The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and RA smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are  The conventional power grid contains standard power generation units grounded on fossil fuels. With the soaring energy prices, the need for renewable energy sources and climate change, the old power grid is becoming outdated and facing various limitations, such as cybersecurity, privacy and power losses due to one-way communication . This puThis bidirectional communication utilizes advanced computing infrastructure, digital sensing and software capabilities to optimize all the grid components and improve reliability and sustainability. There is a unidirectional flow of energy from the energy provider to the consumer in a traditional grid, and consumers are charged based on their consumption. However, in a smart grid system, the users in the grid can consume, produce, store and trade energy with other users . The smaThis paper used the DSGC model to define and relate the price to the grid frequency ,4. The mThe model involves real-time pricing, and thus the grid stability has to be maintained with fluctuations in reaction times and electricity price of all users. It is critical to evaluate grid stability as the process is time-critical dynamically. This is because smart grid stability prediction helps increase efficiency through grid optimization, improves electrical supply reliability and consistency and analyses disturbances and fluctuations in energy consumption or production.Before the utilization of modern techniques to predict smart grid stability, traditional approaches consisted of simulations combining fixed values for one subset and fixed distribution of values for the remaining subset variables ,7. The gVarious statistical approaches have been investigated, including autoregressive moving average, Kalman filter and Markov chain model , which cAdditionally, some research involved using conventional parametric methods that include linear regression, auto-regressive moving average and the general exponential methods. Although such models return satisfactory prediction accuracy, they persist with major disadvantages, such as improper response and complex computational problems to meteorological variables and nonlinear electrical load . A probaHowever, some uncertainties have been observed between regular grid operation and cascading failure operation in the simulation result. Adding on, techniques used for stability assessment require extensive computation time and massive data analysis volume, which makes it tough to obtain a reliable prediction and makes it difficult to take decisions for an operating power system .A few hybrid systems used for dynamic stability prediction have been based on unreliable self-organized maps and responded slowly ,18. AnotThey have multiple training algorithms, do not require significant dataset pre-processing and can produce high accuracy values during training and testing . FurtherComprehensive review work in  concludeThe classic FFNN is designed to predict the stability of the smart grid system of a four-node star network with complete input data.The sub-neural networks are proposed to predict the missing input variables, which are caused due to a sensor, network connection or other system failures. Then, the system’s stability is forecast using these predicted missing input data.The performance of the proposed approach is evaluated in four different case studies in which at least one input variable is missing.Being motivated by the literature, this paper proposes a novel method to predict the smart grid stability of a four-node star network using a neural network with complete and missing input data, consisting of missing input variables. Thus, the significant contributions of this paper are highlighted as follows:The subsequent sections of the paper are organized as follows: In this section, an extensive literature survey is conducted on smart grid stability using neural networks. This literature review shows that various neural-network-based techniques have been used for analysis, and the data analyzed in the works are with complete input data. The developed approaches are robust and accurate due to their complex structure that helps classify problems and recognize correlations in raw data and hidden patterns.A summary of works focused on smart grid stability prediction using various neural networks is highlighted in The most popular architectures are IEEE bus systems ,18,22,23In the analysis, several types of neural networks and hybrid networks were identified, as depicted in In addition, the performance of DNN for stability prediction was improved by hybridizing with RNN, RL , CNN andtansig and tanh are the most frequently used hidden layer activation functions [purelin activation function followed by Sigmoid is most commonly used in the output layer of the neural network [unctions ,34. In c network ,36.No work was conducted to predict stability when there is a missing parameter. Most studies showed that missing data had been either omitted, unreported or replaced with mean/median values.The most popular architectures used for the case studies are IEEE bus systems and node network types see .Among the several types of conventional and hybrid neural networks proposed in the literature, the FFNN and its hybrid versions, such as FF-BPNN and FF-DNN, are widely presented see .The Levenberg–Marquardt algorithm is the most frequently used training algorithm for various networks to predict smart grid stability see .tansig and purelin activation functions have frequently been used in various networks’ hidden and output layers to predict smart grid stability  communicates directly with the consumer nodes in a star network topology. The consumer nodes are connected to the central (generation node), enabling bidirectional communication between each node, which helps them to operate at lower power levels. One of the main advantages of star topology is that the networks are independent. In case of failure or errors in one of the consumer nodes, the other consumer nodes are not affected, and the network operates typically.The network is formed with one power producer in the center  and three consumers . Star topologies depend heavily on delay and averaging time. Intermediate delays in a four-node star topology benefit stability, making it a simple, effective and efficient system . From thThe mathematical model of the DSGC system is obtained for the four-node star architecture network given in The first step in the modeling is applying the energy conservation law. As per the energy conservation law, the power balance equation is given as follows:j is the node index (either generator or load), In (1), Similarly, in (1), By substituting (2), (4) and (5) in (1), Now, substituting The final step in the modeling is binding the electricity price to the grid frequency The power consumed or produced For the four-node star network shown in Therefore, the final dynamic equation of DSGC system for the four-node star architecture network is obtained by substituting (7), (9) and (10) in (11) as follows:In the first stage of analyzing the network’s dynamical stability around the grid’s steady-state operation, the fixed points of the network are computed by solving The above equation shows that the fixed point exists only if the grid has an adequate coupling strength coefficient J is calculated as,Next, the Jacobian matrix of the system is obtained to compute the eigenvalues that determine the network’s stability. Thus, the Jacobian matrix The eigenvalues j ranges from 1 to 4, in which index 1 is the generator node, and the remaining indices  are consumer nodes. Further, the values of simulation constants From the differential Equation (12), it is to be noted that the parameters The generated dataset contains 60,000 samples for all the 12 predictive variables and one dependent variable, The Pearson’s correlation matrix between the predictive features of the network Nh=1tansig and purelin activation functions are utilized in the hidden and output layers. The training algorithm and activation functions are chosen as per the results of the comprehensive literature review conducted as shown in The third layer represents the output layer, consisting of 1 node, the output parameter (Stability). The dataset is divided into 80% and 20% for training and testing. The neural network is trained using the Levenberg–Marquardt algorithm. The  and MSE ,72,73,74This section develops and evaluates a novel prediction model to handle missing inputs. The flowchart for the methodology adopted for predicting the missing input data is represented in The first stage includes data collection, analysis, pre-processing and defining the missing inputs. A prediction model is prepared using a sub-neural network to handle the missing inputs in the second stage. After the missing input parameters are predicted, the prediction model that handles missing inputs is prepared to predict stability. The primary neural network is an FFNN trained using the Levenberg–Marquardt algorithm for each case.tansig and purelin transfer functions are used in the hidden and output layers. The dataset consists of 60,000 samples, out of which 80% are used for training and 20% for testing. The input layer consists of 12 nodes corresponding to the 12 input parameters. The output layer consists of one node corresponding to the one output parameter. The number of nodes in the middle layer, i.e., the hidden layer ‘The tansig and purelin transfer functions are used in the hidden and output layers. The dataset, consisting of 60,000 samples, is divided into 80% for training and 20% for testing. The training algorithm used for the sub-neural network is the Levenberg–Marquardt algorithm. Different missing input variables are considered in each layer for each of the four cases, as explained underneath.Standard specifications for each sub-neural model in the four cases are as follows: One missing input variable is considered in the first case, which will be predicted using a sub-neural network. The sub-neural-network model for this section is an FFNN that consists of three layers refer to . The firThe training and testing outputs for the case 1 sub-neural network is shown in The primary neural network was trained to predict stability using the predicted output variables. The testing output variables of the sub-neural network are substituted in the primary neural network. Finally, the leading neural network is tested after predicting the missing input variables. The training and testing outputs for the case 1 primary neural network is shown in Case 2 involves two missing input variables for stability prediction using FFNN as shown in Upon prediction of the missing input variables, the primary neural network is tested. The MSE and RThe next step of case 2 involves training the primary neural network by utilizing the obtained testing output variables from the sub-neural network that is also measured in terms of MSE and RFurther, Case 3 uses two missing input variables for a feedforward sub-neural network for stability prediction as shown in The RNext, the primary neural network was trained by utilizing the obtained testing output variables from the sub-neural network measured using MSE and RFinally, the study of case 4 considers one missing input variable that is predicted using a sub-neural network as shown in Once the missing input variable is predicted, the primary neural network is trained similarly to previous cases. The model’s performance handling the missing data is measured using RFurther, the primary neural network was trained similarly to other cases by utilizing the testing output variables from the sub-neural network. The model obtains an MSE of 0.0084 during training and 0.0084 during the testing phase. The RThe primary goal of this paper was to tackle the issue of stability prediction when there are missing variables involved. This missing variable could be due to the failure of a sensor, network connection or other system. This paper successfully solved this issue by proposing a novel FFNN model that handles missing inputs. The model’s performance was evaluated on a four-node star network. In this study, four cases of missing input variables were taken.For each case, a sub-neural network was first prepared to predict the missing variables, and then these predicted values were fed into the primary neural network to predict the stability. Among all four cases, case 4 showed the best performance with an MSE value of 0.0001 and an RHowever, this work was limited to predicting the power parameter using a sub-neural network because the algebraic sum of the power consumed or generated was assumed to be zero, and uncertainties and disturbances were not considered. Moreover, the reaction time and price elasticity parameters are highly nonlinear in the considered dataset. As a result, this proposed model faces a shortcoming in predicting the missing variables (reaction time and price elasticity) using a sub-neural network that predicts the stability using a primary network. Therefore, extending the proposed model to predict these highly nonlinear input parameters will be addressed in our future work.


The prolonged coronavirus disease 2019 (COVID-19) pandemic has raised concerns about the failures in the public health measures used to manage the spread of this deadly virus. This review focuses its attention on research papers that at their core highlight the individual public health measures instituted by organizations, institutions, and the government of the United States (US) since the start of the COVID-19 pandemic and that were published in 2019 to 2022. Together, these sources help paint a well-rounded view of the US management of this pandemic so that conclusions may be drawn from mistakes that were made and this country may respond better in the future to such situations. This paper is unique because it highlights the areas where improvement is needed, whereas other published work describes the measures taken and how they were carried out, not the failures, which leaves a gap in the literature that this paper hopes to fill. Through a deep dive into public health measures, seven areas in which improvements could be made were pinpointed by the authors. Such measures included mask mandates, social distancing, lockdown/quarantine, hand hygiene, COVID-19 testing, travel screening, and vaccine hesitancy. In exploring each measure, a discussion was carried out about its benefits and shortcomings in alleviating the ramifications of a global pandemic. In addition to the poor supply chain for critical products like personal protective equipment (PPE), the miscommunication between states and federal policies did not allow for the entirety of the US to respond cohesively in the face of the COVID-19 pandemic. This general review is crucial to know what is working and what needs to be changed to increase the benefits provided to the population. The coronavirus disease 2019 (COVID-19) virus, also known as severe acute respiratory syndrome coronavirus (SARS-CoV-2), was identified in late 2019 and quickly became a super spreader virus across the world. COVID-19 is an incredibly fast-spreading virus, spreading through respiratory droplets. The disease may not show symptoms right away. This contributes to how vastly spread it became and why it became a pandemic. Most of the population will experience mild to moderate symptoms but will recover without issues . To dateMethodsA review of the failures of public health measures in the US during the COVID-19 pandemic was performed using online sources such as PubMed, Google Scholar, and the Centers for Diseases Control and Prevention (CDC) website. Within the research, the main search words used to find articles were COVID-19, public health measures, mask mandates, lockdown, and vaccinations. The articles selected were about the main safety measures implemented to prevent the spread of COVID-19, as well as articles showing COVID-19 progression throughout different states. Articles were excluded in which public health measures related to COVID-19 were not mentioned. Specifically, the main reason for excluding 2106 articles in the first pass, was no mention of COVID-19 in the title or abstract of the articles. The CDC, with the World Health Organization (WHO), was used for data regarding the number of cases, deaths, and modes of contamination. Articles from before 2019 were excluded. The study was conducted according to the latest Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The inclusion criteria for the 61 articles included in the review consisted of articles specific to the US population, published after 2019, mentioning a specific public health measure taken during the pandemic, and relevant to COVID-19.DiscussionMasksThe use of masks to prevent the spread of the COVID-19 virus was widely recognized across the US. Mask mandates came into play at varying times throughout the pandemic for each state and faced criticism on both sides of the spectrum. Masks were majorly used indoors in most states, while few states required their use in outdoor environments. Masks have a 99.98%, 97.14%, and 95.15% efficacy for N95, surgical, and homemade masks, respectively in preventing the spread of avian influenza . The aviPublic health failed with the implementation of mask mandates because it was not implemented in a timely and uniform matter across the US. The states made the individual decisions of when to require the mask mandates and the specific regulations surrounding it. Some states failed to implement any mandates at all and left the local government to decide for themselves. States such as New York and Washington took on universal mask mandate policies, which showed a decrease in the spread of COVID-19 . These uIssues also became apparent with the type of masks used. The CDC recommended the use of N95 masks for healthcare workers, as they would have the highest exposure to the virus [Lastly, there was a shortage of personal protective equipment, specifically face masks, that the US was unprepared for when the pandemic hit. Increased use of surgical and N95 masks led to dwindling resources because the demand far exceeded the supply available . HospitaThe timely and uniform execution of mask mandates, adequate supply, and properly informing the public are major considerations for future purposes. Several states showed much better results and decreased COVID-19 cases when those guidelines were followed . FurtherCOVID-19 TestingDuring the past year and a half, diagnostic testing has been critical for quickly and accurately identifying individuals infected with COVID-19 and battling the spread of the virus. Identifying failures within the diagnostic methods used during this pandemic can be especially useful in helping us respond to future pandemics more efficiently.Throughout this pandemic, several types of diagnostic tests were employed to determine who had contracted COVID-19 and was fighting a current infection. The first type of testing most used was a polymerase chain reaction (PCR) test done on samples acquired on a swab from the nasal passage. PCR tests require three reagent kits and need to be analyzed in a lab with specific lab equipment making the process lengthier . UltimatOne major failure in COVID-19 diagnostic testing was the depletion of testing kits. At the start of the pandemic, the PCR method was the test used most in the US because serologic testing kits had not been mass-produced and dispersed yet. As the virus continued to spread and more individuals got sick, the demand for testing increased exponentially while the supply decreased due to the lack of available materials to conduct the PCR tests. Several months into the pandemic the US Strategic National Stockpile (SNS), which stores medical equipment for emergency events like this one, was running low due to the worsening situations in hospitals across the country . As mentIn conjunction with the low availability of testing kits, there was also a lack of access to testing sites. When testing became available, testing sites were set up by government agencies throughout counties across the US in places like stadiums and churches and many others. The location of such testing sites highlights a huge disparity in the access that different racial and ethnic groups have to medical services. As detailed in a recent study done on COVID-19 testing site distribution in New York City, data show that African American and Latino communities have fewer sites in their zip codes than traditionally white communities . Health Another prevalent issue with COVID-19 testing was the long wait times for testing results. With the huge demand for testing, especially in highly populated cities, the turnaround time for results was greatly affected. Results would take anywhere from 24 to 72 hours which then took one to five days to be relayed to the patient . Many faDuring the COVID-19 pandemic, there were many issues in the way testing methods were developed and employed to battle the quick spread of the virus. To be better prepared, the US can work on building a larger stockpile of raw materials for the tests discussed above, and in doing so, be sure that there are enough kits to diagnose individuals and stop the spread of disease. It would be beneficial to work on having these materials sourced in the US as opposed to being out of the country so that the US is less affected by another global shutdown. Better policies can be established to ensure that racial background or the zip code someone lives in is not a determinant of their access to medical services, such as testing. These are just a few things that if changed can make a world of difference in response to any future pandemic. Social DistancingOne of the main public health measures used to contain the spread of any contagious disease is social distancing. Theoretically, the fewer people one is in contact with, the spread of disease decreases, and the chance of getting sick is lower. In a study comparing the COVID-19 incidence and mortality rates of 3,054 US counties during the first wave of the pandemic, it was determined that increased social distancing measures decreased incidence by 26% and mortality by 31% . TherefoThe physical parameters of social distancing have been widely studied to determine what is a safe distance that ensures low to no transmission of the virus. One of these studies assesses the distance traveled by viral particles during regular breathing, sneezing, and coughing to determine the appropriate social distance to avoid the zone of viral exposure . This stWhen evaluating the US use of social distancing measures in response to COVID-19, the question of timing is always discussed. Were social distancing measures established at the appropriate times or would it have been beneficial from an earlier start? This very question has been statistically evaluated through a longitudinal study of 37 countries done after the first wave of the pandemic . The stuAnother facet of social distancing that needs to be discussed is how it differed from state to state. One of the key issues at the start of the COVID-19 pandemic was the differing mandates proposed from state to state which often led to different states being at various stages of pandemic management. In a state-level analysis done with data collected from the start of the COVID-19 pandemic, it was discovered that states that had stay-at-home orders and public gathering bans in place by the time they reach their 500th COVID case had lower transmission rates and longer case doubling times . The traWith retroactive analysis of data gathered from the start of the COVID-19 pandemic and peak points during the last year, there were failures in instituting adequate social distancing measures. Discovering the safe social distance needed to avoid exposure took some time and therefore not established in public settings. Another big failure was the timing of when these measures were enforced. As discussed above, based on data gathered from different countries, an earlier start time has been mathematically proven to lead to decreased overall mortality. A final failure in implementing social distancing measures was the lack of cohesive policy enforced by all states in the US. Various policies from state to state led to varying degrees of enforcement and in the long run compliance by the population which ended up affecting some states more negatively than others.Vaccine HesitancyFor months, the primary way to combat the spread of COVID-19 was through non-pharmaceutical measures, such as lockdowns and mask mandates . These pVaccine hesitancy found in the US can be attributed to the three C model, which is composed of the three categories of convenience, confidence, and complacency . These cThis three-C model is furthered with the addition of two more categories, communication, and context . These cAn increase in vaccine hesitancy within the US has also been correlated with communities of ethnic and racial minorities . A revieTo increase vaccination rates in the US, some corporations and state governments have started to offer monetary motivation as well as the promise of other material rewards. States, such as Ohio, North Carolina, and Michigan, have offered their citizens a drawing in a lottery with a chance at winning a sum of a million dollars or more in exchange for getting a vaccination . Some ciLockdown & QuarantineAs the COVID-19 pandemic began its initial spread, many countries were not prepared with a plan on how to contain the virus since there had not been a threat like this since the 1918 Influenza. By the middle of March, many countries had implemented emergency measures, ranging from a complete lockdown to stay-at-home orders that put a curfew into effect. The US began to respond to the threat of COVID-19 with the enforcement of a lockdown toward the end of March, but the federalist structure of its government and the temperament of the political climate caused inconsistency and confusion in the response of the country.When enforced properly, a lockdown can be one of the most effective non-pharmaceutical interventions applied within society . A lockdWhile lockdowns were useful, they could have been more effective if implemented earlier . A studyThe time at which a lockdown was implemented was not the only reason a lockdown was not as effective in the US; inconsistency between the decisions at the level of the state governments could also be to blame. At the beginning of the pandemic, the federal government cast the responsibility for the response to COVID-19 on the states, therefore, the US did not have a unified reaction like many other countries . Some stThe various containment strategies within each state reflected the political polarization found in the US as well . BecauseThe first of the 50 states to enact a shelter-in-place order was California. These lockdown measures are estimated to have led to between 160.9 to 194.7 per 100,000 population reduction in COVID-19 cases within the state as well as a 3.6 to 3.9 per 100,000 population reduction in COVID-19-related deaths per 100,000 . WithoutTravel ScreeningOut of the measures taken during the beginning of the COVID-19 pandemic, one that perhaps should have been better managed to prevent another public health failure in how the pandemic was handled by societies across the globe was the screening of travelers out of mainland China as well as contact tracing of who they encountered . The meaHand HygieneHand hygiene is a highly effective method of preventing the spread of pathogens and diseases that only became commonplace in the US a few decades ago, in the 1980s, when the firsthand hygiene guidelines made their debut into public health measures . Today, For hand hygiene measures to be effective, they must be successfully enforced amongst the public and healthcare workers. Hand hygiene compliance is directly related to the accessibility and availability of resources that make proper hygiene possible. One of the most widespread methods of hand sanitizing available to the public became hand sanitizing stations available in drug stores, grocery stores, retail stores, schools, restaurants, and airports. Furthermore, handwashing stations were made available at places commonly visited by thousands of people. Most importantly, hand hygiene adherence was promoted amongst healthcare workers, who were most likely to encounter COVID-19. However, it seems that this was not entirely successful; a recent study showed that adherence to the hand hygiene guidelines established by WHO only significantly increased among healthcare.Public health failed with the implementation of mask mandates because it was not implemented in a timely and uniform matter across the US. The states made the individual decisions of when to require the mask mandates and the specific regulations surrounding it. Some states failed to implement any mandates at all and left the local government to decide for themselves. States such as New York and Washington took on universal mask mandate policies, which showed a decrease in the spread of COVID-19 . These uIssues also became apparent with the type of masks used. The CDC recommended the use of N95 masks for healthcare workers, as they would have the highest exposure to the virus [Lastly, there was a shortage of personal protective equipment, specifically face masks, that the US was unprepared for when the pandemic hit. Increased use of surgical and N95 masks led to dwindling resources because the demand far exceeded the supply available . HospitaThe timely and uniform execution of mask mandates, adequate supply, and properly informing the public are major considerations for future purposes. Several states showed much better results and decreased COVID-19 cases when those guidelines were followed . FurtherFor future directions, the largest factor found that would lead to improvement in preventing the spread of COVID-19 is an earlier implementation of prevention methods. COVID-19 diagnostic methods also fell short and could have contributed to the spread of the disease. The first test that originally came out took about one week to deliver results, leaving the potentially COVID-positive patient to spread the virus to those around him/her. Once rapid testing began to be performed, which reduced the diagnosis time to two to three days, many turned out to be false positives or false negatives. These discrepancies in the testing process need to be addressed and more sensitive tests need to be provided to individuals who have had contact with COVID-19. While it is impossible to predict any future disease, this pandemic has a lot to teach about future preparations. Hospitals and healthcare facilities should remain stocked with personal protection equipment for worst-case scenarios to aid healthcare workers which will be facing new diseases. Healthcare facilities have many plans within their policies regarding major catastrophic events, with the experience they have had during COVID-19, they can use this as an opportunity to establish protocols and guidelines to enhance response to any future pandemics. Lastly, with the vaccine recently coming out, there was a lot of skepticism throughout the general population about taking the vaccine. The media and politics played a major role in deterring people from taking the vaccine. There was misinformation being spread about symptoms and possible uses of the vaccines which led the population to doubt their’ efficacy. Informing the public, instead of sensationalizing and profiting from such a challenging time would have created a better understanding for those with questions.


Recently, the possibilities of detecting psychosocial stress from speech have been discussed. Yet, there are mixed effects and a current lack of clarity in relations and directions for parameters derived from stressed speech. The aim of the current study is – in a controlled psychosocial stress induction experiment – to apply network modeling to (1) look into the unique associations between specific speech parameters, comparing speech networks containing fundamental frequency (F0), jitter, mean voiced segment length, and Harmonics-to-Noise Ratio (HNR) pre- and post-stress induction, and (2) examine how changes pre- versus post-stress induction  in each of the parameters are related to changes in self-reported negative affect. Results show that the network of speech parameters is similar after versus before the stress induction, with a central role of HNR, which shows that the complex interplay and unique associations between each of the used speech parameters is not impacted by psychosocial stress (aim 1). Moreover, we found a change network  with changes in jitter being positively related to changes in self-reported negative affect (aim 2). These findings illustrate – for the first time in a well-controlled but ecologically valid setting – the complex relations between different speech parameters in the context of psychosocial stress. Longitudinal and experimental studies are required to further investigate these relationships and to test whether the identified paths in the networks are indicative of causal relationships. Stress is an increasingly relevant topic in modern society, with the majority of people experiencing regular stress symptoms. Considering the broad range of physiological and psychological factors that are influenced by stress, a plethora of methods have been developed to assess individuals’ stress levels. Currently, commonly used methods determine stress levels by self-report questionnaires on factors that are affected by stress  or broader indicators of psychological well-being Monroe, . BesidesSpeech production is a complex process that requires the involvement of many different parts of the body. To produce speech, one first considers what words to say, tone of voice, and many more conscious aspects. However, the practical part happens more automatically, which is the actual sound production. When producing speech, the body modulates the tension of numerous muscles to push air through the vocal folds and out the vocal tract to produce sound waves  Fundamental frequency (F0) can be considered a key speech parameter in the context of different types of stressors. F0 refers to the frequency at which the vocal cords vibrate, and gives rise to the idea of the pitch of the voice. Research suggests a universal trend of increase in F0 in stressed subjects  Harmonics-to-Noise Ratio (HNR), which indicates one’s vocal quality by measuring the additive noise in the speech signal during voiced periods . HNR has mainly been studied in physical stress tasks , and has shown to decrease with increased physical task stress  Jitter refers to the frequency variation from cycle to cycle and has been found to reduce in the context of stress, however this trend has not shown to be universal  formants have been opted as promising features of speech in distinguishing stress from speech, more specifically, the shifting of formant 1 (F1) and formant 2 (F2) have shown to be decent indicators of psychological stress  Mean voiced segment length can be used to gain insight into such speaking patterns, as it is the mean length of the continuously voiced regions which is expected to decrease under stress.However, relatively little is known with regards to how specific speech parameters interact in a context with or without stress, and how this interaction of speech parameters changes following a stressor. That is, speech research in the context of stress is still in its infancy and has mostly flourished at the fundamental level of parameter identification and development. Speech consists of many different parameters , which are contingent on many factors, both conscious and automatic. Fundamental frequency, Harmonics-to-Noise Ratio, and jitter are such speech parameters that have been found to change in stressed subjects , and that the interrelation between each of these constructs in the context of stress  remains to be explored, we will make use of psychological network models  was recruited through flyers, social media, and University of Ghent mailing lists informing them on the duration of the experiment, the possibility to win a 25 euro gift card, and a link to “Papa en Marloes staan op het station. Ze wachten op de trein. Eerst hebben ze een kaartje gekocht. Er stond een hele lange rij, dus dat duurde wel even. Nu wachten ze tot de trein eraan komt. Het is al vijf over drie, dus het duurt nog vier minuten. Er staan nog veel meer mensen te wachten. Marloes kijkt naar links, in de verte ziet ze de trein al aankomen.” From: van de Weijer and Slis (1991)Participants were seated in one of two nearly identical rooms in front of a Huawei MediaPad M5 tablet. The task was written in Java using Android Studio. Before any instructions commenced, participants signed the informed consent form. Then, participants were instructed on the procedure, how the tablet and application worked , and the cover story (cf. infra) was repeated to minimize the likelihood of the actual purpose of the study being identified. Next, participants were given a smartwatch (Chill+ Band) to put on their dominant hand , and ECG electrodes were placed on the sternum. The participants were informed on the purpose of each of these measures with the cover story of it being used to validate the smartwatch measures. Data quality was shortly inspected before the actual experiment started. Firstly, participants were requested to rate the VAS. Next, participants were instructed to rest for 5 min to ensure they were relaxed and to minimize the impact of any events occurring previous to the experiments . Following the resting phase, participants were instructed to read-out-loud a five-sentence piece of text that was the same for all participants and an often-used text in Dutch speech therapy:Participants were instructed that the recording of the speech was to train speech-to-text algorithms to hide the actual purpose but to ensure the accurate pronunciation of the text. Next, once again, the VAS sliding scales were answered, providing a baseline measure for NA in a relatively unstressed state . After that, the MIST (see Stress induction procedure header) commenced, starting with instructions and 2 min of practice trials during which no social comparison was made and without a trial time limit. After the testing phase of the MIST, another speech recording and VAS segment was conducted, which corresponds to the post-stress measurement. The end phase of the experiment consisted of another 5-min resting block to prevent participants from leaving the experiment in a stressed state, followed by another block of VAS questions. The experiment was concluded by conducting the Ruminative Response Scale (RRS) and the Depression, Anxiety, and Stress Scale (DASS) in order to get an estimation of the sample characteristics. At the end, the participants were debriefed and informed on the actual purpose of the study.‘average participant’, which in reality was an unfeasible, fictional benchmark. Participants were instructed that they should not deviate from the average performance too much, and that if they would the data would be unusable for the purpose of the study. Throughout the task, the experimenter was sitting across from the participant and taking notes. Since the participant is always performing worse than the ‘average participant’, and with the experimenter taking notes and the constant time pressure, stress is induced. As a cover story, participants were told that the study attempts to link biometric signals to quick arithmetic solving skills.In order to induce acute stress in our participants and investigate the effects of stress on networks of speech parameters, we used the Montreal Imaging Stress Task  as an indicator of stress, self-reported mood was measured at four time points , by using the three NA items of a seven-item mood questionnaire (adopted from the Profile of Mood States (POMS); Rossi & Pourtois, Speech parameters were extracted using OpenSmile 2.3.0  were performed with the ‘emmeans’ R Package . As part of the manipulation check, we fitted generalized linear mixed models (GLMMs) using the ‘lme4’ , (2) a network including the speech parameters, assessed immediately following the stress induction procedure (referred to as stress network), and (3) a network including the change scores for each of the speech parameters and the compound measure for NA (referred to as stress reactivity network). Change in NA / speech parameters was computed by subtracting the resting state measure from the post-stressor measure. As such, a positive value reflects an increase in NA / the speech parameters throughout the induction procedure.We relied on Gaussian graphical models (GGMs), also referred to as regularized partial correlation networks, to model the impact of stress on the unique associations between the speech parameters of interest , as well as the relation between change in speech parameters and change in NA throughout the stress induction procedure. For this purpose, we estimated three separate GGMs. In particular, we computed: (1) a network including each of the speech parameters of interest, assessed following a resting phase  strength of connectivity . Therefore, the EDA data (together with self-reported mood data) that was collected accurately is used to validate the stress induction method, but will not be included in the network analyses (n = 148).Due to technical malfunctions, all ECG data was unusable and a part of the EDA has not been collected properly for some participants throughout the experiment  pre- and post-stress induction, and EDA pre-, during-, and post-stress induction. Given the non-normality of the EDA data, a series of (G)LMM  were conducted to ensure the use of a statistical model that best fits the underlying distribution . Based on the Akaike information criterion (AIC), EDA was best described by a gamma model with a log-link (AIC = 802.5).time (pre - post for negative affect (two levels) and pre - during - post for EDA (three levels)) as an independent variable and subject ID as random intercept. The LMM for negative affect showed a significant effect of time  = .19, p = .04, was found following the expected trend of negative interrelatedness, therefore supporting the stress-induction method.To further underline the stress induction effectiveness, we ran a Pearson correlation between the delta scores computed for EDA and negative affect. The delta scores were computed by subtracting the data from the resting state measure from the post-stressor measure for these variables, resulting in scores that indicate an increase after the stress induction when positive, and a decrease when negative. A significant correlation, n = 148). The strongest connection in the network occurs between HNR and F0 (.75). HNR is positively associated with F0 and VO. The latter two constructs are negatively associated with one another. In addition, VO and HNR are negatively associated with JIT. For HNR, an additional negative edge emerges with F1/2. Finally, we observe a negative association between JIT and F1/2.Our first aim was to model the impact of stress on the interrelations between the speech parameters of interest by comparing a pre-stressor network with a post-stressor network. These networks consist of nodes representing variables, connected by edges representing regularized partial correlations. As such, every edge (connection) between two nodes (variables) represents the sign (positive/negative) and the weight (strength) of the connection, depicting the unique associations between two nodes while controlling for all other nodes in the network , is highly similar to the resting state network  or strength of connectivity .In line with the visual interpretation of the obtained network models, a statistical comparison of the models suggested strong overlap. That is, we observed a correlation of n = 148) depicts the unique associations between change in negative affect (NA) and change in the speech parameters following the stress induction procedure with every edge (connection) between two nodes (variables) represents the sign (positive/negative) and the weight (strength) of the connection, depicting the unique associations between the two nodes while controlling for all other nodes in the network  the unique associations between specific speech parameters  after as compared to before experiencing psychosocial stress, and (2) how change in these speech parameters was uniquely associated with change in self-reported NA following the stressor. We measured (change in) speech in the context of experimentally induced stress in a large sample of individuals selected from the community. The psychosocial stress induction was successful, as evidenced by higher skin conductance levels after the stress induction as compared to baseline, as well as increased negative affect following the stress induction. Moreover, we observed a significant positive association between these measures, indicating that the more skin conductance levels were increased following the psychosocial stress induction, the more negative mood was reported, providing support for the validity of the stress-induction method. As such, the comparison between the resting state and stress network allows us to test for changes in interrelations between the speech parameters after experiencing stress (aim 1).First, network analyses were conducted on the selected speech parameters of interest at baseline, representing the unique associations between each of these parameters in a resting non-stressed state. This network shows Harmonics-to-Noise Ratio (HNR) as the most central node, being connected to all other speech parameters. The strongest connection that occurs is the positive connection between HNR and fundamental frequency (F0), implying that less noise is present in higher-pitched voices and vice versa, as has been reported by Ferrand . FurtherWhen comparing this baseline network with the post-stressor network, no differences between the interrelations of the different speech parameters were observed, suggesting that the relations between the parameters (selected as nodes in the current study) do not change after a stress induction procedure. More specifically, in both models, (1) HNR emerged as the most central node, (2) the strongest connection was observed between HNR and F0, and (3) all parameters were connected to at least two out of four other nodes in the network. The fact that the network of speech parameters was highly similar after versus prior to the stress induction procedure is an interesting and innovative observation, as it shows that the complex interplay between each of the above presented core speech parameters is not impacted by stress, and as such cannot be used as an indicator for stress. In particular, the unique interrelations remained stable in a stressed versus a non-stressed state.In addition to comparing the pre- and post-stress networks of speech parameters, we composed an individual network model of change (delta) scores of each of the parameters and self-reported negative affect to gain insight into the unique relations between speech parameters and individual differences in stress reactivity (aim 2). We found that changes in jitter (JIT), a fairly central speech parameter in the estimated network, were directly positively related to changes in self-reported negative affect, after controlling for the influence of other parameters in the network. Even though after the regularization procedure the strength of this association was relatively weak, this finding is important as it suggests a unique association between speech and self-reported negative affect. Jitter quantifies the modulation of the periodicity of the voice signal and as such is related to the amplitude variation of the sound wave and is mainly affected by the lack of control of vibration of the cords . Moreover, especially a strong association between change in jitter and change in HNR is observed, which together with jitter form the most central nodes of the network. Prior studies have demonstrated that HNR is more sensitive to subtle differences in vocal function than is jitter . Our findings provide several implications for the measurement of speech in the context of psychosocial stress, as well as for the measurement of stress via speech features. That is, our findings point towards the stability of the network structure of speech features in the context of stress, and the role of jitter as the only speech feature which showed a direct association with self-reported negative affect, suggesting the importance of jitter in the context of stress assessment via speech. The present study used a standardized method of psychosocial stress induction in a highly controlled lab setting. The analysis has been conducted using an exploratory and data-driven method which allows to model complex interrelations in an intuitive manner. Therefore, the present study’s main strength is the generation of trustworthy hypotheses.Future studies using large sample sizes whilst maintaining a within-subject design in a controlled setting are absolutely warranted. On the other hand, considering the accessibility of high-quality microphones, combining frequent speech recordings with continuous smartwatch recordings of heart rate and skin conductance will generate more dynamic results that could withstand and overcome prior limitations of controlled lab settings and can uncover the stability and strength of the different relations. However, this is to be confirmed by basic experimental research investigating the complex relation between speech and stress in a well-controlled setting, which was the aim of the current study. Finding the key parameters of stressed speech and being able to use these to assess stress levels in a wide variety of settings, swiftly and cost-effectively, will enable us to monitor excessive stress levels and set up interventions where necessary.Even though the current study has several strengths such as such as its innovative nature and ecological validity, some limitations should be discussed. Firstly, it should be noted that network models are merely descriptive rather than predictive. These networks are undirected and therefore do not allow any statements regarding the direction of the observed effects. This data-driven, explorative analysis, is hypothesis generating as the identified paths in the networks might be indicative of causal relationships which should be tested in future prospective or experimental research. Secondly, as network models value each individual relation between the different parameters in an unguided manner, we were limited in the number of parameters that could be included in the model due to the sample size. The current set of parameters was selected based on literature research and has brought forth a network of interesting relations. However, an expanded network would give more insight into the stability of these relations, as well as further explain the dynamics between speech parameters and negative affect. Thirdly, the required larger sample size to do so would also increase the strength of the network comparison (resting state vs. stressed) made. However, in this context both networks were highly similar. As such, the non-significant findings for the network comparison test are unlikely to be driven by a lack of power. Overall, jitter seems to be a central node in the relation between speech and negative affect, which should therefore be further studied using confirmatory analyses. Fourthly, due to some technical setbacks, most of the collected data for ECG and EDA was not usable. This is especially unfortunate as this would give insight not only into the interplay between speech and self-reported negative affect but also into the relations with other indicators of objectively experienced stress .Stress has long been a much-discussed topic, and as such many different methods for stress measurement have been proposed over the years. Recently, speech analysis has been proposed as a possible physiological marker for stress which can be measured in a remote and non-invasive matter. The current study deployed network analysis to investigate the unique associations between specific speech parameters prior to and following exposure to a psychosocial stressor (aim 1), and to model the unique associations between specific speech features and self-reported stress . For this purpose, we relied on a well-validated stress induction procedure in a controlled lab setting. The network of speech parameters was highly similar after versus before the stress induction, suggesting that the complex interplay between each of the used speech parameters was not impacted by stress. Interestingly, changes in jitter were directly positively related to changes in self-reported negative affect, indicating that this speech feature may be of particular interest in the context of stress assessment. These findings warrant further investigation in the diagnostic value of speech features to monitor stress in daily life, which requires intensive time series data.


The Internet of Things (IoT) is being deployed to provide smart solutions for buildings, logistics, hospitals, and many more. It is growing with billions of connected devices. However, with such tremendous growth, maintenance and support are the hidden burdens. The devices deployed for IoT generally have a light microcontroller, low-power, low memory, and lightweight software. The software, which includes firmware and applications, can be managed remotely via a wireless connection. This improves flexibility, installation time, accessibility, effectiveness, and cost. The firmware can be updated constantly to remove known bugs and improve the functionality of the device. This work presents an approach to update firmware over-the-air (OTA) for constrained IoT devices. We used Narrowband IoT (NB-IoT) as the wireless communication standard to communicate between the managing server and devices. NB-IoT is one of the most promising low power wide area (LPWA) network protocols that supports more than 50k devices within a cell using a licensed spectrum. This work is a proof of concept demonstrating the usage of NB-IoT to update firmware for constrained devices. We also calculated the overall power consumption and latency for different sizes of the firmware. Billions of IoT devices are expected to work for more than ten years operating at very low-energy consumption considering their limited capabilities such as battery life, processing power, and memory. It means the microcontroller, sensors, and communication need to be low-power. These devices are controlled and monitored remotely using network connections, usually over the Internet. However, to connect these devices to the network, traditional power-hungry Wi-Fi or mobile technology cannot be used. We can use a low-power wide-area network (LPWAN) technology such as NB-IoT, which is a communication standard that provides low energy consumption, reliable connection and deep-indoor coverage for the device. There are two techniques, namely extended discontinuous reception (e-DRX) and power saving mode (PSM), which are applied in NB-IoT to lower the energy consumption of the device. In , we alreThe firmware is usually stored in electrically erasable programmable read-only memory (EEPROM) or flash memory. The firmware of IoT devices has features such as working with real-time behaviour, deterministic execution, fast boot time, closed system with fixed-function, and fixed boot target . The reqThe major contribution of our work is the development of a remote firmware update architecture using NB-IoT as a low-power energy consumption network and effectively transferring the firmware images to a large number of devices by having a negligible impact on the battery lifetime. The remainder of the paper is organized as follows. In The IoT device firmware update generally consists of the development of a management framework, device design for supporting the remote update, and transferring the firmware remotely. A study of the firmware update for embedded IoT systems is described by Nikolov . He showSmart systems use technologies such as Wi-Fi, Bluetooth, and ZigBee for software update transfer. Texas Instruments has developed the Z-Stack protocol stack-based wireless firmware updating technique, which can meet the basic need of ZigBee nodes . A few rHowever, other communication standards for low-powered devices include LoRaWAN and NB-IoT. LoRaWAN restricts the maximum amount of data communication per time period, making the system slower for updating. The updates interfere with data communication and the device becomes non-communicable for a long time. On the other hand, the NB-IoT standard supports group services with the multimedia broadcast multicast service (MBMS), which can help send update notifications. The single cell-point to multi-point (SC-PTM) framework is one of the approaches that extend MBMS to provide group communications within a single cell . More reA few works analyzed the NB-IoT firmware update theoretically. Feltrin et al.  comparedSome works have focused on calculating the power consumption of NB-IoT modules from different vendors on different deployed networks. Alobaidy et al.  performeThere are many platforms such as Mender , ARM PelThere are a few solutions available online ,25,26. TSensor: It is a device connected to the NB-IoT network and senses various data such as temperature, humidity, and GPS position. It sends the sensed data periodically to a server via the NB-IoT network. The sensor needs to update its functionalities and becomes a target for the wireless firmware update over NB-IoT.Server: It is a self-deployed server connected to the Internet or a cloud service. It is used to maintain the database of the system and store the sensors’ identities and measurements. In addition to that, the server manages firmware versions and handles the sensors’ firmware update process. The update process can be triggered automatically by the server application or manually by the administrator using a web interface. Before transferring the firmware update file, the server notifies all the registered sensors about the upcoming files via NB-IoT.In this section, the system architecture is described. We define the system with end-nodes, and the NB-IoT network is used as a communication interface between them, as shown in Push firmware update: Whenever a new firmware version is available, the server starts the update process.Pull firmware update: In this case, the sensor or the user at the sensor-side can initiate the update process. Practically, the sensors have a dedicated button that needs to be long pressed at bootup to start the firmware update.The update image can be a partial update, differential binary patching, or full firmware image replacement at once. There could be two modes of firmware update, as below:The firmware update operations should fulfill the following requirements.Notification about the update: The availability of a new firmware version should be notified to all the eligible sensors, which is done by the server.Version control at server-side: The server should manage all the firmware versions so that the firmware rollback is possible.Authenticity of update file source: The sensors should accept the firmware updates only from authenticated servers.Reliability in transfer: Packet loss or file corruption can affect firmware update operations and can fail the process. Therefore, the file transfer protocol should be reliable, which is generally controlled by the destination-feedback mechanism.Integrity of the firmware-image: The complete transfer of the firmware should be verified in a way so that a corrupted file can be rejected.Backward compatibility: The sensors should be able to revert the firmware version to the last running one. To support backward compatibility, the sensor needs to have a large flash memory, which could affect the cost of its deployment.Hindrance to the user data communication: The firmware update files should be transferred so that they do not block the main data communication processes.Automated process: The complete process of updating should be automated such as the sensors should be able to boot automatically to the new firmware; it should check the integrity, negotiate the timing of the update, and much more.Secure Channel: The communication between the server and the sensor should be secure. A secure handshake should be performed to negotiate security parameters, and transmitted data should be encrypted. Furthermore, the firmware can be signed for additional security.background firmware update. The software running at the sensor handles both the data reporting and the firmware update in parallel. The background update approach is favourable for large firmware updates. The second approach is a foreground firmware update, in which two different software agents or threads are separately responsible for the data reporting and the firmware update. In our architecture, we used the second approach: the two agents are the application and the bootloader. The application is responsible for collecting data and reporting them to the server via the NB-IoT network, whereas the bootloader is a program that handles the firmware update operations. This way we separate responsibilities between the two agents which results in easy maintenance of the code and less code change in the application.As mentioned before, the sensor device must manage its main function, which is reporting various sensors’ data to the server via the NB-IoT network while the firmware update process is running. There are two approaches to performing it. The first approach is referred to as Mailbox, which enables data sharing between these two agents. When the sensor application receives a firmware update notification from the server, it stores the notification parameters in the mailbox and jumps to the bootloader. Whenever the bootloader finds a firmware update request in the mailbox, it starts the firmware update process. The complete procedure is explained in the next sections. Our focus in this article is on the firmware update process rather than on data communication.Both of them are stored in the sensor flash memory. The flash memory layout is shown in STM32 Nucleo: The development board STM32 Nucleo L496ZG is used to build the prototype. It runs both the sensor application and the bootloader for data reporting and firmware updates. It has many peripherals, and internal flash memory of one megabyte (MB) that can host both the application and the bootloader firmware [firmware .NB-IoT module: It is the communication module used to receive the firmware update file from the server using the NB-IoT network. It can also be used to exchange user data between the device and the server. We have used a SARA-N210 [ARA-N210  based moOcta Extension board: A custom board plugged in the STM32 Nucleo GPIO socket that provides different connectors where various sensors and modules can be added.Flash memory: This is an extended external non-volatile memory and is used to store sensor application data and the downloaded firmware segments when performing the firmware update. It also hosts the mailbox to share data between the application and the bootloader. We have used S25FL256 flash memory [h memory  of 32 MBSensor modules: There can be many sensor modules plugged into the Octa extension board and controlled by the STM32 Nucleo board using SPI, I2C, or UART. These modules are used to gather data such as GPS coordinates, temperature, and humidity.In this section, we describe the prototype designed to implement the required functionalities. Our aim is to show a proof of concept that NB-IoT can be used for the firmware update of IoT devices. Therefore, a prototype is presented without considering optimization and encryption. Our solution is modular and open to including additional features. This section describes the storage layout of the bootloader and application entities, and the management of the flash memories to perform the firmware update operations. The memory layout can be visualized as shown in Application usage: This section is dedicated to the sensor usage such as storing data or future usage to implement new additional features.Downloaded firmware: This area is used by the bootloader to store the downloaded firmware from the server during the firmware update process and before moving it to the internal flash. The firmware is sent from the server in segments, so the bootloader stores each one in this area using a buffering mechanism allowing it to be stored in contiguous blocks.Mailbox: As already explained, it is used to share data between the application and the bootloader regarding firmware update requests and application status. When a firmware update notification is received from the server, the sensor application updates this area that contains various fields such as application status which represents the current state of the application as defined in firmware length, packet number to know the next expected segment counter, firmware checksum to validate its integrity, and token which is used to establish a firmware update session between the bootloader and the server. A token is generated by the server dedicated to a sensor and sent with the firmware update notification. Furthermore, some memory areas are reserved for future use.We chose to use the external flash to store firmware update-related data, in addition to other usage needed by the application. The external flash is divided into three sections as follows:This section discusses the firmware update procedure in detail. The activity diagram is presented in (S1) The CPU starts the execution from the bootloader address at the start of the boot up process. It begins by initializing the system and the required modules. It loads the mailbox data from the external flash and checks the application status field. If the status is OK, the CPU jumps to the application address and performs the actions from step S5. However, if the status is Firmware update request or a long press on the update button is detected, the bootloader executes the firmware update operation and starts executing step S2. On the other hand, if the application status field is Firmware Downloaded, which means that the firmware file is downloaded successfully from the server and written to the external flash but not yet written into the internal flash, the bootloader executes the instructions starting from step S4.(S2) The bootloader initializes the NB-IoT module and connects to the NB-IoT network. It accesses the token that is sent from the server in the firmware update notification. This token is stored in the mailbox associated with the IMEI of the sensor. In the current session, the device uses this token to establish a firmware update session from the server. In the case of pull firmware update mode, the sensor uses the saved token from its last firmware update session. The server checks the identity of the sensor and uses it to validate the session request. However, for a new sensor device that would not have any token, it can initiate the request using its IMEI number. Upon receiving the request, the server checks its database to verify if any established previous bootstrap firmware update session exists from the requested IMEI number. It generates a new token and confirms the firmware update request. Then, the bootloader starts downloading the firmware from the server by following the instructions in step S3. Otherwise, it sets the application status to Error(x) to reset the Firmware update request. If the failure occurs while establishing the NB-IoT connection x is 1, if it fails during receiving the new token x is set to 2, and to 3 if the server is unable to verify the token. The server updates its database with the firmware update operation status, to track each sensor.(S3) The bootloader retrieves the number of firmware segments from the mailbox that were already received from the server before it starts the reception process. The device can check its energy level and request the server to send the firmware segments one by one. The server responds each time with the requested segment file and its checksum. The bootloader verifies if the checksum received matches with the calculated checksum. If the checksum matches, the received segment is written to the Downloaded Firmware slot of the external flash. This operation uses buffering to guarantee that segments are written in contiguous blocks of the flash memory. If all the segments are downloaded, the bootloader executes the instructions in step S4. If the checksum does not match, the bootloader re-requests the same segment from the server. The mismatching of the checksum indicates that the downloaded segment is corrupted or downloaded incompletely. The device also maintains a number of maximum retry attempts to request the same segment to avoid infinite requests from the bootloader. After reaching the maximum number of attempts, the bootloader closes the firmware update session with the server. It sets the application status to Error(4) to reset the firmware update request. The server updates its database with the failure reason.(S4) This step starts when all the firmware segments are downloaded, i.e., the complete firmware image is received. The successful completeness is verified by comparing the checksum for the complete firmware file with the received value from the server. The bootloader writes the downloaded firmware to the internal flash memory in the application section on successful verification. It also closes the firmware update session with success status, sets the application status field back to OK. Otherwise, if the checksum does not match, the bootloader closes the firmware update session with the server with the wrong firmware reason. It sets the application status to Error(5) to reset the firmware update request. Enhancement concerning the processing of this case is left for future work. The server updates its database with the firmware update operation status to know which sensors successfully updated their firmware, and which ones failed.(S5) The application initializes the system, the sensors and the NB-IoT module, and connects to the NB-IoT network. Then it sends an identification message to the server containing the IMEI of the sensor, IP address of the NB-IoT module, Application software version, and timestamp. After that, step S6 is executed.(S6) The application starts gathering the sensors’ values periodically at the default period and sends them to the server, which stores them in its database. When a firmware update is triggered, the server sends a firmware update notification to the sensor containing the size of the firmware, number of packets, checksum, and a token. In this case, the application writes this information in the mailbox and sets the application status field to Firmware update, then jumps to the bootloader to restart step S1.The exchanged messages between the sensor and the server in normal operations and during the firmware update are shown in The user powers on the sensor. Its bootloader starts the application after checking the OK status of the application in the mailbox. The application initializes the system, the sensors and the NB-IoT module, and connects to the NB-IoT network. It sends an identification message to the server containing the IMEI of the sensor, the IP address of the NB-IoT module, the application software version, and the timestamp. Later, it starts recording the sensors’ data and sends it periodically to the server.The second step is enabling firmware updates. When it is enabled by the user, the server sends a firmware update notification to the sensor containing the size of the firmware, number of packets, checksum, and a token. As shown in The final step is the bootloader operations. The bootloader initializes the system and required modules, loads the mailbox data from the external flash and checks the application status field. The status is firmware update request, the bootloader executes the firmware update operation as described in S3 and S4. These steps are detailed in The communication between the sensor and server is established using UDP sockets. The server can be configured to listen on a particular UDP port. We define some specific command formats for each message exchanged over the NB-IoT channel. They are presented in In this section, we describe the software stacks of each entity of the system. As mentioned before, these consist of the sensor running the application and the bootloader, and the server.The software architecture of the sensor application and bootloader running on the STM32 Nucleo is described in Application: The main program of the sensor application. It initializes the system, the sensors and the NB-IoT module, and connects to the NB-IoT network. Then it starts gathering data from the sensors and sends them periodically to the server via the NB-IoT network.Bootloader: The main program of the sensor bootloader. It initializes the system and reads the application status in the mailbox. If the status is OK, it jumps to the application. If not, it initializes the NB-IoT module and connects to the NB-IoT network. Then it starts performing the firmware update operations.HAL: STM32 standard hardware abstraction layer assuring the portability of the software applications between different STM32 boards.SARA-N210 NB-IoT driver: A C++ driver class that configures and manages the communication over NB-IoT of the SARA-N210 NB-IoT module via AT commands using the serial port. It uses the standard STM32 serial driver.S25FL256 flash memory driver: A C++ driver class that initializes the S25FL256 external flash memory and manages the read and write operations via SPI. It uses the standard STM32 SPI driver.Various sensors drivers: The temperature, GPS, humidity and other sensors drivers are used to initialize the sensors and read their values.STM32 drivers: Standard STM32 peripherals drivers such as I2C, SPI, UART, etc...The software architecture of the server that is hosted on a dedicated machine or cloud machine publicly accessible via the Internet is described in socket: The standard Python low-level networking interface library.mysql connector: It is used as a standardized database driver for MySQL database.mysql server: A database server that hosts the databases and manages all the clients’ requests.database: It stores sensors’ information in three tables. The first one is Sensors which stores the IMEI, the IP address, the software version, the last firmware update status and the timestamps of a sensor. The second one is Measurement table which stores IMEI, power, timestamps of the measurements and the values of the different sensors. The third one is Firmware table which contains the path of the recent firmware and an enable field of the download.Application: The script that stores all the sensor’s measurements in the database and handles firmware update operations of the sensors. It listens on UDP sockets and parses received messages from the sensors. It puts sensors’ identities and measurements, respectively, in the sensors and measurements database tables. It also triggers the firmware update of the sensors and handles the exchange with the sensors’ bootloaders during the process. It runs as a daemon to be active on booting up the server.Experiments were conducted for different sizes of firmware, ranging from 2 to 1000 KB which is sufficient for many IoT devices ,31. We uThe power consumption of the complete setup is measured using the off-the-shelf Nordic-Power Profiler Kit-II (PPK-II), which has a resolution of 0.1 μA . We instNow, consolidating all the steps, we measure the device energy consumption as shown in It is observed that NB-IoT consumes around 38 J to transfer 120 kB firmware, while the LoRa network consumption varies between 3 J and 250 J depending on the considered data rate . MoreoveThe total firmware transmission latency includes the latency of all the states in The experiments performed in this work target the firmware update requirements such as version control, integrity, error-free transmission, operability check and reduced user interaction. However, at this moment it lacks authenticity which can be easily implemented by using digital signatures and confidentiality, which can be introduced by encrypting the transmitted update segments. The encryption can triple the latency of each segment  and so wWe use the Orange network in Belgium and Sara N210 NB-IoT module. The results might vary for other operators, as different operators have different algorithms for channel scheduling and link quality adaptation, and so experiences different timings for network acquisition or data transfer. Moreover, different NB-IoT modules, such as Nordic nRF9160  and QuecIn this paper, we present the framework and implementation for firmware updates on energy-constrained NB-IoT devices. Our design is modular, portable, efficient and can support many features such as differential updates. The implementation is optimized for segment retransmission only when any segment of the firmware update is lost or corrupted in the transmission process (rather than retransmission of the complete firmware). Moreover, we measure the power consumed by the NB-IoT device to perform the firmware update. It can be observed that IoT devices powered by batteries with a capacity of 1500 mAh or more can be updated at a cost of less than 0.75% of their total energy.


This paper proposes a new methodology called TOpic modeling Based Index Assessment through Sentiment (TOBIAS). This method aims at modeling the effects of the topics, moods, and sentiments of the comments describing a phenomenon upon its overall rating. TOBIAS is built combining different techniques and methodologies. Firstly, Sentiment Analysis identifies sentiments, emotions, and moods, and Topic Modeling finds the main relevant topics inside comments. Then, Partial Least Square Path Modeling estimates how they affect an overall rating that summarizes the performance of the analyzed phenomenon. We carried out TOBIAS on a real case study on the university courses’ quality evaluated by the University of Cagliari  students. We found TOBIAS able to provide interpretable results on the impact of discussed topics by students with their expressed sentiments, emotions, and moods and with the overall rating. Different researchers in time have investigated universities and their activities. They have analyzed aspects as the scientific research and its impact in different fields  for inferring the effects of the topics, moods and sentiments of the students expressed in their comments on lecturers and courses upon their overall rating. That is built by combining different techniques and methodologies. Firstly, Sentiment Analysis identifies sentiments, emotions, and moods, whilst Topic Modeling finds the main relevant topics inside comments. Then, Partial Least Square Path Modeling estimates how topics, moods, emotions and sentiments affect the overall rating that summarizes the performance of the analyzed phenomenon.Four sections, besides the introduction, complete this study. Sect. refsec:relatedspsworks reports the background of the methodologies included in our proposal and the related works. The methodological framework of TOBIAS has been illustrated in Sect. Sentiment analysis (SA) has been defined as a process to extract and process textual data automatically to obtain sentiment information contained in an opinion  or quantitative (a number in a given range) sentiment score to a given text; thirdly, the Discrete Emotions, which is considered as a more refined grain analysis to extract emotions such as joy and love from human language.More in detail, SA attempts to detect firstly the Dictionary based, which involves the use of a dictionary of terms built by linguistics experts, who assign a score relative to the sentiment of every single term; and in Corpus based approaches, which relies on co-occurrence statistics or syntactic patterns in text corpora and a set of predefined positive and negative seed words  is defined as a mechanism for discovering low-dimensional, multi-faceted summaries of documents or other discrete data Wang and McCallum . It is aN words and it is denoted by M documents denoted by As evidenced before, different approaches to topic modelling can be used. Among them, LDA is one of the most used because it allows working with extensive collections of text documents , Variational Bayes inference (VB), and Gibbs sampling  Regression and (iii) Path Modeling  of the original matrix by a matrix with a lower rank . Latent variables are the model measurements that are not directly observed, while manifest variables are measurements of the model directly observable. These relationships are represented in the following equationouter model describes the relationships between latent and manifest variables. Manifest variables are represented in two modes: i) Reflective (Mode A) and ii) Informative (Mode B). The reflective model has causal relationships from the latent variable to the manifest variables in its block. Thus, each manifest variable is assumed to be generated as a linear function of its latent variables and the residuals.In Fig. the Eqs. .8\documeOuter approximation of the latent variable scores  considered in them.Let us consider a collection of Affectiveness. According to the emotional states, these manifest variables K subblocks The first step of the algorithm consists in emotional features extraction. Let us consider a set of manifest emotional features The second step of TOBIAS is the topics’ assignment. Let us define Satisfaction) as perceived by final users.The third step consists in determining the manifest indexes expressing the latent overall quality of the services p, c) are aggregated modifying the number of the observations from Next, the values referred to the same pairs  questionnaire, and the final output corresponds to quantitative indexes that are comparable between courses. Studying the specific reasons that produced the values of those quantitative indexes is difficult since the possible causes that could influence the overall satisfaction of the students vary between courses, and, at the same time, it is not possible to customize the questionnaires on the courses too much otherwise, there would be a risk of losing comparability. Nonetheless, an essential source of information that can be considered to overcome this problem is the open-ended question, usually set at the end of the questionnaire, that gives to the students the opportunity to express their issues. Since its answer consists of a text, often it is read by the analyst without being used in the analysis.In this section, we present a case of study of the University of Cagliari during the first two years of the COVID-19 pandemic (2020-21), in which we carry out TOBIAS to model and infer students’ overall satisfaction assessments by using the information extracted from students’ issues reports.The QA questionnaire filled by the students of the University of Cagliari is organized in three main areas of interest: i) course subject; ii) teaching; iii) interest and satisfaction. In addition to the open-ended question where the student can report their issues about the teacher and the course, the latter contains two questions that concern the overall satisfaction: i) the overall satisfaction with the subject of the course; ii) the overall satisfaction of the student with the teaching. All the closed questions of the QA questionnaire have an ordinal scale with four responses: (i) “NO”; (ii) “more NO than YES”; (iii) “more YES than NO”; (iv) “YES”.To elaborate on the results, the analysts of the University of Cagliari use the ordinal responses frequencies to evaluate the following QA indexes defined in Eqs.  and 11)11): PosiIn this study, we analyzed entclass1pt{minimaSentiment  elements associated. Specifically, considering the subset i-th values are the followingIn the Emotional Features extraction step, worried  SpaCy  trained ·) SpaCy . spaCy cs provided by the LDA, the i-th values of the manifest variables of the topics are computed as followsi-th value isi-th value of Each latent variable is characterized by a single manifest variable. Considering sentiment) and those of the four emotions, as defined by the function In order to study the relationship between the features previously defined, we performed an exploratory analysis. Figure Exam is the one with the highest frequency, while the other three have similar occurrences.The distribution of the prevalent topic of the 3871 sentences is showed in Fig. By considering the relative contingencies tables, we analysed the degree of association between the two sentiments (either positive or negative) and the four emotions, as defined respectively by the functions Furthermore, we tested their independence by Chi-Square test, obtaining that they are significantly associated  and negative association with negative evaluation (DD) and the latter has a negative association with positive scores  and positive association with negative evaluation (DD). We can also note that anger emotion provides the highest contribution for the C evaluation, which is a slightly positive evaluation with some critical aspects and a wide range of improvement. The highest contribution are given by joy and sadness for the DD evaluation class (with opposite association).The contribution of each cell to the total Chi-square score is obtained by the following ratio: Exam topic and positive sentiment and joy emotion, while the highest attraction (positive association) is between Exam topic and negative sentiment as sadness emotion. There are other relationships, such as the attraction (positive association) between the topic Teacher and the emotion fear. Figures affectiveness expressed by students in their comments, and among the evaluation of students with respect to the teaching and their affectiveness. These relationships motivated us to apply our TOBIAS model to this study case, in order to better understand the underlying relationship among topic discussed, affectiveness expressed, and final quality assessment of students.Figure Figure Affectiveness, which in turn has an overall positive influence to Satisfaction. We can also note that the highest impact of topics on affectiveness is provided by the topic Exam while the lowest negative impact is due to the Slides. The outer model shows a similar contribution of Satisfaction, while Affectiveness leads to interpretable insights. We can see that Affectiveness which we recall has a positive effect on the Satisfaction. Positive moods such as By fitting TOBIAS model the results shown in Figs. Slides topic. We thus conclude that the comments of students concerning teaching material and slides are not significant .The evaluation of the TOBIAS model is summarized in Tables Besides the results of the regression equations, the quality of the structural model is evaluated by examining three indices or quality metrics: the Affectiveness with the four topics. The total variance of the Satisfaction explained by Affectiveness is 12%.The Satisfaction has a high . Here The redundancy index reflects the ability of a set of independent latent variables to explain variation in the dependent latent variable. In our case study the redundancy index confirms the interpretation of the This study proposes a methodology to model the effects of the topics, moods, and sentiments expressed in service users’ comments describing a phenomenon, upon its overall rating. We presented the TOpic Based Index Assessment through Sentiment framework. TOBIAS is constructed by combining different natural language processing techniques and statistical methodologies. The first step is the Sentiment Analysis which identifies sentiments, emotions, and moods, and Topic Modeling analysis which finds the main relevant topics discussed in the comments. In the next step, the Partial Least Square with Path Modelling estimates how they affect an overall rating that summarizes the performance of the analysed phenomenon.The new methodologies have been applied to the text of the comments written by the University of Cagliari  students to evaluate the lecturers and courses. In addition, we conducted a case study on the student evaluation of the teaching quality, and we applied the TOBIAS framework. We conducted our case study during the last two years of the COVID-19 pandemic. The spread of Coronavirus disease, which shattered the world in 2019, profoundly changed our daily life habits. Universities worldwide have had to quickly adapt to a remote mode of teaching and exams.Exam on sentiments and emotions, and with the TOBIAS model we found that the highest negative contribution to Affectiveness is given by the Exam latent variable. In other words, thanks to TOBIAS we have been able to understand better the existing relationships between the topics discussed and the affectiveness expressed by students in their comments, which are not identifiable through solely an exploratory analysis. Although the results obtained are encouraging, our approach poses some limitations. The dimensions of Affectiveness discussed in the motivating example showed some correlation, for example between Emotion and Moods. These correlations may impact the final output of the model and will be considered in future works.TOBIAS model highlighted the negative impact of the topic discussed by the student on the overall sentiment, emotion, and moods which on the contrary positively influence on the overall satisfaction expressed on the teaching. These results were supported by the explorative analysis and extended in interpreting the TOBIAS model. The preliminary analyses showed the highest impact of the Topic