Public hospital spending consumes a large share of government expenditure in many countries. The large cost variability observed between hospitals and also between patients in the same hospital has fueled the belief that consumption of a significant portion of this funding may result in no clinical benefit to patients, thus representing waste. Accurate identification of the main hospital cost drivers and relating them quantitatively to the observed cost variability is a necessary step towards identifying and reducing waste. This study identifies prime cost drivers in a typical, mid-sized Australian hospital and classifies them as sources of cost variability that are either warranted or not warranted—and therefore contributing to waste. An essential step is dimension reduction using Principal Component Analysis to pre-process the data by separating out the low value ‘noise’ from otherwise valuable information. Crucially, the study then adjusts for possible co-linearity of different cost drivers by the use of the sparse group lasso technique. This ensures reliability of the findings and represents a novel and powerful approach to analysing hospital costs. Our statistical model included 32 potential cost predictors with a sample size of over 50,000 hospital admissions. The proportion of cost variability potentially not clinically warranted was estimated at 33.7%. Given the financial footprint involved, once the findings are extrapolated nationwide, this estimation has far-reaching significance for health funding policy. Patient-level cost drivers may be classified as either predictable or non-predictable, based on whether relevant information is available prior to the hospital admission. We propose that predictable cost drivers contain information independent of the care provided in hospital and thus explain warranted variability of patient-level cost of care in hospital. In this scenario, the hospital has no way of easily influencing these factors, with the associated cost variability being ‘pre-determined’ and therefore warranted from the hospital’s perspective. For example, a patient’s prior health status , socioeconomic status as well as demographic factors may justifiably lead to varying consumption of health resources while in hospital. As a further example, the widely used DRG (Diagnosis-Related Group) system, which classifies hospital admissions into similar groups, is a variable that links the clinical characteristics of a patient with the expected resource requirements; it can also be viewed as a cost driver of warranted cost variability. Each DRG , without catastrophic or severe complications”) describes the patient’s condition as well as its severity , featureCost drivers that are not known or predictable prior to admission do not relate to the underlying unique patient health predicaments. Such factors may however relate to aspects of patient care provided during hospital admission and may thus contribute to unwarranted  variability of cost of care. Large cost variability has been observed between hospitals , 3 and iThe concept of unwarranted nature of hospital cost variability, or equivalently preventability thereof is not yet consistently defined. Useful parallels can be drawn with preventability of patient harm in health care, because harm is known to be strongly correlated with increased cost , 7. LikeCost variability in health care—both warranted and not—has been typically modeled with patient-related predictors –12 usingThe first issue is remedied explicitly by the design of this study see . A largeLASSO (Least Absolute Shrinkage and Selection Operator) to overcome this challenge. A lasso model is one that fits a linear regression via a penalised maximum likelihood [l1 optimisation problem to select out unimportant coefficients but to keep the necessary ones in the model. This results in an interpretable model that includes only the pertinent, or principal cost drivers. Moreover, an l1 method seems appropriate, given its solution exhibits robustness-like properties [While the amount of information is greatly enriched by this inclusion of additional variables, the need to handle correlations between them is potentiated. This is because the possibility of co-linearities increases with the inclusion of additional predictors. This study uses the technique of sparse group kelihood . It solvoperties  born outoperties . NaturalThe sparse group lasso optimisation is a variation of the more generic lasso technique that accounts for the fact that some variables need to be grouped , 19. ThiRegression Penalty in Sparse Group Lasso:λ  tuning parameterα  grouping parameterm  number of coefficient groupsn  total number of coefficientsβj) as an initial dimension reducing step. An excessive number of dimensions would make the computing requirements impractical. The use of this technique to pre-process large data is well recognised  and it hThe sparse group lasso approach provides values of regression coefficients of the predictors left in the model. Comparison of their absolute values provides estimation of the predictors’ relative importance in terms of explaining patient-level cost variability. In the case of grouped variables, the PCA conveniently converts any group of related variables  into a group with an orthogonal set of elements, i.e., the principal components, which are then analysed by the sparse group lasso method. The orthogonality justifies summation of the absolute values of regression coefficients of all members of each group to accurately estimate the group’s total effect on cost variability via computation of an aggregate coefficient for each group.Research was undertaken in a public hospital, which is ultimately under the jurisdiction of the Queensland Department (ie Ministry) of Health. The study has been approved by a Human Research and Ethics Committee (HREC), which is the relevant Institutional Review Board. The particular committee is the Royal Brisbane and Womens’ Hospital’s HREC. The Australian approval number is HREC/16/QRBW/61. The Human Research and Ethics Committees in Australia grant approvals in accordance with the Australian National Statement on Ethical Conduct in Human Research (2007), which in turn fulfills the obligations under the Declaration of Helsinki. As the study is only a statistical analysis of large data, with negligible risk of patients being identified, the above-mentioned ethics committee has granted a waiver of patient consent. This waver has been subsequently endorsed and approved by the Queensland Department of Health. The current dates of the ethical approval are from 09 March 2016–09 March 2019.The data were all hospital admissions to a mid-sized hospital during the 2014/15 financial year. This totaled 53,224 admissions. Some admissions pertain to the same person, with the number of unique patients being 31,449. The hospital serves a geographically contained population and lacks any notable sub-specialisation skews or unusual service gaps which may occur in geographical areas with less defined servicing boundaries . This should justify extrapolation and wider applicability of the results.The potential cost predictors analysed are listed in Several potential cost drivers available from the hospital information system were then deliberately excluded from further analysis as they were felt to merely represent likely consequences of other variables; see the causal diagram in The potential driver of days spent in the ICU (Intensive Care Unit) was left out, as it depended strongly on other variables left in the model, in particular the type and severity of the patient condition , as well clinical decision making . In fact, the decision to admit to intensive care essentially reflects clinical interpretation of the nature of the patient condition, in the context of limited intensive care resources . The limThere are strong parallels between the decision to transfer a patient to a different facility and to accept a patient to intensive care, as discussed above. Transfer out can occur either because the clinical capacity of the hospital under analysis is thought to be exceeded, because the patient condition necessitates admission under a medical team that is not available locally, or perhaps because the patient condition is considered stable enough for transfer to a lower acuity facility. All these are dependent on clinical decision making within the context of the patient condition. Hence, the potential cost driver of “transfer out” was left out, mirroring exclusion of the variable of intensive care admission.The flag for selected clinical incidents was left out because clinical incidents were related to other variables left in the model. Clearly, rates of clinical incidents would be necessarily associated with the patient condition (as described by the DRG). For instance, patients with conditions that require multiple medications would be more likely to experience medication-related adverse than those who may take fewer medications. Also, those with medications that are known to decrease blood pressure or cause confusion would be more likely to experience in-hospital falls. Furthermore, medical conditions that limit mobility may render a patient more likely to develop pressure injuries.There is also likely to be a random component that contributes to when clinical incidents take place. As mentioned above, as the aim at this stage was to identify the proportion of deterministic and thus potentially rectifiable cost variability that is unwarranted, these variables were left out of the model.Clinical incidents are known to be associated with problems in clinical assessment, communication and decision making, the nature and staff profile of the relevant clinical unit as well as the physical attributes of the surroundings . These aParallels exist between the CHADx flag  and clinical incidents. Information encapsulated in the CHADx variable is likely to lead to varying DRG assignment and was left out of the model on the basis of the DRG staying in. This is supported by the fact that the CHADx uses routinely abstracted hospital diagnosis and condition-onset information about in-hospital complications .The amount of health care received by patients, when standardised by patient related characteristics is known to be largely driven by clinical decision making . The amoIn summary, the white boxes represent such potential cost drivers, not likely to represent the primary drivers of patient-level costs. This exclusion specifically applied to the hospital length of stay (LOS) and its non-inclusion in the model mirrors other recent work on cost driver analysis in hospitals . The greHealth Roundtable, an organisation which computes the index for subscribing health providers.The CCI (Charlson Comorbidity Index) has been shown to have a predictive association with health care costs . It was The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) and the Index of Relative Socio-economic Disadvantage (IRSD) were obtained from the Australian Bureau of Statistics SEIFA 2011  which weOther than the postal code data, missing data was negligible. Only one patient had missing data for private insurance (both private insurance as well as DVA or veteran insurance status), smoking, obesity or alcohol flags. All these fields were given modal values, calculated based on all remaining information, in line with the categorical nature of the data.All categorical variables, except for the CCI, were first represented as sets of binary variables. The relationship of the CCI , IRSAD, IRSD and age were first both assumed as linear with respect to the patient hospital costs. These assumptions were felt to be justified in relation to the IRSAD and the IRSD, given the design of both these indices. Appropriateness of these assumptions in relation to the CCI and age was however analysed further with the sparse group lasso re-run, but this time including a squared term for each of these two variables. Computational efficiency necessitated that the re-run was carried out on a random 10% of the data; however it was repeated three times.α and λ were derived.PCA was used to pre-process the data and reduce its size without losing vital information. See Once λ  was determined, the corresponding coefficients of the selected variables were used to compare their respective influences on patient-level hospital costs variability. Absolute values of all regression coefficients were first determined, in keeping with the focus being on cost variability rather than the absolute cost values. Absolute values of all coefficients within groups were added together to provide aggregate coefficients for each multi-factor cost driver . Each aggregate coefficient is therefore a resultant estimation of the relative contribution of each grouped variable to patient-level cost variability.Please see the lm, available in the stats package in R. Like sparse group lasso, this analysis was also performed on the data pre-processed by the PCA, to facilitate ease of comparison. Grouping of binary parameter estimates pertaining to the same cost driver and handling of the age, IRSAD, IRSD as well as the CCI coefficients was carried out in a manner identical to the case of the sparse group lasso.The results of the sparse group lasso were paralleled by also analysing the data using simple linear regression, for comparison purposes. This used the command To illustrate the significance of using the sparse group lasso technique to eliminate the artifacts of co-linearity, Note: the horizontal line between predictors separates the predictable (top) from the not predictable (bottom)Note: the horizontal line between predictors separates the predictable (top) from the not predictable (bottom)To further illustrate the issue of co-linearity, Also, the relative values of the same coefficients or aggregate coefficients as proportion of the overall cost variability are provided in The values of the coefficients observed towards the right side of The benefit of using Principal Component Analysis to pre-process the data is demonstrated in α that minimised the cross-validation error was 0.15, i.e., close to maximal grouping. The small value of α is consistent with previously published, non-hospital-based research [The value of research , althougWe re-ran the sparse lasso, this time relaxing the linearity assumptions with respect to the CCI and the patient age data fields. This tested the suitability or otherwise of relaxing the linearity assumptions of both variables. Adding a squared term to each provides for a wide range of alternative, non-linear relationships with the outcome variable, i.e. patient-level hospital cost. The results demonstrated no significant impact on outcomes for either the CCI or age. Specifically, in each case, the sum of the coefficients of both the linear and square terms were well within the 95% confidence range of the previously obtained coefficients based on linearity assumptions. This indicates that the linearity assumptions were justified.The study has identified the main drivers of hospital patient-level cost variability. They are: DRG, age, choice of the discharge ward, choice of the discharge medical unit, as well as the two measures of socio-economic status, the IRSAD and the IRSD. The admitting ward, admitting medical unit and the CCI are secondary, albeit still significant cost drivers. Identification of this set of “principal cost variables” is important from a practical perspective as it provides a potential structure for analysis of cost over-runs in a hospital. Any mismatch between budgeted and actual costs could be first broken down by each principal variable and then any problems narrowed down to a particular principal variable could be analysed further, relying on the associations with other non-principal cost variables. This would provide a logical structure for cost analysis which follows the empirically-derived cost architecture. Crucially, such analysis could also be replicated longitudinally, facilitating comparability across time periods and monitoring effects of any cost containment initiatives.potential waste; i.e., it is the upper bound for the waste estimate. However, because of the comprehensive selection of potential cost predictors, there should be good proximity between the upper bound of the unwarranted variability and the actual amount of unwarranted variability. The list of potential cost predictors listed in As summarised in LASSO model is based on the fact that it is an example of models known to encourage sparsity [The choice of the sparse group sparsity . Furthersparsity .The technique of pre-processing large data with the PCA has been demonstrated as a crucial step in the analysis, enabling it to be conducted by extracting pertinent information from the original dataset. This carries wider implications for analysing hospital-generated large data that may be subject to both measurement and input error.LASSO and linear cases are large. Therefore, the benefit of adequately addressing the inaccuracies stemming from co-linearity of predictors is obvious. In fact, the fundamental difference in results obtained from the co-linearity remedying sparse lasso-based technique as opposed to previously used models that do not adequately address this prominent phenomenon  makes it imperative that further research in this field analyses the overlap between potential patient-level cost drivers and uses techniques to remedy the otherwise misleading effects of co-linearity. Our findings may also call into question any previous decisions made on the basis of cost driver identification models. As a minimum, researchers should at least test for the presence of co-linearity using the (intuitive) variance inflation factor (VIF).The observed differences between the relative magnitudes of the coefficients / aggregate coefficients in the Variation between the practice of individual medical practitioners if often proposed as the reason for unwarranted cost variation. It is therefore intriguing that, despite featuring prominently in the simple regression case, consultant  doctor—both at admission and at discharge—are not significant lasso cost predictors. These variables are excluded readily by the sparse group lasso method, most likely reflecting the importance of a team-based nature of health care delivery, rather than individual practices. One could argue that in complex systems such as hospitals, characterised by high usage of technological aids, it is the sum of interactions between all members of the treating team as well as with non-human agents that is more significant in terms of patient care and thus patient-level cost outcomes than doctor variability. This is reinforced by greater prominence of the variables of discharge ward and discharge medical unit in the lasso results, that perhaps better reflect these complex interactions. Useful parallels may be drawn with the view of health care as a complex socio technical system, such that its overall performance cannot be examined by viewing the performance of its components in isolation. This mirrors the theory of distributed situation awareness which explains ergonomic and quality control outcomes in complex systems characterised by multiple interactions between agents, both human and non-human, in preference to situational awareness of any one agent in isolation . The praLASSO regularisation. This illustrates the imperfect nature of the DRG as a sole costing tool; and is a likely reflection of the lack of complete homogeneity of all episodes of care classified within a particular DRG. Moreover, this point also illustrates why the focus of analysis in this paper has been on groups of related factors rather than on individual factors: it is important to know how much cost variability is associated with different diagnostic groups  rather than with a particular DRG. DRG assignment is based on the approximately 68,000 ICD 10 codes, as classified by the World Health Organization. It evolves all the time but the contribution of the entire group of DRGs is likely to be more stable over time than that of a particular DRG.Use of the lasso method decreases the contribution of DRG as a driver of cost variability to 29.0% from 54.2% in the simple linear regression. The compensatory emergence of discharge ward, discharge medical unit, age, the IRSAD and the IRSD as significant cost drivers following the sparse group lasso analysis  may perhFrom a conceptual viewpoint, the apparent large amount of non-predictable variability of patient-level hospital costs may of course be related to several causes . FirstlyThe significant presence of non-predictable  sources of cost variability should be of major concern to funders of hospital services. If extrapolated, even if only a proportion of it were actually related to unwarranted variation in clinical practice, the potential saving to the national expenditure would be extraordinarily large. Given these far-reaching conclusions, the analysis would benefit from being replicated at other hospital sites. Furthermore, if confirmed, a model for predicting future health care costs could be developed based on these findings. Such a predictive model, perhaps including allowances for stochastic variability to account for different patients responding differently to treatment, is likely to be more accurate than the currently used methods that tend to rely solely on linear regression techniques. There is therefore room for further research to build on the results obtained herein in estimating the upper bound for waste. It would be highly beneficial to investigate the magnitude of the gap, if any, between the upper bound and the actual amount of waste. This could include the use of simulation (as part of the suite of techniques known as Approximate Bayesian Computation) to reproduce the actual distribution of hospital costs, more accurately estimating the amount of randomness. As natural randomness is not predictable, this would further reduce any estimate of the actual waste. This further work could also better contextualise the practice of setting future efficiency targets based on benchmarks derived from observed costs, as the random nature of the cost distribution may render such targets as unrealistic.This article describes a practical method for estimating the drivers of hospital costs and patient-level cost variability. It applies well known and validated statistical techniques of Principal Component Analysis and Sparse Group Lasso to overcome the challenges of multi-dimensionality and co-linearity, respectively. These challenges are commonly encountered with hospital-generated large data and otherwise prevent accurate cost driver estimation. In fact, predicting expenditure in hospitals has long been challenging, with prevalent models producing sub-optimally estimated true costs . While tOnce estimated, the drivers can be classified as either warranted or unwarranted sources of cost variability. The proportion of variability attributed to unwarranted variability appears rather large. This is postulated as an estimate of the potential waste that could, at least theoretically be minimised. If the findings were further substantiated, the policy implications would be large, perhaps shifting the hospital funding policy focus from matching the cost drivers by appropriate levels of funding to more decisively driving greater care standardisation and thus containing the unwarranted variability. Findings also suggest that clinical care standardisation may be best achieved by focusing on systems of care delivery rather than clinical practice of individuals.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file.S5 Text(PDF)Click here for additional data file.S6 Text(PDF)Click here for additional data file.S7 Text(PDF)Click here for additional data file.S8 Text(DOC)Click here for additional data file.S9 Text(R)Click here for additional data file.S10 Text(R)Click here for additional data file.S1 Fig(XLS)Click here for additional data file.S2 Fig(TXT)Click here for additional data file.


Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world. Brain-computer interfaces (BCIs) have provided an entirely new communication way to interact for both disabled and healthy people with the external world. Such a BCI system is achieved through sensing, processing, and actuation  control by using a left/right-hand MI-based BCI and semi-autonomous navigation for indoor target searching, where MI EEG is simply employed to choose or not choose the current feasible direction  quadcopter  was chosen as the external device for the BCI experiment due to the extensive open source platform, which was suitable for scientific research, and it was able to expand the capabilities of the aerial platform with an onboard embedded system that supported serial communication as well as DJI SDK. It provided the interface to program a wide range of speed and yaw rate in three dimensions. In addition, the M100 quadcopter provided a stable and reliable flight, with up to 40 min of flight time.Before steering an actual quadcopter, a training phase was carried out in a virtual quadcopter flight simulator developed by DJI with the main purpose being to calibrate subjects' control signals. A target instruction consisting of left-forward, right-forward, rise, fall, as well as eye blinking were informed to the subject by sound. EEG signals were collected to train the recognition method for the three modes. The training would not stop until subjects obtained a success rate of 80% or above for three separate modals  were recruited to participate in this experiment. The details of the subjects are listed in d and the minimum absolute height of the trough h. Two constraints we set here aimed at differentiating conscious eyes blinking from normal blinking or other noise like head movements or frowning. The parameter d was set as 0.75 s long, while the parameter h was averaged among the filtered EEG measurements of calibration dataset. If the number of troughs was >2, the subject was considered as eye blinking; otherwise, it was further recognized within MI/SSVEP modes.Eye blinking was used to switch between the two flight modes. Conscious eye blinking is defined as excessive effort blinking with a fixed frequency; that is, blinking twice within a 1.5 s EEG data block. The EEG time series of the conscious eye blinking was recorded by the Fp2 channel, which is located at the upper right orbital in the prefrontal lobe. It demonstrated that the downward vertical eye movement could generate a large-magnitude negative deflection, representing thus a remarkable trough for an eye blinking  which can be described as below:In this approach, the CCA coefficients are calculated between EEG measurements recorded by channel ONh = 2 represents the number of harmonics. The frequency with the largest coefficient corresponds to the one of SSVEP , which were distributed symmetrically over two hemispheres of the sensorimotor area, were calculated to extract MI features using an improved common spatial pattern method called the complete information common spatial pattern (CICSP). The aim of the conventional CSP is to construct an optimal spatial filter which maximizes the variance of one class while minimizing that of the other using the simultaneous diagonalization of two covariance matrices mathematically  to extract the most useful information, leading to Where diag is the diagonal element of the matrix, tr[·] is the sum of the diagonal elements, f1 and X′ was separated into a 0.5-s time window EEG series k = {1, 2, 3}. Q2, which was calculated in the calibration phase, then the CSP features In addition, during the online implementation, the 1.5-s time window EEG measurement The analogous information transfer rate (ITR) metric was created for the physical world BCI task.The numerator of formula (6) is an index of difficulty computed using the Fitt's law formalization . All nine subjects performed remarkably in eye blinking, six of whom reached 100%. Five subjects  performed at least an 80% success rate for each mode in which they participated in the experiment. The average success rates of SSVEP, MI, and eye blinking among these five subjects were 92.06, 90.20, and 98.75%, respectively. The success rate of the entire task was up to 93.67% among these five subjects.The success rate of the three modes was obtained using the whole EEG samples in calibration phase for nine subjects, which is listed in The performance of the quadcopter flight experiment using the multi-modal BCI system is presented as below. As shown in In order to provide a comparison between the BCI control and the common approach of control as well as the baseline level, two other experiments were also performed by using a remote controller in absence of the subjects' intent instead of a BCI. In the remote controller experiment, an experimenter who had experience but did not achieve proficiency completed the complex task. In this protocol, the maximum rise and fall speeds were equal to those of BCI. For the horizontal direction, the motion could be in any direction such that not only the two actuations occurred, and the maximum speed was also restricted to that in the BCI control. In addition, the baseline level aimed to identify the extent to which the subjects' performance could be attributed to the success of the BCI system. A subject was instructed to sit quietly and to watch a video of a quadcopter flight in the experimental site, which was considered as fake feedback, without executing the mental or visual task or any eye movement. The BCI system was set up identically to the actual experimental protocol. EEG signals were recorded and controlled the actuation of quadcopter after recognition. It should be noted that poor performance cannot be attributed to the selection of an unacceptable magnitude of EEG or random noise, due to the fact that true EEG signals were employed as the input to the system.In the complex task, subjects passing through two gates sequentially was considered a success, while passing through one gate was regarded as a half success. Subjects were successful in achieving accurate control of the quadcopter in the actual environment. The performances in various metrics are listed in The present work demonstrates the capacity for subjects controlling a quadcopter in the real world by a multi-modal EEG-based BCI. The purpose of multi-modal BCI is to increase the number of control commands, reducing the task burden and improving the recognition success rate  in the blink-recognizing algorithm would remain constant. The parameter h (the minimum absolute height of the trough) needs to be estimated using the dataset recorded in calibration phase. Therefore, the blink-recognizing algorithm is robust, and the rest of the BCI system worked properly as well. The quadcopter remains hovering instead of engaging in reckless movement in the case of eye blinking as it does not include mental information.We would like to remark that the usage of blinking to facilitate multi-modal BCI is the result of our careful consideration. At the very beginning, the transformation of two modes was completed automatically. If the coefficient in the detection of SSVEPs was higher than a threshold, the EEG block was considered as the SSVEP mode—otherwise, it was considered as the motor imagery mode. Since the detection of the two modes owed to the channels located in two separate areas of the brain, the evoked potential, and the induced potential would not affect each other. Under this circumstance, the LEDs had to keep flickering during the whole process of the experiment. However, gazing at the flickering lights would distract the concentration required for the mental tasks, which led to the increasing number of flights beyond boundaries. Therefore, a switch was introduced in our work to turn on/off the LEDs. Considering the fact that navigating a quadcopter outdoors at a lower altitude is a time-sensitive task, some prototypes have incorporated non-mental features such as eye blinking and muscle movement as a precise switch to minimize the manipulation delay. Since the detection of facial muscle movement needs more electrodes, eye blinking was selected in our work. Once a subject's conscious eye blinking is well-trained with a fixed frequency and the same strength (at least higher than the trained strength), the parameter During the initial phase of the actual quadcopter controlling experiments, the noises generated by the flying quadcopter in the flight field caused the nervousness of some of the subjects to affect the experimental results. Subjects adapted to the environment over time. Since subjects could clearly see the consequence of the task failure such as flying into the bushes, hitting obstacles, and even crashing, this could influence the motivation of the subjects' success in the experiment.The control commands generated by our current multi-modal BCI system could be further extended. Since CICSP has been demonstrated as an efficient method for multi-class motor imagery classification, in future work we plan to extend the CI-CSP method for multi-class ERD/ERS classification as well as place more flickering LEDs on the screen for generating SSVEP features to actuate more motions on three dimensions.Generally speaking, the multi-modal EEG signals could be used together to generate a series of control commands for subjects interacting with the real world. The performance of our multi-modal BCI system presents some advantages compared with previous studies (LaFleur et al., This paper presents a multi-modal BCI system to accurately and stably control a quadcopter to pass through gates in three-dimensional physical space. The MI and SSVEP modes—which are associated with two types of regular EEG patterns, ERD/ERS, and SSVEP—were employed to actuate the flight on both horizontal and vertical directions. Two modes were rapidly switched by eye blinking. The ERD/ERS and SSVEP patterns were analyzed by using the CICSP feature extraction method and the CCA frequency recognition method, respectively. Eye blinking was detected by counting the peak method within each EEG data block. Subjects were able to navigate the quadcopter passing through a series of gates in the outdoor environment continuously, accurately and rapidly. Several metrics for real-world BCI systems were used to assess the performance of this system. The PTC reached 86.5% and the analogous ITR attained 1.69 bit/min for five subjects, with the average gate acquisition time being nearly 0.80 min. This system could build the ability for people who suffer from paralyzing disorders to interact with three-dimensional real world. The multi-modal BCI could increase the accuracy rate while alleviating both the mental burden and the visual fatigue to a large extent.This study was carried out in accordance with the recommendations of guiding research into human subjects, Northwestern Polytechnical University Hospital Ethics Committee with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Northwestern Polytechnical University Hospital Ethics Committee.XD, SX, and XX contributed equally to this work. They conceptualized the idea and proposed the experimental design. XD also programmed and performed all data analysis as well as wrote the manuscript at all stages. SX and XX revised the manuscript at all stages. YM and ZX conducted the experiments. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Minimally invasive pancreaticoduodenectomy (MIPD) was introduced in the attempt to improve the outcomes of the open approach. Laparoscopic pancreaticoduodenectomy (LPD) was first reported by Gagner and Pomp . Unfortunately, due to its complexity and technical demand, LPD never reached widespread popularity. Since it was first performed by P. C. Giulianotti in 2001, Robotic PD (RPD) has been gaining ground among surgeons. MIPD is included as a surgical option in the latest NCCN Guidelines. However, lack of surgical standardization, however, has limited the reproducibility of MIPD and made the acquisition of the technique by other surgeons difficult. We provide an accurate description of our standardized step-by-step RDP technique.We took advantage of our 15-year long experience and > 150 cases performed to provide a step-by-step guidance of our RPD standardized technique. The description includes practical “tips and tricks” to facilitate the learning curve and assist with the teaching/evaluation process.17 surgical steps were identified as key components of the RPD procedure. The steps reflect the subdivision of the RPD into several parts which help to understand a strategy that takes into accounts specific anatomical landmarks and the demands of the robotic platform.Standardization is a key element of the learning curve of RPD. It can potentially provide consistent, reproducible results that can be more easily evaluated. Despite promising results, full acceptance of RPD as the ‘gold standard’ is still work in progress. Randomized-controlled trials with the application of a standardized technique are necessary to better define the role of RPD. Pancreaticoduodenectomy (PD) is the only effective treatment of pancreatic head cancers –5. NamedMore recently, the robotic surgery helped to overcome some of the limitations of the traditional laparoscopy.The increased dexterity of the endowristed instruments, the superior quality of the motion with more degrees of freedom, the three-dimensional magnification of the camera, the software-mediated filtration of surgeon tremor, and the improved ergonomics allow unprecedented precision in the context of a long and accurate dissections required for pancreatic surgery , 13, 14.The first robotic PD was performed by P. C. Giulianotti in 2001 and reported in 2003 . After tWith a 15-year long experience of > 150 cases, we present our standardized 17 steps Operative Technique for RPD (Table See Table The patient is placed on a bean bag in a supine 20-degree reverse Trendelenburg position with slight left-side tilt (arms tucked and lower limbs parted in the French position). The Assistant stands between the patient’s lower limbs. Pneumoperitoneum is induced with Veress needle at the Palmer’s point. A diagnostic laparoscopy is performed after the insertion of a 5-mm trocar in the left-upper quadrant. This is done to detect potential surgical contraindications  and to place the other ports under vision. Standardization of the trocars placement is highly recommended, allowing for minor modifications according to the patient’s body conformation Figs. , 2. For A grasper is used via the R3 to lift the stomach cranially. The gastrocolic ligament is put under tension to identify the Bouchet lamina and to proceed to its dissection with the hook or other energy-based devices . Access into the lesser sac allows inspection of the anterior pancreatic surface (body and neck) and the posterior gastric wall to exclude signs of local tumor spread/carcinosis. In case of pylorus-preserving PD (PPPD), the right gastroepiploic arcade must be identified and preserved in order to maintain adequate blood supply to the pylorus. Finally, the dissection of the gastrocolic ligament is carried out up to the short gastric vessels, which are preserved. Following the middle colic vessels, the confluence of the SMV with the portal vein at the neck of the pancreas is easily explored. The evaluation of this important venous confluence is part of the resectability assessment , 17, 18.The takedown of the hepatic colonic flexure is carried out with the combined use of hook cautery (in R1) and bipolar forceps (in R2). The reverse Trendelenburg with left tilt rotation favors a downward retraction of the colon. A wide takedown of the hepatic colonic flexure is carried out up to the origin of the right colonic vessels medially and sometimes extended down to the cecum. This extended maneuver exposes the duodenum, SMV, and pancreatic head. An incomplete mobilization leads to difficult duodenal dissection and inadequate exposure of the ventral pancreatic surface , 17, 18.The Kocher maneuver Fig.  must be During hepatic hilum exploration, the vascular anatomy must be carefully assessed, in particular, the possible presence of an accessory right hepatic artery originating from the SMA. If this is the case (up to 20%), the hilar dissection is more challenging. Depending on the size and relevance of the vessels , as well as its relationship to the cancer, a decision regarding ligating, preserving, or reconstructing must be made. The dissection starts from the hilar plate exposing the right and left hepatic arteries and bile duct. The use of ICG fluorescence may be useful at this stage to confirm the anatomy of the biliary tree. Regional lymph nodes are sampled and sent for permanent pathology. Not all lymph nodes can be harvested en-block, and some of them can be retrieved station by station , 17, 18.The right gastric artery is identified, skeletonized, ligated, and transected at the origin. This vessel may originate from the proper hepatic, common hepatic, or even left hepatic artery. The in-depth knowledge of such anatomic variations is essential to preserve enough pyloric blood supply while performing an accurate lymphadenectomy. Usually, the artery is ligated with 4/0 prolene suture , 17, 18.The dissection around the of the right gastroepiploic artery is essential: the aim is to achieve a satisfactory nodal dissection maintaining a good blood supply of the pylorus. A vertical cranial lifting of the gastric antrum with R3 is beneficial to reach enough tension to identify the anatomy. The gastroepiploic artery is dissected out 1 cm from its origin and divided with vessel sealer or vascular stapler , 17, 18.The pyloric vascular supply and possibly the innervation have to be spared in case of PPPD. The vascular transection of the right gastroepiploic and right gastric artery has been already carried out. At this point, the posterior wall of the duodenum is dissected out until enough room is made to position the stapler and transect D 1, usually 1 cm distally to the pyloric sphincter. The stomach is then retracted to the left side. By doing this, a proper exposure of supra-pancreatic lymph nodes and the hepato-duodenal ligament is obtained , 17, 18.An anterograde cholecystectomy is carried out down to the cystic duct. In this way, an en-block resection is performed and the cystic duct is left attached to the distal common bile duct (CBD). Sutures or clips are applied to the cystic artery which is then divided with scissors , 17, 18.The CBD is divided with cold-scissors if the duct is thin and small in size or by hook diathermy in case of a thick and fibrotic duct. The CBD division is usually cranial to the origin of the cystic duct and its proximal margin is sent for frozen section. The transection at this level allows adequate preservation of the proximal bile duct blood supply. A gentle bulldog is placed on the proximal CBD to prevent bile spillage in the surgical field. The distal portion of the bile duct is sutured and it remains attached to the specimen. The suture on the distal portion can be used to retract the CBD and get a better exposure of the portal vein , 17, 18.The GDA stump can represent a considerable source of post-operative complications and its transection it is not an easy step. Whenever there is enough room to apply it, vascular stapler is the safest approach. An accurate dissection and the division of some collaterals may be required in order to properly place the stapler. Suturing or ligating the GDA may be a backup plan in case not enough space is available. In any case, for the GDA vascular control, it is preferable not to use clips because of the risk of displacement. Attention should be made in identifying small tributaries of the portal vein . Any of these veins should be sutured and divided before approaching the gastroduodenal artery , 17, 18.The duodenojejunal flexure division and the right-sided derotation of the duodenum must be carried out before performing the transection of the neck of the pancreas. The mesocolon is retracted cranially with the R3 in order to achieve exposure and enough tension on the suspension ligament. Monopolar hook or harmonic shears can be used for this maneuver. The aim is to extensively mobilize the fourth part of the duodenum. The first jejunal loop is divided with stapler. Of note, the more dissection is done at the Treitz, including ligation of some small jejunal vessels from the left side of the SMV, the easier will be to achieve complete derotation of the duodenojejunal flexure and the detachment of the uncinate process , 17, 18.The neck of the pancreas is divided anteriorly to the portal vein. The safest way to carry out this step is to reach an optimal exposure of the inferior and superior margin of the pancreatic neck. On each side of the transection line, two 3/0 polypropylene stay sutures are placed on the inferior edge in order to retract and lift the pancreas and to control the bleeding from the pancreatic section line. The pancreatic neck is divided with the da Vinci Harmonic ACE™ Curved Shears . Increasing tension is applied on the stay sutures to retract the edges of the pancreas as the transection line advances. The classically described tunnel between pancreas and portal vein should be done only if it easy to do so. In case of inflammatory adhesions, the tunnel is developed progressively with the transection of the pancreatic parenchyma. This maneuver avoids to proceed safely within the periadventitial plane.Following the parenchyma transection, the pancreatic duct is identified and cannulated with an endoluminal stent which is secured with 5/0 PDS (polydioxanone). If the pancreatic duct is inadvertently sealed by harmonic thermal lateral spread, it should be re-opened and stented. In order to have confirmation of free margins, a frozen section on the pancreatic duct is sent to pathology , 17, 18.This is the last and by far the most difficult step of the dissection. We believe that it is in this phase that the advantages of the robotic approach vs the open and laparoscopic ones are most evident. This is related to the overall superiority of the robotic platform when precise endowristed microsurgical skills are required. Also the R3 has a fundamental role in maintaining a stable retraction/exposure. All the venous branches originating from the SMV are selectively ligated. The dissection is conducted with the harmonic shears with application of prolene stitches for vessels of bigger caliber.The venous “hanging maneuver” is performed by placing a vessel loop around the SMV Fig. . This isFor benign or pre-malignant lesions, the dissection can be done with an energy base along the surgical plane of thin adipose/lymphatic tissue between the artery and the uncinate process of the pancreas. The harmonic shears can be very effective even in controlling the inferior pancreaticoduodenal artery if few precautions and tricks are put in place , 17, 18.In the same way of the laparotomic approach, the reconstructive phase can vary according to (1) the pylorus preservation, (2) the pancreatic duct size, (3) the quality of the pancreatic parenchyma, and (4) the size of the bile duct. When a standard Whipple procedure is performed (with antrectomy), the pancreatogastrostomy can be more difficult and pancreatojejunostomy may become the best option. According to our standardized technique, a jejunal loop is passed where the D IV normally crosses the mesocolic root (retromesenteric route) and brought to the upper quadrants to reach the digestive reconstruction.The main factors influencing the kind of pancreatic anastomosis to be performed are the pancreatic parenchyma texture and the pancreatic duct diameter. If the pancreatic tissue is fragile/soft or the duct is very small (< 3 mm), a transgastric pancreaticogastrostomy is the preferred option. Conversely, in case of firm/fibrotic pancreas and for duct > 4 mm a pancreaticojejunostomy is the best choice , 17, 18.Multiple options are available to perform the pancreaticojejunostomy. The robotic approach guarantees the same technical level of sophistication of the laparotomic strategy, adding scaling effect and magnified view. In this way, the operative setting is very similar to the use of a surgical microscope platform.An end-to-side duct to mucosa reconstruction is the preferred option when the pancreatic duct has a diameter ≥ 3 mm. The posterior capsule of the stump of the pancreas is anchored to the jejunal serosa via polypropylene sutures. A small opening in the jejunal mucosa is performed and the duct of the pancreas is anastomosed with PDS 4/0 or 5/0 interrupted stitches. Alternatively, two half-running sutures if the duct is ≥ 5 mm can be used. Inside the duct, a small stent is placed and secured with 5/0 PDS.Another 4/0 polypropylene suture fixes the anterior aspect of the pancreatic capsule to the jejunum. Some additional interrupted stitches may be added to strengthen the suture.If the capsule of the pancreas is fragile, a Blumgart’s technique can be used to anchor the pancreas to the jejunal loop , 17, 18.This is the preferred option for high-risk pancreatic parenchyma. The pancreatic stump needs to be freed for at least 4–5 cm. Any branch of the splenic vessels is divided between sutures, allowing mobilization of pancreatic stump. A longitudinal incision is performed on the stomach anteriorly; through an additional small opening on the posterior gastric wall, the pancreas is invaginated into the lumen. This maneuver is achieved by pulling the stay sutures on the pancreatic parenchyma through the anterior opening. Several 4/0 PDS interrupted stitches are placed between the pancreatic capsule and the gastric mucosa endoluminally -enhanced fluorescence test can be performed to check adequate microvascular blood supply to the duodenal margins before making the anastomoses , 17, 18.The specimen is placed in an endobag and extracted through a small Pfannenstiel incision. Alternative extraction sites may be considered in case of previous abdominal surgery. The specimen retrieval can be performed before starting the reconstruction or at the very end of the procedure (if there is no needed for tissue banking).Two drains are usually placed: one close to the pancreatic anastomosis and the other one near the biliary anastomosis , 17, 18.Recent scientific debate surrounding minimally invasive versus open pancreatic surgery focused on perioperative complications and oncological outcomes. After an initial period of skepticism, robotic pancreatic surgery has been embraced by some surgeons and MIPD is even included among the options in the latest NCCN Guidelines (Version 3.2017 Pancreatic Adenocarcinoma NCCN Evidence Blocks™) . DespiteTo ease the learning curve of other surgeons approaching this robotic technique.For each step of the procedure to facilitate homogeneous and reproducible results among different centers/surgeons , 21.Our technique stems from a 15 years of experience and > 150 RPDs. The number of steps is arbitrary, but it reflects a careful consideration of the different anatomical fields encountered during surgery. The steps also reflect specific challenges encountered at certain points in the procedure, which are better faced when frame-worked within a specific step. This subdivision also allows for a better quantitative and qualitative assessment of the learning process and skills acquisition .The learThe stepwise approach facilitates also the teaching process focusing on specific anatomical landmarks of every step. Moreover, the step-by-step approach facilitates also (a) the interaction with the bedside team; (b) how to prevent dangerous situations or potentially life-threatening complications. All the aforementioned points are better addressed focusing on the single specific step of the procedure.In general, RPD is a procedure that requires excellent laparoscopic and robotic surgical skills. However, there are specific steps that need more time and experience to be mastered:The execution of an extended Kocher maneuver.The reason of such an extended kocherization is twofold: (a) functional to successful and safe completion of uncinate process dissection; (b) fundamental for the assessment of the resectability which is one of the weaknesses of the MIPD.Uncinate process dissection.In open surgery, many authors perform a cranial–caudal approach. On the contrary, during MIPD a caudal–cranial technique has to be preferably used and associated with the venous hanging maneuver.Complex reconstruction with a small pancreatic duct and/or a soft pancreas.This step requires considerable experience to be performed proficiently.A flawless technique is necessary to decrease the chances of related complications. With the standardized approach, operative time can be shorter; and a smoother/faster learning curve may potentially lead to a progressive reduction in morbidity and a progressive reduction in morbidity. Despite promising results, full acceptance of RPD is hindered by the absence of randomized-controlled trials, by the paucity of specialized centers, and by a high variability of surgical techniques and perioperative management described in published studies , 27. A lWhenever a new technique or surgical technology is introduced in clinical context, its data-driven validation is essential  Randomiz


The operational efficiency of remote environmental wireless sensor networks (EWSNs) has improved tremendously with the advent of Internet of Things (IoT) technologies over the past few years. EWSNs require elaborate device composition and advanced control to attain long-term operation with minimal maintenance. This article is focused on power supplies that provide energy to run the wireless sensor nodes in environmental applications. In this context, EWSNs have two distinct features that set them apart from monitoring systems in other application domains. They are often deployed in remote areas, preventing the use of mains power and precluding regular visits to exchange batteries. At the same time, their surroundings usually provide opportunities to harvest ambient energy and use it to  power the sensor nodes. This review provides a comprehensive account of energy harvesting sources, energy storage devices, and corresponding topologies of energy harvesting systems, focusing on studies published within the last 10 years. Current trends and future directions in these areas are also covered. Environmental wireless sensor networks (EWSNs) are typically used to obtain information about environments that is needed for decision making. In this context, “environmental monitoring” may refer to observation of indoor or outdoor spaces, whether natural or man-made. Relevant approaches include remote sensing using aircraft and satellites, laboratory analysis of field-collected samples, and in-situ monitoring using sensor devices and networks. They monitor chemical, biological or population-related parameters of the environments under surveillance . As an eThe development of environmental monitoring nodes still poses many research challenges. EWSNs are often deployed far from inhabited centers, and thus without access to mains electricity . This isThe selection of an appropriate power supply is a crucial step in EWSN design. Primary (non-rechargeable) batteries are typically the first option for powering a field-installed EWSN. Energy harvesting devices offer an alternative (or supplement) to primary batteries that can greatly extend the lifetime of EWSN devices. Harvesting sources are often combined with primary batteries and other energy storage devices .The remaining sections of this review article are organized as follows. In general, the goal of energy harvesting is to convert energy from one form to another that can be used to power electronic devices. When implemented in environmental monitoring nodes, it can directly extract ambient energy from the environment under surveillance and use it to power the nodes of the EWSN, improving their performance and/or extending their lifetime. Outdoor environments offer plenty of opportunities to take advantage of the elements naturally present in the surroundings, such as wind or sun. However, there are also other types of energy, such as radio frequency (RF) signals resulting from human activities that be scavenged and used to power the nodes. This section provides a systematic overview of common harvesting sources suitable for environmental monitoring nodes, and their comparison.Energy harvesting sources can be categorized as ambient or external . The ambOne of the most commonly used sources of energy for harvesting is the sun —an afforSolar power is transformed into electrical power using photovoltaic cells. The amount of output power generated by a cell depends on the intensity of light as well as cell size and effectiveness, according to the photovoltaic principle . To incrA photovoltaic system is capable of producing power ranging from 5 mW/cm2 . Energy 5 mW/cm2 .Correct position of the solar panel is important to achieve its maximum possible efficiency. As shown in e north) .The global horizontal irradiance As illustrated in eristics ,17,18,19The utilization of available solar energy can be improved using numerous modeling and forecasting approaches. Harvested energy prediction using environmental shadow detection can be used to adapt sensor nodes’ scheduling plans according to energy availability and residual battery levels . A systeThermal energy can be converted to electricity by thermoelectric transducers depending on spatial variations in temperature or pyroelectric transducers depending on temporal variations in temperature.p- and n-type semiconductor blocks ordered in parallel and connected electrically in series. The open circuit voltage of a thermoelectric element depends on the temperature difference (Thermoelectric transducers and generators are based on the Seebeck effect . They arThermoelectric generators have low efficiency (only about 5–6%). When placed in a stable heated environment, the thermoelectric system will stop generating power after a while when both plates achieve the same temperature ,25.p is the component of the pyroelectric coefficient vector, A is the surface area of the electrode, and Pyroelectric converters can be realized as an alternating current source in combination with a capacitor. The pyroelectric current depends on the speed of the temperature change of the pyroelectric material:Thermal energy harvesting systems have been used in many outdoor applications. A prototype device for harvesting thermoelectric energy from asphalt pavement roadways collects heat energy from the pavement surface and transfers it to the thermoelectric generators embedded at the edge of the pavement . A 64 × Outdoor WSN applications can use energy extracted from the wind. This type of energy harvesting converts kinetic energy into electricity using turbines, rotors, and the principles of electromagnetic induction ,25.A common approach is to use a small wind turbine. Turbine selection starts with the determination of a suitable airfoil, which depends on the Reynolds number. For an average chord length of 2 cm and for a wind speed of 6 m/s, the Reynolds number is around 10,000. An analysis of horizontal axis wind turbines with low Reynolds number is described in , along wAnother option is the use of a wind flutter generator  based onA wind generator capable of supplying up to 10 mW using a 6.3 cm diameter turbine and a 16 km/h wind speed is described in . Anotherergy use . Baranovergy use  use a hyRF-based energy harvesting uses the transmission of radio waves ranging from 3 kHz to 300 GHz, and their subsequent conversion to direct current. This can be accomplished using a single-stage or multistage converter, depending on the requirements of power, voltage, or efficiency. The amount of harvestable power is given by the source power, antenna gain, and by the distance from the RF source. The conversion efficiency ranges from 50% to 75%.There are two models for RF power harvesting and following communication with other sensor nodes: single radio or two radios. In the latter case, one radio receives the RF signal and the other is used for communication. In the other case, a single radio serves both purposes, reducing the complexity of harvesting and communication software.One major limitation of RF power sources is that the strength of the signal decreases with distance, leaving only very low power levels available for harvesting . Due to The main advantage of RF-based energy harvesting in comparison with solar, thermal, or flow-based sources is its availability in indoor environments . PotentiA prototype of an embedded microcontroller-enabled sensor platform powered by an ambient ultrahigh-frequency digital TV signal (512–566 MHz) has been proposed by Kim et al. . The autThe comparison of main energy harvesting sources provided in  shows thThe term energy storage describes technology to convert energy from a form that is difficult to store  to a storable form . The stored energy can then be converted back into a directly usable form. There are various types of energy storage with different properties, such as capacity, power, and charge/discharge rates. The choice of a particular technology depends on the application requirements. In the context of environmental monitoring, energy storage units must satisfy a specific set of requirements related to their small size, adequate capacity, and low environmental impact.Primary or secondary batteries;Supercapacitors;Hybrid combinations of supercapacitors and rechargeable batteries.The following types of storage devices are typically used to power environmental monitoring sensor nodes:The energy storage subsystem is a very important component of a sensor node, greatly affecting its overall efficiency. The choice of energy storage technology also affects the size, cost, and operating life of the node .The choice of battery can be approached from many perspectives. The most important factors affecting the choice are application requirements . Batteries typically serve not just to supply the system with energy, but also to efficiently store energy harvested from the environment. This way, energy can be stored for times when it cannot be directly extracted from the surroundings.Important battery specifications include storage technology, energy density, internal resistance, depth of discharge, self-discharge, and tolerance to overcharging. From the application perspective, it is important to clearly specify operating conditions and choose appropriate battery devices to avoid operational problems. One example is battery selection for different climatic conditions  .Batteries can be primary or secondary. Primary batteries are non-rechargeable and can be one of the choices for EWSNs. They have many advantages, including high capacity and temperature stability. Their main disadvantage is the need for periodic maintenance and replacement at the end of life. Secondary batteries are rechargeable. However, their number of charge/recharge cycles is still limited by cycling capacity.Non-rechargeable dry cell batteries can be divided to alkaline and acidic categories. The alkaline batteries have slightly better performance, while the acidic batteries are more dependable and less expensive.Most batteries, except lithium-ion, do not perform well in cold temperatures due to the increase of their internal resistance, which leads to a loss of capacity. The opposite holds true for their operation at elevated temperatures, but at the cost of a significant shortening of their service life or even permanent damage. The estimated overall efficiency of battery storage is in the range from 60% to 80%, depending on the operational cycle and the electrochemistry type within the batteries .The energy density (Wh/kg) indicates the maximum density of the stored energy in the battery per unit of mass, and differs for individual battery chemistries. The battery capacity is the amount of energy that can stored in the cell at the full charge. The lifetime of most electrochemical batteries is on the order of hundreds to thousands of charging/discharging cycles. During this time, the battery capacity gradually decreases because of the chemical corrosion of its electrodes. The lifetime is greatly influenced by charging and discharging, as well as by the operating temperature . The basThe ambient temperature of the battery plays an important role in estimating its real life. A typical battery achieves nominal characteristics at temperatures around 20  charges . RechargLead-Acid batteries are most commonly used for medium-sized devices. Their advantages include low cost, high reliability, and high efficiency. However, they have low cycling capacity and poor performance in extreme conditions . NiCd barge rate .Supercapacitors are characterized by high power density compared to batteries and common capacitors. They are constructed either as electrochemical double layer capacitors (EDLCs) or pseudocapacitors . The EDLLarge number of charge/discharge cycles without a significant decrease of performance and storage capacity: around 500,000 to 1,000,000 cycles, depending on the manufacturer ,51.High charge/discharge efficiency (up to 98%) and fast charging process .Wide range of operating temperatures between pacitors . Some soCompared to rechargeable batteries, supercapacitors have several advantages:However, the use of supercapacitors is often affected by self-discharge—a problem related to the terminal voltage of the energy stored in the element . The magInitially, it was assumed that all energy losses in supercapacitors are due to leakage. However, a study by Merret et al.  expandedAccurate estimation of the state-of-charge (SOC) is an important task for any energy storage application. It is a complex task because there are many phenomena that cause energy storage aging. They include loss of charge acceptance of the active material on the electrodes, changes in physical properties of the electrolyte, and corrosion of the current conductors . For thiFor effective operation of EWSN power supplies, the estimation of SOC is important to properly set-up their operational parameters  . Common Coulomb counting  is the most common primary battery management circuit in modern electronic embedded designs with energy storage. The corresponding electronic circuit, called the “gas gauge” , is baseulations . An alteulations . Impedanulations . The mosulations . As suchMost new trends in storage technology are connected to the development of new materials. The use of graphene-based materials in Li-ion, Li–S, Li–Ouated in . The resuated in  demonstruated in .Bichat et al.  developeThe ultimate goal of a typical wireless sensor node is to collect data of interest ad infinitum. In order to operate indefinitely, a system cannot consume, on average, more power than a harvested source can provide. Otherwise, if the consumption exceeds the production, the system will eventually deplete its energy stockpile and stop working due to the empty energy reservoir and absence of environmental energy. This leads to undesirable system performance.Kansal et al.  take a sThere are three main topologies for energy harvesting systems: autonomous, hybrid autonomous, and battery-supplemented. Depending on the configuration, energy management strategies with different design goals are required .Autonomous harvesting systems fully satisfy their energy needs from ambient sources, without batteries ,75. AutoThe structure of an autonomous harvesting system is shown in The distribution of power over time Autonomous hybrid harvesting systems are the most common type of energy harvesting system. They have an energy reservoir implemented using a secondary battery or ultracapacitor ,79. The t. t. Variable An autonomous hybrid harvesting system with energy leakage is described in  Joules) .A method to determine vided in . A device memory . The higAn autonomous hybrid harvesting system with an ideal energy storage device can be described as follows:Real storage devices leak energy,  follows:∫t1t2PD . This syA battery-supplemented harvesting system is shown in It is likely that several techniques described in the previous sections need to be combined to achieve an effective EWSN design. These techniques include the selection of energy harvesting sources and methods, the choice of storage technology, the application of appropriate data acquisition and processing methods, the implementation of suitable communication techniques, and the design of a control algorithm. Selected techniques are then combined into a complex strategy suitable for a specific-purpose EWSN. At the beginning of the EWSN design process, an analysis of the available energy sources should be performed. One or more energy sources must be selected and composed into the energy harvesting module. The selection of an appropriate harvesting system topology  is driven by the amount of harvestable energy and the time distribution of its availability. In the case of hybrid and battery-supplemented topologies, an energy storage technology must also be added to the design. The next step is the selection of a suitable communication technology. This decision depends primarily on the amount of data to transfer, transmission period, and required wireless range. There may also be the deployment site-specific criteria such as local availability of gateways or wireless network coverage. The final step is the design of the control algorithm. On the node level, different algorithms can be utilized, including adaptive and predictive control techniques. At the communication or cloud level, data reduction/compression and cloud control algorithm update can be employed.The future research in the area of energy-independent environmental monitoring will likely concentrate on several topics found at various levels of monitoring system infrastructure, as depicted in The main research challenges at the level of a single monitoring node perspective are shown in These parameters are related to a particular deployment environment. For instance, measurement nodes deployed in tropical regions will have different requirements compared to those to be deployed in the Arctic ,82.Specific research challenges concentrate on the transfer of energy from harvesting source to storage. They range from the design of energy transducers, through the optimization of power transfer , to the development of devices converting electrical energy to a different form matching the selected energy storage. Corresponding research goals focus on increasing the efficiency of power conversion. In the area of solar- and thermal-based energy transducers, there have been many advances related to material research with the same goal. Kinetic energy converters for wind harvesting are miniaturized to fit the form factor of embedded devices. RF-based harvesting is very promising for EWSN applications due to their ability to obtain energy from wireless transmitters combined with the continuous reduction of node power consumption.Another research challenge considers a measurement node and its focus on operating cycle. A previous study  providedDifficulties due to diurnal or seasonal variability can be addressed using advanced control algorithms based on soft-computing or artificial intelligence methods. One example is the use of a fuzzy controller for the adaptive control of sensing and transmission parameters in measurement nodes . AlternaAdvanced control approaches can also be combined with various optimization methods, such as differential evolution, particle-swarm optimization, or genetic algorithms. Our previous study  developsOther research challenges can be identified at the network level. When communication with a measurement node is established, the data from that node can be sent to a cloud as an uplink data stream. Many communication technologies described in this section can also setup a downlink communication channel, which allows a specific data frame to be sent from the cloud to the node itself.The most important research objective at the network level concentrates on reducing the volume of data that needs to be sent in both uplink and downlink directions. The underlying communication technology usually strictly limits the amount of data transferred within a specific window of time . TherefoThe first scenario describes data reduction by an internal compression algorithm, where the node sends only compressed data. The raw dataset is not stored on the measurement node. In the second scenario, the measurement node sends a reduced data set and the raw data file is stored on the node. The raw data is then available for manual download during maintenance of the device. This configuration commonly requires a large internal data storage unit, such as an SD card.Another research topic concerns the possibilities of dynamic sensor network control through a downlink communication channel, as shown in The usage of a downlink communication channel enables the implementation of algorithms for updating the nodes’ operating parameters. This connection link allows the node to actively cooperate on specific monitoring tasks. There is also a possibility to predict the operating parameters at the cloud level and then cascade them down to the nodes. Data processing and prediction at the cloud level plays an important role in this operational paradigm. In many communication interfaces, a downlink is limited to few bytes per day, which makes it an interesting research area aiming for the development of data-effective control algorithms.Future trends in the area of energy harvesting include the discovery and exploitation of unconventional energy sources . ExampleThe field of energy storage is dominated by the search for batteries with high energy density as well as improved longevity and safety. In the case of storage devices for EWSN, an additional requirement is the minimization of their potential environmental impact. A prime example of an immature technology is the lithium–air battery ,92 that State-of-the-art EWSN devices use static operational strategies preset during network installation. Current research focuses on dynamic control and online energy management strategies . These aThe distributed network character of modern EWSNs provides additional opportunities for the minimization of energy consumption. One example is the use of clustering and zoning to design energy-aware data collection protocols . The pinThis paper identifies and describes opportunities for future research in the area of environmental wireless sensor networks (EWSNs), which are typically deployed in remote locations without the possibility of frequent maintenance. Additionally, the paper provides an overview of state-of-the-art technologies that can be integrated into EWSNs so as to improve their reliability and operational efficiency.One of the research challenges from a single node perspective is selection of energy management-related parameters which have direct impact on the utilization of energy harvesting sources and energy storage. Their optimization would increase the system reliability. Since there is always a trade-off between environmental data measurement rate and data transmission rate, the application of advanced control algorithms, such as fuzzy logic controller or reinforcement learning (both introduced in our previous studies), is an interesting research challenge and outlines one of the directions for our future work.On the network level, one of the main incentives is to reduce the amount of data sent in both uplink and downlink directions via a network, which typically has some limitation in terms of data throughput per specific window of time. Therefore, the development of such solutions represents one of the most important research challenges.Yet another research challenge could be the development of methods for data processing and the prediction of EWSN operating parameters at the cloud level. This data could then be cascaded down to sensor nodes through the downlink communication.Since one of the weak points of every EWSN is its power system, the selection of hardware components suitable for a particular deployment environment is crucial. This paper reviews current solutions and new trends in the area of energy harvesting sources and alternatives for energy storage, where one of the directions for further development might be using components that incorporate new materials , which could greatly increase their performance.Our future work will focus on addressing the research challenges mentioned above so as to arrive at a better EWSN design, which will help to collect good-quality environmental data.