Dynamic Navigation is a computer-aided technology that allows the surgeon to track the grip instruments while preparing the implant site in real time based on radiological anatomy and accurate pre-operative planning. The support of this technology to the zygoma implant placement aims to reduce the risks and the errors associated with this complex surgical and prosthetic treatment. Various navigation systems are available to clinicians currently, distinguished by handling, reliability, and the associated economic and biological benefits and disadvantages. The present paper reports on the different protocols of dynamic navigations following a standard workflow in correlation with zygomatic implant supported rehabilitations and describes a case of maxillary atrophy successfully resolved with this technology. An innovative and minimally invasive dynamic navigation system, with the use of an intraoral anchored trust marker plate and a patient reference tool, has been adopted to support the accurate insertion of four zygomatic implants, which rapidly resolved maxillary atrophy from a 75-year-old male system. This approach provided an optimal implant placement accuracy reducing surgical invasiveness. Pre-implant bone reconstructions can now often be avoided thanks to the introduction of short, narrow, and standard-length tilted implant ,2,3. In A pre-operative plan of the correct position of ZIs with dedicated software, according to the anatomical and prosthetic scenario, must be followed by a precise system to transfer the virtual project to the clinical reality. The printed surgical template in a computer-guided (static) system is not able to direct the burs and the fixtures to the right position due to the length of the drill path-way, with a considerable deviation of the planned axes ,15,16,17Since Schramm et al.  first suWe report a case of extreme maxilla atrophy treated with ZIs insertion supported by DN system; this is characterized by a unique minimally invasive frame carrying both fiducial markers and an intraoral patient reference tool. A workflow of the DN protocols taking into account the technology currently available is illustrated with comments and discussions.A 75-year-old man was referred to the Oral and Maxillofacial Surgery Unit of the Department of Biomedical and Neuromotor Science, University of Bologna. The patient was in good general health and he was wearing a removable maxillary prosthesis and a mandibular implant-supported overdenture. The primary complaint of the patient was to restore the instability of the upper denture, adversely affecting speech and masticatory function, with a fixed denture. Following the clinical and radiological examination, a horizontal bone atrophy was evidenced, at the level of the premaxilla, and severe bone resorption posteriorly .The patient refused any bone grafting and required the quickest possible solution. Several treatment options were evaluated: narrow, tilted, pterygoid, intra-sinus, and zygomatic implants. The patient opted for a zygomatic implant rehabilitation. Consequently, upon completion of the informed consent form, the patient was enrolled in a clinical trial approved by the Ethical Committee of S. Orsola-Malpighi University Hospital, Bologna, Italy. A provisional denture was made to investigate the inter-arch relationship and occlusion for planning an immediate loading. A resin-based duplicate of the prosthesis was prepared as a radiographic template. A provisional narrow implant with the coronal spires machined  was placed under local anesthesia with a flapless technique into the premaxilla. A marker plate containing the fiducial markers for the navigation system calibration was directly screwed onto the provisional implant. After removing any interference, the radiographic template was placed in situ  and the Then, the marker plate was unscrewed and repositioned on the day of the surgery. The ImplaNav dynamic navigation system  was used for zygomatic implant planning and placement. The planning of the ZIs position was made using the navigation system software. The three-dimensional implant trajectory was planned according to the prosthesis position aiming to reach a good prosthetic emergence .® Moto, Esacrom, Imola, Italy)  but with different geometries, were fixed: one onto the patient directly jointed on a spherical connection on the marker plate, and the second onto the surgical handpiece  . In this way, the infrared camera of the navigation system could record both the position of the patient and the surgical handpiece and the system could display onto the screen the drill position on the patient’s CBCT images. The system registration/calibration proceeded with the use of the three fiducial markers. They are located on the marker plate and must be touched in sequence with the first drill to allow the navigation system to identify the patient position. The system can detect the position of the handpiece by setting up the calibration tool as a drill and exposing it for a few seconds at the navigation system camera. A full-thickness flap enables access and visualization of the malar bone. The site preparation for the zygomatic implant, from the residual alveolar ridge to the zygomatic bone, was done using the navigation system that tracked each drill. The surgeon was able to follow the intended trajectory of the implant on the real-time navigation system screen .An extra-sinus technique was used due to the favorable anatomy of the patient. In total, four ZIs  were placed to achieve a fixed full-arch implant support. The provisional implant was considered stable and integrated, and it was used for the provisional prosthesis support. The prosthesis was manufactured, allowing the provisional implant to be removed in the future if required. Four conical abutments were positioned before suturing . Four pick-up transfers were then placed and rigidly connected with a hard resin  and impressions were taken using a customized tray and polyether impression material . Within 72 h, a screw-retained metal-resin prosthesis was delivered and connected to ZIs . Postoperative radiographs were performed and sutures  were removed after 2 weeks. Clinical and radiological follow-ups were performed at 1, 3, 6, and 12 months after implant placement showing good soft and hard tissue conditions. No intraoperative nor postoperative complications occurred . Step 1. Scanning of the patient and the scan prosthesis/registration template, external registration frame, or bone markers.Step 2. Software planning of the implant position.Step 3. Image-to-patient registration via registration templates, external registration frames, or bone markers.Step 4. Surgery: navigation of the drill along the predefined surgical plan.Zygomatic implantology can benefit from DN support technology more than static computed guidance. Since the drill path is about four times longer than that for standard fixtures, the stereolithographic templates used in static computer aided systems adds small angular or positional entrance errors, resulting in a risky apical tip moving away from the planned safe position. Hung et al.  reportedThe reliability of DN technology depends on systematic and non-systematic errors. The first category is linked up to the inherent starting characteristic of the used instrumentation, while the second depends on a non-appropriate use of the machine for operator inexperience or for setbacks. Systematic errors can be categorized in relation to the different types of technologies that may impact tracking or registration processes. The CT/CBCT image characteristics influence both tracking and registration phases. The set thickness of the CT layer at 0.625 to 1.0 mm and the voxels size from 0.3 to 0.5 mm are considered good for accuracy in ZIs placement procedure .Tracking is related to the dynamically continuous following of the surgical instruments in the space in relation to patient position and the previous acquired radiological anatomy and implant insertion planning. Two principal tracking systems are available so far: optical and electro-magnetic . Most ofRegistration is the determination of the relationship between the imaging data and the surgical field, allowing the system to associate any point on the patient to the correspondent imaging datum ,32. The Newer DN systems, such as the one used in this case report, eliminated the necessity of bone anchored screws as FMs. Lopes et al. described a case of one ZI placement where the presence of three fixtures inserted in a previous rehabilitation allowed the operator to anchor a markers plate directly to the maxilla. The authors concluded that a simpler fiducial markers protocol could make the surgery faster and less complicated, considering that the screws insertion is time consuming and could condition the implant positioning .The herein employed navigation system takes advantage from the placement of one mini-implant: this anchors the frame to the pre-maxilla, carrying the marker plate with three FMs and the patient reference tool ,27. NeveIt is useful that the provisional implant for maxilla anchoring, put in at least one month before surgery, can be used to support the provisional prosthesis in case of delayed prosthetic loading. The removal of this implant is not compulsory.The major drawbacks of DN surgery are the economic costs and the long learning curve for a novel variety of ergonomics. This second aspect is particularly important due to the difficulty of steadily using the handpiece, since the operator is not able to simultaneously examine the computer screen and the surgical field. Augmented reality technology is intended to overcome this problem with the use of special glasses superimposing more imaging information to the vision of the surgeon . AnotherIn conclusion, the exposed minimally invasive characteristics should encourage the use of dynamic navigation systems with intraoral reference anchorages for the placement of zygomatic. Nevertheless, it needs to be underlined that this is a one-case report and that the described technique must be further investigated to validate and determine the correct position of the zygomatic implant in a cohort of patients.


Asian-Australasian Journal of Animal Sciences (AJAS) was established in March 1988 as the official journal of the Asian-Australasian Association of Animal Production Societies (AAAP). The journal’s mission was to serve the animal agricultural industry and academia in the Asian-Australasian region through the efficient publication and distribution of information on animal science. Since its inception, the AJAS has made tremendous contributions to the development of animal science in the Asian-Australasian region over the past 30 years. The AJAS now publishes more than 200 original articles and review papers over 12 issues per year, which is a substantial leap from 33 articles over four issues in the first few years. A more dramatic increase was observed in the number of submissions, which grew from fewer than 50 to 1,000 annually.The Asian-Australasian Journal of Animal Sciences (AJAS) to Animal Bioscience (AB), effective from January 2021 . We hope that this change will enhance the journal’s brand identity by providing a clearer and more distinct message, enabling the journal to draw more international readership, and providing us with a greater opportunity to encompass diverse interests of future animal agricultural science and industry in the AAAP region.To further increase its international representation, the editorial board has decided to change the title of the journal from the Animal Bioscience will continue to publish high-quality, original research and review articles in the area of animal breeding and genetics, reproduction, nutrition, products, health, welfare, and the environment. We also hope that the new title will contribute to creating and fostering new emerging areas of animal sciences. Considering our past contribution to animal industry and academia through our publications and future direction toward becoming a top archival journal, the new title will attract more attention from authors, subscribers, and readers.Animal Bioscience, beginning with the January 2021 issue . All submissions from January 2021 must be completed via the new online system (animbiosci.org). You can contact us at animbiosci@gmail.com ; however, the older email address (jongkha@hotmail.com) will also be functional until the end of 2021. Please do not hesitate to contact us if you have any questions regarding the change of the journal title or other publication-related matters.All manuscripts in various processing stages , once accepted, will be published under the new journal title,


Chronic traumatic encephalopathy (CTE) results from brain injuries and traumas due to accelerated impacts on the head. In severe cases, the diseases cause brain damage, given the head trauma. On the other hand, vasculitis occurs through antibodies that mistake protein vessels as foreign, hence fighting them and resulting in their damage. Examination is usually conducted through blood tests, with antibodies being identified in the antineutrophil cytoplasm. It is unfortunate that its devastating effects also affect the brain of a human, hence leading to dis-functioning. When vasculitis is left untreated, it results in multiple adverse effects on the human body and health both in the short term and in the long term. This study aims to bring to the awareness of neurosurgeons the associations between CTE and vasculitis. This study has proved that there is a close correlation between the progression of CTE and vasculitis. The inflammatory of the blood vessels, as witnessed in vasculitis, increases the risk factors for CTE. The scaling of the vessels and manifestation of different vasculitis conditions in active central nervous system cells results in the worsening of neurodegeneration of the CTE disease. Chronic traumatic encephalopathy (CTE) is a condition that occurs as a result of multiple mild traumatic brain injuries (TBIs) -2. The nThis paper will use a traditional review to gather information about CTE and vasculitis. The study will take advantage of clinical trials to examine the scientific analysis of the diseases and genetic factors that might contribute to the condition, and identify any gaps in the research .The primary characteristic that links CTE to vasculitis is the inflammation of the blood vessels. Research shows that a combination of these conditions occurs in adult patients, especially military veterans, American footballers, and other athletes who play sports such as hockey and boxing. Research shows that sub-concussive or repetitive concussive blows increase the risk of brain injury trauma, which occurs as a result of the blood-brain barrier and microvasculature.Pathophysiology of the diseaseMainly, the pathophysiology of CTE is a result of damage to the axons, which occurs due to repetitive traumatic injury. The neurodegeneration of the affected brain tissues is worsened by the glutamate receptors and pro-inflammatory cytokines . The gluResearch by the National Institute of Bioengineering and Biomedical Imaging and the National Institute of Neurological Disorders and Strokes explained that CTE is considered progressive, with the neurodegenerative disease being characterized by increased P-tau brain deposits. However, its pathophysiology is quite unclear, with animal tests proving deposits of pathogenic cis of phosphorylated Thr231-Pro tau protein related to head trauma. P-tau deposits are then consistent with their diagnosis and characteristics of CTE, with much accumulation triggering neurodegenerative cascades that result in cell deaths alongside dysfunctions in brain networks. In other instances, pathology CTE is evident among patients suffering from temporal lobe epilepsy despite an absence of TBI history.Clinicians also argue that CTE pathological clinical health demands increased safety and sober assessment of scientific insights and data-enhancing evidence-based practices in clinical manifestations. Clinical symptoms of all individuals must be accessed given their implications on individual health. CTE is dangerous as its exacerbating perceptions may result in increased disability of patients, hence the need to understand and solve the issue accordingly.Genetics of the diseaseCTE has been implicated with the TMEM106B genes, which has been proven scientifically to partially explain why some athletes encounter CTE in severity than others despite being exposed to the same level of head trauma . GeneticScientific analysisAn accurate scientific study of the CTE condition involves the evaluation of the underlying causes, its diagnosis, and examination of the appropriate treatment method. The experimental investigation of this disease shows an existing gap in research and clinical findings due to the lack of a means to diagnosis CTE while people are still alive . HoweverClinical analysis of the diseaseMild CTE is associated with depressive symptoms and, short-term memory loss, as well as aggressive traits . In stagThe unanswered questionsThe underlying pathogenic mechanism or the events initiating the occurrence of vasculitis remains to be unknown. The research also uncovers that the pathogenic antibodies linked with the inflammation of the large-vessels vasculitis have not been identified . There iThis research article has shown a strong correlation between CTE and vasculitis. Despite the gaps in our understanding, the primary risk factors of CTE are repeated head injuries, whereas the presence of vasculitis increases inflammation, which worsens a patient condition. There is a need to urge the research community to focus more on these associations and come up with strong statistics between vasculitis and CTE.


The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a  The radio map is the most important part of the WLAN fingerprint localization systems and the key to ensuring the positioning accuracy of the system. Regardless of the deterministic or probabilistic positioning algorithm, the radio map is required to provide accurate mapping from the received signal strength indication (RSSI) sample to the physical location coordinates to complete localization. Building a high-accuracy radio map requires engineers to set enough reference points (RPs) in the positioning area, collect sufficient RSSI samples at these reference points, and these RSSI samples should include the access points (AP) information in the area as much as possible. Therefore, in the indoor complex environment where APs are densely deployed, building a high-accuracy radio map is time-consuming and labor-intensive ,2. This The essence of the WLAN fingerprint localization systems is a process of pattern recognition, and the process of building a radio map is the calibrating of pattern recognition. In order to reduce the consumption of timing and labor cost when building a radio map, this paper proposes a radio map construction method merging self-adaptive local linear embedding (SLLE) algorithm  and grapThis paper is organized as follows. With the increasing importance of location-based services, the construction of radio maps in indoor environments has gradually formed a research point, especially in terms of reducing time-consumption and labor costs. This includes methods based on crowdsourcing ,6, semi-The fundamental idea of crowdsourcing refers to allocating a workload to several participants, in this case, including both professional surveyors and general users. Molé  and FreeThe authors in ,8,9 emplFor reducing the calibration work, the authors in ,11 emploIn , the sysThe researchers in ,15 used The authors in  proposedThere is an assumption in manifold learning that the processed data is sampled on a potential manifold or that there is a potential manifold for this set of data. Different methods have different requirements for the properties of the manifold, which also leads to the assumption of different properties of the manifold. The local linear embedding (LLE) algorithm assumes that the sampled data resides is locally linear in the low-dimensional manifold, and each sampling point can be linearly represented by its nearest neighbors. Similar to manifold learning, in graph-based semi-supervised learning methods, there are certain assumptions about the internal relationships of processed data. For example, the GLP algorithm hopes that the data meets that: points with similar characteristics tend to have the same label. Whether the semi-supervised learning or the manifold learning, all of them have potential assumptions on the sample data. To achieve a good learning effect, the sample data must meet the assumptions.In order to verify whether the RSSI in the indoor complex environment meets the assumptions of LLE and graph-based semi-supervised learning, we built a radio map of the office environment, and select five adjacent reference points from the radio map. As shown in Figure 4 in , the fivFirst, we verify whether the RSSI samples meet the assumption of LLE. Taking The RSSI collected in the indoor complex environment contains information of multiple APs, which can easily form a Curse of Dimensionality , which iGiven a set of RSSI samples The selection of the parameter To overcome the problem, we propose a self-adaptive lgorithm  procedur(1) Chromosome codingk as the chromosome, if there are N high-dimensional data, we can assume that the value interval of k is [1, Take the parameter (2) InitializationM is The population number (3) Fitness FunctionThe Let Then let k is increasing. The inflection point of the data density appears when Therefore, we can use Equation (3) as the fitness function and select the best fit (4) Individual evaluation(5) Termination conditionThe search is terminated when an inflection point occurs in After searching for the parameter LLE hopes that each sample point and its neighboring points have local linear structural features. Using linear coefficients to describe this local geometric feature, each sample point can be reconstructed through its neighboring points. This reconstruction error can be calculated by the following cost function :(4)ε(W)=Consider any RSSI sample point If Equation (7) has a unique solution, then the covariance matrix In order to minimize the reconstruction error, the weight matrix Before mapping nal data ,21,22. Cnal data . When caAfter determining the weight matrix Equation (9) is similar to Equation (4), but the weight k adaptive LLE algorithm, we use them to reduce the dimensionality of the Swiss volume graph, and the results are shown in In order to compare the performance of the traditional LLE algorithm with our proposed parameter After obtaining a low-dimensional RSSI set, we will use the GLP algorithm to perform semi-supervised learning on this data set. The data set will be redefined below. The letters and labels used in the definition have nothing to do with the previous content.U through the label information in The RSSI set contains The GLP algorithm is a graph-based semi-supervised learning algorithm. We need to use the relationship of sample data to build a fully connected graph. The nodes of the graph are data points and contain all labeled and unlabeled data. The edges of nodes (1)Execution propagation: (2)Lock the marked data by replacing the first (3)Repeat steps (1) and (2) until (4)Assign labels to unlabeled data according to Step (1) is to left-multiply matrix In the process of the GLP algorithm, we found that after calculating Then there are:We iterate Equation (14) until convergence. It can be seen from Equation (14) that the label distribution In the indoor WLAN area, we obtained a small number of location fingerprints (labeled data) and a large number of high-dimensional RSSI samples. The location fingerprints consist of RSSI samples and their corresponding physical location. The individual RSSI samples are unlabeled data. Our task is to use the method proposed in this paper to reduce the dimensionality of RSSI samples, and predict the physical location corresponding to each unlabeled RSSI sample through the physical location information of a small number of location fingerprints.When collecting individual RSSI samples and location fingerprints, we need to pay attention to two things. The first is to ensure that the data dimensions of all RSSI samples are equal, which is a necessary condition for using the LLE algorithm for dimensionality reduction. Due to the interference in a complex indoor environment, it is difficult for us to observe the RSSI of all APs at each location. In this case, we can record the RSSI of the missing AP as −99 dBm, so that all RSSI samples have the same dimension. The second is to try to ensure that the radio map can cover the entire localization area. In the GLP algorithm we assume that all labels are known and appear in the labeled data. In a radio map, the physical location is the data label, but unlike the traditional classification problem, we cannot collect the location fingerprints for all physical locations. In order to satisfy the assumption of the GLP algorithm, we need to collect the position fingerprint in a sparse but full-coverage manner, which can be achieved by choosing a larger RP interval when building a radio map.(1)For each RSSI sample (2)The (3)The SLLE algorithm is used to reduce the dimension of the RSSI sample (4)Replace the high-dimensional data with low-dimensional data to establish a new sample set.(5)Use the GLP algorithm to label the physical location of (6)The weighted sum of the probability distribution of We assume that there are (1)Use the self-adaptive method to calculate the most suitable neighbor number (2)Find the (3)Construct the weight (4)Use During online positioning, the system collected a high-dimensional RSSI, and needed to reduce the dimensionality before using the propagation algorithm for localization. However, due to the limitation of the LLE algorithm principle, we must reduce the dimensionality of the newly collected RSSI and the original RSSI together to maintain the integrity of the manifold. It is not economical to perform dimension reduction and label propagation learning for each localization. Aiming at this problem, and considering the linear relationship between high and low-dimensional data and its physical location, this paper proposes an algorithm that uses the corresponding position labels of low-dimensional data and neighbor weights to achieve localization, bypassing the problem of dimensionality reduction of new RSSI.As the location is obtained by multiplying the positions of the self-adaptive The fourth floor of YiFu building and the DaYueCheng library were selected as the experimental scene. We deployed 13 APs on the 4th floor of the YiFu building, and a radio map was built with 48 RPs and a 2 m interval. The RSSI fingerprint of each RP is a 13-dimensional vector, as shown in We use the SLLE algorithm proposed in this paper to reduce the dimensions of two radio maps and analyze the complexity of the k nearest neighbors algorithm (KNN)  when theAs can be seen from This section uses the reduced-dimensional RSSI samples to test the performance of the label propagation algorithm. Considering the sample size, the experiment only selects the radio map of the library area. The experiment is designed as follows: The established radio map of the library area contains the location fingerprints of 600 RPs, we took the fingerprints of 50, 100, 150, and 300 RPs, according to Next, we used the kSNW algorithm and RM2–5 to conduct a positioning test. Next, the merits of the established approach are shown in the positioning performance following the comparison of estimated locations with those in ,8,9,15. k = 4), and the probabilistic positioning (PL) algorithm [In order to compare the performance of different positioning algorithms, we used kSNW and KNN algorithms (lgorithm ,26 2526 In order to show the performance of the kSWN method, we also used the merging method proposed in this paper for localization. With the same positioning results, the kSWN algorithm takes about 7/8 less time than the merging method. The computation time of five experiments is shown in In the present study, a novel cost-effective method is proposed, merging the SLLE algorithm and GLP algorithm for building a radio map. This method noticeably lowers the calibration effort of location fingerprints and enhances the localization accuracy and robustness. This method first employs the SLLE algorithm for reducing the dimensions of RSSI samples and subsequently adopts a limited number of labeled location fingerprints for propagating the labeling data to those that are unlabeled. Lastly, the kSNW algorithm is developed for incorporating the local linear property to online positioning. In the experiment, we demonstrate that the proposed method has the acceptable positioning accuracy with the radio map construction under only 25% labeled fingerprints, and this has better results than the compared method. The kSNW algorithm has better adaptability than the KNN and PL algorithms with incomplete labeled fingerprints. This method reduces the time and labor cost of building a radio map by 75% while maintaining acceptable positioning accuracy. Future studies will focus on the optimization of the proposed method, for instance using the novel unsupervised learning method  and mult


Significance: Functional near-infrared spectroscopy (fNIRS) is a technique for detecting regional hemodynamic responses associated with neural activation in the cerebral cortex. The absorption changes due to hemodynamic changes in the scalp cause considerable signal contamination in the fNIRS measurement. A method for extracting hemodynamic changes in the cerebral tissue is required for reliable fNIRS measurement.Aim: To exclusively detect cerebral functional hemodynamic changes, we developed an fNIRS technique using reflectance modulation of the scalp surface.Approach: The theoretical feasibility of the proposed method was proven by a simulation calculation of light propagation. Its practical feasibility was evaluated by a phantom experiment and brain activation simulation mimicking human fNIRS experiments.Results: The simulation calculation revealed that the partial path length of the scalp was changed by reflectance modulation of the scalp surface. The influence of absorption change in the superficial layer was successfully reduced by the proposed method, using only measurement data, in the phantom experiment. The proposed method was applicable to human experiments of standard designs, achieving statistical significance within an acceptable experimental time-frame.Conclusions: Removal of the scalp hemodynamic effect by the proposed technique will increase the quality of fNIRS data, particularly in measurements in neonates and infants that typically would require a dense optode arrangement. Compared to other elaborate functional neuroimaging techniques, such as functional magnetic resonance imaging and positron-emission tomography, fNIRS has the advantages of low risks, as it does not involve exposure to a magnetic field or radiation, hardware portability, tolerance for electromagnetic noise, and lower running cost. These advantages of fNIRS have increased its use in various disciplines, but some technical concerns in fNIRS have remained, which have not been disclosed to such users.,The influence of scalp blood flow changes on the measured signal is a fairly well-known issue. In fNIRS measurement, pairs of irradiation and detection optodes are fixed to the surface of the subject’s head. The light from the irradiation optode propagates into the head tissues, which are comprised of several tissue layers with different optical properties, such as the scalp, skull, cerebrospinal fluid (CSF), and gray matter and white matter cerebral tissues. The detection optode receives some of the light propagated in these tissues; thus, the detected light inevitably has transited not only the cerebral tissues but also other tissues, including the scalp. If a temporal change in optical properties occurs only in the cerebral tissues, the fNIRS signal might contain only the cerebral hemodynamic response. However, changes in systemic physiological activities, including changes in blood pressure, heart rate, respiration, physical posture, and vasomotion, often induce changes in scalp blood flow. Since the fNIRS measurement sensitivity at the scalp layer is about 10 times larger than that of the gray matter layer,,,–,,,Several methods have been proposed to remove the signal contamination originating from the scalp tissue; however, these methods have some drawbacks in terms of practical implementation. Some methods have introduced additional optodes, with a closer interoptode distance, mainly for detecting scalp blood flow changes.Taken together, these findings imply that a further method is required to allow detection of a signal exclusively arising from the cerebral tissue, with a high-density channel arrangement. In this study, we propose a new fNIRS technique for exclusively detecting cerebral signals, using neither additional optodes nor physiological prior knowledge. In the conventional fNIRS optode pair arrangement, some of the light travels into the scalp layer and exits to the air through the scalp surface. If an optical mirror is attached to the surface between the optodes in a pair, the light expected to exit will be reflected by the mirror and will again travel into the head tissue. As detailed later, this directly increases the light transiting the scalp layer and differentially changes the partial path length (PPL) of each tissue layer. By modulating the reflectance of the mirror material, such changes in PPLs cause some fNIRS signal changes. If the modulation frequency is sufficiently higher than the frequency range of the cerebral hemodynamic changes, optical characteristics in each tissue layer are likely to be constant. Thus, the difference in the signal amplitude observed through reflectance modulation can be attributed to the changes in the PPLs. Based on linear equations of optical extinction in a multilayered tissue model with different mirror reflectance conditions, the change in optical scattering and absorption in each layer can be calculated.In the following sections, we detail the theoretical principle and experimental evaluation of this technique. In the theoretical section, we formulate the relationship between the absorbance changes in two reflectance-modulated conditions and the absorption changes in the scalp and brain layers. Then, we verify the ill-posedness in terms of the existence, uniqueness, and stability of the solution in the inverse problem for estimating the absorption changes from the absorbance changes. Experiments using a mechanically rotating mirror and a dynamic optical phantom indicated that the technique worked adequately for removing signal changes in the superficial layer and detecting signal changes in the deep layer. In the future, the reflection modulator of the rotating mirror can be replaced with a nonmechanical and flexible modulator, which can form part of the optode holder. In this way, exclusive cerebral signal measurement will be achieved, without requirements for any additional optodes, and a high-density channel arrangement will be possible by combining it with the use of an adequate modulator.22.1We propose a technique for removing scalp blood flow changes from the fNIRS signal of a single optode pair, which utilizes modulation of the scalp surface reflectance and induces changes in light propagation in tissue. Near-infrared light that enters at the scalp is propagated to be absorbed and scattered by biological tissues. In the conventional fNIRS, some of the incident light that reached the scalp surface before arriving at the detector exits to the air and is never observed by the detector . On the –,We assessed this hypothesis using a computer simulation of light propagation in the head tissue when the scalp surface reflectance, The simulation results  and 3 sh surface . On the lp layer . The spaSupplemental Material. The refractive index inhomogeneity in the optical models has little effect on PPL and PMDF. Most remarkably, the PPL in the scalp layer became slightly lower in the heterogeneous refractive indices condition than the original homogeneous refractive index condition.The results in the homogeneous refractive index condition were presented in Figs. S1 and S2 in the 2.2According to the computational assessment described in the previous section, the PPL of each tissue layer varies, due to changes in the scalp surface reflectance. It indicates that the absorbance should change with modulation of the scalp surface reflectance within the time period when the absorption change in the tissue could be assumed to be constant. The relationship between the temporal change in absorption, Equation (3) in both cases of a high and low scalp surface reflectances are described in the following matrix equation:  results , lhigh,λIn cases without preliminary simulation calculations, the matrix 33.1The proposed method for extracting the signal from deep layers was verified by a phantom experiment. A schematic illustration of the phantom experiment is shown in We constructed a tissue-mimicking phantom in which the absorption of the superficial and deep layers could be changed independently.A reflectance modulation disc was attached to the surface of the phantom. The disc-shaped reflection modulator was made of a low-reflection black poly-oxymethylene resin that functioned as an optical absorber. Half of the lower surface of the disc was covered by a mirror film . The reflectance of the mirror film and the black resin were acquired with a spectrophotometer equipped with an integrating sphere  and were 0.53 and 0.08 at a wavelength of 840 nm, respectively. The disc made contact with the phantom surface and was rotated by a stepping motor  by means of a magnetic coupler. The detection and irradiation optodes were fixed to the surface of the phantom at the outer edge and the center of the disc, respectively. For the optodes, custom-made multibundled fibers of The protocol of the phantom experiment is shown in The first experiment was conducted under conditions in which the absorption in each layer was changed. Based on Eq. (9), the path length ratio, As a reference to evaluate the proposed method, the absorption change when the absorber was inserted only in the deep layer, 3.2In Eq. (8), the estimated absorption changes in the gray matter layer fluctuate due to the magnification of noise by the measured absorbance change. Usually, in human experiments, such absorption changes in the gray matter layer can be assumed to fluctuate with a constant standard deviation; thus, sufficient statistical accuracy can be achieved with an appropriate sample size achieved by repetition of the trial sequence. In other words, in order for the proposed method to be used in human experiments with sufficient accuracy, trial repetition should be completed within a time achievable in human experiments. Here, we adopted sample size as a measure of the applicability of the proposed method. The minimal sample size that indicated statistical significance was evaluated through a brain activation simulation, in which an fNIRS experiment, comprised of a certain number of control and task trials, was simulated.In this simulation, the amplitude of the absorbance change caused by brain activation was fixed to be equivalent to that of the adult motor cortex by referring to a previous fNIRS study. To determine the absorbance change, the amplitudes and standard deviations of the changes in path-length-dependent oxyhemoglobin and deoxyhemoglobin concentrations were obtained as 44.1The absorbance changes when each absorber was inserted separately in the superficial and the deep layers are shown in 4.2Absorption changes obtained by the phantom experiment are shown in 4.3We conducted a brain activation simulation to calculate the minimum number of experimental trials required to achieve statistical significance in a two-sample 5In this study, we proposed a technique for removing scalp blood flow changes from the fNIRS signal, assuming that the PPL of each tissue layer changes by switching the reflectance of the scalp surface. Intuitively, the optical path lengths could increase when the scalp surface reflectance is high, because more photons re-enter into and travel through the head tissues to the detector. However, the simulation results in periment  did not periment .In the proposed method for estimating absorbance changes in the gray matter layer, we need to determine the value of The applicability of the proposed method was validated only under limited conditions in this study. The results shown may vary modestly with the structure of tissues and their optical properties, the baseline stability in absorption changes, and signal-to-noise ratios in the measurement system. However, for human fNIRS measurements under prevalent conditions, the results obtained with such measurements will not be very different from those obtained in this study. Familiar tasks in human experiments such as verbal fluency, verbal working memory, finger tapping, and visual checker-board tasks generally indicate higher contrast-to-noise ratios of the hemoglobin concentration changes than that indicated in this simulation,Several issues need to be verified in using the proposed method for human fNIRS measurements. The most important issue may be the optimum irradiation-detection distance for the proposed method. The proposed method is extremely effective for removing scalp blood flow changes in areas where light is not obstructed by hair, such as the human forehead and neonatal head. In particular, for the neonatal head, the irradiation and detection optodes should be attached more densely, with a shorter interval, than for the adult head, because the brains of these subjects are smaller than those of adults. In such densely packed conditions, the multidistance optode arrangement to remove the scalp blood flow effect may not be executed. It is unknown whether removal of scalp blood flow changes based on physiological prior knowledge can appropriately work for fNIRS data of newborns and infants, as recent studies have suggested that neurovascular coupling in neonates and preterm born infants is different from that in adults.6We proposed an fNIRS technique that exclusively detects cerebral functional hemodynamic changes using reflectance modulation of the scalp surface. The theoretical feasibility of the proposed method was proven by a simulation calculation of light propagation. A phantom experiment demonstrated that the influence of the absorption change in the superficial layer was successfully reduced by the proposed method using only measurement data. A brain activation simulation study demonstrated that the proposed method was applicable to human experiments with standard designs in terms of achieving statistical significance in an acceptable experimental time-frame. The removal of the scalp blood flow effect by the proposed technique will increase the quality of fNIRS data, particularly in the assessment of neonates and infants, which requires a dense optode arrangement.Click here for additional data file.


Partial discharge (PD) is a major indicator of various failures in power grid systems. PD exhibits a physical occurrence where a localized electrical discharge happens in insulation materials. This phenomenon causes damage to the insulating parts and, in various circumstances, leads to complete insulation breakdown. As a consequence, it can produce more costly outcomes such as abrupt outages or lost production. Therefore, PD detection plays a vital role in preventing insulation failure. In this work, we propose a system that utilizes heterogeneous sensors for the PD detection along with multi-sensor interface, real-time advanced denoise processing, flexible system operation, and Bluetooth-low-energy (BLE)-based ad hoc communication. Among the variety of heterogeneous sensors, several are developed by the application of nanomaterials and nanotechnology, thus outperforming the regular types. The proposed system successfully identifies the presence of PD from sensor signals using a microprocessor-based processing system and effectively performs an advanced denoising technique based on the wavelet transform through field-programmable-gate-array (FPGA)-based programmable logics. With the development of the system, we aim to achieve a solution with low cost, high flexibility and efficiency, and ease of deployment for the monitoring of power grid systems. The world nowadays has entered an era of rapid evolution owing to advancements in technology. Electricity has become an essential resource to ensure the evolution of the globe, social infrastructure, and personal daily life. Therefore, maintaining a stable and uninterrupted power supply for humanity must be a critical and crucial mission. Furthermore, ensuring the quality of insulation is of great importance because insulation materials are utilized in every phase of the power system from generation to transmission and distribution. Insulation degradation poses a challenge for the reliability as well as the continuity of power grids, and partial discharge is among the primary factors contributing to the breakdown of insulators.The partial discharge (PD) is the initial expression in case of insulation material degradations. The phenomenon takes place when there is a breakdown in the material used for high voltage insulation . It has The article is organized as follows: Previously, the monitoring and examination of power grid systems have been done manually or with wired instruments, which lead to higher cost or inefficiency. The Internet of Things (IoT) era brings many benefits to those operations. Over the years, various works have been conducted to construct systems for monitoring power grid systems more efficiently. The usage of heterogeneous sensors, different computation engines, and a communication mechanism is widely applied in those systems. The authors of ,7 proposThe main components of the system include three parts (aside from the remote server): the sensors and the signal interface, the IoT hardware system platform, and the communication module, as indicated in A sensor device takes a particular stimulus from changes in the physical properties of the surroundings and produces responses to the fluctuations. Combined with the nanotechnology, which is a field of science that performs research to manipulate atoms and molecules in the nanometer scale, nanotechnology-enabled sensors facilitate the detection solution with enhanced sensitivity and improved accuracy . Relyingc-axis, and the center is the place where positive and negative charges cancel each other. Furthermore, ZnO with a wurtzite crystal stands out among various popular piezoelectric materials such as lead zirconate titanate (PZT), polyvinylidene difluoride (PVDF), molybdenum disulphide (MoS2) due to non-toxicity, low cost, and ease of fabrication [The piezoelectric effect is a phenomenon that happens inside semiconductors such as ZnO. The effect stems from the ability of the crystalline structure to generate an electrical current in response to an applied mechanical stress . Acoustirication ,41. WithThis part presents the detail of the hardware platform and the signal processing mechanism. It also explains how the platform is suitable for our purpose as well as elaborates on the denoising algorithm.The heterogeneous sensor system is built around the Zedboard   = mother wavelet. The processing mechanism in this work is based on a fast algorithm for wavelet transform developed by Mallat [cA) and detail (cD). As shown in A denoise technique is implemented as a hardware accelerator in the PL to facilitate the real-time operation of the system. Among 28 denoising techniques such as fast-Fourier-transform-based, lowpass filtering, Wigner–Ville-distribution-based, recursive least squares, and exponentially weighted recursive least squares methods, a wavelet transform-based denoise has demonstrated to give the best performance ,44. Thusy Mallat , which pThe approximation and detail coefficients are the result of a convolution operation between the input signals with low-pass (LP) filters and high-pass (HP) filters of a wavelet, respectively. The filters in the reconstruction stage are reversed in terms of time, compared to the ones used in the decomposition stage , as illui λi=mientation . Therefoentation ,51 with The system is equipped with a BLE connection to form an ad hoc network for flexible deployment and data transmission. In addition to obtaining the sensor data, the PS performs wireless data transmitting through an HM-10 BLE module .The wireThis section describes various experiments conducted on the proposed system in PD detection, denoise processing, and data transmission. It also points out the advantages to which the proposed system brings in comparison with other types.Since PD events occur under extreme situations and require a huge size research equipment and environment to incur the events artificially, the research in this paper utilized sensor signals captured under PD occurrences and provided by the sensor research teams collaborating in a joint-project that Korea Electric Power Corporation (KEPCO) leads. The signals have been recorded as data in memory (DDR) and played back to the system in our overall experimental environment. The sensor has been experimented under the laboratory environment of the sensor teams where the PD was generated to test the reactions of the sensor to the event. When the PD happened, the voltage of the piezoelectric sensor was recorded to be about 30 mV. The sensor performed with satisfactory sensitivity and adequate accuracy, but the produced signals had small magnitudes. Furthermore, the signals might easily be affected by various noise sources such as electromagnetic noise, power electronic components, switching . The comThe wavelet denoise core inside the hardware platform is depicted in n = 8 is the number of elements of each filter for symlet4. As each downsampled input signal is shifted into the line buffer, the convolution result is produced as the summation of multiplications between each weight element and the corresponding value in the line buffer, which is expressed by the following:The Conv and ReConv have the same structure and components . Their differences lie in the purpose of the loaded weights: Conv for Decomposition  and ReConv for Reconstruction (synthesis). Each convolution part is preloaded with filter weights of the symlet4 and has dedicated resources  for calculation, as shown in the left part of so) and swapped (sw) and a sorting module, as in the right part of Algorithm 1: The sorting algorithm on The downsampling operation keeps every other result from the convolution module and is performed before data enters the Conv block. The action of discarding half of the total data samples not only reduces the amount of data but also eliminates the effect of aliasing when the data are restored in the reconstruction step. The threshold step is crucial to the denoising purpose. It dynamically determines a threshold value on the data stream, which in turn is applied to the result of the HP convolution path to obtain the detail coefficients at a particular level. The threshold module consists of two buffers named sorted  of the two auxiliary buffers. This median value is then used to calculate the threshold value for one level, and the threshold application block will apply a soft threshold, as shown in L0 = 1001 and F = 8 were the length of the sensor data and the total filter elements, respectively. After sorting to find the threshold value, delay cycles were inserted to synchronize the data for feeding into the re-convolution operation, which requires two separate sets of computed coefficients: detail (cD’) and approximation (cA’). Because of downsampling at each level, the convolution time was nearly the same across four levels while the latency of the denoise core did not change with the increase of denoise levels. The first level had the highest number of elements for convolution and sort, thus incurring the most operation time while the downstream levels  were running. Therefore, the proposed hardware architecture provided the advantage of giving better signals without introducing more time.Another experiment was conducted to test the feasibility of the system for signal interfacing and data transmission. Because of the highly specialized equipment required for the PD generation of the piezoelectric sensor, multi-sensor interfacing was verified through the multi-channel ADC with two supporting sensors: temperature and light. One Zedboard was utilized as the source device and connected to the HM-10 module so that the denoised data could be originated from the source and transmitted through a bridge node before the data reached the destination. The data has been successfully transmitted through the BLE modules and retrieved at the target node. In our experiment, in order to verify the transmission correctness, the data was displayed and compared on a terminal of the destination device, as shown in In terms of denoise processing, our proposed architecture excels when compared with previous implementations . An implIn this article, we present a highly flexible and feasible system for the PD detection of power grid systems. The proposed system can work with heterogeneous sensors, including a newly developed piezoelectric sensor specialized for detecting the presence of PD events. The piezoelectric sensor is fabricated using nanotechnology advancements with satisfactory performance in the detection outcome. In addition to interaction with the novel sensor, the proposed system, which is based on the combination of MCU and FPGA, is easy to deploy because of its capability to form an ad hoc network. The environment of power grid systems is numerous, and the sensor systems are sensitive to the environment because of various noise sources. The research proposed an advanced denoising technique in a hardware design for real-time processing and highly accurate signal quality. The programmable logic provided by FPGA technology brings more flexibility to the system when it requires to offload computation extensive processing tasks such as denoising. The system functions as a sensor node to feed data to central servers for more complex data inspection and even machine learning techniques. The proposed system aids in forecasting imminent insulation failures as well as in the determination of item replacement.


A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types. A complex tissue contains a variety of heterogeneous cell types, each with its own distinct features and function . Based on the gene expression matrix, we utilize EM clustering method to partition these genes into K clusters, which are represented as a collection of K subsets of genes, C = {ck, k = 1, 2, ..., K}.Let For these identified gene clusters, the calculation of their network entropies requires estimating the interaction probabilities among genes. Based on the assumption that two genes will have a greater interaction probability when they are known to interact at the protein level  are used to assign weights to the edges of the PPI-expression network. Specifically, for a given gene neighbor k on cell s is calculated by:We interpret these weights as interaction probabilities between neighboring genes. In a cell with high expression of gene i and gene j, two genes are more likely to interact than those with low expression of i and/or j  curves. The area under ROC curve (AUC) is calculated by the R package ROCR. The second metric is to measure the precision and the recall rate of detecting differential genes, which are calculated as the previous study  is from a study by Kowalcyk et al., which consists of long term hematopoietic stem cells (LTHSC) % of cells. Here, as the previous study  enrichment analysis via DAVID bioinformatics resources (Huang et al., According to the KEGG database, the differential genes are enriched in 304 Pathways. The annotation results are classified according to the type of pathway . The resAlthough the advent of single-cell RNA sequencing has provided new insights into cell dynamics, it has also brought new computational challenges. On one hand, the expression data obtained by scRNA-seq is relatively noisy, and then relevant models need to be considered. On the other hand, given a huge cell population, it is challenge to take on the task of unraveling cell heterogeneity. Analysis of differential gene expression is essential for almost all single-cell transcriptional studies. As previous studies show that genes in the same pathway tend to correlate in expression, the network structure of genes can boost differential expression analysis. In this study, we propose a new computational method, DiffGE, for identifying differential genes from scRNA-seq data. DiffGE extends the notion of local entropy to network entropy, which incorporates protein interactions to improve sensitivity and specificity of detecting differentially expressed genes from scRNA-seq data. It first groups genes, calculates the network entropy according to the PPI network of each gene group, and then detects highly differential genes groups by selecting gene clusters with higher network entropies. We evaluate the detection accuracy of the proposed method using three real scRNA-seq datasets and compare its performance with three widely-used differential expression analysis methods. The comparison results indicate that employing the gene interaction network can make a significant contribution to the accuracy and the proposed method has an powerful performance in detecting highly differential genes.All datasets generated for this study are included in the article/supplementary material.YG was responsible for the main idea, as well as the completion of the manuscript. SL have developed the algorithm and performed data analysis. GZ have coordinated the data pre-processing and supervised the effort. All authors have 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.


Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models. Given that the emergency medicine reserve is one of the strategic materials to deal with large-scale public health events, emergency medicine reserve management plays an important roles in building up the emergency response capacity. Intuitively, in case of a public health event, the lack of emergency medicines probably wastes valuable rescue time and results in much more loss. For example, at the end of 2017 when the influenza virus was rapidly spreading in several regions of China, the specific medicine “Duffy” was out of stock due to the unexpected demand surge, e.g., a shortfall of 310,000 boxes of "Duffy" only in Beijing. However, overstocking emergency medicines also leads to some major problems like the great stock cost and huge expiration cost. Only with the medicine reserve decision based on the accurate demand forecast, can the above problems be effectively solved. However, the emergency medicine reserve demand is affected by the mixed effects of very complex factors like seasonality, response speed, virus transmission rate (in case of an epidemic), communication network of the people, among others. Consequently, the observed data are composed of different but hard-to-distinguish components; therefore, the traditional demand forecasting method does not work for this case, which presents China Food and Drugs Administration (CFDA) an urgent challenge to optimize the safety stock of emergency medicine reserve. Motivated by the above theoretical as well as practical concerns, this work aims to propose a new forecast algorithm of more accuracy based on the decomposition-ensemble methodology.The rest of this paper is organized as follows: “The most widely applied paradigm of forecasting the medicine demand is composed of two phases. The first phase predicts the number of infected cases (persons) while the second generates the forecast of the quantity of medicine demand based on the outcome in the first phase. In terms of forecasting the number of the infected cases, system dynamics models and statistical models are the two mainstream approaches to predict the number of patients in public health events. The most typical system dynamics model is the classic SIR model proposed by Kermack and McKendrick, which lays the foundation of infectious disease dynamics . Since tStatistical models generally predict the evolution trend of public health events based on the historical observations. For examples, –12 used The advantage of the statistical models is that the models include fewer predefined parameters compared with the system dynamics models. However, these kinds of models are heavily dependent on the historical data, which explain why a statistical prediction model shows inconsistent performance in different studies. The “data-driven” nature makes the input data dominate the outcome of the statistical models, namely “garbage in garbage out”. To provide the suited input data, it is necessary to decompose the original data into different components and select the statistical model according to the component characteristics. Empirical Mode Decomposition (EMD) provides a way to solve this problem. EMD is an analysis and processing method for non-linear and non-stationary signals  and has There are some studies presenting the whole forecasting process, very few though. These scanty studies include, Guo et al. used the BP neural network method to predict the population of wounded and deaths in the Yushu earthquakes, and then used the safety stock theory to estimate the demand for emergency supplies . Zhang eThe current related studies on emergency medicine reserve demand forecasting present 4 characteristics: (1) a majority of studies are focused on forecasting the number of cases, only few studies present a complete solution to forecasting the emergency medicine reserve demand. (2) Compared with the system dynamics model, the statistical model need much less predefined parameters and can capture the development trend in the data-driven way. (3) The statistical models like ARIMA and neural network have been widely used in the research of medicine demand forecasting. However, the performance is sample-sensitive, namely the forecasting accuracy values are different on different samples. (4) Empirical Mode Decomposition has widely been applied to improve the calculation accuracy of the single prediction models in various fields.Based on the above-mentioned, this paper proposes the EMD-ELMAN-ARIMA (ELA) hybrid forecasting model. ELA uses EMD method to decompose the historical data of the infected cases into different components, then ARIMA and ELMAN neural networks are employed to capture the linear and the non-linear components, respectively. Furthermore, based on the projection from the number of the infected cases to the medicine reserve demand dominated by nature of the public health events , the emergency medicine reserve demand can be obtained. Finally, to validate the model, an empirical study is carried out based on the actual data of influenza cases during the period from 2014 to 2018 in Beijing. To the best of our knowledge, this paper is the first to apply EMD to the emergency medicine reserve demand forecasting.A.Characteristics of emergency medicine demandBefore forecasting the emergency medicines reserve demand, it is necessary to identify the characteristics of the data so as to select the matching forecasting methods. The demand for emergency medicines is characterized by suddenness, surge, and urgency. First, it is difficult to foresee the outbreaks of public health events, especially to accurately predict its infection scope and population, so the need for emergency medicine that will take on a sudden character. Second, the denser population and complex movement of people within cities accelerate the spread of epidemic, with the spread of online public opinion, the sales of emergency medicines will show a stronger surge character. Finally, the needs of emergency medicines are also extremely urgent, as the availability of emergency medicines cannot be delayed after the disease outbreak, the lack of emergency medicines reserve tends to fuel social panic, which can lead to more serious social problems.B.Paradigm of emergency medicine reserve demand forecastingIn combination with the above characteristics, it is easy to infer that the data on the number of people affected by public health events are destined to be strongly non-stable, so the traditional time series prediction method, which requires a high requirement for data stationarity, will not be too accurate. At the same time, traditional regression analysis methods are difficult to apply to such issues because of the difficulty of identifying and quantifying the factors that lead to the surge in demand for emergency medicines. In addition, considering the rapid population movements, frequent trade and commerce, and rapid development of the cities, premature demand data do not provide much guidance for the current forecast results. Therefore, models of emergency medicine reserve requirements also require the ability to handle small sample data. To sum up, forecasting the demand for emergency medicines is not only a hot academic issue, but also a very difficult practical issue.In practice of CFDA, the medicine reserve demand is estimated by multiplying the number of suspected cases by the effective infections coefficient . Therefore, this paper proposes a paradigm of emergency medicine reserve demand forecasting see Fig. .Fig. 1Pap the predicted value of cases in a public health event, the emergency medicines reserve demand can be expressed as follows:The proposed paradigm proceeds as follows: First, historical data are collected on the actual number of people suffering from public health events in cities. Second, historical data are used to forecast the specific number of people suffering from diseases and the trend over the coming period. Finally, based on the prediction of the patients, the demand for emergency medicine reserve is estimated according to the medicine production capacity, reserve demand characteristics, as well as the guidance and recommendations of experts, all the mechanisms and relevant factors can be summarized as the demand coefficient of emergency medicine The demand coefficient A.Autoregressive integrated moving average modelStatistical prediction models can classified into three categories including regression models , time series models , and artificial intelligence models . In previous studies, the reliable performance of ARIMA and neural network models like ELMAN motivates this paper employ them as individual models.B.ELMAN neural network modelThe Autoregressive Integrated Moving Average Model (ARIMA) is one of the most frequently used models. The ARIMA model is based on the idea of transforming a non-stable time series into a stable time series Neural network models have been proved to be a kind of effective prediction models in a wide range of fields –34. ELMAC.Distinguishing components by EMDTo describe the non-linear state space of the ELMAN neural network model , the defAlthough the above-mentioned models have seen some successful applications in emergency medicine demand forecasting studies –39, theiD.The EMD-ELMAN-ARIMA (ELA) modelTo overcome the above problem, inspired by the studies , 43, thiIt is a unanimous agreement in the forecasting field that the models, including time series models, econometric models and artificial intelligence methods, should be carefully tailored to fit the data characteristics. Given that the emergency medicine demand against the public healthy events comprise multiple components of different characteristics, a single model is not sufficient. Considering some studies have shown the power of combined forecasting models in this case , 45, thiA.Data introductionWith the support of CDFA, we obtained the weekly data on historical influenza cases from Beijing Center for Disease Prevention and Control (Beijing CDC) during the period of 01/07/2014 to 25/12/2018. The first 200 observations are used to tune the parameters of the models, and the remainder 21 observations from August to December 2018 are used to validate the model.B.Data processingFigure Due to the complexity of influenza in megacities, the cases of influenza patients tend to be highly volatile and unstable. Using the Augmented Dickey–Fuller test, the stationarity of the data can be tested. Table This section uses the proposed ELA model to predict the influenza cases. First, the data were decomposed by EMD using the MATLAB software package. As shown in Figs. We tested various combinations of 8 modes by “trial and error”. According to the results, the sum of IMF1 to IMF3, denoted by C.Forecasting processThe ELMAN neural network and ARIMA models are used to capture the nonlinearity in × 1 and seasonality in × 2, respectively Figs. , 10, 11.The whole forecasting process is composed of 4 steps. First, ARIMA model is used to predict the original time series. Because the data fluctuate excessively and include the growth trend brought by the progress of influenza virus detection technology, it is necessary to strip the trend items brought by the progress of detection technology from the original data before using ARIMA model. With the help of Hodrick–Prescott filtering method, the original time series is divided into the period term and trend term, and the trend term is added to ARIMA model as an explanatory variable for model fitting .Because the original data fluctuate violently, the original time series is processed by difference, and the first-order and second-order difference series are tested by the ADF test.As shown in the above test results, at the significance level of 0.05, the null hypothesis is rejected, indicating that the second-order difference series of the original influenza cases data is stationary. The Box–Jenkins method is used to determine the order of the model, and the ARIMA  model is obtained on the premise that the residual error sequence is a white noise sequence.Second, in the ELMAN neural network, we used the training data set  is used to evaluate the prediction error. Denote On the one hand, for the prediction results of the number of influenza patients, the combined prediction model constructed in this paper is superior to the traditional time series model and machine learning model alone.According to Table Table Based on the decomposition-ensemble methodology, this paper constructs a new combined prediction model based on EMD, ARIMA, and ELMAN, namely the ELA model, to forecast the emergency medicine reserve demand in response to a public health events. This model employs EMD decompose the original series of cases (patients) into different components, then reconstructs all the components into several combinations to avoid the curse of dimensionality. Subsequently ELMAN neuro network and ARIMA are applied to the suited combinations by “trial and error” according to the characteristics, like linearity and nonlinearity in this study. Finally, the forecast of the emergency medicine reserve demand is generated with a projection coefficient It is notable that although this work promotes better understanding of applying the decomposition-ensemble paradigm to the emergency medicine reserve forecast work, some important issues are left to the future studies. For examples, when EMD generates a large scale of components, it is infeasible to identify the best combinations by “trial and error”. Moreover, there are some optimization problems to be solved in the future, like the optimal length of forecasting horizon, the optimal number of individual models to be integrated, the optimal size of training set, and so forth.


The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological  research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research. Sweat gland activity modulates the conductance of an applied current ,2,3,4. SThe single effector model of the sweat glands is the most generally agreed-upon model for EDA. The changes in the level and phasic shifts of the EDA are the outputs of such a model. Sweat comes through varying numbers of ducts in the sweat glands at different levels, depending on the level of sympathetic arousal. The sweat ducts can be thought of as a set of variable resistors wired in parallel, which is the principle behind the single effector model. The higher the amount of sweat rises and the more ducts that are filled up, the lower the resistance in that variable set of parallel resistors. In this manner, changes in the level of sweat in the ducts produce observable variations in EDA .The neurotransmitter involved in the mediation of eccrine sweat gland activity is acetylcholine, which is the primary neurotransmitter of the parasympathetic nervous system, rather than noradrenaline, which is typically associated with peripheral sympathetic activation . For thaThe most salient characteristic of an EDA signal is the occurrence of skin conductance responses (SCRs) resulting from an underlying sympathetic reaction to a stimulus. The SCRs are the rapid and smooth transient events noticeable in the EDA signal . At leasMeasures of the SCRs are used to evaluate a subject’s response to event-related experiments (“startle-like” stimuli) or tonic stimuli tests . In event-related experiments, the occurrence of an SCR is expected after the stimulus is applied. In such experiments, the SCRs are usually called the event-related SCRs (ERSCRs) . QuantitThe skin conductance level (SCL) and nonspecific skin conductance responses (NSSCRs)  are measDespite the source that caused the SCRs (specific to a stimulus or spontaneous), they are characterized by a rise from the initial level to a peak, followed by a decline . When caWe reviewed advances in EDA data collection and signal processing techniques from the last 10 years. The first author searched PubMed, Web of Science (WoS), and the Scopus database to identify research articles and conference papers published in the last 10 years. As EDA is also referred to in some studies as “galvanic skin response” or “skin conductance response,” these keywords were also used in the search  AND DOCUMENT TYPE (article) AND PUBLICATION YEAR > 2008). From the results of the search in the three databases, we extracted studies in three categories of our main interest: (1) EDA recording devices and electrodes, which included all the studies that investigated instrumentation, technologies, and sensors for EDA collection development and testing; (2) processing techniques for EDA signals, which included the signal processing techniques developed to create novel, sensitive, and quantitative measures based on the EDA; and (3) EDA quality, which included studies that evaluated the reproducibility and consistency of measures of EDA, as well as techniques for managing motion artifacts and noise.The search found 365 entries in Pubmed, 377 in WoS, and 417 in Scopus. From these search results, 16 manuscripts were classified as describing EDA recording devices and electrodes, 28 reported on processing techniques for the EDA, and 9 studies investigated EDA quality. For context, the previous work has been included, as described in .It is possible to collect EDA signals without an external source of electricity, in which case it is called endosomatic recording. In this case, the voltage between an active site and a reference electrode at a relatively inactive site is collected. Endosomatic devices are thought to be simpler as they require only a high input impedance amplifier (>10 MΩ); however, they require an amplifier gain and a floating reference to measure the potential difference between the two electrodes. The signal obtained with this method is termed the skin potential response. It has a direct relationship to SCRs . HoweverMost EDA devices use an exosomatic approach, in which an external constant current or voltage source is applied via electrodes on the skin . ExosomaAlthough either a constant current or constant voltage can be used for exosomatic recordings, the latter is widely recommended . The colExosomatic recordings using an AC voltage or current source require more sophisticated instrumentation than for the DC approach. Given that conductance and capacitance (resulting from the capacitive properties of the stratum corneum) are physically in parallel, using constant amplitude voltage excitation is recommended (the resulting current is measured). To separate the components representing the conductive and capacitive contributions, a phase-sensitive rectifier is typically used. The foremost advantage of AC-source devices compared to DC-source devices is the avoidance of electrode-polarization issues or a counter emf . AlthougDue to the nature of EDA and the location where the signal is acquired, recording EDA signals may require special electrodes, electrode gels, and recording devices that differ from the ones used for other psychophysiological measures. Electrodes are an important factor in the quality of the EDA measurements. The use of the same material for the two EDA electrodes is very important for EDA data recording, mainly in DC-source devices, because a difference in metals will introduce a potential difference. The use of silver-silver chloride (Ag/AgCl) electrodes is the standard for EDA recording. Ag/AgCl electrodes help minimize both the polarization of the electrode and the bias potential between the electrodes. The hydrogel improves the signal quality by lowering the impedance that exists at the electrode–skin interface. However, it reduces the shelf life of hydrogel electrodes as the hydrogel layer that exists in the skin–electrode interface degrades with time due to dehydration. The high impedance produced by dehydration leads to an impairment in the quality of the signal and to an increased signal corruption caused by motion artifacts and noise.Dry electrodes can potentially avoid the signal degradation issues and inventory shelf-life management. Metallic electrodes are widely used, but carbon electrodes have gained some popularity for EDA recordings . In geneA big concern in EDA measurement is the polarization of the electrodes. In exosomatic DC-source devices, the electrodes are polarized by electrolysis because they carry a DC current and become the anode and cathode in an electrical system. Reusable EDA electrodes, which are often commercially available, are much more expensive than disposable AgCl electrodes with very thin layers of silver chloride. In reusable electrodes, it has been observed that even with a low DC current, the charge can become sufficiently high to remove part of the AgCl layer at the cathode after prolonged use, and increase the layer at the anode, producing large bias voltage levels . Multiple studies have reported advances in EDA data collection in recent years, mainly supporting the feasibility and superiority of AC devices over DC devices. A study reported the development of a device to enable the DC potential and AC conductance to be measured simultaneously at the same skin site by using a small AC current in a monopolar system . They foElectrodes for specific applications of EDA measurement have also been developed recently. Stainless steel electrodes are mechanically strong and avoid the risk of corrosion, which makes them suitable for many applications including wearable devices. However, this material is more prone to polarizing than AgCl electrodes. Alternatively, carbon can be used instead of metal for both the wires and the electrodes because this material not only reduces polarization, but can also make the electrodes radiotranslucent, and magnetic resonance imaging (MRI)-compatible . Some grSome groups have developed electrodes that can potentially perform in wearable devices. A group proposed a highly wearable EDA sensor based on flexible conductive foam as the sensing material and designed it to be easily attached and detached from clothing . The senOther research groups have developed novel devices aiming to foster wearable technology to collect EDA data. A group developed a glove with embedded circuitry and based on conductive fabric sensors that is capable of collecting EDA and pulse waves . Later, The scoring of SCRs was done manually for many years. The main benefit of manual scoring is the ability of the experimenter to get a close trial-by-trial inspection of waveforms to make sure that individual SCRs are physiologically related to an event of interest. However, manual scoring is a cumbersome and subjective process. Aiming to facilitate the analysis of emotion-related data, several computer algorithms have been implemented. A group of researchers developed a tool that offers a user-friendly interface for pre-processing and assistance with the peak scoring  of individual SCRs . More reThe availability of wearable sensors for EDA data collection has increased opportunities for the technology to be used in studies looking at the effects of experiences and environment on physiology. In such scenarios, contextual information is required. An architecture and implementation of a system for acquiring, processing, and visualizing EDA and other biophysiological signals along with contextual information was developed . Their rMethods for automatically measuring the SCRs using mathematical models are attractive from a theoretical and procedural standpoint. There are many studies that have examined automatic ways to count spontaneous SCRs, extract amplitude or other measures of a single causal SCR, and manage the superposition of SCRs. The main challenge is the frequent occurrence of a second SCR before the completion of a given SCR. Aiming to resolve the problem of overlapping SCRs, some researchers proposed a method inspired by the analysis of blood-oxygen-level-dependent responses in functional magnetic resonance imaging to detect and analyze SCRs . The metAnother group proposed a non-negative deconvolution method to separate EDA into continuous tonic and phasic components, and obtain discrete compact responses ,51. The An improvement to the model-based analyses was proposed in Bach et al. . The algAnother group proposed a model that describes the EDA as a linear combination of the tonic component, the phasic component, and noise . The phasic component results from the convolution between an infinite impulse response function and a sparse, non-negative sudomotor nerve activity driver. Based on these assumptions, they extracted the tonic and phasic components of the EDA using a convex optimization problem, which was constrained by non-negativity and sparsity of the sudomotor nerve activity . The algThe explicit incorporation of motion artifacts into the tonic component, called the baseline signal, to produce a more realistic EDA model was proposed later  to overcSimilarly, another group proposed using the Hartley modulating function to assure convexity of the optimization formulation for estimating the number, timing, and amplitudes of underlying neural firing from EDA signals, and using Kaiser windows with different shape parameters to emphasize the significant spectral components . This apA very different approach for EDA data processing based on spectral analysis was proposed recently . This apAnother study introduced the EDA-gram, a multidimensional fingerprint of the EDA signal . The EDAA point process approach for characterization of the EDA signal was introduced recently . This teThe state-space approach has also been used for EDA data analysis. Similar to other studies using state-space models to estimate an unobserved neural state from physiological data, in a particular study, the authors related stress to the probability of occurrence of a specific SCR (a phasic driver impulse) in the EDA . The stuA new tool to perform SCR detection from EDA data that accounts for respiration was made available recently in a MATLAB toolbox called Breathe Easy EDA (BEEDA) . IrregulA key concern about the SCL and NSSCRs is that these indices are highly variable between subjects . In addiSeveral studies have focused on motion artifact detection and/or correction of the EDA signal. A machine learning algorithm for the automatic detection of artifacts in EDA signals was presented in Taylor et al. . Their aA method for removing motion artifacts from EDA using a stationary wavelet transform was proposed . The wavCurve fitting and sparse recovery methods have been used to automatically identify and remove artifacts in EDA data . SpecifiAnother simple, transparent, and flexible method for the automatic quality assessment of EDA data was reported . The algThe deployment of indices of EDA in clinical settings and daily use requires repeatable and robust measures. High inter-subject variability of time-domain measures of EDA has been reported ,69. SpecEDA has a long history in psychophysiological research since the studies of Vigouroux in France in 1879. During the first years of use of the technique, it was used to evaluate EDA as a response to mental  stress. More recent observations showing that EDA varies with the state of sweat glands in the skin, which are controlled by sympathetic nervous activity, initiated the practice of using the technique as an indication of not only psychological but also general sympathetic arousal. Despite the wide acceptation of this concept, using EDA for pathophysiological assessment is rather novel. Literature suggests the practical superiority of the constant-amplitude AC-voltage exosomatic method for reliable EDA data collection. This approach avoids the complexity of collecting signals using endosomatic approaches and the error in the EDA signal introduced by electrode polarization. Nevertheless, the constant-amplitude AC-voltage exosomatic method allows for the collection of skin potential, skin conductance, and skin susceptance (the imaginary part of admittance) at the same skin site, which is of interest for some groups attempting to assess the functioning of the sympathetic nervous system ,83. The The development of wearable technologies capable of reliable EDA data collection and analysis is a relevant research topic. It involves the type of sensor, the measuring site, the signal conditioning, motion artifact detection and correction, and the suitability of the resulting measures of EDA. Furthermore, different scenarios, such as sleeping, exercising, driving, and others, have specific requirements, such as quality and data length, to provide the intended measures based on EDA. The feasibility of wearable devices to provide the required chain of capabilities (collection–processing–diagnosing) should be tested in such scenarios.Tonic/phasic decomposition is the most-pursued task for EDA signal processing. The scientific relevance of tools for this task relies on their ability to detect the underlying sympathetic driver that produces a specific phasic shift. If such a task is accurately achieved using EDA, one could use the model to objectively measure subjects’ reactions to specific stimuli or significant situations . Several implementations of the tools are available. However, the main practical constraints of such methods are the feasibility of implementing the mathematical computations in wearable devices and the requirement of subject-specific tuning. In particular, the convex optimization approach (cvxEDA) and the sparse deconvolution approach (sparsEDA) are relatively fast techniques that can be implemented in a wearable device given their low computational cost. Nevertheless, both tools require setting a group of parameters (four or more). If the default values for the parameters are used, the results of the decomposition vary highly depending on the subject and the application. There is no congruency on the results between the decomposition methods. For illustration purposes, in A potential application of EDA is to use it jointly with heart rate variability to develop indices of autonomic function. For instance, EDA can be used to adjust the bands for spectral analysis of heart rate variability under exercise. Also, adaptive filtering approaches are able to use the information in EDA to obtain more accurate, sensitive, and specific indices of autonomic control and balance. The heart rate variability is known to have a nonlinear relationship with the autonomic control . For itsFurthermore, ways to increase the specificity of EDA for the assessment of the sympathetic nervous system in clinical applications need to be explored. The correct diagnosis of many diseases requires more sensitive and reliable measures of sympathetic function. An important example is diabetic cardiovascular autonomic neuropathy, which is present in at least 25% of diabetics. It is a remarkable example of the need for sensitive measures of sympathetic tone ,86 becauFuture applications of EDA may include the multi-parameter approach  based on wearable sensors for assessing diseases that affect the autonomic control . We envision those tools to incorporate artificial intelligence for obtaining and selecting features, as well as for estimating the level of progress of disease. A different application would be the detection of sleep drowsiness, which could be used, for example, to alert drivers when they are too tired to be driving safely because their bodies are showing low levels of responsiveness. The simplicity of the circuitry to collect the signal and the absence of parasympathetic interference make the EDA a valuable source of information and a desirable target for many applications. Several wearable devices already incorporate EDA sensors  but theiThe best electrode type for EDA data collection highly depends on the application. Although Ag/AgCl hydrogel electrodes provide the most reliable signals, they are impractical for wearable applications that require long-duration reliability. More research is required to validate the feasibility of stainless steel and carbon electrodes to provide comparable signal quality. Developing reusable electrodes that are reliable for long-term applications can boost the deployment of EDA applications. As for the electronic devices, AC-voltage devices seem to be the most appropriate approach overall. Despite their higher circuit complexity, they avoid the issues caused by electrode polarization. Current techniques for automatic analysis of EDA signals still require improving their subject independency and their ability to function without setting any external parameters to make the technique feasible for ambulatory settings. Furthermore, alternative techniques like spectral analysis have emerged as potential tools for the analysis of EDA. Some tools for detecting and removing corrupted segments from EDA signals are available but their sensitivity needs to be further evaluated. The consistency of the measures derived from EDA is still a matter of concern, specifically in the presence of motion artifacts.