Fault detection model for a variable speed heat pump

The impact of the COVID pandemic has resulted in many people cultivating a remote working culture and increasing building energy use. A reduction in the energy use of heating, ventilation, and air-conditioning (HVAC) systems is necessary for decreasing the energy use in buildings. The refrigerant charge of a heat pump greatly affects its energy use. However, refrigerant leakage causes a significant increase in the energy use of HVAC systems. The development of refrigerant charge fault detection models is, therefore, important to prevent unwarranted energy consumption and CO2 emissions in heat pumps. This paper examines refrigerant charge faults and their effect on a variable speed heat pump and the most accurate method between a multiple linear regression and multilayer perceptron model to use in detecting the refrigerant charge fault using the discharge temperature of the compressor, outdoor entering water temperature and compressor speed as inputs, and refrigerant charge as the output. The COP of the heat pump decreased when it was not operating at the optimum refrigerant charge, while an increase in compressor speed compensated for the degradation in the capacity during refrigerant leakage. Furthermore, the multilayer perception was found to have a higher prediction accuracy of the refrigerant charge fault with a mean square error of ± 3.7%, while the multiple linear regression model had a mean square error of ± 4.5%. The study also found that the multilayer perception model requires 7 neurons in the hidden layer to make viable predictions on any subsequent test sets fed into it under similar experimental conditions and parameters of the heat pump used in this study.

The Differential Effects of Oil Prices on the Development of Renewable Energy in Oil-Importing and Oil-Exporting Countries in Africa

The shift to renewable sources of energy has become a critical economic priority in African countries due to energy challenges. However, investors in the development of renewable energy face problems with decision making due to the existence of multiple criteria, such as oil prices and the associated macroeconomic performance. This study aims to analyze the differential effects of international oil prices and other macroeconomic factors on the development of renewable energy in both oil-importing and oil-exporting countries in Africa. The study uses a panel vector error correction model (P-VECM) to analyze data from five net oil exporters (Algeria, Angola, Egypt, Libya and Nigeria) and five net oil importers (Kenya, Ethiopia, Congo, Mozambique and South Africa). The study finds that higher oil prices positively affect the development of renewable energy in oil-importing countries by making renewable energy more economically competitive. Economic growth is also identified as a major driver of the development of renewable energy. While high-interest rates negatively affect the development of renewable energy in oil-importing countries, it has positive effects in oil-exporting countries. Exchange rates play a crucial role in the development of renewable energy in both types of countries with a negative effect in oil-exporting countries and a positive effect in oil-importing countries. The findings of this study suggest that policymakers should take a holistic approach to the development of renewable energy that considers the complex interplay of factors, such as oil prices, economic growth, interest rates, and exchange rates.

Sustainability assessment of crops' production in India: empirical evidence from ARDL-ECM approach

PurposeThe study aims to evaluate the long- vs short-run relationships between crops' production (output) and crops' significant inputs such as land use, agricultural water use (AWU) and gross irrigated area in India during the period 1981–2018.Design/methodology/approachThe study applied the autoregressive distributed lag (ARDL) bounds testing approach to estimate the co-integration among the variables. The study uses the error correction model (ECM), which integrates the short-run dynamics with the long-run equilibrium.FindingsThe ARDL bounds test of co-integration confirms the strong evidence of the long-run relationship among the variables. Empirical results show the positive and significant relationship of crops' production with land use and gross irrigated area. The statistically significant error correction term (ECT) validates the speed of adjustment of the empirical models in the long-run.Research limitations/implicationsThe study suggests that the decision-makers must understand potential trade-offs between human needs and environmental impacts to ensure food for the growing population in India.Originality/valueFor a clear insight into the impact of climate change on crops' production, the current study incorporates the climate variables such as annual rainfall, maximum temperature and minimum temperature. Further, the study considered agro-chemicals, i.e. fertilizers and pesticides, concerning their negative impacts on increased agricultural production and the environment.

Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

The breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients with vulnerable skins. Contactless monitoring systems are then gaining attention for respiratory frequency detection. We propose a new non-contact technique to estimate the breathing rate based on the motion video magnification method by means of the Hermite transform and an Artificial Hydrocarbon Network (AHN). The chest movements are tracked by the system without the use of an ROI in the image video. The machine learning system classifies the frames as inhalation or exhalation using a Bayesian-optimized AHN. The method was compared using an optimized Convolutional Neural Network (CNN). This proposal has been tested on a Data-Set containing ten healthy subjects in four positions. The percentage error and the Bland–Altman analysis is used to compare the performance of the strategies estimating the breathing rate. Besides, the Bland–Altman analysis is used to search for the agreement of the estimation to the reference.The percentage error for the AHN method is 2.19±2.1 with and agreement with respect of the reference of ≈99%.

An Ensemble Learning Approach to Predict Employees' Preference for E-Working in the Post-Pandemic World

The Covid-19 pandemic has forced a large segment of the global workforce to shift to e-working. The pandemic has convinced many organizations that e-working has benefits for a successful business. As a result, it is critical to identify employees' suggestions and evaluate their motivation to continue the e-working concept in the post-pandemic world. The study was conducted by randomly surveying employees using various Machine Learning algorithms, including Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and logistic regression. The ensembling algorithm uses 66% of the percentage split method in the Waikato Environment for Knowledge Analysis (WEKA) tool. Accuracy, precision, recall, /-measure values and error rates were used to compare the results. The ensemble learning algorithm shows the best results with 90% accuracy, making it easier to predict employees' preference for e-working and accordingly take decisions.

A Second-Order Crank-Nicolson-Type Scheme for Nonlinear Space-Time Reaction-Diffusion Equations on Time-Graded Meshes

A weak singularity in the solution of time-fractional differential equations can degrade the accuracy of numerical methods when employing a uniform mesh, especially with schemes involving the Caputo derivative (order alpha,), where time accuracy is of the order (2 - alpha) or (1 + alpha). To deal with this problem, we present a second-order numerical scheme for nonlinear time-space fractional reaction-diffusion equations. For spatial resolution, we employ a matrix transfer technique. Using graded meshes in time, we improve the convergence rate of the algorithm. Furthermore, some sharp error estimates that give an optimal second-order rate of convergence are presented and proven. We discuss the stability properties of the numerical scheme and elaborate on several empirical examples that corroborate our theoretical observations.

Bidirectional Data-Driven Trajectory Prediction for Intelligent Maritime Traffic

Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.

Tracing Machine and Human Translation Errors in Some Literary Texts with Some Implications for EFL Translators

This research study aims at drawing a comparison between some internet emerging applications used for machine translation (MT) and a human translation (HT) to two of Alphonse Daudet's short stories: "The Siege of Berlin" and "The Bad Zouave." The automatic translation has been carried out by four MT online applications (i.e. Translate Dict, Yandex, Mem-Source, and Reverso) that have come to light in the wake of COVID-19 breakout, whereas the HT was carried out by Hassouna in 2018. The results revealed that MT and HT made some errors related to (a) polysemy, (b) homonymy, (c) syntactic ambiguities, (d) fuzzy hedges, (e) synonyms, (f) metaphors and symbols. The results also showed that Yandex has dealt with polysemy much better than HT in "The Siege of Berlin," but the opposite has been noticed in "The Bad Zouave." Another crucial result is that HT has excelled all MT systems in homonymy and syntactic ambiguities in the two literary texts. A final result is that both MT and HT have dealt with fuzzy hedges at similar rates with little supremacy on the part of Reverso, whereas MemSource and Translate Dict have dealt with synonyms in the two literary texts much better than HT. The study concluded that EFL learners should be aware of the fact that in spite of the advantageousness of MT systems, their inadequacies should not be overlooked and handled with post-editing.

Four-dimensional variational assimilation for SO2emission and its application around the COVID-19 lockdown in the spring 2020 over China

Emission inventories are essential for modelling studies and pollution control, but traditional emission inventories are usually updated after a few years based on the statistics of "bottom-up"approach from the energy consumption in provinces, cities, and counties. The latest emission inventories of multi-resolution emission inventory in China (MEIC) was compiled from the statistics for the year 2016 (MEIC_2016). However, the real emissions have varied yearly, due to national pollution control policies and accidental special events, such as the coronavirus disease (COVID-19) pandemic. In this study, a four-dimensional variational assimilation (4DVAR) system based on the "top-down"approach was developed to optimise sulfur dioxide (SO2) emissions by assimilating the data of SO2 concentrations from surface observational stations. The 4DVAR system was then applied to obtain the SO2 emissions during the early period of COVID-19 pandemic (from 17 January to 7 February 2020), and the same period in 2019 over China. The results showed that the average MEIC_2016, 2019, and 2020 emissions were 42.2×106, 40.1×106, and 36.4×106 kg d-1. The emissions in 2020 decreased by 9.2 % in relation to the COVID-19 lockdown compared with those in 2019. For central China, where the lockdown measures were quite strict, the mean 2020 emission decreased by 21.0 % compared with 2019 emissions. Three forecast experiments were conducted using the emissions of MEIC_2016, 2019, and 2020 to demonstrate the effects of optimised emissions. The root mean square error (RMSE) in the experiments using 2019 and 2020 emissions decreased by 28.1 % and 50.7 %, and the correlation coefficient increased by 89.5 % and 205.9 % compared with the experiment using MEIC_2016. For central China, the average RMSE in the experiments with 2019 and 2020 emissions decreased by 48.8 % and 77.0 %, and the average correlation coefficient increased by 44.3 % and 238.7 %, compared with the experiment using MEIC_2016 emissions. The results demonstrated that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts. Copyright: © 2022 Yiwen Hu et al.

IMPLICACIONES TECNOLÓGICAS EN LA EDUCACIÓN A DISTANCIA DURANTE LA PANDEMIA EN UNIVERSITARIOS

Durante la pandemia las Tecnología de la Información y Comunicación (TIC) han sido vitales para mantener la operación de las actividades educativas a distancia. Mediante esta investigación se analiza la relación entre las implicaciones tecnológicas, así como también el estrés académico experimentados durante la pandemia COVID-19 en estudiantes de la Universidad Autónoma de Baja California (UABC). En la metodología se utilizó el método cuantitativo de cuestionario, con 95% de confianza y 4% de error, su uso una muestra de 638 universitarios y se usó la asignación proporcional en once facultades. En la comprobación de la hipótesis se empleó la correlación y regresión lineal, asimismo, se analizó el modelo factorial para la determinación de factores. En los resultados, se comprobó la hipótesis: la carencia de recursos tecnológicos, así como el estrés académico afecta de manera negativa la satisfacción en la educación online, los problemas más frecuentes que presentan los universitarios: computadora/laptop, smartphone, software especializado, paquetería office, realizar tareas/proyectos escolares, comunicarse con los profesores y realizar trabajos en equipo. Se concluye, que la carencia de herramientas y problemas tecnológicos así como el estrés académico, tienen impacto negativo en la satisfacción de la educación a distancia, esto puede cambiar dependiendo de la capacidad de recursos disponibles para los estudiantes.Alternate :During the pandemic, Information and Communication Technologies (ICT) have been vital to maintain the operation of distance educational activities. This research analyzes the relationship between the technological implications, as well as the academic stress experienced during the COVID-19 pandemic in students of the Autonomous University of Baja California (UABC). In the methodology, the quantitative questionnaire method was used, with 95% confidence and 4% error, a sample of 638 university students was used and proportional allocation was used in eleven faculties. In testing the hypothesis, correlation and linear regression were used, likewise, the factorial model was analyzed to determine factors. In the results, the hypothesis was verified: the lack of technological resources, as well as academic stress negatively affects satisfaction in online education, the most frequent problems presented by university students: computer/laptop, smartphone, specialized software, office parcel, carry out homework/school projects, communicate with teachers and carry out teamwork. It is concluded that the lack of tools and technological problems, as well as academic stress, have a negative impact on the satisfaction of distance education, this can change depending on the capacity of resources available to students.Alternate :Durante a pandemia, as Tecnologias de Informação e Comunicação (TIC) têm sido vitais para manter o funcionamento das atividades educacionais a distância. Esta pesquisa analisa a relação entre as implicações tecnológicas, bem como o estresse acadêmico vivenciado durante a pandemia de COVID-19 em estudantes da Universidade Autônoma da Baixa Califórnia (UABC). Na metodologia, utilizou-se o método de questionário quantitativo, com 95% de confiança e 4% de erro, obteve-se uma amostra de 638 universitários e utilizou-se a alocação proporcional em onze faculdades. No teste da hipótese, foram utilizadas correlação e regressão linear, da mesma forma, o modelo fatorial foi analisado para determinação dos fatores. Nos resultados, verificou-se a hipótese: a falta de recursos tecnológicos, assim como o estresse acadêmico afeta negativamente a satisfação no ensino online, sendo os problemas mais frequentes apresentados pelos universitários: computador/notebook, smartphone, software especializado, pacote office, realizar trabalhos de casa/projectos escolares, comunicar com os professores e realizar trabalho em equipa. Conclui-se que a falta de ferramentas e problemas tecnológicos, assim como o estresse acadêmico, pactam negativamente na satisfação da educação a distância, isso pode mudar dependendo da capacidade de recursos disponíveis aos alunos.

Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

Human action analysis is an enthralling area of research in artificial intelligence, as it may be used to improve a range of applications, including sports coaching, rehabilitation, and monitoring. By forecasting the body's vital position of posture, human action analysis may be performed. Human body tracking and action recognition are the two primary components of video-based human action analysis. We present an efficient human tracking model for squat exercises using the open-source MediaPipe technology. The human posture detection model is used to detect and track the vital body joints within the human topology. A series of critical body joint motions are being observed and analysed for aberrant body movement patterns while conducting squat workouts. The model is validated using a squat dataset collected from ten healthy people of varying genders and physiques. The incoming data from the model is filtered using the double exponential smoothing method;the Mean Squared Error between the measured and smoothed angles is determined to classify the movement as normal or abnormal. Level smoothing and trend control have parameters of 0.8928 and 0.77256, respectively. Six out of ten subjects in the trial were precisely predicted by the model. The mean square error of the signals obtained under normal and abnormal squat settings is 56.3197 and 29.7857, respectively. Thus, by utilising a simple threshold method, the low-cost camera-based squat movement condition detection model was able to detect the abnormality of the workout movement.

Monic Chebyshev pseudospectral differentiation matrices for higher-order IVPs and BVPs: applications to certain types of real-life problems

We introduce new differentiation matrices based on the pseudospectral collocation method. Monic Chebyshev polynomials (MCPs) were used as trial functions in differentiation matrices (D-matrices). Those matrices have been used to approximate the solutions of higher-order ordinary differential equations (H-ODEs). Two techniques will be used in this work. The first technique is a direct approximation of the H-ODE. While the second technique depends on transforming the H-ODE into a system of lower order ODEs. We discuss the error analysis of these D-matrices in-depth. Also, the approximation and truncation error convergence have been presented to improve the error analysis. Some numerical test functions and examples are illustrated to show the constructed D-matrices' efficiency and accuracy.

Digital experiment system of uniform circular motion with remote laboratory based on website

Circular motion experiment is an important activity to develop the abilities of students in learning physics. The covid-19 pandemic has caused experimental activities in the laboratory don’t be carry out properly. A solution to solve this problem is to develop a uniform circular motion experiment system with remote laboratory based on website. The objective of this research was to determine the accuracy and precision of the circular motion experimental system with remote laboratory based on website. This research can be classified into engineering research, which is an activity in the design of a product that isn’t continuous. Data collection techniques were carried out in two ways, namely direct measurement and indirect measurement. The research data were analyzed by error theory and descriptive statistics. Based on the analysis of the data obtained, it can be stated from the digital uniform circular motion modelling tool with a length of 54 cm, a width of 43 cm and a height of 21 cm with an accuracy and precision of controlling a stepper motor of 99.03 % and 98.84 %. The accuracy and precision of the uniform circular motion modelling tool with a remote laboratory is close to 100%.

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values;then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.

A Comparative Analysis of the Forecasted Mortality Rate under Normal Conditions and the COVID-19 Excess Mortality Rate in Malaysia

SARS-CoV-2, known as COVID-19, has affected the entire world, resulting in an unexpected death rate as compared to the death probability before the pandemic. Prior to the COVID-19 pandemic, death probability has been assessed in a normal context that is different from those anticipated during the pandemic, particularly for the older population cluster. However, there is no such evidence of excess mortality in Malaysia to date. Therefore, this study determines the excess mortality rate for specific age groups during the pandemic outbreak in Malaysia. Before determining the excess mortality rate, this study aims to establish the efficiency of various parametrized mortality models in reference to the data set before the pandemic. This study employs the hold-out, repeated hold-out, and leave-one-out cross-validation procedures to identify the optimal mortality law for fitting the mortality data. Based on the goodness-of-fit measures (mean absolute percentage error, mean absolute error, sum square error, and mean square error), the Heligman-Pollard model for men and Rogers Planck model for women are considered as the optimal models. In assessing the excess mortality, both models favour the hold-out technique. When the COVID-19 mortality data are incorporated to forecast the mortality rate for people aged 60 and above, there is an excess mortality rate. However, the men’s mortality rate appears to be delayed and more prolonged than the women’s mortality rate. Consequently, the government is recommended to amend the existing policy to reflect the post COVID-19 mortality forecast.

IoT-Based Health Monitoring System Development and Analysis

This paper presents the design and implementation of a health monitoring system using the Internet of Things (IoT). In present days, with the expansion of innovations, specialists are always looking for innovative electronic devices for easier identification of irregularities within the body. IoT-enabled technologies enable the possibility of developing novel and noninvasive clinical support systems. This paper presents a health care monitoring system. In particular, COVID-19 patients, high blood pressure patients, diabetic patients, etc., in a rural area in a developing country, such as Bangladesh, do not have instant access to health or emergency clinics for testing. Buying individual instruments or continuous visitation to hospitals is also expensive for the regular population. The system we developed will measure a patient’s body temperature, heartbeat, and oxygen saturation (SpO2) levels in the blood and send the data to a mobile application using Bluetooth. The mobile application was created via the Massachusetts Institute of Technology (MIT) inventor app and will receive the data from the device over Bluetooth. The physical, logical, and application layers are the three layers that make up the system. The logical layer processes the data collected by the sensors in the physical layer. Media access management and intersensor communications are handled by the logical layer. Depending on the logical layer’s processed data, the application layer makes decisions. The main objective is to increase affordability for regular people. Besides sustainability in the context of finance, patients will have easy access to personal healthcare. This paper presents an IoT-based system that will simplify the utilization of an otherwise complicated medical device at a minimum cost while sitting at home. A 95 percent confidence interval with a 5 percent maximum relative error is applied to all measurements related to determining the patient’s health parameters. The use of these devices as support tools by the general public in a certain situation could have a big impact on their own lives.

TRANSLATION ERROR IN GOOGLE TRANSLATE FROM ENGLISH INTO ROMANIAN IN TEXTS RELATED TO CORONAVIRUS

Both the emergence of the pandemic and lack of knowledge and/or time needed to translate texts related to this topic brought about an increased interest in analysing Neural Machine Translation (NMT) performance. In this study, we seek to fulfil the following purposes: 1.to examine types of translation errors from English into Romanian;2.to establish the causes of those errors or find possible explanations for them;3.to evaluate the quality and accuracy of Google Translate when translating health information from English to Romanian. The study provides theoretical insights, by explaining operational concepts, such as error, Machine Translation terminology, and the construction of Machine Translation lexicons and contrastive aspects of the two languages involved in the process. The lexical contrastive analysis, the branch of comparative linguistics, emphasizes the interferences of the languages in contact in the process of translation, the main objective being to optimize the machine translation system of operating. Some areas of knowledge, such as domain terminology, verb or noun patterns, multiword expressions, general vocabulary and domain-specific vocabulary are implemented in order to detect the lexical errors and to improve the lexical knowledge of Romanian in the Machine Translation database. The data collection comprises the coronavirus vaccine prospects and texts collected from official websites and translated using Google Translate and Google Languages Tools. Samples of errors, potential translation issues, and MT bad performance are manually examined in order to conduct a linguistic error analysis. Once the data is collected, the errors will be identified, classified, described and finally analysing the Machine Translation through a descriptive methodology. From the data analysed, there are more than 50 lexical and semantic errors that are approached through descriptive methodology. By examining types of errors in translation from English into Romanian and analysing the potential causes of errors, the results will be used to illustrate the quality and accuracy of Google Translate when translating public health information from English into Romanian, to observe how much the message is affected by the error, in order to sharpen up linguistic awareness. The results of the study can ultimately help improve of the quality of NMT in terms of better lexical selection and attempt to give inputs as a contribution for a more adequate translation into Romanian by Google Machine Translation. The proposed research will focus on the identification and typology of translation errors. In order to fulfil the objectives and to prepare the analysis of the lexical and semantic errors, the English texts will be paired with the equivalent version from Romanian given by Google Translate. This work presents a first step towards automatic analysis of machine translation output. The current research that analyses the raw English-Romanian translations from Google Translate about coronavirus will show the degree of intelligibility and the errors that could lead to misinformation or ambiguous contexts. © 2021, National Defence University - Carol I Printing House. All rights reserved.

A remote field course implementing high-resolution topography acquisition with geomorphic applications

Here we describe the curriculum and outcomes from a data-intensive geomorphic analysis course, “Geoscience Field Issues Using High-Resolution Topography to Understand Earth Surface Processes”, which pivoted to virtual in 2020 due to the COVID-19 pandemic. The curriculum covers technologies for manual and remotely sensed topographic data methods, including (1) Global Positioning Systems and Global Navigation Satellite System (GPS/GNSS) surveys, (2) Structure from Motion (SfM) photogrammetry, and (3) ground-based (terrestrial laser scanning, TLS) and airborne lidar. Course content focuses on Earth-surface process applications but could be adapted for other geoscience disciplines. Many other field courses were canceled in summer 2020, so this course served a broad range of undergraduate and graduate students in need of a field course as part of degree or research requirements. Resulting curricular materials are available freely within the National Association of Geoscience Teachers' (NAGT's) “Teaching with Online Field Experiences” collection. The authors pre-collected GNSS data, uncrewed-aerial-system-derived (UAS-derived) photographs, and ground-based lidar, which students then used in course assignments. The course was run over a 2-week period and had synchronous and asynchronous components. Students created SfM models that incorporated post-processed GNSS ground control points and created derivative SfM and TLS products, including classified point clouds and digital elevation models (DEMs). Students were successfully able to (1) evaluate the appropriateness of a given survey/data approach given site conditions, (2) assess pros and cons of different data collection and post-processing methods in light of field and time constraints and limitations of each, (3) conduct error and geomorphic change analysis, and (4) propose or implement a protocol to answer a geomorphic question. Overall, our analysis indicates the course had a successful implementation that met student needs as well as course-specific and NAGT learning outcomes, with 91 % of students receiving an A, B, or C grade. Unexpected outcomes of the course included student self-reflection and redirection and classmate support through a daily reflection and discussion post. Challenges included long hours in front of a computer, computing limitations, and burnout because of the condensed nature of the course. Recommended implementation improvements include spreading the course out over a longer period of time or adopting only part of the course and providing appropriate computers and technical assistance. This paper and published curricular materials should serve as an implementation and assessment guide for the geoscience community to use in virtual or in-person high-resolution topographic data courses that can be adapted for individual labs or for an entire field or data course.

Embedded Coded Relay System for Molecular Communications

With the emergence of the COVID-19 pandemic, the World Health Organization (WHO) has urged scientists and industrialists to explore modern information and communication technology (ICT) as a means to reduce or even eliminate it. The World Health Organization recently reported that the virus may infect the organism through any organ in the living body, such as the respiratory, the immunity, the nervous, the digestive, or the cardiovascular system. Targeting the abovementioned goal, we envision an implanted nanosystem embedded in the intra living-body network. The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system (i.e., delivery of the therapeutic drug to the diseased tissue or targeted cell). The communication among the nanomachines is accomplished via communication-based molecular diffusion. The control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things (IoBNT). The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward (DF) principle to ensure reliable drug delivery to the targeted cell. Notably, both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into account. In this paper, a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate (BER) performance and high signal-to-noise ratio (SNR), while the detection process is based on maximum likelihood (ML) probability and minimum error probability (MEP). The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position, number of released molecules, relay and receiver size. Analysis results are validated through simulation and demonstrate that the proposed scheme can significantly improve delivery performance of the desirable drugs in the molecular communication system.

A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US

In December 2019, Severe Special Infectious Pneumonia (SARS-CoV-2)–the novel coronavirus (COVID-19)– appeared for the first time, breaking out in Wuhan, China, and the epidemic spread quickly to the world in a very short period time. According to WHO data, ten million people have been infected, and more than one million people have died;moreover, the economy has also been severely hit. In an outbreak of an epidemic, people are concerned about the final number of infections. Therefore, effectively predicting the number of confirmed cases in the future can provide a reference for decision-makers to make decisions and avoid the spread of deadly epidemics. In recent years, the α-Sutte indicator method is an excellent predictor in short-term forecasting;however, the α-Sutte indicator uses fixed static weights. In this study, by adding an error-based dynamic weighting method, a novel β-Sutte indicator is proposed. Combined with ARIMA as an ensemble model (βSA), the forecasting of the future COVID-19 daily cumulative number of cases and the number of new cases in the US are evaluated from the experiment. The experimental results show that the forecasting accuracy of βSA proposed in this study is better than other methods in forecasting with metrics MAPE and RMSE. It proves the feasibility of adding error-based dynamic weights in the β-Sutte indicator in the area of forecasting.