Miracle-dz/UniFAR-FTUs huggingface dataset:
Does biochar addition improve soil physicochemical properties, bacterial community and alfalfa growth for saline soils?

Biochar addition has been proven to have great potential for improving soil physicochemical properties and crop growth in salt‐affected soils. However, whether adding biochar could affect alfalfa growth and soil bacterial community in salt‐affected soils of arid regions remains poorly understood. A pot experiment was carried out to investigate the effects of corn straw biochar addition on soil physicochemical properties, bacterial community and alfalfa yield in saline soils. There were 12 treatments in total, combining three biochar addition rates of 0, 20 and 40 g kg−1 (referred to as B0, B2 and B4, respectively) and four salinity levels of 0, 2, 4 and 6 g kg−1 (referred as to S0, S2, S4 and S6, respectively). The results indicated that biochar addition significantly increased soil organic carbon (SOC), electrical conductivity (EC), soil water content (SWC), available nitrogen (AN) and phosphorus (AP) and mesopores fraction, but decreased soil bulk density (BD), pH and macropores fraction for different salinity levels. In general, biochar addition had an adverse effect on alfalfa yield due to the aggregated stresses from the increased EC and mesopores fraction, especially at high salinity levels. On average, biochar addition decreased the total yield of the two years by 14.2%, 25.3%, 38.3% and 60.4% for S0, S2, S4 and S6, respectively. Biochar addition significantly changed bacterial community composition with higher relative abundances of Proteobacteria and Bacteroidetes but lower relative abundances of Actinobacteria and Acidobacteria under S2–S6. On average, both soil salinity and biochar treatments decreased soil bacterial richness by 5.7–14.6% and 4.1–8.8% compared with S0 and B0, respectively, but they did not significantly affect the bacterial diversity. The redundancy analysis (RDA) suggested that EC, SWC, pH, AN and AP were the most critical factors driving the change of soil bacterial community structure accounting for 69.9%, 64.7% 61.6%, 57.0% and 51.8% of the variation, respectively. Therefore, our results provided valuable insight into how biochar addition would affect alfalfa yield and soil properties, including bacterial community richness and composition and structure of saline soils in arid regions.
The short- to medium-term predictive validity of the HoNOS-Secure on violence in a medium-secure forensic psychiatric ward in New Zealand

ABSTRACT Violence risk prediction in forensic mental health care settings is important in facilitating its management. Although not specifically developed for risk assessment, previous studies have demonstrated that the Health of the Nation Outcome Scales-Secure (HoNOS-secure) and its predecessor may have some predictive validity for violence. This retrospective cohort study examines the predictive validity of HoNOS-secure scores rated on admission in a medium-secure forensic psychiatric ward in New Zealand for inpatient violence in the short- to medium-term at 30 and 90 days respectively. A sample size of 33 eligible admissions was obtained within the study period (2005–2017), and incident reports were rated by two independent clinicians for the occurrence and level of inpatient violence. Poisson, negative binomial and Cox regression analyses were used to investigate HoNOS-secure predictivity for the occurrence of inpatient violence, number of violent events, and time to the first violent event respectively. Although the HoNOS-secure total and subscale scores were not found to be significant predictors, post hoc analysis at the item level demonstrated association of various items with violence. Future studies could consider pooling data from multiple study sites, extending follow-up periods and adopting a prospective design.
Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence

Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.
Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

Purpose Distinguishing obstructive sleep apnea (OSA) in a high-risk population remains challenging. This study aimed to investigate clinical features to identify children with OSA combined with craniofacial photographic analysis. Methods One hundred and forty-five children (30 controls, 62 with primary snoring, and 53 with OSA) were included. Differences in general demographic characteristics and surface facial morphology among the groups were compared. Risk factors and prediction models for determining the presence of OSA (obstructive sleep apnea-hypopnea index>1) were developed using logistic regression analysis. Results The BMI (z-score), tonsil hypertrophy, and lower face width (adjusted age, gender, and BMI z-score) were showed significantly different in children with OSA compared with primary snoring and controls (adjusted p<0.05). The screening model based on clinical features and photography measurements correctly classified 79.3% of the children with 64.2% sensitivity and 89.1% specificity. The area under the curve of the model was 81.0 (95% CI, 73.5–98.4%). Conclusion A screening model based on clinical features and photography measurements would be helpful in clinical decision-making for children with highly suspected OSA if polysomnography remains inaccessible in resource-stretched healthcare systems.
Prevalence and Associated Risk Factors of Urinary Tract Infection among Diabetic Patients: A Cross-Sectional Study

Urinary tract infections (UTIs) are one of the most common long-term complications of diabetes mellitus (DM). Additionally, various factors, such as socio-demographics, type of DM, fasting blood glucose, regular diabetes monitoring, comorbid chronic diseases, HbA1c, body mass index (BMI), and duration of DM, are also thought to predispose individuals to developing UTIs more frequently when they have DM. This research aims to evaluate the risk factors for UTIs and their prevalence among people with DM in Saudi Arabia (KSA). This cross-sectional study was conducted among 440 adults with type 1, type 2, and gestational DM. The participants had to be at least 18 years old, of both genders, and had been suffering from DM for any period of time. A self-administered questionnaire was utilized to collect data on demographic characteristics, such as sex, age, height, weight, material state, education level, income, and clinical profiles of DM and UTI. The crude (COR) and adjusted odds ratios (AOR) were calculated using logistic regression in the IBM SPSS software. The incidence of types 1 and 2 DM and gestational diabetes reached 34.1, 60.9, and 5%, respectively. Most of the participants had first-degree relatives with DM (65.9%). UTI was common in 39.3% of participants. A chi-squared statistical analysis revealed that the frequency of UTI varied depending (χ2 = 5.176, P = 0.023) on the type of DM. Burning urination and abdominal pain were the most common symptoms. The CORs for sex, marital status, hypertension, and BMI were significant (P < 0.05) and had values of 2.68 (95% CI = 1.78–4.02), 0.57 (95% CI = 0.36–0.92), 1.97 (95% CI = 1.14–3.43), and 2.83 (95% CI = 1.19–2.99), respectively. According to the adjusted model, only sex influenced the occurrence of UTIs. The AOR for sex was 3.45 (95% CI = 2.08–5.69). Based on this study, the authorities related to the health of DM patients can use its findings to guide awareness programs and clinical preparedness.
Radioprotection for Astronauts’ Missions: Numerical Results on the Nomex Shielding Effectiveness

Space missions with humans expose the crews to ionizing radiation, mainly due to the galactic cosmic radiation (GCR). All radiation protection programs in space aim to minimize crews’ exposure to radiation. The radiation protection of astronauts can be achieved through the use of shields. The shields could serve as a suit to reduce GCR exposure and, in an emergency, as a radiation shelter to perform necessary interventions outside the space habitat in case of a solar proton event (SPE). A space radiation shielding that is suitable for exploration during space missions requires particular features and a proper knowledge of the radiation type. This study shows the results of numerical simulations performed with the Geant4 toolkit-based code DOSE. Calculations to evaluate the performance of Nomex, an aramidic fiber with high mechanical resistance, in terms of dose reduction to crews, were performed considering the interaction between protons with an energy spectrum ranging from 50 to 1100 MeV and a target slab of 20 g/cm2. This paper shows the properties of secondary products obtained as a result of the interaction between space radiation and a Nomex target and the properties of the secondary particles that come out the shield. The results of this study show that Nomex can be considered a good shield candidate material in terms of dose reductions. We also note that the secondary particles that provide the greatest contribution to the dose are protons, neutrons and, in a very small percentage, α-particles and Li ions.
Local co structure and ferromagnetism in ion-implanted Co-doped LiNbO3

C. Song,1 F. Zeng,1 Y. X. Shen,1 K. W. Geng,1 Y. N. Xie,2 Z. Y. Wu,2 and F. Pan1,* 1Laboratory of Advanced Materials, Department of Materials Science and Engineering, Tsinghua University, Beijing 100084, People’s Republic of China 2Beijing Synchrotron Radiation Facility, Chinese Academy of Science, Beijing 100039, People’s Republic of China Received 5 April 2006; revised manuscript received 23 April 2006; published 22 May 2006
The influence of spin–orbit coupling on the band gap of Heusler alloys

The band gap of half-metallic ferromagnets can be affected by the spin–orbit coupling, which introduces there a small, but non-vanishing, density of states. We study this effect in the case of Heusler alloys. We find that, as a rule, the spin polarization in the middle of the gap decreases for compounds of heavier elements.
Circumnuclear Star Formation in Active Galaxies

We study the gas kinematics and chemical abundances in the inner regions of six galaxies with active galactic nuclei. The galaxies were selected for having star-forming regions in rings or spirals within a few kiloparsecs of the nucleus. The goal of the project is to search for any connections between the gas-dynamics, the chemical abundances, and the nuclear activity. Narrowband images have been obtained to map out the ionized gas. Medium-dispersion long-slit spectroscopy at several positions over the nuclear region has been used to obtain the gaseous velocity field and distribution of excitation. The H ii regions in the rings are located near the turnover point of the rotation curves, suggesting association with the inner Lindblad resonance. The nuclear ring in the LINER NGC 1326 may be located between the two inner Lindblad resonances, and the inner rings in the Seyfert 2 galaxy NGC 3081 and the LINER/ Seyfert 1 NGC 7213 may be associated with the Ω – κ/4 resonance. A warm ionized medium (WIM) is found to pervade the inner region in all galaxies. There is a correlation between the [N II]/Hα and [S ii]/ Hα ratios and the FWHM of the [N ii] λ6584 emission line in this WIM, indicating that shocks contribute to the ionization of the gas. We show that contamination by this diffuse emission increases the measured [N ii]/Hα and [S ii]/Hα ratios at H ii regions close to the nuclei and thus may account, in part, for the systematically higher [N ii]/Hα and [S ii]/Hα found in the present work and by previous authors for near-nuclear H ii regions compared with H ii regions farther out in the disk of the galaxy. In the present paper, we correct for this contamination by interpolating the contribution of the WIM at the H ii regions and subtracting it off. Oxygen and nitrogen abundances are obtained for 18 H ii regions and are extrapolated to obtain the abundance of the nuclear gas, which is found to range from solar to 2 times solar for oxygen and from 2 to 4 times solar for nitrogen. Such values are similar to those observed in nonactive galaxies with the same luminosity and morphological type and do not support the idea that the central regions of active galaxies have undergone unusual chemical processing.
Online Robust Subspace Clustering With Application to Power Grid Monitoring

In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of large-scale data such as synchronized phasor measurements in the power grid, which is challenging due to computational complexity, memory requirement, and missing and corrupt observations. In order to mitigate these issues, a low-rank representation (LRR) model-based subspace clustering problem is formulated that can handle missing measurements and sparse outliers in the data. Then, an efficient online algorithm is derived based on stochastic approximation. The convergence property of the algorithm is established. Strategies to maintain a representative yet compact dictionary for capturing the subspace structure are also proposed. The developed method is tested on both simulated and real phasor measurement unit (PMU) data to verify the effectiveness and is shown to significantly outperform existing algorithms based on simple low-rank structure of data.
Multi-objective optimization design in a centrifugal pump volute based on an RBF neural network and genetic algorithm

The hydraulic and acoustic performance of centrifugal pumps are interrelated and contradictory in hydraulic structure design. An optimization design method of a volute based on a radial basis function (RBF) neural network and genetic algorithm (GA) is proposed to solve this problem. The efficiency and total sound pressure level of the centrifugal pump are used as the optimization objectives. The diameter of the base circle, the width of the base circle, the installation angle of the volute tongue, and the height of the volute diffuser tube are used as the optimization variables. Latin hypercube sampling (LHS) is used to establish the sample space, the RBF neural network is used to build the agent model between the optimization variables and objectives, and the GA is used to multi-objective optimization. For the Pareto solution set obtained, two extremal individuals and initial individuals are selected for comparative analysis of hydraulic and acoustic performance under different working conditions. The result demonstrates that under the rated working condition, compared with the initial individual, the efficiency of the optimal individual of efficiency increases by 3.79%; the internal noise of the optimal individual of sound pressure level decreases by 5.5%, and the external noise decreases by 2.3%.
Management of third molars projected into the temporal fossa and infratemporal fossa: systematic literature review

Introduction: Projection of the maxillary third molar into the temporal or infra temporal fossa is a rare complication. There is no recommendation for the management of such complication. The aim of this work is to try establishing a removal protocol. Materials and methods: A systematic review of the literature was conducted using all cases reports of iatrogenic projection of the tooth into the temporal fossa or infratemporal fossa and how they were treated. The last search was conducted in July 2021. Results: 27 cases involving 28 teeth, published between 1986 and 2020 were included. Discussion: The intraoral approach seems to be preferred in the first instance for teeth in the infra temporal fossa and a cutaneous approach for teeth in the temporal fossa. This should be done within 3–6 weeks after projection to attempt removal. This allows the formation of a fibrous capsule and the downward migration of the tooth to facilitate removal. The use of complementary technological means can improve the chances of success. Conclusion: Resulting a flow chart, who is an aid to management of this type of situation, providing a clear idea of the approach to be taken.
A Wireless Self-Powered Sensor Network Based on Dual-Model Convolutional Neural Network Algorithm for Tennis Sports

More and more scholars turn their attention to the use of digital and systematic management methods to further improve the safety and effectiveness of tennis. At present, most of these systems are based on video monitoring technology, their actual operation process is limited by the deployment environment, and the cost is very high. In order to obtain the track of tennis players, this article uses the sensor array in the intelligent insole to collect the original data. A dual-model convolutional neural network (DMCNN) structure is designed to further improve the computational efficiency and accuracy of tennis players’ gait recognition. Combined with a quaternion and complementary filtering algorithm, the rotation matrix is established to convert the data in the sensor coordinate system to the reference coordinate system. Use multiple sensors in the smart insole to collect linear and curve track data from tennis players. After the unit conversion and normalization of the original data, the acceleration and angular velocity data in the sensor coordinate system are converted to the reference coordinate system by using quaternions to represent the rotation matrix and using a complementary filtering algorithm to update the quaternions. The experimental results show that the displacement error of the scheme is small and the performance is good when the track tracking of tennis players is realized. The error between the estimated distance and the actual distance is within 6%, which proves the accuracy of the linear trajectory experiment.
On the Dynamics of a Storm Track

Abstract An idealized primitive equation model is used to determine the factors controlling the dynamics and maintenance of eddy activity in a storm track. The results show that localized regions of enhanced baroclinicity do not necessarily lead to localization of eddy activity. By studying the energetics of the storm track, it is shown that while baroclinic conversion does indeed correlate with the region of maximum baroclinicity, it is the downstream radiation of energy through the ageostrophic geopotential fluxes which acts as a trigger for the development and maintenance of eddy activity over less baroclinic regions, extending the region of eddy activity much further downstream from the region of high baroclinicity. Examples of eddy life cycles are given that show that convergence and divergence of ageostrophic fluxes can dominate baroclinic and barotropic conversion, especially in regions with weak baroclinicity. Factors that may limit the zonal extent of a storm track are discussed. Evidence of down.
Adaptive visual detection of industrial product defects

Visual inspection of the appearance defects on industrial products has always been a research hotspot pursued by industry and academia. Due to the lack of samples in the industrial defect dataset and the serious class imbalance, deep learning technology cannot be directly applied to industrial defect visual inspection to meet the real application needs. Transfer learning is a good choice to deal with insufficient samples. However, cross-dataset bias is unavoidable during simple knowledge transfer. We noticed that the appearance defects of industrial products are similar, and most defects can be classified as stains or texture jumps, which provides a research basis for building a universal and adaptive industrial defect detection model. In this article, based on the idea of model-agnostic meta-learning (MAML), we propose an adaptive industrial defect detection model through learning from multiple known industrial defect datasets and then transfer it to the novel anomaly detection tasks. In addition, the Siamese network is used to extract differential features to minimize the influence of defect types on model generalization, and can also highlight defect features and improve model detection performance. At the same time, we add a coordinate attention mechanism to the model, which realizes the feature enhancement of the region of interest in terms of two coordinate dimensions. In the simulation experiments, we construct and publish a visual defect dataset of injection molded bottle cups, termed BC defects, which can complement existing industrial defect visual data benchmarks. Simulation results based on BC defects dataset and other public datasets have demonstrated the effectiveness of the proposed general visual detection model for industrial defects. The dataset and code are available at https://github.com/zhg-SZPT/MeDetection.
Food prices response to global and national factors: Evidence beyond asymmetry

Abstract The current series of studies examine how local food prices are affected by domestic and international factors. This research advances the existing body of knowledge by examining this effect at different quantiles, frequencies, and times. We use research data from January 1999 to August 2022 using three local and six global variables as independent variables. Additionally, our study uses recent econometric methods, including Wavelet Coherence, Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ). Moreover, this research uses the Quantile Regression (QR) approach to determine how reliable the findings are. Based on the GCQ approach, the results demonstrate that the correlation persists at most of the quantiles. Moreover, the WC results demonstrate a substantial association between local prices of food and the independent factors across various frequencies and times. Additionally, QQR estimates demonstrate that the impact of exogenous variables on food prices vary among quantiles. These findings are also supported by the QR method. Last but not least, our study offers policy suggestions obtained based on the results of this study.