Proactive Actions Research Articles

Telecommunications Industry: Analysis on Customer Attrition Prediction and Segmentation

Churn prediction is essential for the survival of every business as it allows proactive action planning, whereas customer segmentation is an effective grouping approach commonly used for product marketing and customer relationship management. This paper analyzes the current development of customer attrition prediction and segmentation for the telecommunications (telco) industry, and explores the integration of both approaches that results in an actionable matrix framework for customer retention. This paper consists of four parts: review and analyses on the machine learning techniques used for customer attrition prediction, review and analyses on the machine learning techniques applied for customer segmentation, review on the integration framework, and finally provides discussions for future works. The background and business applications of these approaches are introduced in this paper so businesses can apply the techniques for their business needs. Most importantly, this paper lays out the directions of future works to be done for telco churn management based on the improvements needed form the current state of the art.

Downscaled subseasonal fire danger forecast skill across the contiguous United States

AbstractThe increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead‐times of several weeks can help in wildland fire resource allocation as well as complement short‐term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble‐based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly‐to‐monthly fire business decision making for suppression‐based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.

Invading the Greek Seas: Spatiotemporal Patterns of Marine Impactful Alien and Cryptogenic Species

The Greek Seas are greatly exposed to the proliferation of marine alien species. At least 242 alien species have been reported within Greek territorial waters, three-quarters of which are considered established, while their rate of introduction is increasing. Some of these species exhibit high invasiveness, imposing severe impacts on native ecosystems and ecosystem services. The spatiotemporal proliferation of these species outside their natural boundaries depends on several parameters, including their biological characteristics, native distribution range, introduction pathway, and time of initial introduction. Knowing the current and potential alien species distribution is essential for the implementation of effective management actions. To investigate the distribution of impactful cryptogenic and alien species (ICAS) in the Greek Seas, we combined all records available until the end of 2020 from eight types of data sources: (1) scientific literature, (2) grey literature, (3) offline databases, (4) online scientific databases, (5) personal observations of independent researchers, (6) communications with divers and diving centers, (7) in situ underwater sampling, and (8) social networks. The results of 5478 georeferenced records refer to 60 marine ICAS belonging to 16 taxonomic groups. The number of records and the overall number of ICAS present an increasing trend from the northern to the southern parts of our study area, and there is a clear distinction in community composition between the northern and southern subregions. This latitudinal gradient is mainly due to the large number of thermophilous Lessepsian species of West Indo-Pacific origin, which reach the southern parts of the study area through unaided dispersal. On the other hand, transport stowaways appear to be more prevalent in areas located near large ports, which show significant differences in ICAS numbers and community composition compared to sites located far from ports. Most records (>40% of the total) were associated with rocky reefs, partly reflecting the preference of divers for this habitat type but also the presence of conspicuous, reef-associated impactful fish. The number of published records, as well as the number of reported ICAS, shows a dramatic increase with time, highlighting the urgent need for immediate proactive management actions and scientifically informed control measures.

The silent epidemic of substandard and falsified medicines in low- and middle-income countries: heed lessons from the tragic deaths of children in Indonesia

The silent epidemic of substandard and falsified medicines in low- and middle-income countries: heed lessons from the tragic deaths of children in Indonesia

Ethical use of Artificial Intelligence in Health Professions Education: AMEE Guide No. 158

Health Professions Education (HPE) has benefitted from the advances in Artificial Intelligence (AI) and is set to benefit more in the future. Just as any technological advance opens discussions about ethics, so the implications of AI for HPE ethics need to be identified, anticipated, and accommodated so that HPE can utilise AI without compromising crucial ethical principles. Rather than focussing on AI technology, this Guide focuses on the ethical issues likely to face HPE teachers and administrators as they encounter and use AI systems in their teaching environment. While many of the ethical principles may be familiar to readers in other contexts, they will be viewed in light of AI, and some unfamiliar issues will be introduced. They include data gathering, anonymity, privacy, consent, data ownership, security, bias, transparency, responsibility, autonomy, and beneficence. In the Guide, each topic explains the concept and its importance and gives some indication of how to cope with its complexities. Ideas are drawn from personal experience and the relevant literature. In most topics, further reading is suggested so that readers may further explore the concepts at their leisure. The aim is for HPE teachers and decision-makers at all levels to be alert to these issues and to take proactive action to be prepared to deal with the ethical problems and opportunities that AI usage presents to HPE.

Little fast, little slow, should I stay or should I go? Adapting cognitive control to local-global temporal prediction across typical development.

Adaptive cognitive control (CC), the ability to adjust goal-directed behavior according to changing environmental demand, can be instantiated bottom-up by implicit knowledge, including temporal predictability of task-relevant events. In S1-S2 tasks, either local (trial-by-trial hazard expectation) or global (block-by-block expectation) temporal information can induce prediction, allowing for proactive action control. Recent developmental evidence showed that adaptive CC based on global temporal prediction emerges earlier than when it is based on the local one only. However, very little is known about how children learn to dynamically adjust behavior on the fly according to changing global predictive information. Addressing this issue is nevertheless crucial to unravel the mechanisms underlying adaptive CC flexibility. Here we used a modified version of the Dynamic Temporal Prediction task to investigate how typically developing younger (6-8 years) and older children (9-11 years), adolescents (12-15 years) and adults (21-31 years) use global prediction to shape adaptive CC over time. Specifically, the short-long percentage of S2 preparatory intervals was manipulated list-wide to create a slow-fast-slow-fast fixed block sequence and test how efficiently the response speed adapted accordingly. Overall, results revealed that in all groups behavioral performance is successfully adjusted as a function of global prediction in the late phase of the task (block 3 to 4). Remarkably, only adolescents and adults exhibit an early adaptation of adaptive CC (block 1 to 2), while children younger than 11 show sluggish ability in inferring implicit changes in global predictive rules. This age-related dissociation suggests that, although being present from an early age, adaptive CC based on global predictive information needs more developmental space to become flexible in an efficient way. In the light of a neuroconstructivist approach, we suggest that bottom-up driven implicit flexibility may represent a key prerequisite for the development of efficient explicit cognitive control.

유아교사의 교육과정평가 경험과 인식에 대한 연구

This study examined early childhood teachers’ curriculum assessment experience and perception with a focus on a teacher agency in order to explore the context in which teachers' active and proactive actions occur in curriculum assessment and how the practice in which curricula and assessments are premised on each other is carried out. To this end, 2 focus group interviews were conducted with 30 teachers who had experience in implementing the 2019 revised Nuri curriculum for more than a year, and the results were analyzed. The results showed that early childhood teachers' curriculum assessment experience and perception could be divided into three upper sub-categories of ‘Looking into the world of learning: the meaning of curriculum assessment revisited’, ‘Located in a world where you can’t stay: dilemmas and difficulties faced in curriculum assessment’, and ‘Creating learning entangled with the world: changes implemented through curriculum assessment’, plus seven sub-categories of ‘Acts occurring in the field of education,’ ‘Discovery here and now,’ ‘The point of looking back on the journey,’ ‘A labyrinth wandering in questions,’ ‘Limits one wants to escape,’ ‘Creation together with the educational community,’ and ‘The route we open.’ Through this, this article shows the meaning, difficulties and limitations, and conflicts and dilemmas of curriculum assessment entangled with the here and now. It also discusses early childhood teachers’ curriculum assessment experience and perception as a phenomenon and material in itself by revealing the stories of creation achieved together.

An Exploratory Study on the Impact of the Construction Industry on Climate Change

In the last decade, the construction industry in Egypt has been booming, and many mega projects are under construction. The lack of awareness of the construction industry’s impact on climate change could be very harmful in terms of CO2 emission, water and soil pollution, etc. This paper aims to explore the critical factors in the Egyptian construction industry that contributes to climate change. Moreover, given the shared features between the construction industry in Egypt and those in other countries, this research could also shed light on the general impacts of the construction industry on the various aspects of climate change. So, to achieve this aim, an intensive literature review was carried out to identify various factors contributing to climate change within the construction industry. This is followed by conducting 11 interviews with construction experts to explore any further factors throughout the lifecycle of a construction project. The identified factors from the literature review and the interviews were used to design a questionnaire survey to collect construction professionals’ opinions on the impact of these factors on climate change in Egypt. 48 valid responses were received. The collected data were statistically analyzed to rank and determine the criticality level of these factors. The results revealed that the most significant factors influencing climate change are the impact of industrial construction on climate change, the use of primary renewable energy as raw materials during the construction and use phases, and the effect of heavy civil and highway construction on climate change. The results also show that managing these factors requires considerable awareness and proactive actions during the project life cycle and pre-construction stage. The findings could inform decision-makers and construction professionals to raise awareness and make informed decisions to handle these key factors and minimize their potential contribution to climate change. Therefore, it can be recommended that construction clients may involve a climate change management plan as a requirement of tender documents.

The Red List for the endemic trees of Colombia: Effective conservation targeted for plants required in biodiversity hotspots

Societal Impact StatementConserving trees is fundamental to maintaining forests, which are home to about half of the global biodiversity and provide unmeasurable benefits to people. Colombia has 5868 known species of trees, 1148 of which are endemic to the country. Unfortunately, 45% of the endemic trees of Colombia are threatened with extinction, and therefore, the ecosystem services they provide might be in jeopardy. This study represents an important contribution to conservation planning for the endemic trees of a megadiverse country and highlights critical considerations for their conservation.Summary Colombia has a very rich tree flora and high levels of tree endemism. As a contribution to the “Global Tree Assessment”, we assessed 860 species to complete the Red List of the 1148 endemic trees of the country. The information is being used to support spatial conservation planning as Key Biodiversity Areas identification. To categorize the species according to the International Union for the Conservation of Nature (IUCN) Red List guidelines, we used geographic occurrences obtained from herbarium specimens. We carried out detailed analysis of habitat quantity and quality (using forest cover and human footprint maps) and documented anthropogenic threats and conservation actions. Almost half (45%) of the endemic trees were categorized as threatened. Most species had small distributions (with an extent of occurrence of less than 40,000 km2). Endemic tree occurrences were more common in the Andes and Magdalena regions, which have very high human footprint and consequently a large proportion of threatened species. The major threat to endemic trees was habitat destruction, due mainly to deforestation for livestock and crops. Many endemic trees were present in protected areas (65%), but threatened and non‐threatened species had low coverage with habitat protection, and many of their populations persist in disturbed habitats. Colombia has hundreds of threatened endemic trees, most of them in global biodiversity hotspots, with very few proactive conservation actions. It is urgent to increase the coverage of endemic tree populations in area‐based conservation strategies and to complement this with ex situ and other conservation actions.

Challenges and Solutions for Establishing Robust Diversity, Equity, and Inclusion Efforts in US Academic Radiation Oncology Departments

Challenges and Solutions for Establishing Robust Diversity, Equity, and Inclusion Efforts in US Academic Radiation Oncology Departments

Entrepreneurial Human Resource Management: A Conceptual Review

Entrepreneurial human resource is best defined as the set or sets of human resources practices that will increase the likelihood that new knowledge will be converted to new products or services through innovativeness and proactive actions. This paper conceptually entrepreneurial human resource management. The paper conceptualised the dimensions of entrepreneurial human resource management as entrepreneurial-based compensation, entrepreneurial-based training and development, entrepreneurial-based performance management. The paper used a desk research methodology since it is largely a literature review. Based on the review of extant literature, the paper concludes that entrepreneurial human resource management influences the efficiency of organizations positively. The paper recommends that this study should be approved for an empirical study to be carried out within Indigenous oil and gas companies in South-South, Nigeria, as data generating unit, so as to test the conceptual model developed and validate the concepts reviewed.

Age Influences on Lifestyle and Stress Perception in the Working Population.

Workplace health promotion programs and services offered by insurers may play a fundamental role to foster health/well-being and to prevent chronic diseases. To this end, they should be tailored to companies/employees’ requirements and characteristics. In particular, age needs to be taken into account, considering both that young age workers are generally healthy, and that young age is the best period in lifespan to address prevention and instilling healthy behaviors. We employed an anonymous, simple web-based questionnaire (filled out by 1305 employees) which furnishes data regarding lifestyle (nutrition, exercise, smoking, stress, sleep, etc.), some of which were used to build a unique descriptor (Lifestyle Index; 0−100 higher scores being healthier). We considered three subgroups accordingly to age: ≤30; between 30 and 50; >50 years. This study showed age influences lifestyle and stress perception in the working population: the youngest employees (both men and women) presented the worst lifestyle index, particularly in its stress component. This observation may potentially be useful to tailor workplace health promotion programs and to personalize insurance protocols and services offered to employees. The practical message of our study is that in healthy young people focusing only on medical parameters (frequently within normal ranges in this cohort), albeit important, may be not sufficient to foster proactive actions to prevent chronic non-communicable diseases in adult life. Vice versa, driving their attention on current behaviors might elicit their proactive role to improve lifestyle, getting immediate advantages such as well-being improvement and the possibility to best manage stress.

How do financial markets reward companies tackling climate change concerns? A natural experiment based on the Brexit referendum

AbstractThis paper explores whether environmental management activities and corporate commitment to tackle climate change concerns play a role in hedging a company's market valuation after a political or economic shock. Based on the literature on corporate social responsibility (CSR) and insurance‐like effects, this study advances knowledge on the impact of companies' engagement to tackle climate change on share price variation after the 2016 Brexit Referendum. The study also contributes to the limited number of studies using a difference‐in‐differences (DID) methodology in the CSR and climate change research area. Specifically, it uses DID, a natural experimental research design, and a multi‐variate regression analysis. The paper concludes that companies' concrete commitment to climate change has a buffer role in mitigating uncertainty related to Brexit. As the study found that financial markets reward companies that pay attention through the adoption of concrete actions and best practices on environmental issues during uncertain periods with respect to those that do not, the findings are in line with previous literature suggesting that corporate environmental commitment plays a buffering role during troubling periods. Results on the buffer role played by screenings, assessments, or disclosure activities to address environmental issues are unclear. We thus argue that during uncertain times, markets sustain companies taking proactive actions to tackle climate change.

Public Health Implications of Drought in a Climate Change Context: A Critical Review.

Extreme weather events are expected to increase due to climate change, which could pose an additional burden of morbidity and mortality. In recent decades, drought severity has increased in several regions around the world, affecting health by increasing the risk of water-, food-, and vector-borne diseases, malnutrition, cardiovascular and respiratory illness, mental health disorders, and mortality. Drought frequency and severity are expected to worsen across large regions as a result of a decrease in precipitation and an increase in temperature and atmospheric evaporative demand, posing a pressing challenge for public health. Variation in impacts among countries and communities is due to multiple factors, such as aging, socioeconomic status, access to health care, and gender, affecting population resilience. Integrative proactive action plans focused on risk management are required, and resources should be transferred to developing countries to reduce their vulnerability and risk.

Learning about simulated adversaries from human defenders using interactive cyber-defense games

Abstract Given the increase in cybercrime, cybersecurity analysts (i.e. defenders) are in high demand. Defenders must monitor an organization’s network to evaluate threats and potential breaches into the network. Adversary simulation is commonly used to test defenders’ performance against known threats to organizations. However, it is unclear how effective this training process is in preparing defenders for this highly demanding job. In this paper, we demonstrate how to use adversarial algorithms to investigate defenders’ learning using interactive cyber-defense games. We created an Interactive Defense Game (IDG) that represents a cyber-defense scenario, which requires monitoring of incoming network alerts and allows a defender to analyze, remove, and restore services based on the events observed in a network. The participants in our study faced one of two types of simulated adversaries. A Beeline adversary is a fast, targeted, and informed attacker; and a Meander adversary is a slow attacker that wanders the network until it finds the right target to exploit. Our results suggest that although human defenders have more difficulty to stop the Beeline adversary initially, they were able to learn to stop this adversary by taking advantage of their attack strategy. Participants who played against the Beeline adversary learned to anticipate the adversary’s actions and took more proactive actions, while decreasing their reactive actions. These findings have implications for understanding how to help cybersecurity analysts speed up their training.

Examining factors impacting acceptance of COVID-19 countermeasures among structurally marginalised Canadians

ABSTRACT The COVID-19 pandemic highlighted and exacerbated inequities in health for structurally marginalised Canadians. Their location on society's hierarchies constrained their ability to access healthcare and follow recommended health behaviours. The aim of this article is to identify, from the perspective of marginalised populations, factors influencing the acceptance or rejection of COVID-19 countermeasures by structurally marginalised Canadians. Interviews were conducted with Canadians 18 + who identified as Black (n = 8), First Nations, Métis, or Inuit (n = 7) and low-income (<40,000 annual household income) (n = 8) between August and December 2021. Measures were said to impact well-being and interfere with revenue generating activities. Longstanding unfavourable living and environmental conditions as they relate to structural marginalisation was said to fuel anger toward the government and lead to a greater reluctance to accept countermeasures. Participants described concerns about government decisions being made without considering their unique contexts, or knowledge of the experiences of the population for whom these decisions were being made. Effective proactive action from government is important to foster trust with marginalised populations to support acceptance of health information and address growing inequities. Action that demonstrates government competence and commitment to the interests of marginalised populations is critical.

BIG DATA ANALYTICS: INTEGRATION, IMPORT SUBSTITUTION, OPERATIVENESS, RELIABILITY AND AVAILABILITY

Big data analytics faces the complexities of dealing with big, unstructured, and rapidly changing data that traditional methods of managing are faltering. The exponential growth of the internet and the digital economy has led to a huge increase in demand for data storage and data analysis. This new type of information, often referred to as unstructured data or big data, includes documents, images, audio, video, and social media content, rather than the traditional structured data found in databases. Big Data Analytics aims to extract valuable insights from these vast amounts of information. The article explores diverse technologies, such as Apache Flume, Pig, Hive and ZooKeeper, along with the open-source MongoDB NoSQL database, which collectively make up the big data analytics system. This system is used to predict volumes, gain ideas, take proactive actions and make effective strategic decisions. The article delves into the implementation, use and impact of Big Data Analytics on the value of an enterprise, taking advantage of algorithms designed for large data sets such as Hadoop and MapReduce. In addition, he provides illustrative examples of big data processing, specifically leveraging the integration of Hadoop and Matlab to solve handwritten number recognition tasks with a neural network trained using the MNIST dataset. Moreover, the article briefly touches on software import substitution in the context of the Russian Railways company, noting the use of Postgres Pro and Greenplum DBMS, and the loginom analytical platform. The article also provides a brief description of approaches to predictive assessment of the operation, reliability and availability of Big Data Processing Systems.

An Intelligent Support System for Additive Manufacturing technology investment decisions

When an unexpected crisis occurs, managers often seek such scientific solutions that can help them to take proactive actions before any threat arises. Crisis can be treated as an increase in the cost of energy consumption, employee rotation, lack of access to technology on account of sanctions. In this article, a system is presented that can aid decision-making when implementing additive manufacturing (AM) technology in a manufacturing enterprise. This technology may be necessary to reduce production process stages, minimize material waste, and optimize production costs in the use of resources. The application allows for analysis of the selected parameters of manufacturing process that are currently in use compared with those parameters of the AM process. The tool can be used to support decision-making in the area of changing manufacturing technology and the implementation of AM technology.

Forces et faiblesses du pouvoir municipal

Drawing on his municipal experience, Patrice Leclerc, mayor of Gennevilliers, questions residential diversity as a tool for reducing social inequalities. He prefers proactive political action, particularly in the construction of social housing, which is currently slowed down. Likewise, in the Greater Paris Metropolis, he declares himself a fervent supporter of active “municipalism” in the face of the powers of the Region and the State.

Predicting separation errors of air traffic controllers through integrated sequence analysis of multimodal behaviour indicators

Predicting separation errors in the daily tasks of air traffic controllers (ATCOs) is essential for the timely implementation of mitigation strategies before performance declines and the prevention of loss of separation and aircraft collisions. However, three challenges impede accurate separation errors forecasting: 1) compounding relationships between many human factors and control processes require sufficient operation process data to capture how separation errors occur and propagate within controller-in-the-loop processes; 2) previous human factor measurement approaches are disruptive to controllers’ daily operations because they use invasive sensors, such as electroencephalography (EEG) and electrocardiography (ECG), 3) errors accumulated in using the tasks and human behaviors for estimating system dynamics challenge accurate separation error predictions with sufficient leading time for proactive control actions. This study proposed a separation error prediction framework with a long leading time (>50 s) to address the above challenges, including 1) a multi-factorial model that characterizes the inter-relationships between task complexity, behavioral activity, cognitive load, and operational performance; 2) a multimodal data analytics approach to non-intrusively extract the task features (i.e., traffic density) from high-fidelity simulation systems and visual behavioral features (i.e., head pose, eyelid movements, and facial expressions) from ATCOs’ facial videos; 3) an encoder-decoder Long Short-Term Memory (LSTM) network to predict long-time-ahead separation errors by integrating multimodal features for reducing accumulated errors. A user study with six experienced ATCOs tested the proposed framework using the Phoenix Terminal Radar Approach Control (TRACON) simulator. The authors evaluated the model performance through two types of metrics: 1) point-level metrics, including precision, recall, and F1-score, and 2) sequence-level metrics, including alignment accuracy and sequence similarity. The results showed that 1) the model using the task and visual behavioral features significantly improved the prediction performance compared to the model using one single feature (eyelid movements), with an improvement of up to 26.95% in alignment accuracy for 10s-ahead prediction; 2) the model that combined task and visual behavioral features had a higher or comparable performance to models with different hybrid features, achieving an alignment accuracy of 82.38% for 50s-ahead error prediction; and (3) the proposed method outperformed three baseline models – Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and classic LSTM – by 8.21%, 3.47%, and 3.14% in alignment accuracy, respectively, for predicting 50s-ahead separation errors. These results suggest that the proposed model can effectively predict separation errors in air traffic control.