In this paper, we construct a classroom emotion recognition algorithm by classifying visual emotions for improving the quality of classroom teaching. We assign weights to the training images through an attention mechanism network and then add a designed loss function so that it can focus on the feature parts of face images that are not obscured and can characterize the target emotion, thus improving the accuracy of facial emotion recognition under obscuration. Analyze the salient expression features of classroom students and establish a classification criteria and criteria library. The videos of classroom students' facial expressions are collected, a multi-task convolutional neural network (MTCNN) is used for face detection and image segmentation, and the ones with better feature morphology are selected to build a standard database. A visual motion analysis method with the fusion of overall and local features of the image is proposed. To validate the effectiveness of the designed MTCNN model, two mainstream classification networks, VGG16 and ResNet18, were tested and compared with MTCNN by training on RAF-DB, masked dataset, and the classroom dataset constructed in this paper, and the final accuracy after training was 78.26% and 75.03% for ResNet18 and VGG16, respectively. The results show that the MTCNN proposed in this paper has a better recognition effect. The test results of the loss function also show that it can effectively improve the recognition accuracy, and the MTCNN model has an accuracy of 93.53% for recognizing students' facial emotions. Finally, the dataset is extended with the training method of expression features, and the experimental study shows that the method performs well and can carry out recognition effectively. Human emotions are closely related to behavior. Emotions originate from objective facts and are the key to determining the quality of human daily life, and they are related to human needs. When human needs are met, humans tend to generate positive emotions and show positive behaviors . The claSentiment analysis, also called opinion mining, is the process of processing and analyzing user-generated content to study the emotions, opinions, and attitudes expressed by users and to judge or evaluate them. Sentiment, as an important attribute of humans in social behavior, represents an individual's viewpoint and attitude toward a specific goal and has a great impact on all aspects of human life such as cognition, socialization, decision-making, and reasoning . The curIn recent years, artificial intelligence plays an increasingly important role in education, and AI and deep learning-related technologies  are gradually implemented into specific fields, providing technical support for the development of adaptive education. The conference pointed out that one of the applications of AI in education is real-time monitoring of students′ learning expressions, learning postures, and other valuable information . In tradTraditional visual sentiment analysis methods mainly use knowledge from fields such as visual cognition and psychology to extract underlying visual features such as color, texture, and shape of images and perform sentiment analysis through statistical methods . The UniA good teacher can accurately grasp classroom emotions to improve students′ attention, make them feel happy and satisfied, and eventually make them enjoy their lessons, while a teacher who is not good at grasping classroom emotions may cause the classroom to fall into a boring narrative, students′ attention is not focused, and learning is inefficient. Scholars have done enough research on classroom emotions and have deeply analyzed teachers′ and students′ behaviors and emotional expressions in the classroom and what kind of teaching effect the classroom will have after these expressions. Professor Luo H argues that the affective dimension can be seen as a continuum of hierarchical order and describes emotions not only as simple or complex, figurative, or abstract interpretations but also as characteristics of control from the unconscious to the internal and external, which can be called internalization . Rani etIn classroom teaching, teachers should pay attention to students′ learning emotions and promptly adjust their teaching strategies based on the feedback from expressions to mobilize students' positive learning emotions. Scholars have conducted many studies on classroom teaching and learning, which have provided comprehensive and in-depth analyses of classroom teaching and learning processes from many different perspectives, and have thus contributed to further development of teaching and learning. Since 2000, the integration of computer sentiment analysis and educational teaching has been greatly promoted. Researchers have used video analysis to understand learners′ emotional states, providing teachers with timely feedback so that they can revise course content, adjust course difficulty, select teaching methods, and control the teaching schedule, greatly promoting changes in the teaching model and improving teaching quality . The bigIn classroom teaching, students′ expressions and behaviors can greatly express their mental and emotional states. For courses they are interested in or like, they tend to show happy, focused, and positive emotions, with facial expressions such as smiles and concentration. For courses that they do not like or even dislike, they tend to show negative emotions such as resistance and disdain, and their faces are accompanied by negative expressions such as frowns. These negative emotions are partly caused by the difficulty of the course and the difficulty of adapting to the teacher′s teaching style, while another part is caused by the students′ psychological problems. Therefore, the identification and analysis of students′ emotions in the classroom environment can not only help teachers to make better adjustments to the course teaching but also help to detect whether students have psychological problems in time and provide timely psychological counseling and other treatments for students with psychological problems, to promote the overall development of students′ body and mind. This paper realizes the prediction of dimensional emotional data based on related networks such as recurrent neural network and finally uses the quantitative algorithm of emotional eigenvalues to calculate emotional intensity, which provides a database for the system to perform adaptive adjustment.Get the students' dynamics in class in real time through the camera, then extract features through image processing, machine learning, deep learning, and other technologies, classify emotions, and feed back the real-time emotional status to the teacher in time. The teacher makes corresponding adjustments according to the students' current emotional status. When students are depressed, adjust the teaching progress, activate the classroom atmosphere, and improve students' learning motivation. When students are generally in a low mood, the teacher will adjust the teaching schedule to improve students′ learning enthusiasm and efficiency; when students are generally in a high mood, the teaching schedule can be accelerated to further improve the teaching efficiency of the course.m is the number of neurons in the hidden layer, n is the number of neurons in the visible layer, and Wmn is the weight matrix between them, v= is the state of the visible layer, and h= is the state of the hidden layer. For example, if we assume that there are n visible units in the visible layer and m hidden units in the hidden layer, the energy function between the nodes in the visible layer and the nodes in the hidden layer can be expressed as follows when the values of both visible and hidden units are 0 or 1.The convolutional neural network is a special kind of feed-forward neural network with numerous neurons in its structure, and these neurons come with weights and biases. It uses local connections to avoid the problem of redundant data caused by full connections . In the vi represents the state of the ith unit of the visible layer, hj represents the state of the jth unit of the hidden layer, wij represents the connection weight between vi and hj, and ai and bj represent vi and the bias value of hj.In the formula, A deep neural network (DNN) is also a neural network, but it has multiple layers of perceptron and multiple hidden layers and consists of a series of Boltzmann machines (RBMs) stacked and trained layer by layer. The lower layers represent the original information of data features, while the higher layers represent the attribute categories of data, so its learning process proceeds from the lower to the higher layers. In the learning process, the higher layers will continuously acquire deep abstract features, which is the essential feature of deep learning data. According to the above research status, although researchers have proposed many feature extraction methods and recognition classification methods for pictures, there are still some problems to be solved if we want to realize a system of expression recognition with high accuracy and in real time: one is the problem of how to perform multi-person face detection in real time; the other is the problem that some external factors and similarity between faces under natural conditions can affect the accuracy rate.In 1976, Paul Ekman created the facial action coding system (FACS), which divided the face into independent and related AU—action unit—based on the anatomical characteristics of different muscles of the face. The FACS provides a detailed classification and elaboration of motor units and expression composition, which has become the main theoretical basis for later research, and many classical algorithms or expression databases are based on the FACS. The classroom expression status categories should indicate whether the learners are interested in the content, whether they agree with the teacher′s teaching style, and whether they are comfortable with the pace of knowledge taught. Teachers can use this as a basis for adjusting the content and teaching process and providing timely emotional compensation. Teaching observers can use this to evaluate the quality of teaching, reflect on teaching activities, and make changes. Since the focus and application scenarios of classroom expressions are different, they are both the same and different from the common expressions.The discrete emotion description model describes emotions into discrete categories, such as happy, angry, and sad. Ekman, an American psychologist, proposed 6 basic human emotions: happy, angry, surprised, sad, disgusted, and afraid. The Chinese emotion corpus of the Chinese Academy of Sciences Institute of Automation (CASIA) also basically follows these six basic emotion categories, except that disgust is replaced with neutrality according to the corpus data . The preBecause of the flexible nature of human facial expression, the change of expression is nonrigid, and considering the requirement of real-time detection of multiple targets, this study will use the multi-task convolutional neural network (MTCNN) proposed in 2016 in the part of implementing face detection. It has very good experimental and application effects, MTCNN uses three different depths of network types to achieve face detection, and its cascade structure + CNN + bounding box method can achieve real-time face detection, which can better meet the task of multiple face detection in classroom scenes. The captured video is fed into a multi-task convolutional neural network (MTCNN), which detects the faces obtained from the video in real time and locates the key parts of the face: eyes, nose, and mouth. This model mainly uses three cascade networks, P-Net, R-Net, and O-Net, to detect and locate faces more accurately by stepwise fine-tuning.The original face images have the problem of pose and scale differences due to different shooting conditions. Geometric normalization includes face correction and scale scaling, which can correct the angularly shifted faces and scale the faces of different scales to a uniform scale for subsequent dataset construction and use. Face correction is based on the angular difference between the original face pose and the standard face pose, and the original image is corrected to the standard face pose by coordinate rotation to ensure the consistency of face orientation. The center coordinates of the left and right eyes in the intercepted face image are obtained by feature point detection, the angle between the line connecting the center coordinates of the two eyes and the horizontal direction is calculated, and the original image is rotated by coordinates with the center of the line connecting the center coordinates of the two eyes as the rotation center to obtain the orthographic image.d between the two eyes, the center of the line connecting the two eyes is used as the origin, the area with distance d is cropped to the left and right, and the area with distance d and 1.5 d is cropped upward and downward, respectively, to obtain the standard face rectangular area. The intercepted standard face area is scale transformed to a uniform size by bilinear interpolation to achieve normalization in scale. This paper realizes the prediction of dimensional emotional data based on related networks such as recurrent neural network and finally uses the quantitative algorithm of emotional eigenvalues to calculate emotional intensity, which provides a database for the system to perform adaptive adjustment.The original face images often have different face sizes and scales due to differences in shooting locations and individual differences. Through scale normalization, the faces in the original images are scaled to a uniform standard to reduce or even eliminate noise interference, making the accuracy of the subsequent expression recognition algorithm evaluation more reliable. Scale normalization includes cropping and scaling of the corrected face images. The feature points of the corrected face image are calculated according to the previous  as the dxl denotes the constant mapping, f denotes the residual mapping, and wl denotes the convolution kernel parameter. From this equation, the output of any layer can be represented by the output of any layer shallower than it and the sum of the residuals between the two layers. The input image is passed through the deep residual network, and the final output is obtained as the feature representation F ∈ RW×H×C of the overall image feature.To achieve a deeper network to extract deeper features while reducing the network parameters and improving the computational efficiency, a bottleneck architecture is used for the residual unit. A 1 × 1 convolutional kernel is used at the beginning and the end, and only a 3 × 3 convolutional kernel is used in the middle. The information transfer in the residual cell consists of two types of mapping, namely, residual mapping and constant mapping. The mapping generated by the convolutional and activation operations is called residual mapping; at the same time, it allows the direct transfer of information between residual units in the form of jump connections, and the mapping generated by this direct connection is called constant mapping, which is directly used as the input of the next residual unit and propagated from the top to the bottom layer. In traditional neural networks, some information is lost when the information is passed in the convolutional and fully connected layers. The information transfer between residual units can be expressed as the following equation.xi, yi)=1, where xi denotes the sentiment image samples, yi ∈ {1,2,…, n} like the sentiment labels corresponding to the samples, N denotes the number of image samples in the training set, and k denotes the number of sentiment categories. For each sentiment image sample, the overall feature, i.e., the output of the last convolutional layer of the deep convolutional neural network, is F ∈ RW×H×C. For the feature map of each channel, the richer the sentiment information contained in a location, i.e., the more strongly the location expresses the sentiment, the larger the value of that location in the feature map. The sentiment activation map is then generated by CAM using the image-level sentiment labels.For the training set , this paper validates the recognition based on convolutional neural network and recurrent neural network database contents, while the best model for emotion recognition is used in the adaptive learning system to build the emotion recognition module of the student model. The experiments are divided into emotion recognition for discrete emotions and emotion recognition for two-dimensional arousal-valence. Discrete emotion recognition uses a convolutional neural network, the experiments are performed on the original image data for face cropping, to increase the generalization ability of the model, data augmentation is used, and the leaky ELU activation function is used for the experiments to get the best model; compared with discrete emotion, dimensional emotion data can not only reflect the spatial information of the data but also describe the temporal information of the data. To this end, this paper implements the prediction of dimensional sentiment data based on recurrent neural networks and other related networks, and finally uses a quantitative algorithm of sentiment eigenvalues to calculate the sentiment intensity, which provides the database for the system to perform the adaptive adjustment.In this chapter, the global features extracted by IS10 and IS13 are screened using FC for better feature fusion, and the experimental results show that the combined features (IS10 + IS13) of global features extracted by IS10 and IS13 work best compared with the global features extracted by each of IS10 and IS13 . The temIn this study, a single ID photo of 100 people is selected as the original sample and a small sample dataset is constructed. Because the ID photos have the same background, it can make the faces in the images at the same angle, which can eliminate the influence of different image backgrounds and avoid the influence of face skin color, etc. Based on this, data enhancement techniques such as mirror transformation, multi-region cropping, Gaussian noise, symmetric expansion, and bit-plane method are carried out to expand the sample database . To achiTo further verify the effect of the representative region loss function designed in this paper on the model recognition, the MTCNN without RRL was tested in RAF-DB with added feature images. The accuracy of the MTCNN without RRL on the RAF-DB test set is 76.5%, which compares with that of the pretrained model in The MTCNN model with RRL has a faster fitting speed with the same training parameters and achieves the fit in the 15th round, while the model without RRL has oscillations in the test set, a slow fitting speed, and relatively low accuracy . TherefoThe proposed method in this paper was evaluated on three datasets Twitter I, Twitter II, and Emotion ROI to demonstrate its effectiveness. The three datasets were divided into 80% training set and 20% test set using random partitioning. The proposed method achieves 79.83% and 78.25% classification accuracy on Twitter I and Twitter II datasets, respectively, which are higher than the traditional visual sentiment analysis method GCH and Sent bank based on intermediate semantic representation [The proposed method achieves 49.34% classification accuracy on the multi-category sentiment image dataset Emotion ROI, which is more accurate than the traditional visual sentiment analysis method GCH and the intermediate semantic representation-based visual sentiment analysis method Sent bank. The classification accuracy of DA-MLCNN is 6.81% and 3.88% higher than that of DeepSentiBank and VGGNet-16, and 1.78% and 1.21% higher than that of PCNN and COIS models, respectively. By comparing the classification results of the various methods on the multiclassification dataset, it can be shown that the DA-MLCNN method proposed in this paper can also be adapted to the multiclassification task of visual emotion [X-axis represents the values of the parameters, and the corresponding Y-axis represents the corresponding posterior probabilities; if the posterior probabilities are larger, then the probability that the parameters μ andσ are the true values will be larger [μ andσ do not have any correlation, meaning that the two parameters are independent of each other and will not be linearly correlated. Finally, a maximum posterior density (HPD) interval is run. An HPD interval is the smallest interval that contains a certain proportion of probability density and is often used to describe the dispersion of the posterior distribution of the parameters.Through scale normalization, the face in the original image is scaled to a unified standard to reduce or even eliminate noise interference, which makes the evaluation accuracy of the subsequent expression recognition algorithm more reliable. First, a class is sampled and analyzed as Classroom A. Classroom A is analyzed by running the program. The e larger . In suchWhen making statistical inferences, the size of the sample that can be relied upon is often limited by various conditions. Direct use of means is intuitive and simple, but the meaningfulness of statistical inference cannot be determined by the size and complexity of the computation alone. The mean value calculated directly is easily affected by the sample characteristics, the way the sample is drawn, and other factors, especially when the sample size is limited, making it more difficult to make reasonable inferential statistics. Using the Bayesian probability model for statistical analysis, the MCMC under the framework of the PyMC3 probability model estimates the posterior distribution of parameters by random sampling, and then the highest probability value in the posterior distribution is used as the estimator of the parameters, the more the samples, the more the posterior distribution converges, and the resulting estimates will be closer to the real situation.In this paper, we perform face detection and image segmentation with the help of a multi-task convolutional neural network (MTCNN), select the ones with better feature morphology to build a standard database, and further improve and build a classroom-based facial expression classification standard. A visual motion analysis method with the fusion of overall and local features of images is proposed. The method designs a multi-scale full convolutional neural network for detecting the salient regions of images and extracting the features of the salient regions of images, while generating class activation mappings of emotional images using only the image-level emotional labels, and finally generating emotional activation maps of images and extracting the features of emotional regions by superimposing class activation mappings of multiple emotional categories. Suitable parameters are designed according to the characteristics of the experimental data, and the constructed dataset is trained. After testing and analysis, the results show that the model of constructing a task convolutional neural network performs well on the student classroom facial expression dataset, and the recognition rate using the test set is at 91%, which indicates that the method is scientifically feasible and can reduce the reliance on the training sample size, reduce the collection workload, and alleviate the pressure of data storage. However, due to the small sample size of the classroom emotion dataset constructed in this paper, the emotion labels are representative but incomplete and cannot fully characterize the recognition effect of the model designed in this paper. For the construction of the emotion dataset in a classroom environment, future work, other emotion categories, or even compound emotion categories can be added to further expand the sample size of the dataset. Increase the number of labelers to further enhance the accuracy of the dataset picture labels.


The sensitivity analysis indicates highly consistent and sturdy results regarding the optimal selection. This study recommends adopting a hybrid system of anaerobic digestion and gasification technologies, as this offers a well-balanced system under all of the evaluation criteria compared to the standalone systems. The results of the current study may help the government of Ghana and other prospective investors select a suitable WtE technology, and could serve as an index system for future WtE research in Ghana.Energy recovery from waste presents a promising alternative for several countries, including Ghana, which has struggled with unsustainable waste treatment methods and an inadequate power supply for several decades. The current study adopts a comprehensive multi-criteria decision-making approach for the selection of an optimal waste-to-energy (WtE) technology for implementation in Ghana. Four WtE technologies are evaluated against twelve selection criteria. An integrated AHP-fuzzy TOPSIS method is applied to estimate the criteria’s weights and rank the WtE alternatives. From the AHP results, technical criteria obtained the highest priority weight, while social criteria emerged as the least important in the selection process. The overall ranking order of WtE technologies obtained by fuzzy TOPSIS is as follows: anaerobic digestion  Energy is a critical element in human existence, and is essential to a society’s sustainable economic growth. Globally, fossil fuels have been the primary source of electrical generation. Based on the most recent Statistical Review of World Energy, fossil fuels account for around 84% of global electricity generation . HoweverMSW management has become a topic of interest in most studies due to its numerous socioeconomic and environmental benefits. The generation and management of MSW has evolved dramatically over the last few decades. Globally, the trend of solid-waste management has begun to shift from uncontrolled landfilling to more sustainable options like recycling, composting, and energy recovery. The rates of recycling and energy recovery have increased from 6% and 0% of MSW generated in 1960 to around 32% and 12% in 2018, respectively. On the other hand, landfilling decreased significantly, from 94% of the amount generated in 1960 to 50% of the amount generated in 2018 . TherefoConversely, in most developing countries, including Ghana, waste management is presently dependent on a linear economy, where materials are manufactured, utilized, and dumped into landfill . The draAs one of the fastest developing economies in Africa, Ghana’s electricity demand has increased steadily over the years due to economic growth, urbanization, industrialization, and rural electrification . Ghana’sThe government of Ghana has identified renewable energy as one of the promising alternatives to contribute to the energy supply mix while reducing the environmental menaces of energy production . A targeThis necessitated the creation of the Renewable Energy Master Plan (REMP) in 2019 to develop a roadmap focused on socioeconomic progress and reducing the negative effects of climate change by 2030 [Like most developing economies, waste generation in Ghana is increasing rapidly, especially in urban areas, due to rapidly growing populations, urbanization, and industrialization. An estimated 12,710 tons of waste is generated daily, which translates into 0.47 kg/person/day . MSW conEffective waste collection in urban areas accounts for only 52% of the total waste generated, with the remaining either being burned by households, dumped indiscriminately in the open, and/or disposed of in water bodies . DespiteIn an attempt to lessen the problem-solving time and complexities and boost productivity, several approaches have received particular attention in the last few decades, including machine learning ,25 and mIn model application, AHP has been frequently utilized to evaluate WtE technologies and determine the development and investment priority. Yap and Nixon  applied Other methods like TOPSIS, VIKOR, and DEMATEL have also been applied to solve multi-objective problems related to WtE selection. Alao et al.  employedThe use of integrated MCDM models in sustainable energy planning has also increased considerably in recent years. In WtE selection, Shah et al.  integrat3 of biogas produced from MSW in Kumasi may provide up to 36 MJ of energy, which is equivalent to 10 kW/h of electricity [3 of biogas. From a techno-economic standpoint, Osei-Appiah and Dioha [In the past, few studies have been conducted to thoroughly evaluate the potential and viability of WtE implementation in Ghana. A recent study investigating the energy recovery potential of MSW in one of Ghana’s major cities, Kumasi, found that 1 mctricity . Anotherctricity  suggestectricity  also revnd Dioha  establisSimilar to the studies from different countries/regions, as reviewed above, the use of MCDM for WtE technology selection has been studied for Ghana. To the best of our knowledge, only two studies—i.e., ,43—have Despite their study’s key contribution and significance, the sole use of the AHP method has often been criticized. The conventional AHP is a subjective method that depends on experts’ opinions. In addition, the random adjustment of the consistency ratio until the desired value is obtained is also subjective. Subjective decisions are prone to ambiguity, vagueness, and bias, which makes the reliability of the sole use of the AHP method questionable to some extent. The second study is by Afrane et al. , where tWhat are the technical, economic, environmental, and social implications of the WtE technologies chosen?What are the relative weights of several indicators that influence the choice of WtE alternatives in the Ghanaian context?In light of the experts’ and stakeholders’ interests, which WtE technology is most suited for the conditions of Ghana?In summary, the primary aim of the current research is to assess the feasibility of WtE technologies for implementation in Ghana through a multi-criteria decision analysis of the chosen WtE alternatives against a set of defined criteria. This offers an initial evaluation of the significance of several variables affecting the selection of WtE technologies from the standpoint of experts and stakeholders in the waste management and energy recovery sector. The goal is to ascertain the most technically feasible, most cost-effective, least polluting, and most socially acceptable waste-to-energy conversion technology among other selected alternatives for the generation of electricity from MSW in Ghana. The particular questions to be answered are:This research is useful in providing information on feasible treatment technologies for the management of the country’s waste through energy recovery. The present study’s findings may help the government of Ghana and other prospective investors choose a suitable WtE technology that offers the greatest benefit under multiple criteria for minimizing Ghana’s electricity and waste management problem. Additionally, assessing the most important sustainability criteria could serve as an index system for future waste-to-energy research in Ghana.Technologies that can efficiently recover energy from waste with little to no environmental damage have been considered as alternatives for conventional fossil fuel generation . The conThe WtE technologies considered for this analysis include AD, conventional gasification, pyrolysis, and plasma gasification. The justification for choosing these technologies is based on two primary considerations: firstly, the technical maturity of the WtE technologies in Ghana; secondly, the availability and accessibility of data on these technologies to make an informed decision. However, for comparative purposes, incineration and landfill gas recovery technologies are reviewed alongside the selected WtE technologies in this section.The incineration of waste is the most common WtE technology integrated into waste management systems worldwide. It is the oxidation of combustible waste constituents, and is utilized for a diverse variety of MSW. The efficiency of the incineration process is estimated to be about 25–30%, and it is mainly influenced by the constituents of the waste stream, particularly for MSW, which may be extremely diverse ,47. In gThe incineration of MSW operating at an uncontrollably high temperature can produce a net energy of about 544 kWh/ton MSW, but this process is more environmentally damaging. Combusting MSW at uncontrolled temperatures produces chlorinated dibenzo-p-dioxins and corrosive gases that could destroy the steam pipes and cause health-related problems. Dioxins, particulate matter, sulfur dioxide, hydrochloric acid, and heavy metals are all possible contaminants in flue gases. In previous years, the environmental impact of dioxins was one of the most crucial challenges related to MSW incineration . In receGasification is a process in which organic materials are partly oxidized at high temperatures  with reduced oxygen . The gasBiological waste, sewage sludge, industrial waste, and wood waste have previously been treated using gasification methods. However, in recent years, the gasification of MSW is increasingly gaining popularity. Several gasification technologies for the cogeneration of heat and electricity from syngas have been developed during the previous two decades and are now commercially accessible . GasificPlasma gasification uses an electrically driven plasma torch to volatize waste and organic materials with a regulated infusion of oxygen . The orgPyrolysis is the thermal breakdown of a substance in the absence of or with a restricted supply of an oxidizing agent  to produce the thermal energy needed for the process. Compared to gasification, relatively modest temperatures  are used . Syngas Pyrolysis processes are classified under three main categories; slow pyrolysis, rapid (or flash) pyrolysis at high temperatures, and flash pyrolysis at low temperatures . The mos2, and hydrogen by acetate-forming bacteria. The last step of methanogenesis, in which methanogens may develop at low redox potentials using substrate-level or electron transport phosphorylation, generates biogas, a combination of gaseous molecules—mostly methane and carbon dioxide—through volatile fatty acid breakdown [Anaerobic digestion is the breakdown of biodegradable material by microorganisms in the absence of oxygen. For the digesting process, special reactors are utilized, and within the reactors, certain variables—such as pH, moisture content, and temperature, among others—are regulated . These preakdown ,65.Biogas may be used to generate electricity, fuel (biomethane) for combustion engines, and space heating, water heating, and process heating. Over 90% of the energy available in the wastes is kept in the biogas as methane during the anaerobic conversion or fermentation of MSW, with the remainder being sludge . Anaerob3 [Landfilling has been the only disposal technique capable of dealing with all of the materials in the solid waste stream, as well as the easiest and, in many cases, cheapest disposal option. The generation of landfill gas from a sanitary landfill plant is similar to anaerobic digestion, with the main difference being the operational control of the sanitary landfill . Because3 . In mostThe four WtE alternatives were chosen with the goal of integrating both the universal solution and certain new technologies. However, each of these alternatives has its benefits and disadvantages. As a result, choosing the optimal WtE technology among the four alternatives is challenging for decision-makers based on only one criterion or the other. There is, therefore, a need to integrate all of the criteria that have a relevant impact on the optimal selection. A thorough literature study on energy recovery from waste in general and the MSW and energy sectors in Ghana was performed to choose the criteria that would be utilized in assessing the technologies. Based on the literature review, the assessment criteria for the selection of WtE technologies may be classified in four major categories: technical, economic, environmental, and social aspects. The technical aspect concerns factors that affect energy production, such as conversion efficiency, power generation capacity, and technology maturity. The economic component is concerned with cost-related factors such as capital cost, O&M cost, and the cost of energy. The environmental component is concerned with the detrimental effect on the environment, such as carbon dioxide emissions and the production of hazardous residue. The social component defines the maximum of people’s social well-being, such as social acceptance and job creation. The various sub-criteria adopted for assessing the optimal WtE alternative based on the four main sustainability criteria are summarized and reviewed in This research aims to evaluate the feasibility of various WtE technologies for implementation in Ghana, and to make an optimal selection against different indicators. To begin with, a thorough literature review was conducted on the various WtE technologies, the factors that affect their feasibility, and different MCDM techniques and their applications in sustainable energy planning and waste management strategies. A study of the different WtE alternatives led to the establishment of appropriate selection criteria for the decision-making process. This study’s criteria include a variety of technological, economic, environmental, and social aspects. The weights for the criteria might be qualitative, quantitative, or a combination of both. Quantitative or objective weights represent the characteristics of the possibilities available numerically, and they are best suited for studies for which adequate and easily accessible research data are plentiful. In contrast, qualitative or subjective weights are dependent on decision-makers’ views and judgments regarding the characteristics of the options, and thus are best suited for study areas with relatively scarce research data, such as Ghana. In order to achieve the intended results, the present research uses a qualitative decision-making method. A questionnaire is created and sent to ten experts with extensive expertise and background information on WtEs for developing nations with MSW characteristics and economies similar to Ghana’s. An integrated MCDM technique that combines AHP and fuzzy TOPSIS is used for the analysis. The AHP methodology is used to calculate the weights of the selection criteria and sub-criteria. The obtained weights are then used to rank the optimal WtE technology for investment in Ghana using the fuzzy TOPSIS method. Because expert judgments may be confusing, unclear, and imprecise, the subjective characteristics are represented numerically using both Saaty’s basic scale of absolute numbers and the fuzzy conversion scale. Finally, a sensitivity analysis is conducted to see how changing the experts’ original weights would affect the overall priority ranking of the WtE technologies. The decision framework used to identify the optimal WtE selection is shown in Saaty developed the AHP technique in the 1970s . AHP enaA pairwise comparison matrix is created for each comparison. Following the construction of the pairwise comparison matrices, the rankings are determined by computing a priority vector for each comparison matrix. There is a stringent consistency requirement to maintain the validity of preference patterns between the compared components. A consistency check is performed to ensure that the pairwise comparisons are properly assessed. In this study, the consistency check was performed using Saaty’s consistency ratio, as Saaty’s relative measurement scale is used for scoring and the consistency ratio is simpler to understand and use than the other techniques.The main steps involved in the AHP technique are outlined simply below :Step 1: Identify the decision problem and determine its goal.Step 2: Create a four-tiered hierarchical structure, as shown in Step 3: Create a pairwise comparison matrix Step 4: Determine the significance of the pairwise comparison by creating a matrix of relative ranks for each hierarchy stage.CI) and consistency ratio (CR).n is the number of criteria;Step 5: Calculate the consistency index  was proposed by Hwang and Yoon , and it Regarding fuzzy set theory, as mentioned earlier, subjective human judgements are prone to uncertainties, ambiguity, and vagueness during the decision-making process. For the exactness of a selection, words can be more imprecise than numbers. As a result, Zadeh  developeA fuzzy set is a group of objects with membership grades ranging from 0 to 1, with the membership grade being an intermediate value between 0 and 1. A fuzzy subset The fuzzy TOPSIS approach is detailed below; it was derived from Nădăban et al. K members. The fuzzy rating of the thk decision maker regarding alternative Step 1: Assign a rating to the alternatives. We assume that we have a decision group with Step 2: Compute the aggregated fuzzy ratings for the alternatives and the aggregated fuzzy weights for the criteria. The aggregated fuzzy rating Step 3: Compute the normalized fuzzy decision matrix. The normalized fuzzy decision matrix is Step 4: Compute the weighted normalized fuzzy decision matrix. The weighted normalized fuzzy decision matrix is Step 5: Compute the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS). The FPIS and FNIS are calculated as follows:Step 6: Compute the distance from each alternative to the FPIS and the FNIS. LetStep 7: Compute the closeness coefficient Step 8: Prioritize the alternatives. The ideal scenario is the one that has the highest closeness coefficient.The relevant criteria were selected for the assessment of WtE alternatives through the literature review.A digital questionnaire was developed, and respondents were requested to access it online and fill in the required scores for each criterion depending on their relative importance in the decision-making process in order to assess various WtE alternatives based on the Saaty’s relative measurement scale.The respondents were also required to rate how each criterion affects the alternatives using the fuzzy linguistic terms in order to rank the WtE technologies based on their feasibility against all of the criteria.When deciding on a complicated problem, it is critical to appoint qualified experts, as these specialized professionals know how to manage and address the decision problem. Furthermore, experts’ opinions are crucial when major choices must be made quickly and data in a particular field are limited or unavailable. The goal of the survey in this study was to obtain expert opinion on various thermochemical and biochemical technologies, focusing on the most significant criteria, and to evaluate these technologies as waste management strategies as well as energy generation alternatives. Experts were carefully selected from institutions that are relevant to this field. Two academic experts were selected from the University of Energy and Natural Resources (Ghana), two from Tianjin University (China), and one from the University of KwaZulu-Natal (South Africa); one technical expert was selected from the Environmental Protection Agency (Ghana), two from the Energy Commission (Ghana), and two from the Ministry of Sanitation (Ghana). The procedures listed below include the qualitative research design for the expert survey:A case is presented for the assessment of the technical, economic, environmental, and social feasibility of four WtE technologies—namely anaerobic digestion, conventional gasification, plasma arc gasification, and pyrolysis—in order to fully understand the practicability and efficiency of the suggested technology for investment and implementation in Ghana. The goal of this study is achieved by applying a multi-criteria model to select the optimal technology that is best suited for the Ghanaian context against the chosen evaluation criteria. The first part of this section presents the weights of the evaluation criteria and sub-criteria in terms of their significance for the advancement of the WtE technologies based on the judgements of the selected experts. Then, using this relationship (weight) in the establishment of the rankings for the various WtE alternatives, the experts scored the value of each WtE technology in the second part. The results of the sensitivity analysis are presented at the end of the section.The main evaluation criteria and sub-criteria results were acquired using the AHP weighting technique in this section. The following sub-sections show the aggregated priority vectors of the four main criteria and their respective sub-criteria derived from these weights.The priority weights of the four main evaluation criteria are shown in From the technical standpoint, the results in Within the economic criteria, it can be seen in 2 emissions are ranked as the most significant environmental sub-criterion to influence the decision of WtE selection in Ghana, with a priority weight of 0.523, followed by pollution potential of WtE technologies, with a weight of 0.263, and health impacts, with a weight of 0.214. Due to the increasing global concern about climate change impacts, many countries are making considerable efforts to reduce their greenhouse gas emissions, and Ghana is no exception. This concern is reflected in the experts’ opinion for the selections of an optimal WtE technology, such that priority should be given to a technology that can reduce CO2 emissions significantly. The wide gap between the weight of this criterion and the other environmental criteria emphasizes its importance in the development of WtE technologies in Ghana.As shown in From the social perspective, the results in 2 emissions, are more closely linked to the evaluation of WtE technologies than health effects, and hence are given higher priority. The findings from the Fuzzy TOPSIS theory application to choose the optimal WtE alternative in Ghana based on the four key sustainability criteria are presented in this section. The weighted normalized fuzzy decision matrix is computed based on the sub-criteria weights obtained from the previous section and shown in 2 emissions (kgCO2eq) obtained relatively high importance in the ecological assessment of the alternatives under consideration. Even though all of the selected WtE alternatives present lower CO2 emissions than Ghana’s current waste management strategies, anaerobic digestion was found to have the least damaging potential in terms of global warming [With a closeness coefficient of 0.63 to the ideal solution, anaerobic digestion ranks first as the optimal WtE alternative for the generation of electricity from MSW in Ghana, followed closely by gasification, with 0.56, pyrolysis, with 0.31, and plasma arc gasification as the least feasible WtE alternative, with 0.28. Anaerobic digestion was found to be the most appropriate for the overall aim; it also ranked first under economic, environmental, and social criteria, as shown in  warming . Further warming ,86,87. TGasification was revealed to be the most favourable technology in terms of its power generation capacity and conversion efficiency, which are the first and third most important among all of the evaluation criteria. Despite this, gasification was ranked as the second most feasible WtE alternative in Ghana in terms of the overall goal. This could be attributed to gasification obtaining the first rank in only three out of twelve evaluation criteria. Nevertheless, this WtE alternative performed best among all of the thermochemical options in terms of all of the evaluation criteria except for health impacts and safety. Currently, the government of Ghana is concentrating its efforts on developing a plan to avoid the spread of uncontrolled dumpsites, as the current waste management system is failing to keep up with expanding waste volumes and requires significant waste reduction. Because gasification can produce a 50–90% decrease in waste volume , this teFrom a different perspective, plasma gasification could be the most appropriate method for MSW treatment in Ghana, where waste segregation is not performed effectively. This is because the plasma torch used in this process can break down any type of waste into its fundamental constituents without discrimination. Unfortunately, this technology is very limited in terms of its high capital and operating cost, as well as its high electricity demand. Even though the high conversion efficiency and electricity generation capacity make it attractive for an energy-poor country like Ghana, the construction of a plasma gasification plant is extremely costly, presumably due to its high demand for a higher level of plant automation, special construction materials to withstand the extreme temperatures, and the cost of plasma sources . The extThe final WtE alternative ranking results of this study are comparable to previous studies in this field conducted for other developing countries or regions. Qazi et al.  comparedSensitivity analysis shows how particular elements have a positive or negative effect on the outcome of decision-making. Because the criterion weight greatly influences the rank, any changes in the weight value should be carefully considered. By varying the initial weights of the various criteria, what will be the effect? A sensitivity analysis was used to determine the influence of underlying uncertainty in the experts’ judgments. The AHP results from the previous section indicated that technical criteria have the highest significance in the determination of the optimal WtE alternative and are thus considered the most sensitive. Therefore, in the current study, the global priority weights of the three technical criteria  were modified from 0.05 to 0.3 in increments of 0.05 in order to evaluate the influence of these key criteria on the overall ranking order of the alternatives.The results of the sensitivity analysis of the criteria weights are presented in this section. It can be seen that the ranking order from the first to the third scenario was maintained even though the It is worth noting that even though the ranking order of pyrolysis changed after the third scenario, its The sensitivity analysis results suggest that the combined decision framework is sensitive to the technical criteria. As a result, they must be given serious attention during the pragmatic decision-making process by identifying the design and requirements of the country’s power generation industry ahead of time. An integrated system combining gasification and AD technologies, rather than their standalones, might be ideal for the achievement of a well-balanced performance across various criteria. Similar studies conducted in the past have suggested that a waste-to-energy hybrid system that combines anaerobic digestion and gasification is not only cost-effective but also offers the greatest potential for recovering energy from waste ,91.The current study applied an integrated multi-criteria decision-making method to the optimal selection of waste-to-energy technologies for implementation in Ghana. The analytical hierarchy process, which allows a decision-maker the flexibility to quantitatively rank criteria with respect to the overall goal using priority calculations, was applied to determine the weights of the evaluation criteria. Consequently, the fuzzy TOPSIS method, which helps to reduce ambiguity and vagueness in human judgement using linguistic variables, was used to rank WtE alternatives. In this study, four WtE technologies  were evaluated against twelve indicators covering technical, economic, environmental, and social criteria.The priority weighting results of the main criteria indicated that technical criteria are the most important, followed by economic and environmental criteria, while social criteria ranked as the least important in the selection process. Amongst the sub-criteria, the power generation capacity was given the highest consideration, while health impacts obtained the lowest weight. The overall ranking results of the WtE alternatives reveal that anaerobic digestion is the most suitable technology for adoption in the Ghanaian context, given the relative importance of the key criteria. Gasification and pyrolysis ranked in second and third place, respectively, while plasma gasification was considered the worst alternative. In order to determine the stability and robustness of the proposed model, a sensitivity analysis was carried out to evaluate the degree of reliability of the obtained results. The results demonstrated a high degree of consistency and robustness, irrespective of the variations in the baseline scenario. It was also deduced from the sensitivity analysis that if more emphasis is placed on the most critical criteria , the optimal selection shifts towards gasification technology; therefore, a hybrid system combining both anaerobic digestion and gasification technologies might be more viable for energy generation and waste management in Ghana.2 emissions, which can assist Ghana in accomplishing its goal of reducing GHG emissions. In addition to producing green electricity, these two technologies can have important urban, industrial, and transportation applications to improve the socioeconomic and environmental needs of the country. For instance, the production of biogas from anaerobic digestion may promote clean cooking and provide a solution to numerous socioeconomic and health issues. Liquid bio-carbon dioxide, a byproduct of converting biogas and syngas, may be stored and used as a chemical platform for various industrial purposes. In order to enhance green transportation, high-quality fuels may be generated, such as bio-methane (biogas from anaerobic digestion) and hydrogen gas (syngas from gasification). In order to realize the above-mentioned practical implications of the anaerobic digestion and gasification technologies, this paper recommends that the decision-makers and stakeholders adopt the following measures: (1) create avenues for foreign investment and establish particular financial assistance for industries that use both technologies for MSW treatment, such as low interest and subsidies; (2) make conscious efforts to set realistic targets that increases the share of WtE technologies in Ghana’s total installed renewable generation capacity; (3) train more engineers in these two technologies to enhance the skill set; and (4) encourage proper waste segregation among the public in order to improve the efficiency of both anaerobic digestion and gasification.According to Ghana’s current renewable energy master plan, electricity generation from MSW is anticipated to reach 50.1 MW of utility-scale power in 2030 in order to contribute to the country’s overall energy supply mix while reducing the environmental consequences of energy production. The implementation of a hybrid of anaerobic digestion and gasification, both of which are suitable renewable energy production technologies, can help to achieve this goal. The municipal solid waste composition in Ghana is particularly suitable for energy recovery by these two technologies. The two technologies also have the benefit of producing little to no COIt is worth noting that the current work has its share of limitations. First, the proposed model relies solely on experts’ judgement to rank various WtE alternatives, thereby lacking a certain degree of objectivity, which could be supplemented by using actual data with units to express the alternatives in relation to the selection criteria. Thus, future studies should develop a method for the integration of experts’ judgement with the input data of various alternatives in order to obtain an optimal selection so that the benefits of both subjective and objective techniques may be realized. Next, the twelve selection criteria—spanning only technical, economic, environmental, and social factors—taken into account in this study appear not to be all-inclusive. In order to guarantee that the evaluation procedure is thorough, future studies must consider additional selection criteria, including those related to political, regulatory, and institutional capacity. These are needed in order to emphasize the concept of a circular economy in waste management and the production of renewable energy. Furthermore, with the development of new and better technologies in the global WtE market, research in the future can assess the feasibility of new and growing alternatives for integration into the Ghanaian system. In the future, a specialized study will be required in order to assess the conditions under which WtE projects and infrastructure may be modelled to fit the country’s specific needs.


Advances in digital medicine now make it possible to use digital twin systems (DTS), which combine (1) extensive patient monitoring through the use of multiple sensors and (2) personalized adaptation of patient care through the use of software. After the artificial pancreas system already operational in children with type 1 diabetes, new DTS could be developed for real-time monitoring and management of children with chronic diseases. Just as providing care for children is a specific discipline—pediatrics—because of their particular characteristics and needs, providing digital care for children also presents particular challenges. This article reviews the technical challenges, mainly related to the problem of data collection in children; the ethical challenges, including the need to preserve the child's place in their care when using DTS; the legal challenges and the dual need to guarantee the safety of DTS for children and to ensure their access to DTS; and the societal challenges, including the needs to maintain human contact and trust between the child and the pediatrician and to limit DTS to specific uses to avoid contributing to a surveillance society and, at another level, to climate change.  Throughout history, the practice of medicine has been constantly impacted by technological advances and societal developments. The first 2 industrial revolutions led to the development of new techniques for obtaining new information about the human body, resulting in the industrial collection of objective and quantitative data in the 20th century, including sensor-based physiological , biological, imaging, functional test, and increasingly “omics”  data . From thDTS comprise a physical element—the patient—a cyber element—the patient's DT—and 2-way interactions between the physical and cyber elements: Sensors transform the patient's signal into the patient’s DT, and software processes them to act through recommendations to the physician or automated adaptations on the patient's management . In the The challenges associated with the use of DTS have already been discussed -10. HoweThe first requirement of digital medicine is digital data. However, both accessing and processing digital data are more complex for CYP than for adults.Smartphones have a central role in digital health by enabling (1) data collection through built-in functions  and interfaces for patient-reported outcome measures, (2) connection to the IoT via a Bluetooth connection, and (3) patient feedback. In the best-equipped countries, smartphone ownership is limited to 25%, 50%, and 75% of children aged 8 years to 9 years, 10 years to 11 years, and 12 years to 13 years, respectively, complicating the use of digital health interventions for the remote management of children’s diseases . FurtherAll parents have experienced the recurrent changes in size of clothing and shoes as children grow. A child's height doubles in the first 4 years, from an average of 50 cm to 100 cm, and then increases again by at least 1.5 times over the next 15 years. Similarly, a child's abilities develop impressively, from the infant who does not yet walk and talk to the adolescent capable of the most extreme sports and complex reasoning. Finally, in the medical field, physiological values vary constantly with the age of the child , as do the expected results of additional tests. This complicates the task for manufacturers of connected devices who need to provide different model sizes, develop appropriate interfaces for children and adolescents of different ages, and adapt the standards for physiological parameters according to the age of the child. These adaptations are not without risk: In the field of cardiac medical devices, an attempt to miniaturize an implantable cardioverter defibrillator resulted in a higher risk of failure in young patients . In the The use of connected objects to collect data from children poses particular risks. Similar to a toy with a defective design, children may choke on, or simply ingest, small parts that may come off the object; they can also be exposed to chemicals that are carcinogenic or mutagenic or to endocrine-disrupting substances. Thus, devices intended to collect data from children’s bodies must be manufactured taking into account the additional risks they may pose to children, which is another challenge compared with manufacturing devices for adults.In the other direction, children pose specific risks to the connected objects. In the same way that children regularly come home having lost or broken their glasses, connected objects are more likely to be lost or broken when used with children than with adults. This is the consequence of their age-related activities but also of a less cautious attitude toward their personal belongings. For example, connected inhalers that automatically record children's use of asthma medication were reported lost or damaged by up to 50% of families ,19.CYP pose particular problems when it comes to creating models with traditional supervised approaches from real-world data. The amount of data available is lower, due to the young age of patients , logistical and legal difficulties in obtaining the data, and the lower prevalence of diseases in children than in adults in high-income countries . FurtherThe main ethical question related to the use of digital health in pediatrics is whether the use of DTS is in the best interests of the child. The notion of “best interests of the child” is derived from Article 3 of the United Nations Convention on the Rights of the Child . This isAutonomy means being governed by oneself, in thought and action . As the Pediatricians encourage children to talk about their symptoms themselves to recognize their place as individuals in the consultation but also because their statements are frequently more accurate than those of their parents . A firstCurrently, CYP gradually become responsible for the day-to-day management of their disease, in agreement with their parents and pediatricians. This process can be delayed if DTS are complex to use, whether the complexity is due to the hardware or software components of the DTS. Conversely, DTS can also increase children's autonomy in managing their daily care if they rely on easy-to-use devices and software: In the case of type 1 diabetes, most CYP and parents reported greater control and autonomy in managing their diabetes with insulin pumps after an initial learning period than with injections .Beneficence refers to the responsibility of professionals to promote the well-being of their patients . It is cBy improving their health, DTS are expected to enable children to participate more in play and recreational activities. However, DTS can also prevent children from playing or participating in certain sports: Children using the artificial pancreas system for their type 1 diabetes have reported difficulties when playing sports due to the tendency of the insulin pump to fall . Using aChildren need to be protected from chronic stress in order to be in the best condition to develop their ability to control their emotions, focus on tasks, and form healthy social relationships ,33. DTS Children need to interact with their peers, including forming strong friendships, to develop their social skills . Again, The principle of justice refers to the delivery of equal treatment and care according to the particular patient’s needs, as well as just allocation of available resources . ProvidiIn conclusion, ethical dilemmas can arise in many ways when developing a DTS for CYP, given the need to preserve the health of the child; preserve their physical, mental, and social development; and be anticipated.As stated by the policy guidance on AI for children from the United Nations International Children's Emergency Fund, children need protection from AI (do no harm) and provision of effective AI systems (do good) . In healChildren need special protection from DTS risks for 2 reasons: They have special characteristics that expose them to greater or potentially different risks in comparison with adults, and they are developing beings who may face dramatic consequences for the rest of their lives if their health is adversely affected. Because of these specificities, the approval of a drug for children is subject to a specific process in the United States and European Union. Sponsors are required to provide a pediatric study plan to the Food and Drug Administration (FDA) in the United States or its equivalent pediatric investigation plan to the European Medicines Agency (EMA), which are reviewed by specific committees taking in account pediatric considerations: the FDA's Pediatric Review Committee in the United States and the EMA's Paediatric Committee in the European Union .DTS are not subject to this regulation. Under the current regimes, they are considered medical devices and not medicines. The regulation of medical devices, although recently strengthened in the European Union by the regulation 2017/745 . In the If legislators must protect children from the risks that a DTS could pose, they must also protect children from the risk of not having access to DTS that could improve their health. Industry has consistently been less interested in developing drugs for children because of the small number of children affected, increased regulatory constraints, and difficulty of conducting clinical trials in this population, and this trend continues with medical devices including DTS -52. As aIn conclusion, the risk is that, in the absence of legislative constraints or incentives, market forces will continue to widen the gap between the quantity and quality of DTS developed for adults and children.DTS in pediatrics is currently limited to a few indications , and pediatricians prescribing DTS are unlikely to feel they are participating in societal change. However, in combination with other digital transformations in society, DTS are likely to lead to profound changes in our lifestyles with important consequences on children. Apart from the risk of exacerbating social inequalities already detailed, DTS can contribute to several societal challenges .If most medical decisions are made autonomously and remotely by DTS, this would result in fewer in-person visits to pediatricians, in line with a more general trend of reduced face-to-face interactions in society . The risThe use of DTS in pediatrics may contribute to a growing distrust of pediatricians. The digitalization of the world, whether through access to medical information on the Internet or conspiracy groups on social networks, has already increased parents' distrust of their pediatricians ,57. FamiThe use of DTS requires the collection of a large amount of data, which are increasingly being collected continuously from the child's home or even body. This constant monitoring of children to ensure their safety is a general trend in our societies, in the same way that the proliferation of surveillance cameras was intended to ensure the safety of the population. It can lead to a surveillance society where everyone watches each other, with children being a main target because of their vulnerability. Parents could demand to monitor their children at all times for their safety, since the data are already available, being continuously collected for health purposes. Pediatricians may also ask to use indirect information about parents' behavior collected from the home devices of the child to monitor the risk of child abuse. If such a surveillance society was to emerge, children would grow up in an optimized state of physical health but would probably be more anxious, dependent, and conventional in adulthood, unable to make decisions on their own values ,60. AgaiClimate change is one of the major challenges of our century. Young people understand this and are actively campaigning for policies to reduce carbon emissions . CYP are2 emissions equivalent to those of a passenger making 300 flights between New York and San Francisco [Among the sectors responsible for greenhouse gas emissions, the digital sector is growing in importance every year, increasing in the contribution to global emissions from 2.5% to 3.7% between 2013 and 2019 . Medicalrancisco . Thus, erancisco . Conversrancisco . This wiThe use of DTS for children poses specific challenges at individual and societal levels .Since the values at stake are at different levels , the ethical approach that seems most appropriate when developing and evaluating a DTS is that of “value pluralism” . This apThe evaluation of a pediatric DTS must balance the expected effects on the child's health and its beneficial consequences  against the risks posed by the DTS, whether individual , societal , or global (climate change). This specific evaluation should be supported by specific legislation on pediatric DTS and by incentives by governments and private foundations to promote children's access to DTS. Indeed, children should not be deprived of DTS, which, if effective, could be a real game changer in the management of their diseases.


This article describes the process of creating a prehabilitation unit in our center and the role of perioperative nursing. Initially, the project was launched with the performance of a research study on prehabilitation for gastrointestinal cancer surgery. The results of this study encouraged us to continue the implementation of the unit. Progressively, multimodal prehabilitation programs focused on each type of patient and surgery were developed. Currently, our prehabilitation unit is a care unit that has its own gym, which allows supervised training of cancer patients prior to surgery. Likewise, the evolution of perioperative nursing in the unit is described: from collaboration and assistance in the integral evaluation of the patient at the beginning to current work as a case manager; a task that has proven extremely important for the comprehensive and continuous care of the patient.Multimodal preoperative prehabilitation has been shown to be effective in improving the functional capacity of cancer patients, reducing postoperative complications and the length of hospital and ICU stay after surgery. The availability of prehabilitation units that gather all the professionals involved in patient care facilitates the development of integrated and patient-centered multimodal prehabilitation programs, as well as patient adherence. This article describes the process of creating a prehabilitation unit in our center and the role of perioperative nursing. Initially, the project was launched with the performance of a research study on prehabilitation for gastrointestinal cancer surgery. The results of this study encouraged us to continue the implementation of the unit. Progressively, multimodal prehabilitation programs focusing on each type of patient and surgery were developed. Currently, our prehabilitation unit is a care unit that has its own gym, which allows supervised training of cancer patients prior to surgery. Likewise, the evolution of perioperative nursing in the unit is described: from collaboration and assistance in the integral evaluation of the patient at the beginning to current work as a case manager; a task that has proven extremely important for the comprehensive and continuous care of the patient. Despite advances in surgery safety, postoperative complications continue to have a marked negative impact on clinical outcomes, quality of life, and economic costs ,2,3. CurPrehabilitation programs are patient-tailored, typically multimodal, and address several aspects for improving risk factors. The combination of exercise training together with other measures such as optimization of nutritional status, especially in cancer patients, and psychological support  are propDespite this growing evidence, prehabilitation has not yet become part of standard referral practices by surgeons at most centers. Prehabilitation programs should be designed from a multidimensional approach taking into account all stakeholders involved . An implementation research approach, combining experience-based co-design and quality improvement methodologies, was recently conducted at Hospital Clinic de Barcelona . As partThe aim of the current report is to summarize from a more pragmatic point of view the process of implementing a prehabilitation unit as an integrated care program at the Hospital Clinic of Barcelona (HCB), emphasizing the evolution of the role of perioperative nursing, from its beginnings as an expert aid to the anesthesiologist until its consolidation as a case manager at the present time.Prehabilitation is a complex process that requires a multidisciplinary team, proper facilities, and adequate equipment. To be efficient and adopted by individual patients, an adequate design of patient workflow through the perioperative pathway, where many professionals are involved, is crucial. Hence, the deployment of prehabilitation in a real-world scenario involves several logistic challenges.After almost a decade from our first study, the prehabilitation service at HCB currently receives more than 200 high-risk surgical referrals per year from different surgical specialties. Patients are referred by the surgeon or the anesthesiologist after the preoperative assessment. The intervention is carried out by a multidisciplinary team led by the Anesthesiology Department combining hospital and community-based actions. The phases of the implementation process of the unit are described below. The unit is fully focused on the high-risk surgical patient, who is empowered to participate in his/her surgical process in order to achieve the best possible results. The sense of accompaniment by the members of the unit during this stressful process is also a key aspect for the success of the intervention .Throughout the years, the demands of an increasingly complex healthcare system have meant that the contribution of nurses has undergone a process of redefinition. Whereas in the past nurses were not considered integral members of a clinical care team (despite their responsibilities), today they have much more autonomy and recognition, enjoying an increasingly collaborative relationship with physicians and other healthcare professionals. In our opinion, nurses may play a key role in delivering appropriate and coordinated care interventions before surgery, to prevent postoperative complications and enhance surgical recovery. Prehabilitation is based on the concept: everyone working together as a team caring for the preoperative optimization of the patient. The nurse would act as a trusted first point of contact to reassure patients and link them with the rest of the team.Generating solid evidence was our first step. We began with a three-year (2013–2016) double-blind RCT that evaluated the efficacy of prehabilitation in high-risk patients undergoing major gastrointestinal surgery. Patients at high risk for postoperative complications were specifically included in the study . The preIn this phase, the role of perioperative nursing was to collaborate supporting the team for the initial assessment of the patients, as well as in the pre-surgery re-evaluation. Once visited by the anesthesiologist, the tasks performed included assisting the patients to fill in the different questionnaires predetermined in the study as well as collaborating in performing physical functioning tests such as the 6-Minute Walk Test (6MWT) the 30” Sit-to-Stand Test or the hand-grip strength. These tests were repeated in the pre-surgery evaluation of the patients. In addition, nurses were also involved in performing administrative assistant tasks such as receiving and registering patients in the hospital’s local system.Following these encouraging results, the prehabilitation service was deemed ready for implementation as a mainstream service at HCB, leading to the creation of the Prehabilitation Unit in 2016. During the initial 12 months period, the main purpose was to demonstrate the feasibility of a real-world prehabilitation program in a tertiary care hospital, and to generate the service workflow design of the intervention. Due to limited resources and program capacity, patients with lower baseline functional reserve were prioritized since they were more likely to benefit from prehabilitation [A preoperative schedule that allowed at least 4 weeks of prehabilitation was designed . In addiPatients initiated the program after the preanesthetic visit. On the first day, a holistic characterization of the patients was carried out as patients were introduced to the multidisciplinary team and the three pillars of the program. Identification of relevant clinical features and comorbidities ,20, asseAt that time, due to the absence of specific facilities for supervised training, the program only included home-based physical training and a personalized promotion of physical activity plan designed after a motivational interview with the physiotherapist. Replacing sedentary time with physical activity by increasing the amount of daily movement in short sessions is a well-known strategy to improve health, especially in previously inactive individuals . SpecifiThe implementation of a multidisciplinary unit made the flow of patients somewhat more complicated. On a typical day in the unit, patients who came for the first assessment mixed with those who had follow-up visits with one or several professionals, in addition to those who came for their pre-surgery evaluation. Nurses began to perform other tasks such as the coordination and scheduling of all activities, accompanying and guiding the patients during the program, and informing patients and caregivers about the surgical process.In 2017, the availability of an appropriate space for a hospital gym made it possible to consolidate a supervised training program in this second period. Endurance exercise training is a key component of prehabilitation as it is the best approach to improve aerobic capacity. Particularly, high-intensity training can be more effective than moderate-intensity exercise training or an unsupervised home exercise program when limited time is available to achieve changes in oxygen consumption ,33.As an integrated care service , there w® Excite Bike; Cesena; Italy) and muscle strength training  two or three days a week under the supervision of the physical therapist in the unit’s gym. This program is prioritized for the most deconditioned patients and for the most aggressive surgeries.Supervised training: This modality includes 45 min sessions of high-intensity supervised interval training with a stationary cycle ergometer or treadmill : Patients who for different reasons cannot attend the supervised training program  are referred to this modality, which includes a weekly session supervised by the physiotherapist to reinforce instructions, assess progress, and review goals and two or three non-supervised sessions at home or local gyms.(1) Personalization of an aerobic and resistance exercise program, respiratory incentive, and promotion of physical activity. Therefore, two modalities of the program were developed with physiotherapists deciding which is more appropriate for each patient The programs currently available are:The exercise training is performed on top of encouragement of daily physical activity through fitness trackers or pedometers.(2) Optimization of nutritional status. When individuals increase their level of physical activity, they need an adequate protein substrate to cope with the gain in muscle mass during this period. In addition, certain pathologies condition an inadequate intake and the surgery itself frequently determines prolonged periods of fasting. The MUST  has been used as a nutritional screening tool. In addition, the GLIM   has beenThe nutritional intervention is managed by a registered dietitian. Individualized diet therapy is recommended, and nutritional supplementation is prescribed if it is necessary.In addition, those patients with documented iron loss or with gastrointestinal cancer and anemia (Hb < 12 g/dL) are treated with intravenous iron.(3) Psychological intervention. One weekly 2 h session of mindfulness is organized, and open to all patients who wish to attend upon request. In the unit, there is also a reference psychologist who can visit patients individually depending on patient needs.https://eithealth.eu/product-service/paprika, accessed on 30 July 2022) developed by the prehab team. The team conceptualized the need for covering: (i) patients’ accessibility and empowerment; (ii) enhanced management of care paths; and (iii) collaborative work between two or more stakeholders , eventually from different healthcare tiers/providers. The application tracks patients’ daily steps, sends motivational messages, and provides access to educational material in the three main pillars  [Since 2019, the prehabilitation program has been complemented by a digital solution (support) .The duration of the prehabilitation program is mainly determined by the waiting time until surgery. At the end of the program, a re-evaluation of the patient’s functional, nutritional, and psychological status is performed before surgery, in order to monitor the effects of our intervention. At the same time, patients are asked to complete a satisfaction survey to evaluate their experience with the care provided, and the SUS   questionSince our unit is targeted to high-risk patients, especially those with chronic complex conditions, it is necessary to provide more joined-up care (continuum of care). Typically, such patients are under the care of multiple therapists  as well as the broader health and social care apparatus . Therefore, this complexity of closely collaborating multilevel caregivers and their patients needs a professional to act as a “common thread” dedicated to ensuring that the program provides the maximum preventive effect in an efficient manner. Good coordination among all individuals involved in the program would result in better patient experience, better care team cooperation, and fewer unplanned events.Therefore, the role of nursing evolved in this phase from collaborating in the evaluation of patients and accompanying them during visits, to carrying out case management tasks. Case management is defined by the Case Management Society of America (CMSA) as “a collaborative process of assessment, planning, facilitation, care coordination, evaluation and advocacy for options and services to meet an individual’s and family’s comprehensive health needs through communication and available resources to promote patient safety, quality of care, and cost-effective outcomes” ,40,41,42Patients’ follow-up is also performed by the prehabilitation nurses . They ma®) [Another important task performed by nurses is the preparation, updating, and monitoring of the unit’s database (Redcap®) ,44, in oCurrently, the multimodal prehabilitation program has been consolidated at HCB as a standard service for approximately 250 candidates per year undergoing major surgeries in different specialties. It is of note that the capacity of the prehabilitation unit covers less than 20% of the estimated demand, mainly due to the limited capacity of the exercise training facilities at HCB. This aspect, together with patients’ logistic/accessibility limitations, prompted a pilot project offering physical training for selected patients in the community. The monitoring of physical activity is carried out through our digital solution if patients have a smartphone, or by telephone otherwise. In addition to the explained tasks, nurses have an important role in patients’ remote monitoring in this phase. As such, they act as the reference person for patients within the prehabilitation team so that patients can discuss any doubt regarding the program or the surgical process either by the app or by phone, and prehabilitation nurses can solve these questions themselves or refer them to the specific professional if necessary.Prehabilitation nurses are a key part of the team since they maintain the continuity of care, ensure the proper use of resources, and accompany patients during the whole process, acting as a link between them and the rest of the team. Their tasks have been extended  parallelThe creation process and evolution of a prehabilitation unit in a tertiary hospital have been described. The initial project evolved from a research study to a multidisciplinary prehabilitation unit for high-risk patients scheduled for different types of major surgical procedures. Currently, the unit provides assistance to a large number of patients, most of them frail patients with high comorbidity. The role of perioperative nursing has also changed parallel to the development of the unit, with nurses becoming mainly case managers with tasks involving patient accompaniment, assessment, and coordination, in order to improve patient care.