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Awarding contracts based solely on the lowest price is unsuitable for every project. Consequently, most procurement systems in developed countries have progressed to the multicriteria selection practices (MSPs) for tender evaluation. MSPs consider a range of quality measures, such as completion time, life cycle cost, functional characteristics, environmental impact and innovation, alongside bid price. This study examines the prevailing MSPs in Sri Lankan public tender evaluations to enhance the effectiveness of the local tender evaluation process.

A desk study approach was employed to collect bidding documents, resulting in the identification of 66 documents. A systematic screening process was then applied to identify those bidding documents that incorporated MSPs. Subsequently, content analysis was conducted to determine the common features of the functions used in MSPs.

The study identified six primary functions related to MSPs incorporated in the bidding documents to procure building and substation projects. Three functions follow the price-to-quality method, while the remaining three follow the quality-to-price method. Among these identified functions, four functions employ objective evaluation criteria, such as thickness, capacity and operational loss. The other two functions utilize subjective evaluation criteria, such as the project’s design and technical specifications. Contract awarding will be based on either the highest score or the lowest bid, depending on the function type.

This study’s originality lies in exploring MSPs in the Sri Lankan public tender evaluation process and in disclosing their characteristics to promote the MSPs in Sri Lanka and developing countries.

Managers and academics alike focus on value creation in business relationships. This paper adds to existing literature by analyzing functions of business relationships and their impact on value perception. Applying a customer perspective, direct relationship functions are concerned about payment, quality, and volume. Indirect functions include innovation, access, and scouting. Furthermore, trust and number of alternative suppliers are included in the study. The empirical results illustrate the important role of direct and indirect functions for value creation. Understanding these functions is instrumental for driving customer value, both for the supplier and the seller. Direct functions do have a much stronger impact on value than indirect functions that still do have a significant impact. Thus, increasing direct function fulfillment is much more effective in order to gain key supplier status than relying only on indirect functions. But indirect functions may offer ample differentiation opportunities. Being a strong driver of relationship value, trust is also driven by function fulfillment. Thus, relationship value depends on rational elements (functions) and social elements (trust). Availability of alternative suppliers increases the importance of relationship function fulfillment on customer value and customer trust. In highly competitive markets, suppliers need clear understanding and communication of relationship value in order to succeed.

The current study seeks first to examine the prediction of school functioning in crises during the COVID-19 pandemic by school principals’ self-efficacy; second, to explore the differences in all dimensions of self-efficacy and school functioning during crises in Arab and Jewish schools in Israel and third, to determine which of school principals’ self-efficacy dimensions best predicted school functioning during the COVID-19 crisis.

Data were aggregated at the school level for structural equation modeling (SEM) analysis using AMOS analysis of 103 middle schools across Israel, 53 from Jewish and 50 from Arab society. Participants included 103 school principals (who answered the school principals’ self-efficacy questionnaire) and 1,031 teachers who answered the school functioning during crises questionnaire (477 Jewish teachers and 554 Arab teachers).

The findings showed that the principals’ self-efficacy positively predicted school functioning during the crisis. Among the five self-efficacy dimensions (general management efficacy, leadership efficacy, human relations efficacy, efficacy in managing external relations and pedagogical management efficacy), significant differences were found only in “external relations efficacy,” which was higher for “Arab” school principals; the only dimension that predicted school functioning during crises in both societies was “human relations efficacy.”

The current results emphasize the importance of principals’ self-efficacy in general and specifically caring leadership practices “human relations efficacy” in their relations with the school staff, the students and the parents for effective school coping and functioning during crises in two societies in Israel: Arab and Jewish. Further, no previous studies have explored this correlation.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

In this study, we investigate how capability in managing intellectual property may be treated as a type of “dynamic capability,” and we seek to understand how, when it is linked to the new technology development capability of a complex technological organization, these two types of dynamic capabilities may coevolve.

We conducted a longitudinal empirical case study of Airbus, incorporating an abductive research methodology that required investigating theory and empirical data concurrently and iteratively. The data, ranging over a period of two decades from 2000 to 2021, was collected from four different sources, including interviews, internal company documents, publicly available information and patent data.

Our main findings are that the capabilities and roles of the Intellectual Property function in Airbus and their interaction with the company's Technology function have indeed influenced the overall innovation strategy of the organization, and that three coevolutionary phases may be identified in the interactive development of the two functions.

Our investigation into how new technology development and intellectual property (IP) management capabilities coevolve within complex technological organizations, exemplified by Airbus, provides significant theoretical and practical implications. Theoretically, it enhances understanding of capability co-development dynamics in complex organizations, particularly in strategic IP management. Practically, it suggests aligning IP strategy with overall corporate objectives and optimizing organizational structures to promote collaboration and efficiency across IP and technology teams. This alignment may foster innovation, maximize the value of intellectual assets and strengthen collaborations, positioning organizations for long-term success in competitive landscapes.

This study makes a fresh contribution to the innovation studies literature by showing how if intellectual property management is treated as a core function of a complex technological organization – rather than simply as a vehicle for protecting new inventions and products after the fact or simply as a constituent part of the organization's legal function – it may contribute proactively to the organization's technological innovation performance. We also address the current gap in the academic literature for a clear understanding of the processes by which different function-specific dynamic capabilities may coevolve in a complex organization operating as part of a dynamic and complex adaptive system.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

A system for solving a wide variety of inverse and optimization problems in solid mechanics is introduced. The system consists of a general purpose finite element method (FEM) analysis system “Elfen” and a shell which controls this system. The shell functions as a stand‐alone programme, so the system is physically divided into two separated parts. The “optimization part”, which corresponds to the shell, possesses optimization and inverse problem solution algorithms. The “analysis part”, which corresponds to an FEM system, serves for the definition of the objective function to which these algorithms are applied. The shell has a user interface implemented in the form of file interpreter which imposes a great flexibility at the definition of various optimization and inverse problems, including parameter identification in constitutive modelling, frictional contact problems and heat transfer. Concepts of the shell are discussed in detail.

Presents a procedure for obtaining an improved finite element solution of boundary problems by estimating the principle of exact displacement method in the finite element technique. The displacement field is approximated by two types of functions: the shape functions satisfying the homogeneous differential equilibrium equation and the full clamping element functions as a particular solution of the differential equation between the nodes. The full clamping functions represent the solution of the full clamping state on finite elements. An improved numerical solution of displacements, strains, stresses and internal forces, not only at nodes but over the whole finite element, is obtained without an increase of the global basis, because the shape functions are orthogonal with the full clamping functions. This principle is generally applicable to different finite elements. The contribution of introducing two types of functions based on the principle of the exact displacement method is demonstrated in the solution procedure of frame structures and thin plates.

Defines and investigates fuzzy symmetric functions with don't‐care conditions and most‐unsymmetric functions. Represents and illustrates by examples algorithms for finding the grade of membership function and the number of most unsymmetric functions. Also presents applications to function representation, data reduction and error correction. The results may have useful applications to fuzzy logics, finding most‐unsymmetric images, fuzzy neural networks and related areas.