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The purpose of this research note is to investigate the implications of supply chain management of e‐business for the macroeconomic phenomenon of business cycles.

The paper provides a list of propositions, which form the broad basis of an empirical research agenda, to explore and investigate the mechanisms through which supply chain innovations can influence business cycle.

Economic research literature has pointed out that there are linkages between inventory investment and business cycle fluctuation. Given that the e‐business supply chain management drastically alters inventory investment across a range of industries, it is likely to affect the behaviour of economic fluctuation.

This research has the potential to contribute to a better‐informed formulation of economic policies at national and global level.

This paper analyzes the advance selling and pricing strategies of fresh products supply chain where the e-retailer provides wholesale contract or agency contract to the fresh products supplier.

This paper constructed a two-period sequential-move game of fresh products supply chain members.

This analysis showed that the supply chain members had different preferences for contracts under different market conditions. The advance selling of fresh products was not a decision of the seller, but also required the support of other supply chain members. And the advance selling strategy was not always beneficial to all supply chain parties. Under the two contracts, there were market conditions in which the profits of supply chain members were Pareto-improved through the implementation of advance selling.

The model presented in this study focuses solely on the context of monopoly, overlooking the competition from alternative suppliers or retailers. Consequently, exploring the competitive landscape within the fresh products supply chain, particularly in relation to pre-sale pricing, emerges as a crucial avenue for further investigation. By employing empirical research methods, valuable insights are gleaned, thereby significantly augmenting the existing body of relevant theories.

The decision to pre-sell fresh products should be based on market conditions. Supply chain members can control production costs and fresh products circulation losses to maximize profits.

From the perspective of game theory, this study analyzed the optimal advance selling and pricing strategies of fresh products supply chain members under two kinds of contracts. These results can provide practical implications for fresh products suppliers and e-retailers.

Concerning the radiation effects on the three-dimensional (3D) packaging in space environment, this study aims to investigate the influence of the total dose effect on the transmission characteristics of high-frequency electrical signals using experimental and simulation methods.

This work carries out the irradiation test of the specimens and measures their S21 parameters before and after irradiation. A simulation model describing the total dose effect was built based on the experimental test results. And, the radiation hardening design is evaluated by the simulation method.

The experimental results demonstrate that the S21 curve of the interconnection decreases with the increase of the irradiation dose, indicating that the total dose effect leads to the decline of its signal transmission characteristics. According to the simulation results, decreasing the height of the through silicon via (TSV), increasing the radius of the TSV, reducing the length of Si and increasing the number of grounded through silicon via have positive effects on improving the radiation resistance of the interconnection structure.

This work investigates the effect of radiation on the transmission characteristics of interconnection structures for 3D packaging and proposes the hardening design methods. It is meaningful for improving the reliability of 3D packaging in space applications.

The purpose of this paper is to investigate the effects of Sn-Ag-x leveling layers on the mechanical properties of SnBi solder joints. Four Sn-Ag-x (Sn-3.0Ag-0.5Cu, Sn-0.3Ag-0.7Cu, Sn-0.3Ag-0.7Cu-0.5 Bi-0.05Ni and Sn-3.0Ag-3.0 Bi-3.0In) leveling layers were coated on Cu pads to prepare SnBi/Sn-Ag-x/Cu solder joints. The microstructure, hardness, shear strength and fracture morphology of solder joints before and after aging were studied.

The interfacial brittleness of the SnBi low-temperature solder joint is a key problem affecting its reliability. The purpose of this study is to improve the mechanical properties of the SnBi solder joint.

Owing to the addition of the leveling layers, the grain size of the ß-Sn phase in the SnBi/Sn-Ag-x/Cu solder joint is significantly larger than that in the SnBi/Cu eutectic solder joint. Meanwhile, the hardness of the solder bulk in the SnBi/Cu solder joint shows a decrease trend because of the addition of the leveling layers. The SnBi/Cu solder joint shows obvious strength drop and interfacial brittle fracture after aging. Through the addition of the Sn-Ag-x layers, the brittle failure caused by aging is effectively suppressed. In addition, the Sn-Ag-x leveling layers improve the shear strength of the SnBi/Cu solder joint after aging. Among them, the SnBi/SACBN/Cu solder joint shows the highest shear strength.

This work suppresses the interfacial brittleness of the SnBi/Cu solder joint after isothermal aging by adding Sn-Ag-x leveling layers on the Cu pads. It provides a way to improve the mechanical performances of the SnBi solder joint.

The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities.

The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime.

The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality.

The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information.

The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem.

The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method.

The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices.

It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime.

The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach.

An enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing.

The enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools.

The study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.

The purpose of this paper is to identify and examine the major forces that are changing the way biopharmaceutical medications are purchased, distributed, and sold throughout the supply chain. This will become important as healthcare reform moves forward, and logistics will be transformed in this industry.

Multiple interviews with key informants at each level of the value chain were combined with manifest text analysis from practitioner articles to derive key insights into the primary change drivers influencing the future of the biopharmaceutical supply chain.

The research discovered radical shifts in the structure of the biopharmaceutical supply chain. Future research into biopharmaceutical supply chain practices will need to explore three primary issues: How will supply chain member compensation influence the power of parties within the network? How will the role of supply chain intermediaries change the landscape of medication delivery to the end customer? What impact will the role of regulatory constraints on product pedigree and proliferation have on this network? The relationship between these forces is mediated by operations strategy concerning inventory policy, supply chain visibility, and desired service levels.

The research was based on multiple interviews with a convenience sample, as well as text analysis from practitioner articles. These findings are an initial step to guide future more in‐depth research for this dynamic and contextually rich supply chain environment that impacts consumers in every country in the world.

The paper adds insights into the pharmaceutical supply chain, examining this from multiple perspectives.

The purpose of this paper is to focus on measuring the contribution generated by social media when used for business purposes, distinguishing between metrics and methods for data collection and data analysis. Organizations worldwide have widely endorsed social media, but available studies on the contribution generated by these technologies for organizations are fragmented. A performance measurement system (PMS) framework to monitor social media is theoretically derived, highlighting the methods for data collection and data analysis and metrics to quantify social media impacts in terms of financials, network structure, interactions, conversations and users’ opinion.

This is a qualitative research based on a literature review of papers in management, information technology, marketing and public relations.

A PMS framework to quantify the contribution of social media is theoretically derived, distinguishing between metrics and methods. PMS metrics support the measurement of the financial and relational impact of social media, as well as the impact of social media conversations and users’ opinions. PMS methods comprise different approaches for data collection and data analysis that range from manual to automated data collection and from content to sentiment analysis techniques.

The PMS framework contributes to the academic literature by integrating a unique model of the available approaches for social media measurement that can serve as a basis for future research directions. The framework also supports practitioners that face necessity to quantify financial and relational contributions of social media as well as the contribution of social media conversation and users’ opinion.

This paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.

The authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.

With these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.

This study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.