Improve Knowledge Transfer Research Articles

Understanding eco-innovation in the Hungarian wine sector

PurposeA growing body of literature analyzes eco-innovation in the wine industry, specifically focusing on the internal and external factors that contribute to sustainable innovation in wineries. The purpose of this study is to classify wineries according to their eco-innovation strategy and investigate variations in innovation groups concerning networking and socio-demographic characteristics.Design/methodology/approachThe authors use an original firm-level survey conducted between June 2022 and January 2023 to study eco-innovation practices of 234 Hungarian winemakers using four groups of indicators: marketing, organizational, process and product innovations. The authors apply principal component analysis and cluster analysis to identify eco-innovation dimensions and group wineries. The authors also investigate the relationship between eco-innovation strategies, determinants and firm-specific characteristics.FindingsThe estimations identify two innovation modes: with Cluster 1 (characterized by innovative wineries) surpassing Cluster 2 (comprising less innovative wineries) in all aspects. Marketing and process innovations were more widespread than product and organizational innovations. The results confirm the importance of firm and manager characteristics, corporate organization and size, age and education of management influence innovation activity.Research limitations/implicationsThe cross-sectional nature of the survey provides a snapshot of the wine industry. To identify changing trends, it is beneficial to conduct follow-up research and ongoing monitoring for a more comprehensive understanding of the dynamics of eco-innovation over time. The study offers valuable information about the Hungarian wine sector, but generalization of results to other geographic areas or wine-producing regions should be careful. The unique characteristics of particular regions might have a different effect on the dynamics of eco-innovation. Comparative studies across different wine regions or countries may reveal regional variations in eco-innovation strategies and the impact of local contexts.Practical implicationsThe study highlights the priority of human elements, such as the commitment of owners and managers to sustainability in driving force for innovation. The ability of businesses to absorb new knowledge and the exchange of information within organizations is crucial in promoting innovation. Considering the results of the survey, managers should proactively strive to improve knowledge transfer and information sharing within their organizations and establish cooperative alliances with universities, research institutes, suppliers, industry bodies and associations and global consultancy firms. Such knowledge-based cooperation can counterweight the innovative inertia of the micro and small enterprises representing the Hungarian wine sector.Social implicationsTo address the issue of “innovative inertia” faced by micro and small businesses in the wine sector, policymakers may implement focused supports, such as educational initiatives and grants, to augment their understanding and promote sustainable innovation. They should initiate intra- and inter-cluster connections and cooperations. Incentives, such as tax reduction or financial support, may stimulate wineries to introduce eco-innovation practices. Policymakers could also streamline the process of obtaining funding or grants for wineries interested in investing in cutting-edge solutions, such as renewable energy or advanced cultivation technologies to accelerate the adoption of sustainable practices.Originality/valueThis study contributes to the eco-innovation literature by providing insights into the drivers and practices of wineries in Hungary. The results emphasize the significance of networking, sharing of knowledge and firm/manager-specific characteristics in influencing eco-innovation in the wine industry.

COVID-19 infection prevention and control program for aged care providers: a quasi-experimental study of Telegram-based training

ABSTRACT Given the high turnover of care workers in residential aged care facilities, continuous training on infection prevention and control (IPC) remains essential even after the COVID-19 pandemic. This study examined the impact of a Telegram-based training on aged care providers’ IPC knowledge, attitudes, self-efficacy. A two-arm, pre-post quasi-experimental study with repeated measures at week 2 (T1) and week 4 (T2) was conducted for seventy-two (72) participants from 14 residential aged care facilities in Malaysia, with 60 completing the study (30 per arm). All instructional activities and data collection for both groups were delivered through Telegram. The intervention group received a 2-day synchronous workshop (3 h/day) alongside e-materials for a 10-unit IPC module. Primary outcomes were measured using a knowledge test, COVID-19 Elderly Caregiver Questionnaire (attitude component), and an adapted General Self-Efficacy Scale. Telegram’s acceptability was also assessed using an adapted Technology Acceptance Model questionnaire. Results showed that the intervention group had a faster knowledge transfer (91.3%–93.5%; T0-T2 = ∆22.9; p ≤ .002;), higher knowledge scores (∆7.2–16; p ≤ .002; large effect sizes 0.9–1.7), and a small-to-modest increments in self-efficacy (∆13.2; p = .002) and attitude (∆5.2; p = .045). Integrating synchronous sessions into an online program improved knowledge transfer, enhanced self-efficacy and promoted positive attitudes by enabling real-time interpersonal communication and instant feedback. Telegram proved effective for mobile learning in aged care settings with limited resources.

Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses.

Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is,local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

Survey on colostrum management by German dairy farmers focusing on frozen colostrum storage

Because calves are born with low levels of antibodies, effective colostrum management is one of the most critical factors for successful calf rearing. A timely and adequate supply of sufficiently high-quality colostrum immediately after birth is essential to ensure the passive immunization of calves. Frozen colostrum reserves are recommended to fulfill the immunological and nutrient requirements of newborn calves, even in exceptional situations; however, the implementation rates on German dairy farms and challenges of realization remain unclear. A 33-question online survey, focused on frozen colostrum reserves, was developed to obtain an overview of colostrum management practices on German dairy farms. The questionnaire was divided into 3 sections: 1. personal data; 2. farm characteristics; 3. colostrum management. Of the 155 responses we received, 63.9% were from female farmers, and 35.5% were from male farmers. Conventional farming was practiced on 89.0% of farms, and organic farming was practiced on 7.1% of farms. Of the respondents, 89.0% froze colostrum. The main reasons for freezing colostrum were: 1. the dam does not produce enough colostrum; 2. the dam cannot be milked; or 3. in cases where the dam dies during birth. Farmers primarily froze colostrum from cows during their third to fifth lactation. Before freezing, 33.1% of the respondents measured indicators in the colostrum to estimate Ig concentrations, whereas 2.3% determined the colostrum quality after freezing. Reusable and disposable PET deposits (23.1%, 22.3%) and ColostroBags (20.0%) were the primary containers used to freeze colostrum. The main reasons for not freezing colostrum were the high labor intensity and the availability of fresh colostrum from other cows. Thawing methods included buckets (47.7%) and professional water baths (13.8%). The survey identified areas in which improved knowledge transfer could enhance colostrum management. Furthermore, there appeared to be a lack of specific, feasible instructions for employees concerning the practical implementation of colostrum management. Most importantly, the regular determination and documentation of Ig concentrations should be emphasized. The added value of stored colostrum, relative to a greater workload, should also be promoted, particularly on smaller farms.

Content of a wound care mobile application for newly graduated nurses: an e-Delphi study

BackgroundWound care represents a considerable challenge, especially for newly graduated nurses. The development of a mobile application is envisioned to improve knowledge transfer and facilitate evidence-based practice. The aim of this study was to establish expert consensus on the initial content of the algorithm for a wound care mobile application for newly graduated nurses.MethodsExperts participated in online surveys conducted in three rounds. Twenty-nine expert wound care nurses participated in the first round, and 25 participated in the two subsequent rounds. The first round, which was qualitative, included a mandatory open-ended question solicitating suggestions for items to be included in the mobile application. The responses underwent content analysis. The subsequent two rounds were quantitative, with experts being asked to rate their level of agreement on a 5-point Likert scale. These rounds were carried out iteratively, allowing experts to review their responses and see anonymized results from the previous round. We calculated the weighted kappa to determine the individual stability of responses within-subjects between the quantitative rounds. A consensus threshold of 80% was predetermined.ResultsIn total, 80 items were divided into 6 categories based on the results of the first round. Of these, 75 (93.75%) achieved consensus during the two subsequent rounds. Notably, 5 items (6.25%) did not reach consensus. The items with the highest consensus related to the signs and symptoms of infection, pressure ulcers, and the essential elements for healing. Conversely, items such as toe pressure measurement, wounds around drains, and frostbite failed to achieve consensus.ConclusionsThe results of this study will inform the development of the initial content of the algorithm for a wound care mobile application. Expert participation and their insights on infection-related matters have the potential to support evidence-based wound care practice. Ongoing debates surround items without consensus. Finally, this study establishes expert wound care nurses’ perspectives on the competencies anticipated from newly graduated nurses.

FM-OV3D: Foundation Model-Based Cross-Modal Knowledge Blending for Open-Vocabulary 3D Detection

The superior performances of pre-trained foundation models in various visual tasks underscore their potential to enhance the 2D models' open-vocabulary ability. Existing methods explore analogous applications in the 3D space. However, most of them only center around knowledge extraction from singular foundation models, which limits the open-vocabulary ability of 3D models. We hypothesize that leveraging complementary pre-trained knowledge from various foundation models can improve knowledge transfer from 2D pre-trained visual language models to the 3D space. In this work, we propose FM-OV3D, a method of Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection, which improves the open-vocabulary localization and recognition abilities of 3D model by blending knowledge from multiple pre-trained foundation models, achieving true open-vocabulary without facing constraints from original 3D datasets. Specifically, to learn the open-vocabulary 3D localization ability, we adopt the open-vocabulary localization knowledge of the Grounded-Segment-Anything model. For open-vocabulary 3D recognition ability, We leverage the knowledge of generative foundation models, including GPT-3 and Stable Diffusion models, and cross-modal discriminative models like CLIP. The experimental results on two popular benchmarks for open-vocabulary 3D object detection show that our model efficiently learns knowledge from multiple foundation models to enhance the open-vocabulary ability of the 3D model and successfully achieves state-of-the-art performance in open-vocabulary 3D object detection tasks. Code is released at https://github.com/dmzhang0425/FM-OV3D.git.

DSP-KD: Dual-Stage Progressive Knowledge Distillation for Skin Disease Classification.

The increasing global demand for skin disease diagnostics emphasizes the urgent need for advancements in AI-assisted diagnostic technologies for dermatoscopic images. In current practical medical systems, the primary challenge is balancing lightweight models with accurate image analysis to address constraints like limited storage and computational costs. While knowledge distillation methods hold immense potential in healthcare applications, related research on multi-class skin disease tasks is scarce. To bridge this gap, our study introduces an enhanced multi-source knowledge fusion distillation framework, termed DSP-KD, which improves knowledge transfer in a dual-stage progressive distillation approach to maximize mutual information between teacher and student representations. The experimental results highlight the superior performance of our distilled ShuffleNetV2 on both the ISIC2019 dataset and our private skin disorders dataset. Compared to other state-of-the-art distillation methods using diverse knowledge sources, the DSP-KD demonstrates remarkable effectiveness with a smaller computational burden.

A lightweight residual network based on improved knowledge transfer and quantized distillation for cross-domain fault diagnosis of rolling bearings

Predictive maintenance advocates the use of artificial intelligence to analyze big data and provides support for monitoring health conditions and planning maintenance activities in smart manufacturing systems. However, deep networks with massive parameters incur high computational costs, hindering its practical applications. Knowledge distillation (KD) transfers knowledge from a deep network to a lightweight network at the cost of an evident decline in diagnostic accuracy. Instead of an accuracy-size trade-off, a framework for creating a lightweight residual network with higher diagnostic accuracy is proposed in this paper. First, the generalization ability of the teacher network is enhanced based on a domain-adversarial neural network (DANN) with normalization attention mechanism (NAM) and ResNet18. Then, a small student network, ResNet6, efficiently learns the knowledge from its well-learned teacher network through the improved probability KD (IPKD) and uniform quantization. The IPKD is designed to obtain the utmost knowledge, represented as probability distributions of labels and feature space, and narrow the gap between teacher and student networks. Meanwhile, the uniform quantization is incorporated into the distillation process for quantization and distillation co-optimization, further compressing the student network. Experiments on two open-source and one wheelset bearing sets are conducted for performance verification and comparison. The results demonstrate that the obtained lightweight student network has a similar accuracy to the deep network for cross-domain fault diagnosis of bearings with various damages and speeds, even bearing sources, and its quantized network has a much smaller size while retaining a comparatively similar accuracy, which makes it more practical for actual industrial equipment.

A Knowledge Guided Transfer Strategy for Evolutionary Dynamic Multiobjective Optimization

The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-optima closer to the true one as soon as possible once a new environment occurs. Previous dynamic multiobjective evolutionary algorithms (DMOEAs) normally focus on DMOPs with regular environmental changes, but neglect widespread random one, limiting their applications in real-world fields. To address this issue, a knowledge guided transfer strategy based DMOEA is proposed in the paper. First, knowledge described as a two-tuple is extracted under each historical environment and preserved to a knowledge pool. Redundant knowledge is recognized and adaptively removed so as to guarantee the diversity of the pool. Second, a knowledge matching strategy is developed to re-evaluate the representative of each stored knowledge under a new environment, with the purpose of finding the most valuable one to promote positive knowledge transfer. Third, an improved knowledge transfer mechanism based on subspace alignment is introduced. By integrating it with knowledge reuse mechanism, a hybrid transfer strategy is constructed to adaptively select the most suitable one in terms of the similarity degree of selected knowledge on the current environment, and then generate a new initial population. Experiments on 20 benchmark problems demonstrate that the knowledge guided transfer strategy outperforms five state-of-the-art algorithms, achieving good versatility in solving DMOPs with both regular and random changes.

The potential for graphical abstracts to enhance science communication.

Science communication, including formats such as podcasts, news interviews, or graphical abstracts, can contribute to the acceleration of translational research by improving knowledge transfer to patient, policymaker, and practitioner communities. In particular, graphical abstracts, which are optional for articles published in Translational Behavioral Medicine as well as many other journals, are created by authors of scientific articles or by editorial staff to visually present a study's design, findings, and implications, to improve comprehension among non-academic audiences. The use of graphical abstracts in scientific journals has increased in the past 10-15 years; however, most scientists are not trained in how to develop them, which presents a challenge for creating graphical abstracts that engage the public. In this article, the authors describe graphical abstracts and offer suggestions for their construction based on the extant literature. Specifically, graphical abstracts should use a solid background, employ an easily readable font, combine visuals with words, convey only the essential study design information and 1-3 "take-home" points, have a clear organizational structure, contain restrained and accessible use of color, use single-color icons, communicate ways to access the full-text article, and include the contact information for the lead author. Authors should obtain feedback on graphical abstract drafts prior to dissemination. There is emerging research on the benefits of graphical abstracts in terms of impact and engagement; however, it will be essential for future research to determine how to optimize the design of graphical abstracts, in order to engage patient, policymaker, and practitioner communities in improving behavioral health.

VerifyTL: Secure and Verifiable Collaborative Transfer Learning

Getting access to labeled datasets in certain sensitive application domains can be challenging. Hence, one may resort to transfer learning to transfer knowledge learned from a source domain with sufficient labeled data to a target domain with limited labeled data. However, most existing transfer learning techniques only focus on one-way transfer which may not benefit the source domain. In addition, there is the risk of a malicious adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper, we construct a secure and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Verif</b> iable collaborative <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ransfer <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> earning scheme, VerifyTL, to support two-way transfer learning over potentially untrusted datasets by improving knowledge transfer from a target domain to a source domain. Furthermore, we equip VerifyTL with a secure and verifiable transfer unit employing SPDZ computation to provide privacy guarantee and verification in the multi-domain setting. Thus, VerifyTL is secure against malicious adversary that can compromise up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n-1$</tex-math></inline-formula> out of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> data domains. We analyze the security of VerifyTL and evaluate its performance over four real-world datasets. Experimental results show that VerifyTL achieves significant performance gains over existing secure learning schemes.

Eye Motion Tracking for Medical Image Interpretation Training.

The significance of Eye Motion Tracking in aiding learners in training search patterns, pattern recognition, and efficiently using their gaze in terms of time and scanning distribution has been highlighted in the USAF Pilot Training Next initiative. The innovation described further builds on this concept in the realm of medical imaging and the provision of real-time feedback of eye direction and gaze duration. This real-time indicator enables the trainer to adapt verbal queueing of the trainee in a personalized manner to improve knowledge transfer, and to increase the confidence of the trainer and trainee in the competency of the trainee. The initial experiment data set included bone radiographs, digital subtraction angiograms, and computed tomography images. Preliminary results and formative feedback from participants was encouraging with expert viewers able to use Eye Motion Tracking to successfully guide novice readers through search and gaze protocol patterns of the medical images.

Health Care Trainees' and Professionals' Perceptions of ChatGPT in Improving Medical Knowledge Training: Rapid Survey Study.

ChatGPT is a powerful pretrained large language model. It has both demonstrated potential and raised concerns related to knowledge translation and knowledge transfer. To apply and improve knowledge transfer in the real world, it is essential to assess the perceptions and acceptance of the users of ChatGPT-assisted training. We aimed to investigate the perceptions of health care trainees and professionals on ChatGPT-assisted training, using biomedical informatics as an example. We used purposeful sampling to include all health care undergraduate trainees and graduate professionals (n=195) from January to May 2023 in the School of Public Health at the National Defense Medical Center in Taiwan. Subjects were asked to watch a 2-minute video introducing 5 scenarios about ChatGPT-assisted training in biomedical informatics and then answer a self-designed online (web- and mobile-based) questionnaire according to the Kirkpatrick model. The survey responses were used to develop 4 constructs: "perceived knowledge acquisition," "perceived training motivation," "perceived training satisfaction," and "perceived training effectiveness." The study used structural equation modeling (SEM) to evaluate and test the structural model and hypotheses. The online questionnaire response rate was 152 of 195 (78%); 88 of 152 participants (58%) were undergraduate trainees and 90 of 152 participants (59%) were women. The ages ranged from 18 to 53 years (mean 23.3, SD 6.0 years). There was no statistical difference in perceptions of training evaluation between men and women. Most participants were enthusiastic about the ChatGPT-assisted training, while the graduate professionals were more enthusiastic than undergraduate trainees. Nevertheless, some concerns were raised about potential cheating on training assessment. The average scores for knowledge acquisition, training motivation, training satisfaction, and training effectiveness were 3.84 (SD 0.80), 3.76 (SD 0.93), 3.75 (SD 0.87), and 3.72 (SD 0.91), respectively (Likert scale 1-5: strongly disagree to strongly agree). Knowledge acquisition had the highest score and training effectiveness the lowest. In the SEM results, training effectiveness was influenced predominantly by knowledge acquisition and partially met the hypotheses in the research framework. Knowledge acquisition had a direct effect on training effectiveness, training satisfaction, and training motivation, with β coefficients of .80, .87, and .97, respectively (all P<.001). Most health care trainees and professionals perceived ChatGPT-assisted training as an aid in knowledge transfer. However, to improve training effectiveness, it should be combined with empirical experts for proper guidance and dual interaction. In a future study, we recommend using a larger sample size for evaluation of internet-connected large language models in medical knowledge transfer.

Forms of Knowledge Transfer in IT Project Management

This qualitative research study explores the experiences of project managers in IT projects, using an investigative approach to explore the intricacies of knowledge transfer in these contexts. The research framework emphasizes the complexity of social dynamics and the need for a deeper understanding, prompting the use of qualitative methods. Expert interviews were chosen as the primary data collection technique due to their alignment with the research objectives. The study adopts an exploratory perspective, with online interviews conducted using a co-expert approach. The philosophical stance of the study is based on phenomenalist principles and aims to gain insights into the personal experiences of project managers. The experts selected for the study met specific criteria, including certification and experience in IT project management. Thematic analysis revealed key categories: Forms of knowledge transfer, challenges, and opportunities. Personalized spontaneous knowledge transfer emerged as the dominant form, facilitated by informal interactions, and efforts to codify this knowledge in a documented form were observed. Challenges included issues of quantity, distribution, team dynamics, technology platforms, organizational culture, and time constraints. Successful knowledge transfer was associated with increased efficiency, improved quality, faster project completion, and continuous improvement of methods and standards. The study suggests that project-based organizations should develop strategies to improve knowledge transfer, incorporate it into project management standards and focus on facilitating effective communication between team members.

Bridging the gap in healthcare: creating an online health information prototype for individuals with intellectual disabilities to promote equity and knowledge transfer.

The prevalence of intellectual disabilities (ID) in Austria is estimated to be approximately 1% of the population. Growing awareness of the major challenges faced by individuals with ID, including difficulties in comprehending written health information, communication barriers with healthcare professionals, and limited inclusion in health education research, necessitates concerted efforts to address these issues. The utilization of guidelines promoting easy-to-read language, web accessibility, and usability can significantly enhance access to health information and improve health literacy among people with ID. The "LUCHS-Gesundheitsinformationen (Health Information)" project aimed to improve knowledge transfer of health information for people ID by creating a website prototype. Unstructured interviews were conducted with two focus groups (n = 20) people with ID and their caretakers to elicit relevant topics and ensure the quality, comprehensibility, and usability of a website prototype. A convenience sample of 20 adults employed in sheltered workshops of the Caritas Lower Austria West facility who met the inclusion criteria participated. The prototype addressed four main topics, namely nutrition, medical specialization, first aid, and patient information, offering comprehensive information using easy-to-read language and pictorial content. The website prototype followed web accessibility suggestions and incorporated external resources, such as brochures and emergency contact details, to enhance usability and provide a reliable source of health information for individuals with ID. The findings suggest that a website format is a feasible means of providing accessible online health information for people with ID. However, further research is warranted to explore the potential of digital health applications for individuals with ID, as inclusion in the Austrian register for digital health applications necessitates meeting multiple quality standards, thereby instilling trust among the target group.

Unsupervised Domain Adaptation with CycleGAN: Adapting Image Style and Content for Improved Cross-Domain Performance

Abstract: In order to improve model performance while transferring information from a source domain to an unlabelled target domain, unsupervised domain adaptation is a crucial problem in deep learning. The CycleGAN design, a well-known generative adversarial network architecture, is used in this study to explore the domain adaptation challenge. Improved cross-domain performance is the goal, and both picture style and content adaptation are being focused on. In this study, we explore the use of CycleGAN to modify images from a source domain to more closely reflect the look and feel of a target domain. With no need for paired data, the architecture's built-in capacity to learn domain mappings makes it easier to transfer knowledge between dissimilar domains. By placing a strong emphasis on content and style adaptation, we hope to get beyond the problems of domain shift and distribution mismatch, which will improve generalization and classification accuracy in the target domain. We extensively experiment on benchmark datasets to show the efficacy of the suggested approach. Comparing cross-domain performance to baseline models and other adaption strategies, quantitative evaluation using known measures demonstrates the significant improvement attained. Additionally, qualitative assessments demonstrate the successful alteration of images, demonstrating CycleGAN's competence in adjusting both visual appeal and semantic information. By highlighting the need of simultaneously addressing visual style and content, this study makes a contribution to the field of unsupervised domain adaptation. The outcomes highlight CycleGAN's potential as a formidable domain adaptation tool, opening the door for improved knowledge transfer and performance in real-world settings across several domains

Experiences with Lessons Learned Method in it Projects

The project manager plays a central role in project integration and successful project completion. Leadership skills, especially knowledge-based leadership skills, are critical to project success as they enable the effective creation, sharing and use of knowledge. Project managers’ social cognitive, interpersonal and strategic skills contribute to effective knowledge sharing. The Lessons Learned method is generally recommended for knowledge transfer in projects, but its effectiveness and implementation are controversial. The aim of this study is to investigate project managers’ experiences with the Lessons Learned method in IT project management. The focus is on how these experiences influence knowledge transfer in IT projects. A qualitative research design was used to gain insight into the experiences of project managers. Eleven experts responsible for IT projects were interviewed in guideline-based expert interviews. The collected data was analysed using content structuring qualitative content analysis. The study found that project managers are responsible for knowledge transfer, but often do not perceive this task. Different perceptions of knowledge transfer methods were observed between traditional and agile training. The Lessons Learned approach showed unsatisfactory results and project managers defined their own methods. To improve knowledge transfer, project management standards should include clear tasks and support decision making. Generic models need to be integrated in the project management standards to ensure consistent approaches in practice.

Improving sewage sludge treatment and utilisation in China: a German perspective on barriers to and measures for the dissemination of innovative technologies

Abstract In China, there is enormous development potential for resource recovery from sewage sludge. Government plans indicate that there will be greater investment in sewage sludge treatment, recycling and associated new technologies. Expert interviews and an expert workshop with representatives of German firms and institutions were held in order to identify barriers to and possible measures for the dissemination of technologies geared towards the utilisation of sewage sludge, such as anaerobic digestion, thermal hydrolysis and hydrothermal carbonisation. The following measures were identified as crucial: influence and support for the development of suitable regulations for sewage sludge in China, improvement in the processes and practices within specialised authorities, improved knowledge transfer, the development of appropriate business or operator models and persuasion of decision-makers within public authorities. German companies cannot implement most of these measures easily.

Current knowledge and attitudes of students towards biotechnology: A long way to go

Thanks to technological innovations, science is rapidly evolving, and therefore, development of effective and engaging learning strategies is needed to ensure knowledge transmission. But first, we need to know what the current situation is. In this work, we focused on biotechnology, a scientific branch that is present in many of aspects of our daily life, but also a source of controversy. We developed a questionnaire to investigate the knowledge of 124 Baccalaureate students about biotechnology and its uses, as well as their awareness about the transgenic food as a socioscientific issue, and their attitudes towards applications of transgenics. Most students cannot give an appropriate definition of biotechnology. Besides, students have misconceptions about transgenics, and easily change their attitudes. Thus, new strategies to get students interest and improve knowledge transfer should be implemented.

Artificial intelligence-based group decision making to improve knowledge transfer: the case of distance learning in higher education

ABSTRACT In this paper, we propose a method based on multicriteria classification and a dominancebased rough set approach (DRSA) to support teachers in decision making. The objective is to use teachers’ knowledge and preferences to identify ‘atrisk students’, i.e. students who are likely to drop out, and ‘leader students’, i.e. students who are likely to help their peers, in distance learning. The proposed method is composed of two phases: phase I builds collective decision rules from teachers’ preferences, and phase II classifies students into two decision classes: ‘atrisk students’ and ‘leader students’. This method was designed, tested, and validated in higher education, with teachers who have acquired rich experience in teaching in online-synchronous mode since the Covid-19 pandemic.