bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advanced Deep Learning-Based Supervised Classification of Multi-Angle Snowflake Camera Images
Authors: Key C. , Hicks A. , Notaroš B. M. 
Published: June 2021

AbstractWe present improvements over our previous approach to automatic winter hydrometeor classification by means of convolutional neural networks (CNNs), using more data and improved training techniques to achieve higher accuracy on a more complicated dataset than we had previously demonstrated. As an advancement of our previous proof-of-concept study, this work demonstrates broader usefulness of deep CNNs by using a substantially larger and more diverse dataset, which we make publicly available, from many more snow events. We describe the collection, processing, and sorting of this dataset of over 25,000 high-quality multiple-angle snowflake camera (MASC) image chips split nearly evenly between five geometric classes: aggregate, columnar crystal, planar crystal, graupel, and small particle. Raw images were collected over 32 snowfall events between November 2014 and May 2016 near Greeley, Colorado and were processed with an automated cropping and normalization algorithm to yield 224x224 pixel images containing possible hydrometeors. From the bulk set of over 8,400,000 extracted images, a smaller dataset of 14,793 images was sorted by image quality and recognizability (Q&R) using manual inspection. A presorting network trained on the Q&R dataset was applied to all 8,400,000+ images to automatically collect a subset of 283,351 good snowflake images. Roughly 5,000 representative examples were then collected from this subset manually for each of the five geometric classes. With a higher emphasis on in-class variety than our previous work, the final dataset yields trained networks that better capture the imperfect cases and diverse forms that occur within the broad categories studied to achieve an accuracy of 96.2% on a vastly more challenging dataset.
Prehospital Variables Alone Can Predict Mortality After Blunt Trauma: A Novel Scoring Tool
Authors: Stopenski Stephen , Grigorian Areg , Inaba Kenji , Lekawa Michael , Matsushima Kazuhide , Schellenberg Morgan , Kim Dennis , de Virgilio Christian , Nahmias Jeffry 
Published: June 2021

Background We sought to develop a novel Prehospital Injury Mortality Score (PIMS) to predict blunt trauma mortality using only prehospital variables. Study Design The 2017 Trauma Quality Improvement Program database was queried and divided into two equal sized sets at random (derivation and validation sets). Multiple logistic regression models were created to determine the risk of mortality using age, sex, mechanism, and trauma activation criterion. The PIMS was derived using the weighted average of each independent predictor. The discriminative power of the scoring tool was assessed by calculating the area under the receiver operating characteristics (AUROC) curve. The PIMS ability to predict mortality was then assessed by using the validation cohort. The score was compared to the Revised Trauma Score (RTS) using the AUROC curve, including a subgroup of patients with normal vital signs. Results The derivation and validation groups each consisted of 163 694 patients. Seven independent predictors of mortality were identified, and the PIMS was derived with scores ranging from 0 to 20. The mortality rate increased from 1.4% to 43.9% and then 100% at scores of 1, 10, and 19, respectively. The model had very good discrimination with an AUROC of .79 in both the derivation and validation groups. When compared to the RTS, the AUROC were similar (.79 vs. .78). On subgroup analysis of patients with normal prehospital vital signs, the PIMS was superior to the RTS (.73 vs. .56). Conclusion The PIMS is a novel scoring tool to predict mortality in blunt trauma patients using prehospital variables. It had improved discriminatory power in blunt trauma patients with normal vital signs compared to the RTS.
Causal Relationship Between the Risk Factors and Work-Related Musculoskeletal Disorders Among Professional Drivers: A Systematic Review
Authors: Joseph Leonard , Vasanthan Lenny , Standen Miles , Kuisma Raija , Paungmali Aatit , Pirunsan Ubon , Sitilertpisan Patraporn 
Published: June 2021

Objective This review evaluates the evidence on the strength of causal relationship between categories of risk factors (RFs) and work-related musculoskeletal disorders (WRMSDs) among professional drivers. Background A compilation of evidence on the causal relationship between RFs and WRMSDs among professional drivers is lacking. Methods A systematic search of the literature was conducted in major electronic data bases that include Medline (1946 + via OvidSP), Embase (1974 + OvidSP), CINAHL (1982+), AMED, and Web of Science. The methodological quality of the studies was assessed and scored. A descriptive analysis on the categories of RFs associated with WRMSDs was conducted. The Bradford–Hill causation criteria and evidence interpretation tool were used to evaluate the causal relationship between RFs and WRMSDs in professional drivers. Results Among the 54 studies reviewed, a strong evidence suggests a causal relationship between RFs such as whole-body vibration, awkward postures, lifting tasks, manual material handling, job stress, job demand, and previous pain episodes with WRMSDs. Moderate evidence was observed on RFs such as uncomfortable seat and low job satisfaction. The evidence on causal relationship between RFs such as years of professional driving, driving duration, and individual characteristics such as age and body mass index was inconclusive. Conclusion There is strong to moderate evidence on the causal relationship between the physical and psychosocial RFs and WRMSDs among professional drivers. Application Potential application of this review highlights evidence to occupational health practitioners, policy makers, and stakeholders on the strength of causal relationship between RFs and WRMSDs among professional drivers.
Multi-Tiered System of Supports: An Ecological Approach to School Psychology Service Delivery
Authors: Loftus-Rattan Susan M. , Wrightington Mahri , Furey Jenlyn , Case Jessica 
Published: June 2021

Introduction: School psychologists work to support the academic, behavioral, and social-emotional well-being of students in schools. Their roles have become increasingly diverse in recent years due to the implementation of Multi-tiered System of Supports (MTSS) frameworks in schools throughout the United States. Statement of the Problem: Despite a national shortage of school psychologists, psychology students are often unaware of the profession or may have an outdated understanding of the role of school psychologists. Psychology students may also be unfamiliar with the MTSS framework, which promotes research-based, data-driven, equitable, and culturally responsive practices through a systemic, ecological approach to service delivery in schools. Literature Review: We review the main components of a typical MTSS prevention and intervention framework and describe the various roles of school psychologists and other key stakeholders within this model. Teaching Implications: This review and supporting resources can be used to introduce the field of school psychology to students. Additionally, it can be used to generate discussions regarding the provision of evidence-based and equitable academic and psychological services to students in the school through an ecological model. Conclusion: This article will help students gain an understanding of school psychologists’ roles within a current MTSS educational service delivery model.
Vision-based human activity recognition for reducing building energy demand
Authors: Tien Paige Wenbin , Wei Shuangyu , Calautit John Kaiser , Darkwa Jo , Wood Christopher 
Published: June 2021

Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.
Efficacy of Repetitive Transcranial Magnetic Stimulation (rTMS) for Tinnitus: A Retrospective Study
Authors: Yang Haidi , Cheng Gui , Liang Zhengrong , Deng Wenting , Huang Xiayin , Gao Minqian , Zheng Yiqing 
Published: June 2021

Objective: Current studies still find insufficient evidence to support the routine use of repetitive transcranial magnetic stimulation (rTMS) in tinnitus. This study aimed to assess response of tinnitus to treatment with rTMS and identify factors influencing the overall response. Methods: Between January 2016 and May 2017, 199 tinnitus patients were identified from a retrospective review of the electronic patient record at the Sun Yat-sen Memorial Hospital. All patients received rTMS treatment. Their clinicodemographic profile and outcomes, including the tinnitus handicap inventory (THI) and visual analog scale (VAS) scores, were extracted for analysis. Results: Regarding the THI results, 62.3% of all patients responded to rTMS. The analysis of the VAS score revealed an overall response rate of 66.3%. Both percentages were close to the patient’s subjective assessment result, of 63.8%. Patients with tinnitus of less than 1-week duration had the highest response rate to rTMS in terms of either THI/VAS scores or the patient’s subjective assessment of symptoms. Tinnitus duration was recognized as a factor influencing the overall response to the treatment. Conclusions: Repetitive transcranial magnetic stimulation treatment is effective for patients with tinnitus, but its efficacy is affected by tinnitus duration. Tinnitus patients are advised to attend for rTMS as soon as possible since therapy was more effective in those with a shorter duration of disease of less than 1 week.
Effects of β-glucan and inulin consumption on postprandial appetite, energy intake and food consumption in healthy females: A randomized controlled trial
Authors: Alptekin İsmail Mücahit , Çakiroğlu Funda Pınar , Örmeci Necati 
Published: June 2021

Background: To date, several researchers have investigated the association between dietary fibre consumption and satiety. However, there is no study that includes both inulin and β-glucan to compare energy intake (EI) and satiety ratings. Aim: The current study investigated the effects of two dietary fibres, β-glucan and inulin, on satiety and food intake. Methods: The study was carried out among 24 woman over 18 years of age. The dietary fibres β-glucan (6 g/day) or inulin (6 g/day) were consumed by participants for five weeks. On the first and fifth week visits, the participants consumed a standard breakfast followed by an ad libitum test meal. Appetite was assessed using visual analogue scales (VAS) before and after breakfast. EI was measured at the test meal using plate waste. Results: Both dietary fibres significantly reduced the VAS scores of hunger, prospective food consumption and desire to eat, and increased satiety compared with the control group. However, the area under curve data for the VAS scores did not exhibit a significant difference. Significant reductions in EI and anthropometric values between the first and fifth week measures were observed in both dietary fibre groups. Statistically significant changes occurred in the body weight [-1.25 (1.27) kg], body mass index [-0.41 (0.42) kg/m2], waist circumference [-1.25 (1.04) cm] and waist/hip ratio [-0.01(0.01)] in the β-glucan group, whereas a statistically significant change occurred in body fat percentage in the inulin group [-2.16% (7.49)]. Conclusions: Overall, these findings demonstrate that the participants consuming β-glucan over the course of the five weeks had less EI, felt less hunger and had more satiety.
Internet Addiction Status and Related Factors among Medical Students: A Cross-Sectional Study in Western Iran
Authors: Khazaie Habibolah , Lebni Javad Yoosefi , Abbas Jaffar , Mahaki Behzad , Chaboksavar Fakhreddin , Kianipour Neda , Toghroli Razie , Ziapour Arash 
Published: June 2021

Background In recent years, Internet and social media technology use have emerged as an integral tool of human society, and the evolution of technological integration, cyberspace, and web-technology has become a common practice in educational institutions. Internet usage among students has played an indispensable role in learning behavior; however, the excessive usage of the internet and social media leads to internet addiction. This original study has performed a focalized scrutiny on revealing relationships between internet addiction and associated factors among the students of medicine, dentistry, and pharmaceutical departments. Methods This descriptive and analytical study recruited medical students from the Self-governing Education Incubator of Kermanshah. This survey distributed questionnaires among the respondents’ three departments, and this statistical data reported on 420 valid responses of the respondents. They represent first and second-semester medical students of the academic year 2017–2018. The study selected medical students by applying Cochran's Sample Size Formula through Stratified Random Sampling and cross-sectional research design. The survey has utilized a demographic questionnaire of Young's Internet Addiction Test (IAT) for the data collection. The study analyzed received data by using SPSS version 23 and performed the descriptive statistics, and analytical statistics (t-test and ANOVA). Results The results of the present study established that the majority of subjects were female students (53.3%), and the average age was 23.84 ± 2.14, including the students of all departments. Besides, findings specified that the overall mean and standard deviation scores were 3.34 and ±0.88. Internet addiction revealed mean and the standard deviation score measured for all students 3.29 ± 0.73, 3.17 ± 0.92, and 3.57 ± 0.64 correspondingly. The survey results illustrated that medical students’ internet addiction substantially correlated with demographic variables, such as age, marital status, the field of study, academic term, significant time of consuming the internet, the key reason of utilizing the internet, and daily usage of the internet ( p < .05). Conclusion The results of the study specified that 25% of medical students showed internet addiction. The students are increasingly using the internet, and it has penetrated among students. The design and implementation of adequate educational programs and the application of internet-based efficiency interventions are essential for both knowledge acquisition and medical students’ healthy behavior.
Invariance of the Household Food Insecurity Access Scale Across Different Groups of Adolescents and Young Adults
Authors: Masa Rainier , Sharma Anjalee 
Published: June 2021

Background: Cross-group comparisons of household food insecurity and its associations using multiple-item scales assume that scale scores can be interpreted as identical across groups. However, scores should not be interpreted as identical across groups without evidence of measurement invariance. Noninvariant measures indicate that the underlying construct may be different across groups. Objective: To determine whether the Household Food Insecurity Access Scale (HFIAS) is invariant across different groups of Ghanaian and South African youth aged 15 to 24. Methods: We analyzed cross-sectional quantitative data from 1437 and 4165 young South Africans and Ghanaians, respectively. Multi-group confirmatory factor analysis was used to examine whether the HFIAS was invariant across different groups of youth, including sex (male or female), age group (middle adolescence, late adolescence, or emerging adulthood), and receipt of child support grant (yes or no). We assessed 3 levels of invariance: configural, metric, and scalar. The model fit between nested models was compared using χ2 difference testing. Results: Invariance tests indicated that the HFIAS had configural, metric, and scalar invariance across different groups of Ghanaian and South African youth. Model fit statistics across all invariance levels indicated good fit of our hypothesized model with the observed data. χ2 difference testing results were not statistically significant across all nested models. Conclusions: Food insecurity, as measured by the HFIAS, meant the same thing for different groups of Ghanaian and South African youth. Evidence of invariance means that the HFIAS scores could be interpreted as identical across youth groups in our study.
Electrically conductive epoxy/polyaniline composite fabrication and characterization for electronic applications
Authors: Rashid Iqra A , Tariq Asra , Shakir HM Fayzan , Afzal Ayesha , Ali Fahad , Abuzar Muhammad , Haider Toseeq 
Published: June 2021

This study reports on the electrical conductivity, dielectric, and electromagnetic interferenc (EMI) shielding properties of conductive epoxy/PAni blend containing various concentrations. Polyaniline (PAni) was synthesized using oxidative chemical polymerization technique and then dispersed into epoxy resin using a sonication bath. Infrared spectra confirm the curing of composites. Increasing the aspect ratio of PAni in epoxy increased the electrical conductivity and improves the microwave absorption properties of composites in the microwave range (0.1 GHz–20 GHz). Electrical conductivity was measured by using the four-probe method, and the maximum conductivity of the composite was achieved 3.51 × 10−13 Scm−1 with 30 wt% of PAni. The maximum porosity of the composite with 30 wt% of PAni was 15.5%. EMI shielding was measured by a vector network analyzer (VNA) in the microwave region (0.1 GHz–20 GHz), which gives the maximum value of 63 dB. IR shielding was measured by IR spectroscopy and less than 0.5% transmission was observed in NIR (700 nm–2500 nm) region. The average particle size of PAni is found to be 113 nm. These composites were used as a potential candidate for conductive coatings, EMI shielding purposes, and electronic applications.
Hospital Pharmacy Response to Covid-19 Pandemic in Italy: What We Learned From the First Outbreak Wave
Authors: Damuzzo Vera , Bertin Riccardo , Mengato Daniele , Chiumente Marco , Rivano Melania , Palozzo Angelo Claudio 
Published: June 2021

Background: When COVID-19 pandemic started, Italian hospital pharmacists faced multiple challenges and change their work practices. Objectives: The aim of this study was to describe the impact of COVID-19 emergency on pharmaceutical care provided by pharmacists during the first wave of the pandemic. Issues related to pharmacist’s involvement in the pandemic management were: changes in activities, support received by authorities and pharmacists’ own perceived role in the Health System. Methods: A cross-sectional study based on a web survey was conducted between May and June 2020 collecting information from pharmacists, members of Italian Society of Clinical Pharmacy and Therapeutics. 113 (11.4%) completed the questionnaire. The cohort was divided in 2 arms: pharmacists who worked in severely COVID-19 affected areas (High Spread Regions) and those employed in less affected areas (Low Spread Regions). Results: The changes in pharmacy work settings reflected the increase of logistics area and non-sterile clinical galenic, and reduction of clinical tasks. The most demanding challenge was referred to shortages of medical devices and drugs, 61/113 pharmacists reported difficulty in obtaining products compliant to quality standards. National Institutions and Regional Governments provided a greater perceived support. More than 50% of participants felt that their role did not change if compared to other health professionals. Conclusions: Despite some limitations related to their clinical activity, pharmacists played a crucial role in supplying personal protective equipment, medical devices and medications to improve health outcomes during this emergency. The results may guide pharmacists in future actions to improve the management of the pandemic.