Past Actions Research Articles

THE APPLICATION OF DATA MINING AND OLAP TECHNOLOGIES IN HEALTHCARE

The article examines the application of Data Mining and real-time Online Analytical Processing (OLAP) in healthcare. The calculation of analytical indicators and solving planning problems in healthcare are complex and multi-stage processes. The extraction of knowledge from large volumes of data accumulated in databases is referred to as Data Mining. Today, databases are measured in terabytes, containing strategically significant information. One of the most critical issues is determining methods for discovering important information within such vast amounts of data. Data Mining technology, by utilizing a set of analytical tools, helps to uncover relationships within large datasets and make predictions about future trends. Simply defined, Data Mining automatically identifies relevant patterns within databases. For many years, analysts manually recorded statistically significant relationships within databases. Today, Data Mining automates this process. The goal of Data Mining is to create decision-making models that predict future behavior based on the analysis of past actions. With the advent of computers and the rise of the digital information age, there have been changes in both the scale and complexity of existing problems in terms of analytical analysis. Advances in the storage, management, and examination of data have led to the emergence of a new field that promotes the development of bioinformatics and introduces analytical analysis and computational problems in the fields of biology and medicine. Keywords: Data Mining, OLAP, database, factor analysis, healthcare network, multidimensional models.

Multi-agent reinforcement learning with synchronized and decomposed reward automaton synthesized from reactive temporal logic

Multi-agent systems (MAS) consist of multiple autonomous agents interacting to achieve collective objectives. Multi-agent reinforcement learning (MARL) enhances these systems by enabling agents to learn optimal behaviors through interaction, thus improving their coordination in dynamic environments. However, MARL faces significant challenges in adapting to complex dependencies on past states and actions, which are not adequately represented by the current state alone in reactive systems. This paper addresses these challenges by considering MAS operating under task specifications formulated as Generalized Reactivity of rank 1 (GR(1)). These synthesized strategies are used as a priori knowledge to guide the learning. To tackle the difficulties of handling non-Markovian tasks in reactive systems, we propose a novel synchronized decentralized training paradigm that guides agents to learn within the MARL framework using a reward structure constructed from decomposed synthesized strategies of GR(1). We initially formalize the synthesis of GR(1) strategies as a reachability problem of winning states of the system. Subsequently, we develop a decomposition mechanism that constructs individual reward structures for decentralized MARL, incorporating potential values calculated through value iteration. Theoretical proofs are provided to verify that the safety and liveness properties are preserved. We evaluate our approach against other state-of-the-art methods under various GR(1) specifications and scenario maps, demonstrating superior learning efficacy and optimal rewards per episode. Additionally, we show that the decentralized training paradigm outperforms the centralized training paradigm. The value iteration strategy used to calculate potential values for the reward structure is compared against two other strategies, showcasing its advantages.

Mobility of trace elements in a coastal contaminated site under groundwater salinization dynamics

Water pollution is a significant issue resulting from past long-term actions. The remediation projects carried out under law constraints for industrial plants, which have been the major contributors to environmental and water pollution, are currently providing a significant amount of data about contaminated soil, surface waters, and groundwater. Most of such plants worldwide are in coastal zones. Based on a significant amount of chemical and environmental data for a coastal contaminated site subject to variable groundwater salinization, this study aimed to understand the mobility of some trace elements because of coastal zone dynamics. Data concerned 688 groundwater samples, including As, Hg, Cd, Crtot, Cu, Ni, Pb, V, Se, Zn, pH, electrical conductivity, chlorides, total organic carbon and organic contaminants as quantitative variables, enhanced by additional qualitative variables such as groundwater salinity, season, water level, precipitation, and industrial activity type to make the dataset as representative as possible of the site under investigation. The study used robust multivariate statistical analyses to analyse the complex dataset and explain the relevant factors influencing contaminant behaviour under different environmental conditions. The Multivariate Statistical Analysis distinguished three clusters of trace elements with diverse reactivity to changes in groundwater salinization. The first includes Se, Cu, Crtot, V, and Ni, showing the highest correlation with electrical conductivity and chlorides because of their high affinity to form chloride or organic chloride complexes and for ion competition. Zn and Pb cluster in the second group: they are less reactive to groundwater salinization and more influenced by cation and anion competition and organic matter. The mobility of Hg and As (third cluster) significantly correlates with Fe and Mn, underlining the dominant role of reductive dissolution of trace elements-bearing minerals (Fe/Mn/Al-oxy-hydroxides) and metal-organic complexes. The correlation between the clustering of variables in groundwater and soils shows the influence of sediment structure, mineral composition, and physical and chemical soil conditions on the distribution in soils of trace elements and their transport to groundwater. The study proposes a valuable approach for assessing the effects of salinization in contaminated coastal aquifers. It supports planning multi-purpose characterization and monitoring campaigns of contaminated coastal sites and provides guidance on the correct associated remediation projects.

Policy learning in crisis governance: translating narratives and reframing actions for co-evolving futures

ABSTRACT Most policy learning literature assumes identifiable lessons, comparable points of consideration, transferable solutions, and expectable performative changes. However, all these assumptions are challenged in the context of crises, where complexity makes learning across contexts difficult, if not impossible. This article aims to bring in policy learning from critical perspectives. It argues that, in the face of complexity, policy learning that can support crisis governance is instead an internal process involving the translation of lessons and the co-evolution of interrelated actors. In this lens, policy learning goes beyond the conventional understanding of lessons, drawing on actual practices within the learning loops of the particular policy cycle. Such loops and cycles, in this view, are not truly present. Grounded in Hyogo, Japan’s empowered participatory governance in responding to the crisis, policy learning is instead a complex everyday practice for translating narratives about past actions that, in turn, help frame future directions. With that framework, it can stimulate the co-evolution of state and societal actors without clear changing points, leading to more robust governing practices.

Digital activism in the future past: How Dutch Extinction Rebellion activists connect past, present and future in their memory work on Instagram

This article investigates the ways in which Dutch Extinction Rebellion activists discursively engage with the past, present and future on Instagram and leverage the platform’s affordances for mnemonic purposes. We argue that Extinction Rebellion activists, in their communication on the platform, connect these temporal planes retrospectively and prospectively. First, Extinction Rebellion activists use the past as a resource for present action aimed at producing lasting change. This relates to cultural reservoirs and repertoires of past injustices. Second, the present is perceived by Extinction Rebellion activists as a concern for future memory. The recording and documenting of climate injustices and the actions of the movement itself are examples of this. Both positive and negative imagined futures are used by Extinction Rebellion activists to legitimise and delegitimise past and present (in)action. In other words, Extinction Rebellion activists’ future imaginaries not just shape present action, but also shape their memory work. Instagram’s affordances of archivability, connectivity and spreadability are used by Extinction Rebellion to support their digital memory work. These socio-technical affordances are thus appropriated as mnemonic affordances for rhetorical and practical purposes. We support these claims through in-depth interviews with Extinction Rebellion activists and a thematic analysis of Instagram content shared by them.

Evaluating the effects of nest management on a recovering raptor using integrated population modeling

AbstractEvaluating population responses to management is a crucial component of successful conservation programs. Models predicting population growth under different management scenarios can provide key insights into the efficacy of specific management actions both in reversing population decline and in maintaining recovered populations. Bald eagle (Haliaeetus leucocephalus) conservation in the United States has seen many successes over the last 50 years, yet the extent to which the bald eagle population has recovered in Arizona, an important population within the Southwest region, remains an area of debate. Estimates of the species' population trend and an evaluation of ongoing nest‐level management practices are needed to inform management decisions. We developed a Bayesian integrated population model (IPM) and population viability analysis (PVA) using a 36‐year dataset to assess Arizona bald eagle population dynamics and their underlying demographic rates under current and possible future management practices. We estimated that the population grew from 77 females in 1993 to 180 females in 2022, an average yearly increase of 3%. Breeding sites that had trained personnel (i.e., nestwatchers) stationed at active nests to mitigate human disturbance had a 28% higher reproductive output than nests without this protection. Uncertainty around population trends was high, but scenarios that continued the nestwatcher program were less likely to predict abundance declines than scenarios without nestwatchers. Here, the IPM‐PVA framework provides a useful tool both for estimating the effectiveness of past management actions and for exploring the management needs of a delisted population, highlighting that continued management action may be necessary to maintain population viability even after meeting certain recovery criteria.

Active Inference and Reinforcement Learning: A Unified Inference on Continuous State and Action Spaces Under Partial Observability.

Reinforcement learning (RL) has garnered significant attention for developing decision-making agents that aim to maximize rewards, specified by an external supervisor, within fully observable environments. However, many real-world problems involve partial or noisy observations, where agents cannot access complete and accurate information about the environment. These problems are commonly formulated as partially observable Markov decision processes (POMDPs). Previous studies have tackled RL in POMDPs by either incorporating the memory of past actions and observations or by inferring the true state of the environment from observed data. Nevertheless, aggregating observations and actions over time becomes impractical in problems with large decision-making time horizons and high-dimensional spaces. Furthermore, inference-based RL approaches often require many environmental samples to perform well, as they focus solely on reward maximization and neglect uncertainty in the inferred state. Active inference (AIF) is a framework naturally formulated in POMDPs and directs agents to select actions by minimizing a function called expected free energy (EFE). This supplies reward-maximizing (or exploitative) behavior, as in RL, with information-seeking (or exploratory) behavior. Despite this exploratory behavior of AIF, its use is limited to problems with small time horizons and discrete spaces due to the computational challenges associated with EFE. In this article, we propose a unified principle that establishes a theoretical connection between AIF and RL, enabling seamless integration of these two approaches and overcoming their limitations in continuous space POMDP settings. We substantiate our findings with rigorous theoretical analysis, providing novel perspectives for using AIF in designing and implementing artificial agents. Experimental results demonstrate the superior learning capabilities of our method compared to other alternative RL approaches in solving partially observable tasks with continuous spaces. Notably, our approach harnesses information-seeking exploration, enabling it to effectively solve reward-free problems and rendering explicit task reward design by an external supervisor optional.

Reinforcement Learning for Jointly Optimal Coding and Control Policies for a Markovian System Controlled over a Communication Channel

This paper develops approximation and optimality results for the optimal control of a networked system. In this system, a Markovian process is managed over a finite-rate, noiseless communication channel. Solving this problem involves determining joint optimal coding and control policies that minimize cost over time. While theoretical results on optimal coding and control structures exist, practical implementation has remained largely infeasible for non-linear systems due to the computational complexities and uncountable state spaces. This research introduces an approach that combines structural results with reinforcement learning (RL) techniques. The method uses regularity properties of the system to approximate the uncountable state space with a countable one, which allows reinforcement learning algorithms to achieve near-optimal solutions. Specifically, we establish that finite model approximations (where infinite state spaces are quantized to finite ones) and sliding finite window approximations (where a finite memory "window" of past control actions is maintained at each time step) can be employed to develop near-optimality. These approximations allow the system to be reformulated as a Markov Decision Problem (MDP) with a finite state space, making RL algorithms implementable. The convergence of the reinforcement learning algorithm to a near-optimal policy (under both the previous approximations) is supported by theoretical analysis and performance simulations. We ensure that the solutions obtained are not only computationally feasible but also nearly optimal with respect to the original problem. The applications of this work extend to many networked control systems, especially those with zero-delay coding and partially observable Markov decision processes (POMDPs). By integrating structural results with learning algorithms, this paper provides a practical framework for implementing optimal control in finite-rate environments.

Evaluating the Role of Competency-Based Behavioral Interviewing in Holistic Medical School Admissions.

Holistic review is a multifaceted concept that aims to increase diversity and applicant fit with program needs by complementing traditional academic requirements with appraisal of a wider range of personal characteristics and experiences. Behavioral interviewing has been practiced and studied in human resources, business, and organizational psychology for over 50 years. Its premise is that future performance can be anticipated from past actions. However, many of the interview approaches within the holistic framework are resource intensive and logistically challenging. The Vanderbilt University School of Medicine instituted a competency-based behavioral interview (CBBI) to augment the selection process in 2012. Behavioral interviews are based on key competencies needed for entering students and require applicants to reflect on their actual experiences and what they learned from them. The authors reviewed 5 years of experience (2015-2019) to evaluate how CBBI scores contributed to the overall assessment of applicants for admission. The final admission committee decision for each applicant was determined by reviewing multiple factors, with no single assessment determining the final score. The CBBI and summary interview scores showed a strong association ( P < .005), suggesting that the summary interviewer, who had access to the full applicant file, and the CBBI interviewer, who did not, assessed similar strengths despite the 2 different approaches, or that the strengths assessed tracked in the same direction. Students whose 2 interview scores were not aligned were less likely to be accepted to the school. The review raised awareness about the cultural aspects of interpreting the competencies and the need to expand our cultural framework throughout interviewer training. Findings indicate that CBBIs have the potential to reduce bias related to overreliance on standardized metrics; however, additional innovation and research are needed.

Intrinsic Motivation in Dynamical Control Systems

Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain poorly understood. In this study, we investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment (the mutual information between its past actions and future states). We show that this approach generalizes previous attempts to formalize intrinsic motivation, and we provide a computationally efficient algorithm for computing the necessary quantities. We test our approach on several benchmark control problems, and we explain its success in guiding intrinsically motivated behaviors by relating our information-theoretic control function to fundamental properties of the dynamical system representing the combined agent-environment system. This opens the door for designing practical artificial, intrinsically motivated controllers and for linking animal behaviors to their dynamical properties. Published by the American Physical Society 2024

The polder systems legacies in the early twentieth century affect the contemporary landscape in the Jianghan Plain of Hubei, China

The long-term interaction between the environment and humans has significantly shaped contemporary landscapes worldwide. As a heritage landscape, the polder is essential for understanding the impact of the past human activities on contemporary landscape on low-lying area of land. In this case study, we reconstructed the regional spatial distribution of the polders in the early twentieth century and examined their coherence over the past century using historical maps and other materials in the Jianghan Plain. The results revealed several key findings: (1) In the early twentieth century, there were 1571 polders covering an area of 11,826.1 km2 with a perimeter of 17,035.9 km. (2) These polders exhibited a J-shaped distribution, with few large-scale polders despite their substantial areas. (3) The shape of these polders reflected an ability to balance human needs and the natural environment. (4) Considerable regional differences in these polders were attributed to centuries-old interactions between humans and the environment. (5) The remnants of these early twentieth century polders have had a lasting influence on the contemporary landscape, particularly in rural areas. This case study underscores how taking into account a broader historical perspective can enhance our understanding of present-day landscapes and, more importantly, the role that past human actions have played in shaping them.

Sosialisasi Penyuluhan Stop Bulying oleh Mahasiswa KKN Universitas Muslim Nusantara Al Washliyah di SD Negeri 102001 Gunung Meriah Kab.Deli Serdang

One of the forms of bullying in school is mocking, telling, molesting, resisting, and even beating. Some people believe that socio-economic variables, physical variations, and past actions are the causes of bullying. Bullying can lower academic achievement, leave school, resign, and even leave school. The purpose of anti-bullying training is to help students avoid actions that may lead to mocking, commanding, molesting, discrimination, and beating. At Mount Meriah State Elementary School 102001, there's an anti-bullying scholarship. For two hours, 75 students participated. re-test and post-test are used to evaluate students' knowledge about bullying.

Do future actions matter more than past deeds? Temporal moral attribution in U.S. and Chinese school-age children.

This study examines how children attribute moral responsibilities to their past and future actions and what role culture plays in children's temporal moral attribution. A total of 346 U.S. and Chinese 6-7 and 8- to 9-year-old children were randomly assigned to a past or future condition, in which they answered questions about their moral/immoral actions in hypothetical scenarios described as occurring in the last week or the next week. Whereas U.S. 8- to 9-year-olds favored more praise and reward for their future good deeds than past ones, Chinese 8- to 9-year-olds favored more praise and reward for their past good deeds than future ones. Chinese children also moralized their actions to a greater extent than U.S. children, and children reasoned about their moral/immoral actions in line with their cultural beliefs. Interesting age differences also emerged, suggesting the continuing development of mental time travel and moral cognition across middle childhood. The findings shed new light on the important role of time in moral judgment that is constrained by development progressions in mental time travel and specific to the cultural context. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Privacy resignation, apathy, and cynicism: Introduction to a special theme

The growing trend of collecting data about individuals to track past actions and infer future attitudes and behaviors has fueled popular and scholarly interest in the erosion of privacy. Recent shifts in technologies around machine learning and artificial intelligence have intensified these concerns. This editorial introduces the articles in the special theme on digital resignation and privacy cynicism: concepts developed in the past decade to explain the growing powerlessness individuals feel in relation to their digital privacy even as they continue to experience consternation over the collection and use of their personal information. The papers in this special theme engage and extend existing research on these topics. The original articles and commentaries pose theoretical and practical questions related to the ways people confront the powerful institutional forces that increasingly shape many aspects of the information environment. They employ several methodologies and theoretical perspectives and extend the range of geographic, political, cultural, and institutional contexts in which privacy cynicism and digital resignation can be identified and examined. In addition to contextualizing these contributions, this editorial maps a range of related concepts including digital resignation, privacy cynicism, privacy apathy, surveillance realism, privacy fatigue, and privacy helplessness. It concludes by identifying key themes across the papers in this collection and provides directions for future research.

Students’ Grammatical Mistakes on the Use of Language Features in Composing Narrative Text

A narrative text with several grammatical and linguistic aspects is one of the texts learned in senior high school. In fact, according to the researcher's prior observation, students faced numerous challenges when attempting to write it, especially grammatical mistakes in using language features. This study aims to detect grammatical errors in the utilization of language elements in narrative texts produced by students, as well as to determine the underlying reasons contributing to these errors. The recent research employed a descriptive qualitative methodology. Eight of the eleventh-grade students studying Online Business and Marketing major in SMK Negeri 1 Kota Sorong have been selected as the participants in this research. Due to the COVID-19 pandemic situation which restricted social interactions, only those students than could be reached out to gather data. The data was derived from a writing exam and a semi-structured interview. Upon analyzing the student's writing, it was discovered that they made errors in utilizing the simple past tense and action verbs. Simultaneously, the interview data indicated that the causes contributing to grammatical errors in using language features in this study were limitations, lack of attention, and carelessness. This research finding implied that the teacher must identify the most effective technique to engage the student's attention and enable them to concentrate on acquiring language skills for future use in creating narrative texts.

Single-Trial Representations of Decision-Related Variables by Decomposed Frontal Corticostriatal Ensemble Activity.

The frontal cortex-striatum circuit plays a pivotal role in adaptive goal-directed behaviors. However, it remains unclear how decision-related signals are mediated through cross-regional transmission between the medial frontal cortex and the striatum by neuronal ensembles in making decision based on outcomes of past action. Here, we analyzed neuronal ensemble activity obtained through simultaneous multiunit recordings in the secondary motor cortex (M2) and dorsal striatum (DS) in rats performing an outcome-based left-or-right choice task. By adopting tensor component analysis (TCA), a single-trial-based unsupervised dimensionality reduction approach, for concatenated ensembles of M2 and DS neurons, we identified distinct three spatiotemporal neural dynamics (TCA components) at the single-trial level specific to task-relevant variables. Choice-position-selective neural dynamics reflected the positions chosen and was correlated with the trial-to-trial fluctuation of behavioral variables. Intriguingly, choice-pattern-selective neural dynamics distinguished whether the incoming choice was a repetition or a switch from the previous choice before a response choice. Other neural dynamics was selective to outcome and increased within-trial activity following response. Our results demonstrate how the concatenated ensembles of M2 and DS process distinct features of decision-related signals at various points in time. Thereby, the M2 and DS collaboratively monitor action outcomes and determine the subsequent choice, whether to repeat or switch, for action selection.

Doing and belonging: A photo-elicitation study on place attachment of older adults living in depopulated rural areas

ABSTRACT Background The relationship between doing and belonging is rooted in individuals’ engagement in meaningful occupations, which contributes to their sense of connectedness. The meaningfulness of occupation relates closely to the context in which it is performed. However, despite its importance, context is often overlooked and underexplored. Aim To explore how older adults living in depopulated rural areas depict positive and negative features of their neighbourhoods and how belonging was connected to doing and participation. Methods We utilised data from photo-elicited interviews with 7 men and 10 women, aged 68 to 88 years, from depopulated rural areas in southern Sweden. Data were analysed using Rowles’ place attachment framework with a thematic analysis. Results The participants expressed how belonging was connected to doing by depicting resources in the area, how community was created through interaction, the importance of past actions for present belonging, how they acted on pride and feelings of obligation, as well as how they experienced challenges for doing and belonging. Discussion Our study revealed a reciprocal relationship of doing and belonging. Specifically, doing strengthened participants’ belonging, while feelings of belonging resulted in loyalty and motivated supportive community actions. Importantly, participants’ actions were more driven by a collective interest in community welfare than personal gain. Conclusions This study contributes to occupational science by knowing how doing supports belonging and vice versa. Furthermore, our study showed that belonging can serve as a catalyst for older adults to engage in their community, which likely reduces the risk of social exclusion.

Fishers (Pekania pennanti) are forest structure specialists when resting and generalists when moving: behavior influences resource selection in a northern Rocky Mountain fisher population

BackgroundStudies of animal habitat selection are important to identify and preserve the resources species depend on, yet often little attention is paid to how habitat needs vary depending on behavioral state. Fishers (Pekania pennanti) are known to be dependent on large, mature trees for resting and denning, but less is known about their habitat use when foraging or moving within a home range.MethodsWe used GPS locations collected during the energetically costly pre-denning season from 12 female fishers to determine fisher habitat selection during two critical behavioral activities: foraging (moving) or resting, with a focus on response to forest structure related to past forest management actions since this is a primary driver of fisher habitat configuration. We characterized behavior based on high-resolution GPS and collar accelerometer data and modeled fisher selection for these two behaviors within a home range (third-order selection). Additionally, we investigated whether fisher use of elements of forest structure or other important environmental characteristics changed as their availability changed, i.e., a functional response, for each behavior type.ResultsWe found that fishers exhibited specialist selection when resting and generalist selection when moving, with resting habitat characterized by riparian drainages with dense canopy cover and moving habitat primarily influenced by the presence of mesic montane mixed conifer forest. Fishers were more tolerant of forest openings and other early succession elements when moving than resting.ConclusionsOur results emphasize the importance of considering the differing habitat needs of animals based on their movement behavior when performing habitat selection analyses. We found that resting fishers are more specialist in their habitat needs, while foraging fishers are more generalist and will tolerate greater forest heterogeneity from past disturbance.

Trees to remember: culturally modified boab trees in the face of climate change

ABSTRACT Culturally modified trees (CMTs) are a unique form of archaeology and cultural heritage. There are several factors affecting the survival of culturally modified trees in Australia, and these will all likely be exacerbated by climate change. Boab trees (Adansonia gregorii), which are endemic to northwest Australia, have been subject to modification by Indigenous people both prior to and following the settlement of the Kimberley region by European and Anglo-Australians. Many of the potential impacts of climate change on boab tree survival are yet to be determined, but a range of new threats are emerging as potential endangerment. Through the insights of Indigenous knowledge, this paper discusses one particularly significant boab tree in Nyikina Country and how its demise may be linked to erroneous human actions in the recent past. This provides a unique perspective on how the complexities of climate change may be conceptualised through living knowledge and experience.

Using Green Solutions to Cover the Dark, Red History of Urban Heat Islands in New York City

An urban heat island (UHI) is an area in a city that experiences higher temperatures relative to surrounding areas. New York City (NYC) experiences the UHI effect and is vulnerable to extreme heat and precipitation. Although multiple interventions were made on the issue, there exists a knowledge gap regarding the historical root of this phenomenon in present-day NYC. This paper uses secondary data, such as satellite images and climate databases, and conducts linear regressions to shed light on the interlink of heat and income inequality caused by redlining, the now-illegal practice by the federal government labeling non-white neighborhoods undesirable for real estate investment and depriving them of investment. The study analyzes the key issues of various stakeholders’ past actions and ultimately proposes city-wide green remodeling as a sustainable intervention. This research seeks to propose the most effective and feasible method for NYC to reduce its UHI effect and to emphasize the importance of considering historical injustices when investigating environmental issues.