future events
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2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pilar Gil Fombella ◽  
Shaun West ◽  
Marleen Muehlberger ◽  
Thomas Sautter ◽  
Guenter Zepf ◽  
...  

PurposeThis paper describes the impact of COVID-19 on manufacturing firms in the DACH region of Europe (DACH is an acronym used to describe Germany, Austria and Switzerland). The purpose of the study was threefold: first to describe crisis resilience empirically through the actions taken by the firms using the elements of resilience; the paper then goes on to compare the DACH region with Northern Italy; finally, based on the findings, an existing crisis management model is expanded.Design/methodology/approachA mixed method of quantitative research based on survey data and qualitative interviews was applied for data collection. The findings are based on 57 survey results and 13 interviews from December 2020 to March 2021. The findings are presented based on the resilience elements and are discussed based on processes, technologies and people. The findings are compared with those from an Italian study made 6–9 months before this study. The comparison provides the basis for the adaptations to the crisis management model.FindingsThe findings describe the actions taken by firms in the DACH region to overcome the challenges posed by COVID-19. The findings were, in most cases, very similar to those from the Italian study. The most resilient firms had well-defined processes in place, adaptable employees who were well-led, and had (digital) technologies that could be quickly implemented.Originality/valueThe timing for the crisis was later in the DACH region and firms were able to learn from Italy. The crisis management model based on the Italian study was refined; the resulting model will support managers to face future crises. This model needs testing and extending to link to past and future events.


2022 ◽  
pp. 1-44
Author(s):  
Wei Zhong Goh ◽  
Varun Ursekar ◽  
Marc W. Howard

Abstract In recent years, it has become clear that the brain maintains a temporal memory of recent events stretching far into the past. This letter presents a neutrally inspired algorithm to use a scale-invariant temporal representation of the past to predict a scale-invariant future. The result is a scale-invariant estimate of future events as a function of the time at which they are expected to occur. The algorithm is time-local, with credit assigned to the present event by observing how it affects the prediction of the future. To illustrate the potential utility of this approach, we test the model on simultaneous renewal processes with different timescales. The algorithm scales well on these problems despite the fact that the number of states needed to describe them as a Markov process grows exponentially.


2022 ◽  
Author(s):  
Benjamin J. De Corte ◽  
Sean J. Farley ◽  
Kelsey A. Heslin ◽  
Krystal L. Parker ◽  
John H. Freeman

To act proactively, we must predict when future events will occur. Individuals generate temporal predictions using cues that indicate an event will happen after a certain duration elapses. Neural models of timing focus on how the brain represents these cue-duration associations. However, these models often overlook the fact that situational factors frequently modulate temporal expectations. For example, in realistic environments, the intervals associated with different cues will often covary due to a common underlying cause. According to the 'common cause hypothesis,' observers anticipate this covariance such that, when one cue's interval changes, temporal expectations for other cues shift in the same direction. Furthermore, as conditions will often differ across environments, the same cue can mean different things in different contexts. Therefore, updates to temporal expectations should be context-specific. Behavioral work supports these predictions, yet their underlying neural mechanisms are unclear. Here, we asked whether the dorsal hippocampus mediates context-based timing, given its broad role in context-conditioning. Specifically, we trained rats with either hippocampal or sham lesions that two cues predicted reward after either a short or long duration elapsed (e.g., tone-8s / light-16s). Then, we moved rats to a new context and extended the long-cue's interval (e.g., light-32s). This caused rats to respond later to the short cue, despite never being trained to do so. Importantly, when returned to the initial training context, sham rats shifted back toward both cues' original intervals. In contrast, lesion rats continued to respond at the long cue's newer interval. Surprisingly, they still showed contextual modulation for the short cue, responding earlier like shams. These data suggest the hippocampus only mediates context-based timing if a cue is explicitly paired and/or rewarded across distinct contexts. Furthermore, as lesions did not impact timing measures at baseline or acquisiton for the long cue's new interval, our data suggests that the hippocampus only modulates timing when context is relevant.


2022 ◽  
pp. 56-66
Author(s):  
Rimsy Dua ◽  
Samiksha Sharma ◽  
Rohit Kumar

This chapter describes how risk management deals with the detection, the evaluation and the precedence of the risks in the process of project management. There is always an uncertainty factor related to the decisions of an investment while managing a project. Risk management is a proactive approach to deal with such future events that can lead to slow performance of the software project management. For successful risk management; there are different metrics that have been used in the past and are being getting used in the present for inspecting the progress of a project at specific points in a timeline that help in reducing the amount of risk. For the adoption of effective metrics for risk management, data is required. All of the metrics can be applied to the different domains of project, process and product. The chapter also covers strategies to advance, distinguish, estimate, and forecast the risk management process. A review of the key point indicators (KPIs) are also integrated along with the project metrics to signify the future and the present renderings.


2022 ◽  
Vol 11 (2) ◽  
pp. 159-166
Author(s):  
Yuyun Hidayat ◽  
Titi Purwandari Sukono ◽  
Jumadil Saputra

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.


2022 ◽  
Vol 2161 (1) ◽  
pp. 012024
Author(s):  
Padmashree Desai ◽  
C Sujatha ◽  
Saumyajit Chakraborty ◽  
Saurav Ansuman ◽  
Sanika Bhandari ◽  
...  

Abstract Intelligent decision-making systems require the potential for forecasting, foreseeing, and reasoning about future events. The issue of video frame prediction has aroused a lot of attention due to its usefulness in many computer vision applications such as autonomous vehicles and robots. Recent deep learning advances have significantly improved video prediction performance. Nevertheless, as top-performing systems attempt to foresee even more future frames, their predictions become increasingly foggy. We developed a method for predicting a future frame based on a series of prior frames that services the Convolutional Long-Short Term Memory (ConvLSTM) model. The input video is segmented into frames, fed to the ConvLSTM model to extract the features and forecast a future frame which can be beneficial in a variety of applications. We have used two metrics to measure the quality of the predicted frame: structural similarity index (SSIM) and perceptual distance, which help in understanding the difference between the actual frame and the predicted frame. The UCF101 data set is used for testing and training in the project. It is a data collection of realistic action videos taken from YouTube with 101 action categories for action detection. The ConvLSTM model is trained and tested for 24 categories from this dataset and a future frame is predicted which yields satisfactory results. We obtained SSIM as 0.95 and perceptual similarity as 24.28 for our system. The suggested work’s results are also compared to those of state-of-the-art approaches, which are shown to be superior.


2022 ◽  
pp. 222-244
Author(s):  
Pushpalatha M. N. ◽  
Parkavi A. ◽  
Sini Anna Alex

The healthcare scheme in India has a lot of differences between rural and urban areas in terms of quality along with changes in private and public healthcare systems. The healthcare system is massive in India and full of inconsistencies and complexities like the other countries. Predictive analytics will help to improve the healthcare systems by providing valuable insight in healthcare. A huge amount of different data sets is generated because of the digitization of healthcare. This digitization allows us to use predictive analytics for better patient outcomes. Predictive analytics is utilized in decision-making activities and prediction making about the future events which are unknown. In this chapter, a brief overview of the Indian healthcare systems is given, along with data representations, challenges, issues, and risks associated with applying predictive analytics in healthcare and case studies with respect to regression and classification models.


2021 ◽  
Vol 23 (1) ◽  
pp. 336
Author(s):  
Michele Provenzano ◽  
Raffaele Serra ◽  
Carlo Garofalo ◽  
Ashour Michael ◽  
Giuseppina Crugliano ◽  
...  

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.


2021 ◽  
Author(s):  
Alan Kadin

<div>Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity.</div><div>Paper presented at virtual IEEE International Conference on Rebooting Computing (ICRC), Nov. 2021. To be published in conference proceedings 2022.</div>


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