Low back pain in medical workers and its influencing factors.

PURPOSE: To investigate the prevalence of occupational lower back pain (OLBP) among medical workers and identify the contributing factors. METHODS: An electronic questionnaire was distributed to medical workers at Yuebei People's Hospital to gather information on various factors, including gender, age, body mass index (BMI), length of employment, job role, education level, professional title, marital status, fertility status, frequency of night shift, weight lifting daily, duration of daily standing at work, frequency of bending, work-related stress, experience with low back protection training, and frequency of waist exercises. Univariate and multivariate logistic regression analyses were conducted to identify the factors associated with OLBP in medical workers. RESULTS: Out of the 98 medical workers surveyed, 67 experienced OLBP (68.37%). The results of multivariate logistic regression analysis revealed that working for more than 5 years, holding a nursing position, and lacking training in low back protection were significant risk factors for developing OLBP in medical workers (all P<0.05). CONCLUSION: OLBP is a prevalent issue among medical workers, and various factors such as length of employment, job role, and training in low back protection can influence its occurrence.

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment.

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie-Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.

Insights into "wheat aroma": Analysis of volatile components in wheat grains cultivated in saline-alkali soil.

The wheat grains that are cultivated in saline-alkali soil exhibit a richer "wheat aroma" compared to their counterparts. This study characterized the composition and content of volatiles in five wheat kernel varieties, harvested from two fields with varying pH levels and total salt content in the soil. The wheat grown in soil with high pH and total salt content had significantly lower levels (p < 0.05) of ethyl 3-methylbutanoate and 1-octen-3-one and significantly higher levels (p < 0.05) of 1-butanol and 1-octen-3-ol. Among all factors, plant site contributed the highest F-value contribution rate (more than 77 %) for these four volatile compounds. Six e-nose sensors responsive to these four compounds exhibited consistent trends. Therefore, the lower of ethyl 3-methylbutanoate and 1-octen-3-one, the higher of 1-butanol and 1-octen-3-ol in wheat, grown on saline-alkali soil, served as characteristic markers for "wheat aroma".

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3 days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Insight into N2O emission and denitrifier communities under different aeration intensities in composting of cattle manure from perspective of multi-factor interaction analysis.

Nitrous oxide (N2O) emission from composting is a significant contributor to greenhouse effect and ozone depletion, which poses a threat to environment. To address the challenge of mitigating N2O emission during composting, this study investigated the response of N2O emission and denitrifier communities (detected by metagenome sequencing) to aeration intensities of 6 L/min (C6), 12 L/min (C12), and 18 L/min (C18) in cattle manure composting using multi-factor interaction analysis. Results showed that N2O emission occurred mainly at mesophilic phase. Cumulative N2O emission (QN2O, 9.79 mg·kg-1 DW) and total nitrogen loss (TN loss, 16.40 %) in C12 composting treatment were significantly lower than those in the other two treatments. The lower activity of denitrifying enzymes and the more complex and balanced network of denitrifiers and environmental factors might be responsible for the lower N2O emission. Denitrification was confirmed to be the major pathway for N2O production. Moisture content (MC) and Luteimonas were the key factors affecting N2O emission, and nosZ-carrying denitrifier played a significant role in reducing N2O emission. Although relative abundance of nirS was lower than that of nirK significantly (P < 0.05), nirS was the key gene influencing N2O emission. Community composition of denitrifier varied significantly with different aeration treatments (R2 = 0.931, P = 0.001), and Achromobacter was unique to C12 at mesophilic phase. Physicochemical factors had higher effect on QN2O, whereas denitrifying genes, enzymes and NOX- had lower effect on QN2O in C12. The complex relationship between N2O emission and the related factors could be explained by multi-factor interaction analysis more comprehensively. This study provided a novel understanding of mechanism of N2O emission regulated by aeration intensity in composting.

The aging behavior of degradable plastic polylactic acid under the interaction of environmental factors.

Microplastics leaching from aging biodegradable plastics pose potential environmental threats. This study used response surface methodology (RSM) to investigate the impact of temperature, light, and humidity on the aging characteristics of polylactic acid (PLA). Key evaluation metrics included the C/O ratio, functional groups, crystallinity, surface topography, and mechanical properties. Humidity was discovered to have the greatest effect on the ageing of PLA, followed by light and temperature. The interactions between temperature and light, as well as humidity and sunlight, significantly impact the aging of PLA. XPS analysis revealed PLA underwent aging due to the cleavage of the ester bond (O-C=O), resulting in the addition of C=O and C-O. The aging process of PLA was characterized by alterations in surface morphology and augmentation in crystallinity, resulting in a decline in both tensile strength and elongation. These findings might offer insights into the aging behavior of degradable plastics under diverse environmental conditions.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Spraying performance and deposition characteristics of an improved air-assisted nozzle with induction charging.

Electrostatic spraying technology can improve the efficiency of pesticide deposition on the surface of leaves and reduce the environmental pollution caused by pesticide drift, which has an important prospect in agricultural pesticide application. To improve the deposition and penetration of droplets in the crop canopy, we designed and optimized an air-assisted electrostatic nozzle and conducted the spraying performance experiment. Parameters, such as charge-to-mass ratio (CMR) and particle size, were tested and analyzed to obtain the suitable operating parameters of nozzle. The results proved that the improved air-assisted electrostatic nozzle has good atomization and chargeability. There is a good charging effect with a charging voltage of 3,000-5,000 V, the CMR increased 127.8% from 0.86 to 1.97 mC/kg as the charge voltage increases from 1,000 to 4,000 V, at an air pressure of 1.0 bar and liquid flow rate of 200 ml/min. Furthermore, we designed a multi-factor orthogonal experiment, which was conducted using a four-factor, three-level design to investigate the effects of operational parameters and canopy characteristics on droplet deposition and penetration. Analysis of variance (ANOVA) and F-test were performed on the experiment results. The results showed that the factor effect on droplet penetration, in descending order, was as follows: spray distance, leaf area index, air pressure, and air pressure × spray distance. The factor effect on abaxial leaf deposition, in descending order, was as follows: air pressure, spray distance, air pressure × charge voltage, spray distance × charge voltage, and charge voltage. For optimal droplet penetration and abaxial leaf deposition, option A 3 B 1 D 2 (air pressure 1.5 bar, spray distance 0.2 m, charge voltage 2,500 V) is recommend. The spray nozzle atomization performance and deposition regulation were studied by experimental methods to determine the optimal values of operating parameters to provide a reference for electrostatic spray system development.

Strengthening Multi-Factor Authentication Through Physically Unclonable Functions in PVDF-HFP-Phase-Dependent a-IGZO Thin-Film Transistors.

For enhanced security in hardware-based security devices, it is essential to extract various independent characteristics from a single device to generate multiple keys based on specific values. Additionally, the secure destruction of authentication information is crucial for the integrity of the data. Doped amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) using poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) induce a dipole doping effect through a phase-transition process, creating physically unclonable function (PUF) devices for secure user information protection. The PUF security key, generated at VGS = 20 V in a 20 × 10 grid, demonstrates uniformity of 42% and inter-Hamming distance (inter-HD) of 49.79% in the ß-phase of PVDF-HFP. However, in the γ-phase, the uniformity drops to 22.5%, and inter-HD decreases to 35.74%, indicating potential security key destruction during the phase transition. To enhance security, a multi-factor authentication (MFA) system is integrated, utilizing five security keys extracted from various TFT parameters. The security keys from turn-on voltage (VON), VGS = 20 V, VGS = 30 V, mobility, and threshold voltage (Vth) exhibit near-ideal uniformities and inter-HDs, with the highest values of 58% and 51.68%, respectively. The dual security system, combining phase transition and MFA, establishes a robust protection mechanism for privacy-sensitive user information.

Study on Thin Lamination of Carbon Fiber Based on Mechanical Broadening.

Carbon fiber has excellent mechanical properties and plays an important role in modern industry. However, due to the complexity of the carbon fiber widening process, the industrial application of carbon fiber is limited. By designing the carbon fiber widening equipment of automaton, the relationship between the widening width of carbon fiber and the process parameters is studied, and the optimum developing process parameters are obtained, to improve the performance of carbon fiber composites to a certain extent. In this study, the widening process of carbon fiber was studied based on the mechanical broadening method. Firstly, an automatic broadening equipment was designed, and the effects of the initial tension, the number of straight rods, the number of convex rods, and the drawing speed on the widened width during the broadening process were discussed. The widening effect was evaluated by SEM imaging and mechanical testing. At the same time, the factors affecting the broadening width and broadening defects during the broadening process were analyzed, and the optimal broadening process parameters were obtained. The results showed that within a specific range, a higher initial tension, a greater number of convex rods, and an appropriate speed resulted in relatively smaller damage to the broadening of carbon fibers. Through the design of automatic broadening, this experiment explores optimal broadening process parameters, provides a reference for the improvement of the carbon fiber broadening process and further promotes large-scale industrial applications of carbon fiber.

A study of the spatial network structure of ethnic regions in Northwest China based on multiple factor flows in the context of COVID-19: Evidence from Ningxia.

As an underdeveloped and low-income region, the development of minority regions in Northwest China is crucial. As an important part of minority regions, Ningxia Hui Autonomous Region has insufficient endogenous power for stable economic development and high risk of returning to poverty. On the whole, the Ningxia county network shows a spatial pattern of high in the north and low in the south. However, there are great differences in the centrality of different factor flow networks. The factor connections between most counties are weak, and a close innovation network has not yet been formed. There is an obvious administrative clique structure, showing a certain degree of self-enclosure. The factor flows between counties are relatively uniform and greatly affected by geographic distance. From the perspective of integrated flow, the Ningxia county network presents a distinct core-periphery circle structure. Population size and GDP are the main factors affecting the spatial network. The policy implication of this study is that Ningxia Autonomous Prefecture should coordinate the planning of the region's economy, technology, and transportation, so as to reduce the development gap between counties by enhancing the closeness of the county spatial association network, and ultimately realize the region's high-quality development.

Estimation of aboveground biomass of senescence grassland in China's arid region using multi-source data.

Aboveground Biomass (AGB) in the grassland senescence period is a key indicator for assessing grassland fire risk and autumnal pasture carrying capacity. Despite the advancement of remote sensing in rapid monitoring of AGB on a regional scale, accurately monitoring AGB during the senescence period in vast arid areas remains a major challenge. Using remote sensing, environmental data, and 356 samples of grassland senescence period AGB data, this study utilizes the Gram-Schmidt Pan Sharpening (GS) method, multivariate selection methods, and machine learning algorithms (RF, SVM, and BP_ANN) to construct a model for AGB during senescence grassland, and applies the optimal model to analyze spatio-temporal pattern changes in AGB from 2000 to 2021 in arid regions. The results indicate that the GS method effectively enhances the correlation between measured AGB and vegetation indices, reducing model error to some extent; The accuracy of grassland AGB inversion models based on a single vegetation index is low (0.03 ≤ |R| ≤ 0.63), while the RF model constructed with multiple variables selected by the Boruta algorithm is the optimal model for estimating AGB in arid regions during the senescence period (R2 = 0.71, RMSE = 519.74 kg/ha); In the span of 22 years, the annual average AGB in the senescence period of arid regions was 1413.85 kg/ha, with regions of higher AGB primarily located in the northeast and southwest of the study area. The area experiencing an increase in AGB during the senescence period (79.97 %) was significantly larger than that with decreased AGB (20.03 %).

A multi-factor combination prediction model of carbon emissions based on improved CEEMDAN.

As the global greenhouse effect intensifies, carbon emissions are gradually becoming a hot topic of discussion. Accurate carbon emissions prediction is an important foundation to realize carbon neutrality and peak carbon dioxide emissions. To accurately predict carbon emissions, a multi-factor combination prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory optimized by lemurs optimizer (LOBiLSTM) and least squares support vector machine optimized by lemurs optimizer (LOLSSVM), named ICEEMDAN-LOBiLSTM-LOLSSVM, is proposed. Firstly, the influencing factors of carbon emissions are selected by Spearman correlation coefficient, and carbon emissions are decomposed into intrinsic mode functions (IMFs) by ICEEMDAN. Secondly, the influencing factors and IMFs are input into LOBiLSTM and LOLSSVM respectively for prediction. Then, the point prediction results are obtained by weighting the prediction results of LOBiLSTM and LOLSSVM. Finally, probability density function of point prediction error is calculated by kernel density estimation, and the interval prediction results are calculated according to different confidence intervals. Carbon emissions of China and Germany are selected to verify the superiority of ICEEMDAN-LOBiLSTM-LOLSSVM. The experiment shows that RMSE, MAE, MAPE, and R2 of the proposed model are 0.4468, 0.3612, 0.0120, and 0.9839 respectively for China, which is the best among the nine models, as well as for Germany.

Factors associated with intervertebral cage subsidence in posterior lumbar fusion.

BACKGROUND: The interbody fusion apparatus is a key component of the operation and plays a key role in the postoperative efficacy. Cage subsidence is one of the common complications after lumbar fusion and internal fixation. Clinical studies on the risk factors of cage subsidence are incomplete and inaccurate, especially paravertebral muscle atrophy and intervertebral bone fusion time. METHODS: Among the patients who underwent PLIF surgery in our hospital from January 2016 to January 2019, 30 patients with cage subsidence and 30 patients without cage subsidence were randomly selected to be included in this study. The differences between the two groups were compared, and the relevant factors of cage subsidence were explored by single factor comparison and multiple logistic regression analysis. RESULTS: Bone mineral density (T) of the subsidence group [(- 1.84 ± 1.81) g/cm2 vs (- 0.87 ± 1.63) g/cm2, P = 0.018] was significantly lower than that of the normal group. There were 4 patients with end plate injury in the subsidence group (P = 0.038). Preoperative end plate Modic changes [I/II/III, (7/2/2) vs (2/5/8), P = 0.043] were significantly different between the two groups. In the subsidence group, preoperative rCSA of psoas major muscle [(1.43 ± 0.40) vs (1.64 ± 0.41), P = 0.043], CSA of paravertebral muscle [(4530.25 ± 776.55) mm2 vs (4964.75 ± 888.48) mm2, P = 0.047], paravertebral muscle rCSA [(3.03 ± 0.72) vs (3.84 ± 0.73), P < 0.001] and paravertebral muscle rFCSA [(2.29 ± 0.60) vs (2.89 ± 0.66), P < 0.001] were significantly lower than those in normal group. In the subsidence group, the vertebral body area [(1547.81 ± 309.89) mm2 vs (1326.48 ± 297.21) mm2, P = 0.004], the height of the immediately corrected vertebral space [(2.86 ± 1.10) mm vs (1.65 ± 1.02) mm, P = 0.020], immediately SL corrective Angle [(5.81 + 4.71)° vs (3.24 + 3.57) °, P = 0.009), postoperative PI-LL [(11.69 + 6.99)° vs (6.66 + 9.62) °, P = 0.029] and intervertebral fusion time [(5.38 ± 1.85) months vs (4.30 ± 1.49) months, P = 0.023] were significantly higher than those in the normal group. Multivariate logistic regression analysis showed that the time of intervertebral fusion (OR = 1.158, P = 0.045), the height of immediate intervertebral space correction (OR = 1.438, P = 0.038), and the Angle of immediate SL correction (OR = 1.101, P = 0.019) were the risk factors for cage subsidence. Bone mineral density (OR = 0.544, P = 0.016) and preoperative paravertebral muscle rFCSA (OR = 0.525, P = 0.048) were protective factors. CONCLUSION: Intervertebral fusion time, correctable height of intervertebral space, excessive Angle of immediate SL correction, bone mineral density and preoperative paravertebral muscle rFCSA are risk factors for cage subsidence after PLIF.

Optimization of xylanase production by Pichia kudriavzevii and Candida tropicalis isolated from the wood product workshop.

Enzymatic compounds can be found abundantly and provide numerous advantages in microbial organisms. Xylanases are used in various pharmaceutical, food, livestock, poultry, and paper industries. This study aimed to investigate xylanase-producing yeasts, xylose concentration curve and their enzymatic activity under various factors including carbon and nitrogen sources, temperature, and pH. Enzyme activity was evaluated under different conditions before, during, and after purification. The yeast strains were obtained from the wood product workshop and were subsequently cultivated on YPD (yeast extract peptone dextrose) medium. Additionally, the growth curve of the yeast and its molecular identification were conducted. The optimization and design process of xylan isolated from corn wood involved the use of Taguchi software to test different parameters like carbon and nitrogen sources, temperature, and pH, with the goal of determining the most optimal conditions for enzyme production. In addition, the Taguchi method was utilized to conduct a multifactorial optimization of xylanase enzyme activity. The isolated species were partially purified using ammonium sulfate precipitation and dialysis bag techniques. The results indicated that 3 species (8S, 18S, and 16W) after molecular identification based on 18S rRNA gene sequencing were identified as Candida tropicalis SBN-IAUF-1, Candida tropicalis SBN-IAUF-3, and Pichia kudriavzevii SBN-IAUF-2, respectively. The optimal parameters for wheat carbon source and peptone nitrogen source were found at 50 °C and pH 9.0 through single-factor optimization. By using the Taguchi approach, the best combination for highest activity was rice-derived carbon source and peptone nitrogen source at 50 °C and pH 6.0. The best conditions for xylanase enzyme production in single-factor optimization of wheat bran were 2135.6 U/mL, peptone 4475.25 U/mL, temperature 50 °C 1868 U/mL, and pH 9.0 2002.4 U/mL. Among the tested yeast, Candida tropicalis strain SBN-IAUF-1 to the access number MZ816946.1 in NCBI was found to be the best xylanase product. The highest ratio of enzyme production at the end of the delayed phase and the beginning of the logarithmic phase was concluded by comparing the growth ratio of 8S, 16W, and 18S yeasts with the level of enzymatic activity. This is the first report on the production of xylan polymer with a relative purity of 80% in Iran. The extracellular xylanases purified from the yeast species of C. tropicalis were introduced as a desirable biocatalyst due to their high enzymatic activity for the degradation of xylan polymers.

Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses.

Background: Ovarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies. Methods: Gene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance. Results: A total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results. Conclusion: This study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

Land use carbon emissions estimation and carbon emissions control strategy effect scenario simulation in Zhejiang province.

Carbon emission control is an important aspect of regional sustainable development. However, there are few studies on predicting the impact of different socio-economic development strategies on carbon emissions under the premise of established government policy objectives, and then evaluating the degree of achievement of policy objectives. In order to research the effect of social and economic development strategies on carbon emissions, remote sensing land use data of Zhejiang Province from 2000 to 2020 were utilized in this paper to quantify carbon emissions, combined with Kaya identity and LMDI decomposition method, and multi-scenario carbon emissions simulation and prediction were carried out under the STIRPAT model framework. The results demonstrate that land use carbon emissions increased by four times in the last 20 years, and increased rapidly between 2000 and 2010, and became stable in recent years. Economic growth is the primary motivator; According to the existing economic development model, carbon emissions will peak and turn around in 2030; If appropriate economic transformation measures are taken to increase the proportion of low-carbon economic components, it is possible to achieve the development model of scenario 2 and scenario 3, and carbon emissions will reach the peak 2-5 years earlier. In general, this study offers a significant conclusion for the investigation of the connection between social and economic growth strategies and carbon emissions, and it can serve as a guide for the formation of government policy.

Synergistic relationship and interact driving factors of pollution and carbon reduction in the Yangtze River Delta urban agglomeration, China.

The urban agglomeration is the most concentrated region of economy, population, and industry. It is also the key area of carbon emissions (CE) and air pollution management. CE and air pollution have the possibility of collaborative governance due to the same root and the same source of them. To achieve the goal of sustainable development, it is important to study the coordinated relationship of CE and atmosphere pollutants in urban agglomerations. However, most researches have ignored the synergistic relationship between CE and air pollutants. Furthermore, there is limited current study on the driving factors of the synergistic relationship between air pollutants and CE. To fill these research gaps, we first explore the spatial-temporal evolvement law of CE and PM2.5 utilizing satellite remote sensing data sets. Secondly, we analyze the synergistic relationship of CE and PM2.5 in the Yangtze River Delta (YRD) urban agglomeration using the coupling coordination degree (CCD) model from 2000 to 2020. At last, we further study the influencing factors of the synergistic relationship of CE and PM2.5 based on the geo-detector model. The findings display that (1) in 2020, the total CE in the YRD urban agglomeration is 2.24 billion tons, accounting for 22.5% of China, but its growth rate has gradually dropped to 7.25%. Besides, the PM2.5 concentration shows a waving upward-downward tendency. In 2020, the range of higher PM2.5 regions significantly decreased, and air quality gradually improved. (2) The CCD of PM2.5 and CE is at the coordination level in general (CCD > 0.6) between 2000 and 2020, which can realize the coordinated governance of pollution and carbon reduction. (3) Digital elevation model (DEM), topographic relief (RDLS), and population density have a higher degree of influence on the synergistic relationship between CE and PM2.5. Besides, the interaction of topographic and socio-economic factors is the main driving factor between the two. This paper can provide a referral for decision-makers to synergistically make plans for pollution and carbon reduction and facilitate the sustainable development of urban agglomerations.

Muti-factor analysis of sport activity level after high tibial osteotomy.

BACKGROUND: Although many studies have shown that high tibial osteotomy is appropriate for active patients, there are limited multifactorial studies on patients' sport activity level after HTO in general population. METHODS: 158 patients who underwent HTO for knee osteoarthritis between January 2016 and December 2019 are included, with a 36-month follow-up. Information was collected from X-rays and questionnaire. The independent variables were age, sex, breadwinner (provide more than 50% income), sport activity level when the knee was pain-free before and after surgery, concomitant meniscal treatment history, Lysholm knee score, desire level for returning to sports. The 158 cases are divided into three groups according to their sports participation before and after operation, Chi-square tests and ANOVA analysis were adopted to identify the effect of these variables on sport activity level after HTO, and factors with statistical differences and clinical relevancies, or provided by previous research were assessed with the ordinal logistic regression analysis. RESULTS: According to sport activity level analysis, 28(17.7%) patients were categorized into the sport level-reduced group, 97(61.4%) patients into the sport level-unchanged group, and 33(20.9%) patients into the sport level-improved group. Upon ordinal logistic regression analysis, postoperative MA%, age, BMI, and preoperative Lysholm knee score were statistically significant. CONCLUSIONS: Higher postoperative MA%, younger age, lower BMI, and lower Lysholm score are associate with improvement on activity level after HTO. This finding provides valuable references in operation option and rehabilitation planning.

Study on Deterioration Characteristics of a Composite Crossarm Mandrel in a 10 kV Distribution Network Based on Multi-Factor Aging.

This paper presents a study that conducted 5000 h of multi-factor aging tests on 10 kV composite crossarms, considering the natural environment in coastal areas and actual power line operations. Various aging conditions, such as voltage, rain, temperature, humidity, salt fog, ultraviolet light, and mechanical stress, were applied during the tests. The research initially analyzed the influence of multi-factor aging on the bending and tensile properties of the full-size composite crossarm. Subsequently, a detailed investigation was carried out to assess the impact of aging on the mechanical properties, electrical insulation properties, and microscopic characteristics of the composite crossarm core bar. Results indicated that the tensile strength and bending strength of the full-size composite crossarm mandrel experienced minimal changes after aging, remaining well within operational requirements. However, the silicone rubber outer sheath's hydrophobicity decreased, leading to the appearance of cracks and holes on the surface, which provided pathways for moisture and salt infiltration into the mandrel. As a consequence, the bending strength and shear strength of the mandrel material were reduced by 16.5% and 37.7%, respectively. Moreover, the electrical performance test demonstrated a slight change in the mandrel's leakage current, while the electrical breakdown strength decreased by 22.8%. Microscopic analysis using SEM, three-dimensional CT, and TGA revealed that a small amount of resin matrix decomposed and microcracks appeared on the surface. Additionally, the fiber-matrix interface experienced debonding and cracking, leading to an increased moisture absorption rate of the mandrel material.