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Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

This paper aims to analyse the differential impact of foreign and domestic remittances on household expenditure shares.

This study uses micro-data from a very large and detailed income-expenditure survey in Mexico and runs consumption-share Engel equations to estimate income (expenditure) elasticities for different consumption goods groups. Trying to account for the standard problems of endogeneity, this paper considers only nuclear households with migrant fathers and compare households that receive remittances from abroad, from within Mexico and those not receiving remittances.

This study finds that international remittances have a larger impact on the expenditure shares of women’s clothes, insurances and durable goods, while domestic remittances have a larger impact on the share of income dedicated to food, health and education.

Based on the results, differences in consumption shares between families receiving foreign and domestic remittances might depend not only on the relative size of the income transfer but also on the nature of the transfer and the sender’s capacity to monitor in person the use of those remittances. The results indicate that households that receive remittances from abroad present higher shares of consumption of some goods the literature commonly associates with the mothers’ preferences.

Construction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.

Due to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.

According to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.

ICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.

In this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

The purpose of this paper is to discuss the operation of new generation electromagnetic current-to-voltage transducer. The aim of research was analysis of behaviour of considered current-to-voltage transducers during operation. The main problem was to estimate whether the external fields are able to change the value of the secondary voltage and that the replacement of the casing material by a conductive or ferromagnetic material will increase the immunity of the transducer to external magnetic fields. The immunity of current-to-voltage transducers to the external fields is very important because it influences the proper functioning of the protection system.

The use of analytical methods to assess the influence of external fields was impossible due to the complexity of the geometry. The 3D computations were necessary because of different cross sections of circuit boards at different heights. Therefore the numerical 3D field-and-circuit method based on finite element method was applied. The wide range of dimensions in computation system, ranging from 0.15 mm (print paths) to 0.22 m, made it necessary to use the mesh of millions of elements. The division of this type of system into elements requires a diverse and extremely dense mesh in the area of printed circuits board (PCBs).

The 3D analysis of magnetic field distribution was performed for different external field effect upon a current-to-voltage transducer. The magnetic field distributions and the induced secondary voltage for several different cases were presented. As a conclusion it can be said that in this particular case the magnetic shield is most effective. The influence of external magnetic fields caused by currents passing through the other neighbouring phase bars near are insignificant for the transducer with non-magnetic core.

Commonly used in measuring and protection systems of the transmission lines are induction instrument transformers. The instrument transformers are very precise devices and their errors are counted in tenths of a per cent, and phase displacement of signals in minutes. Especially in HV systems they are very big and their cores are heavy. Replacement of instrument transformers by the current to voltage transducers cooperating with electronic measuring systems will reduce the size and cost of devices.

The requirements set for protective current transformers concern the transformation of currents, with high accuracy, especially at transient states. Therefore magnetic characteristics of their cores should be linear. It causes that cores are large and have some air gaps. Current-to-voltage transducers based on Rogowski coil are particularly suitable for the replacement of the protective current transformers because of their linearity. The traditional technologies used for making Rogowski coil consisted in winding a wire on a non-magnetic carcass. The development of technology has enabled the use of new technologies PCB high density interconnect in the production of Rogowski coil.

This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

THIS scheme of exact classification has now been long enough upon trial to justify the publication of a few explanatory notes, adjustments, and revisions which may be useful to present and future users of the system. For an entirely new scheme, which to some extent broke fresh ground, its reception has been extremely kind and flattering, and although it has not escaped criticism, nothing has appeared which has been anything but reasonable and helpful. A surprising circumstance has been that, notwithstanding the very controversial nature of much of the subject, so few points of difference have appeared. These are all more or less directed against the mere placing of certain topics and do not to any extent reflect upon the theory or structure of the system as a whole. One mistake has been made, however, of a more important nature, but this must have arisen either through misapprehension or carelessness. It has been assumed that the Subject Classification claims to be thoroughly scientific, and that each class is arranged in a logical and evolutionary order, so as to modulate or merge naturally into its successor. Any modest claim which may have been made to an attempted logical order is invariably qualified by a statement in the “Introduction” to the effect that such perfect order is only to be expected to a very limited extent. On page eight it is stated that—“The departments of human knowledge are so numerous, their intersections so great, their changes so frequent, and their variety so confusing, that it is impossible to show that they proceed from one source or germ, or that they can be arranged so that each enquirer will find the complete literature of his special subject at one fixed place.” All through the tables and the introduction the same kind of limitation is insisted upon, and it can only be due to misunderstanding to say that I have made such a preposterous claim to sequential perfection. No librarian who has attempted to compile a system of exact classification would ever dream of claiming that he did more than get as near as possible to an ideal arrangement in accordance with his basal plan.

Knowledge management (KM) is widely adopted by organisations to improve their performance and make informed decisions. Prior research has confirmed that Information Systems (IS) play a critical role in effective KM. The purpose of this study is to examine the existing literature on the role of cloud-based KM systems (C-KMS) in small- and medium-sized enterprise (SMEs) by understanding its impact on the five KM processes: knowledge acquisition, creation, storage, sharing and usage.

This study conducted a systematic literature review by examining 133 journal articles and 24 conference papers from 2010 to 2021 on the role of cloud computing in KM for SMEs.

This study revealed that there are numerous empirical analyses on KM processes and tools in SMEs; however, only few studies demonstrate how the whole gamut of KM processes can adopt cloud computing in SMEs. Therefore, SMEs are ineffective at KM with limited IS intervention. This paper offers a proposition on how C-KMS can impact all five KM process, thereby increasing its effectiveness of KM in SMEs. This study analysed the benefits of C-KMS that brings to SMEs in terms of availability, scalability, reliability, security and cost.

This systematic review is restricted to certain databases (ScienceDirect, Sage journals, Scopus and Emerald Insight) and specific IS conference proceedings to source articles. The selection of search criteria and time frame is based on this study’s assessment and choice. This study adds value to our understanding of the role of KM in SMEs, and it reinforces the role of cloud computing in effectively managing knowledge in SMEs. The proposal of C-KMS for the enhancement of KM has significant implications for SMEs to effectively use knowledge for their survival and superior performance.

This study suggests three practical implications. First, adopting and using C-KMS provide a strong foundation to manage knowledge for SMEs in a cost-effective way. Second, C-KMS improves the effectiveness of KM by increasing availability of knowledge artifacts, which in turn aids SMEs’ growth. Third, C-KMS is useful to codify SME’s knowledge, and accordingly supports employees to acquire and use knowledge based on their requirements.

This study discussed C-KMS with contemporary social issues, such as the COVID-19 pandemic challenges for SMEs and demonstrated how C-KMS can support SMEs to handle such crises by managing knowledge effectively.

This research highlights the importance of the implementation of a C-KMS for the enhancement of KM in SMEs. The review provides empirical evidence on the challenges faced by SMEs regarding KM, as they often only have enough resources to focus on a single KM process, predominantly knowledge sharing. Consequently, a holistic approach to KM cannot be realised by SMEs. In this context, the findings of this study offer theoretical and practical insights into the role of cloud computing by addressing the challenges of KM in SMEs.

The purpose of this paper is to discuss whether the outcomes of a reference transaction can be improved by focusing on the ethical dimension of the conversation between user and librarian. The aim is to provide a better understanding of the ethical implications of the reference interview.

The starting point is the ethical questions raised by the results of Gunhild Salvesen's PhD thesis, where she analyses authentic reference interviews by the method Conversation Analysis (CA). She found several traits in the conversations between librarian and user with a negative influence on the quality of the transactions. To visualise the ethical implications of the same conversation, Løgstrup's ethics of proximity are used.

Analysing the conversations from an ethical perspective revealed that increased ethical consciousness among the librarians clearly would have improved the quality of the actual transactions. It is concluded that the total quality of reference transactions will increase by focusing on the ethical dimension.

Using data from a researcher to validate the same data in another context might not be considered appropriate. However, using the transcripts from Salvesen's dissertation would serve the purpose of revealing the ethical implications of the interviews and their influence on the quality.

This work might contribute to increasing the ethical consciousness among reference librarians, and encourage them to improve their communication skills, to raise the quality of the interaction.

This way of using ethical theories in the analysis of authentic conversations between user and librarian is new. Likewise, using CA on reference interviews in a library setting is also new.