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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.

This study examines the functions of informal communication in organizations. Informal communication can be characterized as any interaction within an organization in which two or more individuals assume personal roles rather than their professional roles and in which employees do not intend to solve work-related problems. Despite its central role in organizations, a comprehensive understanding of the specific functions of informal communication within workplace dynamics is lacking.

We reviewed existing literature to identify the functions of informal communication and then surveyed employees (N = 1,380). We asked them to indicate their agreement with statements measuring the functions of informal communication that we derived from the literature.

Using exploratory factor analysis, we identified four core functions: stress management and recreation, nurturing private relationships, fostering a sense of belonging and connectedness and facilitating information exchange and coordination. We then examined how these core functions related to job-related and sociodemographic characteristics. Our findings underscore the importance of informal communication in organizations, particularly in facilitating the development and maintenance of social relationships as well as information exchange and coordination, both of which play critical roles in organizational success.

This study is among the first to systematize the functions of informal communication in organizations and to empirically extract core functions that can be used for further research. It is also highly relevant to practitioners of organizational management and organizational communication.

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.

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.

Organizations are affected top-down by the overarching societies and bottom-up by foundational face-to-face encounters: societies provide norms, values, laws, institutions, beliefs, markets, political structures, and knowledge bases. What happens within organizations is done by people interacting with other people, arguing, discussing, convincing each other when preparing and making decisions. Organizations operate within social environments that leave their – however indirect – imprint on what is going on within organizations. This article argues that organizational sociology can benefit from an integrated theoretical framework that accounts for the embeddedness of organizations within the micro- and macro-levels of social order. The argument is developed in two main points: First, this article introduces the multilevel framework provided by Niklas Luhmann’s systems theory to demonstrate how organizations are shaped by the functionally differentiated macro-structure of society. Organizations follow and reproduce the operational logics of societal domains such as the political system, the economy, science, law, religion, etc. Second, this paper demonstrates how organizations are shaped by micro-level dynamics of face-to-face interactions. Face-to-face encounters form a social reality of its own kind that restricts and resists the formalization of organizational processes. Here, this article draws on Erving Goffman’s and Randall Collins’ work on interaction rituals, emotions, and solidarity, which is inspired by Durkheimian micro-sociology. At the end, this article brings together all the elements into one general account of organizations within the context of their macro- and micro-structural social environments. This account can yield a deeper and more sociological understanding of organizational behavior.

This study aims to extend the known design guidelines for the polymer-based fused filament fabrication (FFF) 3D printing process with the focus on function-integrated components, specifically optomechanical parts. The potential of this approach is demonstrated by manufacturing function-integrated optomechanics for a low-power solid-state laser system.

For the production of function-integrated additively manufactured optomechanics using the FFF process, essential components and subsystems have been identified for which no design guidelines are available. This includes guidelines for integrating elements, particularly optics, into a polymer structure as well as guidelines for printing functional threads and ball joints. Based on these results, combined with prior research, a function-integrated low-power solid-state laser optomechanic was fabricated via the FFF process, using a commercial 3D printer of the type Ultimaker 3. The laser system's performance was assessed and compared to a reference system that employed commercial optomechanics, additionally confirming the design guidelines derived from the study.

Based on the design goal of function integration, the existing design guidelines for the FFF process are systematically extended. This success is demonstrated by the fabrication of an integrated optomechanic for a solid-state laser system.

Based on these results, scientists and engineers will be able to use the FFF process more extensively and benefit from the possibilities of function-integrated manufacturing.

Extensive research has been published on additive manufacturing of optomechanics. However, this research often emphasizes only cost reduction and short-term availability of components by reprinting existing parts. This paper aims to explore the capabilities of additive manufacturing in the production of function-integrated components to reduce the number of individual parts required, thereby decreasing the workload for system assembly and leading to an innovative production process for optical systems. Consequently, where needed, it provides new design guidelines or extends existing ones and verifies them by means of test series.

The aim of this study is to investigate the influence of Project Management Office (PMO) functions on project performance in businesses in Lower Middle-Income Countries (LMIC) using Partial Least Square–Structural Equation Modelling (PLS–SEM).

Utilizing close-ended questionnaires in a cross-sectional survey, 256 project professionals from 10 business sectors in Ghana views were elicited. The questions were developed through a comprehensive literature review and involved 27 PMO functional measures grouped into 6 and 19 project performance measures grouped into 6. Data collected was then analysed using the PLS–SEM to validate the hypothetical relationship.

The PLS–SEM model supported 13 (t > 1.65) out of 36 hypotheses investigating the relationship between PMO functions and project performance. Variations in the PMO functions moderately (R2 = 0.34) explained the performance of projects. The aggregate activities of the PMO had the highest significant influence on environmental performance (R2 = 0.467). The topmost PMO function was identified as monitoring and controlling project performance, and it was significantly associated with cost (ß = 0.265, p < 0.05), quality (ß = 0.291, p < 0.05) and project scope (ß = 0.265 p < 0.05) performance.

This research has brought more illumination to the functions of PMOs and its influence on project performance. The results suggest that PMO activities, when tailored to the business context, can significantly change project performance variables.

Most research on PMO and project performance has been limited to developed countries or a single sector. This study uniquely expands the business sectors and focuses on LMICs.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

This paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.

A six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.

Inspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.

The proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.

The CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.