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Machine learning (ML) offers high potential in manufacturing industry; moreover, for example the effectiveness of quality prediction and evaluation can be greatly improved using Machine Learning, which can generate significant competitive advantages. However, the potentials of ML are not fully exploited by small and medium-sized enterprises. A qualitative empirical study was conducted with 60 companies from different industry sectors to determine when SMEs are more likely to use ML. Here, it is shown that the willingness to invest in applications is substantial for the implementation of ML. Also, the availability of sufficient qualitative data within the SME is imperative for applying ML. Furthermore, recommendations for action for SMEs are established to close the technology adoption gap in SMEs and to leverage the benefits of ML. [ABSTRACT FROM AUTHOR]

Federated learning, as a distributed machine learning framework, aims to protect data privacy while addressing the issue of data silos by collaboratively training models across multiple clients. However, a significant challenge to federated learning arises from the non-independent and identically distributed (non-iid) nature of data across different clients. non-iid data can lead to inconsistencies between the minimal loss experienced by individual clients and the global loss observed after the central server aggregates the local models, affecting the model's convergence speed and generalization capability. To address this challenge, we propose a novel federated learning algorithm based on update bias (FedUB). Unlike traditional federated learning approaches such as FedAvg and FedProx, which independently update model parameters on each client before direct aggregation to form a global model, the FedUB algorithm incorporates an update bias in the loss function of local models—specifically, the difference between each round's local model updates and the global model updates. This design aims to reduce discrepancies between local and global updates, thus aligning the parameters of locally updated models more closely with those of the globally aggregated model, thereby mitigating the fundamental conflict between local and global optima. Additionally, during the aggregation phase at the server side, we introduce a metric called the bias metric, which assesses the similarity between each client's local model and the global model. This metric adaptively sets the weight of each client during aggregation after each training round to achieve a better global model. Extensive experiments conducted on multiple datasets have confirmed the effectiveness of the FedUB algorithm. The results indicate that FedUB generally outperforms methods such as FedDC, FedDyn, and Scaffold, especially in scenarios involving partial client participation and non-iid data distributions. It demonstrates superior performance and faster convergence in tasks such as image classification. [ABSTRACT FROM AUTHOR]

Purpose: Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge. Design/methodology/approach: The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms. Findings: The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting. Research limitations/implications: Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further. Practical implications: Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully. Originality/value: To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms. [ABSTRACT FROM AUTHOR]

Industry 4.0 technologies create a connected ecosystem that allows for real-time monitoring, control, and optimization. This enables the manufacturing industry to move the ultimate goal of their businesses from lean to leagile. Then, to meet the needs of leagile manufacturing, an integrated production planning and scheduling system is needed. The academic world responds to the need by the growth of papers. Many methodologies, models, frameworks, and methods have been proposed. Literature abounds on classifying them, yet it remains inconclusive mainly on their applicability to Industry 4.0. In this regard, this paper conducts a literature review aiming to show the state of the art and discover the most promising production planning and scheduling method for Industry 4.0 among the many. Lastly, on the basis of this study, recommendations on possible future research directions are provided for researchers, and the design of the next-generation production planning and scheduling system for Industry 4.0 is also suggested. [ABSTRACT FROM AUTHOR]

Managing operators is an important issue, especially in work-intensive environments and companies with hundreds or thousands of employees. The challenge of optimizing skilled labour selection on the production line, using measurable indicators and supported by highly sophisticated models and software solutions, is a significant global research topic. In this manuscript, authors present a novel model for managing human operators based on a matrix of selected indicators. This model aims to enhance the planning, selection, and deployment of human resources. By optimizing the allocation of human resources to different workstations while utilizing available resources, the model facilitates monitoring and supervision of employee qualifications. To implement this model, a software solution designed for advanced labour optimization has been introduced. This software utilizes simulation and testing of various scenarios, which is increasingly crucial in intensive working environments. [ABSTRACT FROM AUTHOR]

Scientists seek to understand the causal processes that generate sustainability problems and determine effective solutions. Yet, causal inquiry in nature–society systems is hampered by conceptual and methodological challenges that arise from nature–society interdependencies and the complex dynamics they create. Here, we demonstrate how sustainability scientists can address these challenges and make more robust causal claims through better integration between empirical analyses and process- or agent-based modeling. To illustrate how these different epistemological traditions can be integrated, we present four studies of air pollution regulation, natural resource management, and the spread of COVID-19. The studies show how integration can improve empirical estimates of causal effects, inform future research designs and data collection, enhance understanding of the complex dynamics that underlie observed temporal patterns, and elucidate causal mechanisms and the contexts in which they operate. These advances in causal understanding can help sustainability scientists develop better theories of phenomena where social and ecological processes are dynamically intertwined and prior causal knowledge and data are limited. The improved causal understanding also enhances governance by helping scientists and practitioners choose among potential interventions, decide when and how the timing of an intervention matters, and anticipate unexpected outcomes. Methodological integration, however, requires skills and efforts of all involved to learn how members of the respective other tradition think and analyze nature–society systems. [ABSTRACT FROM AUTHOR]

Currently, the autonomy of artificial systems, robotic systems in particular, is certainly one of the most debated issues, both from the perspective of technological development and its social impact and ethical repercussions. While theoretical considerations often focus on scenarios far beyond what can be concretely hypothesized from the current state of the art, the term autonomy is still used in a vague or too general way. This reduces the possibilities of a punctual analysis of such an important issue, thus leading to often polarized positions (naive optimism or unfounded defeatism). The intent of this paper is to clarify what is meant by artificial autonomy, and what are the prerequisites that can allow the attribution of this characteristic to a robotic system. Starting from some concrete examples, we will try to indicate a way towards artificial autonomy that can hold together the advantages of developing adaptive and versatile systems with the management of the inevitable problems that this technology poses both from the viewpoint of safety and ethics. Our proposal is that a real artificial autonomy, especially if expressed in the social context, can only be achieved through interdependence with other social actors (human and otherwise), through continuous exchanges and interactions which, while allowing robots to explore the environment, guarantee the emergence of shared practices, behaviors, and ethical principles, which otherwise could not be imposed with a top-down approach, if not at the price of giving up the same artificial autonomy. [ABSTRACT FROM AUTHOR]

Weather forecasts are indispensable for the decision on the direction of operation of a runway system. Since the forecasts contain uncertainties, additional challenges arise for runway configuration management (RCM). With developments in machine learning, numerous models have been developed to improve forecasts and assist management. In this contribution, an intrinsically explainable Self-Enforcing Network (SEN) is presented as a decision support system for the RCM at Frankfurt Airport. [ABSTRACT FROM AUTHOR]

After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer. [ABSTRACT FROM AUTHOR]

This study employs bibliometric methods utilizing VOSviewer analysis of Scopus data from 2013 to 2023 to investigate trends in artificial intelligence (AI) research within the aviation industry. The analysis reveals a substantial increase in publication volume over time, peaking at 406 articles in 2022, signifying a heightened interest in AI implementation within the aviation sector. Key publication sources notably include conferences such as AIAA IEEE Digital Avionics Systems Conference Proceedings and ACM International Conference Proceeding Series. Leading contributions in publications emerge from countries such as the United States, China, India, Germany, the United Kingdom, and France, reflecting global involvement in AI research within the aviation industry. Citation analysis identifies highly cited articles addressing topics such as Digital Twin (DT) optimization processes in aviation, AI application in aircraft navigation, and machine learning for weather forecasting. These findings underscore researchers' interest in fundamental topics such as aviation, aircraft-related artificial intelligence, flight delay, and deep learning. Furthermore, co-citation analysis delineates research clusters, illustrating thematic similarities within AI research in the aviation industry. Overall, this bibliometric analysis provides comprehensive insights into the evolution of AI research in the aviation industry, potentially guiding researchers, practitioners, and stakeholders in directing research efforts, formulating policies, and understanding current trends in the application of artificial intelligence within the aviation sector. [ABSTRACT FROM AUTHOR]

Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers. [ABSTRACT FROM AUTHOR]

Background: In India smoking and tobacco dipping are major health problems and one of the largest preventable causes of disease and premature death. Nicotine has been reported to improve attention, learning, reaction time (RT), problem solving, and stimulus evaluation and discrimination. Previous research has established that cholinergic systems are important for cognitive functioning and nicotine is a potent cholinergic stimulant that affects many central nervous system (CNS) pathways, including the auditory pathway. It is a measure of function of sensory motor association and performance of an individual. It involves stimulus processing, decision making, and response programming. Material and Methods: This is a Prospective, observational, comparative study was conducted in the Departments of Physiology at Index Medical College. Audio-visual reaction time - Measurement of reaction time is a simple test which is measured by =audiovisual reaction time apparatus' (Anand agency, Pune). Reaction time is a commonly used parameter for measuring implicit learning. In measurement of reaction time subject was asked to respond by pressing the button as soon as he had received the stimulus in the form of sound or light. The apparatus provides both auditory (low and high pitched sounds) and visual (red and green) stimuli. The arrangement consists of a source of stimulus, response key and the time recording device. The response is given by the subject by pressing a key with his index finger. Results: In the present study, in age groups ART values were significantly different (Graph-1) in all three groups, ART and VRT values increase in control, smokers and dipping tobacco users as age advances. As regards ART values, age group 31-40 years differs significantly from age group 11-20 years and 21-30 years (p<0.05), similarly age group 41-50 years differs significantly from age groups 11-20 years, 21-30 years and 31-40 years. As regards, VRT values age groups differ from each other but age group 41-50 years differ significantly from age group 11-20 years and age group 21-30 years. Conclusion: Significant difference was also observed after smoking and after 12 hours of abstinence in study group subjects. This indicated increased reaction time required for integration process in CNS and delayed conduction in reflex arc after abstinence. Thus, the evidence presented in this article supports that temporary abstinence from nicotine has adverse effect on reaction time which may force them to smoke again. Health education and nicotine replacement therapy may help them to achieve the ultimate goal of complete cessation from smoking. [ABSTRACT FROM AUTHOR]

In this article a motion cueing algorithm (MCA) based on model predictive control (MPC) for a hexapod-based dynamic driving simulator is derived. The design objective of the MCA is to reproduce the real world accelerations and angular velocities for a test person in the simulator while respecting its actuator limitations. This results in an underlying nonlinear, state-constrained optimal control problem. In order to exploit the full predictive potential of the described algorithm, a method to predict the future driver behavior and thus the future desired values for the MPC is derived by modeling the driver as an optimal controller. The OCP weights that mainly influence the predicted driving actions are learned from demonstration using an inverse optimal control (IOC) approach. Furthermore, the direct incorporation of human perception models into the motion planning process is considered. An online driver-in-the-loop experiment with the Daimler driving simulator shows the high potential of the derived MPC scheme compared to the commonly used filter-based approach as well as the efficiency of the underlying optimization method. [ABSTRACT FROM AUTHOR]

Knowledge and skills in the field of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC) are more and more important for industry. Therefore, it is crucial to know what current students and future employees can offer to the industry. University students develop their knowledge and skills to support the industry in implementing modern technologies in the future. It can be expected that the first source of information for students will be lectures and other activities at the university. However, they may obtain knowledge from other sources. This article presents the results of research conducted among students assessing their own knowledge and skills in the field of IoT, AI, and EC. The research was preceded by an analysis of curricula at selected universities in terms of topics related to AI, IoT, and EC. Based on the results of the analysis, survey questions were prepared. The developed questionnaire was made available to students. The research sample for the survey participants was 563 students. The results obtained were analyzed. The results of the analysis show which issues are better known to students and which are worse. The information presented in this paper can be a source of information for the industry that can assess the competences that are or will be available on the labor market in the near future. Additionally, universities can obtain information on the areas in which there are competency gaps and which methods of teaching AI, IoT, and EC are better perceived by students. [ABSTRACT FROM AUTHOR]

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In 2014, the European Council set energy and climate targets for 2030 that will have far-reaching implications for the decarbonisation of Europe's economy as well as the nature and success of the energy integration project, the Energy Union. The targets mark a qualitative shift towards a more technology-neutral EU climate and energy policy than represented in targets for 2020, decided in 2007. Existing explanations emphasise the role of non-state actors but do not fully account for the muted advocacy for a multiple targets approach. This article examines the alternative explanatory power of the concepts of problem surfing and spillover, taken from the multiple streams approach, for interpreting the inability of multiple targets advocates to make a real impact on the policy agenda. Findings suggest that the problematisation of the EU's 'flagship' instrument, the EU-ETS, by policy entrepreneurs and subsequent structural internal alignment of the policy community offer a complementary explanation. [ABSTRACT FROM AUTHOR]

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability. [ABSTRACT FROM AUTHOR]

Abstract: This article discusses (a) sustained critiques of framing theory and of collective action frames, (b) the development and implications of a dialogic and relational alternative, and (c) suggestions for how to pursue this alternative approach using mental models. Proponents of framing theory have advocated that a dialogic and relational alternative could prove fruitful in advancing sociological understanding of the variety of contexts in which social movement discourse takes place. Drawing insights from the work of several scholars, I propose what this alternative entails in terms of both theory and research. Parallel to the development of relational sociology, academics have developed discourse mapping techniques that blend network analysis with cultural analysis. I suggest that one way researchers can integrate a dialogic and relational approach into their analyses of framing is through the use of mental models. [ABSTRACT FROM AUTHOR]

Exam scheduling, or exam timetabling, is one of the most important challenges of credit hour systems. Credit hours system are characterized by their large number of courses, students, and professors. However, there are many constraints regarding exam time tabling. The time tabling problem is a well known graph coloring problem. In this paper, we present a novel approach for solving the exam time tabling problem by enhancing the performance of the greedy algorithm using by modifying the chicken swarm optimization (CSO). In the proposed approach, the modified CSO is used to optimize the ordering of vertices fed into the greedy algorithm. Results show that, statistically wise, using the proposed approach, significant enhancements are obtained compared to using manual timetabling or even using the greedy algorithm individually. Moreover, the soft constraints are considered on the proposed approach using a proposed objective function. From time perspective, the execution time of the algorithm is approximately 1.5 minutes on the average, even in case of dense graphs. Furthermore, the cost function of the proposed approach is significantly less than that of using greedy algorithm only. [ABSTRACT FROM AUTHOR]

Nutrient limitation of oceanic primary production exerts a fundamental control on marine food webs and the flux of carbon into the deep ocean. The extensive boundaries of the oligotrophic sub-tropical gyres collectively define the most extreme transition in ocean productivity, but little is known about nutrient limitation in these zones. Here we present the results of full-factorial nutrient amendment experiments conducted at the eastern boundary of the South Atlantic gyre. We find extensive regions in which the addition of nitrogen or iron individually resulted in no significant phytoplankton growth over 48 hours. However, the addition of both nitrogen and iron increased concentrations of chlorophyll a by up to approximately 40-fold, led to diatom proliferation, and reduced community diversity. Once nitrogen-iron co-limitation had been alleviated, the addition of cobalt or cobalt-containing vitamin B12 could further enhance chlorophyll a yields by up to threefold. Our results suggest that nitrogen-iron co-limitation is pervasive in the ocean, with other micronutrients also approaching co-deficiency. Such multi-nutrient limitations potentially increase phytoplankton community diversity. [ABSTRACT FROM AUTHOR]

In orientation to new developments in evolutionary biology we propose an extension of evolutionary algorithms in two dimensions, the regulatory algorithm (RGA). It consists of two levels of vectors, the regulatory vector and the structural vector. Each element of the regulatory vector is connected with one or several elements of the structural vector, but not vice versa. The connections can be interpreted as steering connections, the switching on or off of the structural elements and/or as switching orders for the structural elements. An RGA that operates with the usual genetic operators of mutation and crossover can be used for avoiding rules like penalty or default operators, it is in certain problems significantly faster than a standard genetic algorithm, and it is very suited when modeling and optimizing systems that consist themselves of different levels. Examples of RGA usage are shown, namely, the optimal dividing of socially deviant youths in a hostel, the optimal introduction of communication standards in information systems, and the allocation of employees to superiors by taking into regard the different personality types. [ABSTRACT FROM AUTHOR]

Sarcopenia in patients with liver cirrhosis (LC) has been attracting much attention these days because of the close linkage to adverse outcomes. LC can be related to secondary sarcopenia due to protein metabolic disorders and energy metabolic disorders. LC is associated with profound alterations in gut microbiota and injuries at the different levels of defensive mechanisms of the intestinal barrier. Dysbiosis refers to a state in which the diversity of gut microbiota is decreased by decreasing the bacterial species and the number of bacteria that compose the gut microbiota. The severe disturbance of intestinal barrier in LC can result in dysbiosis, several bacterial infections, LC-related complications, and sarcopenia. Here in this review, we will summarize the current knowledge of the relationship between sarcopenia and dysbiosis in patients with LC. [ABSTRACT FROM AUTHOR]