Search results

The purpose of this paper is to synthesise a disperse dye based on benzisothiazole and to characterise its crystal morphology, dispersing stability, to study the relationship between the chemical structure and the dyeing property of the dye.

The disperse dye based on benzisothiazole, 3-(3-methyl-4-N-ethyl-N-benzyl-phenyldiazenyl)-5-nitro-2,1-benzisothiazoles, was synthesized. The disperse dye based on benzisothiazole, 3-(3-methyl-4-N-ethyl-N-benzyl-phenyldiazenyl)-5-nitro-2,1-benzisothiazoles, was synthesised. The chemical structure of the dye obtained was characterised by infrared spectrum Fourier transform infrared spectroscopy and nuclear magnetic resonance (1HNMR), and the crystal morphology was observed by Field Emission Scanning Electron Microscopy. Sodium salt of polycondensated naphthalenesulphonic acid (dispersing agent sodium salt of polycondensated naphthalenesulphonic acid [MF]) and a sulphonated amino polyether (anionic surfactant B600) were employed to grind and disperse the dye crystals. The dispersion property of the dye particles was characterised. Dyeing property of the dispersion system was also studied.

The dye formed spherical crystals that were made up of a large number of acicular crystals similar to spherical chrysanthemum. The crystals had warping crystal centres inside the spheres. The particle sizes of the dispersion with the mixture of B600 and MF had an uniform distribution and were smaller than that of the dispersion with only single dispersing agent MF. Dyeing with the dispersion system had an excellent reproducibility under alkalinic condition.

An alkalinic dyeing method for poly(ethylene terephthalate) (PET) with disperse dyes as a cleaner wet process had been developed. Such a process combined pretreatment and dyeing process using the alkali-stable disperse dyes and reduced the consumption of water and energy and improved production efficiency.

The crystal morphology, dispersion and dyeing properties of the synthesised disperse dye for dyeing PET fabric under alkalinic condition were discussed. This disperse dye has an important potential application in alkalinic dyeing method.

The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.

This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.

Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.

The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.

At present, research on the preparation of corrosion inhibitors using modified pyrimidine derivatives is still blank. The purpose of this study is to synthesize a new cationic mercaptopyrimidine derivative quaternary ammonium salt, known as DTEBTAC, that can be used as a corrosion inhibitor to slow down the metal corrosion problems encountered in oil and gas extraction processes.

A new corrosion inhibitor was synthesized by the reaction of anti-Markovnikov addition and nucleophilic substitution. The weight loss method was used to study the corrosion inhibition characteristics of synthetic corrosion inhibitors. Electrochemical and surface topography analyses were used to determine the type of inhibitor and the adsorption state formed on the surface of N80 steel. Molecular dynamics simulations and quantum chemistry calculations were used to investigate the synthetic corrosion inhibitor’s molecular structure and corrosion inhibition mechanisms.

The results of the weight loss method show that when the dosage of DTEBTAC is 1%, the corrosion rate of N80 steel in hydrochloric acid solution at 90? is 3.3325 g m-2 h-1. Electrochemical and surface morphology analysis show that DTEBTAC can form a protective layer on the surface of N80 steel, and is a hybrid corrosion inhibitor that can inhibit the main anode. Quantum chemical parameter calculation shows that DTEBTAC has a better corrosion inhibition effect than DTP. The molecular dynamics simulation results show that DTEBTAC has stronger binding energy than DTP, and forms a network packing structure through hydrogen bonding, and the adsorption stability is enhanced.

A novel cationic mercaptopyrimidine derivative quaternium-ammonium salt corrosion inhibitor was designed and provided. Compared with the prior art, the preparation method of the synthesized mercaptopyrimidine derivative quaternary ammonium salt corrosion inhibitor is simple, and the presence of nitrogen-positive ions, sulfur atoms and nitrogen-rich atoms has an obvious corrosion inhibition effect, which can be used to inhibit metal corrosion during oil and gas field exploitation. It not only expands the application field of new materials but also provides a new idea for the research and development of new corrosion inhibitors.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

OF all the delightful recreations classed, for divers professional reasons, under the general designation of work, which cause the librarian's existence to be regarded with envious eyes as one of the most joyous and irresponsible on earth, the most delectable is surely that of cataloguing ; and the moments when the cataloguer feels himself fullest of enthusiasm, when he knows it would be impossible to exchange his lot with any human being, are those spent in the absorbing occupation of correcting proofs, for then to the more sensuous delights of the game are added the zest and ardour of combat. Some day I may, with the editor's sanction, make a few observations on the pleasures of cataloguing in general: for the present I am going to consider only this final phase. A curious feature of the pastime or “work,” to adopt the conventional phraseology, is that some people are unable to see the fun of it and innocently suppose the term “work” to be meant seriously. Still, when one reflects that every sport is looked upon by outsiders either as a deadly form of depravity, or as idiotically tedious and laborious, it is clear that this feature is neither wonderful nor exceptional. Golf, angling, football, punting, mountaineering, even book‐collecting, are each looked upon as “work” by those who love other kinds of recreation, which may yet be in reality not a whit less arduous.

The review examines the existing literature on blockchain-based small and medium enterprise (SME) finance and highlights its trend, themes, opportunities and challenges. Based on these factors, the authors create a framework for the existing literature on blockchain-based SME financing and lay down future research paths.

The review follows a systematic approach. It includes 53 articles encompassing multiple dimensions of blockchain-based SME finance, including peer-to-peer lending platforms, supply chain finance (SCF), decentralized lending protocols and tokenization of assets. The review critically evaluates these approaches' theoretical underpinnings, empirical evidence and practical implementations.

The review demonstrates that blockchain-based SME finance holds significant promise in addressing the credit gap by leveraging blockchain technology's decentralized and transparent nature. Benefits identified include reduced information asymmetry, improved access to financing, enhanced credit assessment processes and increased financial inclusion. However, the literature acknowledges several challenges and limitations, such as regulatory uncertainties, scalability issues, operational complexities and potential security risks.

The article contributes to the growing knowledge of blockchain-based SME finance by synthesizing and evaluating the existing literature. It also provides a framework for the existing literature in the area and future research paths. The study offers insights for researchers, policymakers and practitioners seeking to understand the potential of blockchain technology in filling the SME credit gap and fostering economic development through improved access to finance for SMEs.

The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.

Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.

In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.

It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.

Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

The purpose of this paper is to examine the efforts of an ethnic Miao migrant worker association to recreate and engage with festivals both in the host society of the Pearl River Delta and back home in Southeastern Guizhou province of Southwest China. It analyzes how and under what conditions the disadvantaged migrant workers collectively demonstrate and assert their cultural identity in festival activities, rekindling and strengthening their ethnic consciousness.

Based on ethnographic field data, this study focuses on the connections between migrant workers’ lives in modern host societies and their traditional culture back home. Special attention is paid to the temporal dynamics of migrant workers’ cultural identity and socio-economic development.

The leaders of the Miao migrants’ association created network linkages to channel the flow of labor, capital and culture between the host society and the migrants’ hometown, and made efforts to secure institutional embeddedness at both ends of the flow. Their use of festivals and related heritage as cultural capital has facilitated the cultivation of network linkages and institutional embeddedness for economic advancement and overcoming ethnic prejudices and institutional disadvantages.

By illustrating how the economic development has been imbricated with culture, this research enhances understanding about the role of network linkage and institutional embeddedness in the flow of labor, capital and culture between host society and home place of migrant communities.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.

The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.

The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.

The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.

The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.