Experimental study on the influence of turbulence on hail impacts.

Hailstorms, characterized by their intensity, are often accompanied by strong winds and heavy rain, posing significant destructive potential. Data indicate that the economic losses caused by hail to buildings, particularly solar panels, have been increasing annually. However, research on the hail resistance of photovoltaic panels has predominantly focused on the isolated effects of hail impacts and wind loads, neglecting the coupling effects between wind and hail. In this study, a device was designed to couple both wind and hail. The effects of turbulence, hail size, and velocity on hail impact behavior were systematically studied and quantified. A predictive formula for the peak load of hail impact on structures was established. The results indicate that the impact of turbulence on hail is significant. When turbulence intensity varies with hail velocity, hail impact force increases as turbulence decreases and hail velocity increases. When both turbulence and hail diameter vary, the impact force of smaller hailstones shows less variation with increasing turbulence. According to variance analysis, hail velocity is the most significant factor affecting hail impact, followed by hail diameter and finally turbulence. The regression equation is given by F = - 0.624 I u + 5116.25 D + 7.85 V hail - 259.709 , where F represents the peak impact force in Newtons (N), I u denotes the turbulence intensity, D is the hail diameter in meters (m), and V hail is the hail velocity in meters per second (m/s).

Supplementary decompression and extended surgical time contribute to hidden blood loss In percutaneous endoscopic lumbar discectomy.

Background: It is widely believed that the Percutaneous endoscopic lumbar discectomy (PELD) is associated with minimal blood loss. However, significant perioperative hidden blood loss (HBL) is frequently unaccounted for. This study aimed to investigate HBL and peri-operative factors contributing to HBL in a series of individuals undergoing PELD. Method: ology: A total of 156 consecutive patients with a mean age of 43.6 years (ranging from 18 to 80 years) who underwent PELD at our department from May 2019 to November 2020, were included in the study. Factors including gender, age, body mass index, symptom duration, operation approach/technique, operation duration, the presence of associated chronic diseases, and improvements in the Visual Analog Scale (VAS) score, Japanese Orthopaedic Association (JOA) score and the Oswestry Disability Index (ODI) were analyzed, and Gross's formula was applied to calculate blood loss, which was used to determine HBL. Results: The average total blood loss (TBL) was 221.0 ± 126.2 mL, while the average HBL was 181.7 ± 119.0 mL (82.2 % of TBL). There was no statistically significant difference in HBL between the transverse surgical approach and the interlayer approach. Additionally, no significant differences were observed in improvements in VAS, JOA, and ODI scores between the two surgical approaches. However, the multivariate linear regression analysis revealed that longer surgical time and foraminal decompression were factors contributing to the increase in HBL, which subsequently led to the occurrence of post-operative anemia. Conclusion: HBL is significant in PELD cases with long surgical time and lumbar foraminal decompression.

Balance Control is Sequentially Correlated with Proprioception, Joint Range of Motion, Strength, Pain, and Plantar Tactile Sensation Among Older Adults with Knee Osteoarthritis.

BACKGROUND: Patients with knee osteoarthritis (KOA) are at high risk for falls, which is attributed to their impaired balance control. Identifying factors associated with balance control facilitates the development of precise KOA rehabilitation programs. This study was to investigate the correlations of balance control with proprioception, plantar tactile sensation (PTS), pain, joint range of motion (ROM), and strength among older adults with and without KOA, as well as the magnitudes and sequence of correlation of these factors to balance control. METHODS: A total of 240 older adults with (n = 124, female: 84, age: 68.8 ± 4.0 years) and without (n = 116, female: 64, age: 67.9 ± 3.5 years) KOA were recruited and assigned to the KOA and control groups. Their proprioception, PTS, pain, ROM, and strength were measured. Pearson or Spearman correlations were used to test whether they were significantly related to their Berg Balance Scale (BBS), and factor analysis and multivariate linear regression were used to determine the degrees of correlation between each factor and the BBS. RESULTS: Compared to the control group, the KOA group had lower BBS score, larger proprioception and PTS thresholds, smaller ROM, and less strength (p: 0.008, < 0.001-0.016, < 0.001-0.005, < 0.001-0.014, and < 0.001-0.002, respectively). In the KOA group, the BBS was weakly to moderately correlated with proprioception, PTS, pain, ROM, and strength (r: 0.332-0.501, 0.197-0.291, 0.340, 0.212-0.508, and 0.236-0.336, respectively). While in the control group, the BBS was correlated with proprioception and strength (r: 0.207-0.379, and 0.212-0.410). In the KOA group, BBS = 54.41+ (0.668*strength) - (0.579*PTS) - (1.141*proprioception) + (1.054* ROM) - (0.339*pain). While in the control group, BBS = 53.85+ (0.441*strength) - (0.677*proprioception). CONCLUSION: Worse proprioception and PTS, smaller ROM, and less strength were detected among older adults with KOA, and their proprioception, PTS, pain, ROM, and strength were all related to balance control. Proprioception had the strongest correlations, followed by ROM, strength, pain, and PTS. Precise KOA rehabilitation programs may be proposed following the sequence of improving the five factors.

Biometric factors associated with the postoperative visual performance of a multifocal intraocular lens.

Purpose: To identify the biometric factors associated with postoperative visual performance after uneventful phacoemulsification with multifocal intraocular lens (MIOL) implantation. Methods: In this retrospective cohort study, 72 eyes of 72 patients implanted with the HumanOptics Diff-aAY MIOL were included. Preoperative examination data including the white-to-white distance (WTW), anterior chamber depth (ACD), axial length and corneal astigmatism were gathered through the electronic medical records. One month postoperatively, the pupil parameters, corneal aberrations, corneal astigmatism, IOL tilts and IOL decentrations were measured using an OPD-Scan III aberrometer. Postoperative visual performance parameters were recorded as the visual acuity, depth of focus, modulation transfer function (MTF) and point spread function (PSF) values, area under log contrast sensitivity function (AULCSF), retinal straylight and visual function questionnaire scores. Univariate and multivariate linear regression analyses were then performed to evaluate the associations between the potential biometric factors and postoperative visual outcomes. Results: Younger age predicted greater MTF and PSF values, better AULCSF and better retinal straylight (P < 0.05). A lower corneal trefoil predicted better MTF and PSF values (P < 0.05). Smaller IOL decentration predicted better distance-corrected near visual acuity, greater AULCSF and better retinal straylight (P < 0.05). A less negative spherical equivalent (SE) predicted better MTF values (P = 0.017), while a more negative SE predicted better Visual Function Index-14 (VF-14) questionnaire scores and satisfaction scores (P < 0.05). A higher IOL power predicted better best corrected distance visual acuity (P = 0.005). Lower preoperative corneal astigmatism predicted greater MTF values (P = 0.020). Lower postoperative corneal astigmatism, smaller corneal high-order aberrations (HOAs), smaller photopic pupil size, larger WTW and deeper ACD predicted a better AULCSF (P < 0.05). Conclusions: IOL decentration, IOL power, age, preoperative and postoperative corneal astigmatism, SE, photopic pupil size, corneal trefoil, WTW, ACD and corneal HOAs were significantly associated with postoperative visual performance. These findings might aid in patient selection prior to MIOL implantation.

Hidden blood loss and its influencing factors after cement augmentation for vertebral metastasis.

Introduction: Few studies have focused on the risk factors for hidden blood loss (HBL) during cement augmentation surgery for pathologic vertebral compression fraction (PVCFs). Method: From January 2014 to December 2020, the clinical data of 169 PVCF patients (283 levels) who underwent cement augmentation were retrospectively analysed. HBL was calculated according to the linear Gross formula using the patient's average Hct during the perioperative course and PBV. Multivariate linear regression analysis was performed to evaluate the independent factors associated with HBL. Results: The mean HBL was 448.2 ± 267.2 ml, corresponding to 10.8% ± 6.2% of the patient blood volume (PBV). There were significant differences between pre- and postoperative haematocrit (Hct) (P < 0.001) and Hb (P < 0.001), and 132 patients developed anaemia postoperatively, while 79 patients had anaemia preoperatively (P < 0.001). Multivariate linear regression revealed that bone lesion quality (p = 0.028), number of PVCFs (p = 0.002), amount of bone cement (p = 0.027), bone cement leakage (p = 0.001), and percentage of vertebral height loss (VHL) (p = 0.011) were independent risk factors for HBL. Conclusion: In conclusion, patients with lytic vertebral destruction, larger amounts of bone cement, greater amounts of bone cement leakage, more PVCF(s), and greater percentages of VHL may be more prone to HBL.

Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme.

BACKGROUND: Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance. METHOD: 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies. RESULT: The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies. CONCLUSION: Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.

Co-occurrence of organophosphate esters and phosphorus fractions in river sediments: Implications for pollution prediction and environment risk assessment.

Organophosphate esters (OPEs) and phosphorus (P) are widespread pollutants in aquatic ecosystems, presenting potential ecological risks. However, there is still a lack of comprehensive understanding of their relationships in sediments. In this study, we investigated the co-occurrence and behaviors of the OPEs and P in urban river sediments. The results indicated serious OPE and P pollution in the study area, with substantial spatial variations in the contents and compositions. The OPE congeners and P fractions exhibited different correlations, particularly more significant linear relationships (R = 0.455 - 0.816, p < 0.05) were observed between the aryl-OPEs and P fractions, potentially due to the influence from sources, physicochemical properties, and total organic carbon. About 56 to 71% of variability in predicting the concentrations of aryl-OPE can be explained by the multiple linear regression model using the Fe/Al- and Ca-bound P contents. The study regions exhibited greater aryl-OPEs ecological risks were consistent with the regions with more serious Total P pollution levels. This study represents the first report demonstrating the potential of Fe/Al-P and Ca-P contents in predicting aryl-OPE contents in heavily polluted sediments, providing a useful reference to comprehensively assess the occurrence and environmental behaviors of aryl-OPEs in anthropogenic polluted sediments.

Mechanistic Studies on Aluminum-Catalyzed Ring-Opening Alternating Copolymerization of Maleic Anhydride with Epoxides: Ligand Effects and Quantitative Structure-Activity Relationship Model.

Previous work has indicated that aluminum (Al) complexes supported by a bipyridine bisphenolate (BpyBph) ligand exhibit higher activity in the ring-opening copolymerization (ROCOP) of maleic anhydride (MAH) and propylene oxide (PO) than their salen counterparts. Such a ligand effect in Al-catalyzed MAH-PO copolymerization reactions has yet to be clarified. Herein, the origin and applicability of the ligand effect have been explored by density functional theory, based on the mechanistic analysis for chain initiation and propagation. We found that the lower LUMO energy of the (BpyBph)AlCl complex accounts for its higher activity than the (salen)AlCl counterpart in MAH/epoxide copolymerizations. Inspired by the ligand effect, a structure-energy model was further established for catalytic activity (TOF value) predictions. It is found that the LUMO energies of aluminum chloride complexes and their average NBO charges of coordinating oxygen atoms correlate with the catalytic activity (TOF value) of Al complexes (R2 value of 0.98 and '3-fold' cross-validation Q2 value of 0.88). This verified that such a ligand effect is generally applicable in anhydride/epoxide ROCOP catalyzed by aluminum complex and provides hints for future catalyst design.

The Study of Factors Affecting the Covid-19 Death Rate.

This study focuses on the analysis of the effects of potential factors on the Death Rate of Covid-19. The factors, in both demographic and economic aspects, have been discussed in the study through the extensive application of statistical inference, such as the Analysis of Variance (ANOVA), Univariate Linear Regression, and Multivariate Linear Regression. In the result section, the significance of the effects of these factors on Covid-19 has been carefully discussed.

The consumer price index prediction using machine learning approaches: Evidence from the United States.

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics-mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination-were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

The relationship between preoperative anemia and length of hospital stay among patients undergoing radical surgery for esophageal carcinoma: a single-centre retrospective study.

BACKGROUND: Although it is unclear if preoperative anemia affects patients undergoing radical resection of esophageal cancer, it does increase the length of stay (LOS) for surgical patients. Accordingly, the purpose of this study was to investigate if, after adjusting for other covariates, anemia was independently associated with LOS in people undergoing radical resection of esophageal cancer. METHODS: The retrospective cohort study included 680 patients undergoing radical esophageal cancer surgery between January 2010 and December 2020. Preoperative anemia was the targeted independent variable, while LOS was the target independent variable. Demographics, comorbidities, laboratory tests, surgery and anesthesia, postoperative outcomes, and complications were collected. Multivariate linear analyses were performed for variables that might influence preoperative anemia and LOS selection. Subgroup analysis using hierarchical variables was then used to test the potential relationship. RESULTS: The 647 individuals that were randomly chosen had an average age of 61.06 ± 8.16 years, and 77.43% of them were male. The prevalence of anemia was 36.6%. All patients recruited had an average length of stay (LOS) of 26.31 ± 13.19 days, 25.40 ± 11.44 days for patients who had no preoperative anemia, and 27.89 ± 15.66 days for patients who had preoperative anemia, p < 0.05. After adjusting for covariates, the results of fully adjusted linear regression revealed that preoperative anemia was significantly associated with LOS (ß = 2.04, 95%CI (0.13, 3.96) ), p < 0.05. The results of the subgroup analysis were basically accurate and steady. Regardless of gender, same outcomes were seen when preoperative anemia was defined as a Hb level < 13 g/dL (ß = 2.29, 95%CI (0.33, 4.25) ), p < 0.05. In addition, the LOS was shortened with the increase of preoperative hemoglobin (Hb) (ß= -0.81, 95%CI (-1.46, -0.1) ), p < 0.05. CONCLUSION: Preoperative anemia is typical in Chinese patients undergoing radical esophageal cancer resection and is independently associated with prolonged LOS.

Impact of, Factors for the Success of, and Concerns Regarding Transplant Patients' Skin Cancer Campaigns: Observational Study.

BACKGROUND: Due to rising health care costs, patients have sought alternative ways of addressing medical expenses. In particular, transplant patients have complex and expensive medical needs-including skin cancer surveillance-that may not be fully covered by insurance. One such method of financing medical costs is by crowdsourcing through web-based platforms, most notably GoFundMe. OBJECTIVE: Previous work identified factors associated with GoFundMe campaigns' fundraising success for dermatologic diseases. We sought to characterize these factors in transplant recipients' campaigns for funds raised for covering skin cancer-related costs. These factors include demographics, campaign traits, and subjective themes. METHODS: From January to April 2022, we analyzed GoFundMe campaigns using the following search terms chosen on the basis of author consensus: "transplant skin cancer," "transplant basal cell," "transplant squamous," "transplant melanoma," and "dermatologist transplant." Demographic data were coded from campaign text or subjectively coded based on author consensus. Campaigns were read completely by 2 independent coders and associated with up to 3 different themes. Linear regression was performed to examine the qualities associated with success, which was defined as funds raised when controlling for campaign goals. Logistic regression was used to examine qualities associated with extremely successful campaigns, defined as those raising funds over 1.5 times the IQR. RESULTS: Across 82 campaigns, we identified several factors that were associated with fundraiser success. Patients who experienced complications during infectious disease treatment, those who received a pancreas transplant, or those who died from their disease raised significantly more money. Patients older than 61 years raised significantly less money. Extremely successful campaigns (>US $20,177) were associated with campaigners who emphasized a disability from their disease, those who were reluctant to ask for help, or those who died due to their disease. CONCLUSIONS: Demographic and thematic factors are associated with transplant patients' skin cancer-related fundraising success, favoring those who are younger, in more extreme situations, and appear reluctant to ask for help; these findings are consistent with those of previous studies. Additionally, transplant patients have complex and expensive dermatologic needs that may not be fully covered by insurance, as reflected in their GoFundMe campaigns. The most commonly mentioned reasons for fundraising included living expenses or loss of income, inadequate or no insurance, and end-of-life costs. Our findings may inform transplant patients how to maximize the success of their campaigns and highlight gaps in health care coverage for skin cancer-related costs. Limitations include the possibility for misclassification due to the data abstraction process and limiting data collection to fundraisers available on GoFundMe while excluding those on other websites. Further research should investigate the ethical implications of crowdfunding, financial needs of this patient population, and potential ways to improve access to routine skin cancer surveillance among patients receiving transplants.

Machine learning-based approach to predict ice meltdown in glaciers due to climate change and solutions.

The fate of humankind and all other life forms on earth is threatened by a foe, known as climate change. All parts of the world are affected directly or indirectly by this phenomenon. The rivers are drying up in some places and in other places, it is flooding. The global temperature is rising every year and the heat waves are taking many souls. The cloud of "extinction" is upon the majority of flora and fauna; even humans are prone to various fatal and life-shortening diseases from pollution. This is all caused by us. The so-called "development" by deforestation, releasing toxic chemicals into air and water, burning of fossil fuels in the name of industrialisation, and many others have made an irreversible cut in the heart of the environment. However, it is not too late; all of this could be healed back with the help of technology and our efforts together. As per the international climate reports, the average global temperature has increased by a little more than 1 °C since 1880s. The research is primarily focused on the use of machine learning and its algorithm to train a model that predicts the ice meltdown of a glacier, given the features using the Multivariate Linear Regression. The research strongly encourages the use of features by manipulating them to determine the feature with a major impact on the cause. The burning of coal and fossil fuels is the main source of pollution as per the study. The research focuses on the challenges to gather data that would be faced by the researchers and the requirement of the system for the development of the model. The study is aimed to spread awareness in society about the destruction we have caused and urges everyone to come forward and save the planet.

Changing patterns in the highly contributing aerosol types/species across the globe in the past two decades.

With the rapidly changing aerosol emissions due to the increase in urbanization, energy consumption, population density, and industrialization in the past two decades across the globe, there is an evolution of different chemical properties of aerosols that are yet not quantified properly. Therefore, a rigorous attempt is made in this study to obtain the long-term changing patterns in the contribution of different aerosol types/species, to the total aerosol loading. This study is carried out only over those regions exhibiting either increasing or decreasing trends in the aerosol optical depth (AOD) parameter on a global scale. Applying the multivariate linear regression trend analysis on Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) aerosol species dataset obtained between 2001 and 2020, we found that despite the overall statistically significant decrease in total columnar AOD trend values over North-Eastern America, and Eastern and Central China regions, an increase in the dust and organic carbon aerosols is observed, respectively. As the uneven vertical distribution of aerosols can alter the direct radiative effects, the extinction profiles of different aerosol types obtained using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) dataset between 2006 and 2020, are further partitioned, for the first time, based on their presence in different altitudes (i.e., within the atmospheric boundary layer and free-troposphere) as well as measurement timing (i.e., daytime and night-time) regimes. The detailed analysis showed that there exists an overall higher contribution of aerosols persisting in the free troposphere region which in turn can have a long-term effect on climate due to their higher residence time, particularly absorbing aerosols. As the trends are mostly associated with the changes in energy use, regional regulatory policies, and/or changing background meteorology conditions, therefore this study also elaborates on the effectiveness of these factors with the changes obtained in different aerosol species/types over the region.

Geostatistical estimates of groundwater nitrate-nitrogen concentrations with spatial auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability.

Groundwater nitrate-nitrogen contamination typically involves several natural and anthropogenic factors, including those related to hydrology, hydrogeology, topography, and land use (LU). DRASTIC-LU-based aquifer contamination vulnerability could be used to characterize the pollution potentials of groundwater nitrate-nitrogen and to determine groundwater protection zones. This study used regression kriging (RK) with environmental auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability to investigate groundwater nitrate-nitrogen pollution in the Pingtung Plain of Taiwan. First, the relationship between groundwater nitrate-nitrogen pollution and assessments of aquifer contamination vulnerability was determined using stepwise multivariate linear regression (MLR). Subsequently, the residuals between the nitrate-nitrogen observations and MLR predictions were estimated by kriging techniques. Finally, the groundwater nitrate-nitrogen distributions were spatially analyzed using RK, ordinary kriging (OK), and MLR. The findings indicated that the land used for orchards and the medium- and coarse-sand fractions of vadose zones were associated with groundwater nitrate-nitrogen concentrations. The fertilizer used for orchards was identified as the primary source of groundwater nitrate-nitrogen pollution. The RK estimates could be used to analyze the characteristics of the pollution source for land used for orchards and exhibited high spatial variability and accuracy after residual correction. Moreover, RK had an excellent estimate ability for extreme data compared to MLR and OK. Correctly determining groundwater nitrate-nitrogen distributions using RK was useful for administering environmental resources and preventing public health hazards.

The effect of traffic at speed bumps in residential areas on noise and air pollution.

Although transportation systems are an increasing necessity in today's connected world, the over-reliance on internal combustion engine vehicles has caused an increase in air and noise pollution. Air and noise pollutions are among the negative environmental factors that contribute to disease occurrence due to their negative health consequences. Literature has showed that air and noise pollutions were responsible for thousands of premature deaths in Europe. This has pushed scientists to search for models to calculate the effect of traffic on air and noise pollution, to help predict future scenarios, and to work on ways to mitigate that increase in pollution. In this paper, a statistical model is done by using data measured for 25 speed bump locations in Kuwait, which included traffic flow data in the form of vehicle count and classification, as well as noise level measurements taken through an Amprobe SM20 sound meter, in addition to air pollutant data obtained from the Environment Public Authority (EPA) in Kuwait. The results of the multivariate linear regression model showed that high traffic counts resulted in significantly higher noise levels, reaching more than 70 decibels in certain locations, which is considered unhealthy for extended time periods. The model also showed that sulfur dioxide levels were impacted by both light and heavy vehicles, while particulate matter less than 10 µm was affected mainly by heavy vehicles. An online survey on speed bumps was completed by 803 participants to understand the behavior of people at speed bumps in Kuwait and understand if age and gender might be a predictor of how people would behave, and this was done by doing Pearson's chi-squared correlation tests on the results obtained from the survey.

Effectiveness of clear aligners in achieving proclination and intrusion of incisors among Class II division 2 patients: a multivariate analysis.

BACKGROUND: The predictability of incisor movement achieved by clear aligners among Class II division 2 patients is poorly understood. The aim of this retrospective study was to determine the effectiveness of clear aligners in proclining and intruding upper incisors and its influencing factors. METHODS: Eligible patients with Class II division 2 malocclusion were included. For clear aligner therapy, three types of incisor movements were designed: proclination, intrusion and labial movement. Pre-treatment and post-treatment dental models were superimposed. The differences between predicted and actual (DPA) tooth movement of incisors were analyzed. Univariate and multivariate linear regression were used to analyze the potential influencing factors. RESULTS: A total of 51 patients and their 173 upper incisors were included. Actual incisor proclination and intrusion were less than predicted ones (both P < 0.001), while actual labial movement was greater than predicted one (P < 0.001). Predictability of incisor proclination and intrusion was 69.8% and 53.3%, respectively. Multivariate linear regression revealed that DPA of proclination was significantly positively associated with predicted proclination (B = 0.174, P < 0.001), ipsilateral premolar extraction (B = 2.773, P < 0.001) and ipsilateral canine proclination (B = 1.811, P < 0.05), while negatively associated with molar distalization (B = - 2.085, P < 0.05). The DPA of intrusion was significantly positively correlated with predicted intrusion (B = 0.556, P < 0.001) while negatively associated with labial mini-implants (B = - 1.466, P < 0.001). The DPA of labial movement was significantly positively associated with predicted labial movement (B = 0.481, P < 0.001), while negatively correlated with molar distalization (B = - 1.004, P < 0.001), labial mini-implants (B = - 0.738, P < 0.001) and age (B = - 0.486, P < 0.05). CONCLUSIONS: For Class II division 2 patients, predicted incisor proclination (69.8%) and intrusion (53.3%) are partially achieved with clear aligner therapy. Excessive labial movement (0.7 mm) of incisors may be achieved. Incisor movement is influenced by predicted movement amount, premolar extraction, canine proclination, molar distalization, mini-implants and age.

Risk factors for perioperative hidden blood loss after one-segment posterior circumferential decompression surgery on thoracic ossification of the posterior longitudinal ligament: a finding of the double-layer sign on CT.

BACKGROUND: Hidden blood loss (HBL) is of increasing interest to spine surgeons. This retrospective study aimed to evaluate perioperative HBL and its risk factors in patients undergoing one-segment posterior circumferential decompression surgery on thoracic ossification of the posterior longitudinal ligament (T-OPLL). METHOD: We retrospectively studied 112 patients diagnosed with T-OPLL following posterior circumferential decompression surgery from August 2015 to June 2020. Patient demographics, blood loss-related parameters, surgery-related data and imaging parameters were extracted. Postoperative complications were also recorded. Pearson or Spearman correlation analysis was used to investigate the correlation between patient demographics and HBL. Multivariate linear regression analysis was performed to determine the independent risk factors associated with HBL. RESULTS: Forty-five men and 67 women were involved in this research, with an average age of 56.4 ± 10.2 years. The mean HBL was 459.6 ± 275.4 ml, accounting for 56.5% of the total blood loss. Multiple linear regression analysis showed that double-layer sign (P = 0.000), ossification occupancy ratio (OOR) > 60% (P = 0.030), age (P = 0.010), hematocrit (Hct) loss (P = 0.034), and postoperative Hct (P = 0.016) were independent risk factors for HBL. However, OPLL morphology (P = 0.319), operation time (P = 0.587), hemoglobin (Hb) loss (P = 0.644), and postoperative Hb (P = 0.952) were not significantly different from HBL. CONCLUSION: A high proportion of HBL was found after posterior circumferential decompression surgery on T-OPLL during the perioperative period, which should not be overlooked. Double-layer sign, OOR > 60%, age, Hct loss and postoperative Hct are independent risk factors for HBL.

State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles.

Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.

Development of a Machine Learning Algorithm for Drug Screening Analysis on High-Resolution UPLC-MSE/QTOF Mass Spectrometry.

BACKGROUND: Ultra-performance liquid chromatography (UPLC)-MSE/quadrupole time-of-flight (QTOF) high-resolution mass spectrometry employs untargeted, data-independent acquisition in a dual mode that simultaneously collects precursor ions and product ions at low and ramped collision energies, respectively. However, algorithmic analysis of large-scale multivariate data of comprehensive drug screening as well as the positivity criteria of drug identification have not been systematically investigated. It is also unclear whether ion ratio (IR), the intensity ratio of a defined product ion divided by the precursor ion, is a stable parameter that can be incorporated into the MSE/QTOF data analysis algorithm. METHODS: IR of 91 drugs were experimentally determined and variation of IR was investigated across 5 concentrations measured on 3 different days. A data-driven machine learning approach was employed to develop multivariate linear regression (MLR) models incorporating mass error, retention time, number of detected fragment ions and IR, accuracy of isotope abundance, and peak response using drug-supplemented urine samples. Performance of the models was evaluated in an independent data set of unknown clinical urine samples in comparison with the results of manual analysis. RESULTS: IR of most compounds acquired by MSE/QTOF were low and concentration-dependent (i.e., IR increased at higher concentrations). We developed an MLR model with composite score outputs incorporating 7 parameters to predict positive drug identification. The model achieved a mean accuracy of 89.38% in the validation set and 87.92% agreement in the test set. CONCLUSIONS: The MLR model incorporating all contributing parameters can serve as a decision-support tool to facilitate objective drug identification using UPLC-MSE/QTOF.