Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10−9 to 10−10 and absolute error close to 10−5 to 10−7. The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.

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Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽

10.3390/s21113771 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3771

Author(s):

Alexey Kashevnik ◽

Walaa Othman ◽

Igor Ryabchikov ◽

Nikolay Shilov

Keyword(s):

Respiratory Rate ◽

Evaluation System ◽

Body Movement ◽

Absolute Error ◽

The Body ◽

Meditation Practice ◽

Body Parts ◽

Mental Health Training ◽

Negative Thoughts ◽

The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

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Country-level determinants of the severity of the first global wave of the COVID-19 pandemic: an ecological study

BMJ Open ◽

10.1136/bmjopen-2020-042034 ◽

2021 ◽

Vol 11 (2) ◽

pp. e042034

Author(s):

Tiberiu A Pana ◽

Sohinee Bhattacharya ◽

David T Gamble ◽

Zahra Pasdar ◽

Weronika A Szlachetka ◽

...

Keyword(s):

Mortality Rate ◽

Vaccination Coverage ◽

Urban Areas ◽

Ecological Study ◽

Environmental Parameters ◽

The Philippines ◽

International Travel ◽

Bcg Vaccination ◽

Country Level ◽

The Mean

ObjectiveWe aimed to identify the country-level determinants of the severity of the first wave of the COVID-19 pandemic.DesignEcological study of publicly available data. Countries reporting >25 COVID-19 related deaths until 8 June 2020 were included. The outcome was log mean mortality rate from COVID-19, an estimate of the country-level daily increase in reported deaths during the ascending phase of the epidemic curve. Potential determinants assessed were most recently published demographic parameters (population and population density, percentage population living in urban areas, population >65 years, average body mass index and smoking prevalence); economic parameters (gross domestic product per capita); environmental parameters (pollution levels and mean temperature (January–May); comorbidities (prevalence of diabetes, hypertension and cancer); health system parameters (WHO Health Index and hospital beds per 10 000 population); international arrivals; the stringency index, as a measure of country-level response to COVID-19; BCG vaccination coverage; UV radiation exposure; and testing capacity. Multivariable linear regression was used to analyse the data.Primary outcomeCountry-level mean mortality rate: the mean slope of the COVID-19 mortality curve during its ascending phase.ParticipantsThirty-seven countries were included: Algeria, Argentina, Austria, Belgium, Brazil, Canada, Chile, Colombia, the Dominican Republic, Ecuador, Egypt, Finland, France, Germany, Hungary, India, Indonesia, Ireland, Italy, Japan, Mexico, the Netherlands, Peru, the Philippines, Poland, Portugal, Romania, the Russian Federation, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, Ukraine, the UK and the USA.ResultsOf all country-level determinants included in the multivariable model, total number of international arrivals (beta 0.033 (95% CI 0.012 to 0.054)) and BCG vaccination coverage (−0.018 (95% CI −0.034 to –0.002)), were significantly associated with the natural logarithm of the mean death rate.ConclusionsInternational travel was directly associated with the mortality slope and thus potentially the spread of COVID-19. Very early restrictions on international travel should be considered to control COVID-19 outbreaks and prevent related deaths.

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Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽

10.3390/drones5030068 ◽

2021 ◽

Vol 5 (3) ◽

pp. 68

Author(s):

Jiwei Fan ◽

Xiaogang Yang ◽

Ruitao Lu ◽

Xueli Xie ◽

Weipeng Li

Keyword(s):

Deep Learning ◽

Autonomous Navigation ◽

Detection Method ◽

Active Role ◽

Absolute Error ◽

Face Mask ◽

Learning Technology ◽

Flight System ◽

Crowd Density ◽

The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

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Explanation Plus Prediction—The Logical Focus of Project Management Research

Project Management Journal ◽

10.1177/8756972821999945 ◽

2021 ◽

pp. 875697282199994

Author(s):

Joseph F. Hair ◽

Marko Sarstedt

Keyword(s):

Project Management ◽

Statistical Models ◽

Predictive Power ◽

Mean Absolute Error ◽

Explanatory Power ◽

Absolute Error ◽

Model Parameters ◽

Mean Square ◽

Management Research ◽

The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

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An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽

10.3390/s21051867 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1867

Author(s):

Tasbiraha Athaya ◽

Sunwoong Choi

Keyword(s):

Blood Pressure ◽

Cardiovascular Health ◽

Estimation Method ◽

Absolute Error ◽

British Hypertension Society ◽

Non Invasive ◽

Arterial Blood ◽

Blood Pressure Waveform ◽

The Mean ◽

Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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Asynchrony of Gambierdiscus spp. Abundance and Toxicity in the U.S. Virgin Islands: Implications for Monitoring and Management of Ciguatera

Toxins ◽

10.3390/toxins13060413 ◽

2021 ◽

Vol 13 (6) ◽

pp. 413

Author(s):

Justin D. Liefer ◽

Mindy L. Richlen ◽

Tyler B. Smith ◽

Jennifer L. DeBose ◽

Yixiao Xu ◽

...

Keyword(s):

Harmful Algal Blooms ◽

Algal Blooms ◽

Environmental Parameters ◽

Virgin Islands ◽

Coastal Communities ◽

Trophic Levels ◽

Us Virgin Islands ◽

Benthic Dinoflagellates ◽

The Mean ◽

The U.S

Ciguatera poisoning (CP) poses a significant threat to ecosystem services and fishery resources in coastal communities. The CP-causative ciguatoxins (CTXs) are produced by benthic dinoflagellates including Gambierdiscus and Fukuyoa spp., and enter reef food webs via grazing on macroalgal substrates. In this study, we report on a 3-year monthly time series in St. Thomas, US Virgin Islands where Gambierdiscus spp. abundance and Caribbean-CTX toxicity in benthic samples were compared to key environmental factors, including temperature, salinity, nutrients, benthic cover, and physical data. We found that peak Gambierdiscus abundance occurred in summer while CTX-specific toxicity peaked in cooler months (Feb–May) when the mean water temperatures were approximately 26–28 °C. These trends were most evident at deeper offshore sites where macroalgal cover was highest year-round. Other environmental parameters were not correlated with the CTX variability observed over time. The asynchrony between Gambierdiscus spp. abundance and toxicity reflects potential differences in toxin cell quotas among Gambierdiscus species with concomitant variability in their abundances throughout the year. These results have significant implications for monitoring and management of benthic harmful algal blooms and highlights potential seasonal and highly-localized pulses in reef toxin loads that may be transferred to higher trophic levels.

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A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis

Applied Sciences ◽

10.3390/app11041667 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1667

Author(s):

Kerstin Klaser ◽

Pedro Borges ◽

Richard Shaw ◽

Marta Ranzini ◽

Marc Modat ◽

...

Keyword(s):

Medical Image Analysis ◽

Absolute Error ◽

Magnetic Resonance Images ◽

The Body ◽

Whole Body ◽

Image Size ◽

Body Images ◽

Image Translation ◽

The Mean ◽

Synthetic Ct

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

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Visualizing the Variance of a Random Variable

Open Systems & Information Dynamics ◽

10.1142/s1230161211000054 ◽

2011 ◽

Vol 18 (01) ◽

pp. 71-85

Author(s):

Fabrizio Cacciafesta

Keyword(s):

Stochastic Dominance ◽

Mean Absolute Error ◽

Random Variable ◽

Absolute Error ◽

Second Order ◽

Taylor Formula ◽

Order Stochastic Dominance ◽

The Mean ◽

Second Order Stochastic Dominance ◽

Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

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The Application of Visual Environmental Economics in the Study of Public Preference and Urban Greenspace

Environment and Planning B Planning and Design ◽

10.1068/b33140 ◽

2009 ◽

Vol 36 (2) ◽

pp. 355-375 ◽

Cited By ~ 16

Author(s):

Richard Laing ◽

Anne-Marie Davies ◽

David Miller ◽

Anna Conniff ◽

Stephen Scott ◽

...

Keyword(s):

Environmental Economic ◽

Environmental Economics ◽

Environmental Conditions ◽

Three Dimensional ◽

Computer Models ◽

Time Of Day ◽

Public Preference ◽

Daily Lives ◽

Urban Greenspace ◽

Physical Variables

Urban greenspace has consistently been argued to be of great importance to the wellbeing, health, and daily lives of residents and users. This paper reports results from a study that combined the visualisation of public results from a study that combined the visualisation of public greenspace with environmental economics, and that aimed to develop a method by which realistic computer models of sites could be used within preference studies. As part of a methodology that employed contingent rating to establish the values placed on specific greenspace sites, three-dimensional computer models were used to produce visualisations of particular environmental conditions. Of particular importance to the study was the influence of variables including lighting, season, time of day, and weather on the perception of respondents. This study followed previous work that established a suitable approach to the modelling and testing of entirely moveable physical variables within the built environment. As such, the study has established firmly that computer-generated visualisations are appropriate for use within environmental economic surveys, and that there is potential for a holistic range of attributes to be included in such studies.

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Statistical Characteristics of Unsteady Storms in Radar Observations for the Beijing–Tianjin Region

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0043.1 ◽

2015 ◽

Vol 54 (1) ◽

pp. 106-116 ◽

Cited By ~ 1

Author(s):

Yu Wang ◽

Hong-Qing Wang ◽

Lei Han ◽

Yin-Jing Lin ◽

Yan Zhang

Keyword(s):

Warm Season ◽

Environmental Parameters ◽

Statistical Characteristics ◽

Movement Speed ◽

Movement Direction ◽

Upper Troposphere ◽

Maximum Value ◽

Dynamic Factors ◽

The Mean ◽

Wind Profiles

AbstractThis study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

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