bluuebunny/crossref_metadata_2025 huggingface dataset:
Depression, anxiety and stress, during COVID-19 pandemic among midwives in ethiopia: A nationwide cross-sectional survey
Authors: Kassahun Eskeziaw Abebe , Kassie Belayneh Ayanaw , Tilahun Sewbesew Yitayih , Bizuneh Asmamaw Demis
Published: July 2022

Introduction

COVID-19 has rapidly crossed borders, infecting people throughout the whole world, and has led to a wide range of psychological sequelae. Midwives who come close in contact with women while providing care are often left stricken with inadequate protection from contamination with COVID-19. Therefore, this study aimed to assess the level of depression, anxiety, and stress (DASS) among midwives in Ethiopia.

Methods

A cross-sectional study was conducted from 20 June to 20 August 2020, among 1,691 practicing midwives in Ethiopia. A simple random sampling technique was used to select study participants. Data were collected through a structured telephone interview. A 21-item depression, anxiety, and stress scale (DASS-21) was used. Data were entered using the Google forms platform and were analyzed with SPSS version 24. Both bivariate and multivariable logistic regression analyses were employed. Variables with a p-value < 0.05 in the final model were declared statistically significant. Adjusted odds ratio (AOR) with the corresponding 95% confidence interval (95% CI) was used to determine independent predictors.

Results

The prevalence of DASS among midwives in Ethiopia was 41.1, 29.6, and 19.0%, respectively. Being female [AOR = 1.35; 95% CI: 1.08, 1.69], working in rural areas [AOR = 1.39; 95% CI: 1.06, 1.82], having poor knowledge of COVID-19 [AOR = 1.40; 95% CI: 1.12, 1.75], having poor preventive practice [AOR = 1.83; 95% CI: 1.47, 2.28], and substance use [AOR = 0.31; 95% CI: 0.17, 0.56] were significantly associated with depression; while, working in the governmental health facility [AOR = 2.44; 95% CI: 1.24, 4.78], having poor preventive practice [AOR = 1,47; 95% CI: 1.16, 1.85], and having poor attitude [AOR = 2.22; 95% CI: 1.04, 1.66] were significantly associated with anxiety. Furthermore, working in rural areas [AOR = 0.57; 95% CI: 0.39, 0.83], substance use [AOR = 2.06; 95% CI: 1.51, 2.81], having poor knowledge [AOR = 1.44; 95% CI: 1.20, 1.90], and having poor preventive practice [AOR = 1.60; 95% CI: 1.23, 2.10] were associated with stress.

Conclusion

In this study, the overall magnitude of depression, anxiety, and stress were high. Addressing knowledge gaps through information, training, and safety protocols on COVID-19 and the provision of adequate personal protective equipment (PPE) is essential to preserve the mental health of Midwives during COVID-19.
Digital phenotype of mood disorders: A conceptual and critical review
Authors: Maatoug Redwan , Oudin Antoine , Adrien Vladimir , Saudreau Bertrand , Bonnot Olivier , Millet Bruno , Ferreri Florian , Mouchabac Stephane , Bourla Alexis
Published: July 2022

Background

Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.

Objective

The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.

Methods

We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.

Results

Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).

Conclusion

The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.
Usual care for youth with autism spectrum disorder: Community-based providers’ reported familiarity with treatment practices
Authors: Lerner Matthew D. , Brown Cynthia E. , Sridhar Aksheya , Tschida Jessica E. , Felsman Peter , Libsack Erin J. , Kerns Connor M. , Moskowitz Lauren J. , Soorya Latha , Wainer Allison , Cohn Elizabeth , Drahota Amy
Published: July 2022

Objective

To examine patterns and predictors of familiarity with transdisciplinary psychosocial (e.g., non-pharmacologic) practices for practitioners treating youths with autism spectrum disorder (ASD) in the United States.

Method

Practitioners (n = 701) from behavioral, education, medical, and mental health backgrounds who worked with youth (ages 7–22) with ASD completed the Usual Care for Autism Survey, which assessed provider demographics and self-reported familiarity with transdisciplinary treatment practices for the most common referral problems of ASD. We examined relations between provider-, setting-, and client-level characteristics with familiarity of key groups of the treatment practices (practice sets). Practice sets were identified using exploratory factor analysis (EFA), and demographic predictors of practice subsets were examined using generalized estimating equations (GEE).

Results

The EFA yielded a three-factor solution: (1) environmental modifications/antecedent strategies; (2) behavior analytic strategies; and (3) cognitive strategies, with overall familiarity ranked in this order. Medical providers indicated the least familiarity across disciplines. More experience with ASD and treating those with intellectual disabilities predicted greater familiarity with only environmental modifications/antecedent strategies and behavior analytic, but not cognitive strategies. Experience treating low SES clients predicted familiarity with environmental modification and behavior analytic strategies while experience treating high SES clients predicted familiarity with behavior analytic and cognitive strategies.

Conclusion

This is the first study to identify transdisciplinary, interpretable sets of practices for treating youth with ASD based on community providers’ reported familiarity. Results highlight factors associated with familiarity with practice sets, which is essential for mapping practice availability, and optimizing training and dissemination efforts for youth with ASD.
Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder
Authors: Wei Yanyan , Wang Tingting , Li Guoguang , Feng Junhui , Deng Lianbang , Xu Haiting , Yin Lu , Ma Jinbao , Chen Dongning , Chen Jingxu
Published: July 2022

Background

The systemic immune-inflammation index (SII), system inflammation response index (SIRI), neutrophil/high-density lipoprotein (HDL) ratio (NHR), lymphocyte/HDL ratio (LHR), monocyte/HDL ratio (MHR), and platelet/HDL ratio (PHR) have been recently investigated as new markers for inflammation. The purpose of this research is to use large-scale clinical data to discuss and compare the predictive ability of the SII, SIRI, NHR, LHR, MHR, and PHR in patients with schizophrenia (SCZ) and bipolar disorder (BD), to investigate potential biomarkers.

Materials and methods

In this retrospective, naturalistic, cross-sectional study, we collected the hematological parameter data of 13,329 patients with SCZ, 4,061 patients with BD manic episodes (BD-M), and 1,944 patients with BD depressive episodes (BD-D), and 5,810 healthy subjects served as the healthy control (HC) group. The differences in the SII, SIRI, NHR, LHR, MHR, and PHR were analyzed, and a receiver operating characteristic (ROC) curve was used to analyze the diagnostic potential of these parameters.

Results

Compared with the HC group, the values of the SII, SIRI, NHR, LHR, MHR, and PHR and the levels of neutrophils, monocytes, and triglycerides (TG) were higher in SCZ and BD groups, and levels of platelets, cholesterol (CHO), HDL, low-density lipoprotein (LDL), and apoprotein B (Apo B) were lower in SCZ and BD groups. Compared to the BD group, the values of the SIRI, lymphocytes, monocytes, and HDL were lower and the values of the SII, NHR, PHR, and platelet were higher in the SCZ group. In contrast to the BD-D group, the values of the SII; SIRI; NHR; and MHR; and levels of neutrophils, monocytes, and platelets were higher in the BD-M group, and the levels of CHO, TG, LDL, and Apo B were lower in the BD-M group. The MHR and NHR were predictors for differentiating the SCZ group from the HC group; the SIRI, NHR, and MHR were predictors for differentiating the BD-M group from the HC group; and the MHR was a predictor for differentiating the BD-D group from the HC group. The combination model of the indicators improved diagnostic effectiveness.

Conclusion

Our study highlights the role of systemic inflammation in the pathophysiology of SCZ, BD-M, and BD-D, the association between inflammation and lipid metabolism, and these inflammation and lipid metabolism indicators showed different variation patterns in SCZ, BD-D, and BD-M.
Safe menstrual hygiene management practice and associated factors among female adolescent students at high schools in central Ethiopia: A mixed–method study
Authors: Deriba Berhanu Senbeta , Garedew Girma , Gemeda Diriba , Geleta Tinsae Abeya , Jemal Kemal , Bala Elias Teferi , Mekuria Mulugeta , Nigussie Tadesse , Dirirsa Dejene Edosa , Legesse Elsabeth
Published: July 2022

Background

Menstrual Hygiene Management (MHM) is a much-neglected issue in developing countries, including Ethiopia. Menstruating women and girls are forced into isolation, prevented from movement, dietary restrictions, and can be prevented from participating in daily routine activities. Furthermore, the way almost all previous studies conducted in Ethiopia measured the practice of MHM did not meet standard definition of safe MHM. This study aimed to assess safe management of menstrual hygiene practice and associated factors among female adolescent students in public high schools in central Ethiopia.

Methods

A mixed-methods approach was employed in this study. Systematic random sampling technique was used to select 846 study participants. The collected data were entered through EPI INFO version 7 and exported to SPSS version 23 for cleaning and analysis. Bivariate and multivariate logistic regression analysis were performed to identify the association between MHM and independent variables. Finally, AOR, 95% CI, and p-value < 0.05 were considered statistically significant. The qualitative data was analyzed by ATLAS.ti in order to extract the main themes and categories. Direct quotations were presented with a thick description of the findings.

Results

The safe management of menstrual hygiene was 28.20%. Living with parents (AOR = 2.51, 95% CI:1.11–5.68), living with relatives (AOR = 7.41, 95% CI:2.55–21.54), having a merchant mother (AOR = 1.81, 95% CI:1.14–2.9), having a mother who has private work (AOR = 4.56, 95% CI:1.31–5.90), having a farmer father (AOR = 1.53, 95% CI:1.1–2.31), rural resident (AOR = 1.61, 95% CI: 1.17–2.21) and realizing the absence of container for storing sanitary napkins in the toilet of the school latrine (AOR = 1.44, 95% CI: 1.1–0.94) were factors associated with MHM. Findings from a qualitative study were discussed under four themes to explore barriers to menstrual hygiene management, and three themes emerged as enablers to menstrual hygiene management.

Conclusions

The safe management of menstrual hygiene was low among adolescent girls. People with whom adolescent girls live, the occupational status of mother and father, residence, the availability of a container to dispose of sanitary napkins in school toilets were factors associated with menstrual hygiene management. Behavioral change communications must be provided to female students about menstrual hygiene.
Proteolysis dysfunction in the process of aging and age-related diseases
Authors: Frankowska Natalia , Lisowska Katarzyna , Witkowski Jacek M.
Published: July 2022

In this review, we discuss in detail the most relevant proteolytic systems that together with chaperones contribute to creating the proteostasis network that is kept in dynamic balance to maintain overall functionality of cellular proteomes. Data accumulated over decades demonstrate that the effectiveness of elements of the proteostasis network declines with age. In this scenario, failure to degrade misfolded or faulty proteins increases the risk of protein aggregation, chronic inflammation, and the development of age-related diseases. This is especially important in the context of aging-related modification of functions of the immune system.
A human-in-the-loop haptic interaction with subjective evaluation
Authors: Fang Ying , Qiao Yangjun , Zeng Fanrong , Zhang Keke , Zhao Tiesong
Published: July 2022

To date, one of the challenges in Human-Computer Interaction (HCI) is fully immersive multisensory remote physical interaction technologies. The applications of haptic perception in HCI can enrich the interaction details and effectively improve the immersion and realism of interaction. In the human-in-the-loop haptic interaction system, the quality of experience (QoE) of the human operator plays an essential role. However, QoE in haptic interaction is still in its infancy. Based on the typical application scenarios of haptic operation, the paper constructs a haptic-visual interaction framework and analyzes the QoE influencing factors. Through subjective evaluation experiments, the paper establishes a haptic interaction database that can provide a research basis for further exploring the relationship between various influencing factors and interactive QoE.
Transition between individually different and common features in skilled drumming movements
Authors: Takiyama Ken , Hirashima Masaya , Fujii Shinya
Published: July 2022

Why do professional athletes and musicians exhibit individually different motion patterns? For example, baseball pitchers generate various pitching forms, e.g., variable wind-up, cocking, and follow-through forms. However, they commonly rotate their wrists and fingers at increasingly high speeds via shoulder and trunk motions. Despite the universality of common and individually different motion patterns in skilled movements, the abovementioned question remains unanswered. Here, we focus on a motion required to hit a snare drum, including the indirect phase of task achievement (i.e., the early movement and mid-flight phases) and the direct phase of task achievement (i.e., the hit phase). We apply tensor decomposition to collected kinematic data for the drum-hitting motion, enabling us to decompose high-dimensional and time-varying motion data into individually different and common movement patterns. As a result, individually different motion patterns emerge during the indirect phase of task achievement, and common motion patterns are evident in the direct phase of task achievement. Athletes and musicians are thus possibly allowed to perform individually different motion patterns during the indirect phase of task achievement. Additionally, they are required to exhibit common patterns during the direct phase of task achievement.
Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT
Authors: Wang Dongdong , Wu Zhenhua , Fan Guoxin , Liu Huaqing , Liao Xiang , Chen Yanxi , Zhang Hailong
Published: July 2022

Introduction

Three-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.

Materials and Methods

A total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction.

Results

The average Dice score of IFFCT in the local test dataset for “proximal femur”, “fragment” and “distal femur” were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p < 0.01). Significant differences were observed between 2D masks generated from semantic segmentation and conventional threshold-based segmentation (p < 0.01). The average FHD in the automatic segmentation group was 47.5 ± 4.1 mm (41.29∼56.59 mm), and the average FHD in the manual segmentation group was 45.9 ± 6.1 mm (40.34∼64.93 mm). The mean absolute error of FHDs in the two groups were 3.38 mm and 3.52 mm, respectively. No significant differences of FHD measurements were observed between the two groups (p > 0.05). All ICCs were greater than 0.8.

Conclusion

The proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs.
Medial plantar artery perforator (MPAP) flap is an ideal option for reconstruction of complex soft tissue defect in the finger: Clinical experience from 11 cases
Authors: Xu Xiang , Wang Cheng , Chen Zhenbing , Li Jin
Published: July 2022

Introduction

Soft tissue defects of fingers are common in reconstructive plastic surgery, and reconstruction of the defects remains challenging for plastic surgeons. In our study, we reported our experience in finger reconstruction with a medial plantar artery perforator (MPAP) flap, especially using a lobulated MPAP flap for the complex multifinger soft defect.

Patients and methods

From the period April 2012 to October 2018, 11 patients (9 males and 2 females) with an average age of 44 years old (ranging from 11 to 58) received finger reconstruction with a free MPAP flap. In total, 11 flaps (8 single-lobulated flaps and 3 two-lobulated flaps) were raised from the ipsilateral or contralateral instep area. Trauma and scar contracture caused hand soft tissue loss in all cases.

Results

The sizes of the flaps ranged from 2×3 to 5×7.5 cm2. All flaps survived intact with no complications. One donor site was closed primarily, and other donor sites were covered with a full-thickness skin graft. The mean follow-up time was 6 months (ranging from 3 to 8 months). During the follow-up period, the patients were satisfied with their appearance without any traces of flap plastic surgery.

Conclusion

The MPAP flap is a reliable and acceptable option for the reconstruction of complex soft tissue defects in the finger. Depending on the two branches of the medial plantar artery, the use of the lobulated MPAP flap holds promise in the treatment of multifinger soft tissue defects.
Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter
Authors: Koganti Srilakshmi , Koganti Krishna Jyothi , Salkuti Surender Reddy
Published: July 2022

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.
Simulating Spring Barley Yield under Moderate Input Management System in Poland
Authors: Czembor Elzbieta , Kaczmarek Zygmunt , Pilarczyk Wiesław , Mańkowski Dariusz , Czembor Jerzy H.
Published: July 2022

In recent years, forecasting has become particularly important as all areas of economic life are subject to very dynamic changes. In the case of agriculture, forecasting is an essential element of effective and efficient farm management. Factors affecting crop yields, such as soil, weather, and farm management, are complex and investigations into the relation between these variables are crucial for agricultural studies and decision-making related to crop monitoring, with special emphasis for climate change. Because of this, the aim of this study was to create a spring barley yield prediction model, as a part of the Advisory Support platform in the form of application for Polish agriculture under a moderate input management system. As a representative sample, 20 barley varieties, evaluated under 13 environments representative for Polish conditions, were used. To create yield potential model data for the genotype (G), environment (E), and management (M) were collected over 3 years. The model developed using Multiple Linear Regression (MLR) simulated barley yields with high goodness of fit to the measured data across three years of evaluation. On average, the precision of the cultivar yielding forecast (expressed as a percentage), based on the independent traits, was 78.60% (Model F-statistic: 102.55***) and the range, depending of the variety, was 89.10% (Model F-statistic: 19.26***)–74.60% (Model F-statistic: 6.88***). The model developed using Multiple Linear Regression (MLR) simulated barley yields with high goodness of fit to the measured data across three years of evaluation. It was possible to observe a large differentiation for the response to agroclimatic or soil factors. Under Polish conditions, ten traits have a similar effect (in the prediction model, they have the same sign: + or -) on the yield of almost all varieties (from 17 to 20). Traits that negatively affected final yield were: lodging tendency for 18 varieties (18-), sum of rainfall in January for 19 varieties (19-), and April for 17 varieties (17-). However, the sum of rainfall in February positively affected the final yield for 20 varieties (20+). Average monthly ground temperature in March positively affected final yield for 17 varieties (17+). The average air temperature in March negatively affected final yield for 18 varieties (18-) and for 17 varieties in June (17-). In total, the level of N + P + K fertilization negatively affected the final yield for 15 varieties (15-), but N sum fertilization significantly positively affected final yield for 15 varieties (15+). Soil complex positively influenced the final yield of this crop. In the group of diseases, resistance to powdery mildew and rhynchosporium significantly decreased the final yield. For Polish conditions, it is a complex model for prediction of variety in the yield, including its genetic potential.
Seaweed Oligosaccharide Synergistic Silicate Improves the Resistance of Rice Plants to Lodging Stress under High Nitrogen Level
Authors: Liu Guoxiu , Ouyang Xin , Li Zhiming , Fu Youqiang , Shen Hong
Published: July 2022

The objective of this study was to determine the effect of seaweed oligosaccharide synergistic silicate (Si) fertilizer (SOSSiF) on rice resistance to lodging stress. The results showed that a spraying SOSSiF decreased apparent lodging index and enhanced rice yield significantly under a high N level. The spraying test indicated that the apparent lodging rate of rice was the lowest when SOSSiF was sprayed for four times, and the dosage was 45 kg/ha each time. Morphological and anatomical analysis indicated that SOSSiF decreased plant height and the lower internode length of ZCSM and increased culm cross-sectional area and wall thickness of JNSM significantly compared with the control. Furthermore, SOSSiF enhanced bending strength of rice culm by 38.8% to 63.6%, and reduced lodging index by 36.8% to 42.6%. Chemical component analysis found that SOSSiF elevated the contents of soluble sugar, cellulose, Si, and lignin in the culms of ZCSM and JNSM. Correlation analysis revealed that the lodging index was positively correlated with the length of the lower internode, and was negatively correlated with culm bending strength and culm thickness. The above results suggested that spraying SOSSiF elevates culm contents of Si and lignin and enhances bending strength, thus improving rice lodging resistance and production.
Increased Soil Fertility in Tea Gardens Leads to Declines in Fungal Diversity and Complexity in Subsoils
Authors: Yan Peng , Shen Chen , Zou Zhenhao , Fan Lichao , Li Xin , Zhang Liping , Zhang Lan , Dong Chunwang , Fu Jianyu , Han Wenyan , Shi Lingling
Published: July 2022

Soil fungi are key drivers regulating processes between ecosystem fertility and plant growth; however, the responses of soil fungi community composition and diversity in deeper soil layers to the plantation and fertilization remain limited. Using soil samples along with vertical soil profile gradients with 0–10 cm, 0–20 cm, 20–40 cm, and 40–60 cm in a tea garden, we used Illumina sequencing to investigate the fungal diversity and assemblage complexity, and correlated to the low, middle, and high-level fertilize levels. The results showed that the fungal community dissimilarities were different between adjacent forests and tea gardens, with predominate groups changed from saprotrophs to symbiotrophs and pathotrophs after the forest converted to the tea garden. Additionally, the symbiotrophs were more sensitive to soil fertility than pathotrophs and saprotrophs. Subsoil fungal communities present lower diversity and fewer network connections under high soil fertility, which contrasted with the trends of topsoil fungi. Soil pH and nutrients were correlated with fungal diversity in the topsoils, while soil K and P concentrations showed significant effects in the subsoil. Overall, the soil fungal communities in tea gardens responded to soil fertility varied with soil vertical spatial locations, which can be explained by the vertical distribution of fungal species. It was revealed that fertility treatment could affect fungal diversity, and alter network structure and potential ecosystem function in tea garden subsoils.
Preferences of Dairy Cattle for Supplemental Light-Emitting Diode Lighting in the Resting Area
Authors: Wilson Angela M. , Wright Tom C. , Cant John P. , Osborne Vern R.
Published: July 2022

Light from the environment is important for vision and regulating various biological processes. Providing supplemental lighting in the stall area could allow for individually targeted or group-level control of light. This study aimed to determine whether dairy cattle had preferences for short-term exposure to white (full-spectrum) light-emitting diode (LED) light or no LED light, yellow-green or white LED light, and blue or white LED light in the stall area. In total, 14 lactating cows were housed in a free-stall pen with unrestricted access to 28 stalls. LED light was controlled separately for each side of the stall platform. Two combinations of light were tested per week, and each week consisted of three adaptation days and four treatment days. Lying behaviour and video data were recorded continuously using leg-mounted pedometers and cameras, respectively. Preference was assessed by the amount of time spent lying and the number of bouts under each light treatment. No differences occurred between treatments within each week for daily lying time and number of bouts. Similarly, no differences occurred between treatments within each time period. Further controlled studies of long-term exposure to different LED wavelengths and intensities are required to determine potential benefits on metabolic processes.
Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features
Authors: Ahmad Ghulab Nabi , Shafiullah  , Fatima Hira , Abbas Mohamed , Rahman Obaidur , Imdadullah  , Alqahtani Mohammed S.
Published: July 2022

Heart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models’ classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique’s training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease.
Study of the Dynamic Response of a Rigid Runway with Different Void States during Aircraft Taxiing
Authors: Hu Guizhang , Li Peigen , Xia Haiting , Xie Tao , Mu Yifan , Guo Rongxin
Published: July 2022

In this study, a set of numerical simulation analysis methods for studying the dynamic response of runway under the action of aircraft taxiing load are presented. An aircraft-runway coupled vibration model was established, and the runway pavement roughness was taken as the vibration excitation source; then, the aircraft taxiing dynamic load was obtained. A three-dimensional finite element model of the runway was established, and the vertical dynamic displacement (VDD) response and its variation law of the runway under different void conditions were studied under the action of the dynamic load of a taxiing aircraft. In addition, using wavelet packet transform, the acceleration signals at different positions of the pavement slab under the sliding load were decomposed into three layers. The relationship among the wavelet packet energy ratio (WPER), the wavelet packet energy entropy (WPEE) of each frequency band, and the void under the pavement slab was obtained. The results show that the established model could quickly and accurately solve the aircraft taxiing dynamic load. In the case of a slight void, the VDDs in the runway center and under the main landing gears had negative exponential and logarithmic relationships with the reduction coefficient of the dynamic elastic modulus of the base layer, respectively. When there was a severe void, the pavement slab was separated from the base layer. The VDDs in the runway center and under the main landing gears were exponentially and linearly related to the slab’s void area, respectively. The vibration signals were extracted at three measuring points, and the wavelet packet energy characteristics of the signals were compared and analyzed. It was found that the WPER and WPEE of the vibration signals in the void area of the slab corner could better reflect the void state of the slab bottom.
Feature Normalization Reweighting Regression Network for Sugar Content Measurement of Grapes
Authors: Jia Mei , Li Jiuliang , Hu Tianyang , Jiang Yingzhe , Luo Jun
Published: July 2022

The measurement of grape sugar content is an important index for classifying grapes based on their quality. Owing to the correlation between grape sugar content and appearance, non-destructive measurements are possible using computer vision and deep learning. This study investigates the quality classification of the Red Globe grape. The number of collected grapes in the range of the 15~16% measure is three times more than in the range of <14% or in the range of the >18% measure. This study presents a framework named feature normalization reweighting regression (FNRR) to address this imbalanced distribution of sugar content of the grape datasets. The experimental results show that the FNRR framework can measure the sugar content of a whole bunch of grapes with high accuracy using typical convolution neural networks and a visual transformer model. Specifically, the visual transformer model achieved the best accuracy with a balanced loss function, with the coefficient of determination R = 0.9599 and the root mean squared error RMSE = 0.3841%. The results show that the effect of the visual transformer model is better than that of the convolutional neural network. The research findings also indicate that the visual transformer model based on the proposed framework can accurately predict the sugar content of grapes, non-destructive evaluation of grape quality, and could provide reference values for grape harvesting.
Determination of Field of View of a Dawn–Dusk Sun-Synchronous Orbit Satellite Based on Improved Observation Mode
Authors: Xie Dandan , Huang Yawei , Yan Changxiang
Published: July 2022

We report a method for determining the field of view (FOV) of a dawn–dusk sun-synchronous orbit satellite based on an improved observation mode. The target trajectory distribution model in geosynchronous orbit (GEO) is established, the natural rendezvous mode is improved, and the observation mode of the satellite is determined. A scheme for determining the thresholds of cross-orbit field of view (COFOV) and the along-orbit field of view (AOFOV) was developed. The result shows that the coverage of the satellite can reach more than 95% when the improved observation mode is used to observe the GEO target. When the revisit period of the satellite is one day, the threshold of the COFOV is 15°, and the threshold of the AOFOV is 12°.
Simulation of the Boreal Winter East Asian Cold Surge by IAP AGCM4.1
Authors: Lin Renping , Dong Xiao , Zhang He , Wu Chenglai , Jin Jiangbo
Published: July 2022

In this study, we evaluate the performances of the Institute of Atmospheric Physics atmospheric general circulation model (IAP AGCM version4.1) and atmospheric component of Chinese Academy of Science Earth System Model, version 1 (CAS-ESM1) in the simulation of the cold surge (CS) events in East Asia. In general, the model can capture the main features of anomalous precipitation and circulation associated with the cold surge days. Compared with climatological means of boreal winter, on CS days, the precipitation increases in the southern part of the South China Sea (SCS), while decreases in the subtropical regions near the southern China. In addition, the climatological northeasterly wind over the SCS region strengthens on CS days. In the first day composites of CS events, it shows a dipole pattern in middle latitude over East Asia, with a positive (negative) sea level pressure (SLP) anomaly in the west (east). Based on the anomalous SLP signs in the two centers of the dipole pattern, the CS days can be further classified into two types: positive-west–negative-east-type and positive-west–positive-east-type. All these features can be reasonably reproduced by IAP AGCM4.1. Although in most CS days there is positive SLP anomaly in the East China, some negative events were investigated in this study. In these negative events the northerly anomaly in SCS is associated with an anticyclonic circulation anomaly around the eastern part of the Tibetan Plateau, rather than descending from the mid-to-high latitude cold air outbreaks. The feature can also be captured by the model.
Response of Natural Gas Consumption to Temperature and Projection under SSP Scenarios during Winter in Beijing
Authors: Min Jingjing , Dong Yan , Wang Hua
Published: July 2022

The present study investigates the response of natural gas consumption to temperature on the basis of observations during heating season (middle November–middle March) for the period 2002–2021 in Beijing, China, and then estimates temperature-related changes in the gas consumption under future scenarios by using climate model simulations from the Coupled Model Intercomparison Project Phase 6. Observational evidence suggests that the daily natural gas consumption normalized by gross domestic product is linearly correlated with the daily average temperature during heating season in the past two decades in Beijing. Hence, a linear regression model is built to estimate temperature-related changes in the natural gas consumption under future scenarios. Corresponding to a rising trend in the temperature, the natural gas consumption shows a decrease trend during 2015–2100 under both the SSP245 and the SSP585 scenarios. In particular, the temperature would increase rapidly from early 2040s to the end of 21st century under the SSP585 scenario, leading to an obvious reduction in the natural gas consumption for heating in Beijing. Relative to that in the present day (1995–2014), the natural gas consumption would show a reduction of approximately 9% (±4%) at the end of 21st century (2091–2100) under the SSP245 scenario and approximately 22% (±7%) under the SSP585 scenario.
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare
Authors: Manickam Pandiaraj , Mariappan Siva Ananth , Murugesan Sindhu Monica , Hansda Shekhar , Kaushik Ajeet , Shinde Ravikumar , Thipperudraswamy S. P.
Published: July 2022

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.
A Miniaturized Silicon Lab-on-Chip for Integrated PCR and Hybridization Microarray for High Multiplexing Nucleic Acids Analysis
Authors: Ventimiglia Giorgio , Pesaturo Massimiliano , Malcolm Alastair , Petralia Salvatore
Published: July 2022

A silicon lab-on-chip, for the detection of nucleic acids through the integrated PCR and hybridization microarray, was developed. The silicon lab-on-chip manufactured through bio-MEMS technology is composed of two PCR microreactors (each volume 11.2 µL) and a microarray-hybridization microchamber (volume 30 µL), fluidically connected by buried bypass. It contains heaters and temperature sensors for the management and control of the temperature cycles during the PCR amplification and hybridization processes. A post-silicon process based on (i) plasmo-O2 cleaning/activation, (ii) vapor phase epoxy silanization, (iii) microarray fabrication and (iv) a protein-based passivation step was developed and fully characterized. The ssDNA microarray (4 rows × 10 columns) composed of 400 spots (spot size—70 ± 12 µm; spot-to-spot distance—130 ± 13 µm) was manufactured by piezo-dispense technology. A DNA microarray probe density in the range of 1310 to 2070 probe µm−2 was observed, together with a limit of detection of about 19 target µm−2. The performances of the silicon lab-on-chip were validated by the detection of the beta-globin gene directly from human blood. Remarkable sensitivity, multiplexing analysis and specificity were demonstrated for the detection of beta-globin and mycobacterium tuberculosis sequences.
Retinal Thickness Correlates with Cerebral Hemodynamic Changes in Patients with Carotid Artery Stenosis
Authors: Kwapong William Robert , Liu Junfeng , Wan Jincheng , Tao Wendan , Ye Chen , Wu Bo
Published: July 2022

Background: We aimed to assess the retinal structural and choroidal changes in carotid artery stenosis (CAS) patients and their association with cerebral hemodynamic changes. Asymptomatic and symptomatic patients with unilateral CAS were enrolled in our study. Material and methods: Swept-source optical coherence tomography (SS-OCT) was used to image the retinal nerve fiber layer (RNFL), ganglion cell-inner plexiform layer (GCIPL), while SS-OCT angiography (SS-OCTA) was used to image and measure the choroidal vascular volume (CVV) and choroidal vascular index (CVI). Computed Tomography Perfusion (CTP) was used to assess the cerebral perfusion parameters; relative perfusion (r) was calculated as the ratio of the value on the contralateral side to that on the ipsilateral side. Results: Compared with contralateral eyes, ipsilateral eyes showed significantly thinner RNFL (p < 0.001), GCIPL (p = 0.013) and CVV (p = 0.001). Relative cerebral blood volume (rCBV) showed a significant correlation with RNFL (p < 0.001), GCIPL (p < 0.001) and CVI (p = 0.027), while the relative permeability surface (rPS) correlated with RNFL (p < 0.001) and GCIPL (p < 0.001). Conclusions: Our report suggests that retinal and choroidal changes have the potential to detect hemodynamic changes in CAS patients and could predict the risk of stroke.
FRP Poles: A State-of-the-Art-Review of Manufacturing, Testing, and Modeling
Authors: EL-Fiky Ahmed M. , Awad Youssef Ahmed , Elhegazy Hosam Mostafa , Hasan Mahmoud Galal , Abdel-Latif Ibrahim , Ebid Ahmed M. , Khalaf Mohamed A.
Published: July 2022

Fiber-reinforced polymers poles were on the increase because they were lightweight, have high strength-to-weight ratios, provide corrosion resistance, can be customized to meet strength and deflection requirements, and have a low life-cycle cost of construction and maintenance. This research presents a comprehensive review of all significant research and existing case studies to review the present knowledge concerning fiber-reinforced polymers poles. The main summary covers 70 works focusing on fiber poles to summarize recent activities on selected relevant topics and highlight possible future implementations. In this context, this study discusses fiber-reinforced polymers poles in six aspects: (i) introduction; (ii) methodology; (iii) Materials properties of FRP poles; (iv) manufacturing techniques of FRP poles; (v) testing of FRP poles (static and dynamic flexure test as cantilever beam); (vi) modeling of FRP poles. Therefore, this critical review will demonstrate an overview of FRP Poles manufacturing techniques (Pultrusion, filament winding, centrifugal process, and hand lay-up) and which Pultrusion technique is the best suited for FRP Poles. Static modeling was the most used of other techniques.