Under the change in fuel ethanol policy, this study could provide scientific support for local agriculture land-use management in realizing the carbon neutralization vision and also set a good example for the development of the cassava fuel ethanol industry in other cassava-planting countries.Speeding up the promotion and application of biofuel ethanol has been a national strategy in China, which in turn has affected changes in the raw material planting structure. This study analyzed the response mechanism of water quality to agriculture land-use changes in a cassava fuel ethanol raw material planting area. The results revealed that an increase in cultivated land and construction land would lead to a rise in the load of TN  and TP , while an expansion in forest land and grassland area would reduce the load. As for crop structures, corn would have a remarkable positive impact on TN and TP, while rice and cassava performed in an opposite manner. Furthermore, scenarios under the carbon neutralization policy were carried out to forecast the nonpoint source pollutants based on the quantitative relations coefficients. It was proven that cassava planting was suitable for vigorous fuel ethanol development, but the maximum increase area of cassava should be 126 km With the development of the global economy from the last century, the human demand for energy has been increasing day by day. In November 2018, the World Energy Outlook issued by the International Energy Agency showed that the global primary energy demand will increase by more than 25% by 2040 . In receDue to fuel policy changes, the development of biofuel ethanol would inevitably lead to planting structure variations in raw material crops . Land-usIn China, the technology of biofuel ethanol production with corn and other foods as raw materials was mature and led to good economic benefits . Corn et(1)Land-use transformation patterns were revealed through the interpretation of remote sensing images for two terms.(2)A nonpoint source pollution model was established after calibration and verification by a MIKE-SHE distributed hydrological model . The influence of land-use changes on nonpoint source pollution in the watershed was obtained by multiple linear regression.(3)A scenario hypothesis of ethanol crop cultivation was set up based on the results of step two, in order to guide the spatial layout of land-use planning in the fuel ethanol planting area.In this study, a typical fuel ethanol raw material planting area was selected: a cassava planting area in the Guiping section of the Yujiang River Basin in Guangxi Province. The research content was mainly divided into the following three tasks:This study divided the cultivated land into second-level classification and will provide scientific support and more elaborated management suggestions to local agricultural land-use planning. Furthermore, it could also set an example for other cassava planting areas worldwide for realizing the goal of carbon neutralization.2. The selected area is a typical raw material planting area for cassava fuel ethanol and contains a cassava plantation which area is 10 ha. Cassava planted in the basin is near the river, and it would help to reflect the influence of cassava planting structure change on nonpoint source pollution. The research takes place in the area where the Yujiang River flows from the southwest to the northeast, with a total length of about 45 km . The cultivated land in the Yujiang River Basin was further divided into cassava, corn, rice, and other cultivated land on the basis of different crop phenological information. The visualized spatial distribution maps of land-use types were displayed by raster data.ijA was a matrix with n columns and n lines; i was the land-use type of the previous period, while j was that of the later period; ijA was the area transformed from type i to j; and n was the number of all of the land-use types.To reveal the internal transfer analysis of land use, a transfer matrix model was used as follows:Compared with point source pollution, nonpoint source pollution had the characteristics of a complex mechanism and a wide spatial–temporal range. The estimation was also much more difficult than that of point source pollution. Therefore, a nonpoint source pollution model often assists in estimating the pollution load . At presThe input data required for the MIKE-SHE model simulation were listed in The land-use types of the Yujiang River Basin included cassava, corn, rice, other cultivated land, forest, water area, and construction land. The interpretation results of the two terms are displayed in 2 is denoted by 0.0 km2; the transfer area between land-use types was not obvious. The largest transfer crop was corn, with a total area of 14.7 km2, of which 46% was transferred to other cultivated land. The largest transferred area was other cultivated land, with a total area of 8.7 km2, of which 77% came from corn. From 2015 to 2020, cassava received 4 km2 of new area, of which 45% came from other cultivated land. Cassava transferred out a total of 3.8 km2, of which 53% was transferred to other cultivated land. This indicated that the conversion between cassava and other cultivated land was relatively easier than that of other types. Therefore, priority to the conversion between other cultivated land and cassava could be considered in future scenarios.According to the land-use transfer matrix from 2015 to 2020 , a transTo sum up, the transfer matrix results could provide a theoretical basis for subsequent scenario assumptions. Other cultivated land could be the first choice to become cassava in the Yujiang River Basin.Calibration period data of the Guiping Hydrological Station was selected from 1 September 2020 to 31 January 2021, and the verification period was set from 1 February 2021 to 13 June 2021 in the Yujiang River Basin. Through parameter adjustment, the final simulated and measured water level values were determined as shown in 2 was 0.95 in the calibration period. Those of the validation period were both 0.98, Nash coefficients >0.5 and R2 > 0.6, which indicated that the hydrodynamic model of the Yujiang River was credible and could reflect the real hydrodynamic situation.The Nash coefficient of the Yujiang hydrodynamic model was 0.93; RAs for the water quality, the simulation was based on the hydrodynamic results. The reliability was evaluated by the PBIAS (Position Bias) evaluation index. The smaller the PBIAS was, the smaller the deviation between the measured value and the simulated value. The water quality model can reflect the actual situation if the PBIAS is less than 25% .In the simulation period, the TN content exceeded the IV class of the surface water quality standard from 15 November 2020 to 13 April 2021. Since the dry season was from mid-November to March of the next year, the growth rate of aquatic plants was slow, and the water flowed steadily at the same time. The attenuation rate of pollutants reduced, resulting in an increase in nitrogen content while nonpoint source pollutants were scoured into the water body. After March, the water flow increased, accompanied with plant growth acceleration and an increase in rainfall. The pollutants’ diffusion and attenuation sped up, causing a decrease in the nitrogen content. In terms of the TP content, it performed better than the class II water quality of the surface water. The TP content began to decline in the middle of September, and to rise from November to June of the next year. The content of phosphorus in the water body was relatively small, and it mainly came from the surface runoff generated by the rainfall. Phosphorus was mainly adsorbed on soil particles. After rainfall events, the topsoil was eroded and entered the river, leading to an increase in the phosphorus content.n = 9) and TN as well as TP load was obtained ; the same principle appeared in the construction land type. There was a very significant negative correlation between forest land change and pollutants , as was also the case with grassland . There was no remarkable correlation regarding the water area.There was a very significant positive correlation between cultivated land change and TN and TP load . There was a very striking positive correlation between the changes in other cultivated land as well as corn area and TN as well as TP load  . The exerted impact of other cultivated land area was greater than that of corn land.Further efforts were made to the extent of crop structures. The Pearson correlation analysis is illustrated in Multiple linear regression analysis was conducted to reveal the quantitative relations between crop structures and pollutant load. The main indexes are shown in The results stated clearly that corn and other cultivated land would have a striking positive impact on TN and TP, while rice and cassava would not have a momentous impact. The results were similar to the conclusions of some scholars. According to Guigang 2020 statistical yearbook, other cultivated land in the Yujiang River Basin was mainly divided into sugarcane and vegetables. In order to improve the yield, a large amount of nitrogen and phosphorus fertilizer needs to be applied. Sun’s research showed that growing corn requires a large amount of fertilization, which increased the risk of nonpoint source pollution . The effThe low impact of cassava on nonpoint source pollution was due to the low fertilizer demand of cassava. It was suitable to plant in soil with low fertility. Cassava has a dense root system that could use nutrients efficiently. It made the cassava has less contribute to TN loads . CassavaIn order to reasonably speculate on the impact of land-use changes on the ethanol raw material planting area in the future, three scenarios were set up in the Yujiang River Basin. Land-use scenario assumption patterns mainly considered policy constraints, water quality constraints, and economic benefit constraints, among which water quality constraints referred to the relation coefficients presented in To achieve the goal of carbon neutrality as soon as possible, the Chinese government cancelled the preferential tax policy for ethanol production with grain crops as raw materials since 2016, and implemented a subsidy plan for nongrain fuel ethanol production to encourage the use of fuel ethanol production based on nongrain crops. In the future, nongrain biofuel ethanol would develop vigorously . China’s(1)Scenario One: According to the results of a previous loss risk assessment of the Yujiang River Basin, the intersection of high-risk loss areas of nitrogen as well as phosphorus and other cultivated land was set to be converted into a cassava area.(2)Scenario Two: The intersection of medium- and high-risk loss areas of nitrogen as well as phosphorus and other cultivated land was set to be converted into a cassava area.(3)2. The area of corn and rice would remain unchanged to ensure food security.Scenario Three: The cassava area continuously increased until the profit of other cultivated land (sugarcane and vegetables) was replaced by the cassava benefit difference. According to the economic data obtained in 2020, the upper limit for the cassava area was 126.0 kmThree scenarios were set up, which are presented below:These scenarios of land use are displayed in Taking the land-use distribution under the three scenarios into the validated hydrological model, the TN loads at the basin outlet were 869.39 tons, 816.61 tons, and 737.68 tons, respectively. Meanwhile, the TP loads were 76.07 tons, 68.84 tons, and 58.26 tons, respectively . The resIn addition to fertilization, planting patterns also affected nonpoint source pollution loads. Single cropping patterns could lead to soil fertility decline and destroy the microbial community. It would reduce crop yields and increase the risk of nonpoint source pollution while crop rotation system could effectively reduce nitrogen and phosphorus loss caused by fertilization . Jiang fThe development of cassava fuel ethanol could be a conducive way to improve the quality of the water environment and realize this goal from a low-carbon perspective. In addition, the increase in fuel ethanol demand might promote an increase in cassava production and demand, which could lead to a rise in its price. The leveling up of the cassava price could uplift farmers’ income and reduce the profit gap with other economic crops. On the other hand, this change may affect the interests of cassava ethanol production companies. In order to eliminate the negative impact, appropriate agricultural policies are required to balance the demand and supply of cassava ethanol. Therefore, the state should provide more aid in terms of agricultural and energy incentives for cassava planting and cassava ethanol production.(1)The main land-use type in the Yujiang River Basin was other cultivated land, while the area of cassava is increasing. From the land-use transition matrix, the conversion between cassava and other cultivated land was the easiest, which gave a case principle for the scenario’s assumption setting.(2)The increase in cultivated land and construction land would lead to a rise in the load of TN and TP, while an expansion of forest land and grassland area would reduce TN and TP load in the watershed. As for the crop structures, corn would have a significant positive impact on TN and TP, while rice and cassava would not have a striking impact.(3)2. If it continues to rise past that level, it could cause negative impacts on farmers’ income and economic benefits.The increase in the cassava area in the Yujiang River Basin was beneficial to reduce nonpoint source pollution. The maximum increase in the area of cassava should be 126 kmThis study selected a typical fuel ethanol planting area in South China to explore the land-use changes that have occurred in recent years. Combined with the typical crop structure changes in the local area, the response mechanism of water quality was revealed by using the distributed hydrological model. Based on the quantitative relations coefficients, scenarios under the carbon neutralization policy were carried out to forecast the nonpoint source pollutants of the river.In summary, the cassava planting area had little influence on NPS pollution in the Yujiang River Basin, which was suitable for the vigorous expansion of fuel ethanol. The threshold for cassava extension was suggested after the scenario analysis. In order to reduce the impact of cassava nonpoint source pollution, managers could adopt rotation and intercropping. Intercropping cassava and crops with shorter growth cycles could effectively reduce the risk of nonpoint source pollution. Tropical countries such as Nigeria, Brazil, and Thailand could choose to promote cassava cultivation. Meanwhile, the introduction of cassava ethanol industry in the local area could improve the national economic efficiency and ensure the global clean energy supply. This research could provide scientific support for local agriculture land-use management to realize the carbon neutralization vision and also set a good example for the development of the cassava fuel ethanol industry in other cassava-planting countries, such as Thailand, Nigeria, and Indonesia.


Accurately forecasting solar plants production is critical for balancing supply and demand and for scheduling distribution networks operation in the context of inclusive smart cities and energy communities. However, the problem becomes more demanding, when there is insufficient amount of data to adequately train forecasting models, due to plants being recently installed or because of lack of smart-meters. Transfer learning (TL) offers the capability of transferring knowledge from the source domain to different target domains to resolve related problems. This study uses the stacked Long Short-Term Memory (LSTM) model with three TL strategies to provide accurate solar plant production forecasts. TL is exploited both for weight initialization of the LSTM model and for feature extraction, using different freezing approaches. The presented TL strategies are compared to the conventional non-TL model, as well as to the smart persistence model, at forecasting the hourly production of 6 solar plants. Results indicate that TL models significantly outperform the conventional one, achieving 12.6% accuracy improvement in terms of RMSE and 16.3% in terms of forecast skill index with 1 year of training data. The gap between the two approaches becomes even bigger when fewer training data are available , breaking new ground in power production forecasting of newly installed solar plants and rendering TL a reliable tool in the hands of self-producers towards the ultimate goal of energy balancing and demand response management from an early stage. This new reality has highlighted the need for a gradual transition to smart cities, where operational efficiency is improved with the use of emerging information and communication technology (ICT) applications3. At the same time, the impact of the digitalization era is more evident than ever, improving quality of life through digital automation of complex processes5. This transformation could not leave the energy sector unaffected, as the convergence of electrical power and data offers opportunities for new services6, with the aim of reducing costs and reshaping business models7. In the context of an ever-evolving urban environment, it is important to promote the concept of energy communities; i.e., the organization of collective energy actions around open, democratic participation and governance8.Urbanization effects are obvious during the last decades, as more and more people move to cities, resulting in more than 90% of anthropogenic carbon emissions being generated in urban environments9. More specifically, rooftop photovoltaic systems are one of the most promising energy generation sources for prosumers in big cities, as more and more PV panels are installed10. Studies have shown that rooftop PVs can cover almost completely the domestic electricity demand for prosumers, indicating that self-sufficient cities are expected to be protagonists of the energy transition era11. However, efficient prosumption schemes are based on accurate solar production forecasting models. The problem becomes more demanding when lack of data is taken into account, which is a common phenomenon in the case of newly installed photovoltaic systems, where it takes a long time to collect a sufficient sample of data. This paper aspires to present a Transfer Learning (TL) approach for PV production forecasting in the case of lack of data, where predictive Deep Learning (DL) models are trained in PV plants with data adequacy, and transfer knowledge to PV plants with a small sample of available data.A major point of interest for energy communities is effectively forecasting electricity production of Renewable Energy Sources (RES), as it is vital for balancing electricity supply and demand, and consequently, for scheduling and analyzing distribution networks and ensuring community autarky13 of power output is the same in the future as in now. These approaches are usually used as reference models against other more complex methods. Apart from persistence methods, many data-driven methods have been proposed including time series forecasting models, spatio-temporal statistics, and machine learning (ML) techniques. According to Bacher et al.14, there are two dominant approaches for solar power forecasting: The first approach requires that solar power is normalized with a clear sky model in order to formulate a more stationary time series facilitating forecasting with classical linear time series methods. The second approach includes utilization of neural networks (NNs) with different types of input to predict the solar power directly. Traditional statistical approaches and time series models have been utilized to a great extent for short-term and long-term predictions. Indicative examples in this category are the Autoregressive Moving Average (ARMA) model proposed by Huang et al.15, a series of statistical regression methods presented by Zamo et al.16 and exponential smoothing for solar irradiance forecasting developed by Dong et al.17. Except from statistical models, the problem of PV-based power forecasting has been successfully tackled by exploiting ML methods. Studies based on Support Vector Machine (SVM) models18, the k-nearest neighbors algorithm19 and tree based models, such as Random Forest20 (RF) and Decision Trees21, are indicative examples of how efficient ML methods can be for this problem. However, the aforementioned approaches have been outperformed by DL models which have emerged in the last decade22. The increased processing power afforded by graphical processing units (GPUs) has resulted in the rise of DL, offering the opportunity to solve a wide range of problems with good accuracy23. More specifically, Feedforward Neural Networks24 (FFNN), Recurrent Neural Network (RNN) models, as well as their most recent variation called Long Short-Term Memory25 (LSTM) models, have gained ground over traditional statistical and ML methods regarding the PV production forecasting problem. In addition, a widely adopted category of methods are the so-called hybrid methods, which are essentially a combination of ML or DL models, either in the form of ensembles26 or using more elaborate methods such as boosting, bagging or meta-learning27. In this paper, a stacked LSTM architecture, i.e., an LSTM model comprised of multiple LSTM layers, is selected for two reasons. Firstly, LSTM can represent the dynamic performance of systems, being capable of efficiently handling sequential data with temporal relationships. Such temporal, non-linear, relationships exist between weather variables and the PV power output, constituting LSTM as an appropriate model for this forecasting problem. Secondly, being a weight-based model, unlike other statistical methods, it is suitable for the application of TL approaches.Several approaches have been proposed for the problem of energy production forecasting in PV plants. The simplest approach is the naive persistence method, which assumes that the generated power will remain the same as the previous observation. A more complex variation of this approach is the smart persistence method, which additionally assumes that the clear-sky indexHowever, DL models are data consuming, in general, requiring a sufficient amount of data in order to achieve high accuracy predictions. Even more interestingly, DL models are more data dependent than traditional time series forecasting techniques and ML models, which is compensated by their increased predictive capability. In this context, it is generally acknowledged that DL models trained with too little data suffer from under-fitting and that they result in poor approximation and, consequently, in high variance estimation of the model’s performance. The cause of this problem is known as data scarcity. More specifically, data scarcity can be defined as the situation where there is a limited amount of training data. In general, there are two different forms of data scarcity when dealing with PV power output data. First and foremost, data scarcity is a common phenomenon in the case of newly installed photovoltaic systems, where it takes a long time to collect a sufficient sample of power output data in order to train the models. Secondly, data scarcity may be attributed to missing values (or data gaps) due to malfunctioning smart-meters. In either case, the result is a lack of data to train a DL forecasting model from scratch. In the case of PV production forecasting a sufficient amount of training data is one calendar year, in order to enable the model to learn seasonal patterns. In order to tackle this problem, we exploit a model which is trained for one location and we apply it to another location where there is too little historical data.28. Traditional DL approaches rely on learning new concepts from scratch, using data from a specific topic in order to train the model. However, the exploitation of data from similar applications can facilitate the learning process. TL has been proven to be very efficient for dealing with problems with insufficient or missing data. Data scarcity often occurs due to data being inaccessible, high cost of data collection mechanisms and Internet of Things (IoT) devices, or lack of appropriate data storage schemes. However, the impact of the digitisation era is more apparent than ever, as data availability and data quality have significantly improved. Thus, big data repositories enable the exploitation of existing datasets in order to address similar problems, rendering TL the most suitable approach29. Focusing on the energy sector, TL emerges as the most popular technique for problems with insufficient or poor-quality data. A typical example is the Hephaestus method for cross-building energy forecasting, considering seasonality and trend factors30. Similar studies have been developed for short-term building energy predictions31 and energy consumption forecasting with poor-quality data32. However, few studies have addressed the problem of PV production forecasting with TL33, allowing room for further research in this area.Humans have the innate ability of exploiting information collected from one task in order to resolve similar tasks. The same applies in the field of DLIn this paper, we explore three different TL strategies and we evaluate the impact of TL in providing accurate PV production forecasts, comparing the efficiency of traditional and TL models with respect to data availability. Results indicate that TL models significantly outperform the conventional one, achieving 24.8% accuracy improvement with 1 year of training data. The gap between the methods is even bigger when fewer training data are available (3-month training set), breaking new ground in power production forecasting of newly installed solar plants and rendering TL a reliable tool in the hands of self-producers towards the ultimate goal of energy balancing and demand response management from an early stage.The rest of the paper is organized as follows. The second section  introduc34 and the Copernicus Atmosphere Data Store35. One PV plant, namely Hourly PV production and weather data  from 7 PV plants are exploited. PV production data are collected directly from the solar plant systems of a Portuguese energy community, while weather data are extracted from a local meteorological stationmentclass2pt{minimThe PV plants are located in 4 cities in Portugal  and the available data vary from 14 to 30 months depending on the PV plant. The selected PV plants also differ in terms of nominal and peak capacities. The base PV has a nominal capacity of 23.52 KW, while the target domain PVs’ capacity varies from 30 KW to 271.53 KW, which is over 10 times the capacity of the base PV. The rationale behind the selection of the specific PV plants is to assess model performance on PV plants that are located both in the same city  and the remaining data are used for testing. It is obvious that the testing period differs for each target PV based on the total number of available data; Source Code). ADAM is the selected optimizer based on existing literature, while the learning rate is set to 0.001. The developed stacked LSTM model is composed of three fully connected intermediate layers  followed by the output layer. The number of epochs is set to 100 and the batch size is set to 128 by the trial-and-error method.Finally, the experimental application is implemented on Python programming language, interacting with open-source libraries, including NumPy and Pandas, as well as the DL application program interface (API) TensorFlow , while the source and target domains are different in terms of location, nominal capacity and weather conditions.Finally, according to Lu et al.41. This is mainly due to the fact that the functionality of the LSTM depends on weight updating between the neurons of the deep learning model, allowing the creation of pre-trained models. Thus, it facilitates pre-training the model on the baseline PV in order to utilise the saved weights of the pre-trained model and apply TL on the target PV. The same applies for other NNs, but LSTM networks have shown the best performance, and the interest in PV power prediction using variations of LSTM networks has been continuously increasing over the past few years42.One of the most suitable models for the application of TL in the PV production forecasting problem is the LSTM model43. The purpose of the LSTM development has been the vanishing gradient problem, which can be described as the exponential increase (or decrease) of the backpropagated error signal as a function of the distance from the final layer, resulting in models which are unstable and incapable of efficient learning44. The LSTM uses an additive gradient structure which incorporates direct access to a forget gate enabling the network to stimulate desired behaviour from the error gradient45.The LSTM is a RNN architecture with the innate ability of capturing long term dependencies in sequence prediction problems46. LSTMs provide a significant advantage over other methods, as they are able to detect linear relationships between nonlinear data. Such relationships may appear in the PV production forecasting problem, between power output and meteorological data. In this respect, the LSTM can benefit from features and detect relationships and patterns that other models would not be able to find. Furthermore, LSTM has been exploited for several energy-related time series problems, including residential energy consumption predictions48, and natural gas demand forecasting49. The architecture of the LSTM cell is illustrated in Fig. The selection of LSTM over traditional ML algorithms and feedforward NNs is based on its suitability for holding long term memory, which is essential when facing problems with sequential data with temporal relationship. LSTM is able to represent the dynamic performance of systems, thus being one of the most widely models for dealing with time series problems, such as PV production forecasting50 and Olah51. The standard LSTM cell includes four NN layers, differing from common RNN architectures which include a single layer. Each line in Fig. The forget gate is the first block represented in the LSTM architecture. The forget gate determines which part of the information must be retained or discarded. The inputs of this gate are the previous hidden state The input gate serves as an input to update the cell status. The input gate’s functionality is performed in two parts. Firstly, the previous hidden state and the current input are passed into the second sigmoid function Finally, the output gate determines the next hidden state The parameters The presentation of the LSTM architecture follows the works of Graves52. The solution to the problem is produced by the last layer of the network. The intermediate layers of deep NNs are called hidden layers. The main idea of introducing hidden layers to the architecture is that each hidden layer generates more advanced representations of the problem leading to higher abstraction levels. Thus, deep NNs can represent any non-linear function with relatively fewer neurons than a single-layer network. DL assumes that a hierarchical model with many layers is exponentially more efficient at approximating some functions than a more shallow one53.The high accuracy achieved by NNs in a variety of challenging forecasting problems is attributed to their complex architecture. Deep NNs incorporate the concept of hierarchy due to the connection of multiple layers of neurons. Each layer is responsible for solving a small task of the main problem and its output is transferred to the next layer54 in their application of LSTMs to speech recognition. Proportional to simple feed-forward networks, stacked LSTM networks result in deeper models with higher levels of approximation accuracy. Moreover, due to the fact that LSTMs are used with sequence data , deeper architectures lead also to deeper level of abstraction of the input data over time providing a representation of the task at different timescales56.This approach can also be applied to LSTMs. The original LSTM model is composed of one single layer which receives the input data and passes the output signal to a single feed-forward output layer. However, in this study an alternative architecture is proposed, which involves multiple hidden layers of multiple LSTM units followed by a feed-forward output layer. Each layer provides a sequence output to the next layer, rather than a single value output. This architecture is called stacked LSTM network, and it has been introduced by Graves et al.TL is exploited through the process of reusing the weights of a model which has been trained on the source domain data to fine-tune a new model based on the target domain data. The pre-trained model is referred to as the base model, while each new model in the target domain is referred to as TL model. The weights of each layer of the base model can be processed differently in order to provide better performance of the TL model in the target domain, using the following approaches: (a) keep the weights of the layer fixed, (b) fine-tune the weights of the layer based on the target domain data and (c) train the weights of the layer from scratch based on the target domain data.TL Strategy 1: In the first strategy, the weights of the initial layers are frozen and the only trainable weights are the weights of the last hidden layer. This strategy is known as weight freezing and it is widely used in order to extract features from the source domain and carry them to the target domain. This is a widely used scheme when treating images, where the first layers are used as feature extraction layers and the last layers are used to adapt to new data.TL Strategy 2: In the second strategy, the base model is used as a weight initialization scheme for the TL model. The weights of all layers of the TL model are initialized based on data from the source domain and they are fine-tuned based on data from the target domain. This approach is extensively used with problems where there is an abundance of data in the source domain, but a scarcity of data in the target domain. However, a high degree of similarity between the source and the target domain is a necessary condition.TL Strategy 3: In the third strategy, the initial layers of the TL model are frozen and the last layer is trained from scratch, popping the last layer of the base model and adding a new layer after the frozen layers. This approach is similar to the first one, but it differs in the fact that the weights of the last layer are not initialized based on data from the source domain. Thus, the TL model serves as a feature extraction mechanism because of the frozen layers, but it can also be fine-tuned to the target domain because of the random initialization of the last layer’s weights.In this paper, three TL strategies are developed and compared in terms of forecasting accuracy for the problem of PV production forecasting.The results of this study are presented in three categories, namely; the forecasting performance of the baseline stacked LSTM model, the TL models performance results compared to the conventional model in the target domain and the results of applying TL with different volume of available data, respectively. By the term conventional model we refer to the LSTM model in which no TL has been applied; in this context, the conventional LSTM model is solely based on training with data from the target PV.The stacked LSTM model has the following lag features: (a) Power output measured value, (b) air temperature, (c) global horizontal irradiance, (d) humidity, (e) month of the year (in the form of one-hot encoding) and (f) hour of the day (in the form of sine/cosine transformation). The above-mentioned features are fed into the LSTM model in the format of “5 inputs - 1 output” of hourly data. More specifically, a point value for each feature is fed into the model for the last five hours and the PV power output for the next hour is predicted (one-hour ahead power output forecast).t of the evaluation period, 58, the accuracy of solar forecasting models  must be inter-comparable across different locations and different time periods through a common metric which is the forecast skill index. The forecast skill index is based on the comparison of the proposed model to a reference model on a specific error metric. However, two issues arise: What reference model and which error metric must be used? The most common reference model to standardize the verification of solar forecasting models is the persistence model. More specifically, the utilization of a smart persistence model as a reference model is highly recommended, rather that using the naive (or simple) persistence model59. Regarding the optimal error metric, the RMSE is the most suitable metric in the case of solar power production, as a metric that is appropriate for capturing large errors57. Thus, the formula of the forecast skill index is the following:Ensuring an accurate base model is a prerequisite for achieving accurate predictions in the target domain. In this context, the performance of the LSTM model for the base PV is evaluated with the following procedure: The base PV dataset is split into train set and evaluation set using a 80-20 split, keeping the first 80% as training and the remaining 20% as testing  and the LSTM model is trained on the training set. The accuracy of the model is evaluated by computing the root mean squared error (RMSE) and the mean absolute error (MAE) of the respective forecasts across the evaluation period considered, as well as the coefficient of determination 59. The same also applies to PV power forecasts, where several smart persistence models have been proposed60. More specifically, a clear sky index has been proposed by Engerer and Mills in case that the characteristics of the PV panel are known61, while another PV smart persistence model based on scaling global horizontal irradiance to PV production value has been presented by Huertas and Centeno62. In this study, the definition of Pedro and Coimbra is adopted, which is based on estimating the expected power output under clear-sky conditions63. The formula of the adopted smart persistence model is described by the following equation:y(t) is the measured power output and 63. The power output expected under clear sky conditions for the base PV  Power output measured value, (b) air temperature, (c) global horizontal irradiance, (d) humidity, (e) month of the year (one-hot encoding) and (f) hour of the day (sine/cosine transformation) and the model output is a one-hour ahead forecast of the PV power output.RMSE, MBE, MAE, NRMSE and The validation process of the proposed TL strategies is implemented in 6 PV plants, with different nominal and peak capacity, located in 4 cities in Portugal. Four architectures are compared, including the presented TL strategies, as well as a conventional model where no TL has been applied. For the TL models, a pre-training is applied on the whole dataset of the base PV (30 months of data). Then, the four models are trained using one year of data (8760 h) and they are tested in the rest of the dataset. For each PV plant the size of the test dataset is different depending on data availability, as presented in Table 20 training repetitions are performed for each model, in order to eradicate randomness. This number of repetitions is generally proposed in the literature. The forecasting performance for all models is presented in Table Firstly, it is worth mentioning that all LSTM models perform better than the smart persistence model in terms of RMSE. This fact illustrates the suitability of the selected model and the selected features for this problem. The only case that the LSTM performs worse than the smart persistence model is for the conventional LSTM of Regarding the comparison between the conventional LSTM and the three TL models, the impact of TL is evident as TL strategies have better accuracy than the conventional one for all six PVs . The boxplots presented in Fig. As mentioned in the introductory section, one calendar year of data is the minimum time interval for a model to be sufficiently trained, in order to incorporate all seasonal and weather patterns of the problem. Also, the presented results indicate that TL models can perform better than conventional models considering a scenario where one year of training data is available, while conventional models are still better than reference smart persistence ones. However, the application of TL offers the possibility to obtain reliable and accurate predictive models, even when the available training data for the target domain are less than one year. In this context, the proposed architectures are compared on the target PVs for different training periods, namely 3 months, 6 months and 9 months of available data. It must be noted that, although the training period has changed, the testing period has been kept the same for comparison purposes between the different scenarios.Figure Finally, the difference in terms of RMSE between the conventional model and the best-performing TL model decreases as more training data are becoming available. This is evident in all six target PVs. For example, the difference in terms of RMSE in Collecting sufficient data from recently installed solar plants is a long process. The urgent need for accurate PV production forecasts has led to the idea of exploiting solar plants with sufficient data to provide accurate forecasts for recently installed ones. In this paper, the purpose of the research is to determine whether TL can be efficiently employed to provide PV production forecasts for solar plants with limited data size. Three TL strategies based on a stacked LSTM architecture are developed and compared to a non-TL approach. The presented methodology is tested in power plants in different cities and with different nominal capacities. The findings of the experimental application indicate that all three TL strategies significantly outperform the non-TL approach in terms of forecasting accuracy, evaluated by several error indexes.Moreover, the models are compared with a smart persistence model based on the clear-sky power output. The models which are trained with the three TL strategies significantly outperform the reference model, having a forecast skill score between 0.28 and 0.56 considered satisfying by the existing literature. On the opposite side, the non-TL LSTM model shows limited forecasting accuracy, quantified by an average decrease of Last but not least, one of the most significant parameters in the concept of TL is the replicability of the presented TL strategies. In order to assess this aspect, the experimental application has been performed on three PV panels located in the same city as the base PV and on three PV panels located in different cities. This enables comparison between the two groups in terms of forecasting accuracy. However, the fact that these PV panels are located in different cities and have different nominal power indicates that the comparison must take place on the forecast skill index, to be in alignment with the proposed verification guidelines for deterministic solar forecasts. Thus, the average forecast skill index for PV panels in the same city is 0.4, while the corresponding value for PV panels in different cities is 0.43. This is undoubtedly a sign that the forecasting accuracy of the models is not affected by the geographical distance between the base and the target PV.This study is the first step towards enhancing our understanding of the impact of TL on solar plant power prediction. Future work will concentrate on assessing the impact of the base model’s training data volume, investigating whether training base models with more data or with data from different solar plants could further improve forecasting accuracy. This could result in the evolution of cross-stakeholder models and data sharing among energy communities, with the aim to promote inclusiveness in smart cities environments. Further studies, which take geographical characteristics differences between the base and the target domain into account , will also need to be performed. Finally, the prospect of being able to use TL for solar plants power forecasting, serves as a continuous incentive for future research on transferring knowledge in similar problems, such as cross-building energy forecasting, wind power prediction, and hydraulic plant generation forecasting, among others.


Closing the prosthesis control loop by providing artificial somatosensory feedback can improve utility and user experience. Additionally, closed-loop control should be more robust with respect to disturbance, but this might depend on the type of feedback provided. Thus, the present study investigates and compares the performance of EMG and force feedback in the presence of control disturbances. Twenty able-bodied subjects and one transradial amputee performed delicate and power grasps with a prosthesis in a functional task, while the control signal gain was temporarily increased (high-gain disturbance) or decreased (low-gain disturbance) without their knowledge. Three outcome measures were considered: the percentage of trials successful in the first attempt (reaction to disturbance), the average number of attempts in trials where the wrong force was initially applied (adaptation to disturbance), and the average completion time of the last attempt in every trial. EMG feedback was shown to offer significantly better performance compared to force feedback during power grasping in terms of reaction to disturbance and completion time. During power grasping with high-gain disturbance, the median first-attempt success rate was significantly higher with EMG feedback (73.3%) compared to that achieved with force feedback (60%). Moreover, the median completion time for power grasps with low-gain disturbance was significantly longer with force feedback than with EMG feedback . Contrary to our expectations, there was no significant difference between feedback types with regards to adaptation to disturbances and the two feedback types performed similarly in delicate grasps. The results indicated that EMG feedback displayed better performance than force feedback in the presence of control disturbances, further demonstrating the potential of this approach to provide a reliable prosthesis-user interaction. Upper limb loss has profound and lasting effects on the quality of life of those affected . AmputeeMost commercial myoelectric prostheses are controlled using a direct proportional approach , whereinHowever, it is well known that the efficiency of such control strategies in clinical settings can be substantially impaired by the various sources of disturbance that perturb the control signals, which are largely absent from the rigorously controlled laboratory environment . A distuAn intrinsic characteristic of closed-loop control is that it should be more robust regarding disturbances and noise injected into the system. However, in the context of prosthesis control, the users’ ability to react and compensate for the disturbance is likely affected by the specific type of feedback they receive. For example, EMG and force feedback are fundamentally different, therefore, it is reasonable to assume that the effectiveness of disturbance mitigation will also differ between them. In the case of EMG feedback, the user receives online information about their muscle contraction (prosthesis control input), even while the hand is in motion and not in contact with an object. This allows predictive force control, since the user can modulate or correct the generated EMG level even before the prosthesis grasps an object. Conversely, in the case of force feedback, the user must wait until the prosthesis makes contact with an object to assess the generated force and correct if necessary. Hence, it can be expected that EMG feedback would facilitate the compensation of control disturbances. In this case, the feedback would provide information on the control signal, thereby allowing the user to notice if the signal is disturbed and then modulate their muscle contraction in time, before the hand closes around the object and an erroneous force is applied.The aim of this study was, therefore, to assess and compare the performance of prosthesis control when using EMG versus force feedback in the presence of control disturbances. The subjects were asked to perform a force matching task using a sensorized myoelectric prosthesis over a number of trials. In some of these trials, the myoelectric signal was amplified or attenuated, unbeknownst to the subjects.We hypothesized that the subjects would perform better when using EMG feedback. More specifically, we assumed that they would be able better compensate for the disturbances as soon as they appeared; and that they would need fewer attempts to adapt to the disturbance in case they failed to compensate immediately. Lastly, a shorter completion time was expected when EMG feedback was used thanks to the real-time feedback flow.Twenty healthy able-bodied subjects (28.5 ± 4.4 years), with no prior experience in myoelectric control, participated in this study. The subjects were divided into two groups one of which performed the experiment using EMG feedback and the other using force feedback. In addition, one subject with left transradial amputation performed the experiment in both feedback conditions in two separate sessions. The amputee was 49 years old, had lost their arm 10 years prior and had been using a simple myoelectric prosthesis for 8 years. Prior to the present experiment, the amputee did not use a prosthesis equipped with artificial somatosensory feedback. The subjects signed an informed consent form before commencing the experiment, which was approved by the Research Ethics Committee of the Nordjylland Region .The experimental setup for able-bodied subjects comprised the following components: (1) a multifunctional myoelectric prosthetic hand , with proprietary controller, two dry EMG electrodes with embedded amplifiers  and a USB Bluetooth dongle, (2) four C3 vibrotactors and a control unit , (3) a standardized box-and-blocks test, (4) a standard laptop , running Windows 10 Professional and an 18″ computer monitor. The able-bodied subjects carried the prosthesis on their right forearm using a specially made 3D printed mount  and theiThe prosthesis controller sampled the EMG, computed the root mean square (RMS) in 100-ms windows, and sent the data to the laptop at a sampling frequency of 100 Hz. The electrodes were placed on the skin above the flexor carpi radialis and extensor carpi ulnaris muscles , identifThe amputee subject was fitted with a custom-made socket, to which the Michelangelo hand was connected . The socThe C3 tactors produce vibrations perpendicular to the skin, with adjustable gain and frequency. In this experiment, the frequency was set at 230 Hz, which corresponds to the maximum sensitivity of the Pacinian corpuscles . The fouThe subjects stood in front of a desk, wearing the setup on their arm . The heiThe Michelangelo prosthesis supports velocity control over two degrees of freedom, namely hand opening/closing using two grasp types  and wrist rotation. In this experiment, the hand was configured to only open and close in palmar grasp. The command input to the prosthesis was a normalized myoelectric signal (ranging from 0 to 1). While the hand was in motion, this signal was proportional to the closing velocity and the grasping force generated upon contact . After contact, a further increase in the prosthesis command input proportionally increased the force. The force was measured using sensors embedded in the hand, transmitted to the laptop at 100 Hz and normalized to the maximum force produced by the hand when it closed at the highest velocity. Importantly, the prosthesis was non-backdrivable and after contact responded only to an increase in the command input, while the grasping force remained unchanged if the myoelectric signal decreased. For instance, the subjects could relax their muscles and the prosthesis would still maintain the generated grasping force.The closed-loop control scheme implemented in this study is shown in Two feedback types were implemented: EMG and force feedback. Each subject received one of the two feedback types, depending on the experimental group to which they were assigned. For the feedback generation, the myoelectric and force signals were divided into five intervals and the feedback conveyed to the subject the discrete level of the signal, i.e., the interval in which the EMG/force lay at any moment . The thrThe feedback encoding scheme is shown in The control of the Michelangelo hand using EMG and force feedback is illustrated in The effect of control perturbations in myoelectric systems has been investigated in the past. Examples include the introduction of delays in the feedback delivery , injectiThe subjects were first asked to rest their arm for 10 s while the baseline recording was performed. The mean value of the recorded signal (EMG RMS) was then subtracted from subsequent measurements. Afterward, the MVC was recorded for both flexors and extensors. The subject contracted each muscle maximally in three intervals of 5 s. The mean value of the recorded signal was computed in each interval and the MVC for each muscle was defined as the average of these three values.Next, the sensation thresholds (ST) for the four C3 tactors were determined. Each tactor was activated in sequence and its gain, which corresponded to the vibration intensity, was slowly increased in steps of 2%. The gain at which the subject reported that they started feeling a vibration was defined as the ST. The gain of each tactor was subsequently set to ST + 0.4 × (MAX-ST), where MAX is the maximum gain . This vaThe feedback training was then conducted, wherein the five levels of vibrotactile feedback were presented sequentially to the subjects, while the experimenter verbally indicated which tactors were active. A short session of reinforced learning followed, where the stimulation patterns for levels 1–4 were delivered in a pseudorandom order and the subject was asked to identify each pattern. If the subject reported the wrong pattern, the experimenter provided the correct answer. Each pattern was presented 10 times in total. In the validation phase, the test was repeated, without the experimenter providing verbal feedback. If the identification rate was above 90%, the feedback training was deemed successful and if not, the procedure was repeated. Most subjects achieved a success rate of above 90% immediately after the first run, indicating that the feedback encoding was indeed simple to learn and interpret .The subjects then performed the main experimental task. They used the prosthesis equipped with the vibrotactile feedback to perform a functional task inspired by the commonly used box and blocks test . The tasWhen a trial was successful, a high-frequency beeping sound was generated by the computer. A lower-frequency sound indicated that the subject had failed, and the trial had to be repeated. The subjects were allowed a maximum of 10 attempts to successfully complete each trial. If they were successful in a smaller number of attempts or if they were still unsuccessful in their 10th attempt, they proceeded to the next trial. All subjects managed to complete all the trials within the 10-attempt limit.The subjects first performed a 100-trial training session , to familiarize themselves with the setup and the task. At this stage, they could see their EMG signals on the monitor, as well as their current target force level, generated force, EMG level and remaining number of attempts. Before commencing, the experimenter verbally explained the setup, the prosthesis control and feedback scheme, and the task to be performed. Initially, the subjects were asked to perform the task while seated and with the prosthesis placed on the desk, so that they could focus on familiarizing themselves with the feedback. After approximately 25 successful trials, the prosthesis was mounted onto the subjects’ arm and the subjects were asked to stand in front of the desk, facing the box and blocks kit and the monitor. They then performed the experimental task until they reached a total of 100 training trials.After that, the subjects performed the main experiment, comprising three sets of 100 trials. Each set was performed in batches of 2 min, until a total of 100 trials was completed. There was a pause of approx. 30 s between the batches and a longer break between the sets. During the trials, the subjects could only see their target force, remaining attempts and a timer counting to zero from 120 s on the monitor. They were also instructed to complete as many successful trials as possible within a 2-minute batch. The time constraint was introduced to promote natural and intuitive control, while generating consistent movement profiles .The first 100-trial run always included only non-disturbed trials to ensure that the subject was well trained in using the feedback and accomplishing the task in the nominal condition. The second and third runs included 30 disturbed trials (15 in each of the target levels) at a single disturbance gain (random order across the two sets). The order of the trials was pseudorandomized, with a constraint that two consecutive disturbed trials were separated by at least one non-disturbed trial. The sequence of the two disturbance gains was randomized among subjects, to minimize any training effects.The able-bodied subjects performed the task with one of the feedback types (EMG or force), depending on the group to which they were allocated, while the amputee performed the experiment using both types. The amputee performed the two experimental sessions two days apart, first with force, then with EMG feedback. The experimental sessions lasted between 2 and 2.5 h.The outcome measures were: (1) the first attempt success rate (SR) defined as the percentage of trials successful in the first attempt, (2) the average number of attempts in the trials that were unsuccessful in the first attempt, and (3) the average completion time of the successful (last) attempt in each trial. The latter was defined as the time from the moment the myoelectric control signal crossed the dead zone until the moment the maximum force was applied. The outcome measures were computed separately for the two types of disturbed trials (high- and low-gain disturbance), as well as for the non-disturbed trials.reaction to the disturbance, i.e., their ability to compensate the disturbance as soon as it appears (in the first attempt).The SR was deemed the most important measure, since an erroneous force potentially results in slippage or breakage of an object. In disturbed trials, this metric can be regarded as a measure of the subjects’ adaptation to the control disturbance.The average number of attempts in trials that were unsuccessful in the first attempt is an indication of how effectively the feedback assisted the subjects in adjusting their control input (muscle contraction) across attempts. In disturbed trials, therefore, this can be regarded as a measure of The metrics used in the analysis were computed for each subject and each gain using 30 disturbed trials at that gain. The baseline metrics were also computed, using 100 initial, non-disturbed trials. The results of the amputee subject were interpreted independently and were not considered in the statistical analysis.t-test or a Mann-Whitney test, depending on the outcome of the normality test. The statistical toolbox of MathWorks MATLAB 2021a and IBM SPSS 27 were used for the statistical analysis. The results are reported in the text as “median {interquartile range}” and the threshold for significance was set at p < 0.05.Outliers were defined as values above or below 1.5 times the upper and lower quartiles, respectively. The data was tested for normality using the Lilliefors test. The outcome measures were compared between the two feedback types, using an unpaired In the high-gain disturbed trials with force feedback , the subjects needed multiple attempts to adapt to the disturbance and complete the trial. With respect to the nominal case, the high gain increased the amplitude of the myoelectric signal generated in response to the same muscle contraction. Therefore, the myoelectric signals (gray lines) generated by the subjects were initially too high, in fact saturated, for both delicate (plot E) and power grasping (plot F). Consequently, the generated force overshot the target (object “broken”). In subsequent attempts, the subjects gradually reduced their contraction levels, finally generating the appropriate contraction (green line), to reach the target force in the third attempt.Conversely, with EMG feedback  the subjp = 0.035) and number of attempts in delicate grasping . However, force feedback resulted in significantly shorter completion time in delicate grasping . In all other cases, the difference in performance between the two feedback types was not significant.Regarding the results in non-disturbed trials, EMG feedback outperformed force feedback slightly but significantly in terms of SR in power grasping  in power grasping with EMG feedback (73.3% {26.7}) than with force feedback (60% {20}). In delicate grasping, the median percentage for force feedback (33.3%) was also lower compared to EMG feedback (46.6%) but the difference was not significant.The summary results for the SR in disturbed trials are displayed in The median number of repeated attempts ranged between 2 and 3 in all disturbed conditions, with no significant difference between the feedback types.p = 0.046) longer time to reach the target compared to those who used EMG feedback (2.48 s {1.66}).The results regarding the completion time of the last attempt in disturbed trials are shown in The results regarding the amputee subject are denoted with asterisks in This study compared the performance of prosthesis grasping in a force-matching task with EMG and force feedback when the prosthesis control signal was disturbed. The disturbed trials, which appeared unbeknownst to the subjects and were interspersed with normal trials, were characterized by a gain that amplified or attenuated the control signal, thereby substantially altering the response of the prosthesis to the subjects’ muscle activity. Overall, the results imply the advantage of EMG over force feedback, albeit in fewer conditions than initially hypothesized.We assumed that the prolonged training would allow the subjects to develop internal models for the control of the prosthesis and establish the mapping between the desired grasping force and the contraction required to achieve that force . HoweverIndeed, EMG feedback enabled the subjects to better react to the high-gain disturbance and achieve higher success in the first attempt. The ability to generate the correct force level despite the disturbance reduces the likelihood of accidents, such as slippage or breakage. However, the performance was significantly better only in the case of power grasping . The sliIn the case of low-gain disturbance, the two feedback types achieved similar SR for both grasp types. This can be attributed to the smoother myoelectric signal due to lower sensitivity and higher stability of the system resulting from the reduced gain. However, and following our initial assumption, the subjects indeed required a significantly longer time to complete a power grasp with force feedback than with EMG feedback . PresumaThe similarity in the numbers of repeated attempts when using either feedback type in both grasp types was another surprising result. As mentioned before, force feedback was expected to underperform compared to EMG feedback, with the subjects adapting to the disturbances over a larger number of attempts per trial. There was, however, no significant difference in the performance of the two feedback types in disturbed trials. Given that the subjects needed only 1-2 additional attempts to adapt to the disturbance, the adaptation appeared not to have been difficult overall. This point, in combination with intrinsic feedback from the prosthesis, might be the reason for the lack of difference between the feedback types. Namely, even though the subjects did not have explicit information about the change in the control signal gain with force feedback (unlike with EMG feedback), they could estimate this indirectly, for instance, by observing the prosthesis motion . Such incidental information might have been enough to drive the adaptation across repeated attempts. The use of incidental cues in prosthesis control has been reported in the past  and inveImportantly, not only did the performance of the amputee subject follow the trends defined by the able-bodied subjects, but the EMG feedback seems to have been particularly beneficial in the case of the amputee. This is a promising result for the prospective clinical application of this approach. Moreover, the placement of the tactors on the upper arm did not have an impact on the quality of the feedback, since it remained intuitive and easily interpretable.The gain disturbances used in the present study intend to simulate the changes in the amplitude of EMG signal that can arise during clinical applications  . These fWith EMG feedback, the subjects are likely to receive stimulation for a longer time (before and after contact) compared to force feedback (after contact only). However, the potential desensitization of the subjects due to prolonged constant stimulation was not assessed in the present study. Nevertheless, habituation is most pronounced when a prolonged stimulation is delivered to the same area on the skin . ConsideAlthough the present study assessed the short-term adaptation to disturbance across successive trials, the potential learning over a longer time scale was not investigated. Nevertheless, it would be interesting to assess the evolution of disturbance compensation strategies as the user becomes increasingly familiar with closed-loop myoelectric control. Such assessment could elucidate if and how the type of feedback affects the learning over extended use.The present study explored how the performance in a functional task with a myoelectric hand prosthesis integrated with EMG and force feedback differed in terms of response to disturbances in the prosthesis control signal. Overall, the results of this study indicate that, when a disturbance gain is introduced, the two feedback types perform similarly during the execution of delicate grasping, but EMG feedback provides a significant advantage in power grasps. A control scheme that displays better performance in the presence of a disturbed control signal is an important step toward a more reliable prosthesis-user interface.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Committee of the Nordjylland Region . The patients/participants provided their written informed consent to participate in this study.JT executed the data collection and analysis. All authors conceptualized the study, wrote the manuscript, and approved it for publication.


Cognitive decline can be screened by the clock drawing test (CDT), which has several versions.This survey aimed to analyze the correlation between two simple methods for scoring the CDT.This cross-sectional study was nested in the Elo-Creati cohort from Passo Fundo, Brazil and comprised 404 subjects. Two raters underwent previous training and scored the subjects’ CDT according to both the Pfizer and Shulman systems. The inter-observer and intra-observer concordance within each method was analyzed with the Spearman’s rank correlation coefficient, as well as the concordance of the scores between the two methods. Age and scholarity were also correlated with the scores.Most of the participants were women (93.8%) and Caucasian (84.6%), with a mean age of 66.9 (±7.8) years and a scholarity of 10.9 years (±5.6). There was significant inter-observer  and intra-observer correlation . Intra-observer correlation was also observed comparing Pfizer and Shulman methods . There was weak correlation of the scores with scholarity  and age . More participants were classified with decreased cognition through the Pfizer system .For this population, our results suggest that the Pfizer system of scoring CDT is more suitable for screening cognitive decline. The majority of older adults with dementia live in low- and middle-income countries, and such a preponderance will increase over the next decades. Early identification of cognitive decline is desirable to allow adequate management and improve outcomes.The number of older people, including those living with cognitive decline and dementia, is rising. One of the most simple of them is the clock drawing test (CDT), which has more than one version with different scoring methods. The 5-item score Shulman system was considered as an accurate method for the widespread use in the diagnosis of dementia, requiring a substantial understanding of its scoring system. In contrast, small cross-sectional studies suggested that CDT is not sensitive enough to detect very mild dementia neither in educated people nor in those with limited education, even though the CDT version employed in the last study had a complicated scoring system. Moreover, additional uses for the several CDT versions have been described in terms of evaluating the progression of cognitive decline, differentiating types of dementia, and even classifying qualitatively the elements of CDT according to the educational level.There are several cognitive screening tools for identifying cognitive decline and dementia, but the validation of these tests in illiterate and low-educated older adults is a challenge.The issue about the usefulness of the CDT is far from being clarified. In this setting, this survey aimed to analyze the correlation between two simple methods for scoring the CDT in a large series of adult and elderly people, as well as their inter- and intra-observer correlations.Estudo Longitudinal do Centro de Referência e Atenção ao Idoso) cohort from the urban area of Passo Fundo, RS, Brazil. This study has been accomplished by the Universidade de Passo Fundo (UPF) and comprises 404 adult and elderly subjects from the community who have been followed since 2014. The Elo-Creati cohort has the objective of accompanying their health status and promoting well-being through the intervention of professors and students from areas related to health care with experience in the application of cognitive tests. All subjects were invited to participate in this study, gave their written consent, and were evaluated between 2014 and 2015. No patient subject declined participation during the period of this research, which was conducted in accordance with the Declaration of Helsinki and was approved by the Ethical Committee of the UPF (Report Number 741.214).This cross-sectional study was nested in the Elo-Creati  assessment. A third researcher was responsible for data collection into the statistic program, and a fourth one performed the statistical analysis comparing the concordance between the two methods regarding subjects’ cognitive status and the inter-observer correlation.To assess intra-observer correlation, 52 drawings were randomly reevaluated according to the Pfizer system and the other 52 drawings taking into account the Shulman method, one week apart from the original assessment. Raters were blinded and not aware of their previous scores. As aforementioned, a third researcher collected data and another one performed the statistical analysis. Categorical data were described as a percentage and absolute frequency, including the dichotomized variable normal/abnormal result in both scoring methods, which were compared with the chi-square test. The analyses were performed with commercially available Statistical Package for the Social Sciences (SPSS) version 16.0 . Statistical significance was assessed with a two-tailed p-value<0.05.Quantitative variables were presented as mean and standard deviation or when appropriate, median and 25–75% interquartile range (IQR). This was employed for calculating the inter-observer and intra-observer correlations with kappa statistics , through Spearman’s rank correlation coefficient with ordinal scores.Demographic and clinical characteristics of the sample are depicted in Since the ordinal variables resulted from the scores in the Pfizer and Shulman systems, the nonparametric Spearman’s rank correlation coefficient was employed. There was a strong inter-observer correlation for both methods .A subset of 52 random drawings was randomly reevaluated with the Pfizer system and the other 52 random drawings through the Shulman method. This was performed by both raters, rendering a strong intra-observer correlation, with the exception of the Pfizer system for rater 1, where a moderate correlation was observed . There wA weak correlation arose between years of scholarity and the scores in the Pfizer and Shulman methods . In contMore participants were classified with decreased cognition through the Pfizer method  than through the Shulman system  . However, even the 5-item score Shulman system was considered to be somewhat difficult. An easier scoring system based on four items was proposed by Borson et al. and adopted by the Consortium to Establish a Registry for Alzheimer Disease (CERAD) and by the Pfizer Inc. and Eisai Inc., a fact that popularized the test with the name Pfizer method in some circles.There are several methods of scoring CDT. Shulman, one of the pioneers of its use, advocates the simpler the scoring system the better because the more complicated and lengthy methods did not appear to add significant value to the psychometric properties or clinical utility of this test.The main purpose was to compare the Shulman and the Pfizer systems in a large series of adult and elderly people to assess the presence of any difference in terms of inter- and intra-observer ratings. The higher the correlations between the scores, the more significant the results. The inter-observer and intra-observer correlations demonstrated that the Shulman and the Pfizer systems are consistently similar when applied by trained raters. This suggests that the two methods could be applied in primary care, rendering similar results in terms of rating properties. In this setting, the choice of an easier scoring system would be more suitable for cognitive assessment through CDT in primary attention. That is the case with the Pfizer system. CDT seems to be a robust screening test for Alzheimer’s disease when compared with Mini-Mental State Examination (MMSE). In contrast, previous studies have already stated that the CDT is not a good screening tool for the diagnosis of mild cognitive impairment. An advisable approach to improve the diagnostic performance of the CDT is the combination with other tests, especially MMSE.A secondary objective was to compare the interpretation of the results of each method. In the non-selected sample, more participants were classified with decreased cognition with the aid of the Pfizer system than through the Shulman method. Although we did not examine such results in the face of a gold standard diagnostic test, a fact that should be considered a limitation of our study, such finding raises the possibility that the Pfizer method could be more sensitive for screening purposes than the Shulman system. A recent systematic review and meta-analysis about the diagnostic accuracy of CDT proposed its widespread use in the diagnosis of dementia, citing the Shulman method as the most studied, but with some concern about the understanding of its scoring system. nor in those with limited education. Illiterate patients can be successfully screened for Alzheimer’s disease using well-known screening instruments in combined protocols . A study that analyzed the qualitative elements of CDT, rather than the sole score, showed that among participants without cognitive impairment, those with lower education often presented graphic difficulties, conceptual deficits, and spatial deficits. Our results confirmed that more educated people exhibit slightly better performance in CDT.The scholarity seems to influence the performance in CDT. Small cross-sectional studies suggested that CDT is not sensitive enough to detect very mild dementia neither in educated people However, in our sample of 404 adults (50–89 years), a weak negative correlation appeared between the scores and age, that is, the older the subjects, the worse the performance. This finding aligns with a large Brazilian study that also showed a negative correlation between age and cognitive performance, although not employing CDT.Finally, there is an issue about the age and the performance in CDT. A previous study with 180 adults (47–82 years) found no influence of age in CDT scores.We must recognize the profile of our sample as a limitation since most participants were Caucasian women living in the urban area. This restrains the generalization of our results for other populational groups, namely, men, non-Caucasian, and people living in the countryside. Another concern is that the Elo-Creati cohort is representative of adult and elderly people engaged in occupational activities related to healthy aging, far from a sample of patients with declared cognitive decline or dementia. Thus, our population seems of a primary care subset, instead of secondary or tertiary care. Our conclusions may not apply to groups composed solely of patients with cognitive problems.In summary, our results suggest that the simplest form of scoring CDT called the Pfizer system can be employed in primary care for evaluating cognitive decline, with an easier interpretation than the Shulman method. The combination of CDT with other cognitive tests, especially MMSE, is advisable for a thorough evaluation, taking into account educational level and age as influencing factors.


The objective of smart cities is to improve the quality of life for citizens by using Information and Communication Technology (ICT). The smart IoT environment consists of multiple sensor devices that continuously produce a large amount of data. In the IoT system, accurate inference from multi-sensor data is imperative to make a correct decision. Sensor data are often imprecise, resulting in low-quality inference results and wrong decisions. Correspondingly, single-context data are insufficient for making an accurate decision. In this paper, a novel compound context-aware scheme is proposed based on Bayesian inference to achieve accurate fusion and inference from the sensory data. In the proposed scheme, multi-sensor data are fused based on the relation and contexts of sensor data whether they are dependent or not on each other. Extensive computer simulations show that the proposed technique significantly improves the inference accuracy when it is compared to the other two representative Bayesian inference techniques. The rapidly growing world population is becoming a relevant issue to solve the problems of efficiency and quality of life for people ,2. SmartThe smart city comprises different types of sensor devices which often produce diverse data. In such a situation, it is necessary to fuse the heterogeneous data obtained from various sensors deployed in the target place. However, it is challenging to accurately infer and make a correct decision based on the multi-sensor data. Furthermore, data from a single source (sensor) are usually insufficient for making the correct decision. Additionally, noisy data may result in wrong inferences about the environment . Thus, nContext awareness allows accurate inference by properly interpreting the context information extracted from the sensor data in an integrated fashion, either passively or actively ,13. The While various multi-sensor data fusion schemes with BI have been proposed, they are mostly based on a single context. We proposed a generic approach for improving reasoning and inference accuracy by sharing and utilizing multiple compound contexts.Since the events in the real situation might be correlated with each other, the inference operation is further specified in two modes to best match the given condition between the contexts of sensor data: (i) Bayesian inference with dependent contexts and (ii) Bayesian inference with independent contexts.A novel belief function of the BI system is developed which effectively represents the conditional dependency between a specific state and contextual information. The proposed modeling approach is general so that it can be adopted for any inference problem handling heterogeneous data.In this paper, a compound context-aware Bayesian inference (CCBI) scheme is presented which allows accurate inference and decision making based on multiple-context data sharing of the smart cities environment. Multi-sensor data are operated in two phases; in the first phase, KF is applied to filter each sensor datum and reduce the error. In the second phase, BI is applied on filtered data to exploit the statistical correlation between the sensor data based on the multiple contexts and fuses them. Altogether, in the CCBI, the heterogeneous data from multiple sensor devices are fused based on context sharing and relation to perform an accurate decision on them. Computer simulation reveals that the CCBI shows considerably better performance than the other two schemes, Bayesian Data Fusion  and BayeThe organization of the paper is: the work related to multi-sensor data fusion for WSN and smart cities is summarized in The issue of sensor data fusion in smart IoT environments has been recognized by several researchers. In , a platfIn , the autA fuzzy-neural-network-based technique was presented in  to extraIn this section, we present the proposed CCBI scheme for heterogeneous data fusion and make accurate decisions on them. It increases the inference accuracy based on compound context awareness and sharing.The design purpose of the proposed scheme is to efficiently fuse the heterogeneous sensor data for accurate inference. The data generated in WSN are usually massive and noisy. Hence, a method to efficiently filter the errors in the sensor data is needed before the fusion. With centralized filterings, such as a base station (BS), the network tends to become congested. So, we consider that each sensor node can accommodate the KF operation for filtering the sensing data to reduce error and noise before transmitting it to the fusion node. The BI operation is deployed at the central node to fuse heterogeneous sensor data. The structure of the CCBI scheme is shown in The data of each sensors are processed using KF ,35 to omAs shown in Equations (2)–(5), the estimation The proposed scheme has two modes of operation: inference with dependent contexts and independent contexts, respectively. These two modes properly deal with heterogeneous data based on the relation between the contexts and data.Bayesian Inference with Dependent ContextsThis mode is applied when the events or measurements of the environment are dependent on each other. Here, the probability of an event or outcome is predicted based on the occurrence of the related events. Making a decision based on multi-sensor data and context information is regarded as inference with the given condition. For example, turning an air conditioner on and off according to the existence of people or fire detection based on multiple context data are such inference operations. Similarly, in the healthcare application, a variety of sensor nodes record the patient’s physical context such as blood pressure and sugar, etc. . When thThe equation of Bayes fusion  is adoptThe following belief represents the conditional dependence between By applying the chain rule, In a similar fashion, Substituting Equations (12) and (13) into Equation (11), we obtain the following equation:By substituting Equation (14), Equation (9) becomesThe data service module produces the meta-information on the system to extract the information relevant to the target application such as updating the alarm threshold or reconfiguring the sensory infrastructure.Bayesian Inference with Independent ContextsThis case is applied when the events or measurements of the sensor are not dependent on each other. After performing the KF operation, individual sensors send data to the central node, where the data are inferred based on the posterior and prior probability, and the measurement of the highest probability is considered as the final value. Let us denote es as in :(16)BelxSince the sensor readings are independent, we obtain the following:By applying the chain rule, Equation (16) is rewritten asEquation (13) can be expanded for the case of three sensors as given below:Here, In this section, we present the simulation environment as well as performance analysis of the obtained results.The simulation environment is set up on a computer with Intel-Core i7 processor and 16 GB RAM running Matlab R2018a at LINK lab, koreatech university, South Korea. The performance of the CCBI is compared with the two representative BI schemes: Bayesian Data Fusion (BDF)  and BayeBayesian Data Fusion (BDF): This is the simplest model of data fusion based on Bayes’ rule combining different knowledge. The BDF model is further extended by BNDF to improve the inference accuracy as follows.Bayesian Network Data Fusion (BNDF): This merges some properties of the surrounding environment, as below :(23)Px|zNote that the BNDF scheme is extended in the proposed CCBI scheme by adding different factors and states of the environment to improve the inference accuracy, as explained in the previous section.The fire detection system is adopted to evaluate the efficiency of the BI schemes as a realistic test case. In fire detection, the key contexts are air temperature, smoke concentration, the wavelength of the flame radiation, humidity, and carbon monoxide (CO). The data used in the simulation are generated randomly for each of the five different contexts. In the simulation, gaussian noisy data of high variation are injected, and then erroneous data are omitted by applying KF . The filIn this paper, a novel, context-aware scheme of multi-sensor data inference was proposed for a smart IoT environment. It employed KF and BI to efficiently deal with the uncertainty and inconsistency issues with sensory data. To enable early warnings of fire, the characteristics of temperature, smoke concentration, and carbon monoxide sensor data in the initial stage of fire were analyzed in this study, and a BI was chosen to achieve the fusion of data. Here, the data were inferred considering the relation between the contexts. The simulation results show that the proposed scheme considerably outperformed the existing BI schemes in terms of decision accuracy, with a realistic test case of fire detection.In the future, the performance of the proposed method will be further extended and improved by employing the Dempster–Shafer theory, which calculates the reliability of each state based on data extracted from various sources. The proposed scheme will also be extended using feature extraction and selection in the high-dimensional data to improve inference and decision accuracy. Moreover, it is also our goal to build a wireless sensor network (WSN) based on Deep Reinforcement Learning to test the algorithm in a realistic fire scenario and apply it to the Internet of Things (IoT) in smart cities.