bluuebunny/crossref_metadata_2025 huggingface dataset:
Translating and Evaluating a Physical Activity Program for Aboriginal Elders on Noongar Boodjar (Country) — A Longitudinal Study
Authors: Gidgup Margaret J. R. , Kickett Marion , Jacques Angela , Weselman Tammy , Hill Keith D. , Coombes Julieann , Ivers Rebecca , Bowser Nicole , Palacios Vilma , Hill Anne-Marie
Published: July 2022

Objective

The primary aim of the study was to translate and evaluate the impact of a Physical Activity (PA) program on the physical function of older Aboriginal Elders on Noongar Boodjar (Country).

Methods

A longitudinal design framed within an Indigenous methodology. Two groups, one metropolitan and one regional, of Aboriginal Elders, aged ≥45 years, participated in the Ironbark PA program. This comprised weekly strength and balance exercises followed by yarning circles. Physical function (primary outcome) and functional ability, cardiovascular risk factors (weight, waist circumference), falls efficacy and health-related quality of life were measured at baseline 6, 12 and 24 months. Data were analyzed using generalized linear mixed effects modeling.

Results

Fifty-two Elders initially enrolled and of those, n = 23 (44.2%) Elders participated regularly for 24 months. There was a 6-month gap in program delivery due to the COVID-19 pandemic. Participants made significant improvement in physical function at 12 months compared to baseline: [short physical performance battery (SPPB) at baseline, 8.85 points (95% CI 8.10, 9.61); 12 months 10.28 (95% CI 9.44, 11.13), p = 0.001: gait speed at baseline 0.81 ms−1 (95% CI 0.60, 0.93); 12 months 1.14 (95% CI 1.01, 1.27), p < 0.001]. Some sustained improvement compared to baseline was still evident at 24 months after the 6-month gap in attendance [SPPB 9.60 (8.59, 10.60) p = 0.14, gait speed 1.11 (0.95, 1.26) p < 0.001]. Cardiovascular risk factors showed a non-significant improvement at 12 and 24 months compared to baseline. All participants reported that they enjoyed the program, found it culturally appropriate and would recommend it to others.

Conclusion

Older Aboriginal people showed sustained improvements in physical function after engaging in a culturally appropriate PA program. Culturally appropriate PA programs provide safety, security and choice for older Aboriginal people to engage in evidence-based PA.
Effectiveness of school-based child sexual abuse intervention among school children in the new millennium era: Systematic review and meta-analyses
Authors: Che Yusof Ruhana , Norhayati Mohd Noor , Mohd Azman Yacob
Published: July 2022

Introduction

School-based child sexual abuse intervention programs were developed to educate the school children to protect them from sexual abuse. The programs were evaluated to make sure the interventions were effective in reducing child sexual abuse cases (CSA). This review aimed to determine the effectiveness of the school-based child sexual abuse intervention programs in the new millennium era (2000–2021) in improving the knowledge, skills, and attitude of school children under 18 years old toward child sexual abuse.

Methods

A systematic search was conducted through MEDLINE (PubMed), EBSCO, and SCOPUS databases to collect full English articles related to school-based CSA intervention programs published from 2000 to 2021.

Results

A total of 29 studies from randomized control trial and quasi-experimental from several countries was analyzed. Comparisons within group of pre-post intervention for knowledge, skills, and attitude were measured by standardized mean difference (SMD) and 95% CI of −1.06 (95% CI: −1.29, −0.84), −0.91 (95% CI: −1.2, −0.61), and −0.51 (95% CI: −3.61, 0.58), respectively. Meanwhile for between intervention and control group comparisons, the SMD of knowledge was 0.9 (95% CI: 0.63, 1.18), skills was 0.39 (95% CI: 0.07, 0.71), and attitude was 1.76 (95% CI: 0.46, 3.07).

Conclusion

The programs were found to be effective in improving the knowledge, skills, and attitude of the students from pre-intervention to post-intervention and between the intervention and control groups.

Systematic Review Registration:www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022312383, identifier: CRD42022312383.
The impact of the COVID-19 pandemic on the prevalence and risk factors of workplace violence among healthcare workers in China
Authors: Qi Miao , Hu Xiuli , Liu Jun , Wen Jing , Hu Xue , Wang Zhizhong , Shi Xiuquan
Published: July 2022

Background

The pandemic of COVID-19 has significantly increased the burden on healthcare workers and potentially affect their risk of workplace violence (WPV). This study aimed to explore the prevalence and risk factors of WPV among healthcare workers during the peaking and the remission of the COVID-19 pandemic in China.

Methods

Using the snowball method, a repeated online questionnaire survey was conducted among Chinese healthcare workers from March 27th to April 26th in 2020 and 2021, respectively. Data included healthcare workers' socio-demographic and occupational characteristics, psychological status, and workplace violence.

Results

A total of 3006 samples in 2020 and 3465 samples in 2021 were analyzed. In 2020, the prevalence of WPV and witnessing colleagues suffering from WPV among healthcare workers were 64.2% and 79.7% respectively. Compared with 2020, the prevalence decreased by 11.0% and 14.4% in 2021, respectively. Logistic regression showed that WPV in 2020 was influenced by males, long working experience, working in the psychiatric department, direct contact with COVID-19 patients, self-discovery of medical errors, moral injury, depression, and anxiety (minimum OR = 1.22, maximum OR = 2.82). While risk factors of WPV in 2021 included males, working in psychiatric departments, self-discovery of medical errors, moral injury, depression, and anxiety (minimum OR = 1.33, maximum OR = 3.32); and protective factors were holding a master's degree (OR = 0.78) and working in other departments (OR = 0.54).

Conclusion

This study retains the common effects of WPV among healthcare workers, though after the baptism of the COVID-19 pandemic, the prevalence of WPV among healthcare workers decreased; however, part of the influencing factors changed. In addition, COVID-19 has seriously affected the mental health of healthcare workers, and the effect of mental health problems on WPV should also attract more attention.
Analysis of the impact of urban summer high temperatures and outdoor activity duration on residents' emotional health: Taking hostility as an example
Authors: Huang Huanchun , Li Yang , Zhao Yimin , Zhai Wei
Published: July 2022

The combined effect of global warming and the heat island effect keeps the temperature of cities rising in the summer, seriously threatening the physical and mental health of urban residents. Taking the area within the Sixth Ring Road of Beijing as an example, based on Landsat remote sensing images, meteorological stations, and questionnaires, this study established a relational model between temperature and hostility and then analyzed the changes in the emotional health risk (hostility) in the study area and the mechanism of how outdoor activity duration influences hostility. Results show that: (1) the area within the Sixth Ring Road of Beijing had a higher and higher temperature from 1991 to 2020. Low-temperature areas gradually shrank, and medium- and high-temperature areas extended outwards from the center. (2) The threat of high temperature to residents' hostility gradually intensified—the sphere of influence expanded, low-risk areas quickly turned into medium-high-risk areas, and the level of hostility risk increased. Level 1 risk areas of hostility had the most obvious reduction—a 74.33% reduction in area proportion; meanwhile, Level 3 risk areas had the most significant growth—a 50.41% increase in area proportion. (3) In the first 120 min of outdoor activities under high temperature, residents' hostility was negatively correlated with outdoor activity duration; after more than 120 min, hostility became positively correlated with duration. Therefore, figuring out how temperature changes influence human emotions is of great significance to improving the living environment and health level of residents. This study attempts to (1) explore the impact of temperature changes and outdoor activity duration on hostility, (2) evaluate residents' emotional health risk levels affected by high temperature, and (3) provide a theoretical basis for the early warning mechanism of emotional health risk and the planning of healthy cities.
A novel prognostic model based on three clinic-related miRNAs for prostate cancer
Authors: Che Ping , Jiang Shihao , Zhang Weiyang , Zhu Huixuan , Hu Daorong , Wang Delin
Published: July 2022

Background

Prostate cancer (PCa) is the second most common malignant tumor in men worldwide. MiRNAs have been reported to play significant roles in prognosis prediction for patients with malignant tumors.

Methods

The survival-related miRNAs (sDMIRs) were identified by Cox regression analysis. A risk score model (RSM) was established based on three sDMIRs. The expression levels of sDMIRs in cell lines and clinical samples were detected via quantitative polymerase chain reaction. The correlations between sDMIRs and clinicopathological characteristics of PCa patients were evaluated using the chi-square test and Fisher's exact probability method.

Results

Four sDMIRs were remarkably related to the prognosis of PCa patients based on univariate Cox analysis, of which miR-10a-5p, miR-20a-5p, and miR-508-3p were used to establish the RSM. The OS in the low-risk group was better than that in the high-risk group. In the verification of various prostate cell lines and clinical samples from 162 PCa patients, the prominently higher expression of miR-10a-5p and miR-20a-5p and lower expression of miR-508-3p were detected in PCa cell lines and tumor tissues, especially the more advanced T-stage. Besides, the higher expression of miR-20a-5p and miR-10a-5p was significantly correlated to the higher level of PSA, Gleason score, more advanced T-stage, and distant metastasis status.

Conclusion

We identify and validate the clinical significance of three sDMIRs and establish a verified RSM to evaluate the prognosis for PCa patients. The findings not only provide a reliable tool for clinical decision-makers to evaluate patients' prognosis but also offer a novel perspective into the field of biomarker identification.
A novel bidirectional side exiting pledgetted suture for valve replacement surgery
Authors: Zheng Junnan , Davies Henry , Ni Yiming
Published: July 2022

The pledgetted mattress suture is one of the most widely used suture techniques for valve replacement surgery. However, the traditional pledgetted suture has several defects including intertwining of the sutures and the pledget flipping over. Here we present a novel side exiting pledgetted suture that can overcome these defects. It offers cardiac surgeons a new alternative for valve replacement surgeries.
Photon Distribution of Sole-Source Lighting Affects the Mineral Nutrient Content of Microgreens
Authors: Vaštakaitė-Kairienė Viktorija , Brazaitytė Aušra , Miliauskienė Jurga , Sutulienė Rūta , Laužikė Kristina , Viršilė Akvilė , Samuolienė Giedrė , Runkle Erik S.
Published: July 2022

In the study, we cultivated basil, beet, and mustard microgreens under different lighting treatments from light-emitting diodes (LEDs) and evaluated the contents of mineral nutrients. Microgreens grew under blue 447, red 638 and 665, far-red 731 nm LEDs, or the same spectrum but with partial substitution of 638 nm red with green 520 (BRG), yellow 595 (BRY), or orange 622 nm (BRO) LEDs (16 h photoperiod; total photon flux density of 300 μmol m −2 s −1). BRG, BRY, or BRO lighting had distinct effects on mineral contents among the microgreen species. BRG increased the content of mineral nutrients, especially in mustard and beet. In all microgreens, Ca and P were associated with BRG; in beet and mustard, Zn and Mg were associated with BRG; in basil, Zn was associated with BRY and Mg with BRO treatments. A broader photon spectrum increased Fe (up to 2.9–fold), K:Ca, P:Mg, and P:Zn in basil, and Fe:Zn in microgreens. We conclude that the partial replacement of red with green light was the most effective at enhancing the mineral nutrient content of microgreens, although responses varied among the crops studied.
Modeling Adaptive Strategies on Maintaining Wheat-Corn Production and Reducing Net Greenhouse Gas Emissions under Climate Change
Authors: Yi Xiaopei , Chang Naijie , Ding Wuhan , Xu Chi , Zhang Jing , Zhang Jianfeng , Li Hu
Published: July 2022

Climate change has posed serious challenges to food production and sustainable development. We evaluated crop yields, N2O emissions, and soil organic carbon (SOC) in a typical wheat–corn rotation system field on the North China Plain on a 50-year scale using the Denitrification–Decomposition (DNDC) model and proposed adaptive strategies for each climate scenarios. The study showed a good consistency between observations and simulations (R2 > 0.95 and nRMSE < 30%). Among the twelve climate scenarios, we explored ten management practices under four climate scenarios (3 °C temperature change: P/T−3 and P/T+3; 30% precipitation change: 0.7P/T and 1.3P/T), which have a significant impact on crop yields and the net greenhouse effect. The results revealed that changing the crop planting time (CP) and using cold-resistant (CR) varieties could reduce the net greenhouse effect by more than 1/4 without sacrificing crop yields under P/T−3. Straw return (SR) minimized the negative impact on yields and the environment under P/T+3. Fertigation (FG) and Drought-Resistant (DR) varieties reduced the net greenhouse effect by more than 8.34% and maintained yields under 0.7P/T. SR was most beneficial to carbon sequestration, and yields were increased by 3.87% under 1.3P/T. Multiple adaptive strategies should be implemented to balance yields and reduce the environmental burden under future climate change.
Development of Data-Driven Models to Predict Biogas Production from Spent Mushroom Compost
Authors: Salehi Reza , Yuan Qiuyan , Chaiprapat Sumate
Published: July 2022

In this study, two types of data-driven models were proposed to predict biogas production from anaerobic digestion of spent mushroom compost supplemented with wheat straw as a nutrient source. First, a k-nearest neighbours (k-NN) model (k = 1–10) was constructed. The optimal k value was determined using the cross-validation (CV) method. Second, a support vector machine (SVM) model was developed. The linear, quadratic, cubic, and Gaussian models were examined as kernel functions. The kernel scale was set to 6.93, while the box constraint (C) was optimized using the CV method. Results demonstrated that R2 for the k-NN model (k = 2) was 0.9830 at 35 °C and 0.9957 at 55 °C. The Gaussian-based SVM model (C = 1200) provided an R2 of 0.9973 at 35 °C and 0.9989 at 55 °C, which are slightly better than those achieved by k-NN. The Gaussian-based SVM model produced RMSE of 0.598 at 35 °C and 0.4183 at 55 °C, which are 58.4% and 49.5% smaller, respectively, than those produced by the k-NN. These findings imply that SVM modeling can be considered a robust technique in predicting biogas production from AD processes as they can be implemented without requiring prior knowledge of biogas production kinetics.
Biochemical and Microbiological Soil Effects of a Biostimulant Based on Bacillus licheniformis-Fermented Sludge
Authors: Caballero Pablo , Macías-Benítez Sandra , Moya Ana , Rodríguez-Morgado Bruno , Martín Luis , Tejada Manuel , Castaño Angélica , Parrado Rubio Juan
Published: July 2022

Biostimulants are substances and/or microorganisms that are applied to plants or to the rhizosphere in order to enhance the natural process improving the absorption of nutrients and the quality of crops as well as the tolerance to abiotic stresses. A new biostimulant was developed from sewage sludge through its fermentation with Bacillus licheniformis as a plant growth-promoting bacteria (PGPB). The fermented product includes three classes of biostimulant components: the B. licheniformis biomass; the enzymatic secretion of said microorganism, which are mainly peptidases and amidases related to nitrogen metabolism and glucanases, related to carbohydrate metabolism; and finally, the hydrolyzed sludge organic matter, with a high content of protein hydrolysates. The biostimulant was evaluated in soil at the biochemical (enzymatic activities) and microbiological levels (metabarcoding analysis). Metabarcoding analysis revealed that the biostimulant complex, mainly the soluble fraction containing the Bacillus multienzyme complex and protein hydrolysate, induced PGPB soil bacteria, and it was detected that the inoculation in the soil of B. licheniformis remained active throughout the study. These results show the fermentation process with B. licheniformis as an interesting option for the total valorization of activated sewage sludge aimed at obtaining products of agronomic/environmental interest.
Population Pharmacokinetic and Pharmacodynamic Analysis of Dalbavancin for Long-Term Treatment of Subacute and/or Chronic Infectious Diseases: The Major Role of Therapeutic Drug Monitoring
Authors: Cojutti Pier Giorgio , Tedeschi Sara , Gatti Milo , Zamparini Eleonora , Meschiari Marianna , Siega Paola Della , Mazzitelli Maria , Soavi Laura , Binazzi Raffaella , Erne Elke Maria , Rizzi Marco , Cattelan Anna Maria , Tascini Carlo , Mussini Cristina , Viale Pierluigi , Pea Federico
Published: July 2022

A population pharmacokinetic analysis of dalbavancin was conducted in patients with different infection sites. Non-linear mixed effect modeling was used for pharmacokinetic analysis and covariate evaluation. Monte Carlo simulations assessed the probability of target attainment (PTA) of total dalbavancin concentration ≥ 8.04 mg/L over time (associated with ≥90% probability of optimal pharmacodynamic target attainment of fAUC24h/MIC > 111.1 against S. aureus) associated with a single or double dosage, one week apart, of 1000 or 1500 mg in patients with different classes of renal function. Sixty-nine patients with 289 concentrations were included. Most of them (53/69, 76.8%) had bone and joint infections. A two-compartment model adequately fitted dalbavancin concentration–time data. Creatinine clearance (CLCR) was the only covariate associated with dalbavancin clearance. Monte Carlo simulations showed that, in patients with severe renal dysfunction, the 1000 mg single or double one week apart dosage may ensure optimal PTAs of 2 and 5 weeks, respectively. In patients with preserved renal function, the 1500 mg single or double one-week apart dosage may ensure optimal PTAs of 2 and 4 to 6 weeks, respectively. Therapeutic drug monitoring should be considered mandatory for managing inter-individual variability and for supporting clinicians in long-term treatments of subacute and chronic infections.
Butein Ameliorates Oxidative Stress in H9c2 Cardiomyoblasts through Activation of the NRF2 Signaling Pathway
Authors: Tungalag Tsendsuren , Park Kye Won , Yang Dong Kwon
Published: July 2022

Oxidative stress, defined as an imbalance between reactive oxygen species (ROS) production and the antioxidant defense system, contributes to the pathogenesis of many heart diseases. Therefore, oxidative stress has been highlighted as a therapeutic target for heart disease treatment. Butein, a tetrahydroxychalcone, has potential biological activities, especially antioxidant properties. However, the effect of butein on oxidative-stressed heart cells has been poorly studied. Thus, we sought to identify the antioxidant effects of butein in H9c2 cardiomyoblasts. To elucidate these antioxidant effects, various concentrations of butein were used to pretreat H9c2 cells prior to H2O2 treatment. Thereafter, measures of oxidative damages, such as ROS production, antioxidant expression levels, and apoptosis, were evaluated. Butein effectively increased cell viability and rescued the cells from oxidative damage through the inhibition of ROS production, apoptosis, and increased antioxidant expression. Furthermore, butein dramatically inhibited mitochondrial dysfunction and endoplasmic reticulum (ER) stress, which are the main ROS inducers. Nrf2 protein translocated from the cytosol to the nucleus and consequently activated its target genes as oxidative stress suppressors. These findings demonstrate that butein has potential antioxidant effects in H9c2 cardiomyoblasts, suggesting that it could be used as a therapeutic substance for the treatment of cardiac diseases.
Moringa oleifera Leaf Extract Protects C2C12 Myotubes against H2O2-Induced Oxidative Stress
Authors: Ceci Roberta , Maldini Mariateresa , Olson Mark E. , Crognale Domenico , Horner Katy , Dimauro Ivan , Sabatini Stefania , Duranti Guglielmo
Published: July 2022

The imbalance between reactive oxygen species (ROS) production and antioxidant defense systems leads to macromolecule and tissue damage as a result of cellular oxidative stress. This phenomenon is considered a key factor in fatigue and muscle damage following chronic or high-intensity physical exercise. In the present study, the antioxidant effect of Moringa oleifera leaf extract (MOLE) was evaluated in C2C12 myotubes exposed to an elevated hydrogen peroxide (H2O2) insult. The capacity of the extract to influence the myotube redox status was evaluated through an analysis of the total antioxidant capacity (TAC), glutathione homeostasis (GSH and GSSG), total free thiols (TFT), and thioredoxin (Trx) activity, as well as the enzyme activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) and transferase (GST). Moreover, the ability of MOLE to mitigate the stress-induced peroxidation of lipids and oxidative damage (TBARS and protein carbonyls) was also evaluated. Our data demonstrate that MOLE pre-treatment mitigates the highly stressful effects of H2O2 in myotubes (1 mM) by restoring the redox status (TFT, Trx, and GSH/GSSG ratio) and increasing the antioxidant enzymatic system (CAT, SOD, GPx, GST), thereby significantly reducing the TBARs and PrCAR levels. Our study provides evidence that MOLE supplementation has antioxidant potential, allowing myotubes better able to cope with an oxidative insult and, therefore, could represent a useful nutritional strategy for the preservation of muscle well-being.
Pandemic Preparedness and Response: A Foldable Tent to Safely Remove Contaminated Dental Aerosols—Clinical Study and Patient Experience
Authors: Rosa Vinicius , Agarwalla Shruti Vidhawan , Tan Bing Liang , Choo Si Yi , Sim Yu Fan , Boey Freddy Yin Chiang , Anantharaman Sudarshan , Duggal Mandeep Singh , Tan Kai Soo
Published: July 2022

The D-DART (Droplet and Aerosol Reducing Tent) is a foldable design that can be attached to the dental chair to prevent the spread of contaminated dental aerosols. The objective of this study was to evaluate the ability of the D-DART to reduce spread of aerosols generated during dental treatment. Thirty-two patients (sixteen per group) undergoing deep ultrasonic scaling were recruited and randomly allocated to groups D-DART or Control (no D-DART). After 20 min from the start of the treatment, the clinician’s face shield and dental chair light were swabbed and the viable microbial load was quantified (ATP bioluminescence analysis, blinded operator). Statistical analyses were performed with Tukey’s Honest Test with a level of significance pre-set at 5%. There were significant increases in ATP values obtained from the operator’s face shield and dental chair light for the Control compared with baseline (31.3 ± 8.5 and fold increase). There was no significant change in microbial load when the D-DART was used compared with baseline (1.5 ± 0.4 fold increase). The D-DART contained and prevented the spread of aerosols generated during deep scaling procedures.
Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
Authors: Chen Yin-Lin , Hwang Wen-Jyi , Tai Tsung-Ming , Cheng Po-Sheng
Published: July 2022

This study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in the sequence is regarded as an object with a pair of key intervals. The detection and classification of each gesture are equivalent to the identification and matching of the corresponding key intervals. A simple automatic labelling is proposed for the identification of key intervals without manual inspection of sensory data. This could facilitate the collection and annotation of training data. To attain superior generalization and regularization, a multitask learning algorithm for the simultaneous training for gesture detection and classification is proposed. A prototype system based on smart phones for remote control of home appliances was implemented for the performance evaluation. Experimental results reveal that the proposed algorithm provides an effective alternative for applications where accurate detection and classification of hand gestures by simple networks are desired.
The Effects of a Combined Pre- and Post-Operative Anterior Cruciate Ligament Reconstruction Rehabilitation Program on Lower Extremity Muscle Imbalance
Authors: Ficek Krzysztof , Gołaś Artur , Pietraszewski Przemysław , Strózik Magdalena , Krzysztofik Michał
Published: July 2022

This study examined whether the 5-week pre-operative progressive exercise rehabilitation program with weekly monitoring contributed to a significantly lower muscle activity imbalance in the treatment group, both before and immediately after anterior cruciate ligament reconstruction (ACLR), as well as during the next 5 weeks in comparison to the control group. Twelve professional soccer players took part in the study (from among the top three Polish levels of competition) (age: 26 ± 5 years, body mass: 73 ± 7 kg, stature: 180 ± 6 cm, training experience: 15 ± 4 years). The participants were randomly assigned to the treatment group (n = 6) or to the control group (n = 6). Both groups performed the same pre- and post-operative progressive exercise rehabilitation program, while the rehabilitation of the treatment group was extended by supplementary body-weight functional stabilization training. The three-way repeated-measures ANOVA revealed a statistically significant interaction for muscle × group × time (p < 0.0001; F = 24.897; η2 = 0.806). The post-hoc analysis for the interaction effect of muscle × group × time indicated a significantly higher muscle activity imbalance for every measured muscle in the control group at any time point than in the treatment group (from p = 0.036 to p < 0.0001). The muscle activity imbalance was significantly higher from the 1st to 4th weeks than in the last week before surgery for quadriceps (p < 0.016 for all) and hamstrings (p < 0.001). However, in the case of gluteal muscles’ activity imbalance, it was significantly higher at every time point of the post-operative phase than in the last week before surgery (p < 0.001). The results of this study showed that the 5-week pre-operative rehabilitation program with weekly monitoring influenced outcomes of the post-operative phase. Nevertheless, performing both pre- and post-ACLR rehabilitation significantly reduced the muscle activity imbalance of lower limbs, but in the case of the quadriceps muscles, not to a sufficient level.
A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series
Authors: Qin Rui , Chen Genliang , Zhang Haibo , Liu Luo , Long Shaoqiu
Published: July 2022

Satellite-derived environmental parameters play important roles in environmental research on global changes and regional resources. Atmosphere effects and sensor limitations often lead to data products that vary in quality. The main goal of time series data reconstruction is to use various statistical and numerical analysis methods and to stimulate changing seasonal or annual parameters, providing more complete data sets for correlational research. This paper aims to develop a time series reconstruction algorithm for LST based on data assimilation according to the current problems of unstable precision and unsatisfactory results, and the simplistic effects of evaluation methods while using remote sensing-derived LST data as the basic parameters and the daily LST data derived from the static meteorological satellite GMS-5 as the input data. The data assimilation system used the Kalman filter as the assimilation algorithm. A complete set of global refined LST time series data sets were obtained by constantly correcting the LST values according to the regional ground-based observations. This method was implemented using MATLAB software (version R2017a), and was applied and validated through partitioning using the principal elevation in the Beijing, Tianjin, and Hebei regions. The results show that the accuracy of the reconstructed LST data series improved significantly in terms of the mean and standard deviation. Better consistency was achieved between the variables obtained over a year from the reconstructed LST data and the ground observations from the LST data set.
Study of Fault Identification of Clearance in Cam Mechanism
Authors: Chang Xuefang , Pan Hongxia , Xu Jian , Wang Tong
Published: July 2022

The clearance between the main roller and the cylinder cam has an important impact on the operational status of the bolt. In order to achieve the clearance and recoil of the cylinder cam mechanism, a bolt model was built and studied. The leading dynamic roller group and the dynamic bolt were analyzed. Positive stress and friction force change quickly on the high-speed leading roller group, influencing multiple aspects. When the main roller suddenly rotates or reverses in several short intervals, the assembling clearance and the massive friction force between the main roller and the cam curve slot are the main wear factors in theoretical analysis and test experiments. The leading roller group, the pure rolling criterion of the cylindrical cam mechanism of the automaton, is derived in this paper. Moreover, a new fault diagnosis method is developed, based on the Variational Box Dimension Kernel Fuzzy Mean Clustering Algorithm (VK) algorithm, which combines the variational mode decomposition and fractal box-counting dimension (VMD-FBCD) and the fuzzy clustering algorithms with a kernelized Mahalanobis distance (KMD-FC). With the simulation validation from 2810 samples, diagnosis using the VK algorithm is found to have a higher accuracy rate and slightly lower fallacy rate and to be faster than other diagnostic methods of similar studies. The results in terms of the recoil cylinder cam mechanism’s multi-body dynamic analysis and high-speed fault identification also have significance as a reference for solving similar problems.
Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models
Authors: Ma Chunyan , Zhai Liting , Li Changchun , Wang Yilin
Published: July 2022

Remote sensing estimation of crop nitrogen content allows real-time monitoring of growth to develop scientific methods. However, most of the current remote sensing estimates of crop nitrogen contents have limitations in accurately reflecting the vertical distribution of nutrients in plants. Firstly, the original hyperspectrum is first-order differential (FD), second-order differential (SD), and continuous removal (CR), and the corresponding sensitive bands were screened by correlation analysis in this paper. Then, the spectral reflectance, vegetation indices, and wavelet coefficients were used as input features to construct models for estimating nitrogen content of flag leaf, upper 1 leaf, upper 2 leaf, upper 3 leaf, and upper 4 leaf based on partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and multiple linear regression (MLR), respectively. The results showed that the accuracy of nitrogen content prediction based on wavelet coefficients was the highest. The combination of MLR and SVM with wavelet coefficients had high accuracy and robustness in the prediction of nitrogen content at different leaf positions. Additionally, the prediction accuracy of nitrogen gradually increased as the leaf position of winter wheat increased. The study can provide technical support for remote sensing estimation of nutrient elements at vertical leaf position of crops. The study can provide a reference for prediction of other crops.
Evaluation of General Health Status of Persons Living in Socio-Economically Disadvantaged Neighborhoods in a Large European Metropolitan City
Authors: Iacoella Claudia , De-Giorgio Fabio , Palaia Gaspare , Ferraioli Mario , Arcangeli Andrea , Ralli Massimo
Published: July 2022

Background: Living in socio-economically disadvantaged neighborhoods can predispose persons to numerous health conditions. The purpose of this study was to report the general health conditions of persons living in disadvantaged neighborhoods in Rome, Italy, a large European metropolitan city. Participants were reached through the mobile facilities of the primary care services of the Dicastery for the Charity Services, Vatican City. Methods: People living in disadvantaged neighborhoods were reached with mobile medical units by doctors, nurses, and paramedics. Demographic characteristics, degree of social integration, housing conditions, and history of smoking and/or alcohol use were investigated. Unstructured interviews and general health assessments were performed to investigate common acute and/or chronic diseases, and history of positivity to COVID-19. Basic health parameters were measured; data were collected and analyzed. Results: Over a 10-month period, 436 individuals aged 18–95 years were enrolled in the study. Most lived in dormitories, whereas a few lived in unsheltered settings. Most participants (76%) were unemployed. Smoking and drinking habits were comparable to the general population. The most common pathological conditions were cardiovascular diseases in 103 subjects (23.39%), diabetes in 65 (14.9%), followed by musculoskeletal system disorders (11.7%), eye diseases (10.5%), psychiatric conditions such as anxiety and depression (9.2%), and chronic respiratory conditions (8.7%). Conclusions: Subjects in our sample showed several pathologic conditions that may be related to their living conditions, thus encouraging the development of more efficient and effective strategies for a population-tailored diagnosis and treatment.
Knowledge Graph Recommendation Model Based on Adversarial Training
Authors: Zhang Suqi , Zhang Ningjing , Fan Shuai , Gu Junhua , Li Jianxin
Published: July 2022

The recommendation model based on the knowledge graph (KG) alleviates the problem of data sparsity in the recommendation to a certain extent and further improves the accuracy, diversity, and interpretability of recommendations. Therefore, the knowledge graph recommendation model has become a major research topic, and the question of how to utilize the entity and relation information fully and effectively in KG has become the focus of research. This paper proposes a knowledge graph recommendation model based on adversarial training (ATKGRM), which can dynamically and adaptively adjust the knowledge graph aggregation weight based on adversarial training to learn the features of users and items more reasonably. First, the generator adopts a novel long- and short-term interest model to obtain user features and item features and generates a high-quality set of candidate items. Then, the discriminator discriminates candidate items by comparing the user’s scores of positive items, negative items, and candidate items. Finally, experimental studies on five real-world datasets with multiple knowledge graph recommendation models and multiple adversarial training recommendation models prove the effectiveness of our model.
Reinforcement-Learning-Based Tracking Control with Fixed-Time Prescribed Performance for Reusable Launch Vehicle under Input Constraints
Authors: Xu Shihao , Guan Yingzi , Wei Changzhu , Li Yulong , Xu Lei
Published: July 2022

This paper proposes a novel reinforcement learning (RL)-based tracking control scheme with fixed-time prescribed performance for a reusable launch vehicle subject to parametric uncertainties, external disturbances, and input constraints. First, a fixed-time prescribed performance function is employed to restrain attitude tracking errors, and an equivalent unconstrained system is derived via an error transformation technique. Then, a hyperbolic tangent function is incorporated into the optimal performance index of the unconstrained system to tackle the input constraints. Subsequently, an actor-critic RL framework with super-twisting-like sliding mode control is constructed to establish a practical solution for the optimal control problem. Benefiting from the proposed scheme, the robustness of the RL-based controller against unknown dynamics is enhanced, and the control performance can be qualitatively prearranged by users. Theoretical analysis shows that the attitude tracking errors converge to a preset region within a preassigned fixed time, and the weight estimation errors of the actor-critic networks are uniformly ultimately bounded. Finally, comparative numerical simulation results are provided to illustrate the effectiveness and improved performance of the proposed control scheme.
Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil
Authors: Wu Dazhi , Zhang Zilong , Chen Keyu , Xia Linling
Published: July 2022

In response to the high carbon emissions and energy consumption of traditional cement curing agents, in this paper, we propose a fly ash/slag-based geopolymer as an alternative to cement for stabilizing soft soils. In this study, the effects of the activator modulus, activator, and slag content on the geopolymer-stabilized clay were investigated by unconfined compressive strength (UCS) tests on Hangzhou soft soils, and the water stability and resistance to wet–dry cycles of the geopolymer-stabilized soils were studied. The changes in the microstructure and mineral phases were investigated using X-ray diffraction and scanning electron microscopy, respectively, and the inner evolution of the properties of the stabilized soft soil under different conditions was clarified. The test results revealed that the UCS of the geopolymer-stabilized soft soils increased and then decreased as the content and modulus of the alkali activator increased. The optimum mix proportion of geopolymer-stabilized soil required a modulus of the alkali activator of 0.6, a content of the alkali activator of 6%, and a slag-to-fly ash ratio of 1:1. Its 28-day UCS of the test specimens reached 2 MPa. When the content of the geopolymer was 25%, the water stability coefficient reached 87.53%, and the strength was still 1.6 MPa after eight wet–dry cycles. Based on the microscopic analysis, the cementing substances in the geopolymer-stabilized clay were calcium silicate hydrate (C-S-H) and sodium aluminosilicate hydrate (N-A-S-H), which made the soil’s structure denser through bonding and filling effects.
A Statistical-Based Model for Typhoon Rain Hazard Assessment
Authors: Gu Jiyang , Cui Xizhong , Hong Hanping
Published: July 2022

Extreme typhoon rainfall can lead to damaging floods near the coastal region in mainland China. In the present study, we calibrate the parameters for a parametric hurricane rain model by using the precipitation radar (PR) data from the Tropical Rainfall Measuring Mission (TRMM) (i.e., PR-TRMM) and the TRMM microwave imager (TMI) data (i.e., TMI-TRMM). To show the applicability of the model for the tropical cyclone (TC) rain hazard assessment, we combine the developed rainfall intensity model with historical and synthetic TC tracks to estimate the T-year return period value of the accumulated rainfall in 24 h, QA24-T. We map QA24-100 for part of the coastal region in mainland China, showing that the spatial variation of QA24-100 is relatively smooth. It was found that the estimated QA24-100 using the model developed, based on the snapshots from PR-TRMM, is about 60% of that obtained using the model developed based on the snapshots from TMI-TRMM. This reflects the differences in the rainfall intensities reported in TMI-TRMM and PR-TRMM. As part of verification, we compare the estimated return period value to that obtained by using the record from surface meteorological stations at a few locations. The comparison indicates that, on average, QA24-100 based on gauge data is about 1.4 and 2.3 times that obtained using the model developed based on the snapshots from PR-TRMM and TRM-TRMM, respectively. This suggests that, for TC rain hazard estimation, one may consider the empirical scaling factor of 1.4 and 2.4 for the rainfall intensity models developed based on snapshots from PR-TRMM and TMI-TRMM, respectively.
The Influence of Multiplicative Noise and Fractional Derivative on the Solutions of the Stochastic Fractional Hirota–Maccari System
Authors: Al-Askar Farah M. , Mohammed Wael W. , Cesarano Clemente , El-Morshedy M.
Published: July 2022

We address here the space-fractional stochastic Hirota–Maccari system (SFSHMs) derived by the multiplicative Brownian motion in the Stratonovich sense. To acquire innovative elliptic, trigonometric and rational stochastic fractional solutions, we employ the Jacobi elliptic functions method. The attained solutions are useful in describing certain fascinating physical phenomena due to the significance of the Hirota–Maccari system in optical fibers. We use MATLAB programm to draw our figures and exhibit several 3D graphs in order to demonstrate how the multiplicative Brownian motion and fractional derivative affect the exact solutions of the SFSHMs. We prove that the solutions of SFSHMs are stabilized by the multiplicative Brownian motion around zero.
A Valid Quantization of a Half-Harmonic Oscillator Field Theory
Authors: Klauder John
Published: July 2022

The usual full- and half-harmonic oscillators are turned into field theories, and that behavior is examined using canonical and affine quantization. The result leads to a valid affine quantization of the half harmonic oscillator field theory, which points toward further valid quantizations of more realistic field theory models.
Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
Authors: Rahman Md. Mominur , Islam Md. Rezaul , Rahman Firoza , Rahaman Md. Saidur , Khan Md. Shajib , Abrar Sayedul , Ray Tanmay Kumar , Uddin Mohammad Borhan , Kali Most. Sumaiya Khatun , Dua Kamal , Kamal Mohammad Amjad , Chellappan Dinesh Kumar
Published: July 2022

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.
Enhanced Adipogenic Differentiation of Human Dental Pulp Stem Cells in Enzymatically Decellularized Adipose Tissue Solid Foams
Authors: Garcia-Urkia Nerea , Luzuriaga Jon , Uribe-Etxebarria Veronica , Irastorza Igor , Fernandez-San-Argimiro Francisco Javier , Olalde Beatriz , Briz Nerea , Unda Fernando , Ibarretxe Gaskon , Madarieta Iratxe , Pineda Jose Ramon
Published: July 2022

Engineered 3D human adipose tissue models and the development of physiological human 3D in vitro models to test new therapeutic compounds and advance in the study of pathophysiological mechanisms of disease is still technically challenging and expensive. To reduce costs and develop new technologies to study human adipogenesis and stem cell differentiation in a controlled in vitro system, here we report the design, characterization, and validation of extracellular matrix (ECM)-based materials of decellularized human adipose tissue (hDAT) or bovine collagen-I (bCOL-I) for 3D adipogenic stem cell culture. We aimed at recapitulating the dynamics, composition, and structure of the native ECM to optimize the adipogenic differentiation of human mesenchymal stem cells. hDAT was obtained by a two-enzymatic step decellularization protocol and post-processed by freeze-drying to produce 3D solid foams. These solid foams were employed either as pure hDAT, or combined with bCOL-I in a 3:1 proportion, to recreate a microenvironment compatible with stem cell survival and differentiation. We sought to investigate the effect of the adipogenic inductive extracellular 3D-microenvironment on human multipotent dental pulp stem cells (hDPSCs). We found that solid foams supported hDPSC viability and proliferation. Incubation of hDPSCs with adipogenic medium in hDAT-based solid foams increased the expression of mature adipocyte LPL and c/EBP gene markers as determined by RT-qPCR, with respect to bCOL-I solid foams. Moreover, hDPSC capability to differentiate towards adipocytes was assessed by PPAR-γ immunostaining and Oil-red lipid droplet staining. We found out that both hDAT and mixed 3:1 hDAT-COL-I solid foams could support adipogenesis in 3D-hDPSC stem cell cultures significantly more efficiently than solid foams of bCOL-I, opening the possibility to obtain hDAT-based solid foams with customized properties. The combination of human-derived ECM biomaterials with synthetic proteins can, thus, be envisaged to reduce fabrication costs, thus facilitating the widespread use of autologous stem cells and biomaterials for personalized medicine.