Improper foot biomechanics associated with uneven bodyweight distribution contribute to impaired balance and fall risks. There is a need to complete the panel of commercially available devices for the self-measurement of BMI, fat, muscle, bone, weight, and hydration with one that measures weight-shifting at home as a pre-specialist assessment system. This paper reports the development of the Early Notice Pointer (ENP), a user-friendly screening device based on weighing scale technology. The ENP is designed to be used at home to provide a graphic indication and customised and evidence-based foot and posture triage. The device electronically detects and maps the bodyweight and distinct load distributions on the main areas of the feet: forefoot and rearfoot. The developed platform also presents features that assess the user’s balance, and the results are displayed as a simple numerical report and map. The technology supports data display on mobile phones and accommodates multiple measurements for monitoring. Therefore, the evaluation could be done at non-specialist and professional levels. The system has been tested to validate its accuracy, precision, and consistency. A parallel study to describe the frequency of arch types and metatarsal pressure in young adults  was conducted to explain the importance of self-monitoring at home for better prevention of foot arch- and posture-related conditions. The results showed the potential of the newly created platform as a screening device ready to be wirelessly connected with mobile phones and the internet for remote and personalised identification and monitoring of foot- and body balance-related conditions. The real-time interpretation of the reported physiological parameters opens new avenues toward IoT-like on-body monitoring of human physiological signals through easy-to-use devices on flexible substrates for specific versatility. Approximately 1.71 billion people are diagnosed with musculoskeletal disorders (MSD), limiting mobility and dexterity, causing disability, and leading to a low quality of life worldwide. The highest prevalence of lower back pain among MSD patients, 568 million people, is the leading contributor to MSD-related disability in 160 countries, raising concerns mainly because the prevalence increases with age, although young people are also affected, and the incidence increases more rapidly in low-income and middle-income countries. Moreover, it is generally acknowledged that pronated  or supinated (high arch) feet are among the leading causes of pain and injury in the foot and ankle area ,3. The iRegrettably, people of all ages present for clinical assessment only when irreversible changes and chronic conditions have occurred  when diaSpecialised literature acknowledged that other non-standard balance assessment methods have their equivalent numerical assessment parameters, and there is little similarity between these parameters and between different methods of corresponding evaluation . Some plStrain of joints, muscles, ligaments, and plantar soft tissues . CliniciWe, therefore, report the development of a wireless platform—Early Notice Pointer (ENP) for the self-assessment of bodyweight, body load distribution on both the right and left forefoot and rearfoot, and early detection of body imbalance. The designed and developed system can measure and map the weight shifting of users as pre-assessment data. The prototype comprises pressure sensors coupled with specific electronic elements to detect and transmit data to a mobile phone and, based on a predetermined setpoint, to display results graphically and numerically for straightforward interpretation and distant communication with the specialists. The prototype has been internally validated against available marketed devices for clinical compliance. The ENP determines user weight distribution and can alert the user when the weight distribution is above or below average level. Therefore, it assists its users with an informed decision regarding their presentation for specialist consultation and specific treatment. Furthermore, the ENP is designed for everyone who wishes to monitor their bodyweight, posture, and plantar pressures: healthy individuals who may detect any affected biomechanics before any clinical signs and symptoms begin and patients who wish to monitor and detect any complications of their biomechanics-related conditions in a timely manner. Since it offers wireless data transmission and emits an early warning regarding the bodyweight distribution cum balance, it could work as an IoT-like device for point of care testing (POCT) for personalised medicine. In conclusion, the ENP is a potential screening device for the home-based evaluation of bodyweight, balance, and plantar pressure for effective prophylaxis of biomechanics-related conditions.Methodology refers to the design of the prototype and the testing for validation. Additionally, a study of foot arch and balance discusses variables such as bodyweight, body height, and carrying bags concerning foot arch and posture.The ENP resembles normal weighing scale functions complemented by the capability to compare the load distributions on a person’s feet and calculate the relative imbalance of the load distributions. Force Sensor Plates: Two identical force plates with compression load cells (ISVASIA SINGAPORE PTE LTD), used for measuring the left and right foot weight loads.Signal Processing: PCB with 8 INA125P (a 16-pin Instrumentation Amplifier) (Texas Instruments) to filter, amplify, and remove noise.Arduino Mega Development board with Wireless transmitter: uses 8 out of 16 analogue inputs (Arduino LLC).Cellphone/tablet Display: User interface for the measurement, analysis, and display of results.Remote Raspberry Pi for MySQL Databased: A pocket PC for hosting Database services for storage records for post-analysis (Adafruit Industry).The screening platform is designed as a flat plate that displays the measurement results in a simple and direct manner. The eight sensory cells collect point weight readings and are distributed symmetrically over the four corners of each foot to collect pressure data from corresponding areas of the right and left foot. The circuits and sensory cells were joined for stability since the platform is transportable. The ENP has a specific workflow  for userThe software can be used in website and mobile phone modes. The website mode starts once the sensing platform and the computer are connected. Premeasurement setup checks are performed automatically to ensure the sync with the database. Furthermore, manual inputs via Home Tab are allowed to correct the initiation. Once the initiation is performed, the Home page displays the twelve most recent user data, as weight chart, percentage by the part chart, weight table, and percentage table a,b. DataThe Mobile phone mode starts upon installing the App, pairing with the Bluetooth, and initialising the check to display the weight and percentage distributions. The platform was programmed using a prototype development board that provided connectivity and a battery shield and enabled easy troubleshooting, fast prototyping, and proof-of-concept.The prototype evaluation followed internal and external validation which involved human subjects. To be eligible to participate in the validation study, participants had to be healthy with no known foot biomechanics-related problems. The study was approved by the Republic Polytechnic Singapore Institutional Review Board (IRB), written informed consent was obtained from all participants, and IRB guidelines were followed in all procedures with human participants .The cutoff was set to determine the level of distribution based on the data collected from 150 subjects . The group studied was a population aged 19 to 70 years with no orthopaedical or podiatric medical history, including no injuries and no deviation of the foot arch.2 = 1) showed the consistency of the signal and confirmed the final placement of sensors and circuit processing units. From the users’ perspective, monitoring the procedure will be under the direct supervision of users or helpers, and the results will be available via mobile phone to both the users and specialists.The design ensures that demographic and anthropometric parameters do not influence the results. First, the ENP has a specific workflow  and usesTwo stages of testing and validation (as a proof-of-concept study) were conducted to compare the prototype’s performance with commercial low to high-end equipment for internal and external validation. The newly created prototype has been tested for validation against specific equipment: two commercial, personal weighing scales (max 150 kg) and two foot scanners .The first stage was the internal validation to compare the weight output given by the prototype with the one given by validated weight scales. The first stage of validation on multiple commercial weighing scales was performed to calculate and compare the standard deviation of the weight output from the prototype. A user’s bodyweight was measured 50 times. Further tests were conducted to reduce the variance in weight with multiple types of scale. Eventually, the testing compared the prototype with the most stable scale.In the second stage, testing for external validation was conducted to validate the weight distribution accuracy against two commercially available professional feet scanning equipment. Therefore, the prototype was used to measure plantar pressure and body sway. Three measurements were performed for each of the enrolled subjects upon informed consent. The foot arches were observed to set up the average profile and the setpoint for the prototype (green colour in t-test (the measure of a difference between the scanners compared) to validate our product’s performance against the two validated foot scanners (Matscan and RS FootScan). The significant preliminary variance was corrected with further fine-tuning to stabilise the device. The measurements were compared with the developed prototype’s results and modifications were implemented to improve the prototype.Data were collated and analysed for significance using a The topmost added functionality resides in implementing a particular software developed in parallel with the theoretical research and screening for analysing and correlating the measurements. The developed prototype includes a more sophisticated method of analysis: the load distribution is measured not only as a difference between the right and left leg but was distinguished, for instance, through a limited number of pressure sensors, specifying the load on the different areas of the foot, the outside and inside edge load. Setpoints were established based on the existing standards for bodyweight and plantar pressures and the mean values of the data collected during the validation testing. The analytical algorithm  enables This highly sensitive platform can screen for body imbalances possibly caused by the foot arch type as a home use device. Therefore, it can issue a timely warning message to the user and indicate the need for specific tests to rule out possible foot arch- and posture-related conditions.Since the structures within the musculoskeletal system, bones, muscles, ligaments, and tendons, maintain the correct posture and respond to external factors such as footwear and loaded bags, monitoring these factors could trigger early awareness in young individuals before any related anatomical changes become permanent.The parallel study regarding the foot arch and balance was conducted in a population of 1034 healthy young adults (18–23 years old) with no orthopaedical or podiatric medical history, including no injuries and no deviation of the foot arch.TM mobile phone application provided the measurements for the posture evaluation. The anatomical landmarks observed to evaluate posture are presented in This study describes the frequency of foot arch types and metatarsal pressure on both right and left feet. It also investigated the relationships between a few anthropological parameters such as the bodyweight, height, pressure distribution on forefoot and rearfoot, and body posture in 54 subjects of the same age (20 years old). Three foot arch scans  were performed for each subject enrolled to identify the foot arch types . A wall-mounted Posture Chart was used to analyse the subjects’ body postures before and while carrying schoolbags. Three measurements were performed for each subject and each school bag type used. The three school bag styles used were the most popular among students . Observational and inferential statistical analysis was used to evaluate the frequency of higher metatarsal pressures and the correlations between the shoe size and the type of foot arch, the loaded torso and the anatomical landmarks’ deviations, and the changes in the front and back plantar pressures.The ENP relies upon the Working Principle of the Weighing Scale andcompares the load distribution between the two feet of a user,calculates the relative imbalance of the load distribution,issues an early warning if measurements exceed certain thresholds (the range of acceptable norms derived from the research data),ensures transmission of data in an IoT manner.The alternative display of weight and percentage charts is from the website. These diagrammatic representations in This chart is a per point display based on the total weight measured, and the accuracy is verified following the summations:The formulas above describe the working principle of the ENP: it is a two-weight scale concept with eight-point load cells compared with the four points in a classic weight scale. The measurement result includes the values from the left and the right scales as in Formula (1). Similarly, Formula (2) shows that the total weight can be extrapolated from the loads measured by the cells at the Toes and Heels. Further analysis of the values corresponding to both sides of Toes and Heel (left and right) also leads to the total weight as presented by Formula (3). The colour code is for each section corresponding to the right and left toes, heels, and right and left sides. For instance, if more than the average + 1.5% was recorded by a cell, the result was considered a warning for that pressure cell a: Left. t-test indicated the measurement stability, with no significant differences in the measurements between the prototype and the commercial scanners . This study introduces data to support the need for early balance monitoring and supports the observation that daily tasks such as standing or carrying bags can influence posture. Therefore, in the long run, these effects could be related to improper posture and biomechanics-related locomotor diseases. The bodyweight distributions on the right and left feet were measured and no significant difference between the right and left feet was observed in the studied population (t = 0.9 for 0.05 significance level).Independent measurements presented the relationship between the type of school bag and the change in body posture in a population of 54 young, healthy subjects (18–23 years old) to strengthen the importance of screening and analysis of body posture in a population at the personal level, and to reinforce self-monitoring and healthy habits. The observations in Similarly, body posture was analysed by observing the load shift in the torso and pelvic balance on the posture chart. The measurements were conducted in correlation with school bag wearing of a backpack, tote bag, or sling/crossbody bag. The results indicated the positive correlations between the shift in the torso and pelvis while carrying the bags.Similar to Generally, assessing body balance includes static, quasi-mobility, and mobility tasks. Since they often complement each other, numerous assessment methods have been developed in recent decades with complex and corresponding balancing devices and measurement tasks. However, most of them, which serve as diagnostic tools, are complex and require professional skills for interpretation ,26,57,58Furthermore, a clear display and easy access to data makes the scanner user-friendly. The ENP resembles a standard bathroom scale with additional functionality, mainly readings on the display window with Bluetooth or Wi-Fi data communication to a personal electronic device. The design of the ENP is straightforward to address patients’ immediate and easy measurement needs, compared with the complex and multifunctional analytical instrumentation available which is used in specialised clinics for professional investigation. The ENP distinguishes the distributed weight through a limited number of pressure sensors outside and inside edge load, repetitive tests, or tests with a determined position of the feet . Moreover, it specifies the fore- and rearfoot loads and displays them in various forms for easy reading and understanding of their significance. The colour codes establish fast recognition of the body balance status, so the user immediately ascertains any changes and decides to either store and monitor locally or transmit the data to a specialist.Meanwhile, the map displayed shows the weight shifts and guides the user towards instant readjustment. For instance, the user will visualize the weight distribution on both sides, right and left or forward and backwards. This distribution directly indicates an imbalanced body and signals risks of falling and injuries ,63,64. TAnother factor influencing healthcare is how data is transmitted and processed. The ENP prototype as an IoT-like device connects the user to healthcare providers for real-time monitoring and therapeutic decisions. The recorded data transmission is secured via protected channels. For instance, the users will activate a virtual private network (VPN) when they create a password-based login into the system prior to starting any measurement with the ENP. Furthermore, the medical systems are protected by special software to ensure the confidentiality of the data. Therefore, when using these channels, the medical specialists in the clinical settings will secure patient registration and medical information. This belongs to the larger group of remote healthcare assistance which support personalised diagnosis and treatment and specialised medical programs for remote areas or during at-home isolation imposed by epidemiological surveillance .Since monitoring posture is one simple observational method, it is a task that, once performed at home, could teach healthy individuals or patients with biomechanical-related conditions about any changes in body balance and possible causes such as the foot arch type. The foot arch and posture study highlighted that 31% of subjects were high archers and 15% were flat footers in the young, healthy population. However, the results showed slight backwards leaning to maintain balance at the expense of body stability. Therefore, the posture trends (the regression model) in the observed population stress the need of knowing the type of foot arch. For instance, young and healthy people with a deviation from a neutral foot arch who are aware of the consequences of this foot type  can improve the mechanics in the ankle with adequate footwear to maintain the correct posture and musculoskeletal system homeostasis . Such exThe regression models presented the correlation between the bodyweight and the two main plantar foot pressures, forefoot and rearfoot, under observation. When a correlation is measured, the two observed variables develop in ways that may explain the body’s responses to changes in the body balance and the tendency to adapt; the deviation from a balanced posture may be in the same or opposite direction. For instance, the negative correlation in Furthermore, the relatively strong positive correlations between schoolbags and the plantar pressures in The observed tendency of leaning backwards while standing and the relationship between the metatarsal pressure and the type of foot arch ,78,79 emEven though the significance of the correlation varies, the information transmitted contributes to a person’s knowledge and decisions regarding the maintenance of a healthy posture by carrying adequate types of bags correctly. Demonstrating the influence of carrying bags on body posture also contributes to better education of young individuals . MedicinTherefore, cost-effective scanners for easy, effective at-home monitoring of bodyweight and weight distribution for the fast understanding of any slight imbalances are crucial for a timely clinical examination, diagnosis, and adequate intervention.Determining the abnormal postural sway associated with an increased risk of falls requires access to laboratory equipment for diagnostics and monitoring. The newly created ENP as a proposed screening platform for the at-home measurement and monitoring of bodyweight and foot load distributions separated right from left, adds value to the current procedural and therapeutic aspects from the consumer perspective. The easy-to-use platform was designed with a self-check calibration error function that runs during its start-up to ensure that every measurement is accurate and provides reliable information to the users at home and to professionals who monitor the condition’s progress and amend the treatment. The developed platform also presents features that assess the user’s balance and bodyweight to permit a straightforward representation of the user’s weight distribution and load distribution on the right-left, front-back foot. Since the data collected is displayed as a simple numerical report and a map, the evaluation could be done at two levels for non-specialists and professionals. Moreover, the possibility of the real-time interpretation of the reported physiological parameters opens new avenues toward IoT-like on-body monitoring of human physiological signals through easy-to-use devices on flexible substrates for specific versatility. Clinicians also may exploit distance communication to monitor their patients’ body-worn postures via at-home, low-cost alternatives, miniaturised or not. Such solutions may be the next generation of user-friendly at-home devices designed for early diagnosis and personalised treatment and monitoring. Furthermore, posture-related data is the key to developing artificial intelligence in the field and personalised medicine. Therefore, the Early Notice Pointer is an IoT-like platform for point-of-care feet and body balance screening for early postural changes and fast data sharing that allows timely and accurate medical decisions, easy monitoring, and prophylaxis of locomotor system-related diseases.


RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points. The fate of RNA transcripts in dead tissues and the decay of isolated RNA are not subject to strict regulation, unlike in vivo normal cells . WhetherTime-series-based gene expression data have become one of the most fundamental methods for studying biological processes. Most biological processes are dynamic, and using time series data can characterize the function of specific genes, discover linkages and regulatory relationships between genes, and find their clinical applications ,7,8. TimIt is the first prediction framework constructed by deep learning methods for time series data of isolated RNA from tissue samples. The time-series-based prediction is able to extract causality because the information on gene expression flows over time in one direction. Overall, we developed a new LSTM-based deep learning model named Multi-LSTM to predict gene expression at target time points in transcriptome time series data. . This moOur data are the expressions of transcripts corresponding to each gene obtained by extracting RNA from mouse brain tissue after transcriptional library building and sequencing. Usually, the normalized Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) value is a standard value to judge the high or low expression of that gene . In thisThe raw gene expression data are shown in The decomposition results are shown in p-values > 0.05.First, the LSTM model was built for each IMF component to obtain the corresponding prediction results. The prediction results of the nine components are shown in RMSE), mean absolute error (MAE), and coefficient of determination (R2) were selected as the prediction performance evaluation indexes. Among them, RMSE can be used to assess the degree of inconsistency between the predicted and true values of the model. Theoretically, the smaller its value is, the better the performance of the model. In addition, the MAE can be used to complement the evaluation of the performance of this model. Meanwhile, the induced effects of the model can be evaluated by the determinants statistic R2. The R2 is an excellent statistical indicator of the closeness between the predicted and true values. The results of R2 of each component are shown in In order to quantify the prediction accuracy of each component more directly, the root-mean-square error . To use this model for disease prediction in Parkinson’s disease, the genes related to RNA degradation can simply be replaced with genes related to Parkinson’s disease. Of course, this model is not limited to the brain, but can also be used to predict retinal degenerative diseases, such as age-related macular degeneration. In conclusion, this model can be very widely used in disease prediction.The data used in this study were obtained from brain tissue samples of normal mice. Both tissues and blood cells should be preserved by proper methods as soon as possible after leaving the optimal cellular life state; otherwise, they will all undergo degradation. This study did not involve biological experiments, and mainly explored the mechanism of RNA degradation in mouse brain tissue. RNA from mouse brain tissue was stored at room temperature (RT) prior to extraction for 3 samples at 0 h time points , 3 samples at 2 h time points , 3 samples at 4 h time points , and 3 samples at 6 h time points . The data obtained from the final sequencing of the samples were used for the analysis of this study. The 12 time points are 12 consecutive points in time. We used the next 11 time points as the training set and the gene expression data from the very first time point as the prediction set. Then, we found the list of genes associated with RNA degradation in existing studies from NCBI. In total, there are 60 genes corresponding to the expression values from time point 1 to time point 12, respectively. The data used in this study were partly obtained from previously published articles . Time-spEMD has been recognized as an effective time–frequency analysis method for dealing with nonlinear, nonstationary signals since it was proposed in 1998 . This meThe variable instantaneous frequency and instantaneous amplitude can enhance the efficiency of signal decomposition. At the same time, the EMD method can better preserve the nonsmooth and nonlinear characteristics of the original signal. The eigenmode function obtained by EMD also has the characteristic of intra-wave modulation, which can cover the information of the same component expressed by different Fourier frequencies into one IMF . The resThe purpose of the EMD algorithm is to decompose the original signal into some series of IMFs, and then obtain the time–frequency relationship of the signal by the Hilbert transform. Taking the terahertz time-domain spectral signal as an example, the basic calculation process of the EMD algorithm is as follows.Step 1: The terahertz time-domain spectral signal Step 2: The mean value of the upper and lower envelopes is calculated by the formula:The components of the EMD algorithm are defined as:Step 3: Determine whether the component Step 4: The first IMF component is raised from the original signal to obtain the residual signal, which is calculated as follows.Repeat the above three steps with The above steps are repeated continuously until the stopping criterion of the EMD algorithm is satisfied. The stopping criterion of the EMD algorithm uses the Cauchy convergence criterion, a test that requires the normalized squared difference between two adjacent extraction operations to be sufficiently small, as defined by:In the formula: T denotes the signal length, and the decomposition ends when RMSE), also known as root-mean-square deviation, is a commonly used measure of the difference between the values. Its formula is:The root-mean-square error (MAE) is the average of the absolute values of the deviations of all individual observations from the arithmetic mean. The mean absolute error avoids the problem of errors canceling each other out and thus accurately reflects the magnitude of the actual prediction error. Its formula is:The mean absolute error (R2) is a statistical measure of how close the regression prediction is to the true data point. The formula for The coefficient of determination  proposed to solve the deficiency of RNN in the long-range dependency problem . LSTM acThe three gates in the LSTM gate structure are the input gate, the output gate, and the forget gate. The input gate mainly reflects the amount of information stored in the cell state Memory unit update:Status update:The formula In this study, we propose a method based on signal decomposition techniques and deep learning, called Multi-LSTM. It is a prediction framework to explore the expression changes of RNA degradation-related genes in the initial hours of the sample, which is important for studying the effect of RNA degradation on gene expression levels. The first module decomposes the collected gene expression data by the empirical mode decomposition algorithm to obtain a series of sub-modules with different frequencies in order to improve data stability and reduce modeling complexity. The second module uses an LSTM neural network as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final transcriptome time series gene expression level prediction results. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides a reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Meanwhile, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points by combining the robust temporal nonlinearity analysis capability of LSTM. The combination of LSTM and the effective EMD decomposition algorithm not only improves the accuracy of prediction results, but also has low prediction errors. The results show that both forward and reverse time series can be predicted accurately. The combination of deep learning neural networks with biomedicine will lead to great breakthroughs in gene function prediction, disease-related gene discovery, and transcriptome time series analysis.


When exploring facial expression recognition methods, it is found that existing algorithms make insufficient use of information about the key parts that express emotion. For this problem, on the basis of a convolutional neural network and long short-term memory (CNN-LSTM), we propose a facial expression recognition method that incorporates an attention mechanism (CNN-ALSTM). Compared with the general CNN-LSTM algorithm, it can mine the information of important regions more effectively. Furthermore, a CNN-LSTM facial expression recognition method incorporating a two-layer attention mechanism (ACNN-ALSTM) is proposed. We conducted comparative experiments on Fer2013 and processed CK  + datasets with CNN-ALSTM, ACNN-ALSTM, patch based ACNN (pACNN), Facial expression recognition with attention net (FERAtt), and other networks. The results show that the proposed ACNN-ALSTM hybrid neural network model is superior to related work in expression recognition. In the field of facial expression recognition based on deep learning networks, the convolutional neural network (CNN) has been proven to be an important means of extracting local spatial features of images. However, CNN is a typical feedforward deep network. Its network structure is monotonously connected and the information flows in only one direction, which makes it lack the recognition of contextual timing information and restricts the accuracy of the algorithm.The recurrent neural network (RNN) has loops because neurons in them not only receive information from other neurons but also receive information from themselves. The input of RNN is sequence data, and a loop unit is executed at each time t. The calculation of the hidden layer at the next time t+1 depends on the current input data and the hidden state at time t [Computer vision problems involving sequence input can be satisfying solved by RNN. As for multilabel image classification tasks , Zhang eParameters of RNN are adjusted through the back propagation algorithm. However, the network likely experience gradient disappearance as the length of the sequence data input to the network increases. Long short-term memory network (LSTM) introduces gating units to control the accumulation speed of information . This noWhen using neurons as information storage units in neural networks, the more information is stored, the more neurons are needed and the corresponding networks structure are more complex . A largeBased on the ability of the attention mechanism to focus on key parts and the excellent memory capability of the cyclic neural network, this paper proposes a CNN-LSTM hybrid neural network that integrates a two-layer attention mechanism (ACNN-ALSTM) , 11. TheA two-layer attention mechanism is introduced to propose a novel ACNN-ALSTM model for facial expression recognition. The two-layer attention mechanism can enhance attention to key areas.The LSTM algorithm is introduced to establish the global spatial dependency of facial information. Afterwards, the effect of different network layers and the number of nodes in the hidden layer of the LSTM model on expression recognition are investigated.Comparative experiments are made to verify that the introduction of the two-layer attention mechanism in the CNN-LSTM model improves the classification performance on Fer2013 and CK + data sets. Comparison with other neural networks that incorporate attention mechanism shows that our work has certain advantages.Our contributions are summarized as follows:A convolution operator across the spatial domain is added to the network by Yang et al. to extract spatial features to automatically learn spectral spatial features from hyperspectral images . Then, aThe model proposed by Lin et al. is composed of a deep LSTM network , eight eDuring recent years, researchers have been trying to optimize the network model using the attention mechanism. For facial expression recognition tasks, Fernandez et al. present a new model, that is end-to-end network architecture with an attention . Li et aIn this chapter, the specific composition of the two network architectures we designed is described. For the first one, a layer of attention mechanism is added to the CNN-LSTM network. For the other, a two-layer attention mechanism is introduced.When the RNN is employed for facial expression classification tasks, the results of average sampling according to time steps are used as classification features. However, the average sampling method blurs the focus and cannot expand the advantages of valuable information. The attention mechanism can make the network focus on effective information, so it is employed to selectively process the information input to the classifier to optimize the classification performance of the neural network. The CNN-LSTM facial expression recognition network model incorporating an attention mechanism (CNN-ALSTM) proposed in this section mainly includes four parts: CNN local feature extraction layer, LSTM feature learning layer, global feature attention layer, and classification layer. The CNN-ALSTM model structure diagram is shown in This layer is mainly responsible for extracting the local abstract features of the facial expression image and serializing the generated feature vector as the input feature of the LSTM network. The CNN local feature extraction layer is composed of a 7-layer convolutional neural network, including 4 convolutional layers and 3 down-sampling layers. The parameters of each layer are shown in Different from the employing the large convolution kernel in the original CNN model directly, the convolution layer C1 executes point-by-point convolution of the input data using a 1 × 1 convolution kernel. This is beneficial to increase the nonlinear representation of the input and improve the feature representation ability. In addition, the 1 × 1 point convolution kernel has few parameters, which can effectively reduce the network calculation complexity.The pooling layer in the CNN local feature extraction layer uses the maximum pooling method to extract the strongest features. This can reduce the resolution of the feature map and reduce the computational complexity.x={x1, x2,…, xN}, and the input data at moment n is xn. The main work of this layer is roughly divided into four parts. First, the facial expression abstract feature vectors obtained by the CNN feature extraction layer are serialized as the input of the LSTM network. Then, according to the order of the time series, they are sequentially input into the main structure of the recurrent network. At the same time, the LSTM memory unit generates state information combining the current input data and historical information at each moment. Finally, the state information is passed to the next layer feature, which contains historical information at all previous moments.The input sequence is h={h1, h2,…, hN}, hn is the hidden state at moment n. To select features that are more relevant to the classification task, a query vector q related to the facial expression recognition task is introduced. A scoring function is used to calculate the correlation between the hidden state and the query vector, which means that the higher the correlation between the hidden state and the classification task, the higher the score. For each hidden state, the correlation score can be obtained by the following equation:After the image is extracted by the LSTM network, the hidden state sequence of the LSTM network is defined as s is the attention scoring function.an of the hidden state:After scoring the relevance of all hidden state tasks, the score is normalized to obtain the attention distribution an is the probability representation of the importance of the hidden state hn in the facial expression recognition task.The attention distribution The weighted average method is employed to fuse the information of all hidden sequences of the LSTM model, focusing on the most relevant features of the task. The following formula is the weighted feature representation of the sequence of hidden states:Finally, there is the classification layer. The classification layer is responsible for classifying the weighted features fused by the global feature attention layer.An ACNN-ALSTM network model is proposed in this section. The network mines the local features through CNN , 19 and The structure of this layer is exactly the same as the CNN local feature extraction layer in the previous section, so it is not repeated.q related to facial expression recognition tasks is introduced. Thus, similar to the previous section, the correlation between each local feature localn and the query vector can be calculated through the scoring function. Local features with high scores are more essential:The role of this layer is to strengthen the expression ability of essential local features. The query vector S is the attention scoring function. After completing the task relevance scoring, the score is normalized to obtain the attention distribution Lan of the local features as shown in the following formula:Lan is the probability representation of the essential of the local feature localn. Then, weight each feature as the input of LSTM:The attention distribution xn is the weighted local feature.The process at this layer is similar to that described in the previous section. The LSTM algorithm employs gating units and strong memory ability to establish the global spatial dependence of facial information.The hidden state output by LSTM is employed as the input of the global feature attention layer. The query vector q of the attention layer is defined as a learnable weight vector whose dimension is consistent with the number of hidden layer nodes of the LSTM network.a={a1, a2,…, aN} for the hidden state h={h1, h2,…, hN} of the LSTM network is calculated. Second, the weighted average result att of the hidden state is calculated according to the attention distribution.This layer implements the weighted fusion of the output features of the LSTM using the attention mechanism in two steps. First, the attention distribution The attention mechanism can increase the weight of the features that are most relevant to facial expression recognition tasks and achieve its dominant role in the feature fusion process, thereby improving the classification performance.eV is obtained. The symbol V corresponds to the attention scoring function s above. Then, a normalized formula  data set was established in 2010 by the Patrick Lucey team and the Zara Ambadar team . The datTwo kinds of image classification experiments are designed to verify the effects of the two deep learning network models proposed in the paper on facial expression recognition. These experiments are implemented on the Linux16.04 64 bit operating system, using python3.6 and the Google framework tensorflow1.12.0-gpu for programming and implementation, and calling Open CV for data enhancement. NVIDIA Ge Force GTX1080ti graphics card with 16G video memory is employed. The FER2013 and CK + data sets are divided into training set, validation set, and test set according to the ratio of 8 : 2. The iteration is 5000 and print the training loss and error every 100 rounds. The loss and error of the test are printed every 400 rounds. Face images of the input model are rescaled to 48 × 48 pixels. The learning rate of the model is 0.001, and Adam optimizer and softmax regression are employed for calculation and classification , 24. CroIt is found that the feature abstraction degree of the LSTM model of the image is related to the number of nodes in the hidden layer and the depth of the hidden layer. Therefore, the influence of the number of nodes in the hidden layer and the depth of the hidden layer on the effect of facial expression recognition is shown in We conducted two types of experiments. First of all, the experiment studied the influence of LSTM parameters on facial expression features extracted by the model. Then, a comparison about various variants of ACNN-ALSTM and related work is executed.The paper considers two essential factors that affect the feature extraction of LSTM: the number of network layers and the number of hidden layer nodes. The classification results of differently constructed LSTM networks on the Fer2013 and the processed CK + data sets are shown in According to the experimental results, the classification accuracy rate continues to increase as the number of nodes increases. At the same time, the abstract features learned from the image data of the LSTM network are more discriminative. However, the increase in the number of hidden layer nodes has an upper limit on the effect of increasing the recognition rate. From CNN and CNN-LSTM models. The CNN-LSTM hybrid neural network is proposed by combining the advantages of CNN and LSTM to mine image features. The CNN and the CNN-LSTM models are demonstrated in Figures On the Fer2013 training set, the classification accuracy of the CNN-LSTM hybrid model surpassed the CNN model after 1900 iterations. On the CK + training set, the training classification accuracy of the CNN-LSTM hybrid model is always better than the CNN model. It can be seen that the CNN-LSTM hybrid model can effectively recognize facial expressions.CNN-LSTM model and its various variants. These models are trained on the Fer2013 and the processed CK + training sets. Compared with that only the local feature or that only global feature is fused with the attention mechanism, the effect of both local feature and global feature are fused with the attention mechanism is better. At the same time, it can be found that the effect of fusing the attention mechanism in the global feature is better than that in the local feature. The above-given research results show that adding an attention mechanism to the CNN-LSTM model can highlight essential features and improve the classification effect.ACNN-ALSTM and related work. Although some results cannot be directly compared due to different experimental setups and different preprocessing methods, it is demonstrated in It is obvious that the efficiency and accuracy of recognition can be promoted by making sufficient use of key parts of facial expression. However, the utilization of the key parts of the existing algorithm can be further improved. Thus, the CNN-ALSTM and the ACNN-ALSTM incorporating an attention mechanism are proposed in this paper. Simulation results indicate that the proposed algorithm can further improve the recognition rate compared with other researchers' work, although both their algorithms and ours incorporating attention mechanisms. At the same time, this work demonstrates the effectiveness of attentional mechanisms for exploiting key information about facial expressions.In this work, a novel ACNN-ALSTM model for facial expression recognition that incorporates a two-layer attention mechanism is proposed. Comparative experimental results indicate that the introduction of the two-layer attention mechanism improves the classification performance of the system more significantly than that of the CNN-LSTM model and its variants. We also investigate the effect of the different network layers and the number of nodes in the hidden layers of the LSTM model on expression recognition. The results indicate that appropriately increasing the network depth and the number of hidden layer nodes is beneficial to improve the recognition accuracy of the LSTM model. In future work, we want to train the network for extreme conditions such as dark, light, and occlusion , 26.


Network pruning techniques have been widely used for compressing computational and memory intensive deep learning models through removing redundant components of the model. According to the pruning granularity, network pruning can be categorized into structured and unstructured methods. The structured pruning removes the large components in a model such as channels or layers, which might reduce the accuracy. The unstructured pruning directly removes mainly the parameters in a model as well as the redundant channels or layers, which might result in an inadequate pruning. To address the limitations of the pruning methods, this paper proposes a heuristic method for minimizing model size. This paper implements an algorithm to combine both the structured and the unstructured pruning methods while maintaining the target accuracy that is configured by its application. We use network slimming for the structured pruning method and deep compression for the unstructured one. Our method achieves a higher compression ratio than the case when the individual pruning method is applied. To show the effectiveness of our proposed method, this paper evaluates our proposed method with actual state-of-the-art CNN models of VGGNet, ResNet and DenseNet under the CIFAR-10 dataset. This paper discusses the performance of the proposed method with the cases of individual usage of the structured and unstructured pruning methods and then proves that our method achieves better performance with higher compression ratio. In the best case of the VGGNet, our method results in a 13× reduction ratio in the model size, and also gives a 15× reduction ratio regarding the pruning time compared with the brute-force search method. Convolutional neural network (CNN) models have been rapidly growing since their appearance in 1990 , autonomTo achieve higher precision, the CNN models always have a wider and deeper architecture. Some of the new state-of-the-art models have over millions or even trillions of parameters. This inevitably raises the enormous needs of massive computations, large energy consumption and huge memory/computational resources ,16. AccoRecently, extensive works have been proposed to compress these large-scale models. Network pruning is one of the most well-known techniques and attracts enormous attention. It enables huge CNN models to run efficiently on edge devices by removing superfluous parts of the models. According to the pruning granularity, network pruning can be roughly categorized as structured pruning ,20 and uIn this paper, we propose a heuristic method that combines the structured and unstructured pruning methods. In our method, we control the compression ratios of the original structured and unstructured pruning methods by repeating these methods under a target accuracy. Through combining these two pruning methods, we will explore the effective pruned model with a higher compression ratio than the single usage of them, respectively, and simultaneously achieve higher accuracy for the inference.We have found a method for model compression that uses the structured pruning and unstructured pruning jointly. We also allow the CNN models to meet and maintain the target accuracy given by their applications. This will achieve the best reduction ratio regarding the parameters of the CNN models.We have developed an algorithm that achieves a better compression ratio than the individual usage of the structured and unstructured pruning methods under a target accuracy.We have shown the efficiency of our proposed method according to evaluations with five actual CNN models and validated the correctness of the algorithm.We also optimized our proposed algorithm to require significantly less computational time to find the best compression ratio compared to the case when we apply brute-force search algorithm.The main contributions of this paper are summarized as follows:The rest of this paper is organized as follows. The next section introduces the background and the definitions of this research focusing on the object detection techniques with CNN and the pruning techniques to compress the network model. Object detection has been a long-standing and fundamental research theme in the computer vision field that detects object instances of a certain class such as a person, animal or vehicle in digital images. The object detection technique has evolved over the past 50 years. Since Krizhevsky  won the However, along with their remarkable improvement on performance, the state-of-the-art CNNs always have more complicated architectures. In other words, there are multiple convolutional layers in such CNNs. These cause a huge increase in the convolution operations and the number of parameters . Since tAs one of the typical model compression methods, network pruning has been widely studied owing to its remarkable performance improvements for the inference process and high compatibility with the original network. Network pruning reduces the size of CNN models by removing unnecessary components from the CNN models while maintaining accuracy for the inference.The major branch of the network pruning method is called unstructured pruning, which is also known as weight pruning since its main focus of the technique is to remove the redundant weights. It has been studied since its appearance in the 1990s . RecentlStructured pruning is another branch of the network pruning method. It compresses the models through removing redundant structures such as channels, filters or layers. Among existing structured pruning methods, channel pruning is considered as the most effective for compressing the model while maintaining the accuracy for inference. Since the channel is the most fine-grained structured level, it can achieve a higher compression ratio compared to the other types of structured pruning methods. Many advanced research works focused on the channel pruning approach have been presented. Liu et al. proposed network slimming , which iIn conclusion, we summarize the discussion above into two directions for the compression techniques of the CNN models. One is to reduce the amount of computations by reducing the number of convolutional layers. Then, we can compress the network structure further by using the structured pruning approach. The other direction is to reduce the network size while maintaining accuracy by reducing the number of fan-ins to each node in the middle layers of the fully connected layers. This can be addressed via the unstructured pruning methods.In this paper, for the structure pruning, we focus on network slimming. For the unstructured pruning, we focus on the weight pruning method utilized in the deep compression.Network Slimming. Deep Compression. Therefore, by combining the structured pruning method related to the convolution layers and the unstructured one for the fully connected layer, it is possible to achieve a higher compression ratio than the case employing individual use of these methods, respectively. Additionally, due to the use of these two approaches during a pruning process, we can gain merits from both the structured and the unstructured techniques. This approach can finally prune the entire structure of a CNN model focusing on both the convolutional and fully connected layers. However, because these two methods can influence each other between the convolutional and fully connected layers, it is not clear on how to decide the order of the two methods to be applied to a CNN model and/or the pruning ratios configured to each method in combining the two methods. Thus, in this paper, we will propose a new method that implements an appropriate combination of these two network pruning methods to find the minimized model with a given accuracy for the inference.Let us present our proposed method in detail including the pruning scheme and the approach for the compression. We setup the approach to achieve the best compression ratio with the structured and unstructured pruning methods. We also introduce the parameters in our proposed method to be defined by application.Pruning scheme. We first apply the structured pruning method to a CNN model to compress the convolutional layers. The reduction ratio is gradually increased, and each ratio is applied to the original network until the model accuracy reaches beneath a given target accuracy. Here, the reduction ratio is defined as the percentage for compression given to the pruning process. Then, the pruning method switches to the unstructured pruning method for compressing the fully connected layers while also increasing the reduction ratio until the accuracy reaches beneath the target accuracy again. Through these two pruning methods, we can finally identify the model that is minimized in size. As we express mathematically above, assume m in a percentage of the reduction ratio p. It returns a pair of the compressed model p and comparing Initial Margin. As illustrated above, the structured pruning method compresses a CNN model with every reduction ratio gradually increased until the accuracy does not meet the target one. The compressed model gained after the pruning process is minimized by this structured pruning. Here, if we directly pass the compressed model to the unstructured pruning method, it might not leave enough room for further compression by the unstructured pruning method. Therefore, our method introduces a margin for the reduction ratio after the former pruning method when p when Parameters to be configured. There are three parameters defined for our proposed method. The parameters are defined by application side. The first one is the target accuracy for the final compressed model. It affects the size of the final compressed model. The second one is the step_width to be used for increasing the reduction ratios p and Step 1 applies network slimming to the initial model until the model’s accuracy becomes beneath the target accuracy (dotted line in gray color). Step 2 draws back (dotted line in yellow color) the reduction ratio during Step 1 according to the margin value (the square in red color) for obtaining the model that can be further pruned (the square in green color). Step 3 switches to the deep compression until the accuracy becomes beneath the target accuracy again. Here, we achieve the reduction ratio derived in the previous accuracy (the circle in blue color) for the deep compression. At the end, our method returns the minimum model derived from the Step 3. In addition, we will repeat Step 2 and Step 3 with different margin values for minimizing the model size. Through the above process, the convolutional layers and the fully connected layers will be effectively compressed. Furthermore, thus, we obtain a model that is minimized in size.Our proposed method employs the three-step procedure as shown in To summarize the overall mechanism, we show the computation flow of the proposed method in Algorithm 1. The algorithm first loads the pre-trained model N is the number of iterations in lines 8–13 of the algorithm. Here, let us consider the elapsed time of our proposed method. We define the valuables related to the execution time of the algorithm as follows. For investigating the efficiency of our algorithm, let us compare it with the brute-force search method to find the minimized model. The elapsed time is defined as follows:Algorithm 1 Pseudocode of algorithm for Minimizing Model SizeHere, the step_width is the value to define the incremental step_width for the reduction ratio during the structured and the unstructured pruning methods. If we increase the reduction ratio at every percentage as an integer value, the step_width becomes 1. Therefore, the elapsed time becomes Through the procedures above, our proposed method will utilize the structured pruning method for focusing on compressing the convolutional layers, and then, the unstructured one for reducing the fully connected layers. In comparison with the other existing approaches, our method not only removes the unnecessary network structures but also directly acts on the reduction of unimportant connections and neurons by utilizing unstructured pruning.In summary, network slimming is used as the structured pruning in our work, which compresses the convolutional layers by removing the unimportant channels. For the unstructured pruning, we take advantage of the deep compression. It works especially well on the fully connected layers where the greater part of the parameters of the entire network is concentrated.Now, we discuss the performance of our proposed method. We evaluate the performance of our proposed method with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on the CIFAR-10 dataset. The results of the evaluations will be discussed from three aspects: model size, calculation overhead and execution time.For our experiment, we use a variation of the VGG-19 with 16 convolutional layers and 1 fully connected layer. For Resnet, a 110-layer pre-activation Resnet with bottleneck structure (denoted as ResNet-110) is used. For DenseNet, we experiment with a 40-layer DenseNet (denoted as DenseNet-40), a 121-layer DenseNet (denoted as DenseNet-121) and a 201-layer DenseNet (DenseNet-201), respectively. Furthermore, for all models, we adopt the network model with batch normalization layer from . The modDuring the performance evaluations below, we implemented the compression system of Algorithm 1 on a GPU-based environment by using the state-of-the-art network models with the CIFAR-10 dataset. Our proposed method is implemented with PyTorch framework. The experiments are performed on a computer using Linux Ubuntu 18.04 operating system with Intel Xeon E5-2698 V4 CPU, 512GB RAM, and one NVIDIA Tesla V100 with 32 GB VRAM.We first evaluate the minimizing performance for model size. Besides performing our method on each CNN model, we also individually implement network slimming and deep compression on these models. According to the original accuracy of the five CNN models, we set three target accuracies for each model. The detailed results of each CNN model are presented in The result for VGG-19 is summarized in The experimental results for the ResNet-110 and DenseNet-40 models are presented in Furthermore, in the case of DenseNet-121 and DenseNet-201 applied to the CNN models that have complex and deeper network structures, our method also achieves the best compression ratio as listed in On the other hand, we notice that both our method and the deep compression perform much better than the network slimming method as shown in It has been confirmed that our method can minimize the model size compared with cases where network slimming and deep compression are applied individually.We further evaluate the calculation overhead and the execution time. The execution times of each CNN model with pre-defined target accuracies are shown in As we can see in lines 7–15 shown in Algorithm 1, the number of iterations in lines 8–13 that occupy the largest part of the execution time is affected by the value of the initial margin. Here, we assigned the initial margin to 50. The execution time of our proposed method for each model is presented in Comparing the results of our method with the brute-force search method, focusing on VGG-19 as shown in Overall, through the results shown in In this section, we discuss the order of the pruning methods used in our proposed method. Our proposed approach tries to minimize the model size by combining two kinds of pruning methods. In order to maximize the benefits of the two pruning methods, we conduct a further experiment to concretely establish the effective execution order of two pruning methods. We try to compress the five models in order from deep compression to network slimming (abbreviated as DC→NS). Then, we compare the compression ratios with the ones achieved by the order (abbreviated as NS→DC) used in our method. Next, we discuss a crucial parameter in our approach, the margin. The margin is used for drawing back the model before the pruning method switches from network slimming to deep compression. Furthermore, the purpose of the initial margin is to control the number of iterations for finding the minimum model by performing further pruning. The model will be over-compressed by network slimming if we draw back too little, and then, it will be difficult to perform further compression through the deep compression. We experimented to minimize the five models applying the margin value while varying from 1 to 50 to evaluate the effect of drawing back during searching the minimum model size. The results are summarized in According to the experimental results in the cases of VGG-19, ResNet-110, DenseNet-40, DenseNet-121 and DenseNet-202 shown in This paper describes a heuristic method for minimizing the model size of CNN by combining the structured pruning and unstructured pruning methods. Compared with the performances in which the pruning methods are applied individually, our method takes advantage of the structured pruning method for compressing the convolutional layers and also removes the dense parameters in the fully connected layers through the unstructured pruning method. The results of evaluations using five CNN models showed that our method could find the minimized model while satisfying the given target accuracy. Furthermore, our method drastically reduces the runtime and computing operations compared with the brute-force search method.For future work, we will apply our proposed method to various CNN models to find the minimum model sizes under given target accuracies. Additionally, we plan to extend our method by combining other compression techniques such as quantization, low rank, or distillation and identify the most effective combination for minimizing the model size.


When implants are required in prosthodontics treatment, one of the most important decisions is the choice of the final crown and the type of connection to the implant through the abutment. Hybrid abutments are becoming a primary choice. They are projected and produced with materials whose properties guarantee the required mechanical features (including resistance) and take advantage of the hybrid abutment crown retention between screw and cement. However, a review of the mechanical resistance of the different abutment types and associated materials is still lacking. This review aimed to study the in vitro mechanical efficiency of the hybrid abutments used in oral rehabilitation. Methods: A systematic review was conducted using the PubMed, B-on, and Google Scholar databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: 75 articles were identified from all databases, and 33 were selected after abstract screening. Thus, 21 studies were included in the review after full-text reading. Among the materials used for crowns, lithium disilicate was, aesthetically, the primary choice for its aesthetic and moderate strength. On the other hand, zirconia showed the best fracture resistance. Regarding the different kinds of abutments, there is still some lack of knowledge about the best design. Conclusions: Within the limitations of this systematic review, we can conclude that hybrid pillars are an excellent choice for oral rehabilitation through implants, showing improved resistance when including materials such as zirconia and lithium disilicate. Reconstructions for problems of the dental arch supported by implants are considered the preferred option thanks to their favorable longevity, aesthetics, and comfort. One of the most important decisions in using dental implants in prosthodontics is the choice of the final crown and the type of connection to the implant through the abutment. The implant–crown abutment can be either cemented or screwed. Both connections have advantages and disadvantages. Before choosing the type of connection between the implant and the crown, the prosthetist should consider the outcomes and features of the crown, the type of abutment, and other issues described elsewhere . Crowns Today, through CAD/CAM technology, the hybrid abutment is projected and produced, with a focus on the mechanical features of the materials . Current-A hybrid abutment crown with the abutment and the crown fabricated as a unique piece that will be attached to the titanium base and screwed to the implant.-A hybrid abutment with a separated crown with the abutment first fixed to the titanium base and then screwed to the implant, followed by cementation of the whole ceramic crown on it.The hybrid abutment, where a bevelled edge is fixed to the palatal side and an intrasulcular component of the restoration is limited to the vestibular side, can be produced by two different designs:The hybrid abutment crown combines the retention advantages between the screw and cement. However, only a few studies have rated the differences between the hybrid crowns and the hybrid abutments with separated crowns ,10.Traditionally, hybrid abutments’ design and materials usually depend on the doctor’s and/or patient’s preferences and the patient’s clinical situation. However, mechanical efficiency is also important to consider. To our knowledge, there is still no review assessing the mechanical efficiency of the design and materials used in the hybrid abutments. Thus, we reviewed and synthesized, for the first time, the resilience results obtained from in vitro studies to contribute to the informed choice of abutment type in oral rehabilitation and to suggest improvements for future research.Population: two-piece hybrid abutment  with crown;Intervention: laboratory mechanical stress tests (duration/strength/frequency);Comparison: one-piece hybrid abutment  with crown;Outcome: hybrid abutment integrity or fracture rates.The method used in this systematic revision met the PRISMA guidelines and was registered at PROSPERO (ID:341060). The PICO strategy was used to build the research questions:This revision aims to evaluate the mechanical resilience of the hybrid abutments with crowns to in vitro mechanical stress tests.https://www.b-on.pt/colecoes/ (accessed on 1 August 2022)). The applied search algorithm was: “dental implant [MeshTerms] AND (hybrid abutment OR titanium base OR Ti-Base) AND crown AND zirconia AND (screw OR cemented)”.The research strategy included the electronic screening of English publications over the last 10 years on PubMed, Google Scholar, and B-on  specimen randomization; (D2) single-operator protocol implementation; (D3) description of the sample size calculation; (D4) blinding of the testing machine operator; (D5) the presence of a control group; (D6) failure mode evaluation; and (D7) use of all materials according to the manufacturer’s instructions. These domains were adapted from the Sarkis–Onofre quality assessment tool .Two independent reviewers (L.F. and P.M.) first screened the titles and/or abstracts identified in the searches. The final selection of studies was independently performed by the same authors who reviewed the paper’s full text based on the inclusion criteria. Only studies that met the eligibility criteria were included for review. Discussion with a third reviewer (P.S.M.) resolved any disagreements or discrepancies.A total of 75 articles were identified from all databases. No studies were identified from the grey literature. The In ® Hybrid abutment was the most commonly used cement. However, other authors applied other resin types of cement. The abutment’s macrostructures of lithium disilicate were treated with hydrofluoric acid of different percentages. For the abutment’s macrostructures of zirconia, it was sandblasted with alumina particles in most cases or with alumina oxide particles. Due to the high heterogeneity of the pieces, used materials, and treatments, the quantitative results reported in articles were not extracted for meta-analysis. Other aspects related to the article’s results are dealt with within the Discussion section.This systematic review enrolled hybrid abutments from in vitro studies. Many studies evaluated the resistance to fractures of materials with different kinds of hybrid abutments as a single piece or two-pieces with separated crowns. In these studies, the different kinds of materials for the crowns and the cementation protocols for the macrostructure of the titanium basis within the cementation of the crown above the macrostructure were also considered. Most authors have concluded that the zirconia hybrid abutment has the best fracture resistance. On the other hand, other authors, such as Elsayed et al.  and RobeIn studies with alternative materials such as PINC and PEEK, the authors have observed that the PINC material might not give enough fracture resistance due to the occlusal forces, even if it does not influence the titanium base or the screw ,17. PEEKRegarding the differences between hybrid abutment types, Pitta et al. have observed in their study that the presence of a second interface between the hybrid abutment and the crown, and the absence of a canal for the screw on the abutments with separated crowns, can contribute to a better dissipation of the tension . Tribst In the in vitro studies, several authors observed different types of fractures or cementation failures during laboratory tests. These faults occurred mainly due to fracture of the titanium base, abutment fracture, failure of the cementation by deformation of the titanium base, and fracture of the crown. Nouh et al.  observedAs stated before, the choice regarding the hybrid abutment type, applied material, and treatment is traditionally dependent on the clinical case, particularly on where the hybrid abutment will be placed. This review highlighted and discussed the mechanical efficiency of the whole set as an additional aspect of major importance and the aspects of current research that still need to be corrected or addressed in future studies.Hybrid abutments are an adequate choice when mechanical resistance is an important requirement for implants, especially in the presence of materials such as zirconia and lithium disilicate. PICN and PEEK one- or two-piece hybrid abutments need more study;Limited evidence supports better mechanical performance in two-piece hybrid abutments due to a more balanced strength distribution;Overall, research in this field is still scarce. Furthermore, it is necessary to carry out more studies on the gap between the macrostructure and the crown and how much it can affect the periodontal tissues and might be a risk factor for the development of peri-implantitis;The mechanical testing research on hybrid abutments needs to adhere more thoroughly to quality assessment guidelines for in vitro studies to prevent the high risk of bias found in current studies for future research.Our review results show the following:


F1-score, and 1856 ms of execution time. The simulation is conducted to test the efficacy of the model, and the results of the simulation show that the proposed Internet of Things-based enhanced machine learning obtains a higher rate of intelligence than other methods.In the current age of technology, various diseases in the body are also on the rise. Tumours that cause more discomfort in the body are set to increase the discomfort of most patients. Patients experience different effects depending on the tumour size and type. Future developments in the medical field are moving towards the development of tools based on IoT devices. These advances will in the future follow special features designed based on multiple machine learning developed by artificial intelligence. In that order, an improved algorithm named Internet of Things-based enhanced machine learning is proposed in this paper. What makes it special is that it involves separate functions to diagnose each type of tumour. It analyzes and calculates things like the size, shape, and location of the tumour. Cure from cancer is determined by the stage at which we find cancer. Early detection of cancer has the potential to cure quickly. At a saturation point, the proposed Internet of Things-based enhanced machine learning model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of  The lung neuroendocrine tumour is a rare neuroendocrine tumour that develops in the lungs. It makes up 20% of all neuroendocrine tumours. This cancer is usually diagnosed in people between the ages of 45 and 60; however, it can occur at any age . It is mTumour sequencing provides insight into changes in patient tumour genes. Tumour DNA sequencing can also be referred to as genetic specification or genetic testing. Sequence results will enable clinical decision-making to develop a personalized cancer treatment plan based on the molecular characteristics of the tumour rather than a one-size-fits-all treatment approach. Tumour sequencing also plays a major role in cancer research . Thanks Racial differences are found in cancers of the same origin, e.g., differences are found in the subtypes of lung cancer between the Jewish and Chinese populations. Because of these large variations in cancer characteristics, a one-size-fits-all treatment may not be a good choice for cancer patients . Once a Although chemotherapy is an important area of cancer treatment and there is a need to control the rapidly growing cancer despite the severe and debilitating side effects, the choice of chemotherapy should be personalized. It provides sequential insights into changes in a patient tumour gene. The tumour sorting results help clinicians make clinical decision-making and personalized cancer treatment plans. Tumour continuity plays an important role in the development of novel targeted treatments for cancer . PersonaCancer genetic/DNA sequencing can help to make more accurate cancer diagnoses, better prognosis, and identify customized treatment options based on cancer's genetic characteristics. However, despite the growing popularity and exaggeration of the benefits and uses of the cancer gene sequence, there are currently only a handful of patients who benefit from it. Tumour genetic sequencing is the technique of obtaining a molecular scan of the type of DNA extracted from tumour cells obtained from a biopsy specimen or the patient blood or bone marrow. Diagnosing the stages of cancer helps researchers find more effective treatments. The research will be done in comparison with the data of the general previous patients of this study. This information provides information on which parts of tumour DNA differ from those of nontumour DNA, and the interpretation of genetic sequencing data provides insight into the key genes and drivers of cancer. More recently, the severity of the disease and the complications it causes have made the disease even more problematic. This type of tumour is the 10th most deadly disease in the world . There hIn this paper, an improved algorithm is developed named Internet of Things (IoT) based on enhanced machine learning. The major contribution of the proposed model is to analyze and calculates things like the size, shape, and location of the tumour. The current analysis of these large cancer population datasets has provided key insights into the changing landscape of cancer treatment protocols worldwide. Cancers of specific tissue origin, such as all breast cancers or all lung cancers, which were previously considered historically similar and treated the same way, are now recognized as very different and are classified as distinct molecular subtypes that need to be treated differently.The authors of  developeIn , computeC-means method based on a modified mean-change to calculate the mean size and type of tumours. It was used to obtain blurry images as input and remove unwanted parts to accurately locate tumours, and its reliability was high because its computational speed was consistent and fast.The authors of proposed  Grade 1: cancer cells look like normal cells and do not grow.  Grade 2: cancer cells do not look like normal cells. At this level, cancer cells grow rapidly.  Grade 3: cancer cells look abnormal. And, those cells are likely to grow or spread aggressively to other organs.The proposed IoT-based enhanced machine learning algorithm was structured, even within the molecular subtype of a particular cancer symptom, and the tumour genetic profile of each individual is different and unique. The genetic analysis of cancer DNA provides information on the major genetic abnormalities (mutations) that drive the disease, and many of these contain specific drugs designed to inhibit their action. The abnormalities of cancer DNA help to better understand the basic mechanisms by which the cancer cell uses its continuous and rapid growth and proliferation and to discover new and more targeted drugs. Therefore, in the case of a disease such as cancer, which is associated with morbidity and mortality, every piece of information that helps to understand the cancer characteristics of the individual will be useful. Listed as follows are the first three reasons why patients should order their DNA and consult a specialist with their results as in In many cases, the site and cause of the primary cancer are not clear, and the genetic sequence of the DNA helps to better identify the primary tumour site and the major cancer genes, thereby providing a more accurate diagnosis. For rare cancers or cancers that are late diagnosed and spread by various organs, an understanding of the cancer characteristics can help determine the most appropriate treatment options as in From the sorted data, one obtains the genetic profile of the cancer DNA. Based on the analysis of cancer demographic data, patterns of various abnormalities are associated with disease severity and treatment response. For instance, the absence of the MGMT gene predicts a better response to DMZ in patients with brain cancer multiform. The presence of a TET2 gene mutation increases the likelihood of patients with leukemia responding to a specific type of drug called agents. So, this information provides insight into the severity and characteristics of the disease and helps to select mild or more aggressive treatment. In general, some specific symptoms can be computed to diagnose tumours. Based on that data, its status and classifications are determined. Its common symptoms are classified in For every IoT sensor indication, a distinct rate is located to evaluate through the experiential rate as follows:  Stage 0: in stage 0, it is said that no cancer cells have formed till now. However, abnormal cells in body organs may have the potential to become cancer cells. This is called carcinoma.  Stage I: this indicates small-scale cancer. It is formed in only one part of the body. This stage is often called the early stage of cancer.  Stage II: stage II indicates that cancer has grown larger than stage I. But it did not spread to other areas.  Stage III: if a person has stage III cancer, it means that cancer has spread to a large extent. It also indicates that cancer has spread to nearby areas.  Stage IV: stage IV cancer is called advanced or metastatic cancer. In this case, the cancerous tumour has spread to other parts of the body. This is the final phase.Cancer is classified into five stages depending on the tumours. However, other types of cancer, such as leukemia, lymphoma, and brain cancer, have different staging systems. But all stages help determine the depth of cancer:a = probability constant for all related IoT indication values to the general indication.For many cancer patients who do not respond to the standard of maintenance chemotherapy treatment, tumour sorting can help better identify major abnormalities that can then be treated with more recently developed target drugs and used only in specific cases. It has the required attribute. In stubborn, recurrent, and resistant cancers, the genetic specification of the tumour DNA will facilitate access and inclusion in clinical trials testing new and innovative target drugs or finding unique alternative and customized drug options (treatments) based on cancer characteristics. The grade of cancer is determined by the appearance of the cancer cells seen under a microscope. A low grade indicates slow-growing cancer, while a high grade indicates fast-growing cancer cells. The indication measurements based on b = sum of all the general indication values.After measuring the indication values, the detection of tumour is very simple.The chances of tumour probabilities obtained from the estimation are presented in T (TP): it is the perfect predicted correct or above the calibration level  Positive-T (TN): it is the negative prediction value below the calibration level  Negative-F (FP): when the exact values are at calibration level and the predicted samples are at the same level  Positives-F (FN): when the exact values are at calibration level but the predicted samples are at a different level  Negative-The proposed IoT-based enhanced machine learning was compared with the existing hybrid fuzzy brain-storm optimization algorithm (HFBSO), multifractal texture estimation (MTE), and tumour segmentation using chi-square fuzzy c-mean clustering (CSFCC), and hybrid feature extraction method (HFEM). 5 parameters are evaluating the water quality, that is, tumour accuracy, tumour precision, tumour recall, tumour F1-score, and computation time. Before understanding the quality rate of the parameters, we will know about the following:Tumour accuracy is the parameter which describes the ratio between perfectly predicted tumour input images from the given samples to the total number of collected image samples as in equation . When thTumour precision measurement is the ratio between the positive true samples and total true samples as in equation . The totTumour recall measurement is the ratio between the positive true samples and the sum of positive true samples and false negative true samples:It is measured by the average sample values of tumour precision and tumour recall of the samples.The computation duration is nothing, but the time taken to calculate the prediction of two different images.F1-score, and 1856 ms of execution time. These computations are very important in the medical field to diagnose the types of tumours in patients. It is especially helpful for physicians to obtain information about the nature of patients and their health from the place where IoT procedures were performed. The future enhancements of the proposed system include IoT-based consultation with the doctor and guided clinical examination, finding out the details of cancer and helping to get better treatment.In this paper, various methods of diagnosing tumours are discussed, and its classification varies greatly depending on its type detection and accuracy. If diagnosed more accurately, the delay in calculating the size of the tumour and sending the tumour to the doctor will further increase the patient's risk. The state-of-the-art model with IoT further enhances time management by analyzing the size and type of tumours and making it possible to send the results immediately to the doctor without compromising accuracy. The proposed IoTEML was getting good accuracy, better precision, great recall rate, fine F1-score, and low computation duration compared with the existing HFBSO, MTE, CSFCC, and HFEM. Hence, the proposed IoT-based enhanced machine learning method was very accurate to identify the tumours with low time consumption. At a saturation point, the proposed IoT-EML model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of


Design codes provide the necessary tools to check the torsional strength of reinforced concrete (RC) members. However, some researchers have pointed out that code equations still need improvement. This study presents a review and a comparative analysis of the calculation procedures to predict the torsional strength of RC beams from some reference design codes, namely the Russian, American, European, and Canadian codes for RC structures. The reliability and accuracy of the normative torsional strengths are checked against experimental results from a broad database incorporating 202 RC rectangular beams tested under pure torsion and collected from the literature. The results show that both the readability and accuracy of the codes’ equations should be improved. Based on a correlation study between the experimental torsional strengths, and geometrical and mechanical properties of the beams, refined yet simple equations are proposed to predict torsional strength. It is demonstrated that the proposed formulation is characterized by a significant improvement over the reference design codes. The efficiency of the proposed formulae is also assessed against another equation earlier proposed in the literature, and an improvement is noted as well. From the results, it can be concluded that the proposed equations in this study can contribute to a more accurate and economical design for practice. In engineering practice, structural members under pure torsion are not a common situation. Usually, torsional effects are combined with other internal forces in the critical sections of the member. However, there are several practical cases in structures in which structural members must sustain primary torsional effects in their critical sections. Typical examples are encountered in bridge structures or geometrically complex building structures, in which reinforced concrete (RC) girders and columns can be subjected to primary torsion due to the high eccentricity of static loads or complex geometry of the members . For such members, an accurate calculation of the torsional strength is essential for the design or assessment of the torsional capacity, namely to guarantee or check the safety at the ultimate limit states. For such purposes, current RC members and structural engineers usually base their calculations on the rules from design codes. Nowadays, each country has its own set of design codes that govern structural design, namely for RC structures, and some of them have a high impact in other countries and also constitute reference codes for the international community ,4,5,6,7.Despite all the research effort made in recent decades by the scientific community, several current design codes for concrete structures are still somewhat scarce in providing detailed and specific design rules for torsion. These include basic reinforcement detailing rules and limits for important design variables to ensure a good performance of RC members under torsion for both the ultimate and serviceability limit states. For example, some design codes do not provide any specific rule regarding the minimum amount of torsional reinforcement, which is considered a basic requirement to avoid a sudden failure after concrete cracking. The same can be stated for the maximum amount of torsional reinforcement to ensure ductility at failure . Although such a maximum amount can be indirectly computed from the maximum compressive stress allowed for the concrete struts, this upper-stress limit can vary substantially among design codes. As there is a lack of specific rules for torsion, some codes refer to the rules related to the reinforcement requirements for other internal forces, such as for bending  and for shear (for the transverse reinforcement). In addition to the aforementioned missing aspects, when the rules from several design codes are used to predict the torsional strength of RC beams, small accuracy and high dispersion of the results, including unsafe predictions, are still observed when they are compared with experimental data ,9. This The first reference design codes, which incorporated specific design rules for torsion for RC members, were based on the so-called skew-bending theory. This model was proposed by Hsu in 1968  and was Nowadays, the torsion design rules from most design codes for concrete structures are based on the space truss analogy. This model, first proposed by Raush in 1929 , was latSince the 1980s, refined versions of models based on the space truss analogy have been proposed that allow one to compute with accuracy the strength of RC beams under pure torsion ,24,25,26Based on these motivations and research needs, this study first presents a critical overview and a comparative analysis of the calculation procedures from design codes to predict the torsional strength of RC rectangular beams. For this, some reference design codes considered important due to their territorial scope were chosen. Such design codes are the following ones: the Eurasian code, SiNiP 2018 , the EurWhen compared to previous research, namely the one from Rahal , which cIt should be noted that this article addresses only the particular case of RC beams under pure torsion. It is well known that in real concrete structures, the critical section of members usually carries combined loadings, for instance, torsion combined with other internal forces . However, for some concrete members, such as girders curved in plan and girders with eccentric loadings, torsion could be the primary internal acting force. Furthermore, the design provisions to check the interaction between internal forces requires the calculation of the torsional strength of the cross-section, considering only torsion as loading. Hence, the ultimate strength of the cross-section needs to be well known. This justifies the importance of this study.From the studied design codes, the SiNiP 2018 , MC90 44, MC10 , EurocodAmerican and Canadian codes incorporate specific rules to design both the longitudinal and transverse torsional reinforcement. In general, the European codes incorporate specific equations to design longitudinal torsional reinforcement. However, they refer to the rules for shear reinforcement to design the transverse torsional reinforcement.The limit of the wall’s thickness of the equivalent hollow beam, which determines and limits the area enclosed within the flow of shear stress acting on the beam’s cross-section;The flow of shear stress, which is induced by the external torque, and the corresponding shear resultant forces in each wall;The criteria to compute the torsional strength, which, depending on the underlying mechanical model and design code, consider separately the strength contributed by the torsional tensile reinforcement and the strength contributed by the compressive concrete;The maximum limit allowed for the compressive stress in the concrete struts to avoid a brittle failure of the beam due to concrete crushing;The angle of the concrete struts to the longitudinal axis of the beam.From the presented equations in A more detailed analysis of the summarized equations in The main properties of the tested RC beams needed to compute the normative torsional strength should be given;The experimental torsional strength should be given and the RC beams should have failed in pure torsion in their ultimate stage as expected;The beams should comply with reinforcement requirements from ACI 318R-19 ). From the scatterplot, a power trendline was computed to fit the data  is linearizable with a logarithmic transformation, a multiple linear regression was performed. Applying a logarithmic transformation and adding the additional “concrete area term,” Equation (1) can be rewritten in the following general linear form:2 equal to 0.984, and a mean squared error equal to 0.013552. For this analysis, IBM SPSS software version 28 was used. The obtained linear fitted model is the following:To perform the multiple linear regression, the data and terms for each reference beam from the database  can be written to compute the torsional strength for under-reinforced beams.2], 2], 2/m], 2], and From the results obtained in the previous subsections, Equations (6) and (7) are proposed as an improvement for Equation (1) to compute the torsional strength of RC beams. Equation (6) constitutes an alternative to Equation (2) proposed by Rahal in 2013 . In EquaAs for Equations (2) and (3), Equation (6) governs the torsional strength for under-reinforced beams, while the upper limit stated in Equation (7) governs the torsional strength for over-reinforced beams, respectively. Equations (6) and (7) can also be applied to arbitrary cross-section shapes.cv = 13%). The results are also good for both plain and hollow RC beams. A comparison with the results from the reference codes used in this study (The results from Equations (6) and (7) are checked against the results from all 202 tested beams from the database. The torsional strengths computed from the previous equations for each reference beam , simple equations to compute the torsional strength were proposed and checked.In general, equations from the studied reference codes still need improvements to increase the accuracy and reduce the observed statistical dispersion;Some reference design codes overestimate the torsional strength of several reference RC beams from the database noticeably, which is not acceptable for design and justifies further improvements;The proposed equations to compute the torsional strength of RC beams showed to be much more reliable and accurate in comparison with code’s equations;The proposed equations are simple and can easily be used for practice to assess with accuracy the torsional strength of RC members, including plain and hollow beams, normal- and high-strength concrete beams, as well as under- and over-reinforced beams;When compared with similar equations from a previous study , the proThis study confirms that simple and reliable design equations can be obtained by simply fitting the results with experimental data existing in the literature and related to the torsional strength of the RC beams.From the obtained results, the following main conclusions can be drawn: