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Seasonal demand forecasting is critical for effective supply chain management. However, conventional forecasting methods face difficulties accurately estimating seasonal variations, owing to time-varying demand trends and limited data availability. In this paper, we propose a Fourier time-varying grey model (FTGM) to tackle this issue. The FTGM builds upon grey models, which are effective with limited data, and leverages Fourier functions to approximate time-varying parameters that allow it to represent seasonal variations. A data-driven selection algorithm adaptively determines the appropriate Fourier order of the FTGM without prior knowledge of data characteristics. Using the well-known M5 competition data, we compare our model with state-of-the-art forecasting methods taken from grey models, statistical methods, and architectures of neural network-based methods. The experimental results show that the FTGM outperforms popular seasonal forecasting methods in terms of standard accuracy metrics, providing a competitive alternative for seasonal demand forecasting in retail companies.

The traditional CO2 hydrogenation reaction in gas phase always requires harsh reaction conditions to activate CO2 , resulting in huge energy consumption. However, with the assistance of 1-butanol solvent, catalytic CO2 hydrogenation can be proceeded at a mild condition of 170 °C and 30 bars. To further improve the catalytic performance of the widely studied Cu-ZnO-ZrO2 catalyst (CZZ), the catalysts were modified by incorporating hydrotalcite (HTC) as a support material. The addition of HTC significantly improved the copper dispersion and surface area of the catalyst. The performance of CZZ-HTC catalysts was investigated at varying weight percentages of HTC, and all showed higher space-time yield of methanol (STYMeOH ) compared to the commercial catalyst. Notably, CZZ-6HTC exhibited the highest methanol selectivity, further highlighting the beneficial role of HTC as a support material.

School bullying is a common social problem among teenagers that occurs in various ways, and physical violence is considered the most harmful. However, all the existing methods for reporting school violence are human-driven, making them inconvenient for victims to use. The author’s research group therefore aims to develop automatic violence recognition methods. First, this thesis proposed a Fuzzy Multi-Threshold algorithm. However, as more activities involving players of different ages and genders were added, it failed to perform effectively. A Proportional K-Nearest Neighbors algorithm was therefore proposed to address this issue, achieving an average recognition accuracy of 80%. Speech emotion recognition was then incorporated. Time- and frequency-domain motion features were extracted for activity recognition, while Mel Frequency Cepstrum Coefficients were extracted for emotion recognition. A Back Propagation Neural Network was used as the classifier, achieving an average accuracy of 87.8%. To further enhance recognition performance, an additional movement sensor was utilized. Motion features were separately extracted from each sensor. An improved Relief-F algorithm was proposed to select useful and low-redundancy motion features, while a Radial Basis Function neural network was used as the classifier. An improved Dempster-Shafer fusion algorithm was proposed to combine the classification results of each sensor. An average recognition accuracy of 93.6% was achieved. To address the problem of bullies removing wearable sensors, an image-based violence recognition was proposed. Surveillance cameras capture images, and target-detecting algorithms and morphological processing methods were utilized to detect foreground targets. A circumscribed rectangular frame (CRF) integration method was proposed to refine the detected targets. CRF features and optical flow features were extracted, and three different selection methods were employed to select useful features., Tiivistelmä Koulukiusaaminen on teini-ikäisten yleinen sosiaalinen ongelma, ja sitä tapahtuu monin tavoin fyysisen väkivallan ollessa vahingollisin. Nykyiset kouluväkivallasta ilmoittamisen menetelmät ovat kuitenkin uhrin aloitteeseen perustuvia ja näin ollen epämukavia käyttää. Tässä tutkimustyössä kehitetään kouluväkivallan tunnistamismenetelmiä, jotka havaitsevat kiusaamistapahtumat automaattisesti. Työssä kehitettiin ensin Fuzzy Multi-Threshold -algoritmi, joka kykeni luokittelemaan seitsemän erityyppistä fyysistä aktiviteettia. Ratkaisu kuitenkin menetti tarkkuuttaan, kun aineistoa laajennettiin eri-ikäisillä ja erisukupuolisilla koehenkilöillä. Seuraavaksi kehitettiin Proportional K-Nearest Neighbors -algoritmi ja tuloksena oli 80 % tunnistamistarkkuus. Ratkaisuun lisättiin emootion tunnistus puhesignaalista. Fyysisen aktiivisuuden piirteet laskettiin liikesignaalin aika- ja taajuusalueista ja puheäänen Mel Frequency Cepstrum Coefficients -piirteet laskettiin tunteiden tunnistamiseksi. Back Propagation -neuroverkko antoi tunnistustarkkuudeksi 87,8 %. Ratkaisuun lisättiin toinenkin liikeanturi ja piirteet laskettiin molemmista anturisignaaleista. Työssä kehitettiin parannettu Relief-F-algoritmi parhaiden piirteiden valintaan liikkeen ominaisuuksista. Luokittelijana käytettiin Radial Basis Function -neuroverkkoa. Anturien tuottamien signaalien luokittelutulosten yhdistäminen tehtiin Dempster-Shafer-fuusioalgoritmilla, jolloin saavutettiin 93,6 % luokittelutarkkuus. Koska on mahdollista, että kiusaajat poistavat puettavat anturit tai älypuhelimet, kehitettiin vaihtoehtoinen tapa tunnistaa kouluväkivalta videokuvien perusteella. Valvontakameran kuvan edustassa sijaitsevien kohteiden paikantamiseksi käytettiin kohteen havaitsemisalgoritmeja ja morfologisia operaatioita. Kohteen ympäröivälle suorakaiteelle laskettiin kuvapiirteet sisältäen optisen virtauksen piirteet, ja niistä valittiin parhaat kolmella eri piirteenvalintamenetelmällä. 2-kerroksinen luokittelija De

We report high-precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section at high values of the Bjorken variable $x_B$. DVCS is sensitive to the Generalized Parton Distributions of the nucleon, which provide a three-dimensional description of its internal constituents. Using the exact analytic expression of the DVCS cross section for all possible polarization states of the initial and final electron and nucleon, and final state photon, we present the first experimental extraction of all four helicity-conserving Compton Form Factors (CFFs) of the nucleon as a function of $x_B$, while systematically including helicity flip amplitudes. In particular, the high accuracy of the present data demonstrates sensitivity to some very poorly known CFFs.

We report high-precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section at high values of the Bjorken variable $x_B$. DVCS is sensitive to the Generalized Parton Distributions of the nucleon, which provide a three-dimensional description of its internal constituents. Using the exact analytic expression of the DVCS cross section for all possible polarization states of the initial and final electron and nucleon, and final state photon, we present the first experimental extraction of all four helicity-conserving Compton Form Factors (CFFs) of the nucleon as a function of $x_B$, while systematically including helicity flip amplitudes. In particular, the high accuracy of the present data demonstrates sensitivity to some very poorly known CFFs.

INTRODUCTION: Embryo implantation failure leads to infertility. As an important approach to regulate implantation, endometrial epithelial cells produce and secrete factors apically into the uterine cavity in the receptive phase to prepare the initial blastocyst adhesion and implantation. Organoids were recently developed from human endometrial epithelium with similar apical-basal polarity compared to endometrial gland making it an ideal model to study endometrial epithelial secretions. METHODS: Endometrial organoids were established using endometrial biopsies from women with primary infertility and normal fertility. Fertile and infertile organoids were treated with hormones to model receptive phase of the endometrial epithelium and intra-organoid fluid (IOF) was collected to compare the apical protein secretion profile and function on trophoblast cell adhesion. RESULTS: Our data show that infertile organoids were dysregulated in their response to estrogen and progesterone treatment. Proteomic analysis of organoid apical secretions identified 150 dysregulated proteins between fertile and infertile groups (>1.5-fold change). Trophoblast progenitor spheroids (blastocyst surrogates) treated with infertile organoid apical secretions significantly compromised their adhesion to organoid epithelial cell monolayers compared to fertile group (P < 0.0001). DISCUSSION: This study revealed that endometrial organoid apical secretions alter trophoblast cell adhesiveness relative to fertility status of women. It paves the way to determine the molecular mechanisms by which endometrial epithelial apical released factors regulate blastocyst initial attachment and implantation.

BACKGROUND: To investigate the common etiologies, characteristics, and management of ureteric injury. METHODS: A nationwide study was performed in 38 medical centers from Mar 2017 to Mar 2019. The data of patients with ureteric injury were retrospectively collected. RESULTS: Overall, 294 patients were included in this study. Ten cases (3.4%) were due to abdominal injuries, and 284 cases (96.6%) were due to iatrogenic injuries, including 48.6% from urological procedures, 41.9% from gynecological procedures, 6.3% from general surgery, and 3.2% from other treatments. Most urological injuries (79.7%) were caused by endourological procedures. Injury occurred in the distal ureter in 178 cases (60.5%), the mid-ureter in 31 cases (10.5%), and the proximal ureter in 85 cases (28.9%). Only 51 patients (17.3%) were diagnosed immediately during primary surgery. Immediate ureter reconstruction was performed in 30 patients (58.8%), while delayed urinary diversion was performed in 148 patients (60.9%) in the delayed diagnosis group. The side and the location of the injury were different between the urological and nonurological injury groups (P<0.001). Ureteric injuries were diagnosed timelier in the urological group than in the nonurological group (P=0.037). CONCLUSIONS: Iatrogenic injury was the most common cause of ureteric injury. The majority of iatrogenic injuries were caused by urological procedures and were primarily due to aggressive endourological techniques. Depending on the condition of the patient, the time of diagnosis, and the location and length of the ureteric injury, different management strategies should be applied.

We report high-precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section at high values of the Bjorken variable $x_B$. DVCS is sensitive to the Generalized Parton Distributions of the nucleon, which provide a three-dimensional description of its internal constituents. Using the exact analytic expression of the DVCS cross section for all possible polarization states of the initial and final electron and nucleon, and final state photon, we present the first experimental extraction of all four helicity-conserving Compton Form Factors (CFFs) of the nucleon as a function of $x_B$, while systematically including helicity flip amplitudes. In particular, the high accuracy of the present data demonstrates sensitivity to some very poorly known CFFs.

Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict N2O emissions. The hybrid model was successfully implemented and validated with the N2O emission data from a full-scale WWTP. This hybrid model is demonstrated to have higher accuracy for N2O emission modeling in the WWTP than the mechanistic model or pure deep learning model. Equally important, the hybrid model is more applicable than the pure deep learning model due to the lower requirement of data and the pure mechanistic model due to the less calibration requirement. This superior performance was due to the hybrid nature of the proposed model. It integrated the essential wastewater treatment knowledge as the first principal component and the less understood N2O production processes by the data-driven deep learning approach. The developed hybrid model was also successfully implemented under different circumstances for the prediction of N2O flux, which showed the generalizability of the model. The hybrid model also showed great potential to be applied for the N2O mitigation work. Nevertheless, the capability of the hybrid model in evaluating N2O mitigation strategies still requires validation with experiments. Going beyond N2O modeling in WWTP, the novel hybridization modeling concept can potentially be applied to other environmental systems.

Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict N2O emissions. The hybrid model was successfully implemented and validated with the N2O emission data from a full-scale WWTP. This hybrid model is demonstrated to have higher accuracy for N2O emission modeling in the WWTP than the mechanistic model or pure deep learning model. Equally important, the hybrid model is more applicable than the pure deep learning model due to the lower requirement of data and the pure mechanistic model due to the less calibration requirement. This superior performance was due to the hybrid nature of the proposed model. It integrated the essential wastewater treatment knowledge as the first principal component and the less understood N2O production processes by the data-driven deep learning approach. The developed hybrid model was also successfully implemented under different circumstances for the prediction of N2O flux, which showed the generalizability of the model. The hybrid model also showed great potential to be applied for the N2O mitigation work. Nevertheless, the capability of the hybrid model in evaluating N2O mitigation strategies still requires validation with experiments. Going beyond N2O modeling in WWTP, the novel hybridization modeling concept can potentially be applied to other environmental systems.

In this article, we propose a procedure for the investigation of the feasibility and effectiveness of recently developed in-field magnetometer calibration methods. Extensive numerical analyses under different noise levels are conducted to identify the proper measurement conditions of each method. The variance analysis is also presented to study the observability of model parameters based on the information matrix under different inclination angles. In accordance with the analysis, several important conclusions are drawn including the condition to ensure the validity of the parameter estimation and the approach to improve the performance of the calibration results. Numerical simulation and experimental validation support our analytical findings. Accordingly, this study provides an in-depth exploration of the limitations of typical in-field magnetometer calibration methods.

This paper presents a novel continuum robot (OctRobot-I) that has controllable stiffness variation capability in both the transverse and axial directions. Robot design, stiffness variation analysis and experimental testing are discussed in detail. Stiffness models based on the Euler-Bernoulli beam theory are developed, and then four static deflection cases are analysed. Experiments are conducted with two types of layer jamming sheaths (overlap numbers n =3, 5) and four different vacuum pressures (0kPa, 25kPa, 50kPa, 75kPa) at three different bending angles (0°, 90°, 180°). The results demonstrate that the stiffness changing tendency is in compliance with the derived models and show that the robot has a good stiffness variable capability. With the jamming sheath of n =3, the stiffness ranges (ratios) are 36.4 to 241.7 N/m (6.6) and 92.9 to 19.3×103 N/m (207.8) in the transverse and axial directions, respectively. With the jamming sheath of n =5, the stiffness ranges (ratios) are 65.7 to 398.3 N/m (6.1) and 106.7 to 20.8×103 N/m (194.9) in the transverse and axial directions, respectively. Additionally, the actuating and gripping experiments demonstrate that this robot has good performance in real-world applications.

Background: Older people receive care from multiple providers which often results in a lack of coordination. The Information and Communication Technology (ICT) enabled value-based methodology for integrated care (ValueCare) project aims to develop and implement efficient outcome-based, integrated health and social care for older people with multimorbidity, and/or frailty, and/or mild to moderate cognitive impairment in seven sites (Athens, Greece; Coimbra, Portugal; Cork/Kerry, Ireland; Rijeka, Croatia; Rotterdam, the Netherlands; Treviso, Italy; and Valencia, Spain). We will evaluate the implementation and the outcomes of the ValueCare approach. This paper presents the study protocol of the ValueCare project; a protocol for a pre-post controlled study in seven large-scale sites in Europe over the period between 2021 and 2023. Methods: A pre-post controlled study design including three time points (baseline, post-intervention after 12 months, and follow-up after 18 months) and two groups (intervention and control group) will be utilised. In each site, (net) 240 older people (120 in the intervention group and 120 in the control group), 50–70 informal caregivers (e.g. relatives, friends), and 30–40 health and social care practitioners will be invited to participate and provide informed consent. Self-reported outcomes will be measured in multiple domains; for older people: health, wellbeing, quality of life, lifestyle behaviour, and health and social care use; for informal caregivers and health and social care practitioners: wellbeing, perceived burden and (job) satisfaction. In addition, implementation outcomes will be measured in terms of acceptability, appropriateness, feasibility, fidelity, and costs. To evaluate differences in outcomes between the intervention and control group (multilevel) logistic and linear regression analyses will be used. Qualitative analysis will be performed on the focus group data. Discussion: This study will provide new insights into t

BACKGROUND:While increased levels of circulating inflammatory cytokines in chronologically aged humans have been linked to the development of ageing-associated chronic disorders (e.g., cardiovascular disease, type II diabetes, osteoporosis and Alzheimer's disease), approaches that reduce circulating cytokines are not yet available. In chronologically aged mice, we recently demonstrated that epidermal dysfunction largely accounts for age-associated elevations in circulating cytokine levels, and that improving epidermal function reduced circulating cytokine levels. OBJECTIVE:We performed a pilot study to determine whether improving epidermal function reduces circulating pro-inflammatory cytokine levels in aged humans. METHODS:Thirty-three aged humans were topically treated twice-daily for 30 days, with ≈ 3 mL of an emollient, previously shown to improve epidermal function, while untreated, aged humans and a cohort of young volunteers served as controls. Changes in epidermal function and levels of three key, age-related, plasma cytokines (IL-1β, IL-6 and TNFα) were measured at baseline and after treatment, using Luminex 200™ system. RESULTS:We also found significantly higher baseline levels of IL-1β, IL-6 and TNFα in aged vs. young humans (P < 0.001), as previously reported. Topical applications of the barrier repair emollient significantly enhanced epidermal permeability barrier function (P < 0.01) and stratum corneum hydration (P < 0.05). In parallel, circulating levels of IL-1β and IL-6 normalized, while TNFα levels declined substantially. CONCLUSION:The results of this preliminary study suggest that a larger clinical trial should be performed to confirm whether improving epidermal function also can reduce circulating pro-inflammatory cytokine levels in aged humans, while also possibly attenuating the downstream development of chronic inflammatory disorders in the aged humans.

There has been rapidly increasing interest in psychedelics internationally in recent years, with decriminalisation of some substances in this class in the United States, establishment of research centres that focus on psychedelics, and expanded industry attention.Recent reviews have identified an emerging evidence base for psilocybin and other psychedelics in treatments for mental health conditions such as PTSD and treatment resistant depression Some drugs in this class have been designated by the US Food and Drug Administration as “breakthrough therapies” for these disorders and the Australian Government has recently dedicated 15 milliondollars toward clinical trials examining the treatment of mental illness with psychedelic drugs.In Australia, the National Drug Strategy Household Survey identified a small but significant increase in psychedelic use in the Australian general adult population, rising from 1 0 in 2016 to 1 6 in 2019 It should be noted that rates have been in a similar range across the past decade.One of the many potential reasons for the apparent increase in psychedelic use may be self treatment of mental health conditions The 2020 Global Drug Survey found almost 6 of their sample 6500 of 110000 reported self medicating with psychedelic drugs. In addition, there is increasing interest in microdosing that is, regular use of subthreshold doses of psychedelics with an aim to improve mood or cognition.In this bulletin we aimed to examine if there was any evidence of change in the use of psychedelic drugs among participants in the Ecstasy and Related Drugs Reporting System ( between 2010 and 2020 and if there was evidence for the very frequent use that would be suggestive of microdosing.

Follicular helper T (TFH) cells control antibody responses by supporting antibody affinity maturation and memory formation. Inadequate TFH function has been found in individuals with ineffective responses to vaccines, but the mechanism underlying TFH regulation in vaccination is not understood. Here, we report that lower serum levels of the metabolic hormone leptin associate with reduced vaccine responses to influenza or hepatitis B virus vaccines in healthy populations. Leptin promotes mouse and human TFH differentiation and IL-21 production via STAT3 and mTOR pathways. Leptin receptor deficiency impairs TFH generation and antibody responses in immunisation and infection. Similarly, leptin deficiency induced by fasting reduces influenza vaccination-mediated protection for the subsequent infection challenge, which is mostly rescued by leptin replacement. Our results identify leptin as a regulator of TFH cell differentiation and function and indicate low levels of leptin as a risk factor for vaccine failure.

We present new precision measurements of the elastic electron-proton scattering cross section for momentum transfer (Q$^2$) up to 15.75~\gevsq. Combined with existing data, these provide an improved extraction of the proton magnetic form factor at high Q$^2$ and double the range over which a longitudinal/transverse separation of the cross section can be performed. The difference between our results and polarization data agrees with that observed at lower Q$^2$ and attributed to hard two-photon exchange (TPE) effects, extending to 8~(GeV/c)$^2$ the range of Q$^2$ for which a discrepancy is established at $>$95\% confidence. We use the discrepancy to quantify the size of TPE contributions needed to explain the cross section at high Q$^2$., Comment: 7 pages, 2 figures

Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

We present new precision measurements of the elastic electron-proton scattering cross section for momentum transfer (Q$^2$) up to 15.75~\gevsq. Combined with existing data, these provide an improved extraction of the proton magnetic form factor at high Q$^2$ and double the range over which a longitudinal/transverse separation of the cross section can be performed. The difference between our results and polarization data agrees with that observed at lower Q$^2$ and attributed to hard two-photon exchange (TPE) effects, extending to 8~(GeV/c)$^2$ the range of Q$^2$ for which a discrepancy is established at $>$95\% confidence. We use the discrepancy to quantify the size of TPE contributions needed to explain the cross section at high Q$^2$., Comment: 7 pages, 2 figures

The recently emerged weakly-supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels. Previous works endeavor to perceive the interval objects from the small and sparse discriminative attention map, yet ignoring the co-occurrence confounder (e.g., duck and water), which makes the model inspection (e.g., CAM) hard to distinguish between the object and context. In this paper, we make an early attempt to tackle this challenge via causal intervention (CI). Our proposed method, dubbed CI-CAM, explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps thus improving the accuracy of object localization. Extensive experiments on several benchmarks demonstrate the effectiveness of CI-CAM in learning the clear object boundary from confounding contexts. Particularly, on the CUB-200-2011 which severely suffers from the co-occurrence confounder, CI-CAM significantly outperforms the traditional CAM-based baseline (58.39% vs 52.4% in Top-1 localization accuracy). While in more general scenarios such as ILSVRC 2016, CI-CAM can also perform on par with the state of the arts.

The recently emerged weakly-supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels. Previous works endeavor to perceive the interval objects from the small and sparse discriminative attention map, yet ignoring the co-occurrence confounder (e.g., duck and water), which makes the model inspection (e.g., CAM) hard to distinguish between the object and context. In this paper, we make an early attempt to tackle this challenge via causal intervention (CI). Our proposed method, dubbed CI-CAM, explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps thus improving the accuracy of object localization. Extensive experiments on several benchmarks demonstrate the effectiveness of CI-CAM in learning the clear object boundary from confounding contexts. Particularly, on the CUB-200-2011 which severely suffers from the co-occurrence confounder, CI-CAM significantly outperforms the traditional CAM-based baseline (58.39% vs 52.4% in Top-1 localization accuracy). While in more general scenarios such as ILSVRC 2016, CI-CAM can also perform on par with the state of the arts.

This paper presents an efficient servomotor-aided calibration method for the triaxial gyroscope. The entire calibration process only requires approximately one minute, and does not require high-precision equipment. This method is based on the idea that the measurement of the gyroscope should be equal to the rotation speed of the servomotor. A six-observation experimental design is proposed to minimize the maximum variance of the estimated scale factors and biases. In addition, a fast converging recursive linear least square estimation method is presented to reduce computational complexity. The simulation results reflect the robustness of the calibration method under normal and extreme conditions. We experimentally demonstrate the feasibility of the proposed method on a robot arm, and implement the method on a microcontroller. We verify the calibration results of the proposed method by comparing with a traditional turntable approach, and the experiment indicates that the results of these two methods are comparable. By comparing the calibrated low-cost gyroscope reading with the reading from a high-precision gyroscope, we can conclude that our method significantly increases the gyroscope's accuracy.

This article presents an innovative in-field calibration method for portable triaxial magnetometers (TMs). For in-field calibration of portable sensors, it is difficult to provide accurately measured inputs to each sensitivity axis due to the unavailability of high-precision turntables. Because of this limitation, most existing in-field calibration methods often require nonlinear optimization to identify model parameters. According to the fact that the angle between the local gravity and magnetic field is invariant, a new in-field calibration approach called inclination-based calibration (I-Calibration) was proposed. The I-Calibration could reliably estimate model parameters of the magnetometers by simply using a linear least square estimator. A 12-observation icosahedron experimental scheme was performed for a microinertial measurement unit, which contains both triaxial accelerometers (TAs) and TMs. The effectiveness, as well as the robustness with the misalignments of the TAs and TMs, of the proposed calibration method, was first verified by using numerical simulations. Thereafter, in real data experiments, the proposed method was compared with an existing calibration approach, namely, magnitude-based calibration (M-Calibration), which is based on the fact that the magnitude of the local magnetic field is invariant. The calibrated results together with the comparison between the two methods demonstrated the effectiveness of the proposed method.

Increasing evidence points to the presence of low-level de novo T790M mutations in patients with non-small cell lung carcinoma (NSCLC) harboring activating EGFR mutations. We utilized digital PCR (dPCR), a highly sensitive gene mutation detection method, to detect pre-treatment T790M mutations in NSCLC tumor samples and correlated the T790M status with clinical features and patient outcomes. DNA extracted from pre-treatment NSCLC tumor tissue with known activating EGFR mutations, diagnosed between October 2010 and May 2017 at PathWest laboratory, was used to perform targeted dPCR for quantitative detection of T790M mutations. T790M was detected in 42 of 109 pre-treatment samples (38.5%). Median variant allele frequency was 0.14% (range 0.02–28.5%). Overall response rate to first generation EGFR tyrosine kinase inhibitors (TKI) was 67% regardless of T790M status. The median progression free survival was 10.7 (IQR 5.6–19.9) versus 6.7 (IQR 3.5–20.8) months in T790M negative and positive patients respectively. T790M positivity correlated with increased rate of early disease progression. It also correlated with increased mortality (HR 3.1 95%CI 1.2–8.1, p = 0.022) in patients who did not respond to TKI treatment. We detected a significant rate of low-level pre-treatment T790M mutations in NSCLC using highly sensitive dPCR. Low-level pre-treatment T790M did not impact treatment response rate or overall survival, but was associated with increased rate of early progression on TKI therapy.

The aim of the study – to evaluate the psychological approach to the social and psychological rehabilitation of paralympic athletes negatively experienced subjective loneliness.Materials and methods are based on theoretical and empirical data, which include the history of the loneliness phenomenon and its modern understanding study. Negative experiences requiring rehabilitation were determined using psychological methods: “Questionnaire for determining the type of loneliness” (S. Korchaginа), “Methods for the subjective feeling of loneliness” (D. Russell, M. Ferguson), “Methods for diagnosing character accentuations” (Leonhard-Shmishyok in the modification A. Lychko), test “Neuropsychic Adaptation” (S. Korchagina at V. Bekhterev Research Institute) with subsequent computer processing of the results using the software package Statistica 6.0 (StatSoft Inc., USA). The study enrolled 50 paralympic athletes aged 18 to 24, who were engaged in powerlifting.Results. Significant intercorrelations between subjective feelings of loneliness and its varieties were found (r = 0.3–0.5) based on the Pearson correlation coefficient, which allowed to consider subjective loneliness as an independent internal phenomenon. The experience of diffuse loneliness did not reveal a significant correlation (r = -0.153) with the subjective state of paralympic athletes. Two directions, which provide a mechanism of development and rehabilitation taking into account the type of loneliness, were identified. The first was the desire to be with others, identification with them through the correction of their own personality. The second one, as a result of the first, was the coping with feeling of loneliness and lack of meaning in life.Conclusions. It was established that the feeling of subjective loneliness negative experiences was associated with the deterioration of neuropsychiatric adaptation and required the socio-psychological rehabilitation of paralympic athletes who specialized in powerlifting, Мета роботи – оцінити психологічний підхід до соціально-психологічної реабілітації негативних переживань суб’єктивної самотності спортсменами-паралімпійцями (на матеріалі пауерліфтингу).Матеріали та методи. Дослідження ґрунтується на теоретичних та емпіричних даних, що включають вивчення феномена самотності та його сучасне розуміння. Негативність переживань, що потребують реабілітації, визначали за допомогою психологічних методик: «Опитувальник для визначення виду самотності» (С. Корчагіна), «Методика суб’єктивного відчуття самотності» (Д. Рассел, М. Фергюсон), «Методика діагностики акцентуацій характеру» (Леонгард–Шмішек у модифікації А. Личко), тест «Нервово-психічна адаптація» (С. Корчагіна, НДІ імені В. М. Бехтерєва) з наступним комп’ютерним опрацюванням результатів за допомогою програмного пакета Statistica 6.0 (StatSoft Inc., CША). У дослідження залучили 50 спортсменів-паралімпійців віком від 18 до 24 років, які займаються пауерліфтингом.Результати. Виявили інтеркореляційні зв’язки між суб’єктивним відчуттям самотності та її різновидами на рівні значущості (r = 0,3–0,5) за коефіцієнтом парної кореляції Пірсона, що дає змогу розглядати суб’єктивну самотність як самостійний внутрішній феномен. Переживання дифузної самотності не показало значущого зв’язку (r = -0,153) із суб’єктивним станом спортсменів-паралімпійців. Визначили дві спрямованості, що забезпечують механізм виникнення та реабілітацію, враховуючи тип самотності: перша – бажання бути з іншими, ідентифікація з ними через корекцію власної індивідуальності; друга, що виникає внаслідок першої, – подолання відчуття самотності та відсутності сенсу життя.Висновки. Відчуття негативних переживань суб’єктивної самотності пов’язане з погіршенням нервово-психічної адаптації та потребує соціально-психологічної реабілітації спортсменів-паралімпійців, які спеціалізуються в пауерліфтингу. Доведено наявність значущого позитивного зв’язку між дифузною самотністю та емотивною акцентуацією характеру спортсмена. Визначили, Цель работы – оценить психологический подход к социально-психологической реабилитации негативных переживаний субъективного одиночества спортсменами-паралимпийцами (на материале пауэрлифтинга).Материалы и методы. Исследование основано на теоретических и эмпирических данных, включающих изучение феномена одиночества и его современное понимание. Негативность переживаний, требующих реабилитации, определяли с помощью психологических методик: «Опросник для определения вида одиночества» (С. Корчагина), «Методика субъективного ощущения одиночества» (Д. Рассел, М. Фергюсон), «Методика диагностики акцентуаций характера» (Леонгард-Шмишек в модификации А. Личко), тест «Нервно-психическая адаптация» (С. Корчагина, НИИ имени В. М. Бехтерева) с последующей компьютерной обработкой результатов с помощью программного пакета Statistica 6.0 (StatSoft Inc., CША). В исследовании приняли участие 50 спортсменов-паралимпийцев в возрасте от 18 до 24 лет, которые занимаются пауэрлифтингом.Результаты. Установлены интеркорреляционные связи между субъективным ощущением одиночества и ее разновидностями на значимом уровне (r = 0,3–0,5) по коэффициенту парной корреляции Пирсона, что позволяет рассматривать субъективное одиночество как самостоятельный внутренний феномен. Переживание диффузного одиночества не показало значимую связь (r = -0,153) с субъективным состоянием спортсменов-паралимпийцев. Выделены две направленности, которые обеспечивают механизм возникновения и реабилитации с учетом типа одиночества: первая – желание быть с другими, идентификация с ними через коррекцию собственной индивидуальности; вторая, возникающая в результате первой, – преодоление ощущения одиночества и отсутствия смысла жизни.Выводы. Ощущение негативных переживаний субъективного одиночества связано с ухудшением нервно-психической адаптации и требует социально-психологической реабилитации спортсменов-паралимпийцев, которые специализируются в пауэрлифтинге. Доказано наличие значимой положительной связи между диффузным о

This study focuses on the interactions between sediment stability and biological and physical variables that influence the erodibility across different habitats. Sampling at short‐term temporal scales illustrated the persistence of the microphytobenthos (MPB) biomass even during periods of frequent, high physical disturbance. The role of MPB in biological stabilization along the changing sedimentary habitat was also assessed. Key biological and physical properties, such as the MPB biomass, composition, and extracellular polymeric substances, were used to predict the sediment stability (erosion threshold) of muddy and sandy habitats within close proximity to one another over multiple days, and within emersion periods. The effects of dewatering, MPB growth, and productivity were examined as well as the resilience and recovery of the MPB community after disturbance from tidal currents and waves. Canonical analysis of principal components (CAP) ordinations were used to visualize and assess the trends observed in biophysical properties between the sites, and marginal and sequential distance‐based linear models were used to identify the key properties influencing erodibility. While the particle size of the bed was important for differences between sites in the CAP analysis, it contributed less to the variability in sediment erodibility than key biological parameters. Among the biological predictors, MPB diversity explained very little variation in marginal tests but was a significant predictor in sequential tests when MPB biomass was also considered. MPB diversity and biomass were both key predictors of sediment stability, contributing 9% and 10%, respectively, to the final model compared to 2% explained by grain size.

Increasing evidence points to the presence of low-level de novo T790M mutations in patients with non-small cell lung carcinoma (NSCLC) harboring activating EGFR mutations. We utilized digital PCR (dPCR), a highly sensitive gene mutation detection method, to detect pre-treatment T790M mutations in NSCLC tumor samples and correlated the T790M status with clinical features and patient outcomes. DNA extracted from pre-treatment NSCLC tumor tissue with known activating EGFR mutations, diagnosed between October 2010 and May 2017 at PathWest laboratory, was used to perform targeted dPCR for quantitative detection of T790M mutations. T790M was detected in 42 of 109 pre-treatment samples (38.5%). Median variant allele frequency was 0.14% (range 0.02–28.5%). Overall response rate to first generation EGFR tyrosine kinase inhibitors (TKI) was 67% regardless of T790M status. The median progression free survival was 10.7 (IQR 5.6–19.9) versus 6.7 (IQR 3.5–20.8) months in T790M negative and positive patients respectively. T790M positivity correlated with increased rate of early disease progression. It also correlated with increased mortality (HR 3.1 95%CI 1.2–8.1, p = 0.022) in patients who did not respond to TKI treatment. We detected a significant rate of low-level pre-treatment T790M mutations in NSCLC using highly sensitive dPCR. Low-level pre-treatment T790M did not impact treatment response rate or overall survival, but was associated with increased rate of early progression on TKI therapy.

We report measurements of the exclusive neutral pion electroproduction cross section off protons at large values of $x_B$ (0.36, 0.48 and 0.60) and $Q^2$ (3.1 to 8.4 GeV$^2$) obtained from Jefferson Lab Hall A experiment E12-06-014. The corresponding structure functions $d\sigma_L/dt+\epsilon d\sigma_T/dt$, $d\sigma_{TT}/dt$, $d\sigma_{LT}/dt$ and $d\sigma_{LT'}/dt$ are extracted as a function of the proton momentum transfer $t-t_{min}$. The results suggest the amplitude for transversely polarized virtual photons continues to dominate the cross-section throughout this kinematic range. The data are well described by calculations based on transversity Generalized Parton Distributions coupled to a helicity flip Distribution Amplitude of the pion, thus providing a unique way to probe the structure of the nucleon.

School bullying is a common social problem, and school violence is considered to be the most harmful form of school bullying. Fortunately, with the development of movement sensors and pattern recognition techniques, it is possible to detect school violence with artificial intelligence. This paper proposes a school violence detecting method based on improved Relief-F and Dempster-Shafe (D-S) algorithms. Two movement sensors are fixed on the object’s waist and leg, respectively, to gather acceleration and gyro data. Altogether nine kinds of activities are gathered, including three kinds of school violence and six kinds of daily-life activities. After wavelet filtering, 39 time-domain features and 12 frequency-domain features are extracted. To reduce computational cost, this paper proposes an improved Relief-F algorithm which selects features according to classification contribution and correlation. By drawing boxplots of the selected features, the authors find that the frequency-domain energy of the y-axis of acceleration can distinguish jumping from other activities. Therefore, the authors build a two-layer classifier. The first layer is a decision tree which separates jumping from other activities, and the second layer is a Radial Basis Function (RBF) neutral network which classifies the remainder eight kinds of activities. Since the two movement sensors work independently, this paper proposes an improved D-S algorithm for decision layer fusion. The improved D-S algorithm designs a new probability distribution function on the evidence model and builds a new fusion rule, which solves the problem of fusion collision. According to the simulation results, the proposed method has increased the recognition accuracy compared with the authors’ previous work. 89.6% of school violence and 95.1% of daily-life activities were correctly recognized. The accuracy reached 93.6% and the precision reached 87.8%, which were 29.9% and 2.7% higher than the authors’ previous wor

In daily life, speech is the main medium of human communication, and interpersonal communication is emotional. People hope that the computer can give a response based on the emotions contained in the voice. In this paper, we build a Wechat program of speech emotion recognition system, which is based on a random forest classifier. Firstly, the system preprocesses the collected speech signals in order to reduce noise. Secondly, 16 acoustic features are extracted from the pre-processed speech signals. The system obtains the emotional features of speech by applying 12 statistical functions to the original acoustic features. The emotional classification of Berlin Speech Emotion Database uses two classifiers: the Random Forest Classifier and the Support Vector Machine. The recognition accuracy of the SVM classifier is 83%. The accuracy of the random forest classifier is 89%. Finally, the random forest classifier is used to build the speech emotion recognition system.

School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This paper proposes a video-based school violence detecting algorithm. This algorithm first detects foreground moving targets via the KNN (K-Nearest Neighbor) method and then preprocesses the detected targets via morphological processing methods. Then, this paper proposes a circumscribed rectangular frame integrating method to optimize the circumscribed rectangular frame of moving targets. Rectangular frame features and optical-flow features were extracted to describe the differences between school violence and daily-life activities. We used the Relief-F and Wrapper algorithms to reduce the feature dimension. SVM (Support Vector Machine) was applied as the classifier, and 5-fold cross validation was performed. The accuracy was 89.6%, and the precision was 94.4%. To further improve the recognition performance, we developed a DT–SVM (Decision Tree–SVM) two-layer classifier. We used boxplots to determine some features of the DT layer that are able to distinguish between typical physical violence and daily-life activities and between typical daily-life activities and physical violence. For the remainder of activities, the SVM layer performed a classification. For this DT–SVM classifier, the accuracy reached 97.6%, and the precision reached 97.2%, thus showing a significant improvement.

School violence has become more and more frequent in today’s school life and caused great harm to the social and educational development in many countries. This paper used a MEMS sensor which is fixed on the waist to collect data and performed feature extraction on the acceleration and gyro data of the sensors. Altogether nine kinds of activities were recorded, including six daily-life kinds and three violence kinds. A filter-based Relief-F feature selection algorithm was used and Radial Basis Function (RBF) neural network classifier was applied on them. The results showed that the algorithm could distinguish physical violence movements from daily-life movements with an accuracy of 90%.

We report measurements of the exclusive neutral pion electroproduction cross section off protons at large values of $x_B$ (0.36, 0.48 and 0.60) and $Q^2$ (3.1 to 8.4 GeV$^2$) obtained from Jefferson Lab Hall A experiment E12-06-014. The corresponding structure functions $d\sigma_L/dt+\epsilon d\sigma_T/dt$, $d\sigma_{TT}/dt$, $d\sigma_{LT}/dt$ and $d\sigma_{LT'}/dt$ are extracted as a function of the proton momentum transfer $t-t_{min}$. The results suggest the amplitude for transversely polarized virtual photons continues to dominate the cross-section throughout this kinematic range. The data are well described by calculations based on transversity Generalized Parton Distributions coupled to a helicity flip Distribution Amplitude of the pion, thus providing a unique way to probe the structure of the nucleon.

Mitigation of nitrous oxide (N2O) emissions is of primary importance to meet the targets of reducing carbon footprints of wastewater treatment plants (WWTPs). Despite of a large amount of N2O mitigation studies conducted in laboratories, full-scale implementation of N2O mitigation is scarce, mainly due to uncertainties of mitigation effectiveness, validation of N2O mathematical model, risks to nutrient removal performance and additional costs. This study aims to address the uncertainties by investigating the quantification, development and implementation of N2O mitigation strategies at a full-scale sequencing batch reactor (SBR). To achieve this, N2O emission dynamics, nutrient removal performance and operation of the SBR were monitored to quantify N2O emissions, and identify the N2O generation mechanisms. N2O mitigation strategies centered on reducing dissolved oxygen (DO) levels were consequently proposed and evaluated using a multi-pathway N2O production mathematical model before implementation. The implemented mitigation strategy resulted in a 35% reduction in N2O emissions (from the emission factor of 0.89 ± 0.05 to 0.58 ± 0.06%), which was equivalent to annual reduction of 2.35 tonne of N2O from the studied WWTP. This could be mainly attributed to reductions in N2O generated via the NH2OH oxidation pathway due to the lowering of DO level. As the first reported mitigation strategy permanently implemented at a full scale WWTP, it showcased that the mitigation of N2O emissions at full-scale is feasible and that widely accepted N2O mitigation strategies developed in laboratory studies are also likely effective in full-scale plants. Furthermore, the close agreement between the validated and predicted N2O emission factors (0.58% vs 0.55%, respectively), showed that the N2

This study focuses on the interactions between sediment stability and biological and physical variables that influence the erodibility across different habitats. Sampling at short‐term temporal scales illustrated the persistence of the microphytobenthos (MPB) biomass even during periods of frequent, high physical disturbance. The role of MPB in biological stabilization along the changing sedimentary habitat was also assessed. Key biological and physical properties, such as the MPB biomass, composition, and extracellular polymeric substances, were used to predict the sediment stability (erosion threshold) of muddy and sandy habitats within close proximity to one another over multiple days, and within emersion periods. The effects of dewatering, MPB growth, and productivity were examined as well as the resilience and recovery of the MPB community after disturbance from tidal currents and waves. Canonical analysis of principal components (CAP) ordinations were used to visualize and assess the trends observed in biophysical properties between the sites, and marginal and sequential distance‐based linear models were used to identify the key properties influencing erodibility. While the particle size of the bed was important for differences between sites in the CAP analysis, it contributed less to the variability in sediment erodibility than key biological parameters. Among the biological predictors, MPB diversity explained very little variation in marginal tests but was a significant predictor in sequential tests when MPB biomass was also considered. MPB diversity and biomass were both key predictors of sediment stability, contributing 9% and 10%, respectively, to the final model compared to 2% explained by grain size.

Free nitrous acid (FNA), the protonated form of nitrite, has historically been an unwanted substance in wastewater systems due to its inhibition on a wide range of microorganisms. However, in recent years, advanced understanding of FNA inhibitory and biocidal effects on microorganisms has led to the development of a series of FNA-based applications that improve wastewater management practices. FNA has been used in sewer systems to control sewer corrosion and odor; in wastewater treatment to achieve carbon and energy efficient nitrogen removal; in sludge management to improve the sludge reduction and energy recovery; in membrane systems to address membrane fouling; and in wastewater algae systems to facilitate algae harvesting. This paper aims to comprehensively and critically review the current status of FNA-based applications in improving wastewater management. The underlying mechanisms of FNA inhibitory and biocidal effects are also reviewed and discussed. Knowledge gaps and current limitations of the FNA-based applications are identified; and perspectives on the development of FNA-based applications are discussed. We conclude that the FNA-based technologies have great potential for enhancing the performance of wastewater systems; however, further development and demonstration at larger scales are still required for their wider applications.

© 1989-2012 IEEE. Ultra-wideband spoof surface plasmon polaritons (SSPPs) based on compact balanced coplanar stripline (CPS) waveguides are proposed. Compared with the conventional CPS-based terahertz (THz) SSPP unit cell, the proposed one has achieved a size reduction of 55.2% under the condition of the same asymptotic frequency. The propagation and attenuation characteristics of the proposed SSPP waveguide can be easily manipulated by adjusting the geometry dimensions of the SSPP unit cell. It indicates that the cut-off frequency of the SSPP waveguide has its tunable flexibility thereby facilitating the filter design. To further validate the proposed idea, a similar topology in microwave regime is designed and measured, where the microwave feeding can be easily realized by embedding a balun structure from unbalanced microstrip line to balanced CPS. The measurement of the microwave filter prototype illustrates ultra-wideband bandpass filtering characteristics with return losses of over 10 dB and average insertion losses of 3.2 dB in the passband of 1.9-14 GHz. The presented work may have significant potentials to develop the miniaturization of various planar plasmonic devices and integrated circuits in microwave and THz regimes.

© 2013 IEEE. Intelligent regulation for human exercise behaviors becomes significantly necessary for exercise medicine after the COVID-19 epidemic. The key issue of exercise regulation and its potential development for intelligent exercise is to describe human exercise physiological behaviors in a more accurate and sufficient manner. Here, a non-parametric modeling method with kernel-based regularization is presented to estimate cardiorespiratory biomarkers (i.e., oxygen uptake ( ${\dot {\text {V}}}$ O2) and carbon dioxide output ( ${\dot {\text {V}}}$ CO2) by merely non-invasively monitoring the indicator of exercise intensity (e.g., walking speed). Using the kernel-based non-parametric modeling, we show that ${\dot {\text {V}}}$ O2 and ${\dot {\text {V}}}$ CO2 behaviors in response to continuous and diversified exercise intensity stimulations can be quantitatively described. Furthermore, the dataset from the stairs experiment with a proper protocol is applied in the kernel parameter selection, and this selection approach is compared with the numerical simulation approach. The comparison results illustrate an improvement of 4.18% for oxygen uptake and 7.63% for carbon dioxide output in a half period, and 11.00% for oxygen uptake and 12.60% for carbon dioxide output in one period when using the kernel parameter selected from the stairs exercise. Moreover, the advantages of using the non-parametric model, the necessity of sufficient stimulation for identification and the importance of the kernel regularization term are also addressed in this paper. This method provides fundamental work for the practice of intelligent exercise.

Mitigation of nitrous oxide (N2O) emissions is of primary importance to meet the targets of reducing carbon footprints of wastewater treatment plants (WWTPs). Despite of a large amount of N2O mitigation studies conducted in laboratories, full-scale implementation of N2O mitigation is scarce, mainly due to uncertainties of mitigation effectiveness, validation of N2O mathematical model, risks to nutrient removal performance and additional costs. This study aims to address the uncertainties by investigating the quantification, development and implementation of N2O mitigation strategies at a full-scale sequencing batch reactor (SBR). To achieve this, N2O emission dynamics, nutrient removal performance and operation of the SBR were monitored to quantify N2O emissions, and identify the N2O generation mechanisms. N2O mitigation strategies centered on reducing dissolved oxygen (DO) levels were consequently proposed and evaluated using a multi-pathway N2O production mathematical model before implementation. The implemented mitigation strategy resulted in a 35% reduction in N2O emissions (from the emission factor of 0.89 ± 0.05 to 0.58 ± 0.06%), which was equivalent to annual reduction of 2.35 tonne of N2O from the studied WWTP. This could be mainly attributed to reductions in N2O generated via the NH2OH oxidation pathway due to the lowering of DO level. As the first reported mitigation strategy permanently implemented at a full scale WWTP, it showcased that the mitigation of N2O emissions at full-scale is feasible and that widely accepted N2O mitigation strategies developed in laboratory studies are also likely effective in full-scale plants. Furthermore, the close agreement between the validated and predicted N2O emission factors (0.58% vs 0.55%, respectively), showed that the N2

© 2001-2012 IEEE. In this paper, an innovative algorithm for Tri-Axial Magnetometers calibration based on the magnetic inclination is proposed. The proposed 'Inclination based Calibration method' uses the fact that the angle between the local gravity and magnetic field is invariant hence overcoming the limitations of most existing in-field calibration methods which require nonlinear optimization. This calibration algorithm is formulated as the solution to a linear least square problem. A commonly used 9-parameter model and its associated 12-observation Icosahedron experimental scheme were developed to evaluate its applicability to calibrated Tri-Axial Magnetometers based on measured acceleration and magnetic data. The results show that the algorithm can provide effective calibration results for the magnetic field in both simulation and experiments. In addition, the influence of accelerometers data applied in this algorithm is investigated by simulation and experiment to demonstrate the importance of accelerometers data accuracy. The acceleration value after effective calibration is demonstrated to make an improvement in the estimation results.

Tillering or branching is an important agronomic trait in plants, especially cereal crops. Previously, in barley (Hordeum vulgare) ‘Vlamingh’, we identified the high number of tillers1 (hnt1) mutant from a γ-ray-treated segregating population. hnt1 exhibited more tillers per plant, narrower leaves, and reduced plant height compared with the wild-type parent. In this study, we show that the hnt1-increased tiller number per plant is caused by accelerated outgrowth of tiller buds and that hnt1 narrower leaves are caused by a reduction in vascular tissue and cell number. Genetic analysis revealed that a 2-bp deletion in the gene HORVU2Hr1G098820 (HvHNT1), encoding a trypsin family protein, was responsible for the hnt1 mutant phenotype. Gene function was further confirmed by transgenic complementation with HvHNT1 and RNA interference experiments. HvHNT1 was expressed in vascular tissue, leaf axils, and adventitious root primordia and shown to negatively regulate tiller development. Mutation of HvHNT1 led to the accumulation of a putative cyclophilin-type peptidyl-prolyl cis/trans-isomerase (HvPPIase), which physically interacts with the HvHNT1 protein in the nucleus of plant cells. Our data suggest that HvHNT1 controls tiller development and leaf width through HvPPIase, thus contributing to understanding of the molecular players that control tillering in barley…

У статті наведено сучасне бачення особливостей електрокардіограми осіб, які займаються спортом. Висвітлено зміни ЕКГ, що найчастіше виявляють у спортсменів: екстрасистолію, синдром ранньої реполяризації шлуночків, неповну блокаду правої ніжки пучка Гіса, зміни кінцевої частини шлуночкового комплексу, ювенільні зміни зубця Т. В огляді наведені власні результати досліджень ЕКГ у групах видів спорту та у спортсменів окремих видів спорту, що розрізняються за спортивною кваліфікацією., The article presents a modern vision of the lectrocardiographic features in persons involved in sports. The report addresses the most common changes on ECG in athletes, namely extrasystole, early repolarization syndrome, incomplete right bundle branch block, changes in the terminal part of the ventricular complex, juvenile changes of T-wave. The review presents our own results of ECG test in athletes of various groups of sports and athletes of certain sports with different sports qualification., В статье представлено современное видение особенностей электрокардиограммы лиц, занимающихся спортом. Рассмотрены наиболее часто встречающиеся изменения ЭКГ спортсменов: экстрасистолия, синдром ранней реполяризации желудочков, неполная блокада пучка Гиса, изменения конечной части желудочкового комплекса, ювенильные изменения зубца Т. В обзоре представлены собственные результаты исследований ЭКГ у спортсменов отдельных видов спорта, различающихся по спортивной квалификации.

The accumulation of soil organic matter (SOM), poorly crystalline Fe oxides and metal-humus complexes is a trait of non-allophanic Andosols. The process of andosolization, which may occur in ferrallitic soils with high organic matter content, can involve transformation from well crystallized Fe oxides to poorly crystallized Fe oxides and Fe-humus complexes. This study investigates such changes in pedogenic Fe mineral associations for a soil toposequence between 1500 and 2260 m altitude along the southern flank of the volcanic Bambouto Mountains, Western Cameroon. The soils consist of highly weathered material, dominated by kaolinite, gibbsite and Fe oxides, grading to Protoandic Umbrisols at high altitude, recording an increase in SOM content with increasing altitude. As revealed by selective extraction analysis, the relative amount of poorly crystalline Fe oxides is low in the Bt and Bo horizons of low-altitude pedons, as well as in deep subsurface horizons of the highaltitude pedons. In contrast, it is significantly higher in the A and Bw horizons of the high-altitude pedons, with a clear increase with increasing altitude. Mössbauer spectroscopy analysis of B horizon samples identifies goethite as the dominant Fe oxide phase in nearly all pedons, with higher hematite contents in a mid-altitude zone marked by lower annual rainfall than in other parts of the toposequence. The Mössbauer spectra also reveal the presence of dissolved organic matter (DOM)-ferrihydrite, whose abundance is greatest in the Bw horizon of the high-altitude pedons, with an increase in relative abundance with increasing altitude. The observed patterns are attributed to dissolution-reprecipitation of Fe oxides that initially formed through ferrallitic weathering of volcanic parent materials that were roughly uniform along the toposequence. At high altitude, coupled hematite dissolution and DOM-ferrihydrite formation are favoured by high organic matter contents and low pH, related to cool humid

It is often argued that psychoanalysis has declined in prominence since its ascendance in the mid-20th century. To assess this claim we examined the trajectory of psychoanalytic concepts from 1900 to 2008 in the massive Google Books database. The changing relative frequency of a sample of English-language psychoanalytic terms was explored and compared to a sample of terms in French. The frequency of the English terms was further explored from 2008 to 2017 using the Corpus of Contemporary American English (COCA). The English terms rose steeply from the 1940s and declined steeply from the early 1990s. In contrast, the French terms rose steeply from the 1960s and plateaued from the 1970s. In addition, psychoanalytic terms were markedly more prominent in French since the 1960s. The findings are discussed in the context of historical trends in the reception of psychoanalysis in the Anglophone and Francophone worlds.

The cross section of atomic electron Compton scattering $\gamma + e \rightarrow \gamma^\prime + e^\prime $ was measured in the 4.40--5.475 GeV photon beam energy region by the {\em PrimEx} collaboration at Jefferson Lab with an accuracy of 2\% and less. The results are consistent with theoretical predictions that include next-to-leading order radiative corrections. The measurements provide the first high precision test of this elementary QED process at beam energies greater than 0.1 GeV., Comment: 6 pages, 4 figures

Bullying in campus has attracted more and more attention in recent years. By analyzing typical campus bullying events, it can be found that the victims often use the words “help” and some other appealing or begging words, that is to say, by using the artificial intelligence of speech recognition, we can find the occurrence of campus bullying events in time, and take measures to avoid further harm. The main purpose of this study is to help the guardians discover the occurrence of campus bullying in time by real-time monitoring of the keywords of campus bullying, and take corresponding measures in the first time to minimize the harm of campus bullying. On the basis of Sunplus MCU and speech recognition technology, by using the MFCC acoustic features and an efficient DTW classifier, we were able to realize the detection of common vocabulary of campus bullying for the specific human voice. After repeated experiments, and finally combining the voice signal processing functions of Sunplus MCU, the recognition procedure of specific isolated words was completed. On the basis of realizing the isolated word detection of specific human voice, we got an average accuracy of 99% of appealing words for the dedicated speaker and the misrecognition rate of other words and other speakers was very low.

School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4%, and that of daily-life reaches 97.3%.

Hundreds of millions of youths suffer from various violence each year. The negative impacts motivate much research and numerous studies on violence. However, those attempts went their own way, making the achieved results, especially from engineering, not so useful. Based on the Sensor and Social Web (SEWEB) concept, Violence Detection (VITEC) was proposed as a possible framework to facilitate multi-disciplinary researchers in their fight against violence. At its core, it consists of a primary agent, which is violence detection using physiological signals and activity recognition, and a secondary agent, which is violence detection using surveillance video. The second layer of the proposed framework contains a cloud computing service with a Personal Safety Network (PSN) database. The cloud computing service manages all data, notifications, and some more thorough processing. The upper layer is for both observed young persons and members of the PSN. The proposed framework offers business opportunities. The existing school violence/bullying intervention programs can take advantage of VITEC by providing almost instant notifications of violent events, enabling the victims to get immediate help and intensifying coordination among different sectors to fight against violence. In the long run, VITEC may provide an answer related to the vision of having a world free from violence in 2030, as addressed by the UN Special Representative of Secretary-General on violence against children.