.g., metallic nanoparticles, nanowires, and nanostars) and their structural features on the SERS effect, this article aims to provide a comprehensive understanding of SERS substrate preparation techniques.

SCIENTIFICLY BASED CALCULATION METHODS AND EXPERIMENTAL DATA BASES FOR THE PREVENTION OF PREMIUM FIRE EXPLOSIONS AND UNSAFE EXPLOSIONS OF PYROTECHNIC MULTICOMPONENT NITRATE-METALLIZED MIXTURES UNDER EXTERNAL THERMAL

Problem statement. Methods of preventing the occurrence of forced fire-hazardous destruction of products in the event of exposure to external thermal actions are of great practical importance. At the same time, they should be based on scientifically based methods for determining the critical values of parameters of thermal effects on products and technological parameters of mixture charges, the excess of which leads to premature fire-hazardous destruction of products. To develop such methods, it is necessary to have the results of theoretical and experimental studies of the processes of heating the shells of mixture charges for various external heat flows and their thermoreaction times, as well as the processes of ignition and development of their combustion under different external conditions. Purpose of the article. To form the results of theoretical studies in the form of scientifically based methods that allow preventing and controlling the premature ignition of mixture charges, the explosive development of their combustion process and the fire-hazardous destruction of products under conditions of external thermal effects, as well as the compilation of the results of experimental studies into a single database with determination of the influence of the technological parameters of the mixtures on the characteristics of their ignition processes and the development of combustion, the use of which allows reducing the number of fire-explosive destruction of products in conditions of elevated heating temperatures and external pressures. Conclusion. Scientifically based methods of determining the critical values of the parameters of external thermal actions for the quantitative assessment of the level of fire safety of pyrotechnic products based on multi-component nitrate-metallized mixtures have been developed. A scientific and technical base of experimental data was created to determine the regularities of the influence of a wide class of technological parameters of mixtures on the main characteristics of the processes of ignition and development of combustion of mixtures (ignition temperature, induction time, speed of combustion development) under conditions of external thermal actions (elevated heating temperatures, external pressures, etc.), which makes it possible to determine the probability of fires occurring at facilities where products are stored.

Biophysical Approach to Understand Life and Cancer

ABSTRACTBackgroundFrom the perspective of biology, the characteristics of life can be categorized as cellular organizations, homeostasis, metabolism, growth, reproduction and heredity, response to stimuli and adaptation to the environment. From the perspective of physics, living organisms are highly ordered, complex, thermodynamically open systems; they are in non‐equilibrium phase. According to the second law of thermodynamics, every system in the universe is going to the disorder spontaneously. Maximum entropy signifies a thermodynamic equilibrium which means the death. Today, cancer is a major global health problem and despite the explosive development of our knowledge about the carcinogenesis the “war on cancer” has not yet been won.AimsTo examine life and cancer together based on current biophysical approach and to shed light on new paradigms.Materials & MethodsA systematic literature search for publications on a thermodynamic approach to understand life and cancer was conducted in PubMed without language restrictions. The reference lists of identified studies were also used as additional knowledge.ResultsLiving organisms exchange entropy and they gain information from the external environment. Information can be stored as memory. Life is a category of emergence like art composed from correct modules. On the contrary, cancer is the emergence of a disease formed by incorrect modules. Cancer is a chaotic disease at least for a limited period and cannot arise from healthy functional tissue units. Chaos of the lower level may be associated with the entropy increase of upper level.ConclusionsBiologic chaos control is possible.

중국 택배기업의 서비스 품질이 고객만족도와 지속사용의도에 미치는 영향에 관한 연구

As an important part of the postal industry after the reform and opening up, Chinese courier services have made rapid development. However, there are also problems with the development of Chinese courier companies, such as uneven service quality and difficulty in protecting the rights and interests of users. With the increasingly explosive development of e-commerce such as C2C, problems are increasing along with the rapid development of the courier industry. Whether it is the final stage of online marketing and distribution or the service industry, the overall service quality of the courier industry has declined. For example, consumer complaints have increased and netizens have complained. As consumers’ rights protection consciousness is strengthened, one of the ways to increase customer satisfaction is to improve the quality of service of the company and increase the customer’s intention to continue using it.

Synthetic Strategies of Highly Bioactive Scaffold Bis(indolyl)methane Under Greener Condition- A Comprehensive Review.

Bis(indolyl)Methanes are a major class of heterocycles with considerable promise for technological and biological applications and being fluorescent active as well. Considering the extensive quantity of work on various synthetic techniques, the objective of this study is to measure the previous and current status of research studies related to different types of Bis(indolyl)methane (BIM) derivatives. Currently, research is focused on developing green synthetic strategies for dependable, sustainable and environmentally friendly synthetic processes. The present literature describes the formation of BIM moieties starting from suitable precursors using conventional reaction procedures, as well as reactions mediated by microwaves, ultrasounds, organocatalysts, transition metal catalysts, metal-free ionic liquid catalysts, and other environmentally friendly reaction protocols. The current review discusses the explosive development of different environmentally friendly synthesis routes for bis(indolyl)methane and its analogues during the past few decades. Moreover, this study includes the biological activities such as antibacterial, anticancer, anti-inflammatory, etc., of BIM derivatives, which have been investigated in recent years.

DNN Adaptive Partitioning Strategy for Heterogeneous Online Inspection Systems of Substations

With the explosive development of power edge equipment and the continuous improvement in power inspection performance, the requirements of substations and terminal equipment, such as drones with limited resources, cannot meet the strict delay and energy consumption requirements. This paper proposes an adaptive partitioning strategy for heterogeneous substation inspection systems. First, a layer delay prediction model and layer energy consumption prediction model are established on each heterogeneous node, and nonlinear characteristics related to delay and energy consumption are trained. On this basis, a deep neural network (DNN) hybrid partitioning strategy is proposed. The DNN task is divided into synchronous cooperative reasoning between terminal devices and multi-heterogeneous edge nodes. The experimental results show that the average absolute percentage error (MAPE) of the delay model was reduced by 31.49% on average. On drones and mobile edge nodes, the energy consumption model MAPE reduced the average by 21.92%, and the DNN end-to-end latency was reduced by 31.48%. The total cost of the system was reduced and the efficiency of UAV inspection was improved.

The transformation of Chacabuco village: From the pre-columbian to the Metropolitan periphery of Santiago, Chile

The process of transformation of the Province of Chacabuco throughout its history according to historical, cartographic and testimonial background, constitutes the central objective in this writing. The facts that describe the process of territorial occupation and the different forms it has taken over time identifying five settlement impulses that are related to the structures that were formed in the territory, with the objective of understanding in detail the occupation of the territory and explaining the causes that impulse it. Since the 17th century, the territory of Chacabuco had scattered human settlements and a countryside organized under the hacienda system for agricultural and livestock use, with a small population around the houses, which after the appearance of the Santiago- Valparaíso railroad generated an agglomeration of dwellings around the stations. From the seventeenth century until the first half of the twentieth century, the territory lacked housing and was mainly devoted to livestock, agriculture or remained unused. However, in the second half of the 20th century, the settlement had an explosive development, linked to urban expansion processes of the city of Santiago, the main metropolis of the country.

A sustainable mineral process for silicon and quartz recovery from quartz crucible waste ash via electrical separation

Driven by the explosive development of the photovoltaic (PV) industry, the treatment of the quartz crucible waste ash (QCWA) from monocrystalline silicon rod production must be considered to combat the shortage of silicon materials and promote sustainable development. In particular, the loss of grade 4 N high-purity silicon in QCWA is a frustrating fact for the silicon supply chain. In this work, an electrical separation process is proposed for the recovery of silicon and quartz from QCWA to realize waste resource reutilization. The charging processes and forces in the feed QCWA are first analyzed. Then, the electric field distribution during electrical separation is simulated to clarify the movement models of silicon and quartz particles and formulate reasonable electrical separation parameters. After systematic theoretical analysis, calculation, and simulation, electrical separation experiments were conducted. The results prove that the content of silicon in the concentrate and the corresponding content of quartz in the tailing respectively exceeded 93 % and 61 % under a particle size of 80 ∼ 120 mesh, a voltage of 40 kV, and a roll speed of 75 r/min. This work demonstrates that electrical separation is a sustainable process that could be recommended for silicon and quartz recovery from QCWA.

Rational Design of Oil-Resistant and Electrically Conductive Fluorosilicone Rubber Foam Nanocomposites for Sensitive Detectability in Complex Solvent Environments.

Recent years have witnessed the explosive development of highly sensitive smart sensors based on conductive polymer foam materials. However, the design and development of multifunctional polymeric foam composites as smart sensors applied in complex solvent and oil environments remain a critical challenge. Herein, we design and synthesize vinyl-terminated polytrifluoropropylmethylsiloxane through anionic ring-opening polymerization to fabricate fluorosilicone rubber foam (FSiRF) materials with nanoscale wrinkled surfaces and reactive Si-H groups via a green and rapid chemical foaming strategy. Based on the strong adhesion between FSiRF materials and consecutive oxidized ketjen black (OKB) nano-network, multifunctional FSiRF nanocomposites were prepared by a dip-coating strategy followed by fluoroalkylsilane modification. The optimized F-OKB@FSiRF nanocomposites exhibit outstanding mechanical flexibility in wide-temperature range (100 cycle compressions from -20 to 200 °C), structure stability (no detached particles after being immersed into various aqueous solutions for up to 15 days), surface superhydrophobicity (water contact angle of 154° and sliding angle of ∼7°), and tunable electrical conductivity (from 10-5 to 10-2 S m-1). Additionally, benefiting from the combined actions of multiple lines of defense (low surface energy groups, physical barriers, and "shielding effect"), the F-OKB@FSiRF sensor presents excellent anti-swelling property and high sensitivity in monitoring both large-deformation and tiny vibrations generated by knocking the beaker, ultrasonic action, agitating, and sinking objects in weak-polar or nonpolar solvents. This work conceivably provides a chemical strategy for the fabrication of multifunctional polymeric foam nanocomposite materials as smart sensors for broad applications.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Fully homomorphic encryption in PPMLAn review

Fully homomorphic encryption (FHE) in privacy-preserving machine learning (PPML) is a current area of research value, aiming to achieve the protection of users private data by applying the concept of full homomorphic encryption to machine learning privacy preservation. The integration of the two involves extensive model modifications and performance issues. The current difficulties mainly focus on how to improve encryption efficiency through hardware or software, and how to apply homomorphic encryption to neural network models such as RNN that process sequence data. This paper introduces this complex research field, outlines two machine learning service models (MLaas and AIaas) that are concerned by the industry, summarizes the most advanced research technologies based on these two models in recent years, and discusses the technical difficulties and future research directions. As a difficult problem that has never been overcome in cryptography in recent decades, homomorphic technology has received extensive attention from experts and scholars and ushered in new opportunities in the current explosive development of machine learning.

Projected future changes in bomb cyclones by the HighResMIP-PRIMAVERA multimodel ensemble

Projected future changes in bomb cyclones by the HighResMIP-PRIMAVERA multimodel ensemble

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods.

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

A comprehensive study of machine learning-based methods to predict epileptic seizures

People with epilepsy have many difficulties as a result of this complicated brain condition, which is typified by frequent convulsions. Symptoms of these seizures include bizarre behaviors, odd sensations, and in extreme cases, loss of awareness. These seizures appear as episodes of aberrant electrical impulses in the central nervous system. Successful epilepsy management depends on early seizure detection and identification, which allows for appropriate intervention to minimize risks and improve patient outcomes. Two major reasons have contributed to the extraordinary advancements in the area of epilepsy investigations in the last few years: the explosive development of machine learning techniques and the decreasing cost of non-invasive electroencephalography (EEG) apparatus. The availability of low-cost EEG equipment has made it easier to gather information on brain activity, which has opened up new avenues for monitoring and analyzing episodes of epileptic seizures away from conventional medical settings. The abundance of data and the advancement of machine learning methods have created new opportunities for the early identification and forecasting of seizures. Machine learning algorithms can predict seizures based on EEG data, providing patients with epilepsy with more control and informed decision-making. This paper offers a current review of current methods for treating epileptic seizures. The feature extraction techniques and classification algorithms receive particular focus. The most popular EEG datasets and their accessibility are listed. The approaches that are examined range from those that use more established machine learning techniques, such as naive Bayes models, Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), to those that take advantage of more recent deep learning techniques, like (Long-Short Term Memory, or LSTM), and deep Convolutional-Neural-Networks (CNN).

Ship Design in the Era of Digital Transition

The evolution of ship design from a manual toward a computer-aided, digital approach has been drastic after the 1970s, with the explosive development of computer hardware and software systems. In today’s era of smart digitalization in the frame of Industry 4.0, recently introduced digital/software tools and systems increase the efficiency and quality of the life-cycle ship design process, but also the operational complexity and the demand for proper training of users of software platforms. Parametric optimisation and simulation-driven design, product lifecycle management, digital twins and artificial intelligence are nowadays frequently used by the maritime industry during the commissioning/quality control activities and in the various phases of ship design, ship operation and ship production. This paper presents an overview of notable developments in the above areas and the way ahead to respond to present and future challenges of the maritime industry.

Research on the Design Method of Rural Lodging Based on the Integration of Architecture and Environment

The concept of modern tourism intelligent application in China is to fully meet the information needs of tourists and customer experience, and the development of intelligent tourism relying on Internet of Things (IoT) is its most basic guarantee condition. With the rise of ethnic village tourism and the change of new consumer lifestyle, B&B shows an explosive development. Tourism consumers are also shifting from hotels to home stays in the process of tourism. Therefore, based on IOT application as the starting point, this paper firstly, gives a more detailed description of the development process and marketing status of home stays, and discusses its various problems. Then, based on the TP theory of ethnic villages, Porter's five forces analysis model and the SWOT analysis method of ethnic villages, the marketing environment of homestays is studied in depth to discover their advantages.The HTOE model develops a marketing mix strategy for B&Bs that meets the actual situation of ethnic village development

Does digital economy affect corporate ESG performance? New insights from China

With the explosive development and innovation of digital and information technologies, a big data era has arrived in China. We examine the influence of the digital economy on corporate ESG performance using panel data from A-share listed firms in China between 2011 and 2021. First, we find that the digital economy can significantly improve corporate ESG performance, especially in environmental and governance dimensions. Second, our mechanism analysis identifies two channels—green innovation and digital transformation—through which the digital economy affects corporate ESG performance. Third, our cross-sectional analysis shows that the effect of the digital economy on corporate ESG performance is more pronounced for listed firms located in areas with lower economic levels, facing higher financing constraints, and belonging to high-polluting industries. Finally, our results remain robust after conducting a series of tests. Our study underscores the policy implication that embracing and promoting digital economy initiatives can serve as a potent lever to enhance corporate ESG performance, particularly in environmental and governance dimensions.

Optimizing strength training protocols in young females: A comparative study of velocity-based and percentage-based training programs

The purpose of this study was to compare the effects of velocity-based strength training (VBT) and percentage-based strength training (PBT) on absolute strength, explosive strength, speed, and agility, as well as markers of muscle damage after 6 weeks of exercise programs. The study included 30 young female individuals, divided into three groups of 10 participants: VBT, PBT, and control group. The main findings indicated that the VBT group and PBT group showed significant improvement in 1RM squat exercise (Δ% 27.87 and Δ% 8.98, respectively) and 1RM bench press (Δ% 14.47 and Δ% 8.65, respectively), but a greater enhancement was observed in the VBT group. In addition, VBT induced substantial changes in SJ (Δ% 14.32) and CMJ height (Δ% 7.69), while PBT had an improvement only in the SJ test (Δ% 6.72). The improvement noted in the VBT group could be attributed to its ability to tailor training intensity according to the speed of movement execution. This approach allows athletes to perform each repetition as fast as possible, thus maintaining an optimal intensity for explosive strength development. The capacity of VBT to adapt training intensity based on the speed of movement execution may be the key factor contributing to these results. Therefore, coaches and athletes should consider implementing VBT as a valuable tool to optimize strength and power development. In conclusion, VBT induced greater improvement in the 1RM squat, 1RM bench press, SJ, and CMJ compared to the group that performed the traditional strength training modality. Therefore, VBT is considered a more effective training tool regarding the development of absolute and explosive strength in young women.

Blockchain-based Pipeline Custody System (BPCS) for Preserving Critical Video Evidence

<p>The explosive development of network technology has driven the evolution of civilization, leading to the higher crime rate. Hence, CCTV cameras have been widely deployed to monitor criminal behaviors. The video of CCTV is considered as significant digital evidence. Due to the property of digital data, it is easily to copy and modify without electronic footprint, unlike the physical records. In case, the law enforcement starts to employ the blockchain technique due to its advantages, decentralization, transparency, and traceability, which can improve the legal effect. Unfortunately, blockchain network produces the tremendous overhead when it processes the data preservation based on the distributed communication. Thus, the gap and burden of data synchronization are the derivative issues. In order to solve the above problems, we aim to realize a blockchain-based pipeline custody system, so-called BPCS. The critical frame determination (CFD) technique is designed to catch the potential evidence image from the monitor, which is able to mitigate the storage cost of blockchain. Especially, the pipeline and directional ring strategy is proposed to aggregate the potential evidence in a batch file. This can reduce the burden on the chain of custody and shorten the time gap. The experimental results have demonstrated that BPCS can firmly possess the features of credibility and efficient through the comparisons and storage simulation.</p> <p> </p>