A rotating body consisting of a rotating shaft and bearings inevitably generates voltage and current. The potential difference between the bearing and the shaft is the main cause of electrical corrosion, which causes motor failure, shortened bearing life, and many safety issues. To prevent corrosion, passive shaft-grounding devices use conductive materials and brushes; however, these devices cannot be completely grounded, so there is a difference in local potential, and brush friction generates a shaft current. The cumulative effect causes electrical corrosion; therefore, in this study, an electrical corrosion protection device for the rotating power supply shaft was developed. It detected current and potential difference and established a feedback system on the rotating shaft. It also energized the rotating shaft using an external power supply to eliminate the potential difference on the shaft and reduce electrical corrosion. The result was prolonged motor life and improved stability, operating efficiency, and operability of related equipment. In this study, a rotating-shaft test rig was set up, and a constant current was applied to simulate the potential difference and verify the performance of the anti-corrosion device. Gradually, the design scheme was optimized; the potential difference on the rotating shaft was accurately quantified; and the goal of controlling the potential difference within 2 mV was achieved. Finally, the electrical corrosion protection device was applied to the rotating shaft of a merchant ship, and the current and potential difference on the rotating shaft were monitored for 30 days. The results showed that the device had excellent performance in reducing the potential difference on the rotating shaft and preventing electrical corrosion. A rotating body consisting of a shaft and a bearing will inevitably generate electrical discharge in electrical machines ,2. The bAs early as 1924, Alger et al. recognized that the drive generates shaft voltage and current . Erdman To solve the electrical corrosion problem, it was very important to supply and control the current to the rotating shaft precisely to balance the potential difference. An algorithm was used to detect the potential difference, calculate the corresponding balance current, and deliver it to the rotating shaft. In this study, a power supply was added to the shaft-grounding device, and PWM technology was used to drive the voltage source inverter (VSI). A complete feedback regulation system was established by designing a potential difference detector, power-control device, monitoring device, and grounding-related accessories. The electrical corrosion protection device of the power supply shaft measured the potential difference between the rotating body and the bearing and calculated the current corresponding to the potential difference flows to the rotating shaft. In this way, the potential difference on the rotating shaft was eliminated, and the various parts of the rotating body were prevented from being burned or damaged from electrolysis, thereby prolonging its service life. The experimental results showed that the anti-corrosion device controlled the potential difference on the rotating shaft within 2 mV, which was superior to the performance of the conventional shaft-grounding device, thus verifying its application to industry. The circuit diagram of the existing marine passive shaft-grounding device and the electrical corrosion protection device of the power supply shaft is shown in According to According to The automatic control potential balance is the core system configuration of the electrical corrosion prevention device. It is mainly composed of a potential difference detector, power-supply control device, monitoring device, and grounding-related accessories. The potential difference detector measures the potential difference between the rotating shaft and the bearing and applies a stable and effective signal detection and noise attenuation protection circuit. It does so by using digital filters to prevent the input signal from causing damage to the system. The power supply control device mainly calculates the current control value and outputs the current to the device between the rotating shaft and the bearing. Like the potential difference sensor, it uses a noise filter and protection circuit to produce precise current output; the monitoring device is mainly a screen control for the storage of potential information including output voltage, current, measured potential difference, system operating status, and alarm. The grounding-related accessories mainly consist of a highly conductive elastic band holder and a grounding brush and holder, which transmits output current. To verify the operation of the rotating shaft anti-corrosion device using a power supply, an experimental bench equipped with the same motor-driven rotating shaft device as a ship or factory equipment was established. By supplying current to the ground and the rotating shaft, a potential difference between the rotating shaft and the bearing was artificially created. The motor frame was fabricated and structurally insulated so that passive- shaft grounding could be done using brushes and brush holders. The experimental bench is shown in The schematic diagram of the experimental setup for the electrical pitting prevention device is shown in The details of the specific coupling device are shown in In the experiment, the same rotating shaft from a ship or factory equipment was used to simulate the working state. As shown in In the second step, the galvanic corrosion protection of the power supply shaft was quantified, and in the third, the electrical corrosion protection device was installed on the commercial hull shaft to further verify its performance. The goal of this study was to control the potential difference on the rotating shaft to within 2 mV. In this study, through three performance tests, an optimal design scheme for the device was proposed, and the 2 mV requirement was realized.To ensure the spark generation condition in the driving shaft with a passive shaft-grounding device and electrical pitting prevention device, the saw-tooth-shaped plate was specially designed and installed as shown in To further verify the performance of the electrical pitting protection device for the rotating shaft of a power supply in a real application, we installed the electrical pitting prevention device on the rotating shaft of a commercial vessel and detected the voltage difference under actual working conditions. The installation is shown in As shown in In this paper, we design the electrical pitting prevention device through two design ideas of ‘dredged’ and ‘resistance’. First, the shaft current is ‘dredged’ through the passive shaft grounding device, and then the automatically controlled balance system input balance current ‘resistance’ the shaft current to balance the shaft potential difference. Finally, the shaft voltage is stably controlled within 2 mV, which can meet most industrial production safety requirements. Compared with the existing shaft electro-corrosion solution, the automatic control balance system algorithm equipped with the electrical pitting prevention device has stable performance and can be applied to equipment with a large shaft potential difference. In order to prevent the equipment from electric corrosion of the rotating shaft, a new equipment design idea is proposed.By measuring the potential difference between the rotating shaft and the bearing, the current corresponding difference was calculated and sent to the rotating shaft, which eliminated the potential difference and prevented electrical corrosion.To generate and remove potential differences within a few mV in rotating machines, a potential difference detector capable of measuring at least 0.005 mV was required. In addition, smooth signals had to be provided by applying noise filters and protection circuits.It was necessary to apply a current-control calculation algorithm to which the PID algorithm was applied as an output current to remove the potential difference as measured by the potential difference detector as basic information.A power-control device, which output the current between the rotary shaft and the bearing as calculated through the control calculation algorithm, was manufactured. Noise filters and protection circuits were applied, and the focus was on maintaining precise current output.A rotating shaft test bench was established to check the performance and operational status of the manufactured parts or devices over time. It took into account various field conditions and is expected to be applied to other research areas of rotating shafts.The electrical corrosion protection device can control the potential difference of the rotating shaft within 2 mV, which means that the current-control algorithm can operate normally. In addition, it was confirmed that it exhibited superior performance compared with that of the existing shaft-grounding device.


Perceptual biases can be interpreted as adverse consequences of optimal processes which otherwise improve system performance. The review presented here focuses on the investigation of inaccuracies in multisensory perception by focusing on the perception of verticality and self-motion, where the vestibular sensory modality has a prominent role. Perception of verticality indicates how the system processes gravity. Thus, it represents an indirect measurement of vestibular perception. Head tilts can lead to biases in perceived verticality, interpreted as the influence of a vestibular prior set at the most common orientation relative to gravity , useful for improving precision when upright . Studies on the perception of verticality across development and in the presence of blindness show that prior acquisition is mediated by visual experience, thus unveiling the fundamental role of visuo-vestibular interconnections across development. Such multisensory interactions can be behaviorally tested with cross-modal aftereffect paradigms which test whether adaptation in one sensory modality induces biases in another, eventually revealing an interconnection between the tested sensory modalities. Such phenomena indicate the presence of multisensory neural mechanisms that constantly function to calibrate self-motion dedicated sensory modalities with each other as well as with the environment. Thus, biases in vestibular perception reveal how the brain optimally adapts to environmental requests, such as spatial navigation and steady changes in the surroundings. When moving through space, our aim is to accomplish a specific goal-directed action, such as reaching the door entrance of a building. To finalize this action, our brain processes all relevant sensory information to build a reliable perceptual representation of physical properties of interest. For instance, the visual system will identify environmental properties, such as the location of the entrance door to the building. This process provides the basis for action to finalize the goal, that is to spatially navigate until we reach the door. While moving, our actions need to be calibrated relative to our perception of the environment. Specifically, in the case of spatial navigation, perception changes as we move through space. We experience a compound of multisensory information that provides our brain with the cues necessary to accomplish our action, e.g., visual , vestibular , haptic/tactile  and auditory . This combination of cues builds a dynamic perceptual representation of the environment as we move through. Although some information has been predefined before performing the action , the environment is subject to changes  and we adapt our action to such changes. Thus, our action needs to be calibrated relative to the perceptual properties of stimulus readout, whereas perception changes as we act in the environment. It follows that such operations mutually influence each other leading to perceptual and motor calibration.From the above presented spatial navigation example, it emerges that perceptual accuracy is fundamental to accomplish actions in the environment and that it is more than the simple product of sensory readout. In order to study this rather complex phenomenon, psychophysical studies take advantage of biases, that is inaccuracies in perception. Such perceptual errors reveal how the brain processes sensory information and permit to infer properties of the brain leading to such inaccuracies. A clear representation of the importance of biases as a side effect of an optimal perceptual system can be observed in 1 and S2) and width (in terms of standard deviation) represents variability. This representation assumes that physical stimuli are grouped in two opponent categories  separated by a reference located at the center of the stimulus space . If the perceptual system is unbiased (in gray), estimates will be equal to the stimulus values, i.e., all data points fall on the diagonal. However, because of perceptual variability, overlaps around the category boundary could lead to category misclassification  as indicated by the gray area between the two Gaussian describing unbiased estimates for S1 and S2. On the contrary, in the biased system (in black), estimates are repelled from the reference thus showing overestimation. Assuming the same perceptual variability as for the unbiased system, overlaps across the category boundary are reduced in the biased system because of the estimates’ shift (S1 and S2 are overestimated). Therefore, discriminability in terms of category classification is improved as the system benefits from the increased separation between the estimates to tell their relevant category. In other words, the ability to discriminate between two different stimuli  as belonging to two different categories  defined by the category boundary would be aided by the overestimation represented in Given this scenario, it can be postulated that discriminability relative to the reference defined by the category boundary would be higher with respect to a system where perceptual space is uniformly distributed relative to the stimulus space. In detail, From the previously mentioned example of spatial navigation, it is clear that not only does our representation of the physical world influence perception, including idiosyncratic differences across individuals , but conTherefore, biases can be considered as the price of optimality for improving other aspects of system performance . Such discrepancies between physical properties of the stimulus and the estimates computed by our brain can be interpreted here as the side effect of a system that operates optimally in two main ways: (1) it avoids misclassification errors by reducing the effect of sensory noise on the readout of stimuli close to the category boundary; (2) it adapts to the changes in the environment by reducing coding redundancy.Often considered as the sixth sense, the vestibular system allows the brain to compute information, such as the direction of gravity and self-motion properties. This system is composed by biomechanical sensors, that can be considered as linear and angular accelerometers that provide information about head movement. The peripheral vestibular organs comprise two main types located in the inner ear in the temporal bones. These are the semicircular canals and the otolith organs see . The thrFrom the vestibular peripheral organs, the signal is directed to the vestibular nuclei in the brainstem. From this site, inertial information is relayed to and processed by several brain structures to maintain posture  and balance, gaze fixation by compensating head movements through the vestibular-ocular reflex (from the vestibular nuclei to motor neurons in the brainstem) and finally body position  and self-motion perception. In the latter case, second order neurons in the brainstem project to thalamic nuclei which project to cortical regions, such as the posterior insula, superior temporal gyrus, inferior parietal lobule , somatosensory cortex, precuneus, cingulate gyrus, frontal cortex , and hippocampus .In the investigation of the vestibular system and perception based on vestibular signals in particular, much research has employed psychophysical methods to investigate the interaction of sensory modalities such as vision, haptic and audition with vestibular. In the context of this review, studies on vestibular perception and its interaction with the other sensory modalities will be presented to elucidate the role of biases in perceptual processing.In situations in which not all sensory modalities are available, the vestibular system may be vulnerable to biases and limitations that reduce its reliability in perceptual judgement. When moving linearly on the earth-horizontal plane while upright, e.g., while driving a car or flying an airplane, the vestibular system codes self-motion as the combination of gravity acting vertically and inertial acceleration acting horizontally on the sensors. Due to Einstein’s principle of equivalence, the otolith organs within the vestibular system are unable to distinguish these two forces. Instead, the combined gravitoinertial vector is sensed see .In order to distinguish the two forces, the brain takes advantage of other sensory information such as the angular velocity signals from the semicircular canals, and environmental information coded via the other senses . In situations where most informative sensory modalities such as vision are unreliable, and rotational components of motion are weak, illusions might occur. For instance, a well-known issue in aviation is the so-called somatogravic illusion in which strong sustained linear acceleration can be misperceived leading to illusory head pitch ,15 see . While fPerception of verticality indicates the ability to correctly process the direction of gravity, in other words graviception. In the context of vestibular perception and the assessment of balancing abilities, the ability of perceiving the direction of gravity is fundamental to maintain balance. Indeed, such an ability is fostered by the ability to detect any deviation from the gravitational axis to avoid falling. Much evidence has shown also the link between altered perceived verticality and clinical conditions that affect the vestibular system from the peripheral to the central processing of balancing information , as wellThe Aubert effect or A-effect  is a welSuch perceptual phenomena were interpreted by Mittelstaedt  as the cA simplified version of Bayesian probabilistic models to predict the A-effect in the perception of verticality ,38 is reX) given the sensory input (Y). In probabilistic terms, this scenario can be represented as follows:In this framework, judgment of own body roll tilt is based on the conditional probability of observing a certain stimulus (P(Y) serves normalization purposes, the equation can be rewritten as follows:P(X|Y) is the posterior proportional to the product between the likelihood P(Y|X), that is the conditional probability of observing the sensory input (Y) given the stimulus (X), and the prior distribution P(X), which indicates a priori knowledge or experience about the occurrence of stimulus (X). In this example, the maximum a posteriori (MAP) rule is used to infer the perceptual estimate, according to which observer’s judgment corresponds to the peak of the posterior distribution.Since As the head is tilted to the side, sensory information transduced by the vestibular system can be represented as a likelihood distribution centered at head tilt see . The estThe development of such a prior corresponding to the upright position may be related to the integration of multiple sensory cues via perceptual experience and redundancies. Although the readout of graviception is mostly vestibular-based as the vestibular system constantly reacts to changes of head orientation with respect to gravity, other sensory modalities definitely play a role in obtaining the association between verticality and the direction of gravity. Let’s consider the orientation of windows, doors, or trees, unless specific cases, these objects tend to be earth-vertical, that is aligned with gravity. Along these lines, gravity priors have been observed in the perception of moving object speed with higher precision for objects moving downward compared to upward . HoweverThe contribution of other sensory modalities in perceived verticality has been studied also with somatosensory loss patients . In partA recent study suggests that haptic judgments of verticality are sensitive to distortions that depend on hand related bias rather than graviception itself . In the Direct measurements of vestibular perception often employ motion simulators that elicit passive self-motion stimulation, e.g., ,50. ThesInterestingly, vestibular heading biases have been confirmed also from a neural perspective. In detail, such biases were predicted with a population vector decoding approach considering the known overrepresentation of lateral directions of movement in brain areas processing heading stimuli, that is MSTd,  . The nonStudies on heading perception have revealed the presence of different pattern of biases depending on factors, such as body and stimulus orientation. Regarding the first, vestibular heading perception in supine position is susceptible to changes compared to the upright position, whilst visual heading accuracy is not affected by body orientation relative to gravity . These fAftereffects can be considered as biases induced by brain adaptation to steady changes in the environment. Therefore, the presence of aftereffects reflects the activation of brain networks underlying the perceptual dimension of interest . In the context of unisensory perception, much research has focused on the investigation of visual motion aftereffects, or MAEs to elucidate the different stages of motion processing in the visual cortex . PercepRegarding vestibular perception, the investigation of within-modality aftereffects has received less attention than in vision. The investigation of aftereffects in self-motion perception has shown the presence of a time window of approximately 3 s within which a passive self-motion stimulus influences accuracy in the perception of a subsequent stimulus . From a In the context of multisensory perception, aftereffects between sensory modalities provide an indistinctive measurement of multisensory interaction at the brain level via purely behavioral stimulation. Specifically, such paradigms unveil cross-modal interactions by testing whether adaptation in one sensory modality influences perception in another tested sensory modality. Much research has investigated such perceptual phenomena, revealing the presence of visual-auditory ,69, audiThe works presented in this review outline how perceptual biases in the vestibular domain can unveil properties of the brain mechanisms that underlie the processing of graviception and self-motion perception. The verticality biases described in


Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects . Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things—the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics. It is the central nervous system, affected to the greatest extent, which manifests in different ways—from major structural defects13 such as corpus callosum hypoplasia, to characteristic neurocognitive and behavioral symptoms17.The term “fetal alcohol spectrum disorders” (FASD) encompasses a spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure (PAE)19. The role of electroencephalography (EEG) as a part of the process of diagnosis is being debated. All three major diagnostic systems include seizure disorder19 as a proof of brain impairment, however they vary in interpretation whether it should be treated as “a hard neurological sign” or just a sign of distorted neurophysiology.The FASD diagnosis is usually established by a group of professionals (physicians and psychologists) according to the clinical criteria22 described the characteristics of EEG patients with FASD. In23 the authors established an increased prevalence of both, epilepsy and seizure episodes among individuals with FASD in a retrospective database study. A prospective study presented by Boronat et. al. in24 demonstrated an increased incidence of EEG abnormalities even among patients without epilepsy, however, the study was performed without a control group.An increased incidence of both, epilepsy and abnormal findings in EEG was documented by several researchers25. This technique enriches neurological interpretation, owing to the fact that it can show a more subtle dysfunction such as concentration and learning disorders or mental and emotional problems. The QEEG has its role in the neuropsychiatric disorders diagnosis, being: epilepsy, stroke, dementia, traumatic brain injury, depression, encephalopathy, learning and attention disorders and many more26.All the aforementioned studies were limited to clinical qualitative analyses only. The quantitative electroencephalography (QEEG) is a type of EEG analysis that involves the use of mathematical algorithms and has brought some new possibilities of EEG signal evaluation, among other things, an analysis of a specific frequency band28 ADHD30 and other childhood conditions32. This study is aimed to investigate the characteristics of the bioelectric activity of the brain in resting-state conditions with quantitative EEG analysis methods.To the best of our knowledge, no studies in which the quantitative analysis of EEG was applied in FASD have ever been published. Quantitative EEG has been studied in other neurodevelopmental disorders such as autism spectrum disorders (ASD)In order to test whether the signal features were statistically different between the control group and the FASD group, the three following types of two-sample statistical tests were performed:U-test; abbreviated as MW in the rest of the publication), which is a non-parametric test and in which the null hypothesis states that the medians of the two groups are equal;Two-sided Wilcoxon rank sum test , which is a non-parametric test, and in which the null-hypothesis states that the data from two groups come from the same distribution;Two-sample Kolmogorov-Smirnov test (abbreviated as KW in the rest of the publication), which is a non-parametric test, and in which the null-hypothesis states that the data from two groups have equal distributions.Kruskal–Wallis test . The inclusion criteria were: FASD diagnosis according to the2 guidelines; age 6–13 years. The exclusion criteria were: a diagnosis of epilepsy, anti-epileptic treatment for other reasons. The group consisted of 12 children  = 9.4 (1.8) years).The study was approved by the Ethical Comitte of Local Board of Physician in Cracow, Poland, on 16th July 2021, approval number: 12/SD) = 9.2 (2.0) years), who were hospitalized for diagnostic purposes and had a diagnosis of a functional abdominal pain. The exclusion criteria were confirmed or unknown prenatal alcohol exposure, a diagnosis of epilepsy, anti-epileptic treatment for other reasons, other neurodevelopmental conditions such as ASD (autism spectrum disorder), ADHD (attention deficit hyperactivity disorder), a known structural brain defect, systemic conditions that could temporarily affect the EEG signal .The controlled group consisted of children from the Department of Pediatric Gastroenterology  from August 2021 to October 2021. Before performing EEG recording, all procedures were explained to both a subject and a legal guardian or parent. The consent form signed by a legal guardian or parent was required prior to the EEG recording. Participants, especially from the experimental group, were asked to leave out the morning dose of any medication that could interfere with the EEG recording outcome. Participants were requested to be well-rested and relaxed on the test date. The recording lasted approximately 10 minutes and was carried out according to the same scheme: eyes open and eyes closed. All subjects were asked to lie still comfortably and in silence, remain relaxed, and instructed to stay awake with their eyes closed during the recording period. EEG was obtained using 19 tin electrodes, with an impedance level set below 36:Delta .It is possible to differentiate the following EEG frequency rangesp-values for MW, KS and KW tests were included in Appendix.To test whether the signal features were statistically different between the control group and the FASD group, as mentioned above—three types of two-sample statistical tests were performed: two-sided Wilcoxon rank sum test (MW), two-sample Kolmogorov-Smirnov test (KS), Kruskal–Wallis test (KW). If all three  tests indicated a significant difference between groups, at 0.05 significance level, data were further analyzed. Full tables of KBL/OIL/2021). The study was carried out in accordance with the Helsinki Declaration.Written informed consent was obtained from a parent or legal guardian prior to EEG recording and oral assent was obtained form children. Appropriate approval from the Ethical Comitte of Local Board of Physician in Cracow (Poland) was obtained on 16th July 2021   and little is known about the stability of this phenomenon. Nooner et al.52 established that the characteristic pattern was observed in QEEG among patients who had been recently subjected to neglect. It is impossible to exclude that patients from our FASD group have a history of abuse or neglect; however, they presented in a setting with adoptive or foster parents and their legal and psychosocial situation had been stable for a significant amount of time, so recent exposure to adverse life events can be excluded.Meiers et al. established that the distribution of alpha rhythm at rest, called alpha asymmetry, is related to neglect and alcohol abuse in adolescents54. Researchers are still debating whether this parameter can be treated as a biomarker of ADHD55. As the ADHD is treated as an important comorbidity of individuals with FASD58. However, the ADHD and FASD partially share the clinical picture, especially in terms of attention deficit59 and a misdiagnosis of a non-dysmorphic FASD being treated as ADHD is not uncommon61.Moreover, an increased theta/beta ratio among patients with FASD seems to be an important finding, as it has been observed that a higher baseline theta/beta ratio occurs in individuals with ADHD59) with FASD and attention deficit (with or without a diagnosis of ADHD) could have contributed to the finding we present. More studies that could correlate clinical data, especially the results of neuropsychological evaluation, should be performed to confirm this effect. Interestingly, theta/beta ratio was also linked to the executive functions. It has been established that a lower theta/beta index is characteristic for developing children with higher levels of executive functions62. The lower theta/beta ratio in the front-line was associated with the greater inhibitory control among adults64.We assume that the deficits observed in both, conditions contribute to similar findings in EEG; however, using this parameter in clinical practice would make the differential diagnosis even more challenging. As neuropsychological problems in FASD differ between patients  and inhibiting the growth of serotonergic neurons72. The influence of PAE on cellular metabolism that reduces glucose uptake is another mechanism that can contribute to impaired brain function that could be reflected in abnormal bioelectric activity.The exact mechanism by which alcohol affects the fetal nervous system remains unknown13 and the evidence supports the crucial role of the corpus callosum in the propagation of bioelectrical impulses73. The patients who participated in the study did not have brain imaging performed, so the presence of structural brain defects can not be confirmed; however, even the defects in the corpus callosum microstructure, not identifiable by brain imaging, can be responsible for the observed effect. Furthermore, as reported by Dylag et al. in74, delayed myelinization of cortical neurons can be observed among individuals with FASD, what might reflect the slow maturation of the neurons in this group of patients75. As the velocity of impulse propagation depends on the myelinization76 this phenomenon can also justify our findings.Moreover, it has been established that the PAE is associated with selected structural brain defects such as corpus callosum hypoplasia21. The study was limited by the absence of clinical data, especially the history of possible neglect or abuse and the results of neuropsychological tests. Furthermore, the lack of data on comorbidities such as ADHD that could complicate the clinical picture and can contribute to alterations in EEG signals can be considered a study limitation.Furthermore, extending the analysis of the neuropsychological data could lead to a more broad spectrum of conclusions and is considered to be carried out in the future.This is one of the first, if not the first study toward exploring the role of QEEG in FASD diagnosis, as this research establishes a quantitative framework to detect QEEG findings, which may be characteristic for the FASD27. The prospective recruitment of the patients and the controlled setting of the experiment used in our pilot study was applied to minimize the risk of potential bias. Caution must be taken in generalizing our results because of the relatively small sample size included in our study. However, this is a pilot study, which provided the promising results, the research will continue. Notwithstanding the relatively limited sample, this work offers valuable insights into the characteristics of bioelectrical activity of the brain in individuals with FASD with quantitative EEG analysis. Further research is required to establish the diagnostic roles of QEEG tools in FASD diagnosis.It should be also mentioned that the physiological variability of the EEG signals among children may affect the final results of the QEEG analysisThe purpose of the current research was to determine the characteristics of the QEEG findings among individuals with FASD. The research has also shown that an increased theta/beta ratio especially in frontal parts of the brain characterizes FASD patients in comparison to the control group. The second major finding was that alpha waves dominate over beta waves, especially in the frontal and temporal parts of the brain. The results of this research support the idea that QEEG might be a cheap and useful diagnostic tool for establishing the diagnosis of FASD. There is abundant room for further progress toward determining the role of quantitative EEG analysis in the FASD diagnosis, especially in terms of other frequency bands analysis, coherence, analysis of connectivity and spatial correlation.Supplementary Information.


DNA topoisomerase I enzymes relieve the torsional strain in DNA; they are essential for fundamental molecular processes such as DNA replication, transcription, recombination, and chromosome condensation; and act by cleaving and then religating DNA strands. Over the past few decades, scientists have focused on the DNA topoisomerases biological functions and established a unique role of Type I DNA topoisomerases in regulating gene expression and DNA chromosome condensation. Moreover, the human enzyme is being investigated as a target for cancer chemotherapy. The active site tyrosine is responsible for initiating two transesterification reactions to cleave and then religate the DNA backbone, allowing the release of superhelical tension. The different steps of the catalytic mechanism are affected by various inhibitors; some of them prevent the interaction between the enzyme and the DNA while others act as poisons, leading to TopI-DNA lesions, breakage of DNA, and eventually cellular death. In this review, our goal is to provide an overview of mechanism of human topoisomerase IB action together with the different types of inhibitors and their effect on the enzyme functionality. TopI enzymes are not dependent on ATP for their catalytic activity, except for reverse gyrase. They are divided according to their structure and mechanism of action: Type IA includes bacterial and archaeal TopI, topoisomerase III, and reverse gyrase, whereas Type IB includes eukaryotic TopI and topoisomerase V. Enzymes belonging to family A form a covalent bond between the 5’-end of the broken strand of the DNA and the catalytic tyrosine, instead the family B form the covalent bond between the 3’-end and the catalytic tyrosine. Type IA topoisomerase requires Mg²+ for the reaction mechanism and relaxes only the negative supercoils, while Type IB appears independent of the Mg²+ and relaxes both positive and negative supercoils. Members of Type IB family do not share any structural or sequence homology with other topoisomerases and they are functionally diverse from the members of Type IA family.Topoisomerase I (TopI) is a class of enzymes responsible for catalyzing the relaxation of supercoiled DNA during cell essential processes such as DNA replication, transcription, recombination, and chromosome condensation,11.This review focuses on human DNA topoisomerase IB (hTopIB) and its mechanism of action. HTopIB catalyzes the relaxation of supercoiled DNA by cleaving, passing, and religating one strand of the DNA. The catalytic active tyrosine (Tyr723) starts the catalytic process through a nucleophilic attack on the backbone of the DNA that results in the breakage of one of the strands with the enzyme covalently attached to the 3’-phosphate forming the cleavage complex. A second nucleophilic attack occurs after changing the linking number, which is driven by the 5’-hydroxy DNA end, resulting in the restoration of intact double strand DNA. After this step, the enzyme is released. The other reason is the study of the hTopIB domains that have been published in the past years and provide valuable insights into how these molecular machines function-15.The interest in studying hTopIB in recent years arises not only because of their important role in managing DNA topology but also for other major reasons. Foremost is the identification of different topoisomerase-targeting drugs, many of which generate cytotoxic lesions by trapping the enzyme in the covalent complex. These studies indicate that the human enzyme is composed of 765 amino acid residues and subdivided into four distinct domains ,18. The N-terminal domain is 24 kDa and composed of 214 amino acids; it constitutes a highly protease-sensitive, hydrophilic, unstructured region of the hTopIB. Crystal structures of different forms of the hTopIB have been determined and show both non-covalent and covalent DNA binding,19,20. The crystal structure was obtained with an N-terminal reduced active form of the enzyme in which the first 174 amino acids are missing. However, the X-ray density was only interpretable beginning from residue 215; therefore, the entire N-terminal domain is still not crystallized. Redinbo et al. determined the crystal structure of a form of the enzyme where the structure extends back to amino acid residue 203 containing 12 residues of the N-terminal domain. Studies also suggest that residues 191-206 are essential for binding of DNA and processing the enzyme functionality. The N-terminal domain is important for catalysis; this theory is also accordant with the study of crystal structure of hTopIB extending to amino acid position 203, which shows a close contact between Trp205 and Gly437 of the core domain to permit rotation. The N-terminal domain contains four basic NLSs  and one acidic NLS. It serves as the site for interaction with different cellular proteins such as nucleolin, SV40 T-antigen, certain transcription factors, p53, and the WRN protein-24. The N-terminal domain is followed by the core domain, which is 54 kDa and is a conserved domain . This domain contains all the catalytic residues  except the active site tyrosine 723 [Figure 1]. Based on the structure, this domain is further divided into three subdomains. Subdomains I (215-232 and 320-433) and II (233-319) form a “cap” region containing a pair of a-helices called the “nose cone”. The core subdomain III (434-635) forms the “cat” region [Figure 2]. As shown in , to close the enzyme around the DNA. HTopIB clamps around the DNA allowing the interaction of lips through a non-covalent interaction between the carboxylic lateral group of Glu497 and the side-chain amino group of Lys369. Opening and closing of the protein clamp during DNA binding and release must involve the breaking of this interaction between the lips and the lifting of the cap away from the base. The linker domain is positively charged and incorporates residue 636-712 with a molecular weight of 5 kDa -30. The linker domain has been demonstrated to be in direct contact with the DNA and is one of the most flexible protein regions, as evidenced by multiple non-isomorphous crystal structures and MD simulation. The most important role played by this domain is its contribution in the process of controlled rotation. The charged residues on the linker domain and nose cone form an electrostatic interaction with the DNA, allowing a free rotation surrounding the protein. The C-terminal domain is an 8-kDa highly conserved domain and contains the catalytically active site Tyr723 . Cleavage of the DNA backbone is performed by nucleophilic attack of Tyr723 on the scissile phosphate; a phosphodiester link is generated between the tyrosine and the 3’ phosphate, releasing a 5’ hydroxyl. The C-terminal region has also been shown to bind ATP (adenosine triphosphate) and carry out splicing factor phosphorylating activity.The structure of the hTopIB (PDB ID 1A36) has been resolved based on different studies such as conservation of sequence, sensitivity to limited proteolysis, hydrodynamic properties, and fragment reconstitution experiments.The topoisomerization reaction begins with the binding of hTopIB to the duplex DNA. For the binding to happen, the enzyme should initially exist in an “open” conformation, which is most likely achieved by a hinge-bending motion situated at the edge between core subdomains I and III (residue Pro431) and the boundary between helices 8 and 9 (residue Lys452). This region of the protein is sensitive to proteolysis when the DNA is not present but becomes resistant upon DNA binding. The binding step occurs through the interaction of the charges present on the surface of the enzyme and DNA. This step culminates with the protein that completely embraces the DNA as a “clamp” such that the lips of core subdomains I and III touch each other. As a result of this event, the active site residues are arranged in position for attacking the scissile phosphate, which leads to the cleavage of the strand and formation of the covalent attachment between the enzyme and the 3’ end of the DNA. Once the covalent intermediate has been formed, the release of superhelical tension can occur through one or more cycles of controlled rotation, which involves the ionic charge interactions between the DNA and both the nose cone helices and the linker domain. Consequently, the religation occurs with a nucleophilic attack driven now from the OH’ of the scissile phosphate on the covalent intermediate between the enzyme and the 3’ DNA. Finally, a DNA molecule with a decreased superhelicity is released, hereby allowing the enzyme to undergo another round of DNA binding and relaxation [Figure 3]Bituminaria bituminosa, was found to inhibit the cleavage in the pre incubation with the enzyme and the compound. Et-KuQ, a pentacyclic-diquinoid synthetic compound, was shown to inhibit the cleavage step of the enzyme reaction efficiently with a mechanism similar to EryC. Once the cleavage occurs, small molecules are found to target hTopIB by stabilizing the cleavable complex et al. identified a novel inhibitor that particularly inhibits the cleavage activity of hTopIB instead of allowing the formation of drug-enzyme-DNA covalent ternary complex. Another study shows the work on betulinic acid, which inhibits the cleavage kinetics of hTopIB by preventing the formation of apoptotic TopI-DNA complexes. The religation step remains the most studied step in the literature because the mechanism behind it is very fascinating. Most of the inhibitors discovered are inhibitors that do not react on the DNA or on the enzyme separately but only after the enzyme cleaves the DNA, transforming the enzyme into a poison -44. There is another class of inhibitors called Indolocarbazoles, which showed potent non-CPT hTopIB poisons initially but further studies showed that they act on other cellular target besides hTopIB,46.The different steps of the hTopIB catalysis are significant events during the cell cycle since cell vitality can be seriously affected by poisoning these steps. As shown in et al. previously demonstrated that deletion of the first 206 amino acids of hTopIB makes the enzyme insensitive towards CPT in the relaxation. The study of mutant Glu418Lys showed for the first time the occurrence of different cleavage substrate specificity, thereby confirming the crucial role of core subdomain I in the recognition of the substrate and the requirement of T base at the -1 position of the scissile strand for binding of CPT. Chrencik et al. suggested that six residues  present in the Lip1-Lip2 region harbor a CPT resistant mutations cluster. Other studies on the Gly363 mutation show the role of this residue in CPT sensitivity,51. Even though the linker domain is located far away from the active site of Tyr723, it can deeply affect the drug resistance or sensitivity of hTopIB. The main reason for this long-distance interaction is its flexibility. Evidence of these phenomena comes from several works where key residues involved in the mobility of the domain were mutated. The mutation of Ala653Pro has demonstrated that a large flexibility of the linker domain is associated to CPT resistance. This correlation has been shown also in the Glu710Gly mutation. This paper demonstrated that a lower degree of linker motion leads to an increase of religation rate and consequently to CPT resistance. On the other hand, in the case of Asp677Gly-Val703Ile double mutant, the reduction of the linker flexibility confers a hypersensitivity to CPT. Other evidence of linker involvement in enzyme religation rate comes from the construction of a chimeric enzyme, in which the human enzyme contains a linker from Plasmodium falciparum TopI (PfTopI). In the chimeric enzyme, the PfTopI linker shows a great flexibility and confers the chimeric enzyme to be CPT resistant. Moreover, inserting a long yeast linker domain in hTopIB drastically altered enzyme function in vivo. Expression of this chimera was toxic in yeast even in the absence of CPT, with no particular changes in enzyme catalysis. Altering the linker structure, either through changing its flexibility by the mutation of specific residues or by deleting the entire domain, affects the enzyme rate and consequently the CPT reactivity. An additional residue that was shown to impact CPT reactivity is Thr729, which is part of the hydrophobic pocket present in the C-terminal domain. Losasso et al. studied this residue extensively by producing different mutations, namely Thr729Ala, Thr729Glu, Thr729Lys, and Thr729Pro, and showed the importance of this residue in modulation of hTopIB DNA binding and drug resistance. Residue Asn722, which is present near the Thr729 residue, was also shown to impart CPT resistance to the protein,58.In the last part of this review, we mention some of the most important studies related to the residues affecting drug reactivity. Lisby HTopIB is still a fascinating enzyme that is worth studying. However, some considerations should be given to try to focus the attention of the scientific community. Many inhibitors of the binding and cleavage phases have never entered into clinical protocols. The most important step for inhibition remains the religation and compounds that reversibly inhibit this phase of the enzyme catalysis. The only derivatives of CPT that are now in clinical use targeting hTopIB are still Topotecan and Irinotecan. It is probably time to look for other natural compounds that could inhibit this enzyme. Our group is focusing on a metabolite from an Antarctic sponge that was found to be a potent inhibitor of hTopIB (unpublished results). Perhaps it is time to study and search different ecosystems to obtain promising drugs for cancer therapy, as sometimes it is from nature that we have the best solution.


Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical. As the energy crisis worsens, more and more renewable energy resources are being connected to the power system . Due to Existing NILM methods are mainly used for residential loads . CommercTo sum up, traditional classification and regression algorithms still dominate non-intrusive commercial load monitoring. These methods typically require manual feature extraction using domain expert knowledge and are less transferable . Additio1.The proposed deep learning method for non-intrusive commercial load monitoring is based on a multi-label classification task, which can simultaneously identify the operating status of multiple commercial electrical devices and decompose the power consumption of multiple devices, reducing the time cost of existing commercial NILM methods. Moreover, the label correlation and class imbalance problems are solved from the model framework and training method, respectively, to improve monitoring accuracy.2.Compared with the existing models, the encoder part of TTRNet designs the structure of stacking multiple identical blocks, and each block is composed of a transformer encoder and max-pooling to automatically extract the characteristics of the total input power sequence. A Temporal Pooling block is added between the encoder and decoder to provide more detailed features by adding contextual information when identifying the activation state. Finally, RethinkNet is introduced in the decoder part to enhance the learning of interrelationships between each power-consuming device and improve the accuracy of multi-label classification.3.By improving the existing single-label Focal Loss for multi-label classification tasks, not only the load imbalance problem in NILM is solved, but also the accuracy of hard-to-identify loads is improved.In response to the above challenges, this paper proposes a deep learning method for non-intrusive commercial load monitoring, which can directly obtain the operating status and power consumption of multiple internal electrical devices from the overall power consumption of commercial buildings without a complex features project. The method includes a novel deep learning framework called Transformer-Temporal Pooling-RethinkNet (TTRNet) and a novel loss function called Multi-Label Focal Loss (MLFL). Considering the need for commercial loads to participate in demand response and the power consumption logic of commercial customers, two different NILM evaluation scenarios, called “seen” and “unseen”, were created and tested to demonstrate that the proposed method can achieve high performance with good transferability in terms of equipment state identification and energy decomposition. The following are the main contributions of this work:The rest of this paper is organized as follows: Recurrent Neural Networks (RNN) and their variants have been the dominant method for solving problems with sequence data. However, the structure of RNN leads to weaknesses in parallel computing. In addition, one of the biggest drawbacks of RNN is the problem of vanishing gradients and exploding gradients when the sequence is too long. To overcome these limitations, the attention mechanism is introduced as a solution . On thisIn recent years, multi-label classification methods have become increasingly popular in NILM. Instead of obtaining the power consumption of individual electrical devices for training purposes, the multi-label classification-based NILM approach requires only the operational status of individual devices, making the load monitoring process truly non-intrusive. In addition, device status can be easily obtained from automated smart building records or from commercial building managers. Moreover, this method can output multiple loads simultaneously, which can reduce the time cost and facilitate the practical application of NILM disaggregated data. Massidda et al.  proposedLabel correlation and category imbalance are two major problems in multi-label classification research. Usually, one expects to improve the correct rate of multi-label classification by learning the correlation between labels or solving the category imbalance. Yang et al.  envisionThe method proposed in this paper has two main parts: a neural network architecture, TTRNet, and a loss function, MLFL. Where TTRNet consists of four components: Input Embedding, Transformer, Temporal Pooling, and RethinkNet, the overall structure is shown in First, perform input embedding on the aggregated loads. Because the Transformer cannot learn the order of the input information, the loading sequence cannot be passed directly to the Transformer part. Input embedding consists of two parts: value embedding and positional encoding.The value embedding transforms the input sequence into a 256-dimensional vector using a 1D convolution with a kernel width of 3 and a stride of 1.The positional encoding is used to generate the relative and absolute position information for the input sequence.Finally, the feature vector of the value embedding transformation is added to the position vector generated by the positional encoding to add position information to the input sequence.The Transformer part of TTRNet, i.e., the encoder part of TTRNet, is used to extract input features. This part uses the Transformer’s encoder structure. It consists of a stack of N identical modified encoder blocks, each of which includes a Transformer encoder layer and a max-pooling layer, whose structure is shown in Each Transformer encoder layer consists of two sublayers. The first layer is multi-head attention, and the second layer is a feedforward network. Each sublayer performs skip connections followed by layer normalization. Multi-head attention consists of several parallel self-attentions. Self-attention operates on the input Q, K, and V matrices. First, Q and K are multiplied, then divided by the square root of the hidden size; softmax is used to generate soft attention and multiplied by V to get the final weighted value matrix.The effect of each max-pooling layer is to halve the temporal resolution of the output of the previous Transformer encoder layer. In this study, 3 Transformer blocks were used, so the temporal resolution of the input sequence was eventually reduced to one-eighth of its original value.TTRNet adds a Temporal Pooling between the traditional encoder-decoder structure for introducing more contextual information, which is shown in As shown in the figure above, the Temporal Pooling module has five channels, one of which does nothing and keeps the original features unchanged. The remaining four channels perform Temporal Pooling operations. In these four channels, feature vectors are first passed through average pooling layers with kernel sizes of 5, 10, 20, and 30 to reduce the temporal resolution. Then, each channel performs a 1D convolution to reduce the feature dimension to a quarter of the original dimension. The Temporal Pooling operation for each channel can be expressed as:At the end of each of the four channels, linear upsampling is performed so that the output of the Temporal Pooling block has the same precise time resolution as the encoder output. Finally, the five channels are concatenated to form a new feature vector. The total number of features with contextual information is doubled compared to the number of features fed into the Temporal Pooling module.The main role of the RethinkNet module is to decode the previously extracted feature vector into an output sequence with multiple labels. The output of the Temporal Pooling module is the input to the transposed convolution, which uses a kernel size and stride of 8 to increase the temporal resolution of features while reducing the number of features. After layer normalization, these features are fed into multiple recurrent networks. Multiple recurrent network layers are arranged in parallel, the input of each layer is from the transposed convolution, and the hidden state of each layer is from the previous recurrent network layer.RethinkNet is a memory label correlation model across recursive network layers. RethinkNet forms initial guesses in the first recursive grid layer stores them in memory and iteratively corrects them using label correlation. Each layer of the recursive network is an iterative process that “rethinking” through multiple iterations.In this study, a long short-term memory network (LSTM) was selected as the recurrent network layer. This is because LSTMs can guarantee good results when dealing with long sequences of data no matter how many iterations are made. In this study, four LSTM layers were used. The output of LSTM is linearly transformed by the fully connected layer, and finally activated by the sigmoid function to realize multi-label classification.In practical applications, existing methods often perform poorly in practice due to the different proportions of different operating states of different electrical equipment of commercial customers. This can be solved by changing the loss function. Larger weights are assigned to smaller proportions of samples, and smaller weights are assigned to larger proportions of samples. Increasing the proportion of small-scale samples in the overall loss function guides network training to favor small-scale samples, thereby improving the classification accuracy of small-scale samples.In addition to this, in the later stages of training, the most identifiable loads are correctly classified, while only a few challenging loads are misclassified. Likewise, the classification accuracy of hard-to-identify loads can be improved by weighting.lth label. Focal Loss is an excellent solution to this problem, but the original Focal Loss was applied to single-label classification tasks. In this study, Focal Loss is improved by designing weights for each label separately, making it usable for multi-label classification tasks. The improved MLFL is shown below:This section focuses on the experiments performed to verify the effectiveness of the proposed method. In this study, the above methods are experimented with using the Commercial Building Energy Dataset (COMBED) . This daIn the field of NILM, it is common to use not only real data but also synthetic data in order to improve the generalization performance of the model . Here, dData for each commercial air conditioning unit and each commercial building in the dataset were preprocessed before the experiments were performed. The first step filters measurement errors by removing outliers from the active power measurement data for each air conditioning unit, limiting the maximum power to the values provided in λ . HoweverThis study was conducted under the following two different NILM assessment scenarios. (1) Seen. The composition of the commercial load is known; that is, trained on multiple loads in multiple commercial buildings, tested on multiple loads in one of the commercial buildings; (2) Unseen. The composition of the commercial load is unknown; that is, trained on multiple loads in multiple commercial buildings and tested on multiple loads in another commercial building which is not trained. This scenario is more in line with practical applications.Therefore, each commercial building in the dataset described earlier was divided into three consecutive time periods containing 70%, 15%, and 15% of the measurements; 70% is used for training, and the remaining 30% is used for validation and testing. It is important to note that the tests in the unseen scenario were performed on the 100% value of the building. The specific division of the dataset in this study is shown in In the seen scenario, we train the first part of building 1 using real data and the first part of buildings 2, 3, 4, and 5 using synthetic data. We verify the second part of Building 1. During the validation phase, the network parameters are saved when a new minimum value is reached throughout the training period. Finally, we test the third part of Building 1. Training and testing the model in this context enables the evaluation of the model’s ability to identify and decompose the load when the composition of the commercial load is known.In the unseen scenario, we train the first part of buildings 1 and 5, validate the second part of buildings 1 and 5, and also save the network weights when the loss function reaches a new minimum. Building 2 is used to simulate an unknown building and test all of its data. Training and testing the model, in this case, enables the evaluation of the generalization performance of the model. Since the original purpose of NILM research is to apply to unknown buildings in real life, the results in this scenario have greater application value.All experiments are performed on a Linux host with the following specifications, CPU: 15-core AMD EPYC 7543 32-core processor 30 GB; Graphics: RTX A5000*1 24 GB. In these two different evaluation scenarios, the network parameters are optimized using the Adam optimization method, which uses the gradient descent technique with a learning rate of 10–5 and a batch size of 32. The above hyperparameter combinations do not reflect the maximum accuracy of the test case, as the purpose of this experiment is mainly to verify the effectiveness of the proposed technique.In this paper, five event detection (ED) metrics and two energy estimation (EE) metrics are used to comprehensively evaluate the performance of the NILM method.The ED indicator measures the performance of the algorithm in detecting device activation. True Positives (TP) are the number of moments when AHU is correctly identified as active. True Negative (TN) is the number of moments when the AHU is correctly assessed to be closed. False Positives (FP) represent the number of moments when the AHU was not working but was reported as working. False Negatives (FN) are the number of moments when the AHU actually worked but was incorrectly evaluated as off. Precision  represenThe EE metric estimates the precise amount of energy consumed by the device. This paper uses mean absolute error (MAE)  and signThis section describes and explains the experimental results for two different NILM evaluation scenarios, the seen and unseen scenarios, respectively.A total of twenty experiments were performed, and the results were averaged. The 90% interval for multiple results is also reported to show the stability of the method. Then, the output states are compared with the real states, respectively. Twenty experiments were also conducted in the unseen scenario. As can be seen from the above table, the method performs relatively poorly in estimating the instantaneous power in the unseen scenario. This study aims to enable non-intrusive monitoring of multiple commercial loads simultaneously. According to the research results, in the see scene: the average score of F1 is 0.957, the average score of SAE is −0.049, and in the unseen scene: the average score of F1 is 0.904, and the average score of SAE is −0.058. The above results show that the method proposed in this paper has achieved good results in the state identification and power decomposition of all four commercial loads, indicating that the method can achieve the preliminary purpose of the experiment.Furthermore, the basic idea of this study is to improve monitoring accuracy by considering the correlation and imbalance of commercial loads. In this approach, correlations are solved by RethinkNet and imbalances are solved by MLFL. To this end, extensive ablation studies were performed to verify the role of each component, and the results are shown in The TP-NILM  was usedThis method is also thoroughly compared with existing NILM methods based on multi-label classification. Here, the existing methods are chosen from CNN and TP-NILM, i.e., Model A and the baseline mentioned before. By comparing the seven performance metrics with existing methods, most of the metrics of TTRNet are far better than other methods, proving that the comprehensive performance of the proposed method in this paper is the best. However, the present method still has some shortcomings. This method is slightly less effective in identifying AHU1, and the specific reasons have been explained above. The most significant advantage of this method is its good power estimation performance while ensuring the correct identification of the load state, especially for high power loads. The MAE of AHU5 in the seen scenario is 380.09, which is 123.08% better than the 847.91 of TP-NILM, and the MAE of AHU5 in the unseen scenario is 592.14, which is 781.18% better than the 1055.06 of TP-NILM. The evaluation of the unseen scenario is more meaningful because it is consistent with the application of NILM in real life. In this scenario, the average F1 score of TTRNet is 0.904, which is 1.92% better than the 0.887 of TP-NILM. Although the results show that the improvement of the proposed method over existing methods is not particularly large, the main reason is that the data of the used public dataset are relatively ideal. In this case, the advantages of this method are still reflected, and the effect of this method will be more obvious when it is applied to more realistic data in the future. To sum up, the method proposed in this paper has higher practical value than existing methods.From the perspective of demand response, this paper introduces the research value of NILM for commercial loads. Then, this paper summarizes the algorithms used and the problems of existing non-invasive commercial load monitoring methods. It is found that the field is currently stuck in traditional algorithm research. These traditional algorithms cannot meet the requirements of today’s demand response, which requires the simultaneous acquisition of multiple loads. Further, the correlation and imbalance of commercial loads are the main issues that limit the application of non-intrusive methods. Therefore, this paper proposes a deep learning approach to address several such problems simultaneously.The main idea of the approach is to propose a deep learning network, TTRNet, whose inputs aggregate commercial load information and end-to-end directly outputs the subcommercial load sequences contained therein. The design of TTRNet is divided into three main steps. First, a deformer-based encoder is designed to enhance the extraction of time series features by invoking and improving the latest techniques in NLP. Secondly, a Temporal Pooling module is designed to pool features extracted from the encoder at different time scales, thus increasing the perceptual domain without affecting the temporal resolution of the output. Finally, a RethinkNet module is designed to utilize its unique rethinking process to learn relevance while achieving the final multi-label classification. The original Focal Loss is improved to adapt it to multi-label classification tasks and solve the load imbalance problem and improve the accuracy of challenging loads.In this study, the method was tested using the public dataset COMBED. To simulate a realistic NILM application environment, a seen scenario and an unseen scenario were designed separately. Then, several experiments were conducted under these two different evaluation scenarios. Not only ED metrics but also EE metrics were used to evaluate the performance of the method comprehensively. The experimental results show that the method has high performance and stability in identifying the activation state, estimating the instantaneous power and total power in both seen and unseen scenarios. In addition, ablation experiments were conducted in this study to demonstrate the role of each component. The experimental results show that considering the correlation and imbalance of loads can effectively improve the accuracy of the NILM method for non-invasive commercial loads. This not only provides a theoretical basis for the NILM study of commercial loads but also provides new ideas for the NILM study of other types of loads.In future work, two approaches will be taken: First, the study will be extended to multivariate heterogeneous commercial loads. This study can be seen as a study of multivariate homogeneous loads. More factors need to be considered to realize NILM for multivariate heterogeneous commercial loads based on this study. Second, the research approach will focus on lightweight models to speed up training and inference and investigate more efficient optimization processes to improve the quality of monitoring for challenging loads.


It was concluded that the oscillometric signal can be used to evaluate brachial artery diameter. Based on the clinical relationship between brachial artery diameter and cardiovascular risk factors such as hypertension, diabetes, and obesity, this study showed and verified a novel method to monitor brachial artery diameter and hence, cardiovascular risks while measuring blood pressure.Cardiovascular diseases are the leading cause of global deaths, making cardiovascular health monitoring important. Measuring blood pressure using an automatic sphygmomanometer is the most widely used method to monitor cardiovascular health due to its accessibility, convenience, and strong correlation with cardiovascular diseases. In this work, in order to estimate brachial artery diameter, stiffness, or thickness using an automatic sphygmomanometer, the correlation between upper arm parameters and the oscillometric signal was intensively investigated through analytical, numerical, and experimental approaches. The parametric studies commonly revealed that the inner radius of the brachial artery is the most influential parameter in determining the amplitude of the oscillometric signal. The experimental results of using a cardiovascular simulator (a  The major causes of such CVDs are arteriosclerosis and atherosclerosis3 where the vessels become narrow and stiff, hindering blood flow. Such changes in the vessels are accompanied by an increase in blood pressure (BP), leading to hypertension, which is a representative symptom and factor of CVDs4. Since such degenerative changes in vessels are progressive and lead to severe diseases, monitoring vessel health is essential for the early diagnosis and treatment of CVDs.Cardiovascular diseases (CVDs) are the number one cause of global death. In 2019, nearly 18 million people died from CVDs, which accounts for almost one-third of the total death6, brachial-ankle8, or finger-toe9—and calculating the transit time the pulse took to propagate. Another method to estimate vessel stiffness includes the augmentation index (AIx), which is calculated from the blood pressure waveform obtained invasively with a catheter or non-invasively with tonometry12. Angiography is a well-known examination to check the vessel diameter and hence, the level of occlusion13. However, as those methods need clinicians and special medical devices, they are not proper for a regular, frequent check-up of vessel health from the user aspect. Hence, recent studies focus on extracting vessel-related information from more accessible devices or signals. One of the most widely studied signals is photoplethysmography (PPG)21, where the contour of the pulse wave can be obtained from the fingertip or wrist. Even though PPG signals can be easily recorded through wearable devices, the signals are vulnerable to small movement, making use of the signals for in-depth evaluation inappropriate.So far, monitoring the vessel health has been done by checking the stiffness and occlusion of vessels which are examined in the hospital with dedicated medical devices. A typical method of measuring arterial stiffness is pulse wave velocity (PWV) by measuring the pulse at two different positions—for instance, carotid-femoral4, measuring BP on a regular basis is recommended for those with a high CVD risk. Moreover, the oscillometric signal obtained during the BP measurement, as shown in Fig. In contrast, BP, which is one of the simplest but most powerful indicators of blood vessel health, can be easily measured using automatic sphygmomanometer without clinician assistance. Indeed, due to the strongest evidence for causation of CVDs26 or the simple hardware simulator that can calibrate an automatic sphygmomanometer30 to improve automatic sphygmomanometer accuracy. Some research has used the oscillometric signal to investigate cardiovascular-related parameters such as vascular endothelial function2. However, as far as we know, no previous work has investigated oscillometric signals with use of a cardiovascular simulator and phantoms with the aim of analyzing the correlation between oscillometric signals and cardiovascular-related parameters. Thus, with the ultimate goal of developing an oscillometric-based sphygmomanometer that can monitor blood vessel health, our work utilized analytical, numerical modeling, and experimental approaches, and examined the correlation between oscillometric signals and upper arm parameters. Analytical modeling with a simplified, axi-symmetric upper arm model was used to reveal the underlying dominant physics in the pressure transmission from the brachial artery to the air cuff. Finite element method (FEM)-based analyses with a simplified upper arm model and a magnetic resonance imaging (MRI)-based, complicated model were additionally investigated to undertake more detailed parametric studies that could not be considered in the analytical model. An experiment with a cardiovascular simulator and upper arm phantoms fabricated using 3D-printing molds that mimicked the human circulatory system and upper arm was conducted as mutual verification of the parametric study results from the MRI-based FEM and the experiment.Previous studies regarding oscillometric signals have focused on either the modeling of the blood pressure measurement processThis work had two main objectives. First, with the understanding of the oscillometric signal and with the upper arm composition, parametric studies between the upper arm parameters and the oscillometric signal were carried out to identify the parameters that affected the oscillometric signal the most. The result was verified by physical experiment with the phantoms and cardiovascular simulator. Second, based on the results from the parametric studies verified by experiment, but utilizing the results in a reverse way, it was examined whether vessel diameter, elasticity, and stiffness could be extracted from the oscillometric signal. The results confirmed a high correlation between the oscillometric signal amplitude and the inner radius of the brachial artery, demonstrating that it is possible to extract brachial artery diameter from oscillometric signals.31 asIn order to identify the effect of upper arm parameters on pressure transmission, the correlation between the upper arm parameters and the oscillometric signal was analyzed via parametric studies with various 2D models , where it can make various input waveforms to the system, and the tubing system (the artery system), where the material and the formation are similar to the human circulatory system. An example of a brachial blood pressure waveform created by the cardiovascular simulator is shown in Fig. The cardiovascular simulatorThe brachial blood pressure waveform was reproduced from the cardiovascular simulator, and the phantom was installed in the upper arm position in the simulator. A sphygmomanometer cuff was put around the phantom to replicate a standard blood pressure test on a real arm and the corresponding oscillometric signals were collected through the sphygmomanometer. The pressure wave right before the phantom was monitored through the whole experiment Fig. a. Figure33 (MAA), which is the most generally used algorithm for commercial automatic sphygmomanometers. In the MAA, the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were estimated to be the cuff pressures that matched to the amplitudes of certain ratios to the maximum amplitude of the oscillometric signal. According to Geddes33, the most generic ratios are 0.55 before maximum for SBP and 0.82 after maximum for DBP. Therefore, the point where the amplitude is 0.55 of the maximum amplitude before maximum value, and the point where the amplitude is 0.82 of the maximum amplitude after the maximum are set as each end of the meaningful range. From here, the amplitude and the shape of the oscillometric signal could be evaluated.Unlike the signals obtained from the simulation with modeling, each oscillometric signal obtained from the experiment had a different time length depending on the maximum pressure the device inflated. Hence, in this work, a “meaningful range” was set Fig. a, where The amplitude of the oscillometric signal was evaluated by the mean amplitude of the signal envelop within the meaningful range, as shown in Fig. p of the maximum amplitude within the meaningful range, as shown in Fig. The shape of the oscillometric signal was compared by the relative position Figure The correlation analysis results based on the amplitude index with the analytical model, the simple FEM model, the MRI-based FEM model, and the experimental phantoms in the cardiovascular simulator are shown in Fig. In Fig. Figure The correlation analysis results based on the shape index show no clear trends in that the changes in the upper arm parameters do not trigger change in the shape index of the oscillometric signal. In Fig. The main result obtained from the analytical/FEM modeling and the experiment is that the artery radius affects the amplitude of the oscillometric signal the most, and the shape of the oscillometric signal is relatively impervious to the variation of upper arm parameters. As seen in Eqs.  and 3),,3), if tThe shape of the oscillometric signal shows no noticeable trends with the upper arm parameters; the shape index showed insignificant change (less than 3.5% change in shape index with 10% change of parameters) with no specific tendency with increasing (decreasing) parameters, like the case described in Fig. Considering the case in which a person measures their blood pressure at rest every time but that the amplitude of the oscillometric signals tend to decrease as times goes by, then this can alert the person to the possibility of a narrowed vessel. Although the change in the artery radius might not be significant, the long-term trend may help to predict any change in the artery radius such as atherosclerosis. It will be appropriate if the person measures their blood pressure regularly for a certain duration of time and store the data, and any trend in the amplitude of the oscillometric signal may give extra information about the artery radius other than blood pressure alone. The inner radius of the artery is important, since it is closely related to CVDs. In fact, atherosclerosis, which results in narrowed blood vessel, is one of the most common factors that causes complications such as coronary artery diseases, carotid artery diseases, or aneurysms, depending on which arteries are blocked. Therefore, regular blood vessel checks will help to detect diseases caused by blood vessel deformation before they have progressed too far.In order to have more clinically meaningful implications, further studies should be conducted. Notably, this work considered brachial artery parameters only. However, degenerative changes in blood vessels are not limited to the brachial artery; atherosclerosis can occur anywhere where blood vessels exist, including in coronary arteries, the aorta, and peripheral arteries. Thus, studies regarding the location where vessel deformation occurs the most should be conducted, and whether parameter changes in other arteries other than the brachial artery can be detected from oscillometric signals obtained at the upper arm should be studied. Indeed, parameters related to larger arteries like the aorta have a more crucial meaning clinically. Therefore, further research should be conducted with the cardiovascular simulator introduced in Fig. 40 have been reported, suggesting the brachial artery diameter as a potential tool to predict cardiovascular events. Also, previous studies regarding the correlation between the brachial artery diameter and right ventricle (RV) mass, RV end-diastolic volume (RVEDV), RV ejection fraction (RVEF)41, and coronary artery disease42 showed the clinical relevance of the brachial artery diameter with cardiovascular events. Considering that the brachial artery diameter is assessed by ultrasonography in current clinical practice, extracting the brachial artery radius from an automatic sphygmomanometer will be helpful for the early diagnosis and treatment of cardiovascular events reported in the previous works42.Nevertheless, the brachial artery diameter itself possesses clinical importance, too. The relationships between the brachial artery diameter and cardiovascular risk such as age, body mass index, systolic blood pressure, diastolic blood pressure, triglyceride level, high-density lipoprotein (HDL) cholesterol level, glucose level, as well as Framingham risk scoreIn summary, this work shows the correlation between the inner radius of the brachial artery and the oscillometric signal, suggesting the possibility of utilizing oscillometric signal to monitor the brachial artery diameter. This work is the first step to further research and develop the use of oscillometric signals as a cardiovascular health monitoring index.In this work, the correlation between upper arm parameters and oscillometric signals were studied. In order to analyze the correlation, parametric studies based on analytical and numerical modeling were conducted. The parametric study results showed that the inner radius of the artery has the most influence on the amplitude of the oscillometric signal among the upper arm parameters, whereas the shape of the oscillometric signal shows no significant changes with different upper arm parameters. After the parametric studies with modeling, upper arm phantoms with various artery inner radii were fabricated in order to verify the effect of the parameters on the amplitude of the actual oscillometric signal through the experiments. The experimental result shows that the radius of the artery affects the amplitude of the oscillometric signal the most, giving an average of 6.5% change in the amplitude when the radius is changed by 10%, while the shape of the oscillometric signal obtained from the experiments shows trivial changes. Throughout the modeling and the experiments, the correlation between the amplitude of the oscillometric signal and the artery parameters are shown and the results demonstrate the possibility that the oscillometric signal can be used as a monitor of brachial artery diameter.


Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progression, many machine learning models have been developed in recent years to automate the histopathological classification of the different types of carcinomas. However, many of them are not scalable to large-scale datasets.In this study, we propose the novel Primal-Dual Multi-Instance Support Vector Machine to determine which tissue segments in an image exhibit an indication of an abnormality. We derive an efficient optimization algorithm for the proposed objective by bypassing the quadratic programming and least-squares problems, which are commonly employed to optimize Support Vector Machine models. The proposed method is computationally efficient, thereby it is scalable to large-scale datasets. We applied our method to the public BreaKHis dataset and achieved promising prediction performance and scalability for histopathological classification.https://1drv.ms/u/s!AiFpD21bgf2wgRLbQq08ixD0SgRD?e=OpqEmY.Software is publicly available at: Bioinformatics online. Every year, approximately 250 000 women in the United States are diagnosed with breast cancer . DiffereModern medical procedures and technologies have increased the number of biopsies performed, and consequently, the number of histopathological images collected has increased far beyond the reasonable workload of pathologists  and ZernpdMISVM) method  has emerged to determine whether an image shows any indication of carcinoma and, if so, where the abnormality is located within the histopathological image. One of the widely used approaches is to use Convolutional Neural Networks (CNNs) trained by the patches extracted from WSIs. CNN is the combination of convolutional layers and consecutive fully connected layers and their concept comes from the working principle of receptive fields and neurons of the human eye and brain. In light of the above issue, multiple instance learning would be the better choice for the disease detection applications, and these types of algorithms have also been evaluated on the BreaKHis dataset previously. This is because, in a SIL model, classification becomes difficult when a single patch of insignificant region on the image is given. Meanwhile, MIL models enable the correct classification from some important patches, even if most patches do not include indication of carcinoma. To deal with the multi-instance dataset, several MIL methods have achieved satisfactory results in the past when performing similar tasks especially on the BreaKHis dataset , and NorpdMISVM that extends to large-scale data. We derive the optimization algorithm based on the multi-block alternating direction method of multipliers (ADMM)   to bypashttps://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/). The BreaKHis dataset was built in collaboration with the P&D Laboratory in Parana, Brazil. BreaKHis was first introduced in ‘A Dataset for Breast Cancer Histopathological Image Classification’ by We perform classifications on the publicly available BreaKHis dataset  of patches for each of the images. However, in the raw tissue segments, the elements of interest such as nuclei may not be clearly visible. In light of this issue, we extract the feature vector through Parameter Free Threshold Statistics (PFTAS)  and GreypdMISVM algorithm designed to handle multi-instance data. Our formulation for pdMISVM provides an efficient solution to avoid dependency on a quadratic programming or least-squares approach.In this section, we develop an objective for the scalable M, vectors as m and scalars as m. The ith row and jth column of M are mj, respectively. Similarly, ith row and jth column of M. The matrix pth column-block of M. Each bag in patches and its associated label of mth class is represented by In this article, we denote matrices as K class multi-instance support vector machine was proposed by The mb by the The MISVM objective in From mb for each mth class in the main article, and leave the derivations for the other variables in the online In this section, we derive the efficient solution algorithm to minimize the proposed objective in b update. By differentiating mb and setting the result equal to zero, we have the following update:mth class are chosen from X. Taking the derivative of mb, setting the derivative equal to zero, and solving for mb gives:mth class.W update (without kernel). We discard all terms in W and optimize the columns of W separately by solving the following K problems for mth class, and X and the corresponding columns of U and ith bag and mth class in T, U and In the calculation of W update (with kernel). The kernel method , therefore our model with the solution in pdMISVM) is more scalable compared to pdMISVM).As can be seen in pdMISVM implementations. The scalability of pdMISVM is assessed across the increasing number of bags and features. Regarding the interpretability of our model, we also identify the disease relevant patches (instances) of each bag (image).In our experiments, we evaluate the classification performance and scalability of the proposed exact and inexact pdMISVM is compared against the following standard MIL benchmarks:The classification performance and scalability of A SIL method that assigns the bags’ labels to all instances during training and produces the maximum response for each bag/class pair at testing time for the training bag’s instances.The two bag-based methods; Normalized Set Kernel (NSK) and Statistics Kernel (STK)  . For the deep learning models , we use the same hyperparameters as in their articles.For these SVM models, the regularization tradeoff is set to 1.0. For the exact and inexact pdMISVM achieves the better or comparable performance to the best performing classical or recent models. In pdMISVM compared against the other MIL algorithms in the classification of benign/malignant bags. For each model, we provide the precision, recall, F1-score, accuracy and balanced accuracy (BACC) across the 10 6-fold cross-validation experiments (six repetitions per experiment).In this section, we evaluate the classification models to investigate whether our exact/inexact pdMISVM show promising performance across the various magnification levels. In particular, our exact pdMISVM outperforms the other models based on recall. A high recall rate is critical in the medical domain, as false negatives may result the serious consequences. This result shows the clinical utility of our model as it is crucial not to miss a malignant tumor in the diagnosis. When the SIL model is compared to the other MIL models, SIL performed the worst because it is difficult to accurately classify labels from individual patches. For example, evidence of malignancy may appear only in some patches of the bag. In this case, it is difficult to classify a patch as a malignancy from a patch where no evidence of a malignant tumor appeared. Our experimental results support the assumption that MIL models will classify better than SIL model.From the results reported in pdMISVM performs better than the inexact version at the smaller magnification levels, and while the opposite results are observed in the larger magnification levels. These results show that the classification pattern of pdMISVM can vary depending on the choice of optimization approach, just like the impact of the optimization algorithm on the deep learning models . In addition, the permutation invariant property is satisfied in Primal-Dual Multi-Instance SVM method and the associated derivations, which scale to a large number of bags and features. We have conducted extensive experiments on the BreaKHis dataset to show the promising performance and scalability of the proposed method when compared to the traditional SVM-based MIL techniques. In addition to the improved classification performance and scalability, the key patches for the classification identified by our model are well supported by previous medical studies. The experimental results illustrate the clinical utility of our approach on the detection of cancerous abnormalities in a large dataset to prevent the progression of breast cancer in a patient.The improvement of the scalability of methods is attracting more attention from machine learning studies as the amount of available data is increasing due to the development of data mining technologies. In this work, we present a novel This work was supported in part by the National Science Foundation (NSF) under the grants of Information and Intelligent Systems (IIS) , Computer and Network Systems (CNS) [1932482].Conflict of Interest: none declared.btac267_Supplementary_DataClick here for additional data file.