Set Of Actions Research Articles

No compromise in solution quality: Speeding up belief-dependent continuous partially observable Markov decision processes via adaptive multilevel simplification

Continuous Partially Observable Markov Decision Processes (POMDPs) with general belief-dependent rewards are notoriously difficult to solve online. In this paper, we present a complete provable theory of adaptive multilevel simplification for the setting of a given externally constructed belief tree and Monte Carlo Tree Search (MCTS) that constructs the belief tree on the fly using an exploration technique. Our theory allows to accelerate POMDP planning with belief-dependent rewards without any sacrifice in the quality of the obtained solution. We rigorously prove each theoretical claim in the proposed unified theory. Using the general theoretical results, we present three algorithms to accelerate continuous POMDP online planning with belief-dependent rewards. Our two algorithms, SITH-BSP and LAZY-SITH-BSP, can be utilized on top of any method that constructs a belief tree externally. The third algorithm, SITH-PFT, is an anytime MCTS method that permits to plug-in any exploration technique. All our methods are guaranteed to return exactly the same optimal action as their unsimplified equivalents. We replace the costly computation of information-theoretic rewards with novel adaptive upper and lower bounds which we derive in this paper, and are of independent interest. We show that they are easy to calculate and can be tightened by the demand of our algorithms. Our approach is general; namely, any bounds that monotonically converge to the reward can be utilized to achieve a significant speedup without any loss in performance. Our theory and algorithms support the challenging setting of continuous states, actions, and observations. The beliefs can be parametric or general and represented by weighted particles. We demonstrate in simulation a significant speedup in planning compared to baseline approaches with guaranteed identical performance.

Double-duty actions addressing the double burden of malnutrition among adolescents aged 10-19: protocol for a scoping review.

The global adolescent population faces the challenge of the double burden of malnutrition (DBM), characterized by the coexistence of undernutrition and overweight/obesity, including diet-related non-communicable diseases (NCDs). This dual challenge, prevalent across various socio-economic backgrounds, necessitates double-duty actions, i.e., integrated interventions designed to concurrently address both sets of conditions. These actions are critical for fostering the overall health and well-being of adolescents. The objective of this review is to describe the content, setting, and delivery mechanisms of double-duty actions, synthesize their impacts on adolescents' nutritional status, and offer policy and program implications for future interventions. As part of this scoping review, we will conduct a comprehensive search across multiple databases, including MEDLINE, Embase, CENTRAL, CINAHL, and Google Scholar, to identify relevant interventions, programs, policies, guidelines, evaluation studies, and strategies targeting DBM among adolescents aged 10-19 years. Inclusion criteria encompass a range of evidence sources with methodologically sound and well-described study designs. All full-text articles and abstracts will be independently screened by two reviewers to ensure a comprehensive inclusion of papers that align with the established criteria. The final protocol is available on Open Science Forum (https://osf.io/kxapb). Addressing DBM through integrated double-duty actions is crucial for achieving global nutrition and public health goals. However, challenges persist in the form of uncoordinated efforts, lack of comprehensive evidence for what interventions work among adolescents, and the need for context-specific strategies to effectively address the heterogeneity of DBM. The results of this scoping review may provide evidence for future policies and interventions, emphasizing integrated, multi-sectoral strategies tailored to the unique needs of the adolescent population.

Shaping Incentives through Measurement and Contracts

ABSTRACT I study productive activity, measurement, and compensation in a principal agent model that relaxes common restrictions on the action set of the agent, the distribution of performance measures, and the shape of the wage schedule. The solution to this relaxed problem unifies insights from extant theory and shares features with well-known empirical phenomena. In particular, the optimal outcome distribution has a kink, optimal measurement is conservative, and optimal wages ensure congruent incentives and resemble accounting-based bonus plans featuring a floor, hurdle bonus, incentive zone, and ceiling, with thresholds that may reference other performance measures. Beyond these specific insights, the paper provides a flexible framework for studying how incentives are shaped through measurement and contracts.

The use of honey in the perioperative care of tonsillectomy patients-A narrative review.

Tonsillectomy is one of the most common surgical procedures in childhood. While generally safe, it often is associated with a difficult early recovery phase with poor oral intake, dehydration, difficult or painful swallowing, postoperative bleeding, infection and/or otalgia. Better pain management and the availability of more child friendly medications are within the top consumer priorities in perioperative medicine, highlighting the importance of alternative pain treatments. This review focuses on the potential role of honey in the postoperative setting, its effects, and mechanisms of action. While the application of honey post-tonsillectomy may offer analgesic and healing benefits, it may also reduce postoperative bleeding. A systematic search was carried out using the search terms honey, tonsillectomy. Filters were applied to human studies and English. No other search terms were used or age filters applied to yield a broader range of results. Seven pediatric, four adult, and two studies of mixed pediatric and adult patients with sample sizes ranging from 8 to 52 patients were included in this review. Effect sizes ranged from small to huge across the studies. While the application of honey post-tonsillectomy may offer analgesic and healing benefits, it may also reduce postoperative bleeding. However, while there are potential benefits based on the chemical composition of honey, the current literature is of variable quality and there is need for high quality clinical trials.

Devising a technique for assessing the accuracy of measuring electric motor torque

The object of this study is a technique for assessing the accuracy of measuring the torques of electric motors. This technique is based on the improvement of dynamic torque measurement processes under transient operating modes. Special attention was paid to devising adaptive methods to normalize the output signal, which take into account vibrations and nonlinearities of the measuring channel. In order to improve the accuracy of measurements under conditions when the controlled parameter has different deviations from its average value, a technique for integrating time measurement intervals was proposed. That has made it possible to generalize parameter fluctuations, providing more stable measurement results. This paper reports the simulation of the process of measuring torques of electric motors under transient operating modes and under vibration conditions. That has made it possible to test the proposed technique for estimating the variance of the methodological error, which includes the integration of measurement time intervals and makes it possible to increase the accuracy of the measurement under the conditions of transient modes in electric motor operation. An algorithm for correcting the error of the dynamic torque of the electric motor has been proposed, which allows it to be corrected. This becomes possible owing to the use of a reference measure and the proposed technique for determining the deviations of the controlled parameter of the measured quantity, as well as the automatic calibration of measuring transducers. The algorithm allows for flexible settings of the corrective action, expanding the potential of electric motor control systems. It can complement soft measurement methods, as well as adjust the threshold values of deviations under different operating modes of electric motors. This allows the measured systems to better adapt to changes in operating conditions, to adjust the specified measurement accuracy under conditions of uncertainty

Reflective learning: a new leadership development framework driving engineering innovation

ABSTRACT Innovation is an organizational learning process that demands a reflexive perspective to take on uncertainties and question deeply held assumptions, propelling leadership and organizational structures forward. Discussions on the relationships between innovation and reflexivity predominantly focus on individuals rather than considering it a shared capability within an organization. This study elucidates the impact of reflexive-learning-based leadership development on promoting collective reflexivity, navigating its implications for organizational leadership and structures in innovation. A reflexive-learning-based leadership development program is a set of collective actions comprising four fundamental steps: acknowledging problems, reassessing assumptions, thinking of alternatives, and developing new perspectives. This study demonstrates its effectiveness in developing collective reflexivity within an engineering organization by repurposing the After-Action Review (AAR) as reflexive learning training. The findings underscore reflexive learning, making leaders exhibit the traits of ambiguity tolerance and interdisciplinary knowledge that favor innovation. This research contributes to an academic framework, opening avenues for future investigations into organizational development from a reflexive perspective and providing a proven managerial practice to improvise an effective solution to overcome those uncertainties in real-world innovations.

Academic publishing requires linguistically inclusive policies.

Scientific knowledge is produced in multiple languages but is predominantly published in English. This practice creates a language barrier to generate and transfer scientific knowledge between communities with diverse linguistic backgrounds, hindering the ability of scholars and communities to address global challenges and achieve diversity and equity in science, technology, engineering and mathematics (STEM). To overcome those barriers, publishers and journals should provide a fair system that supports non-native English speakers and disseminates knowledge across the globe. We surveyed policies of 736 journals in biological sciences to assess their linguistic inclusivity, identify predictors of inclusivity, and propose actions to overcome language barriers in academic publishing. Our assessment revealed a grim landscape where most journals were making minimal efforts to overcome language barriers. The impact factor of journals was negatively associated with adopting a number of inclusive policies whereas ownership by a scientific society tended to have a positive association. Contrary to our expectations, the proportion of both open access articles and editors based in non-English speaking countries did not have a major positive association with the adoption of linguistically inclusive policies. We proposed a set of actions to overcome language barriers in academic publishing, including the renegotiation of power dynamics between publishers and editorial boards.

How to deal with invasive species that have high economic value?

The ever-accelerating process of introduction and establishment of invasive alien species (IAS) in marine waters requires engaging different actors in the management and planning. To address this challenge, stakeholders need an integrated approach for defining actions of control and management within an ecosystem-based framework: to seek and enhance coordination, collaboration and trust among all stakeholders affected by changes in linked marine social - ecological systems. This study explores the complexities and opportunities in managing economically valuable IAS, demonstrating the need for tailored management strategies that consider key ecological details needed for successful control such as timing of removals and key economic considerations such as appropriate incentive mechanisms and time horizons in favor of long-term conservation goals. We propose a set of actions that can serve as a roadmap for effective IAS control and management, considering their dual role as nuisances and valuable resources. These actions pertain to local, regional and international levels, encouraging meaningful stakeholder engagement through co-management structures that can foster the development of robust management plans, enabling better IAS control. The outcome holds potential for positive contributions to the blue economy— sustainable use of marine resources, including fisheries, tourism, and renewable energy— and for meeting sustainability objectives.

Screening of Machine Learning Techniques on Predictive Maintenance: a Scoping Review

Predictive maintenance (PdM) is a set of actions and techniques to early detect failures and defects on machines before they occur, and the usage of machine learning and deep learning techniques in predictive maintenance has increased during the last years. Even with this increase of the literature, there is still a gap concerning the application of such techniques for PdM in the industry, as there are no clear guidelines about which information to use for a PdM system, how to process the information, and what machine learning techniques should be used in order to obtain acceptable results. This scoping review is performed in order to observe the current status on the use of Machine Learning and Deep Learning in predictive maintenance in academia and provide answer to the questions related to these guidelines. For this purpose, a literature review of the last five years is carried out, using those articles that cover information about sources of information used for PdM, the treatment given to such data and the machine learning (ML) methods or techniques used. The Web of Science: Core Collection database is used as a source of information, specifically the Science Citation Index Expanded (SCIE). The review shows that there are different information sources used for machine learning and deep learning in PdM, depending on the origin of the data and the availability of it, and as well whether the data sets are private or public. Also, we can observe that data used for both training and making predictions does not only use traditional pre-processing techniques, but that about one fifth of the articles even propose new techniques in this field. Additionally, we compare a wide range of techniques and algorithms which are used in Deep Learning -being ANN the most used- and in Machine Learning, being SVM the most used algorithm, closely followed by Random Forest. Based on the results, we provide indications about how to apply ML for PdM in industry. Keywords: machine learning, predictive maintenance, artificial intelligence, deep learning, data processing, data collection

Developing mechanisms for implementing the educational potential of subject training for future teachers

Introduction. With the return of upbringing to the global educational space, which is becoming virtual and highly uncertain, the problem of developing mechanisms for realizing the educational potential of subject training for future teachers is urgent. The study aims to scientifically substantiate and design such mechanisms. Materials and methods. The following research methods for theoretical and predictive substantiation of the problem were used: analysis of scientific literature on pedagogical problems; historical-structural, biographical, monographic methods; designing algorithms for implementing educational mechanisms. Results. The main directions of “launching” the aforementioned mechanisms were identified: selecting the subject material; improving the lesson structure; organizing communication and interaction. Two groups of mechanisms were presented: psychological and pedagogical (interiorization, motivational choice, stimulating emotional experiences and emotional saturation, pedagogical support for motivating future teachers to educational activities), and scientific and methodological (scientific support of subject training of the future teacher by modeling pedagogical conditions, pedagogical training laboratory, introducing pedagogical training principles, stimulating forms of independence and activity of the future teacher in subject training and in organizing practices). Conclusion. The data obtained can be used to design and implement the educational potential of subject training for future teachers. Thus, education at a pedagogical university will be aimed at ensuring the professional competence of future teachers in a set of interrelated elements that allow practical implementation of the planned set of teacher actions focused on social activity, self-knowledge, and positive activities in studying disciplines related to personnel training.

Emerging Trends in Play-to-Earn (P2E) Games

This research aims to establish the primary drivers influencing the development and consumers’ decision-making process in web3 games—decentralized games that function according to the play-to-earn paradigm. We observe several types of micro-economies developed within five play-to-earn games and highlight four roles consumers play at any given time. Our study offers a different perspective on rational consumer behaviour in cryptocurrency-based games and paves the way to better understanding their dynamics and evolution. Results shed light on the construction of in-game economies and how individuals of a given type engage in different playing activities. Furthermore, we compare the key features of web3 games with those similar to classic online games and assess if the play-and-earn implementations represent an evolution from previous revenue models. Using our proposed methodology, researchers can compare and classify any P2E games. We conclude by establishing a set of actions that enable consumers to benefit from this new phenomenon.

Intuiciones y racionalidad ecológica

Through an ecological approach, it can be said that a person tries to identify the optimal action for him- or herself by a set of different possible actions through intuition. The debate about the ‘dual process theories’ takes up this idea and postulates two different ways to evaluate decision possibilities: A logical-analytical path and an intuitive (Magrabi and Bach, 2013). Predominantly, it is assumed that the logical-analytical path is related to rationality and therefore the path to strive for in rational decision-making. However, in science it has been shown that bounded rationality is to be assumed. Therefore, the question of the extent to which emotions or feelings, and in this context also intuitions, are rational has come to the fore (De Sousa, 1987; Evans and Cruse, 2004). In this paper, intuitions are considered as a feeling with intentionality: Intuitions always refer to an appropriate environment and are directed toward a decision that results in an action. With Damásio (1994, 2013) it can be shown that an intuition is a feeling that excels in acting as a cue or signal. With the assumption of an ecological rationality (Gigerenzer, Todd and ABC Research Group, 1999; Todd and Gigerenzer, 2012; Todd and Brighton, 2016), it can be shown that intuitive decisions are based on experiential knowledge and implicit structure recognition (Magrabi and Bach, 2013). Accordingly, with the approach of an ecological rationality it can be shown that an intuitive decision is rational in the sense that a person interacts quickly and energy-efficiently successfully adapted with his or her environment and thereby identifies the individual optimal action for him- or herself. This may then be recognized as a ‘gut feeling’ or ‘intuition’. Two challenges will be outlined explained in this paper: The influence of a negative environment on intuitive decisions, and the confusion of an intuition with an emotion or other feeling.

Innovation in security: a proposal for access control with dual authentication through facial recognition and radiofrequency

Property security represents a set of activities and preventive actions aimed at reducing potential losses in organisations. In this context, electric locks that support different authentication methods, such as passwords, digital keys with security tokens, and radio frequency identification (RFID) tags, offer improvements in access control; however, they represent a single barrier of protection. In this article, we propose FaceTag, a prototype with dual authentication via facial recognition and RFID, which has been integrated into an electronic lock to support future access control in educational institutions. To validate our proposal, we implemented a prototype and evaluated its operation in different scenarios. FaceTag proved to be effective and stable in testbed experiments, capable of providing a high degree of accuracy for access control in the desired environment.

Distillation of essential oils: An innovative technological approach focused on productivity, quality and sustainability.

Essential oil (EO) distillation units, which are commonly installed on farms, have difficultly accessing knowledge centers. The apparent simplicity of the process hides unwanted losses and deviations that go undetected and, therefore, not corrected. This article proposes improvements to the process that are based on "4.0" technologies in order to detect and correct two important deficiencies, with an impact on the yield, quality and environmental performance. The first deficiency comprises the steam channels that are formed through green mass (channeling), are well known and are considered normal by EO producers. Without detection and correction, this negatively affects the extraction results. The second is the lack of technology that is able to automatically determine the extraction endpoint. Smart sensing, control, self-configuration and the dynamic determination of improved process parameters make up a set of actions undertaken by a smart extraction plant (50-liter capacity). Nineteen experiments using lemongrass (Cymbopogon citratus) exhibited remarkable 24% and 10% improvements in the yield and quality, respectively. Energy consumption and a more than 50% reduction in the processing complete the set of results achieved. In addition to manufacturing costs and the utilization of capacity, better sustainability indicators are positive consequences of this technological updating.

The concept of forming a system of change management in the domain of railroad passenger transportation in Ukraine under the conditions of war

The object of research is railroad passenger transportation. The subject of research is a change management system. The problem to be solved is to improve the efficiency of railroad transport enterprises in the field of passenger transportation by introducing a change management system. In the study, the concept of the formation of a change management system in the field of passenger transportation of railroad transport in Ukraine under wartime conditions was devised. Since the conditions of war refer to a variety of emergencies, the concept of "emergency situation", its types, characteristics, and signs that describe its existence are considered. The conditions of emergencies are defined. The properties of total and hybrid war affect the fundamental features of the state management of railroad passenger transport in Ukraine, the work of state departments and railroad passenger transport management enterprises. Taking into account the peculiarities of each type of war and the principles of the state, a system of interconnection of the features and principles of the state management of railroad passenger transportation with possible aggressive changes under wartime conditions was built. Such a system is the core of the devised concept of formation and modeling a change management system in the field of railroad passenger transportation in Ukraine under war conditions. Within the framework of the concept, a strategy is formed – a set of actions during an emergency of a military nature. Based on this concept, in the future, it is possible to construct stages of research, the existence of which could make it possible to build a model of the concept, namely the processes of forming a system of changes

On equilibria in constrained generalized games with the weak continuous inclusion property

In this paper, we present results that use Himmelberg's fixed point theorem to highlight substantive trade-offs between compactness, continuity and convexity postulates in the setting of a constrained generalized game. The primary contribution is a focus on weakening the compactness assumption on the action sets and on two versions of the continuity assumption encapsulated as the continuous inclusion property (CIP). We show that the results are invariant to cardinality assumptions on the set of agents and on the dimension of the space in which the action sets are situated. This yields as special cases recent results in both mathematical economics and applied mathematics, and underscores the development of a rich and useful theory in bringing these two registers and two communities together.

Skeleton-based Human Activity Classification in Sparse Image Sequences

Research results on human activity classification in video are described, based on initial human skeleton estimation in video frames. Both single person actions and two-person interactions are considered. The initial skeleton data is estimated in selected video frames by OpenPose, HRNet or other dedicated library. Important contributions of presented work are computational steps of skeleton tracking and -refinement, and relational feature extraction from pairs of skeleton joints. It is shown, that this feature engineering significantly increases the classification accuracy. Regarding the final neural network encoder-classifier, two different architectures are designed and tested. The first solution is a lightweight MLP network, implementing the idea of a "mixture of pose experts". Several pose classifiers (experts) are trained independently on different time periods (snapshots) of single-person visual actions (or 2-person interactions), while the final classification is a time-related pooling of weighted expert classifications. All pose experts use the same deep encoding network. The second (middle weight) solution is based on a LSTM network.Both solutions are trained and tested on the action set of the well-known NTU RGB+D dataset, although only 2D data are used.Our results show comparable performance with some of the best reported STM- and CNN-based classifiers for this dataset. We conclude that by reducing the noise of skeleton data, highly successful lightweight- and midweight-approaches to visual activity recognition in image sequences can be achieved.

An introduction to climate change in management education: an individual-level approach

Purpose This study aims to introduce management students to climate change by providing them with an opportunity to address it in their own lives, through a class exercise. Design/methodology/approach An in-class exercise was designed, carried out and described in this study. Student teams were assigned different questions on how to address major causes of climate change. Each team did research to generate answers, and ranked their answers based on the speed of implementation. Teams reported their answers to the class. The instructor facilitated a debriefing session, during which all responses were ranked with respect to other variables, including cost savings, time savings and lifestyle fit. This exercise uses few resources and can be adapted to different time lengths and teaching/learning formats (e.g. on-ground, virtual, asynchronous online). Findings This exercise can help students to gain an understanding of climate change and its causes and complexities. Students learn how to implement a diverse set of personal actions to mitigate climate change; these can start in the present and continue throughout their lives. In addition, this exercise may help students to make the leap from individual climate change mitigation practices to organizational and societal practices, when they are in the position to do so as future leaders. Originality/value Although classes, exercises, and assignments ask management students to consider issues such as climate change at the organizational level, this individual-level exercise can allow students with limited organizational experience to engage more quickly with climate change and better understand organizational and societal implications in the future. That is, if students first understand how to address climate change in their own lives, they may more effectively transfer and apply that understanding at organizational and societal levels and ultimately synthesize solutions to address climate change in the world.

Physiological evidence of stress reduction during a summer Antarctic expedition with a significant influence of previous experience and vigor

Antarctica provides a unique environment for studying human adaptability, characterized by controlled conditions, limited sensory stimulation, and significant challenges in logistics and communication. This longitudinal study investigates the relationship between stress indicators, with a specific focus on mean sleep heart rate, during a COVID-19 quarantine and subsequent 83 days long summer Antarctic expedition at the J. G. Mendel Czech Antarctic Station. Our novel approach includes daily recordings of sleep heart rate and weekly assessments of emotions, stress, and sleep quality. Associations between variables were analyzed using the generalized least squares method, providing unique insights into nuances of adaptation. The results support previous findings by providing empirical evidence on the stress reducing effect of Antarctic summer expedition and highlight the importance of previous experience and positive emotions, with the novel contribution of utilizing physiological data in addition to psychological measures. High-frequency sampling and combination of psychological and physiological data addresses a crucial gap in the research of stress. This study contributes valuable knowledge to the field of psychophysiology and has implications for expedition planners, research organizations, teams in action settings, pandemic prevention protocols, global crises, and long-duration spaceflight missions. Comprehensive insights promote the well-being and success of individuals in extreme conditions.

Technologies applied to the curriculum for the recomposition of learning in the post-pandemic period

In light of the need for curricular adaptations as a strategy to mitigate the impacts caused by the COVID-19 pandemic, this theoretical study aimed to investigate how digital technologies, when applied to the educational curriculum, can contribute to the reconstitution of learning in the post-pandemic period. To support the theories explored in this article, a Systematic Literature Review (SLR) was chosen, using the Google Scholar database as the main source of consultation. The reconstitution of learning is understood as a set of actions that involve prioritizing competencies and skills of the National Common Curricular Base in line with the new curricula that align with them. Technologies emerge as valuable tools for educators, facilitating the implementation of innovative teaching methodologies and corroborating with learning processes and the necessary curricular prioritization. The insertion of education technologies in teaching methodologies assists the learning process by promoting greater interactivity and involvement of students. In addition, it makes learning more dynamic, playful, meaningful, and enriching. Therefore, this study highlights the importance of integrating education technologies into the school curriculum and teaching methodologies, aiming not only to improve the quality of education but also to prepare students for a world increasingly immersed and interconnected in the digital realm.